UNIVERSITY OF OULU P .O. Box 8000 F I -90014 UNIVERSITY OF OULU FINLAND

A C T A U N I V E R S I T A T I S O U L U E N S I S

University Lecturer Tuomo Glumoff

University Lecturer Santeri Palviainen

Senior research fellow Jari Juuti

Professor Olli Vuolteenaho

University Lecturer Veli-Matti Ulvinen

Planning Director Pertti Tikkanen

Professor Jari Juga

University Lecturer Anu Soikkeli

Professor Olli Vuolteenaho

Publications Editor Kirsti Nurkkala

ISBN 978-952-42-2343-8 (Paperback)ISBN 978-952-42-2344-5 (PDF)ISSN 0355-3221 (Print)ISSN 1796-2234 (Online)

U N I V E R S I TAT I S O U L U E N S I S

MEDICA

ACTAD

D 1528

AC

TAT

iia Honkanen

OULU 2019

D 1528

Tiia Honkanen

MORE EFFICIENT USE OFHER TARGETING AGENTSIN CANCER THERAPY

UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF MEDICINE;MEDICAL RESEARCH CENTER OULU;OULU UNIVERSITY HOSPITAL

ACTA UNIVERS ITAT I S OULUENS I SD M e d i c a 1 5 2 8

TIIA HONKANEN

MORE EFFICIENT USE OF HER TARGETING AGENTS IN CANCER THERAPY

Academic dissertation to be presented with the assent ofthe Doctoral Training Committee of Health andBiosciences of the University of Oulu for public defencein Auditorium 7 of Oulu University Hospital, on 18October 2019, at 12 noon

UNIVERSITY OF OULU, OULU 2019

Copyright © 2019Acta Univ. Oul. D 1528, 2019

Supervised byDocent Jussi Koivunen

Reviewed byDocent Minna TannerDocent Maria Sundvall

ISBN 978-952-62-2343-8 (Paperback) ISBN 978-952-62-2344-5 (PDF)

ISSN 0355-3221 (Printed)ISSN 1796-2234 (Online)

Cover DesignRaimo Ahonen

JUVENES PRINTTAMPERE 2019

OpponentProfessor Jorma Isola

Honkanen, Tiia, More efficient use of HER targeting agents in cancer therapy. University of Oulu Graduate School; University of Oulu, Faculty of Medicine; MedicalResearch Center Oulu; Oulu University HospitalActa Univ. Oul. D 1528, 2019University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland

Abstract

Cancer treatments have remarkably improved over the past years since targeted therapies andimmunotherapy have been introduced to the field of oncology. The benefit of these new therapiesis often limited, however, by de novo or acquired therapy resistances, which should be noticedwhen making clinical decisions.

In this current work, we studied the prognostic and predictive values of several immunologicalmarkers in metastatic HER2-positive breast cancer treated with trastuzumab, because trastuzumabis still given to patients according to the HER2 status only, without certainty of tumor response.We also determined the role of HER2 and HER3 for cancer stem cells (CSC) in ALK translocatednon-small cell lung cancer (NSCLC) cell lines since the CSCs are causing therapy resistance andcancer recurrence.

The results demonstrated that a high number of cytotoxic T cells, together with a high numberof M1-like macrophages in the center of the tumor (CT), are promising and independentprognostic factors in HER2-positive breast cancer. These markers together also can predict theprogression of the disease and the length of trastuzumab discontinuation in tumor response.Expression of HER2 and HER3 increased the stem-like properties of ALK translocated NSCLCcells, which were decreased when the expressions were downregulated. HER2-HER3-dependentCSCs also mediated the ALK therapy resistance.

In conclusion, this study suggests that patients with a favorable immunological tumor profile(high number of cytotoxic T cells and M1-like macrophages in the CT) could be treated in a less-intensive manner, that trastuzumab discontinuation could be feasible for these patients, and thattargeting of HER2 and HER3 receptors can lead to more effective killing of cancer stem-like cellsand should be further studied.

Keywords: ALK, breast cancer, cancer stem cell, ERBB, HER2, HER3, NSCLC,trastuzumab, tumor-infiltrating lymphocytes

Honkanen, Tiia, HER-proteiineja kohdentavien lääkkeiden tehokkaampi käyttösyöpähoidoissa. Oulun yliopiston tutkijakoulu; Oulun yliopisto, Lääketieteellinen tiedekunta; Medical ResearchCenter Oulu; Oulun yliopistollinen sairaalaActa Univ. Oul. D 1528, 2019Oulun yliopisto, PL 8000, 90014 Oulun yliopisto

Tiivistelmä

Syöpähoidot ovat kehittyneet huomattavasti, kun kohdennetut hoidot ja immunologiset hoidotovat tulleet perinteisten hoitojen rinnalle. Usein näiden hoitojen hyötyä kuitenkin rajoittaa joolemassa oleva lääkeresistenssi tai sen kehittyminen, mikä tulisi ottaa huomioon hoitoja suunni-teltaessa.

Tässä työssä tutkittiin immunologisia merkkiaineita, joilla voitaisiin ennustaa trastutsumabi-hoidon vastetta sekä potilaiden ennustetta levinneessä HER2-positiivisessa rintasyövässä. Tällähetkellä trastutsumabi-hoitopäätös tehdään pelkän HER2-geenimonistuman mukaan ilman var-muutta siitä, hyötyykö potilas oikeasti hoidosta. Lisäksi tutkimme HER2- ja HER3-reseptorienmerkitystä syövän kantasoluille ALK-translokoituneessa ei-pienisoluisessa keuhkosyövässä(NSCLC), sillä syövän kantasolut ovat yksi merkittävimmistä tekijöistä lääkeresistenssin kehit-tymisessä ja syövän uusiutumisessa.

Työssä havaittiin, että kasvaimen keskellä oleva suuri määrä sytotoksisia T-soluja sekä M1-tyypin makrofageja on yhteydessä potilaiden parempaan ennusteeseen ja että kyseiset merkkiai-neet ovat toisistaan riippumattomia. Merkkiaineet pystyivät ennustamaan myös taudin etenemis-tä sekä trastutsumabi-hoitokeskeytyksen pituutta. HER2- ja HER3-proteiinien tuotto lisäsi ALK-translokoituneiden NSCLC-solujen kantasolumaisia ominaisuuksia, jotka puolestaan vähenivät,kun proteiinien tuotto estettiin. Lisäksi HER2-HER3 -riippuvaiset syövän kantasolut säätelivätlääkeresistenssiä kyseisessä taudissa.

Työn tulokset viittaavat siihen, että potilaita, joilla on suotuisa kasvaimen immunoprofiili(suuri määrä sytotoksisia T-soluja ja M1-tyypin makrofageja kasvaimen keskellä) pystyttäisiinhoitamaan keveimmillä hoidoilla ja HER2-hoitokeskeytys voisi olla mahdollinen näillä potilail-la. Lisäksi työ korostaa HER2- ja HER3-reseptorien kohdentamista syövän kantasolumaistensolujen tehokkaamman tuhoamisen saavuttamiseksi.

Asiasanat: ALK, ERBB, HER2, HER3, kasvaimeen infiltroituneet lymfosyytit,NSCLC, rintasyöpä, syövän kantasolut, trastutsumabi

“It’s when you cry just a little bit, but you laugh in the middle that you’ve made it – And don’t it feel alright –

And don’t it feel so nice – Lovely” Jason Mraz

8

9

Acknowledgements

This study was carried out at the Department of Oncology and Radiotherapy, Oulu

University Hospital during the years 2015–2019.

I would like to express my deepest gratitude to my supervisor Docent Jussi

Koivunen, MD, PhD, for his great expertise and guidance throughout these past

years. Thank you for introducing me to this great field of science, it has been a

pleasure to work with you and learn from you. I truly admire the enthusiasm you

have for research and work. Thank you for all your support and encouragement. I

was extremely lucky to have you as my supervisor.

I would like to thank the members of my follow-up group, Professor Outi

Kuittinen, MD, PhD, Minna Jääskeläinen, MD, PhD and Nina Kokkonen, PhD for

their expertise and great comments. I appreciate and thank the pre-examiners of

this thesis Docent Minna Tanner, MD, PhD and Docent Maria Sundvall, MD, PhD

for taking the time to examine my thesis and for your great comments, which

improved my work.

I wish to thank all the co-authors and collaborators for their contributions to

my projects: Docent Peeter Karihtala, MD, PhD, Juha Väyrynen, MD, PhD,

Professor Markus Mäkinen, MD, PhD, Professor Peppi Karppinen, MD, PhD,

Docent Päivi Auvinen, MD, PhD, Satu Tiainen, MD, Emmi Wilenius, MSc and

Antti Tikkanen, MB. I also thank Virpi Glumoff, PhD, Elitsa Dimova, PhD, Hanna-

Riikka Teppo, MD, PhD and Ms. Riitta Vuento for their help and technical

assistance in my projects. Special thanks to Peeter and Juha for their statistical and

pathological guidance.

I would like to express my warmest thanks to our research community for

sharing this time, all the struggles and successes with me: Professor Taina

Turpeenniemi-Hujanen, MD, PhD, Professor Outi Kuittinen, MD, PhD and all the

past and current fellow researchers. Special thanks to my dear co-worker Ms. Anne

Bisi. Thank you for everything; for the technical assistance and all the support. You

made the working days so much better.

I want to express my gratitude to my dear friends Meira, Krista, Emmi, Heikki,

Kati, Mikko, Anna and Fawzi, who are also my colleagues. Thank you for

everything; all the time we have spent, all the laughs and adventures, every advice

you have given me, and all the sympathy and support during these years. I would

also like to thank my non-academic friends, especially Suvi and Janne, who have

made it easier to relax and escape the pressure of the scientific world. Suvi, my

10

dear friend, thank you for sticking by me all this time, ever since the childhood.

I’m lucky to have a friend like you.

Special thanks to my family, my brothers Juha and Tomi, and my parents Tuovi

and Pentti. I appreciate the warm relationship I have with both of my brothers. We

support each other no matter what, I love you both. I also want to thank Päivi, it

has been a real pleasure getting to know you. My deepest gratitude goes to my

parents. Without you I would not be where I am now. Thank you for showing me

how to love and truly care about someone. Thank you for always believing in me

and supporting me, I love you. Finally, I want to thank my beloved partner, Toni.

Thank you for all the support and love you have given me during these years. You

mean the world to me. I would also like to thank the whole Valtanen-family for

their care and support.

I acknowledge the financial support of this thesis provided by the Cancer

Foundation Finland, Sigrid Juselius Foundation, Ida Montin foundation, the

University of Oulu Graduate School and the University of Oulu Scholarship

Foundation.

Oulu, August 2019 Tiia Honkanen

11

Abbreviations

4E-BP1 eukaryotic translation initiation factor 4E-binding protein 1

ADCC antibody-dependent cellular cytotoxicity

ADCP antibody-dependent cellular phagocytosis

ALDH1 aldehyde dehydrogenase 1

ALK anaplastic lymphoma kinase

APC antigen presenting cell

ATP adenosine triphosphate

AUC area under the curve

BRCA breast cancer susceptibility gene

BSA bovine serum albumin

CCL2 chemokine ligand 2

CD cluster of differentiation

ChT chemotherapy

CRISPR clustered regularly interspaced short palindromic repeats

CSC cancer stem cell

CSF cerebrospinal fluid

CSLC cancer stem-like cell

CT center of the tumor

DC dendritic cell

DNA deoxyribonucleic acid

EDTA ethylenediaminetetraacetic acid

EGF epidermal growth factor

EGFR epidermal growth factor receptor

EMA European medicines agency

EML4 echinoderm microtubule-associated protein-like 4

EMT epithelial to mesenchymal transition

ER estrogen receptor

ERBB avian erythroblastosis oncogene B

ERK extracellular signal-regulated kinase

ET endocrine therapy

FcγR fragment C gamma receptor

FDA food and drug administration

FoxP3 forkhead box P3

Grb2 growth factor receptor-bound protein 2

GDP guanosine diphosphate

12

GFP green fluorescent protein

GTP guanosine triphosphate

HB-EGF heparin binding EGF-like growth factor

H&E hematoxylin and eosin

HER human epidermal growth factor receptor

HRP horseradish peroxidase

IDO1 indoleamine 2,3-dioxygenase 1

IFN-γ interferon-gamma

IL-10 interleukin-10

IM invasive margin

iNOS inducible nitric oxide synthase

M1 M1-like macrophages

M2 M2-like macrophages

mAb monoclonal antibody

MAPK mitogen-activated protein kinase

MDSC myeloid-derived suppressor cell

MEK mitogen-activated protein kinase kinase

MHC major histocompatibility complex

mRNA messenger RNA

miRNA microRNA

mTOR mammalian target of rapamycin

NK natural killer

NKT natural killer T cell

NRG neuregulin

NSCLC non-small cell lung cancer

OS overall survival

PCR polymerase chain reaction

PD-1 programmed cell death 1

PD-L1 programmed cell death ligand 1

PI3K phosphoinositide 3-kinase

PIP2 phosphatidylinositol 4,5-bisphosphate

PIP3 phosphatidylinositol (3,4,5)-trisphosphate

PTB phosphotyrosine-binding

PTEN phosphatase and tensin homolog

PVDF polyvinylidene difluoride

RB1 retinoblastoma 1

RNA ribonucleic acid

13

ROC receiver operating characteristic

RT room temperature / radiotherapy

RTK receptor tyrosine kinase

S6K1 ribosomal protein S6 kinase 1

SDS-PAGE sodium dodecyl sulfate-polyacrylamide gel electrophoresis

SH2 Src homology-2

Shc Src homology-2 domain containing

shRNA short hairpin RNA

sgRNA single guide RNA

SOS son of sevenless

SOX2 sex determining region Y-box 2

TAM tumor-associated macrophage

T-DM1 trastuzumab emtansine

Th T helper

TGF-β transforming growth factor beta

TIL tumor infiltrating lymphocytes

TKI tyrosine kinase inhibitor

TLS tertiary lymphoid structure

TNF-α tumor necrosis factor alpha

TP53 tumor protein 53

TRAIL TNF-related apoptosis-inducing ligand

Treg regulatory T cell

14

15

Original publications

This thesis is based on the following publications, which are referred throughout

the text by their Roman numerals:

I Honkanen, T.J.*, Moilanen, T.*, Karihtala, P., Tiainen, S., Auvinen, P., Väyrynen, J.P., Mäkinen, M. & Koivunen, J.P. (2017). Prognostic and predictive role of spatially positioned tumour infiltrating lymphocytes in metastatic HER2 positive breast cancer treated with trastuzumab. Sci Rep, 7(1), 18027.

II Honkanen, T.J., Tikkanen, A., Karihtala, P., Mäkinen, M., Väyrynen, J.P. & Koivunen, JP. (2019). Prognostic and predictive role of tumour-associated macrophages in HER2 positive breast cancer. Sci Rep, 9(1), 10961.

III Honkanen, T., Wilenius, E., Koivunen, P., & Koivunen, J.P. (2017). HER2 regulates cancer stem-like cell phenotype in ALK translocated NSCLC. Int J Oncol, 51(2), 599-606.

IV Honkanen T.J. & Koivunen J.P. (2019). HER3 regulates cancer stem-like cell properties in ALK translocated NSCLC. Manuscript.

*Equal contribution

16

17

Table of contents

Abstract

Tiivistelmä

Acknowledgements 9 

Abbreviations 11 

Original publications 15 

Table of contents 17 

1  Introduction 19 

2  Review of the literature 21 

2.1  Cancer ..................................................................................................... 21 

2.1.1  Cancer genetics ............................................................................. 21 

2.1.2  Cancer epigenetics ........................................................................ 23 

2.1.3  Cancer biology ............................................................................. 23 

2.1.4  Main signaling pathways altered in cancer ................................... 27 

2.2  Tumor immunology ................................................................................ 29 

2.2.1  Innate immune system and cancer ................................................ 31 

2.2.2  Adaptive immune system and cancer ........................................... 33 

2.2.3  Tumor immune profile and Immunoscore .................................... 34 

2.3  Cancer stem cells .................................................................................... 35 

2.3.1  Hierarchical and stochastic cancer stem cell models .................... 36 

2.3.2  Signaling pathways related to cancer stem cell phenotype........... 38 

2.3.3  Markers used to identify cancer stem cells ................................... 38 

2.4  HER family ............................................................................................. 39 

2.4.1  Structure and function .................................................................. 39 

2.4.2  HERs and cancer .......................................................................... 41 

2.4.3  HER targeted therapies ................................................................. 43 

2.5  HER2+ breast cancer .............................................................................. 46 

2.5.1  Treatment of HER2+ breast cancer .............................................. 47 

2.5.2  Tumor-infiltrating lymphocytes in HER2+ breast cancer ............ 49 

2.6  ALK in cancer ......................................................................................... 50 

2.6.1  ALK translocated NSCLC ............................................................. 50 

2.6.2  ALK targeting and resistance mechanisms in cancer ................... 51 

3  Aims of the study 53 

4  Materials and methods 55 

4.1  Publications I and II ................................................................................ 55 

4.1.1  Patient material ............................................................................. 55 

18

4.1.2  Immunohistochemistry and immune cell counting ....................... 55 

4.1.3  Statistics ........................................................................................ 57 

4.1.4  Ethics ............................................................................................ 57 

4.2  Publications III and IV ............................................................................ 58 

4.2.1  Cell lines, inhibitors and growth factors ....................................... 58 

4.2.2  Lentiviral knockdown and retroviral overexpression ................... 59 

4.2.3  CRISPR-Cas9 knockdown ........................................................... 59 

4.2.4  DNA extraction, PCR and sequencing .......................................... 60 

4.2.5  Western blot .................................................................................. 61 

4.2.6  Colony formation assay ................................................................ 61 

4.2.7  Tumor sphere formation assay ...................................................... 62 

5  Results 65 

5.1  Immunological markers in metastatic HER2+ breast cancer .................. 65 

5.1.1  Patient characteristics ................................................................... 65 

5.1.2  Staining and image analysis of the studied markers ..................... 66 

5.1.3  Association of T cell subsets with clinical data ............................ 68 

5.1.4  Association of macrophage subsets with clinical data .................. 70 

5.1.5  Association of other markers with clinical data ............................ 70 

5.1.6  Combined analysis of CD8 and M1.............................................. 71 

5.1.7  CD8 and M1 infiltration and trastuzumab benefit ........................ 71 

5.2  The role of HER2 and HER3 for cancer stem-like cell phenotype ......... 73 

5.2.1  Overexpression and knockdown of HER2 and HER3 .................. 73 

5.2.2  The effects of genetic alterations to cancer stem-like cell

marker expression ......................................................................... 73 

5.2.3  The effects of the genetic alterations to cytotoxic response

to ALK inhibition ......................................................................... 74 

5.2.4  Alterations in the Akt and ERK1/2 downstream signaling ........... 75 

5.2.5  Compensatory expression of other HER family members ........... 75 

6  Discussion 77 

6.1  Immunological markers in HER2+ breast cancer ................................... 77 

6.2  The role of HER2 and HER3 for cancer stem-like cells in ALK

translocated NSCLC ................................................................................ 81 

6.3  Implications of the results and future perspectives ................................. 83 

6.4  Limitations of the study .......................................................................... 84 

7  Conclusions 87 

References 89 

Original publications 117 

19

1 Introduction

Cancer has a major impact on society across the world and is one of the leading

causes of death. Cancer incidence has increased in recent years. An estimated 14.1

million new cancer cases worldwide were seen in 2012, whereas the estimation was

increased to 18.1 million cases in 2018 (Bray et al., 2018; Torre et al., 2015).

Fortunately, cancer therapies have been developed over the years with targeted

therapies and immunotherapy taking their place alongside surgery, chemotherapy

and radiation therapy. The new therapies are expensive, however, and have

increased the economic impact of cancer care (Tangka et al., 2010; Torkki et al.,

2018).

In the era of personalized medicine, the cancer therapies are selected for

patients according to their tumor’s genetic status. For example, ALK-positive lung

cancers containing oncogenic alterations of ALK are treated with ALK inhibitors,

because the cancer cells depend on ALK signaling and are thus extremely sensitive

to the receptor’s inhibition (Peters et al., 2017; Shaw et al., 2013; Soda et al., 2007).

However, patient selection still needs improvements, since some patients selected

for a specific treatment do not benefit or respond to the therapy. The

unresponsiveness can be caused by an existing resistance to the therapy that blocks

the therapeutic effects of the treatment. Patients selected for monoclonal antibody

(mAb) therapies, such as trastuzumab, are a great example. Even if the patient and

the tumor seem suitable for a specific mAb treatment, like the presence of HER2

amplification for trastuzumab, the efficacy of the therapy requires a functional

immune system. If the effector immune cells essential for the therapeutic effect of

the treatment are suppressed, the patients will likely not respond. Some of the

HER2-positive breast cancer patients have extreme responses to trastuzumab (over

10 years), but still a major part of these patients are either primary refractory for

the treatment or will develop a resistance against it (Cantini et al., 2018). Selecting

patients more efficiently can increase the tumor responses, and better treatment

options can be designed for the nonresponding patients.

Even if the selected patients respond to the targeted therapy or immunotherapy,

a major limiting factor of these therapies is the emergence of acquired resistance.

Resistance mechanisms for different cancer therapies have been discovered, one of

which is the presence of cancer stem cells (Doebele et al., 2012; Sasaki et al., 2011;

Wang, Qu, & Wang, 2017). Cancer stem cells are able to initiate and sustain the

growth of a tumor and are highly tumorigenic. These cells have been shown to

cause therapy resistance for various cancer treatments (Creighton et al., 2009;

20

McLendon et al., 2006; Phillips, McBride, & Pajonk, 2006; Wang et al., 2017).

Several cancer stem cell targeting agents have been developed and are in clinical

trials (Li, Atkinson, & Zhang, 2017), but the clinical utility of these agents is still

quite unknown. Protein members of the human epidermal growth factor receptor

family have been shown to be tumorigenic and are often linked to cancer stem cells

(Ithimakin et al., 2013; Korkaya, Paulson, Iovino, & Wicha, 2008; Lee et al., 2014;

Shi et al., 2018). Their targeting in the context of cancer stem cell targeting is still

in its infancy, and more studies are required. Since many HER targeting agents are

already in clinical use and are constantly being developed, HER dependency of

cancer stem cells could lead to a rapid clinical testing of the agents in the context

of cancer stem-like cell targeting. The utilization of already existing medical drugs

is not a new idea, since metformin, a mainstay therapy for diabetes mellitus, is now

being tested in clinical trials for cancer stem cell targeting (Buckanovich et al., 2017;

Pernicova & Korbonits, 2014).

21

2 Review of the literature

2.1 Cancer

Cancer is one of the leading causes of death worldwide. 2018 saw an estimated

18.1 million new cancer cases and 9.6 million cancer-related deaths. The numbers

are probably going to increase due to population growth, longer life expectancies

and poor lifestyle behaviors, such as smoking, bad diet and physical inactivity. Over

100 different cancer types exist, but lung and breast cancer are the most common

cancers diagnosed and causing cancer-related deaths in men and women,

respectively (Bray et al., 2018).

Cancer is a genetic disease, meaning that cancer is caused by changes in our

genes, our DNA. These changes occur and accumulate over one’s lifetime; some

can be even inherited from our parents. However, our cells’ behavior is extremely

tightly controlled, and multiple steps are required for cells to become cancerous

and tumors to be developed. This chapter briefly describes these changes.

2.1.1 Cancer genetics

The DNA of both normal and neoplastic cells is continuously altered either by

misincorporation of nucleotides during the DNA replication or by exposure to

exogenous or endogenous mutagens. Most of these changes are repaired by the

cell’s own repair mechanisms; however, some of the changes escape from the repair

machinery, leading to the development of mutations (Stratton, Campbell, & Futreal,

2009). Most mutations required for normal, healthy cells to become cancerous cells

are somatic, nongermline mutations that accumulate over the lifetime of a cancer

patient before the tumor is formed. Some germline mutations and genes, however,

are known to cause heritable cancer, for example, BRCA1 and BRCA2 genes (Hall

et al., 1990; Wooster et al., 1994).

Multiple types of somatic mutations are seen in the cancer genome, but the

most common mutations are single-base substitutions, which means that one

nucleotide is replaced by another one (Vogelstein et al., 2013). The cancer genome

might carry following somatic mutations, in addition to the single-base

substitutions: deletions or insertions of a small or larger DNA segments,

rearrangement of DNA sequence, and gene copy number increases or depletions

(Stratton et al., 2009; Vogelstein et al., 2013).

22

Some of the mutations found in the cancer genome are essential for tumor

progression and maintenance and are thus called the “driver” mutations. Other

mutations seen in the cancer genome are called the “passenger” mutations, which

have neutral effects and do not provide any growth advantages to the cancer cells.

Since the driver mutations give growth advantages to the cancer cells, these

mutations occur in proto-oncogenes and/or tumor suppressor genes that are

essential for cell proliferation and cell survival. Proto-oncogenes are present in

normal cells; they encode for proteins that positively control the cell proliferation

and survival. These genes activate the cell cycle and protect the cells from apoptosis.

Proto-oncogenes become oncogenes after being activated by a mutation or

translocation. Oncogenes can be classified into six groups based on their biological

functions: growth factors, growth factor receptors, signal transducers, chromatin

remodelers, transcription factors and apoptosis regulators (Croce, 2008).

Tumor suppressor genes, conversely, have a negative impact on cell

proliferation. The products of these genes limit cell proliferation and protect the

cells from damages that could lead to uncontrolled cell proliferation; in other words,

they prevent the development of cancer and are, thus, commonly dysregulated in

tumors. Tumor suppressor genes encode various proteins, such as receptors or

signal transducers that inhibit the proliferation (e.g. TGF-β); proteins that control

the cell cycle (e.g. retinoblastoma); cell cycle checkpoint proteins that trigger cell

cycle arrest if the DNA is damaged or there are defects in the chromosomes (e.g.

BRCA1); proteins involved in repairing mistakes in the DNA (e.g. p53 and DNA

mismatch repair protein 2); and apoptosis inducing proteins (e.g. p53) (Hanahan &

Weinberg, 2011; Wang, Li-Hui, Wu, Rajasekaran, & Shin, 2018). However, it

should be noticed that the division of cancer-driving genes into oncogenes and

tumor suppressor genes is not always black and white, since tumor suppressor

genes, such as p53, can also act as oncogenes (Soussi & Wiman, 2015).

In addition to the genetic alteration of protein-encoding oncogenes and tumor

suppressor genes, small non-coding RNAs, microRNAs (miRNA), have been

shown to be involved in cancer development. These small miRNAs play an

important role in cell proliferation, differentiation and survival by binding

complimentary to their target messenger RNAs (mRNAs) and causing mRNA

degradation or inhibition of their translation. miRNAs can be dysregulated in

tumors in a way that they cannot inhibit the translation of proteins, providing

growth advantages to the tumor, or the expression of miRNA can be increased to

inhibit the translation of growth-suppressing proteins (Rupaimoole & Slack, 2017).

23

2.1.2 Cancer epigenetics

Tumors consist of a heterogenous cell population, meaning that one tumor can

contain cancer cells that carry different genetic mutations. The genetic changes

alone, however, cannot explain all the diversity seen within a cancer cell population.

Epigenetic changes can control the activity of genes without altering the genetic

code and can explain why two genetically identical cells can behave differently.

Epigenetic modifications regulate all DNA-based processes such as DNA repair,

replication and transcription of the genes. The most well-known epigenetic

modification is DNA methylation, which means addition of methyl groups into

cytosines preceding guanines, thus preventing gene transcription (Esteller, 2008).

DNA hypomethylation, the loss of DNA methylation, was the first identified

epigenetic modification seen in tumors (Feinberg & Vogelstein, 1983). Several

years after the discovery of hypomethylation in tumors, the tumor suppressor genes

were found to be hypermethylated, causing inactivation of the genes (Greger,

Passarge, Höpping, Messmer, & Horsthemke, 1989).

2.1.3 Cancer biology

Hanahan & Weinberg have described the six hallmarks of cancer that give the

functional abilities for cancer cells to survive, proliferate and disseminate:

sustaining proliferative signaling, evading growth suppressors, resisting cell death,

enabling replicative immortality, inducing angiogenesis and activating invasion

and metastasis. Two hallmarks are emerging, which are not novel findings but are

added to the original six hallmarks: reprogramming of energy metabolism and

evading immune destruction. Genome instability and tumor-promoting

inflammation are two characteristics that also enable cancer cells to gain all the

above-mentioned hallmarks (Hanahan & Weinberg, 2011). This chapter further

examines these cancer characteristics (Fig. 1).

The six hallmarks of cancer

One of the most fundamental characteristics of cancer cells is their ability to sustain

proliferative signaling. The proliferation is usually initiated by growth factors that

bind to their receptors found on the cell surface after which the signals are

transmitted inside the cell, leading to cell proliferation. These proliferative signals

are extremely strictly controlled in normal cells, for example, by limiting the

24

availability of growth factors or the number of growth factor receptors. Cancer cells

can bypass these growth-limiting signals by, for example, producing the growth

factors themselves or stimulating the neighboring cells, even normal cells, to

secrete the growth factors for them (Bhowmick, Neilson, & Moses, 2004; Hanahan

& Weinberg, 2011). Cancer cells may also increase the number of growth factor

receptors, or the receptors can be functionally altered by activating mutations to

reach ligand-independent signaling. Furthermore, the signaling components acting

downstream of the receptors may also be functionally altered, allowing receptor-

independent signaling. Cancer cells might also deregulate negative-feedback

signals, which limit the signaling transmissions, keeping them transient, in normal

conditions (Hanahan & Weinberg, 2011).

Cell proliferation is controlled not only by limiting the growth stimulating

signals but also by activating programs that negatively regulate cell proliferation.

The main negative regulating programs are dependent on the tumor suppressor

genes, of which RB1 and TP53 are the two most well-known. The protein products

of these genes decide whether the cell enters the cell cycle or whether senescence

or apoptosis programs are activated. These negative regulators are commonly

suppressed in tumors to enable uncontrolled proliferation (Hanahan & Weinberg,

2011; Sherr & McCormick, 2002).

Cells with uncontrolled proliferation will be eliminated in normal conditions

by triggering programmed cell death, apoptosis. Cells have two apoptotic programs,

extrinsic and intrinsic; both will activate a complex proteolytic signaling cascade

by regulating the balance of anti- and pro-apoptotic proteins. Cancer cells have

multiple ways to overcome apoptosis. They can damper the damage-detecting

sensors, increase the expression of anti-apoptotic factors or survival signals,

downregulate pro-apoptotic regulators or impair the receptors used to deliver stress

signals outside of the cell (Fernald & Kurokawa, 2013; Hanahan & Weinberg,

2011).

Uncontrolled and unlimited proliferation in normal cells is also restricted by

the length of telomeric DNA, which allows the cell to pass only a certain number

of cell divisions before entering cell senescence or cell crisis that lead to cell death.

Cancer cells have increased the expression of telomerase enzymes, which adds

telomere repeat segments to the ends of telomeric DNA to overcome this issue

(Hanahan & Weinberg, 2011).

Even if cancer cells have bypassed all above-mentioned restrictions and can

proliferate limitlessly, they need to sustain the distribution of oxygen and nutrients

and the removal of metabolic waste and carbon dioxide by angiogenesis.

25

Angiogenesis occurs in normal cells only transiently, for example, during the

embryogenesis or wound healing, but the angiogenic switch is continuously on

during tumor progression (Bergers & Benjamin, 2003; Hanahan & Weinberg, 2011).

A key feature of tumor progression is dissemination of tumor cells by invasion

or metastasis. It is implicated that the invasion and metastasis of carcinomas is

regulated by the epithelial-mesenchymal transition (EMT) program that is

orchestrated by various transcriptional factors, such as Snail, Slug, Twist and

Zeb1/2. These transcriptional factors associate with the loss of adherens junctions,

morphological changes from epithelial to fibroblastic morphology, increased

mobility, expression of matrix-degrading enzymes and increased resistance to

apoptosis (Hanahan & Weinberg, 2011). Indeed, it has been shown that some highly

aggressive carcinomas have downregulated the expression of cell-cell and cell-

extracellular matrix adhesion molecules, such as E-cadherin, and upregulated

adhesion molecules like N-cadherin that are normally seen during embryogenic cell

migrations (Cavallaro & Christofori, 2004). However, it should be noted that the

stromal cells, in other words, the non-malignant cells around the tumor, can also

affect the dissemination of tumor cells, for example, by stimulating invasive

behavior in tumor cells or by supplying matrix-degrading enzymes to facilitate the

invasion (Hanahan & Weinberg, 2011).

The two emerging hallmarks

Cancer cells use glycolysis over oxidative phosphorylation to produce ATP even in

the presence of oxygen. It has been implicated that tumor cells prefer glycolysis,

because it allows the utilization of glycolytic intermediates to generate the

nucleosides and amino acids required for assembling new cells. To compensate the

lower efficiency of ATP production, tumor cells have been shown to upregulate

glucose transporters, which increases the glucose import into the cell (DeBerardinis,

Lum, Hatzivassiliou, & Thompson, 2008; Hanahan & Weinberg, 2011).

Cancer cells must also avoid the immunological destruction barrier to survive.

Tumors can either alter their recognition by immune cells, or they can impair the

immune effector cells. The tumors can change their antigen presenting behavior to

evade the detection by immune cells, for example, by losing the molecules required

for immune cells to detect them, such as major histocompatibility complex I (MHC

I). Cancer cells can impair the immune cells by expressing immune cell inhibitory

molecules on their cell surface, such as programmed cell death ligand-1 (PD-L1),

or by secreting immunosuppressive cytokines or enzymes, which affect the

26

functions of both innate and adaptive immune cells. (Hanahan & Weinberg, 2011;

Vesely, Kershaw, Schreiber, & Smyth, 2011).

The two enabling characteristics

Cancer cells require various mutations, types of which are mentioned in chapter

2.1.1., to orchestrate tumorigenesis and to gain the above-mentioned hallmarks of

cancer. However, the rate of spontaneous mutations is extremely low due to a cell’s

own genome maintenance system, which recognizes and solves defects of the DNA.

Genome instability will provide a higher rate of mutations; cancer cells damage

one or several components of the genome maintenance system to achieve that. The

favorable targets in the maintenance system are genes called the genome’s

“caretakers”. The products of these genes are the ones that detect the DNA damage,

activate the repair machinery or repair the DNA directly. Caretaker gene products

can also inactivate the mutagens even before the DNA is damaged (Hanahan &

Weinberg, 2011).

Inflammation is another enabling characteristic that has been accepted to be

involved in tumorigenesis. The inflammatory conditions can exist before the tumor

has formed, but the cancer cells themselves can also create an inflammatory

microenvironment. The tumor microenvironment contains both innate and adaptive

immune cells as a result of inflammation. Although immune cells destroy tumor

cells, they also provide tumor-promoting factors such as growth factors, survival

factors, proangiogenic factors and matrix-modifying enzymes and, thus, enable the

acquisition of the hallmarks of cancer (Aggarwal, Vijayalekshmi, & Sung, 2009;

Grivennikov, Greten, & Karin, 2010; Hanahan & Weinberg, 2011; Mantovani,

Allavena, Sica, & Balkwill, 2008).

27

Fig. 1. The hallmarks of cancer. Modified from Hanahan & Weinberg 2011.

2.1.4 Main signaling pathways altered in cancer

Intercellular communications are essential for cells functions and survival. Signal

transduction is initiated largely by growth factors binding to their receptors, which

leads to activation of intracellular signaling cascades. Several classes of receptors

are found on the cell surface, such as receptor tyrosine kinases (RTKs), cytokine

receptors and G-protein -coupled receptors (Mendelsohn, Gray, Howley, Israel, &

Thompson, 2014).

RTKs are key regulators of essential cell processes such as proliferation,

differentiation, migration and survival, and their activating oncogenic alterations

are frequently seen in tumors. Human RTKs consist of 20 different subfamilies,

including epidermal growth factor receptors, insulin receptors and vascular

endothelial growth factor receptors. All RTKs share a similar structure: an

extracellular region, a transmembrane helix, and a cytoplasmic region. Binding of

a specific ligand to the extracellular region of its receptor initiates a signaling

cascade by creating receptor dimerization or oligomerization, which in turn leads

to activation of the tyrosine kinase domain in the cytoplasmic region, followed by

autophosphorylation of tyrosine residues in the carboxy terminal tail. These

phosphotyrosines function as recruitment sites for downstream signaling molecules

Sustainingproliferativesignaling Evading

growthsuppressors

Inducingangiogenesis

Activatinginvasion and metastasis

Evadingimmunedestruction

Reprogrammingenergy metabolism

Enablingreplicativeimmortality

Resisting cell death Genomic instabilityTumor-promoting inflammation

Enabling characteristics

28

containing either Src homology-2 (SH2) or phosphotyrosine-binding (PTB)

domains. Downstream signaling molecules can be also recruited to the receptor via

docking proteins that are phosphorylated by the receptor. The downstream

signaling proteins eventually become activated, and the signaling cascade

continues (Lemmon & Schlessinger, 2010; Mendelsohn et al., 2014).

Two well-known RTK signaling pathways are the Ras/Raf/MEK/ERK and

PI3K/Akt/mTOR pathways, which are commonly altered in tumors. Figure 2

presents a simplified scheme of these two pathways. In the Ras/Raf/MEK/ERK

pathway, the phosphotyrosines of an activated RTK recruit the Src homology-2

domain containing (Shc) adaptor protein to the receptor, which instead recruits

growth factor receptor-bound protein 2 (Grb2) and son of sevenless (SOS) protein.

SOS is a guanine nucleotide exchange factor, which activates Ras, a membrane-

bound G protein, by catalyzing the exchange of GDP to GTP. Activated Ras recruits

Raf to the membrane where it will be activated. Raf phosphorylates and activates

mitogen-activated protein kinase kinases 1 and 2 (MEK1/2), which in turn

phosphorylate and activate extracellular signal-regulated kinases 1 and 2 (ERK1/2),

which have multiple targets, both in the cytosol and in the nucleus (McCubrey et

al., 2011; Mendelsohn et al., 2014). Ras and Raf proteins are frequently mutated in

human cancers (Bos, 1989; Davies et al., 2002; Karnoub & Weinberg, 2008),

whereas MEK and ERK mutations have been reported in cancers but are less

common (Nikolaev et al., 2012; Ojesina et al., 2014).

PI3K/Akt/mTOR signaling pathway is initiated when phosphatidylinositol-3-

kinase (PI3K) associates with phosphorylated tyrosine residues on an activated

receptor and becomes activated. PI3K phosphorylates phosphatidylinositol 4,5-

bisphosphate (PIP2), generating phosphatidylinositol 3,4,5-trisphosphate (PIP3),

which functions as a second messenger and is converted back to PIP2 by tumor

suppressor PTEN. PIP3 then attracts Akt proteins to the membrane, where they are

activated via phosphorylation, after which Akt proteins phosphorylate a series of

substrates in the cytosol and in the nucleus. One well-known target of Akt is

mammalian target of rapamycin complex 1 (mTORC1), targets of which include

protein synthesis regulators ribosomal protein S6 kinase 1 (S6K1) and eukaryotic

translation initiation factor 4E-binding protein 1 (4E-BP1). Many components of

the PI3K/Akt/mTOR pathway are mutated or amplified in tumors, most commonly

PI3K and PTEN, but alterations of Akt have also been reported (Liu, Cheng,

Roberts, & Zhao, 2009; McCubrey et al., 2011; Mendelsohn et al., 2014).

29

Crosstalk between signaling pathways

Different signaling pathways interact with each other to create a complex and

dynamic signaling network. The signaling molecules of one pathway can, for

example, activate molecules of another pathway, as figure 2 shows, with GTP-

bound Ras, which can bind and allosterically activate PI3K (Suire, Hawkins, &

Stephens, 2002). The crosstalk between the signaling pathways is highlighted when

components of a specific pathway are blocked. It has been shown that inhibition of

PI3K/Akt/mTOR-pathway leads to activation of Ras/Raf/MEK/ERK-pathway and

vice versa (Carracedo et al., 2008; Won et al., 2012).

Fig. 2. RTK signaling pathways PI3K/Akt/mTOR and Ras/Raf/MEK/ERK.

2.2 Tumor immunology

The most important function of the immune system is to prevent or eradicate

infections. The first idea that the immune system can control cancer arose in the

early 1900s but the whole concept of immunosurveillance was under a constant

debate for almost a century until mouse models were improved enough, and it was

shown that mice lacking adaptive immune cells and IFNγ responsiveness were

more susceptible to cancer development (Schreiber, Old, & Smyth, 2011;

Shankaran et al., 2001). However, the roles of the immune system in tumorigenesis

and in the tumor microenvironment are paradoxical. Even though immune cells

destroy cancer cells, this eradication leads to a selection of a poorly immunogenic

population of cancer cells that are more capable of surviving in an

Grb2 SOS

RasGDP

PPP

P

P

Raf

MEK

ERK

PI3K

PIP2PIP3PIP3

mTORC1

S6K1 4E-BP1

P PP P PP P P

AktP

P P

Nucleus

Shc

Ras

Ras

GTP

GTP

30

immunocompetent host (De Visser, Eichten, & Coussens, 2006; Shankaran et al.,

2001).

Cells of the immune system are guarding throughout the human body and are

also found within the malignant tissue. The cell types of tumor-infiltrated immune

cells vary between different diseases and from patient to patient, and they are

usually located in the center of the tumor, in the stroma or in the tertiary lymphoid

structures (TLS) found near the tumors (Fridman, Pagès, Saut`s-Fridman, & Galon,

2012). The human immune system can be classically divided into the innate and

the adaptive immune system. This chapter will describe the dual roles of both innate

and adaptive immune cells in tumors (Fig. 3).

Fig. 3. Different subtypes of immune cells and their predominant contributions to tumor

progression or tumor suppression. DC=dendritic cells, M=macrophages,

MDSC=myeloid-derived suppressor cells, NK=natural killer cells, NKT=natural killer T

cells, Th=T helper cells, Treg=regulatory T cells.

Tumor suppression Tumor progression

CD8+ T

Th1DC1

Neutrophils

NKM1

NKT

B cells

Treg

DC2

M2

MDSC

Th2

Neutrophils

Mast cells Mast cells

Treg

Th2 Th17Th17

31

2.2.1 Innate immune system and cancer

The first line of defense against foreign pathogens or an injury is the innate immune

system, which comprises of different types of immune cells. The functions of the

innate immune cells are commonly altered in cancer.

Neutrophils, which reflect the state of inflammation, can be involved in tumor

initiation, proliferation and metastasis (Ocana, Nieto-Jiménez, Pandiella, &

Templeton, 2017; Szczerba et al., 2019), and indeed, high levels of neutrophils,

especially in the peripheral blood but also in the tumor, have been associated with

worse patient outcomes in many solid cancers (Jensen et al., 2009; Leibowitz-Amit

et al., 2014; Templeton et al., 2014; Wang, J. et al., 2014). However, the role of

neutrophils in cancer is complex, probably due to a wide scale of different

neutrophil populations (Coffelt, Wellenstein, & De Visser, 2016). Some studies

have shown that neutrophils might have antagonizing effects on metastasis and are

associated with better survival in some cancers (Galdiero et al., 2016; Granot et al.,

2011).

The presence of mast cells, which are both positive and negative regulators of

the immunity (Galli, Grimbaldeston, & Tsai, 2008), has been linked to tumor

development in many studies; their infiltration into tumors has been associated with

poor patient outcomes (Johansson et al., 2010; Nonomura et al., 2007; Ribatti et al.,

2003; Tth, Tth-Jakatics, Jimi, Takebayashi, & Kawamoto, 2000). However,

evidence also exists about a protective role of mast cells in human cancers

(Fleischmann et al., 2009; Rajput et al., 2008).

Dendritic cells, important antigen-presenting cells, are largely defective in

tumors; their differentiation, activation and immune-response stimulation are often

impaired. Indeed, decreased density of tumor-infiltrating dendritic cells is linked to

poor prognosis (Inoshima et al., 2002; Seigo Kashimura et al., 2012; Veglia &

Gabrilovich, 2017). However, evidence also exists that tumor cells can drive the

tumor-associated dendritic cells towards an immunosuppressive phenotype, and

some types of the tumor-infiltrating dendritic cells have been associated with poor

prognosis (Lombardi, Khaiboullina, & Rizvanov, 2015; Saadeh, Kurban, & Abbas,

2016; Tesone et al., 2016).

Natural killer (NK) cells, which have attributes of both innate and adaptive

immunity, recognize malignant cells and kill them by secreting the toxic content of

their cytosolic granules. NK cells also produce cytokines, such as IFNγ and TNF-

α, which will instead, for example, activate adaptive immune responses. Tumor-

infiltrating NK cells have been linked to better prognosis in a variety of cancers;

32

however, the NK cells are commonly suppressed in tumors by the tumor cells

themselves or by immunosuppressive macrophages, dendritic cells or T cells. The

maturation, proliferation and function of NK cells can be directly suppressed via

different immunosuppressive cytokines and other molecules, such as transforming

growth factor beta (TGF-β) and indoleamine 2,3-dioxygenase 1 (IDO1) (Della

Chiesa et al., 2006; Ghiringhelli et al., 2005; Lee, Kang, & Cho, 2017). Still, NK

cells are one of the key players of antibody-dependent cellular cytotoxicity (ADCC),

which is suggested to be the main mechanism of action of the therapeutic

monoclonal antibodies (Arnould et al., 2006; Clynes, Towers, Presta, & Ravetch,

2000; Tian et al., 2017). It was thought for a long time that NK cells were solely

antitumorigenic, but it has become increasingly clear that the NK cells can also

possess tumor promoting effects (Bruno, Ferlazzo, Albini, & Noonan, 2014).

Macrophages are known for their immunomodulatory effects. Macrophages

display variable phenotypes, which form a continuum from M1-like state to M2-

like state. Classically activated M1-like macrophages are pro-inflammatory and

antitumoral, while alternatively activated M2-like macrophages possess anti-

inflammatory and immunosuppressive effects and are, thus, protumoral (Biswas &

Mantovani, 2010; Brown, Recht, & Strober, 2017). Tumor-associated macrophages

(TAMs) have been associated with prognosis and therapy response in many solid

tumors. M1-like TAMs are linked to a better disease course and, conversely, M2-

like TAMs are associated with more adverse outcomes (Biswas, Allavena, &

Mantovani, 2013; Fridman, Zitvogel, Sautès-Fridman, & Kroemer, 2017). M2-like

TAMs are known to suppress innate and adaptive immune responses by promoting

the activity of immunosuppressive T cells, by secreting inhibitory cytokines such

as TGF-β and IL-10 and by expressing/producing inhibitory molecules like PD-L1

and IDO1. M2-like macrophages also promote angiogenesis and facilitate tumor

invasion (Biswas & Mantovani, 2010; Biswas et al., 2013; Mantovani, Marchesi,

Malesci, Laghi, & Allavena, 2017). TAMs are also known to contribute to

antibody-dependent cellular phagocytosis (ADCP), which is induced in TAMs by

therapeutic monoclonal antibodies and leads to phagocytosis of antibody-coated

tumor cells by TAMs (Kurdi et al., 2018; Shi et al., 2015; Vermi et al., 2018).

Yet another cell type of the innate immunity found in tumors is myeloid-

derived suppressor cells (MDSC), which comprise of myeloid cells, immature

dendritic cell, immature granulocytes or immature macrophages. MDSCs suppress

various T cell functions and modulate the cytokine production of macrophages by

converting them into M2-like immunosuppressive state (Gabrilovich & Nagaraj,

33

2009). MDSCs have been shown to be associated with a poor prognosis in many

solid cancers (Zhang et al., 2016).

2.2.2 Adaptive immune system and cancer

The next line defenses against foreign pathogens or an injury are the adaptive

immune responses, which are activated after antigen recognition. The adaptive

immune system comprises B and T lymphocytes. B lymphocytes mediate a

humoral immunity and can recognize various types of molecules -- both soluble

and cell surface-bound forms -- whereas T lymphocytes mediate cell-mediated

immunity and recognize only peptide fragments of proteins introduced to them via

specific receptors. Lymphocytes have an essential role in tumor eradication, and

they are also commonly altered in cancer for immune evasion (Nelson, 2010;

Sharma & Allison, 2015).

B lymphocytes are typically located in conventional lymphoid tissues such as

spleen, lymph node or blood, but they can also be found in other nonlymphoid

tissues, usually in tertiary lymphoid structures and in aggregates with other immune

cells (Drayton, Liao, Mounzer, & Ruddle, 2006). Increasing evidence has shown

that many tumors are infiltrated with B lymphocytes, but the exact effects of B cells

in tumors is unclear. Many B cell subtypes have diverse functions that complicate

the effects of B cells in tumorigenesis and in tumors overall. B cells act directly

through tumor-reactive antibodies or cytotoxic pathways, or they can influence T

cell responses through secretion of chemokines and cytokines, or by facilitating the

formation of TLS, or they can act as antigen-presenting cells (Flynn,

Somasundaram, Arnold, & Sims-Mourtada, 2017; Nelson, 2010). The complex

functions of B cells are also reflected in their clinical significance, since tumor-

infiltrating B cells have been shown to associate with both poor and good prognoses

in many solid cancers (Flynn et al., 2017).

T cells are one of the most abundant immune cells found in the tumors. T

lymphocytes can be divided according to their effector functions into cytotoxic T

cells (CD8+) and helper T cells (CD4+), which comprise Th1, Th2, Th17, regulatory

T (Treg) cells, and natural killer T cells (Grivennikov et al., 2010). The activation

of CD8+ or CD4+ naïve T cells requires not only the recognition of tumor antigens

by T cell receptors but also costimulatory signals via engagement of B7 molecules

(CD80, CD86) to the CD28 receptor found on T cells. The tumor antigens can be

presented to T cells through MHC I or II receptors, which are expressed by many

cell types, but the costimulatory B7 molecules are found mainly on antigen-

34

presenting cells (APCs). The T cells can proliferate, differentiate further and traffic

to the tumor site after activation (Greenwald, Freeman, & Sharpe, 2005; Sharma &

Allison, 2015; Townsend & Allison, 1993).

The antitumoral effects of T cells are mediated through cytokines, chemokines

and direct cytotoxicity, but these T cell actions are often disrupted in cancer by

multiple mechanisms (Lin, Karin, Pearce, & Kleyman, 2007; Swann & Smyth,

2007). First, it is critical that the T cells in the tumor microenvironment are in close

contact with the tumor cells, but their infiltration is often blocked. Second, even if

T cells can infiltrate into tumors, they must overcome additional barriers. The

antitumoral actions of T cells can be suppressed by the tumor cells themselves, by

MDSCs, by M2-like macrophages and by other immunosuppressive cells, even

regulatory T cells. The immunosuppressive effects can be transmitted through

inhibitory cytokines, such as TNFα or TRAIL, inhibitory enzymes like IDO1 or

other inhibitory molecules (Joyce & Fearon, 2015; Sharma & Allison, 2015). One

extremely well-known, inhibitory molecule expressed by various of cell types,

including cancer cells and macrophages, is PD-L1, which binds to PD-1 receptor

on T cells, leading to inhibition of their cytotoxic activities and induction of

apoptosis (Freeman et al., 2000).

T cells have been shown to possess both antitumoral and tumor-promoting

effects. CD8+ cytotoxic T cells and Th1 helper T cells have been shown to correlate

with better survival in many cancer types, whereas Tregs, Th2 and Th17 cells have

contradictory roles (Fridman et al., 2012; Fridman et al., 2017). Tregs are

extensively studied immunosuppressive cells that are essential for developing and

maintaining self-tolerance but are often recruited to the tumor site. They have

multiple ways to inhibit the functions of cytotoxic T cells, such as producing

inhibitory cytokines like IL-10 or expressing inhibitory molecules like PD-L1

(Terabe & Berzofsky, 2004). High densities of Tregs are associated with poor

prognosis in numerous cancer types, but in colorectal and gastric cancer, Tregs have

been linked to favorable prognosis. Th2 and Th17 cells have been similarly

associated with both good and poor survival (Fridman et al., 2012; Fridman et al.,

2017).

2.2.3 Tumor immune profile and Immunoscore

Three different immune profiles of the tumor can be distinguished according to

their inflammatory state. The first profile is the immune-inflamed phenotype,

which is characterized by high infiltration of immune cells, especially CD4+ and

35

CD8+ T cells and high PD-L1 expression. The second profile is the immune-

excluded phenotype, which is characterized by an abundance of immune cells

which, however, are retained in the stroma surrounding the tumor and are unable

to infiltrate into the tumor. The third profile is called the immune-desert phenotype,

which is characterized by a lack of T cells and other immune cells in the stroma

and in the tumor. These tumor-immune profiles correlate with a patient’s response

to anti-PD-1/PD-L1 therapy (Chen & Mellman, 2017).

The significance of the tumor-infiltrating immune cells’ location has been

shown in many studies with different cancer types, demonstrating that the

prognostic value of the tumor-infiltrating immune cells depends on both the

location and the type of the immune cells (Galon et al., 2006; Hermans et al., 2014;

Zhou et al., 2018). Combinatory analysis of CD3+ and CD8+ T cells in the center

of the tumor (CT) and in the invasive margin (IM) has provided a clinically useful

prognostic marker for colorectal cancer. This analysis is described as the

Immunoscore, which categorizes tumors according to their CD3+ and CD8+

immune-cell densities in both CT and IM. Tumors are scored from Immunoscore

0, where the density of both cell types in both locations is low, to Immunoscore 4,

where the density of both cell types in both locations is high. Immunoscore has

been suggested to be included in the classification of colorectal cancer (Galon et

al., 2014; Galon et al., 2006). A recent study with over 2,500 colorectal cancer

patients from 14 different cancer centers and 13 different countries showed that the

Immunoscore (3-tiered: low, mediate, high Immunoscore) provided an even better

estimate of risk for all clinical parameters than the TNM classification (Pagès et al.,

2018). Whether the use of the Immunoscore could be expanded into other cancer

types remains to be seen.

2.3 Cancer stem cells

Cancer cells within one tumor can exist in distinct phenotypic states, thus creating

tumor heterogeneity. Some of the tumor cells have features similar to normal stem

cells and are, therefore, called cancer stem cells (CSCs). These cells can self-renew

and generate various types of non-CSC progenies, which form the rest of the tumor

(Eun, Ham, & Kim, 2017). The first CSCs were confirmed in acute myeloid

leukemia in the 1990s (Bonnet & Dick, 1997), whereas the first CSCs in solid

cancers were found in 2003 in breast cancer (Al-Hajj, Wicha, Benito-Hernandez,

Morrison, & Clarke, 2003). To date multiple cancer types, such as colon, ovarian

and lung cancer, has been shown to contain CSC subpopulations (Medema, 2013).

36

CSCs can evade cell death and metastasize, and their presence in tumors has been

linked to an aggressive disease course and poor survival (Beier et al., 2008;

Charafe-Jauffret et al., 2010; Chiou et al., 2008; Ginestier et al., 2007; Liu et al.,

2007; Pallini et al., 2008). Furthermore, CSCs have been shown to be resistant to

the conventional cancer therapies, targeted therapies and immunotherapies

(Creighton et al., 2009; Maccalli, Parmiani, & Ferrone, 2017; McLendon et al.,

2006; Phillips et al., 2006; Reim et al., 2009; Wang et al., 2017). Thus, these cells

are extremely important also from the clinical viewpoint.

2.3.1 Hierarchical and stochastic cancer stem cell models

Two models -- hierarchical and stochastic -- have been proposed to describe the

tumor progression and heterogeneity driven by the CSCs (Fig. 4). The hierarchical

model states that carcinogenesis occurs when a normal stem cell evades regulation

and becomes a tumorigenic stem cell, thus CSC, and can initiate tumor growth. The

CSCs are defined as a totally distinct population in this model and are the only cells

that can give rise to differentiated cell populations with limited proliferative

capacity. However, this model rules out the interchange between stem-like and

differentiated states within one cell (Melzer, von der Ohe, Lehnert, Ungefroren, &

Hass, 2017; Plaks, Kong, & Werb, 2015; Takebe, Harris, Warren, & Ivy, 2011).

Conversely, the stochastic model suggests that carcinogenesis can be initiated

by any normal cell that has randomly (i.e. stochastically) acquired oncogenic

mutations, epigenetic changes, etc. This model states that every cancer cell within

one tumor is equally capable of maintaining and promoting the tumor growth. The

tumor heterogeneity in this model is mediated through genetic changes or by

extrinsic and intrinsic factors such as tumor microenvironment and different

signaling pathways (Melzer et al., 2017; Plaks et al., 2015; Takebe et al., 2011).

These two models are not mutually exclusive, because the phenotypical states

of the cells in a hierarchical organization are more transitory than earlier believed,

and the stochastic events are also able to generate hierarchical cell populations

(Plaks et al., 2015). To date it is known that a differentiated cell, either a cancer cell

or a normal cell, can dedifferentiate back to the stem-like state (Chaffer et al., 2011;

Gupta et al., 2011; Schwitalla et al., 2013). This dedifferentiation of the cell state

can be either inherited (hierarchical model) or caused by mutations that lead to the

acquisition of stem-like cell properties (stochastic model) (Plaks et al., 2015). This

cancer plasticity is extremely valid to be considered in the clinical targeting of

cancer stem cells.

37

Fig. 4. Hierarchical and stochastic cancer stem cell models. The hierarchical model

suggests that only the cancer stem cells (CSCs) can initiate and maintain tumor growth,

while the stochastic model states that all tumor cells are equally capable of initiating

and maintaining tumor growth. Today it is known that differentiated cancer cells can

dedifferentiate back to the stem-like cell state, creating cell plasticity.

X XX X

CSC Non-CSC

Hierarchical model

Stochastic model

Normal cells

Plasticity

Differentiation

Reprogramming

Normal stem cell

38

2.3.2 Signaling pathways related to cancer stem cell phenotype

CSCs are regulated by specialized microenvironments called CSC niches; several

studies have suggested that CSCs require these niches to maintain their stem cell

properties. The cells and molecules of the CSC niche affect the signaling of CSCs

(Plaks et al., 2015). Normal stem cells and CSCs share similar signaling pathways,

of which the most fundamental ones for the maintenance of the stemness are Notch,

Hedgehog and Wnt/β-catenin pathways (Takebe et al., 2011).

Notch pathway has a critical role in controlling the cell-to-cell interactions in

embryogenesis, cell proliferation and differentiation, and apoptosis (Artavanis-

Tsakonas, Rand, & Lake, 1999). Notch can also affect the self-renewal of stem cells

(Dontu et al., 2004; McKay et al., 2006). Hedgehog pathway acts in the embryonic

development by controlling the tissue polarity and by participating in the

maintenance of the stem cells and is, therefore, often hyperactivated in tumors

(Ingham & McMahon, 2001; Takebe et al., 2011). Wnt signaling has also a vital

role in embryogenesis, as it regulates the development of a variety of organ systems.

In adults wnt regulates tissue self-renewal, for example, in the hair follicles

(Clevers, 2006; Grigoryan, Wend, Klaus, & Birchmeier, 2008). It has been shown

that CSCs require β-catenin to maintain their tumorigenic phenotype (Kim et al.,

2018; Malanchi et al., 2008; Zimmerli et al., 2018).

All the aforementioned pathways have vital roles in the embryogenesis, and

indeed, EMT, an essential process for the embryonic development, occurs also

during tumorigenesis and allows CSCs to become metastatic (Shibue & Weinberg,

2017; Shook & Keller, 2003).

2.3.3 Markers used to identify cancer stem cells

Cancer stem cell markers are commonly proteins that can identify cell populations

enriched within a tumor and that fulfill the functional definition of cancer stem-like

cells. Two key characteristics are used to define CSCs: their enhanced

tumorigenicity and their capacity to self-renew or differentiate. Numerous CSC

markers have been identified, of which the most broadly used are high CD133

expression, high CD44 expression, and high aldehyde dehydrogenase 1 (ALDH1)

expression or activity (Medema, 2013).

CD133, also called prominin-1, is a transmembrane glycoprotein that is used

to identify CSCs in many solid cancers, including breast, lung, brain and colorectal

cancers (Bidlingmaier, Zhu, & Liu, 2008; Cioffi et al., 2015; Liu et al., 2013;

39

Wright et al., 2008). CD44 is also a transmembrane glycoprotein that has been

shown to be potential CSC marker in various cancer types, such as breast, colorectal,

lung and gastric cancers (Morath, Hartmann, & Orian-Rousseau, 2016). ALDH1,

however, is a cytosolic enzyme that is widely used to isolate CSCs in many cancers

(Carpentino et al., 2009; Ginestier et al., 2007; Jiang et al., 2009; van den Hoogen

et al., 2010). In addition to these markers, transcription factors governing

pluripotency of embryonic stem cells, such as SOX2, have been linked to CSCs

and tumor initiation and are potential CSC markers (Leis et al., 2012; Masui et al.,

2007; Zhu et al., 2017).

Universal CSC markers do not exist, because CSCs are an extremely

heterogeneous cell population. A marker identified in one tumor may not be able to

distinguish CSCs in another tumor, even in a tumor of the same origin (Plaks et al.,

2015; Vlashi & Pajonk, 2014). Hence, more than one CSC marker is commonly

used, for example, in breast cancer CD44high/CD24low marker (Wright et al., 2008).

2.4 HER family

Human epidermal growth factor receptor (HER) proteins are essential during

development and in normal adult physiology, and they are expressed in various

neuronal, epithelial and mesenchymal tissues (Hynes & Lane, 2005). HERs belong

to the RTK protein family. As mentioned earlier in section 2.1.3., the RTKs are

crucial to numerous cell functions such as proliferation, differentiation and survival.

The HER family is among the most studied cell-signaling families in biology.

2.4.1 Structure and function

The HER family comprises four members: EGFR/HER1/ERBB1, HER2/ERBB2,

HER3/ERBB3 and HER4/ERBB4. All HER proteins share a nearly similar

structure. They have an extracellular domain that contains four distinct parts

(subdomains I-IV), a single transmembrane segment, an intracellular segment

containing juxtamembrane, a protein kinase domain and a carboxyterminal (C-

terminal) tail (Roskoski, 2014; Ullrich et al., 1984).

The signaling through HER proteins is initiated by the binding of a specific

ligand. Both EGFR and HER4 have seven ligands, whereas HER3 has two ligands,

and HER2 has none (Table 1) (Hynes & MacDonald, 2009). The subdomains I and

III of the receptors participate in the ligand binding. Bivalent ligand, for example,

epidermal growth factor (EGF), binds to the subdomains I and III on a single

40

receptor. The ligand binding initiates major conformational changes in the

extracellular part of the receptor, revealing a dimerization arm in the subdomains

II and IV (Ogiso et al., 2002). The HER2 receptor, however, already exists in this

open conformation where the dimerization arm is not buried (Cho et al., 2003),

which could explain its lack of ligands. After the dimerization arm is revealed, the

HER proteins can form either hetero- or homodimers (or even oligomers), which

will lead to an allosteric activation of the tyrosine kinase domains in the

intracellular parts of the receptors (Hubbard & Miller, 2007; Roskoski, 2014;

Zhang, Gureasko, Shen, Cole, & Kuriyan, 2006). Active kinases then

phosphorylate tyrosine residues on the C-terminal tails of the receptors, which

creates docking sites for the downstream signaling molecules (Roskoski, 2014).

The kinase domain of the HER3 protein is catalytically impaired, but it can still act

as an efficient phosphotyrosine scaffold, and it can activate its dimeric partners via

allosteric activation mechanism (Shi, Telesco, Liu, Radhakrishnan, & Lemmon,

2010).

As mentioned earlier in section 2.1.3., the main downstream signaling

pathways of RTKs are the PI3K/Akt/mTOR and Ras/Raf/MEK/ERK pathways, of

which the former has an important role for cell survival and the latter participates

in cell proliferation (Yarden & Pines, 2012). The HER3 receptor is a major player

in cell survival, since it has several docking sites for the p85 subunit of PI3K. Other

HER proteins are also able to activate the PI3K pathway; HER4 has one binding

site for PI3K, but EGFR and HER2 cannot directly bind the enzyme (Kainulainen

et al., 2000; Roskoski, 2014; Schulze, Deng, & Mann, 2005).

The signaling through HER and other RTK proteins is downregulated by taking

the receptor-ligand complex inside the cell by endocytosis. The receptor-ligand

complex will be transported via endosomes to lysosomes for degradation. Before

the endocytosis, the receptor is ubiquitinated, which is essential for the trafficking

of the endosome to lysosomes. This downregulation is crucial for terminating the

proliferative signals produced by the activated receptors (Katzmann, Odorizzi, &

Emr, 2002; Mizuno et al., 2005).

41

Table 1. Human epidermal growth factor receptor ligands. Modified from Hynes &

MacDonald 2009.

EGFR HER2 HER3 HER4

EGF - NRG1 Betacellulin

TGF-α NRG2 HB-EGF

Amphiregulin Epiregulin

Epigen NRG1

Betacellulin NRG2

HB-EGF NRG3

Epiregulin NRG4

HB-EGF=heparin binding EGF-like growth factor, NRG=neuregulin

2.4.2 HERs and cancer

The HER proteins and their functions are often altered in cancer. The HER genes

can be amplified or mutated, the ligands may be overproduced by the tumor cells

or the surrounding stromal cells, or the downregulation of the receptor may be

impaired. All these alterations can lead to constitutively activated receptors and

prolonged signaling through them, causing tumor progression (Arteaga &

Engelman, 2014; Hu et al., 2015). Activating oncogenic alterations of EGFR or

HER2 are commonly seen in several cancers, while activating alterations of HER3

and HER4 are less frequent, but their altered expressions have been reported in

cancer (Mishra, Hanker, & Garrett, 2017).

In the 1980s Kawamoto et al. showed by blocking EGFR in A431 cells that the

receptor was able to drive the cancer cell growth (Kawamoto et al., 1983). Since

then EGFR amplification, overexpression and activating mutations have been

discovered in several carcinomas (Birkman et al., 2016; Shan et al., 2015). Mutated

EGFR, which lacks exons 2–7 in the extracellular domain and is called EGFRvIII,

is found mainly in gliomas but also in a fraction of lung, breast and ovarian cancers

and leads to enhanced tumorigenicity (Moscatello et al., 1995; Nishikawa et al.,

1994; Sugawa, Ekstrand, James, & Collins, 1990). EGFR mutations were

discovered in the early 2000s in non-small cell lung cancer (NSCLC), further

highlighting the role of the receptor in tumorigenesis (Lynch et al., 2004; Paez et

al., 2004). The two most common EGFR mutations seen in NSCLC are deletion of

exon 19 and leucine-to-arginine substitution at amino acid position 858 (L858R),

which are both located in the kinase domain of the receptor (Pao & Chmielecki,

2010).

42

The most common oncogenic alteration of HER2 is its amplification. In 1987

it was discovered that around 20% of breast cancers have HER2 amplification

which is associated with poor patient outcomes (Slamon et al., 1987). It has been

seen that metastatic mammary tumors are generated when wild-type HER2 is

controlled by a mammary-specific promoter in transgenic mice (Andrechek et al.,

2000; Finkle et al., 2004). HER2 amplification has also been discovered in other

cancers, such as gastric, esophageal and lung cancers (Bang et al., 2010; Cappuzzo

et al., 2005). Activating mutations of HER2 are less common and are usually found

in cancers without HER2 amplification. HER2 mutations have been reported in

several cancers, such as lung adenocarcinomas, breast and colorectal cancer

(Arteaga & Engelman, 2014; Bose et al., 2013; Hanker et al., 2017; Kavuri et al.,

2015; Suzuki et al., 2015).

HER3 has also been linked to several cancers, mainly through its role in

promoting signaling from oncogenic HER2 or EGFR by activating PI3K (Mishra,

Patel, Alanazi, Yuan, & Garrett, 2018). Blocking HER3 has been shown to inhibit

the growth of HER2-dependent tumors in xenograft models, which highlights the

essential role of HER3 for HER2-driven tumorigenesis (Garrett et al., 2013; Lee-

Hoeflich et al., 2008). HER3 alterations have also been discovered, but the

oncogenic activity of HER3 may still be dependent on HER2 or other HER proteins

(Jaiswal et al., 2013; Mishra et al., 2017).

HER4 is much less extensively studied than the other members of the HER

family, and its activating mutations are less frequent in tumors. However, HER4

alterations have been observed in different types of cancer, mostly in melanoma,

and HER4 signaling has been shown to play an important role for cancer recurrence

in lung cancer (Hegde et al., 2013; Kurppa, Denessiouk, Johnson, & Elenius, 2016;

Mishra et al., 2017).

HERs and CSCs

The expression of EGFR, HER2 and HER3 has been linked to cancer stem-like cell

(CSLC) properties in several types of cancer. EGFR expression can identify a

CSLC population, induce CSLC properties, and promote their survival (Mazzoleni

et al., 2010; Shi et al., 2018). HER2 expression is able to drive tumorigenesis and

can maintain and regulate a CSLC population (Ithimakin et al., 2013; Jiang et al.,

2012; Korkaya et al., 2008; Nakanishi et al., 2010; Rainusso et al., 2012).

Neuregulin-1 (NRG1), a HER3 and HER4 ligand, has been shown to induce CSLC

characteristics in breast cancer by triggering HER3, since the NRG1-induced

43

CSLC characteristics are blocked when HER3 is inhibited (Jeong et al., 2014; Lee

et al., 2014). The expression of EGFR, HER2 and HER3 has also been linked to

poor clinical outcomes (Brabender et al., 2001; Cao, Chen, Xiong, & Chen, 2016;

Carey et al., 2007; Cheang et al., 2009; Day et al., 2017; Slamon et al., 1987; Yan

et al., 2018). A connection between HER4 expression and CSLC properties,

however, is quite unexplored. Some studies have shown that HER4 can promote

CSLC tumor progression, although one study showed that HER4 expression alone

was unable to induce tumorigenesis in vivo but was still able to enhance the survival

and growth of Ras- and/or wnt-driven tumors (Jardé et al., 2016; Williams et al.,

2015). HER4 expression is often linked to better prognosis, which supports the idea

that HER4 alone is unable to induce tumorigenesis. However, some studies have

linked the HER4 expression to poor survival (Munk et al., 2013; Wang et al., 2016;

Zhao et al., 2014).

2.4.3 HER targeted therapies

When tumors are addicted to a specific oncogene, they can be easily targeted by

inhibiting the oncogenic product. Multiple EGFR and HER2 targeting therapies

have been developed, some of which are already in clinical use. HER3 targeting

therapies are being developed, whereas HER4 is quite unexplored in the field of

therapeutic targeting. HER targeting agents are commonly either monoclonal

antibodies (mAbs) or tyrosine kinase inhibitors (TKIs). Monoclonal antibodies are

highly specific for their target and are always administered intravenously, whereas

TKIs are usually given orally and are not as specific as the mAbs. TKIs bind the

kinase domain either reversibly or irreversibly, and they can be ATP competitors

binding the ATP pocket directly or they can bind the kinase domain allosterically,

leading to modifications of the ATP pocket. However, it is important to notice that

even though oncogene addicted tumors are easily targeted, the tumors are clever

and will usually develop resistance to the given treatment if it does not already exist

(Arteaga et al., 2011). This chapter briefly describes the HER targeted therapies

and the most common resistance mechanisms.

EGFR

Three generations of small molecule EGFR inhibitors, TKIs, have been developed.

First-generation inhibitors, erlotinib and gefitinib which are reversible and

competitive for ATP binding, have shown drastic clinical responses in EGFR-

44

mutated cancer patients (Lynch et al., 2004; Mok et al., 2009; Rosell et al., 2012).

The mutated EGFRs have a higher affinity for erlotinib and gefitinib than the wild-

type EGFR (Carey et al., 2006; Lynch et al., 2004; Yun et al., 2007). These

inhibitors are in clinical use, but tumors almost inevitably develop acquired

resistance to these first-generation inhibitors. The most common resistance

mechanism is a gatekeeper mutation T790M in the EGFR receptor (Pao et al., 2005).

The second-generation EGFR TKIs, such as ATP-competitive afatinib, bind

irreversibly to the receptor and were developed to overcome acquired gefitinib and

erlotinib resistance. Even though these second-generation inhibitors are shown to

target the EGFRT790M, they also target the wild-type EGFR. Afatinib also binds

HER2 and HER4 proteins. In clinical trials, however, afatinib was unable to

overcome the EGFR TKI resistances and is currently used mainly as an alternative

agent for 1st generation inhibitors in metastatic, EGFR mutant NSCLC (Arteaga &

Engelman, 2014; Li et al., 2008; Miller et al., 2012).

Third-generation inhibitors are much more potent towards the mutated EGFR,

including T790M, than towards the wild-type. AZD9291, also called osimertinib,

has been shown to be well tolerated and has a high rate of clinical responses (Cross

et al., 2014; Jänne et al., 2015; Mok et al., 2017; Soria et al., 2018). Osimertinib is

an irreversible, ATP-competitive inhibitor and has received EMA and FDA

approval, but resistance towards osimertinib has been discovered (Cross et al., 2014;

Thress et al., 2015).

EGFR-neutralizing antibodies have been developed in addition to TKIs, of

which cetuximab and panitumumab are in clinical use, mainly to treat colorectal

cancer and head and neck cancer (Arteaga et al., 2011). Cetuximab in combination

with afatinib has been studied to treat NSCLC, because the combination was shown

to target the EGFRT790M in mice, and the combination has shown some clinical

activity, but the studies are still ongoing (Horn et al., 2017; Janjigian et al., 2014;

Regales et al., 2009).

HER2

Both TKIs and antibodies have been developed to target the HER2 protein.

Trastuzumab (Herceptin) is a monoclonal antibody that binds HER2 on the C-

terminal portion of subdomain IV and is used in the clinic for treatment of HER2-

positive cancers (Cho et al., 2003). Trastuzumab inhibits a cleavage of HER2

ectodomain, uncouples HER2 homodimers leading to a partial inhibition of

downstream signaling, but its main mechanism of action is to induce antibody-

45

dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular

phagocytosis (ADCP) (Clynes et al., 2000; Ghosh et al., 2011; Molina et al., 2001;

Shi et al., 2015). ADCC and ADCP are regulated via fragment c gamma receptors

(FcγR) on immune cells, and the effects of trastuzumab are remarkably diminished

when FcγR are absent (Clynes et al., 2000). These actions of trastuzumab are

mainly mediated by NK cells and macrophages, but it has been shown that T cells

are necessary for its full activation (Arnould et al., 2006; Gennari et al., 2004; Park

et al., 2010). Tumors can develop resistance to trastuzumab via multiple

mechanisms. The binding of trastuzumab can be impeded by expression of

truncated HER2, which lacks the receptor’s extracellular domains. The tumors can

overcome the inhibition of the HER2 signaling by increasing the expression of

EGFR, HER3 or other RTKs, or by altering the downstream signaling molecules.

The PI3K/Akt/mTOR pathway has often been linked to trastuzumab resistance. The

immunomodulatory functions of trastuzumab can be affected by FcγR

polymorphisms and by immunosuppressive cells, which inhibit the function of NK

cells, macrophages and T cells (Arteaga et al., 2011; Elster et al., 2015; Musolino

et al., 2008). However, a recent study has shown that the ADCP in macrophages,

induced by trastuzumab itself, can increase the expression of PD-L1 and IDO1,

leading to immunosuppression (Su et al., 2018).

Pertuzumab is a monoclonal antibody that binds to subdomain II of HER2

protein blocking heterodimerization of the receptor and, thus, leads to partial

inhibition of the receptor signaling and also to activation of the ADCC and ADCP

(Agus et al., 2002). Pertuzumab is given together with trastuzumab, because they

target the different domains and inhibit together all HER2-containing dimers. This

combinatory treatment has shown synergy in preclinical studies and in clinical trials

and is now used as a treatment for HER2-positive breast cancer (Baselga et al.,

2012; Gianni et al., 2012; Scheuer et al., 2009). The resistance mechanisms for

pertuzumab treatment are much less known than for trastuzumab.

Trastuzumab emtansine (T-DM1) is yet another HER2 targeting treatment

containing trastuzumab antibody, which is covalently linked to three to four non-

cleavable maytansinoid molecules that inhibit microtubule polymerization. When

T-DM1 binds to HER2 and the receptor is internalized, the T-DM1 comes along

and will be released after lysosomatic degradation, leading to cell lysis (Lewis

Phillips et al., 2008). The functions of T-DM1 are, otherwise, quite similar to

trastuzumab (Junttila, Li, Parsons, Phillips, & Sliwkowski, 2011). T-DM1 is in

clinical use for HER2-positive breast cancer, but resistance is eventually developed

(Barok, Joensuu, & Isola, 2014; Verma et al., 2012). The suggested resistance

46

mechanisms include impaired lysosomal activity, defects in the system inducing

mitosis and differential endocytosis (Ríos-Luci et al., 2017; Sabbaghi et al., 2017;

Sung et al., 2018).

TKI used for HER2-positive cancers (mainly breast cancer) is lapatinib, which

blocks the HER2 signaling by binding reversibly to the ATP pocket. Lapatinib is

used in clinical practice and has shown activity in HER2-positive breast cancers

that have progressed after trastuzumab. Lapatinib binds also the inactive formation

of EGFR but has not shown activity against EGFR-mutated cancers (Arteaga &

Engelman, 2014; Geyer et al., 2006; Konecny et al., 2006; Wood et al., 2004).

Resistance to lapatinib is proposed to occur via activation of compensatory survival

pathways, for example, increasing signaling trough estrogen receptors and

activating mutations of PI3K (Garrett & Arteaga, 2011).

Neratinib, an irreversible, ATP-competitive TKI that targets EGFR, HER2 and

HER4, has recently been approved for the treatment of early stage HER2-positive

breast cancer patients to follow trastuzumab-based adjuvant therapy (Feldinger &

Kong, 2015; Martin et al., 2017).

HER3 and HER4

Targeting agents for HER3 and HER4 are less studied than for EGFR and HER2.

The signaling through HER3 has been shown to be involved in the resistance to

EGFR and HER2 targeting therapeutic agents, and HER3 targeting inhibitors are

being developed. Since the tyrosine kinase of HER3 is impaired, TKI targeting of

HER3 is unlikely to be effective. The therapeutic approaches for HER3 targeting

focus on inhibiting the ligand binding and dimerization of the receptor, including

the internalization of HER3, and on trapping the receptor in its inactive

conformation (Karachaliou, Lazzari, Verlicchi, Sosa, & Rosell, 2017). Targeting

agents for HER4 are quite unexplored. Few monoclonal antibodies that target

HER4 in vitro and in vivo have been developed (Hollmén, Määttä, Bald,

Sliwkowski, & Elenius, 2009; Okazaki et al., 2016).

2.5 HER2+ breast cancer

HER2 gene is amplified or overexpressed in ~10–20% of all breast cancers (Cortet

et al., 2018; Kohler et al., 2015; Köninki, Tanner, Auvinen, & Isola, 2009). HER2

positivity is associated with large tumor size, lymph-node involvement, high-grade

tumors, high rates of proliferation, higher recurrence rates and poor survival. Anti-

47

HER2 therapies have remarkably improved the patient outcomes of HER2-positive

breast cancer (Andrulis et al., 1998; Baselga et al., 2012; Gonzalez-Angulo et al.,

2009; Marty et al., 2005; Ménard et al., 2002; Payandeh, Shahriari-Ahmadi,

Sadeghi, & Sadeghi, 2016; Slamon et al., 1987; Slamon et al., 1989). The addition

of trastuzumab alone in the treatment practice has made a great difference, but when

HER2-positive metastatic breast cancer patients received pertuzumab in addition

to trastuzumab and docetaxel (a chemotherapeutic agent), an improvement of 15.7

months in the overall survival (OS) was seen reaching a median OS of 56.5 months

(Swain et al., 2015).

2.5.1 Treatment of HER2+ breast cancer

Local disease

Figure 5 shows a scheme of the basic treatment options for local HER2+ breast

cancer. Surgery is the primary treatment for local disease. Mastectomy is a standard

option if the breast conserving surgery is not feasible and the patient has no wish

to conserve the breast. However, if the patient wishes to conserve the breast,

neoadjuvant therapy can be given to downsize the tumor. Neoadjuvant therapy is

also given for patients with inoperable T4 (T, tumor volume) and locally advanced

diseases. Regimens of the neoadjuvant therapies are planned individually for each

patient, but trastuzumab is usually given to all HER2+ breast cancers.

Chemotherapy can be given as neoadjuvant therapy, and endocrine therapy is given

according to the estrogen receptor status of the tumor (Finnish Breast Cancer Group,

2019; Senkus et al., 2015).

Two to six weeks after surgery, adjuvant therapies are planned for each patient

according to the risk of relapse, the predicted sensitivity to the treatment and the

benefit from them. Postoperative radiation therapy is given after breast-conserving

surgery, for patients with locally advanced disease and after mastectomy for T3–

T4 non-advanced tumors. HER2+ breast cancer patients will commonly receive

chemotherapy and trastuzumab therapy and possible endocrine therapy as an

adjuvant therapy. Guidelines exist about which treatments can be given

concomitantly and which cannot, for example, trastuzumab should not be given

together with anthracyclines due to its cardiotoxicity (Finnish Breast Cancer Group,

2019; Senkus et al., 2015). However, much uncertainty exists about the duration of

trastuzumab treatment. Some studies have shown that longer trastuzumab

48

treatments (2 years) do not give additional benefits, but other studies have been

unable to demonstrate that (Goldhirsch et al., 2013; Joensuu et al., 2009; Pivot et

al., 2013). The standard duration of trastuzumab treatment is currently one year. It

should be noted that these therapies are just examples, and every therapy is planned

separately for each patient. The things that must be considered when making the

final decision about the therapies are the predicted late effects of the treatment, the

general health status of the patient, the patient’s biological age and the patient’s

preferences (Finnish Breast Cancer Group, 2019; Senkus et al., 2015).

Fig. 5. Treatment options for early HER2+ breast cancer. Neoadjuvant therapy can be

given before surgery, surgery will be either breast conserving or mastectomy, and

adjuvant therapy will be given after the surgery. ChT=chemotherapy, ET=endocrine

therapy, RT=radiation therapy, T=trastuzumab, P=pertuzumab. Figure modified from

Senkus et al., 2015.

Early HER2+ breast cancer

Small tumorand/or surgery

feasible

Inoperable T4 orlocally advanced tumor

or patient wishes for breast conservation,

but tumor is too large

No wish for breastconservation, or it is

not possible

Neoadjuvant therapy(ChT + (ET) + T + P)

Unsatisfactoryresponse

Sat

isfa

ctor

yre

spon

se

Breast conservingsurgery

Mastectomy

Adjuvant therapy(ChT + (ET) + T)

+ postoperative RT

49

Metastatic disease

The treatment of metastatic disease depends on the tumor/metastasis status. If the

amplification/overexpression of HER2 gene is present, the standard first-line

therapy in the metastatic setting is the combination of chemotherapy, trastuzumab

and pertuzumab. Common chemotherapeutic agents used with the trastuzumab +

pertuzumab combination are docetaxel or paclitaxel. If the first-line therapy

provides benefit, anti-HER2 and ET therapies can be given as maintenance therapy.

According to the guidelines, the maintenance therapy should be used until

progression. However, expert opinion has stated that optimal duration of HER2

therapy is unknown for patients with complete response, and stopping may be

considered if a retreatment option is available. If the disease progresses after

trastuzumab-based therapy, the second-line therapy is T-DM1. Trastuzumab +

lapatinib can be a reasonable treatment option for some patients (Cardoso et al.,

2018).

2.5.2 Tumor-infiltrating lymphocytes in HER2+ breast cancer

Lymphocyte infiltration into HER2-positive breast cancer was first observed in

1990. The significance of tumor-infiltrating lymphocytes (TILs) in HER2+ breast

cancer has been widely studied, mainly by evaluating the abundance of total TILs

present in the tumor or in the stroma, and the high infiltration is associated with

remarkably improved outcomes and increased trastuzumab benefit (Denkert et al.,

2018; Lee et al., 2015; Loi et al., 2014; Luen et al., 2017; Salgado et al., 2015).

TILs may also predict the efficacy of chemotherapy in HER2+ breast cancer

(Denkert et al., 2015; Ingold Heppner et al., 2016).

TILs in HER2+ breast cancer are commonly studied by evaluating the

morphology of the immune cells from hematoxylin and eosin-stained tumor tissues,

even though distinct immune cells may have different impacts on the tumor

progression as section 2.2. described. Some of the most recent studies on HER2+

breast cancer have evaluated the impact of the immune cells to patient outcomes

and treatment responses by analyzing cytotoxic T cells (CD8), regulatory T cells

(FoxP3) and M2 macrophages (CD163 + CD68) separately. High stromal and

intratumoral infiltration of CD8+ cells and a high ratio of CD8/FoxP3 have been

linked to improved progression-free survival, overall survival and objective

response rate of trastuzumab-pertuzumab-docetaxel -treatment (Hou, Nitta, Wei,

50

Banks, Lustberg et al., 2018; Liu et al., 2017; Takada et al., 2018; van Rooijen et

al., 2018).

2.6 ALK in cancer

Anaplastic lymphoma kinase (ALK) is an RTK and a member of the insulin

receptor superfamily. ALK is involved in a normal development and function of the

nervous system, which has been shown to be mediated via MAPK signaling

pathway (Iwahara et al., 1997; Motegi, Fujimoto, Kotani, Sakuraba, & Yamamoto,

2004; Souttou, Carvalho, Raulais, & Vigny, 2001). ALK has been found to be

altered in many solid and hematological tumors and was first identified in

anaplastic large cell lymphoma, after which the ALK is named. The best-

characterized alterations of ALK are the translocations of the gene, but ALK can

also be mutated or amplified (Grande, Bolós, & Arriola, 2011).

2.6.1 ALK translocated NSCLC

Lung cancer is the most common type of cancer and has a high mortality. Lung

cancers are divided into two major classes: non-small cell lung cancer (NSCLC),

which accounts for over 80% of all lung cancers, and small cell lung cancer.

NSCLC is further divided into two categories, squamous cell carcinoma and

nonsquamous cell carcinoma, which include adenocarcinoma, large cell carcinoma

and other cell types. Around 60% of all NSCLC are adenocarcinomas (Cao et al.,

2019; Ettinger et al., 2017).

The most widely recognized translocation of ALK, the echinoderm

microtubule-associated protein-like 4 (EML4)-ALK fusion, was discovered in

NSCLC in 2007 (Grande et al., 2011; Lin, Riely, & Shaw, 2017; Soda et al., 2007).

EML4-ALK translocations are rare in NSCLC, only 3 to 7% of NSCLCs harbor the

fusion (Shaw et al., 2009), but the total amount of these cancers is still rather high

considering the high incidence of lung cancer worldwide. At least 15 different

variants of EML4-ALK translocation have been identified, but the variants 1, 2 and

3a/b are the most common ones. All EML4-ALK translocations contain the kinase

domain of the ALK protein, while the EML4 part differs between the variants.

These translocations lead to a constitutively and ligand-independently activated

ALK kinase, which signals through Ras/Raf/MEK/ERK, MAPK and JAK/STAT-

pathways. The EML4-fusion also changes the location of the ALK kinase, since it

is no longer located at the cell membrane but in the cytosol (Sabir, Yeoh, Jackson,

51

& Bayliss, 2017). ALK rearrangements are a classical example of an oncogene

addiction in cancers, and they can be easily targeted.

2.6.2 ALK targeting and resistance mechanisms in cancer

The first-developed ALK targeting inhibitor was crizotinib, a reversible ATP-

competitive TKI that has gained FDA and EMA approvals for ALK-rearranged

NSCLC and has been shown to be superior to the first- and second-line

chemotherapy in advanced ALK-rearranged NSCLC (Kwak et al., 2010; Shaw et

al., 2013; Solomon et al., 2014; Zou et al., 2007). Like with other TKIs, ALK TKI

treated patients will inevitably progress due to acquired resistance. Second-

generation ALK inhibitors, ceritinib, alectinib and brigatinib, are reversible ATP-

competitive inhibitors and have been developed to overcome crizotinib resistance.

They are approved for the treatment of ALK-rearranged NSCLCs previously treated

with crizotinib (Lin et al., 2017).

The two major classes of ALK TKI resistance mechanisms are ALK-dependent

mechanisms, including ALK secondary mutations or ALK amplification, and ALK-

independent mechanisms, including activation of bypass signaling pathways. ALK

secondary mutations causes around 20–30% of crizotinib resistances and around

56% of the second-generation ALK TKI resistances (Lin et al., 2017). The spectrum

of these ALK secondary mutations is rather broad, but the three most well-known

mutations are L1196M, G1269A and G1202R, which hinder the binding of the TKI

to ALK (Lin & Shaw, 2016). The G1202R mutated ALK has been shown to be

highly resistant to the first- and second-generation ALK TKIs, but the resistance

can be overcome with third-generation ALK TKI lorlatinib, which gained FDA

approval in 2018 (Gainor et al., 2016; Zou et al., 2015). Around half of the ALK

TKI resistances are caused independently of ALK. Numerous bypass signaling

activations have been shown to cause ALK TKI resistance, of which the first-

identified bypass mechanism was the activation of EGFR. The activation of other

HER proteins has also been shown to drive the ALK TKI resistance (Lin et al.,

2017; Wilson et al., 2015). Furthermore, crizotinib resistance can be driven by

pharmacokinetic resistance due to a low penetration of crizotinib through

cerebrospinal fluid (CSF), reducing the effect of crizotinib in the central nervous

system (Costa et al., 2011; Metro et al., 2015).

52

53

3 Aims of the study

This current work focused on more efficient use of HER targeting agents in cancer

therapy. Our aim was to find prognostic and predictive markers for metastatic

HER2-positive breast cancer patients treated with trastuzumab and determine the

role of HER2 and HER3 for cancer stem-like cells in ALK translocated non-small

cell lung cancer. More specifically the main aims of the study were:

1. To study the impact of immunosuppressive molecules and several distinct

immune cell subtypes in different tumor locations for the prognosis of

metastatic HER2-positive breast cancer patients and whether these

immunological markers could predict the benefit from trastuzumab.

2. To investigate the role of HER2 and HER3 receptors for cancer stem-like cells

in ALK translocated NSCLC by altering the gene expression of these proteins

and evaluate the effects of the genetic alteration to the cells.

54

55

4 Materials and methods

4.1 Publications I and II

The following materials and methods were used in publications I and II.

4.1.1 Patient material

All patients who had received at least one dose of intravenous trastuzumab for the

treatment of metastatic HER2-positive breast cancer in Oulu University Hospital

in 2009–2014 (n=54) were retrospectively identified from the pharmacy records

and were included in publications I and II. The studies were limited to patients who

had prechemotherapy samples available (n=48). In publication II the number of

adequate tumor samples was decreased to 40 samples, and the analysis of IDO1 in

publication II was limited to 25 samples due to a limited tumor sample availability

at the time of analysis.

The following patient data were previously (Moilanen, Mustanoja, Karihtala,

& Koivunen, 2017) collected from the electronic patient records: Patients’ age, date

of diagnosis, date of metastatic disease, histological subtype, TNM staging, tumor

grade, Ki-67 staining, estrogen- and progesterone receptor status,

adjuvant/metastatic treatment regimens, treatment durations and therapy responses.

The patients’ HER2 positivity was characterized by the presence of HER2

amplification in chromogenic in situ hybridization. Survival in metastatic disease

was defined as the time from the identification of metastatic disease to death or end

of follow-up. HER2 therapy was interrupted with patients whose HER2 therapy

response lasted >12 months or who had a therapy response but severe, suspected

HER2 therapy-related adverse effects. The length of HER2 therapy discontinuation

was determined from the date of the last administration of trastuzumab (in the

longest therapy interruption period) to the date of trastuzumab re-initiation, death,

or end of follow-up.

4.1.2 Immunohistochemistry and immune cell counting

Immunohistochemistry was conducted on 3.5 μm sections cut from paraffin-

embedded specimens. The sections were deparaffinized in xylene or in HistoClear

(IDO1) and rehydrated through graded alcohols. Antigen retrieval was conducted

56

in a microwave oven with citrate buffer (pH6; IDO1) or Tris-EDTA (pH9) buffer

at 800 W for 2 min and at 150 W for 10 (IDO1) or 15 min. Endogenous peroxidase

activity was neutralized in 3% H202-aqua solution (IDO1) or in Dako REAL

peroxidase blocking solution for 5 min. The incubation with monoclonal primary

antibodies was conducted at different temperatures and times (Table 2). Bound

antibodies were detected using the EnVisionTM system (Dako Denmark A/S,

Glostrup, Denmark). 3,3’-Diaminobenzidine was used as the chromogen and

hematoxylin as the counterstain.

Table 2. Antibodies and protocols used in immunohistochemistry.

Target Antibody Clone/Catalog

number

Manufacturer Dilution Incubation time

(min)1

Study

T cells CD3 PS1 Novocastra 1:50 30 min, RT I

Cytotoxic T cells CD8 4B11 Novocastra 1:200 30 min, RT I

Regulatory T cells FoxP3 236A/E7 Abcam 1:100 30 min, RT II

NK cells CD56 MRQ-42 Cell Marque 1:100 30 min, RT I

Macrophages (Mφ) CD68 PG-M1 Dako 1:100 30 min, RT I

M1-like Mφ/tumor iNOS ADI-905-431-1 Enzo Life Sciences 1:200 60 min, RT II

M2-like Mφ CD163 10D6 NeoMarkers 1:200 30 min, RT II

Tumor CD47 HPA044659 Atlas Antibodies 1:150 60 min, RT II

Mφ/tumor IDO-1 D5J4E™ Cell Signaling

Technology

1:400 o/n, +4°C II

1Primary antibody incubation time

The sections were scanned with Aperio AT2 image-capturing device (Leica

Biosystems) for the analysis of the immunohistochemistry. Imagescope (Aperio

Technologies) software, version 11.2, was used to view the scanned images. Using

the software, 3–6 (median 6) images, depending of the size of the tumor sample,

were captured with ×20 magnification from the center of the tumor (CT) and the

invasive margin (IM) for immune cell counting. The immune cells were counted

using an earlier described and validated computer-assisted counting method

(Väyrynen et al., 2013; Väyrynen et al., 2012) that utilizes ImageJ, a freeware

image analysis software (Abràmoff, Magalhães, & Ram, 2004). The intensity of

tumor cell immunoreaction for iNOS (cytoplasmic), IDO1 (cytoplasmic), or CD47

(membranous) was evaluated as negative (0), weak (1), moderate (2), or strong (3)

(Zlobec, Terracciano, Jass, & Lugli, 2007) by an experienced pathologist. Tertiary

lymphoid structures (TLSs) were manually counted and measured from the H&E

stained sections.

57

4.1.3 Statistics

Statistical analyses were performed with IBM SPSS Statistics 24.0 for Windows

(IBM Corporation, Armonk, NY, USA). Receiver operating characteristics (ROC)

analysis for the whole patient material was used to determine optimal cut-off scores

of CD3, CD8, FoxP3, CD56, CD68, iNOS, CD163, IDO1 and TLS density for

discriminating the survivors from the non-survivors (Table 3). Area under the curve

(AUC) describes the accuracy of a test and also how well the test can separate the

survivors from the non-survivors. AUC values range from 0.5 to 1, and the closer

the value is to 1, the more discriminatory power the test has. The AUC values in

our studies were rather low because of the small number of patients in the studies

(Table 3). The associations between the studied markers and the survival of the

patients were analyzed with Kaplan-Meier method using the log-rank test to

compare the survival in different groups. Multivariate analyses were performed

with Cox regression analysis to test whether the studied markers were independent

prognostic markers. Correlations between different variables were performed using

Spearman’s correlation (2-tailed) and scatter plot analysis. Probability values below

0.05 were considered significant.

4.1.4 Ethics

The patient data collection and tumor analyses were carried out under permissions

from the Northern Ostrobothnia Hospital District ethical committee (114/2011,

amendment 23.02.2015), National Supervisory Authority for Welfare and Health

(9850/05.01.00.06/2010), and the medical director of Oulu University Hospital

(study no. 60/2015).

58

Table 3. ROC analysis AUC and cut-off values of the studied immune cell markers.

Marker Location AUC (CI) Cut-off (cells/mm2)1 Publication

CD3 IM 0.620 (0.409–0.831) 700 I

CT 0.672 (0.457–0.887) 300 I

CD8 IM 0.644 (0.441–0.846) 300 I

CT 0.695 (0.488–0.903) 120 I

CT 0.627 (0.401–0.853) 120 II

FoxP3 IM 0.556 (0.298–0.813) 134 II

CT 0.626 (0.384–0.868) 82 II

CD56 IM 0.683 (0.461–0.905) 17 I

CT 0.722 (0.505–0.938) 12 I

CD68 IM 0.563 (0.347–0.779) 646 I

CT 0.717 (0.544–0.889) 647 I

iNOS IM 0.594 (0.371–0.818) 58 II

CT 0.702 (0.498–0.906) 37 II

CD163 IM 0.504 (0.263–0.744) 307 II

CT 0.459 (0.216–0.703) 241 II

IDO IM 0.632 (0.333–0.930) 62 II

CT 0.706 (0.473–0.939) 19 II

TLS - 0.519 (0.277–0.762) 6.5 I

1Cut-off value for TLS is lymphoid follicles/2x field

4.2 Publications III and IV

The following materials and methods were used in publications III and IV.

4.2.1 Cell lines, inhibitors and growth factors

The cell lines used in the studies included ALK translocated non-small cell lung

cancer (NSCLC) cell lines H3122 and H2228. Both cell lines were further modified

to overexpress HER2 or knockdown HER2 or HER3. The original cell lines were

kind gifts from Dr. Pasi Jänne (Dana-Farber Cancer Institute, Boston, MA, USA).

The cell lines were cultured in RPMI-1640 medium supplemented with 10% fetal

bovine serum and 100 IU/ml penicillin and streptomycin, which were all purchased

from HyClone (Logan, UT, USA). The cells were incubated at 37˚C with 5% CO2

in the atmosphere. Table 4 shows all inhibitors and growth factors used to treat the

cells in studies III and IV. Inhibitors were diluted in dimethyl sulfoxide (DMSO)

and stored at -20˚C, while growth factors were diluted in sterile, distilled water and

stored at -80˚C.

59

Table 4. Used inhibitors and growth factors.

Molecule Target Used concentration Manufacturer

Inhibitor

TAE684 ALK 0.1 M Axon Medchem,

Groningen, Netherlands

Crizotinib ALK 1 M LC Laboratories,

Woburn, MA, USA

Lapatinib HER2, EGFR 1 M Alexis Biochemicals,

Lausen, Switzerland

Afatinib EGFR, HER2, HER4 1 M LC Laboratories,

Woburn, MA, USA

Growth factor

NRG1 HER3, HER4 50 ng/ml Sino Biological Inc.,

Beijing, China

EGF EGFR 50 ng/ml Sino Biological Inc.,

Beijing, China

4.2.2 Lentiviral knockdown and retroviral overexpression

Lentiviral and retroviral vectors were used to achieve knockdown and

overexpression of GFP and HER2 in H3122 and H2228 cell lines in publication III.

HER2 shRNA vector was purchased from Sigma-Aldrich (St. Louis, MO, USA),

while GFP shRNA vector and both retroviral vectors were kind gifts from Dr. Pasi

Jänne (Dana-Farber Cancer Institute). GFP was used as a control for the

overexpression and knockdown. 293T cells were transfected with lenti-/retroviral

expression vectors and packaging plasmids using FuGENE 6 reagent (Promega,

Madison, WI, USA). The packaging plasmids used were Gag&Pol, Rev and VSV-

G with lentiviral vectors and pCL-Eco with retroviral vectors. All packaging

plasmids were gifts from Dr. Pasi Jänne. Lentiviral supernatants were collected 24h

and retroviral supernatants 48h after transfection. Both supernatants were filtered

through 0.45 μm filter and applied to the target cells in the presence of polybrene

(Sigma-Aldrich). After 48h of infections, the target cells were selected with

puromycin (Sigma-Aldrich) for 72–96h.

4.2.3 CRISPR-Cas9 knockdown

HER3 knockdown in publication IV was performed using the CRISPR-Cas9

genome editing system. Single guide RNA (sgRNA) design and CRISPR-Cas9

60

transfection plasmid were purchased from Sigma-Aldrich. A plasmid with non-

targeting sgRNA was a kind gift from Dr. Peppi Karppinen, and the plasmid was

used to create a control knockdown cell line, referred to now on as NEG. Table 5

shows information about the transfection plasmids; vectors, sgRNA sequences and

targeted exons. HER3 has two known transcript variants (National Center for

Biotechnology Information) both of which were targeted with the same plasmid.

Table 5. CRISPR-Cas9 transfection plasmids.

Gene Vector Target site Target exon

HER3 U6gRNA-Cas9-2A-GFP ACTGTACAAGCTCTACGAGAGG 2

NEG pSpCas9(BB)-2A-GFP GCACTACCAGAGCTAACTCA -

H3122 cells were plated on 24-well plates, 25,000 cells/well, allowed to attach for

one day, after which they were transfected with CRISPR-Cas9 plasmids using

Fugene HD (Promega) transfection reagent. Transfection was performed according

to the manufacturer’s instructions using 1 µg of DNA per well. The transfected,

GFP-positive cells were single cell sorted 44–48 hours after transfection into 96-

well plates from where single cell colonies were further grown and analyzed. The

knockdown was verified by sequencing the genomic DNA and analyzing the

changes in protein expression with western blot.

4.2.4 DNA extraction, PCR and sequencing

Genomic DNA was isolated from cultured cells using the NucleoSpin® Tissue Kit

(Macherey-Nagel, Düren, Germany). Target sequences were amplified with

polymerase chain reaction (PCR) using MyTaqTM HS Red Mix (Bioline, London,

UK) with specific primers designed to cover the CRISPR-Cas9 sgRNA sequence

in HER3 (Table 6). The primers were purchased from Sigma-Aldrich. Sizes of the

PCR products were checked on 2% agarose gel, and the products were extracted

from the gel using the GeneJET Gel Extraction Kit (Thermo Fisher Scientific

Baltics UAB, Vilnus, Lithuania) with an extra washing step and pre-warmed elution

buffer. The PCR products were then sequenced with ABI3500xL Genetic Analyzer

by Biocenter Oulu Sequencing Center, and the sequences were analyzed with

CodonCode Aligner (CodonCode Corporation, Centerville, MA, USA).

61

Table 6. PCR primers and their sequences.

Primer Sequence Amplicon length (bp)

HER3 forward 5’-AAA GCT CTC AGG CCA CTA CA-3’ 519

HER3 reverse 5’-GCT ACA ACA GTG AGA CCA TAG G-3’

4.2.5 Western blot

Cultured cells were plated on 6-well plates, allowed to attach for 1–2 days, and

treated with the desired drugs for 5h or 5 days, after which the cells were lysed with

NP-40 lysis buffer (20 mM Tris-HCl pH 8.0, 137 mM NaCl, 10% glycerol, 2 mM

EDTA, 1 mM sodium orthovanadate, 1% igepal CA-630, 10 μg/ml aprotinin and

10 μg/ml leupeptin). Protein concentrations were measured with Bio-Rad Protein

assay (Bio-Rad Laboratories, Hercules, CA, USA). Concentrations between

samples were equalized, after which Laemmli buffer was added, and samples were

boiled and stored at -80˚C.

Equal amounts of proteins were separated on sodium dodecyl sulfate

polyacrylamide gel electrophoresis (SDS-PAGE), proteins were transferred to

polyvinylidene difluoride (PVDF) membrane, blocked with 5% BSA (1x PBS, 0.1%

Tween-20, 0.005% sodium azide) and incubated with primary antibodies overnight

at 4˚C. The membranes were incubated with horseradish peroxidase (HRP)-linked

secondary antibodies for 1h at room temperature, after which the membranes were

developed using chemiluminescence and exposed to radiographic films. The

quantification of western blot images was prepared using ImageJ software version

1.4.3.67, measuring the intensities (pixel percentages) for each sample in the same

membrane with an equal, manually selected area.

Antibodies (Table 7) were diluted in 5% BSA and used at 1:1,000, 1:20,000 (-

actin) or 1:3,000 (secondary antibodies) dilutions. Antibodies were purchased from

Cell Signaling Technology (Danvers, MA, USA), except -actin (Novus

Biologicals, Abingdon, UK) and ALDH1 (BD Transduction Laboratories, Franklin

Lakes, NJ, USA).

4.2.6 Colony formation assay

Cells were plated on 24-well plates, 600–1,000 cells/well, allowed to attach for 1–

2 days, after which they were treated with the desired drugs for 7 days. Table 4

displays the used inhibitors, growth factors and final concentrations. The cells were

then allowed to recover and form several colonies before they were fixed with ice-

62

cold methanol and stained with 0.005% crystal violet (Merck, Darmstadt,

Germany). The plates were imaged with a digital camera.

Table 7. Antibodies used in western blot.

Antibody Phosphorylation site Manufacturer (Product code)

Primary antibodies

ALDH1 - BD Transduction Laboratories (611195)

Akt - CST1 (4691)

Phospho-Akt Ser473 CST (4060)

ALK - CST (3633)

Phospho-ALK Tyr1604 CST (3341)

Cleaved PARP - CST (5625)

CD44 - CST (3570)

ERBB1/EGFR - CST (4267)

Phospho-EGFR Tyr1068 CST (3777)

ERBB2/HER2 - CST (2165)

Phospho-HER2 Tyr1221/1222 CST (2243)

ERBB3/HER3 - CST (12708)

Phospho-HER3 Tyr1289 CST (2842)

ERBB4/HER4 - CST (4795)

Phospho-HER4 Tyr1284 CST (4757)

ERK - CST (9102)

Phospho-ERK Thr202/Tyr204 CST (9101)

SOX2 - CST (3579)

-actin - Novus Biologicals (NB600-501)

Secondary Antibodies

Anti-mouse HRP-linked - CST (7076)

Anti-rabbit HRP-linked - CST (7074)

1CST=Cell Signaling Technology

4.2.7 Tumor sphere formation assay

The cells were treated with ALK TKIs for 5 days, after which the cells were plated

on 6-well ultra-low attachment plates (Corning, NY, USA). A total of 5,000–7,000

cells were seeded on each well in sphere formation media (DMEM/F-12 media

with 20 ng/ml EGF, 20 ng/ml bFGF, 1% B27 supplement and 100 units/ml

penicillin and 100 µg/ml streptomycin) with or without further ALK TKI treatment.

The spheres were allowed to grow for 10–15 days, after which images were taken

with digital camera and the number of spheres were counted manually. Largest

63

spheres from each well were imaged with phage contrast microscope using 10x

magnification in publication III.

64

65

5 Results

5.1 Immunological markers in metastatic HER2+ breast cancer

Immunological markers were studied in publications I and II.

5.1.1 Patient characteristics

As a result of a systematic search, we identified 54 patients who had received

intravenous trastuzumab in 2009–2014 for the treatment of breast cancer and had

metastatic disease at the time of diagnosis or relapsed disease unsuitable for

curative treatment. Of these patients, prechemotherapy samples were available for

48 patients, and further analyses were limited to this group. However, the amount

of adequate tumor samples was decreased gradually, first to 40 samples and finally

to 25 samples. Table 8 demonstrates the types of samples available that were used

in the analyses.

Table 8. Types of tumor specimens.

Total n (publication) Surgical, n (%) Biopsy, n (%)

48 (I) 31 (64.6) 17 (35.4)

40 (I, II)1 25 (62.5) 15 (37.5)

25 (II)2 21 (84.0) 4 (16.0)

1CD56 and CD68 staining in publication I and all in publication II, 2IDO1 staining

Table 9 illustrates the patient demographics relevant to the studies. The patients had

generally an aggressive disease, since most of them had a high-grade tumor and

high Ki-67 value, which describes the proliferation rate of the tumor. Over one-

third of the patients had primary metastatic disease, and 30–40% were identified to

have undergone trastuzumab treatment interruption in a prolonged tumor response.

66

Table 9. Patient demographics.

Factor n (%)1 n (%)2 n (%)3

Histology

Ductal 41 (85.4) 33 (82.5) 20 (80)

Lobular 5 (10.4) 5 (12.5) 5 (20)

Unknown 2 (4.2) 2 (5.0) 0 (0)

Estrogen receptor status

Positive 33 (68.8) 27 (67.5) 16 (64)

Negative 15 (31.3) 13 (32.5) 9 (36)

Progesterone receptor status

Positive 19 (39.6) 18 (45.0) 13 (52)

Negative 29 (60.4) 22 (55.0) 12 (48)

Bone metastasis only

Yes 16 (33.3) 14 (35.0) 12 (48)

No 32 (66.7) 26 (65.0) 13 (52)

Ki-67

Low (5–15%) 5 (10.4) 4 (10.0) 3 (12)

Intermediate (16–30%) 11 (22.9) 10 (25.0) 6 (24)

High (>30%) 30 (62.5) 24 (60.0) 16 (64)

Unknown 2 (4.2) 2 (5.0) 0 (0)

Grade

II 16 (33.3) 10 (25.0) 6 (24)

III 30 (62.5) 28 (70.0) 19 (76)

Unknown 2 (4.2) 2 (5.0) 0 (0)

Adjuvant trastuzumab

No 9 (18.8) 8 (20.0) 6 (24.0)

Yes 20 (41.7) 17 (42.5) 10 (40.0)

Primary metastatic disease 19 (39.6) 15 (37.5) 9 (36.0)

HER2 therapy discontinuation

Yes 14 (29.2) 12 (30.0) 10 (40)

No 34 (70.8) 28 (70.0) 15 (60)

1total n=48, 2total n=40, 3total n=25

5.1.2 Staining and image analysis of the studied markers

The tumors were stained with multiple markers, illustrated in tables 10 and 11.

Most of the staining images can be seen in the original publications (I and II). The

quantitative analysis of the markers was successful, and the stained immune cells

were analyzed separately from the invasive margin (IM) and center of the tumor

(CT). The density of tertiary lymphoid structures (TLSs) could not be analyzed

67

from the biopsy specimen; therefore, TLSs were studied only from the surgical

tumor samples (Table 10). In general, the number of stained immune cells were

more abundant in the IM location, except for the CD68 staining, which showed a

slightly higher quantity of stained cells in the CT. The lowest infiltrations seen in

the tumors were NK cells (CD56), M1-like macrophages (iNOS) and IDO1 positive

macrophages, whereas the most abundant infiltration was seen with CD68 stained

macrophages, M2-like macrophages (CD163) and CD3 positive T cells. CD8

positive cells in the CT have two values, since the CD8 CT staining results were

combined in publication II with other analyses that were performed with a lower

number of specimens (Table 10). The cut-off scores (table 3) were used to define

the low and high number of the studied immune cells.

The tumor cells showed positivity for iNOS, IDO1 and CD47.The staining of

the tumoral tissue was evaluated in publication II according to the intensities of the

staining, which were ranked as low (0), weak (1), moderate (2) and strong (3)

intensities. Almost all of the tumors were IDO1 negative, while differential staining

intensities were seen with iNOS and CD47 (Table 11).

Table 10. Densities of the immune cells and lymphoid follicles.

Antibody

(study)

Target Location of the studied

cells

Median density

(cells/mm2)1

Range

(cells/mm2)1

Sample n

CD3 (I) T cells IM 573 19–2520 48

CT 242 19–2033

CD8 (I) Cytotoxic T cells IM 207 20–1307 48

CT 119 4–1200

CD8 (II) Cytotoxic T cells CT 157 4–1201 40

FoxP3 (II) Regulatory T cells IM 145 14–739 40

CT 92 6–796

CD56 (I) NK cells IM 14 2–140 40

CT 9 1–114

CD68 (I) Macrophages IM 622 112–1465 40

CT 666 319–1857

iNOS (II) M1 macrophages IM 42 4–145 40

CT 37 4–173

CD163 (II) M2 macrophages IM 338 24–957 40

CT 295 15–983

IDO1 (II) Macrophages IM 43 2–320 25

CT 16 1–238

- TLS - 6.5 1–15 31

1The values for median density and range of TLS are lymphoid follicles/2x field

68

Table 11. Staining intensities of the tumor tissues.

Antibody (study) Location Negative (0)

n (%)

Weak (1)

n (%)

Moderate (2)

n (%)

Strong (3)

n (%)

Sample n

iNOS (II) cytoplasm 7 (17.5) 14 (35.0) 10 (25.0) 9 (22.5) 40

CD47 (II) membrane 9 (22.5) 7 (17.5) 12 (30.0) 12 (30.0) 40

IDO1 (II) cytoplasm 20 (80.0) 0 (0.0) 3 (12.0) 2 (8.0) 25

5.1.3 Association of T cell subsets with clinical data

We studied whether the different T cell subsets present in the tumor before any

treatment had effects on the patient outcomes using the Kaplan-Meier survival

analysis. As assumed, a high number of regulatory T cells (Tregs) identified with

FoxP3 nuclear positivity showed a tendency towards worsened survival in both IM

and CT but without reaching statistical significance, however. CD3, a protein part

of the T cell receptor complex, recognizes all sets of T cells, and a high number of

CD3 positive cells was associated with improved survival in the CT (p = 0.007) but

not in the IM. Similarly, a high number of CD8 positive cells recognizing cytotoxic

T cells were associated with improved survival in the CT (p = 0.001) but not in the

IM.

A combined analysis of both CD3 and CD8 positive cells in the IM and in the

CT is a promising prognostic parameter in colorectal cancer, which has been

reported to predict improved survival better than either of the markers alone (Galon

et al., 2014; Galon et al., 2006; Pagès et al., 2018). This parameter is called the

Immunoscore, which categorizes the densities of each of these cell types in both

IM and CT as low (0 points) or high (1 point), yielding a five-tiered classification

from Immunoscore 0 to Immunoscore 4. The significance of the Immunoscore in

breast cancer is quite unknown, and we decided to carry out this combined analysis

of CD3 and CD8 in IM and CT in our material. However, due to a rather small

number of patients, we categorized the Immunoscore values into three groups

instead of five; 0 (n=13), 1–2 (n=24), 3–4 (n=11). A high Immunoscore value

showed a tendency towards better survival but did not reach statistical difference.

We also conducted a combined analysis of CD3 and CD8 only in the CT by using

a two-class grouping: 0–1 low (n=32), 2 high (n=16). A significant association of

CT Immunoscore with survival was seen (p = 0.001); however, it was not higher

than the significance seen with CD8 positivity in the CT alone.

The abundance of CD8 positive cells in the CT was the most prominent

indicator of improved survival, and it was proven to remain as an independent

69

prognostic factor in a multivariate analysis (Table 12). When both CD8 CT density

and receiving trastuzumab as an adjuvant treatment were included in the same

model, both indicators remained significant. On the contrary, when CD8 CT density

was included with ER status in the same model, only CD8 CT remained statistically

significant.

Table 12. Multivariant analyses with Cox regression model.

Model HR 95% CI Significance (p-value)

1

CD8 CT low vs high 5.436 1.477–20.003 0.011

Adjuvant trastuzumab 40.136 4.472–363.473 0.001

2

CD8 CT low vs high 4.621 1.839–11.608 0.001

ER status 2.018 0.821–4.961 0.126

The association between the density of CD8 positive T cells in the CT area and the

patient outcome was further analyzed in different patient subgroups (Table 13). A

significant association between the high number of CD8+ cells in the CT and

prolonged survival was seen in patients who had metastasis not only in the bone,

had smaller or unmeasurable tumor volume and had estrogen receptor positivity,

with the latter showing the most significant association. On the contrary, a

statistically significant difference was not seen in patients having only bone

metastases, large tumor volume and ER negative tumors.

Table 13. Association between the density of CD8+ T cells in the CT and survival in

different patient subgroups.

Factor Significance (p-value) n

Metastasis site

Non-bone only 0.006 32

Bone only 0.079 16

Tumor volume

TX or T1–3 0.003 35

T4 0.454 13

ER status

ER+ 0.001 33

ER- 0.223 15

70

5.1.4 Association of macrophage subsets with clinical data

When macrophages were studied with general macrophage marker CD68, only a

tendency towards improved survival was seen with a high density of CD68 positive

cells both in the IM and CT. However, this marker recognizes both M1- and M2-

like macrophages and also other types of immune cells, such as myeloid cells and

dendritic cells. Thus, we decided to study the M1- and M2-like phenotypes of the

macrophages separately using iNOS and CD163 markers. A high number of M1-

like macrophages in the IM (p = 0.009) and CT (p = 0.009) was associated with

improved survival, whereas a high number of M2-like macrophages in the CT but

not in the IM displayed a tendency towards poor survival, not reaching statistical

significance. Some tumor cells showed positivity for iNOS expression; however,

this did not provide any prognostic value.

5.1.5 Association of other markers with clinical data

We investigated also other immunological markers besides T cells and

macrophages. The number of NK cells, identified by CD56 positivity, was low in

the tumors, which was reflected in the survival studies. There was only a tendency

towards a better survival seen in patients with a high number of NK cells in the CT,

but it was not statistically significant. We also analyzed the density of lymphoid

follicles in the adjacent area of the tumors, called tertiary lymphoid structures

(TLSs), from H&E stained sections, which was shown to be associated with

improved survival (p = 0.021).

Furthermore, we wanted to study a few immunosuppressive molecules

expressed by the tumor cells to reveal if the immune cells were directly suppressed

by the tumors. CD47 is a molecule on the surface of tumor cells that binds to

phagocytosing cells to inhibit their phagocytic effects. CD47 intensities were

divided into two groups, low (int 0 and 1) and high (int 2 and 3) intensities, where

high CD47 intensity predicted worsened survival (p = 0.046). The intensities of

IDO1, an enzyme catalyzing the degradation of tryptophan, were also divided into

two groups, similar to CD47. A high intensity of IDO1 was associated with poor

survival (p = 0.018). In addition to the tumoral expression, IDO1 was observed also

in macrophages in the IM and CT; however, IDO1 expression in macrophages did

not provide any prognostic value.

71

5.1.6 Combined analysis of CD8 and M1

A combined analysis was performed of CD8+ T cells in the CT and iNOS+ M1-like

macrophages in the CT and IM, since these were the most promising prognostic

factors. A high infiltration of CD8+ cell in the CT, combined with high infiltration

of M1 cells in the CT (p = 0.0003, Fig. 6A) or IM (p = 0.0003), were significantly

associated with improved survival, more significantly than high infiltration of

either cell type alone. Multivariant analysis showed that these markers were

independent prognostic factors (Table 14).

Table 14. Multivariant analysis of CD8 and M1 markers.

Model HR 95% CI Significance (p-value)

1

CD8 CT low vs high 2.957 1.336–6.543 0.007

M1 CT low vs high 2.813 1.302–6.077 0.009

2

CD8 CT low vs high 2.923 1.305–6.546 0.009

M1 IM low vs high 3.078 1.298–7.298 0.011

5.1.7 CD8 and M1 infiltration and trastuzumab benefit

The most promising indicators of prognosis in our study, the CD8 CT status and

M1 status, were chosen for further analysis to investigate whether these markers

could predict benefit from trastuzumab-containing treatment in metastatic disease.

Two indicators for trastuzumab benefit were chosen: time from the beginning of

the first-line of trastuzumab treatment to the next line of treatment in metastatic

disease (n=36–43) and the duration of the longest trastuzumab interruption period

in response (n=12–14). The density of CD8+ CT cells correlated with the length of

trastuzumab interruption (Spearman’s correlation coefficient=0.620, p = 0.018),

and the high number of CD8+ CT cells associated with long trastuzumab-free

periods in Kaplan-Meier analysis (p = 0.038). A high number of M1 cells in the CT

associated with both the length of first-line trastuzumab to the next line therapy (p

= 0.026) and with the length of trastuzumab interruption (p = 0.035). A high number

of M1 cells in the IM was associated only with the length of trastuzumab

interruption (p = 0.035). The most significant associations with the trastuzumab

benefit were seen in the combined analysis of CD8 CT and M1 CT/IM. A high

number of both CD8+ CT cells and a high number of M1 CT cells was associated

with both the length of first-line trastuzumab treatment and the length of the

72

trastuzumab-free period (p = 0.002, Fig. 6B and p = 0.001, Fig. 6C), whereas the

high number of CD8+ CT and M1 IM cells was associated only with the length of

the trastuzumab-free period (p = 0.001).

Fig. 6. The most promising indicators of the prognosis and trastuzumab benefit, CD8+

T cells and M1-like macrophages in the center of the tumor (CT). (A) An association

exists between the high number of both CD8+ T cells and M1-like macrophages in the

CT and survival. (B) High infiltration of CD8+ T cells and M1-like macrophages in CT is

associated with the time from the first-line trastuzumab treatment to the next line

treatment in metastatic disease. (C) A link exists between the number of both CD8+ T

cells and M1-like macrophages in the CT and the length of trastuzumab discontinuation.

73

5.2 The role of HER2 and HER3 for cancer stem-like cell phenotype

The role of HER2 and HER3 for cancer stem-like cell phenotype in publications

III and IV was studied using ALK translocated NSCLC cell lines H2228 and H3122.

5.2.1 Overexpression and knockdown of HER2 and HER3

H3122, a cell line that is extremely sensitive to ALK inhibition, was altered to

overexpress HER2 with retroviral expression vector. H2228 cell line, which is only

modestly sensitive to ALK inhibition and expresses HER2 basally, was altered by

knocking down HER2 with shRNA vector. HER3 was knocked down only in the

H3122 cell line, because the HER3 protein expression levels of H2228 control cells

were extremely low, and we were unable to detect it with western blot assay. The

knockdown of HER3 in H3122 line was performed with more advanced gene

editing system called CRISPR-Cas9, and the knockdown cell line was derived from

a single cell. All the genetic alterations were validated and were successful. GFP-

altered cell lines were used as control lines for the overexpression and knockdown

of HER2, whereas a control CRISPR-Cas9 transfection plasmid containing non-

targeting sgRNA was used for the HER3 knockdown.

5.2.2 The effects of genetic alterations to cancer stem-like cell

marker expression

We have previously shown that cancer stem-like cells (CSLCs), identified with

specific markers ALDH1 (H3122) and CD44/ALDH1 (H2228), can mediate ALK

tyrosine kinase inhibitor (TKI) resistance (Jokinen, Laurila, Koivunen, & Koivunen,

2014). Thus, we assessed whether the genetic alterations of HER2 and HER3 had

an effect on the CSLCs by examining the expression of CSLC markers using

western blot analysis. As seen previously, ALK inhibition increased the expression

of CSLC markers, suggesting CSLC-mediated resistance. In the H3122 line, the

overexpression of HER2 did not change the basal ALDH1 expression but led to a

more pronounced increase in the expression in response to ALK inhibition. Basal

expression of CD44 was unaltered when HER2 was knocked down in H2228,

whereas the basal expression of ALDH1 was markedly downregulated. The

expression of CD44, however, was downregulated when ALK was inhibited. HER3

knockdown in H3122 did not change the basal expression of ALDH1 but

downregulated the basal expression of SOX2. The induction of ALDH1 expression

74

in response to ALK inhibition was blocked when HER3 was knocked down, and

the induction of SOX2 was slightly downregulated by the knockdown.

The expression of the CSLC markers was even further increased in the control

cells and HER2 overexpressing cells when NRG1, a HER3 and HER4 ligand,

linked to tumor initiating cells, was introduced simultaneously with ALK inhibition.

A slight recovery of the CSLC marker downregulation in the HER2 knockdown

line was seen by the addition of NRG1 to ALK inhibition, but no changes were

seen in the HER3 knockdown cells, as expected.

5.2.3 The effects of the genetic alterations to cytotoxic response to

ALK inhibition

We investigated whether HER2 and HER3 alterations affected the cytotoxic

response of the cells to ALK inhibition by exploring their colony formation ability.

The main cytotoxic responses to ALK TKI remained unaffected by the alterations;

however, the overexpression of HER2 resulted in a slight increase in the number of

surviving colonies, and knockdown of HER2 or HER3 resulted in a slightly

decreased number of surviving colonies compared to the controls. The increased

survival in HER2 overexpressing cells was blocked by the addition of lapatinib

(targets EGFR and HER2) or afatinib (targets EGFR, HER2 and HER4) together

with the ALK TKI. No differences in the surviving colonies were seen between the

HER3 knockdown and control cells when afatinib or lapatinib was combined with

ALK TKI; however, the number of surviving colonies in HER2 knockdown cells

was decreased compared to the control cells.

More pronounced differences in the cytotoxic responses were seen when HER3

(and HER4) was activated with NRG1 treatment. NRG1 by itself did not alter the

colony formation abilities of the studied cells, however, when it was combined with

ALK TKI differences were seen between the HER2/HER3 altered cells and the

control cells. The addition of NRG1 to ALK TKI treatment increased markedly the

number of surviving colonies in the HER2 overexpressing cells, whereas only a

minor increase was seen in the control cells. No differences were seen between the

HER2 knockdown cells and the control cells when NRG1 was combined with ALK

TKI. The increased colony formation ability seen in the control cells in response to

NRG1 and ALK TKI was blocked by the knockdown of HER3.

The changes in the cytotoxic response to ALK TKI after HER2 alterations were

studied also with tumor sphere formation assay, which is a commonly used assay

to identify CSLCs in vitro. However, the assay was more like a colony formation

75

assay with a CSLC-inducing environment for the H3122 cells, because the formed

spheres were cell rafts instead of spheres due to the cells’ ability to stick together

and difficulty in separating them properly. Despite that, the results from the assay

were similar to colony formation assay and CSLC protein expression patterns,

showing that HER2 knockdown diminished the survival of CSLCs if ALK was

inhibited, and the overexpression of HER2 increased the survival of CSLCs even

when ALK was continuously inhibited for 15 days.

5.2.4 Alterations in the Akt and ERK1/2 downstream signaling

The changes of HER2/HER3 alterations to the most common downstream signaling

pathways of RTKs, PI3K and MAPK were examined by studying the

phosphorylation of Akt and ERK1/2 proteins at 5h or 5 days after the initiation of

ALK inhibition. The alteration did not affect the initial (5h) response to ALK TKI;

instead, the differences were seen in the long-term (5 days) responses. HER2

overexpression in H3122 resulted in reformed downstream RTK signaling with

major upregulation of both phosphorylated Akt and ERK1/2, which was not seen

in the control cells. H2228 control cells were able to recover the downstream

signaling pathways after 5-day inhibition of ALK, which was blocked by the

knockdown of HER2. HER3 knockdown, however, hampered the Akt signaling

recovery, whereas ERK1/2 was able to recover similar to the control cells.

We examined the impact of NRG1 also on the downstream signaling pathway

recoveries. The addition of NRG1 with ALK TKI recovered the downstream

signaling of Akt and ERK1/2 in H3122 control cells, and the expression of

phosphorylated Akt was further increased in the HER2 overexpression cell line.

These signaling recoveries were blocked by the knockdown of HER3.

5.2.5 Compensatory expression of other HER family members

We examined whether the alterations of HER2 and HER3 affected the expression

and activation of the other HER family members. HER2 overexpression in H3122

cells led to an increased activation of EGFR, HER3 and HER4 proteins, of which

the activation of HER3 mimicked the expression patterns of the ALDH1, since the

most prominent activation of HER3 was seen when ALK was inhibited and NRG1

was provided, which also showed the most prominent expression of ALDH1. We

were unable to see activation of the HER proteins in the H2228 cell lines, but the

knockdown of HER2 downregulated the expression of HER3. In H3122 the HER3

76

knockdown increased the phosphorylation of EGFR, but we were unable to see

activations of the other HER proteins in the control and in the HER3 knockdown

cells. The activation of EGFR was unable to recover the Akt signaling in the HER3

knockdown cells.

The addition of NRG1 was able to induce the activation of HER3 in HER2

overexpressed cells even when ALK was not inhibited, but more pronounced

activation was seen with a combinatory treatment of ALK TKI and NRG1, which

was also seen in H3122 control cells. Furthermore, ALK TKI together with NRG1

induced the strongest expression of CSLC markers.

77

6 Discussion

There are many types of cancer treatments these days, such as surgery,

chemotherapy, radiation therapy, targeted therapy, and the latest newcomer,

immunotherapy. It is now well understood that one type of therapy is not suitable

for every patient, and precision medicine has taken its place in the field of oncology.

Targeted therapies, including small-molecule inhibitors and monoclonal antibodies,

and immunotherapy can be given to patients according to their tumor’s genetic

status. However, even though the genetics of the tumors appear to be suitable for

some specific therapy, not every patient will respond to the therapy, or the disease

will progress soon after the response due to development of an acquired resistance.

Many resistance mechanisms for different therapies have been discovered, for

example, the involvement of cancer stem-like cells that are resistant to different

therapies and are extremely tumorigenic. Unfortunately, cancer stem-like cells are

difficult to target, and there are no targeting agents available in the clinic. Therefore,

further research is needed to improve precision medicine in order to enhance patient

selection and develop new targeting agents or utilize the ones that are already

available.

6.1 Immunological markers in HER2+ breast cancer

The disease outcomes of HER2-positive breast cancers have dramatically improved

after HER2 targeting agents, like trastuzumab, became available (Marty et al., 2005;

Swain et al., 2015; Von Minckwitz et al., 2009). However, since its approval,

trastuzumab has been given according to the tumoral HER2 status only, and other

predictive factors for trastuzumab sensitivity or resistance are currently quite

uncharacterized. The mechanisms of action of trastuzumab are based on its ability

to bind the extracellular domain of HER2 and partially inhibit the signaling through

it, but the main effects of trastuzumab have been shown to be the activation of the

immune system through ADCC and ADCP (Clynes et al., 2000; Nami, Maadi, &

Wang, 2018; Petricevic et al., 2013). Some trastuzumab-resistance mechanisms

have been suggested, of course, such as alterations that abolish the signal

transduction inhibition of trastuzumab, like p95 variant of HER2, or alterations of

PTEN and PI3KCA and also compromised host immunoactivation, which prevents

the main effects of the antibody (Gagliato, Jardim, Marchesi, & Hortobagyi, 2016).

Among the metastatic HER2-positive breast cancers, great variation is seen in

treatment outcomes, with some patients surviving even more than 10 years with the

78

disease (Moilanen et al., 2017). This is similar to what is seen with new cancer

immunotherapies; therefore, we speculated that the immunological status of the

tumor would play an important role in patient outcomes.

Our studies assessed whether the tumor-infiltrating immune cells evaluated by

using a quantitative computer-assisted counting method could be used as

prognostic and predictive markers in metastatic HER2-positive breast cancer

treated with trastuzumab. A larger number of patients could have been included in

the study if all the HER2-positive breast cancer patients were included. We selected

to study metastatic disease only, however, since this provides a more homogenous

population and better possibilities to study treatment effects. Furthermore, our

institute has pioneered in trastuzumab discontinuation in responding patients,

therefore providing a unique patient cohort to study factors associated with long

trastuzumab-free periods.

ADCC is mediated mainly through NK and T cells (Clynes et al., 2000; Park

et al., 2010), which were both studied in our retrospective patient material. Previous

studies have shown NK cell infiltration and activation in tumors after the

administration of trastuzumab to be associated with good trastuzumab responses

(Arnould et al., 2006; Tian et al., 2017). We studied the role of NK cells in

pretreatment samples and saw only a minor infiltration of the cells in our tumor

samples. A higher number of NK cells showed a tendency towards a better

prognosis, but the NK cell infiltration may not be optimal for predicting the clinical

outcomes in this subset of patients. It would have been interesting to see whether

the NK cells were able to penetrate the tumor tissue after administration of

trastuzumab, but unfortunately only a few patients had post-treatment samples

available, and the analysis was not performed. Furthermore, treatment decisions are

made based on the available patient and tumor characteristics, and post-treatment

markers provide less information for the clinical decision making.

T cell infiltration has been studied in many cancer types, and in colorectal

cancer the Immunoscore characterizing the abundance and location of CD3 and

CD8 positive T cells has been shown to be a powerful prognostic parameter (Galon

et al., 2014; Galon et al., 2006; Pagès et al., 2018). At the time of our studies, tumor-

infiltrating lymphocytes in HER2-positive breast cancers were studied mainly

using a morphological evaluation of the immune cells or measuring the immune

cell infiltrations in the whole tumor area. We studied the quantity of CD3 and CD8

positive T cells in the center of the tumors (CT) and in the invasive margin (IM)

separately and saw that a high number of the center tumoral CD3 or CD8 positive

T cells were significantly associated with improved outcomes, with the latter being

79

a more powerful prognostic factor. These results highlight the important role of

cytotoxic T cells in cancer eradication and that T cells in the CT and IM might

reflect different types of host immunoreaction to the tumor, which requires further

investigation. During the past few years, more studies on HER2-positive breast

cancer have been examining specific immune cell subtypes in different tumor

locations (in the center of the tumor or in the stroma) in patients treated with

trastuzumab (Hou, Nitta, Wei, Banks, Parwani et al., 2018; Liu et al., 2017; Takada

et al., 2018; van Rooijen et al., 2018). These studies have also confirmed the

importance of cytotoxic T cells in the disease.

Tumor associated macrophages (TAMs) are known to have clinical impact in

the tumor microenvironment, and the ADCP induced by trastuzumab is mainly

mediated by TAMs (Shi et al., 2015). However, little is known about the importance

and prognostic role of macrophages in HER2-positive breast cancer. We first

studied the TAMs using the CD68 marker, which did not give any significant

association with survival, although a high number of CD68 in the CT showed a

tendency towards improved prognosis. The CD68 marker also recognizes other

types of immune cells, not just macrophages, and because it is now well known that

there is a continuum of different macrophage phenotypes shifting from antitumoral

M1-like to pro-tumoral M2-like phenotype (Biswas & Mantovani, 2010; Brown et

al., 2017), we continued our studies using two distinct markers. Our studies

revealed that a high number of M1 polarized macrophages in both IM and CT show

a strong association with better outcomes and are consistent with previous works

studying breast, colorectal, ovarian and gastric cancers (Biswas et al., 2013;

Fridman et al., 2017), whereas the high number of M2 polarized macrophages

showed a tendency towards a poorer prognosis. Thus, our study supports the idea

of targeting the macrophage polarization in HER2-positive breast cancer, but

further investigation is required.

The tumor cells themselves can suppress the functions of the immune cells

required for the proper function of trastuzumab and other monoclonal antibodies,

and our results showed that the low tumoral expression of CD47 and IDO1 are

associated with improved patient outcomes, which were inconsistent with other

reports. CD47 is a commonly known inhibitor of phagocytosis, whereas IDO1 is

associated with an increased M2/M1 macrophage ratio, both of which lead to

immunosuppression and further highlight the importance macrophage-mediated

phagocytosis in tumor eradication (Jaiswal et al., 2009; Wang, X et al., 2014). Anti-

CD47 antibodies are already in clinical trials and have shown promising effects on

80

lymphomas with a favorable safety profile (Advani et al., 2018), while the clinical

trials are still ongoing in solid cancer.

One study (Loi et al., 2014) has shown that adjuvant trastuzumab efficacy is

limited to patients with high TILs. We therefore studied whether our most

promising prognostic markers, high number of CD8+ T cells in the CT and high

number of M1-like macrophages in the IM and CT, could predict the benefit from

trastuzumab in a metastatic setting. This analysis was rather difficult, since the

patients of our retrospective material were treated with trastuzumab at varying

points of their disease. Nevertheless, we selected the treatment length of the 1st

trastuzumab-containing regimen in a metastatic setting (surrogate of progression-

free survival) and the length of trastuzumab interruption in response as indicators

for the treatment benefit, based on the evidence from randomized trials and our

oncologists’ personal experience. In contrast to the recommended clinical protocols,

our institute has aimed to discontinue trastuzumab treatment if a metastatic HER2+

breast cancer patient has achieved a prolonged radiological response, and we have

seen that these patients can experience long trastuzumab-free periods (median of

51 months) without progression (Moilanen et al., 2017). We were unable to see a

connection between the length of the 1st trastuzumab treatment and the number of

CD8 positive CT cells alone, whereas M1 macrophages in the CT showed a

significant association. However, we saw an even more remarkable association

when we combined these markers. When we studied the patients whose

trastuzumab treatment was interrupted, a high number of all the three markers (CD8

CT, M1 CT, M1 IM) were associated with the length of trastuzumab discontinuation,

and the strongest association was seen with a combined analysis of CD8 CT and

M1 CT or IM status. Our results suggest that trastuzumab interruption could be

feasible for patients with a high CD8 CT and M1 status and that CD8 CT combined

with M1 CT should be further studied in the context of trastuzumab efficacy.

Our results support the idea of using distinct immune cell types in specific

tumor locations as prognostic and predictive markers for HER2-positive breast

cancer. However, it should be noted that different cancer therapeutics can alter the

immune cell composition of the tumors, for example, in a favorable way by

increasing the influx of cytotoxic cells but also in an unfavorable way by increasing

the influx of immunosuppressive cells into the tumor (Arnould et al., 2006;

DeNardo et al., 2011). A recent study additionally revealed that ADCP, induced in

TAMs by trastuzumab treatment, increases the expression of PD-L1 and IDO1 in

TAMs leading to immunosuppression (Su et al., 2018). This is quite

counterintuitive when considering the high impact and well-established benefit of

81

monoclonal antibodies in cancer therapies and should definitely be further studied.

Studying the immune cell infiltrations in trastuzumab-treated patients can be

challenging, because trastuzumab is received in combination with multiple

chemotherapeutic agents, which in turn can induce immunogenic cell death and

modulate the composition of immune cell prolife (Casares et al., 2005; García-

Martínez et al., 2014; Obeid et al., 2007; Parikh et al., 2014; Takahashi, Sakakura,

Mito, Ida, & Chikamatsu, 2016). Nevertheless, in the era of personalized medicine,

we need to be able to identify the patients who will or will not benefit from a

specific treatment. Trastuzumab is given to every breast cancer patient with HER2

amplification, even though it cannot be guaranteed that they will really benefit from

the therapy. Since adjuvant trastuzumab received in localized disease is a predictor

of poor prognosis in a metastatic setting (Moilanen et al., 2017; Murthy et al., 2012;

Rier et al., 2017), suggesting that acquired resistance to trastuzumab has been

developed, additional predictive markers for the patient selection are required.

6.2 The role of HER2 and HER3 for cancer stem-like cells in ALK

translocated NSCLC

HER proteins have been linked to cancer stem-like cells (CSLCs) in many cancer

types. NRG1, a HER3/HER4 ligand, has also been shown to induce CSLC features

in breast cancer (Ithimakin et al., 2013; Jeong et al., 2014; Korkaya et al., 2008;

Lee et al., 2014). CSLCs, in turn, have been linked to radiation therapy,

chemotherapy, targeted therapy and immunotherapy resistances, causing cancer

recurrence. CSLCs can prevent the therapeutic effects of monoclonal antibodies,

such as trastuzumab, by secreting immunosuppressive cytokines and modifying the

immune responses (Creighton et al., 2009; Maccalli et al., 2017; McLendon et al.,

2006; Phillips et al., 2006; Reim et al., 2009; Wang et al., 2017). The molecular

mechanisms behind the CSLC acquire are quite unknown, even though some

signaling pathways, like wnt/β-catenin, TGF-β and the HER2, have been linked to

it (Anido et al., 2010; Korkaya et al., 2009; Takebe et al., 2011). Several preclinical

studies and clinical trials are studying potential cancer stem-like cell targeting

agents, but none have been approved for cancer therapy (Li et al., 2017). A Phase

II study with ovarian cancer patients treated with chemotherapy and metformin, a

suggested CSLC targeting agent, showed a decreased number of ALDH+ CSLCs in

the tumors and increased sensitivity of the tumors to cisplatin in vitro (Buckanovich

et al., 2017). However, more data on the clinical utility of CSLC targeting agents

are urgently needed for the development of more efficient cancer treatments.

82

We assessed the significance of HER2 and HER3 for CSLCs in ALK

translocated NSCLC in studies III and IV. We chose to study ALK translocated

NSCLC because this disease represents a subgroup of patients who are extremely

sensitive to ALK inhibition but will inevitably develop a resistance to the treatment

within 1–2 years, which is common for targeted therapies (Peters et al., 2017;

Solomon et al., 2014). In addition, we have previously shown that CSLCs can

mediate the ALK tyrosine kinase inhibitor (TKI) resistance in the disease, and

several reports have linked the activation of bypass signaling via HER receptors to

the ALK TKI resistance (Doebele et al., 2012; Jokinen et al., 2014; Katayama et al.,

2012; Sasaki et al., 2011). Our results highlighted the importance of HER2 and

HER3 for CSLC-mediated ALK TKI resistance. Overexpression of HER2 resulted

in more pronounced expression of CSLC markers in response to ALK TKI, whereas

the knockdown of HER2 or HER3 inhibited this TKI-induced expression of CSLC

markers. The strongest expression of CSLC markers was seen when HER3 was

activated by NRG1, suggesting that CSLC-driven ALK TKI resistance could be

mediated via HER3 signaling. Even though NRG1 also activates HER4, it wasn’t

enough to restore the CSLC marker expression when HER3 was knocked down.

Moreover, our results pointed towards functionality of HER2 and HER3 to cancer

stem-like properties assessed by colony and sphere formation. HER2 and HER3

activation led to increased colony and sphere formation abilities, whereas the

knockdowns downregulated these capabilities. Our results support the idea of

blocking HER2-HER3 signaling to target CSLCs in order to inhibit the tumor

progression after cancer treatments.

HER2-HER3 is an important dimer signaling through PI3K and MAPK

pathways to sustain survival and proliferation of the cells. The HER3 part of the

dimer is essential for the PI3K pathway, since it can directly bind PI3K with its

several phospho-tyrosine residues, and HER2-amplified cancers have been shown

to depend on the HER3 signaling (Garrett et al., 2013; Lee-Hoeflich et al., 2008;

Schulze et al., 2005). HER2 and HER3 alteration of our studies did affect the

signaling through PI3K and MAPK pathways, with a more sustainable impact on

the former one. The overexpression of HER2 was able to reactivate the Akt and

ERK1/2 signaling after long-term exposure to ALK inhibition. This reactivation of

the signaling pathways, especially the Akt signaling, was blocked by the

knockdown of HER2 or HER3. In ALK translocated NSCLC, the Akt and ERK1/2

signaling are mainly driven by the ALK, and the recovery of the signaling pathways

after long exposure to ALK TKI is not entirely known. Our results demonstrate the

importance of HER2 and HER3 in this signaling recovery. Additionally, the

83

signaling recovery, after a long exposure to ALK inhibitor, is likely to reflect a

CSLC acquire in comparison to acute signaling changes that mirror the cytotoxic

responses, further highlighting the role of HER2 and HER3 for the CSLC

phenotype. The exact mechanisms of how HER2 and HER3 are activated after ALK

inhibition are unknown. It might be that the suppression of HER2 and HER3 by

negative feedback pathways is simultaneously inhibited when the downstream

signaling of ALK is blocked. Akt, for example, has been shown to mediate

suppression of HER3, and inhibition of Akt leads to increased expression and

phosphorylation of HER3 (Chandarlapaty et al., 2011). Moreover, the HER2 and

HER3 might not be the only RTKs that are upregulated when ALK is inhibited, as

seen in previous studies (Doebele et al., 2012; Katayama et al., 2012), and this

could be further studied.

Studies by others have shown that inhibition of one HER family member leads

to compensatory activation of other HER family members (Engelman et al., 2007;

Ritter et al., 2007; Yonesaka et al., 2011), which could be a major problem when

targeting these receptors therapeutically. Our results showed that HER2

overexpression can orchestrate the other members of the protein family.

Knockdown of HER3 increased the activity of EGFR; however, it was not enough

to reactivate the Akt signaling blocked by the knockdown. Our studies suggest that

targeting HER2 together with HER3 could be feasible for more efficient killing of

the cancer stem cells.

6.3 Implications of the results and future perspectives

The results of these studies provide suggestions for more efficient use of anti-HER

targeting agents. Our results with metastatic HER2-positive breast cancer patients

suggest that patients with a favorable tumor immune profile (low tumor expression

of CD47 and IDO1, high number of tumor-infiltrating CD8+ T cells and M1 TAMs

in the CT) might be treated in a less intensive manner and remain progression-free

during trastuzumab interruption, while patients with non-favorable immune profile

could be candidates for experimental immunotherapeutic approaches and more

effective cancer therapies should be developed for them (Fig. 7). These hypotheses,

however, must be confirmed in larger prospective studies but, if applicable, this

improved patient selection could lead to increased response rates and economical

savings. Our future studies will investigate further the impact of cytotoxic T cells

and M1-like macrophages utilizing in vitro ADCC and ADCP model. Because the

main mechanisms of action of trastuzumab depends on the activation of the

84

immune system, evaluation of chemokine profile in the tumor microenvironment

and the peripheral blood could be an interesting future study on the subject. A recent

work showed that HER2 signaling can recruit tumor-infiltrating immune cells by

inducing CCL2, which was important for the function of trastuzumab (Triulzi et al.,

2019). Another interesting perspective could be to study the gut microbiota, which

is extremely important for the proper function of the immune system and has been

shown to affect the immunotherapy responses (Routy et al., 2018; Vétizou et al.,

2015).

Since HER2 targeting agents have already been developed and are currently in

clinical use, and HER3 targeting agents are constantly being developed, the HER2-

HER3 dependency of CSLC-driven resistance revealed in our studies supports the

idea of investigating anti-HER agents as stem-like cell targeting agents in different

cancer types. This is not the first time HER proteins have been linked to CSLCs,

but our studies further support their targeting in combination with other treatments

to improve patient outcomes (Fig. 8). Studies combining HER3 targeting or NRG1

inhibitory agents with HER2 targeting agents should be studied in vitro and in vivo

in the future, and the significance of HER2-HER3 dimer could also be studied in

other diseases as well.

6.4 Limitations of the study

Our studies had some limitations. The number of tumor samples was low in

studies I and II, and patients were retrospectively collected from a single academic

cancer center. Patients included in studies I and II were all treated with trastuzumab,

which made the evaluation of the predictive values more challenging. Additionally,

most of the immune cell infiltrations were studied using only a single marker;

however, well characterized and widely used markers were selected for the studies.

Studies III and IV were performed only for two different cell lines, and only in vitro

models were used due to a restricted availability of cell line models of ALK

translocated NSCLC. The results of these studies should be confirmed with in vivo

models, even though it will be challenging.

85

Fig. 7. Main findings of our studies with HER2-positive breast cancer. Our results

suggest that patients with high infiltration of M1-like macrophages and CD8+ T cells in

the center of the tumor and low expression of CD47 and IDO1 in the tumor cells could

be treated less intensively, while more effective treatments are required for patients with

a less favorable tumor immune profile. ChT=chemotherapy.

CD47 highIDO1 high

CD47 lowIDO1 low

CD47 highIDO1 high

ChT +trastuzumab

Trastuzumabdiscontinuation

feasible in response

Trastuzumabdiscontinuation

not feasible

Immune cellinfiltrationshould beimproved

Cancer cell M1-like macrophage CD8+ T cell

86

Fig. 8. Schematic representation of dual targeting of ALK and HER2-HER3 dimer. Our

results suggest that expression of HER2 and HER3 is linked to cancer stem-like cells,

which are causing resistance to ALK TKI therapy and cancer recurrence. Dual inhibition

of ALK and HER2-HER3 dimer could improve cancer cell killing.

ALK TKI

HER2

HER2HER3

HER3

ALK TKI+ anti-HER2/HER3

HER2

HER2

Stem-like cell

Cancer cell

87

7 Conclusions

The current study investigated the possibilities of using HER targeting agents more

efficiently by selecting the HER2-positive breast cancer patients more accurately

for the trastuzumab treatment and by utilizing already available targeting agents to

target cancer stem-like cells. The following conclusions were made based on the

results:

1. A high density of cytotoxic T cells and/or M1-like macrophages in the center

of the tumor (CT) in the pretreatment tumor samples predicts a more favorable

outcome in metastatic HER2-positive breast cancer. The most significant

prognostic value is seen with a combinatory analysis of cytotoxic T cells and

M1-like macrophages.

2. A high density of cytotoxic T cells and M1-like macrophages in the CT can

predict the disease progression and the possibility of interrupting trastuzumab

treatment in metastatic HER2-positive breast cancer. This suggests that

patients with a favorable immune profile could be treated less intensively and

that more efficient treatments should be developed for patients with a low

density of cytotoxic T cells and M1-like macrophages in the CT.

3. An intense expression of CD47 and IDO1 in the tumor cells of HER2-positive

breast cancer is associated with more adverse outcomes, whereas a low

expression predicts more favorable outcomes.

4. HER2 and HER3 have significant roles for the cancer stem-like cell phenotype

in ALK translocated non-small cell lung cancer.

5. ALK tyrosine kinase inhibitor resistance can be mediated by HER2-HER3-

dependent cancer stem-like cells in ALK translocated NSCLC suggesting that

targeting the dimer together with the ALK inhibitor could be beneficial for

overcoming ALK TKI resistances and improving cancer treatments.

88

89

References

Abràmoff, M. D., Magalhães, P. J., & Ram, S. J. (2004). Image processing with imageJ. Biophotonics International, 11(7), 36-41.

Advani, R., Flinn, I., Popplewell, L., Forero, A., Bartlett, N. L., Ghosh, N., . . . Smith, S. M. (2018). CD47 blockade by Hu5F9-G4 and rituximab in non-hodgkin's lymphoma. The New England Journal of Medicine, 379(18), 1711-1721. doi:10.1056/NEJMoa1807315

Aggarwal, B. B., Vijayalekshmi, R. V., & Sung, B. (2009). Targeting inflammatory pathways for prevention and therapy of cancer: Short-term friend, long-term foe. Clinical Cancer Research, 15(2), 425-430. doi:10.1158/1078-0432.CCR-08-0149

Agus, D. B., Akita, R. W., Fox, W. D., Lewis, G. D., Higgins, B., Pisacane, P. I., . . . Sliwkowski, M. X. (2002). Targeting ligand-activated ErbB2 signaling inhibits breast and prostate tumor growth. Cancer Cell, 2(2), 127-137.

Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A., Morrison, S. J., & Clarke, M. F. (2003). Prospective identification of tumorigenic breast cancer cells. Proceedings of the National Academy of Sciences of the United States of America, 100(7), 3983-8. doi:10.1073/pnas.0530291100

Andrechek, E. R., Hardy, W. R., Siegel, P. M., Rudnicki, M. A., Cardiff, R. D., & Muller, W. J. (2000). Amplification of the neu/erbB-2 oncogene in a mouse model of mammary tumorigenesis. Proceedings of the National Academy of Sciences of the United States of America, 97(7), 3444-3449. doi:10.1073/pnas.050408497

Andrulis, I. L., Bull, S. B., Blackstein, M. E., Sutherland, D., Mak, C., Sidlofsky, S., . . . McCready, D. (1998). Neu/erbB-2 amplification identifies a poor-prognosis group of women with node-negative breast cancer. toronto breast cancer study group. Journal of Clinical Oncology, 16(4), 1340-1349. doi:10.1200/JCO.1998.16.4.1340

Anido, J., Sáez-Borderías, A., Gonzàlez-Juncà, A., Rodón, L., Folch, G., Carmona, M. A., . . . Seoane, J. (2010). TGF-β receptor inhibitors target the CD44(high)/Id1(high) glioma-initiating cell population in human glioblastoma. Cancer Cell, 18(6), 655-668. doi:10. 1016/j.ccr.2010.10.023

Arnould, L., Gelly, M., Penault-Llorca, F., Benoit, L., Bonnetain, F., Migeon, C., . . . Coudert, B. (2006). Trastuzumab-based treatment of HER2-positive breast cancer: An antibody-dependent cellular cytotoxicity mechanism? British Journal of Cancer, 94(2), 259-267. doi:10.1038/sj.bjc.6602930

Artavanis-Tsakonas, S., Rand, M. D., & Lake, R. J. (1999). Notch signaling: Cell fate control and signal integration in development. Science, 284(5415), 770-776. doi:10.1126/ science.284.5415.770

Arteaga, C. L., & Engelman, J. A. (2014). ERBB receptors: From oncogene discovery to basic science to mechanism-based cancer therapeutics. Cancer Cell, 25(3), 282-303. doi:10.1016/j.ccr.2014.02.025

Arteaga, C. L., Sliwkowski, M. X., Osborne, C. K., Perez, E. A., Puglisi, F., & Gianni, L. (2011). Treatment of HER2-positive breast cancer: Current status and future perspectives. Nature Reviews. Clinical Oncology, 9(1), 16-32. doi:10.1038/nrclinonc. 2011.177

90

Bang, Y., Van Cutsem, E., Feyereislova, A., Chung, H. C., Shen, L., Sawaki, A., . . . Kang, Y. (2010). Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): A phase 3, open-label, randomised controlled trial. Lancet, 376(9742), 687-697. doi:10.1016/S0140-6736(10)61121-X

Barok, M., Joensuu, H., & Isola, J. (2014). Trastuzumab emtansine: Mechanisms of action and drug resistance. Breast Cancer Research, 16(2), 209. doi:10.1186/bcr3621

Baselga, J., Cortés, J., Kim, S., Im, S., Hegg, R., Im, Y., . . . Swain, S. M. (2012). Pertuzumab plus trastuzumab plus docetaxel for metastatic breast cancer. The New England Journal of Medicine, 366(2), 109-119. doi:10.1056/NEJMoa1113216

Beier, D., Wischhusen, J., Dietmaier, W., Hau, P., Proescholdt, M., Brawanski, A., . . . Beier, C. P. (2008). CD133 expression and cancer stem cells predict prognosis in high-grade oligodendroglial tumors. Brain Pathology, 18(3), 370-377. doi:10.1111/j.1750-3639. 2008.00130.x

Bergers, G., & Benjamin, L. E. (2003). Tumorigenesis and the angiogenic switch. Nature Reviews Cancer, 3(6), 401-410. doi:10.1038/nrc1093

Bhowmick, N. A., Neilson, E. G., & Moses, H. L. (2004). Stromal fibroblasts in cancer initiation and progression. Nature, 432(7015), 332-337. doi:10.1038/nature03096

Bidlingmaier, S., Zhu, X., & Liu, B. (2008). The utility and limitations of glycosylated human CD133 epitopes in defining cancer stem cells. Journal of Molecular Medicine, 86(9), 1025-1032. doi:10.1007/s00109-008-0357-8

Birkman, E., Ålgars, A., Lintunen, M., Ristamäki, R., Sundström, J., & Carpén, O. (2016). EGFR gene amplification is relatively common and associates with outcome in intestinal adenocarcinoma of the stomach, gastro-oesophageal junction and distal oesophagus. BMC Cancer, 16, 406. doi:10.1186/s12885-016-2456-1

Biswas, S. K., Allavena, P., & Mantovani, A. (2013). Tumor-associated macrophages: Functional diversity, clinical significance, and open questions. Seminars in Immunopathology, 35(5), 585-600. doi:10.1007/s00281-013-0367-7

Biswas, S. K., & Mantovani, A. (2010). Macrophage plasticity and interaction with lymphocyte subsets: Cancer as a paradigm. Nature Immunology, 11(10), 889-896. doi:10.1038/ni.1937

Bonnet, D., & Dick, J. E. (1997). Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nature Medicine, 3(7), 730-737. doi:10.1038/nm0797-730

Bos, J. L. (1989). Ras oncogenes in human cancer: A review. Cancer Research, 49(17), 4682-4689.

Bose, R., Kavuri, S. M., Searleman, A. C., Shen, W., Shen, D., Koboldt, D. C., . . . Ellis, M. J. (2013). Activating HER2 mutations in HER2 gene amplification negative breast cancer. Cancer Discovery, 3(2), 224-237. doi:10.1158/2159-8290.CD-12-0349

Brabender, J., Danenberg, K. D., Metzger, R., Schneider, P. M., Park, J., Salonga, D., . . . Danenberg, P. V. (2001). Epidermal growth factor receptor and HER2-neu mRNA expression in non-small cell lung cancer is correlated with survival. Clinical Cancer Research, 7(7), 1850-1855.

91

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer Journal for Clinicians, 68(6), 394-424. doi:10.3322/caac.21492

Brown, J. M., Recht, L., & Strober, S. (2017). The promise of targeting macrophages in cancer therapy. Clinical Cancer Research, 23(13), 3241-3250. doi:10.1158/1078-0432.CCR-16-3122

Bruno, A., Ferlazzo, G., Albini, A., & Noonan, D. M. (2014). A think tank of TINK/TANKs: Tumor-infiltrating/tumor-associated natural killer cells in tumor progression and angiogenesis. Journal of the National Cancer Institute, 106(8) doi:10.1093/jnci/dju200

Buckanovich, R. J., Brown, J., Shank, J., Griffith, K. A., Reynolds, R. K., Johnston, C., . . . Mehta, G. (2017). A phase II clinical trial of metformin as a cancer stem cell targeting agent in stage IIc/III/IV ovarian, fallopian tube, and primary peritoneal cancer. Journal of Clinical Oncology, 35(15), 5556. doi:10.1200/JCO.2017.35.15_suppl.5556

Cantini, L., Pistelli, M., Savini, A., Bastianelli, L., Della Mora, A., Merloni, F., . . . Berardi, R. (2018). Long-responders to anti-HER2 therapies: A case report and review of the literature. Molecular and Clinical Oncology, 8(1), 147-152. doi:10.3892/mco.2017. 1495

Cao, G., Chen, K., Xiong, M., & Chen, B. (2016). HER3, but not HER4, plays an essential role in the clinicopathology and prognosis of gastric cancer: A meta-analysis. PloS One, 11(8), e0161219. doi:10.1371/journal.pone.0161219

Cao, Z., Gao, Q., Fu, M., Ni, N., Pei, Y., & Ou, W. B. (2019). Anaplastic lymphoma kinase fusions: Roles in cancer and therapeutic perspectives. Oncology Letters, 17(2), 2020-2030.

Cappuzzo, F., Varella-Garcia, M., Shigematsu, H., Domenichini, I., Bartolini, S., Ceresoli, G. L., . . . Hirsch, F. R. (2005). Increased HER2 gene copy number is associated with response to gefitinib therapy in epidermal growth factor receptor-positive non-small-cell lung cancer patients. Journal of Clinical Oncology, 23(22), 5007-5018. doi:10.1200 /JCO.2005.09.111

Cardoso, F., Senkus, E., Costa, A., Papadopoulos, E., Aapro, M., André, F., . . . Winer, E. P. (2018). 4th ESO-ESMO international consensus guidelines for advanced breast cancer (ABC 4)†. Annals of Oncology, 29(8), 1634-1657. doi:10.1093/annonc/mdy192

Carey, K. D., Garton, A. J., Romero, M. S., Kahler, J., Thomson, S., Ross, S., . . . Sliwkowski, M. X. (2006). Kinetic analysis of epidermal growth factor receptor somatic mutant proteins shows increased sensitivity to the epidermal growth factor receptor tyrosine kinase inhibitor, erlotinib. Cancer Research, 66(16), 8163-8171. doi:10.1158/0008-5472.CAN-06-0453

Carey, L. A., Dees, E. C., Sawyer, L., Gatti, L., Moore, D. T., Collichio, F., . . . Perou, C. M. (2007). The triple negative paradox: Primary tumor chemosensitivity of breast cancer subtypes. Clinical Cancer Research, 13(8), 2329-2334. doi:10.1158/1078-0432.CCR-06-1109

92

Carpentino, J. E., Hynes, M. J., Appelman, H. D., Zheng, T., Steindler, D. A., Scott, E. W., & Huang, E. H. (2009). Aldehyde dehydrogenase-expressing colon stem cells contribute to tumorigenesis in the transition from colitis to cancer. Cancer Research, 69(20), 8208-8215. doi:10.1158/0008-5472.CAN-09-1132

Carracedo, A., Ma, L., Teruya-Feldstein, J., Rojo, F., Salmena, L., Alimonti, A., . . . Pandolfi, P. P. (2008). Inhibition of mTORC1 leads to MAPK pathway activation through a PI3K-dependent feedback loop in human cancer. The Journal of Clinical Investigation, 118(9), 3065-3074. doi:10.1172/JCI34739

Casares, N., Pequignot, M. O., Tesniere, A., Ghiringhelli, F., Roux, S., Chaput, N., . . . Kroemer, G. (2005). Caspase-dependent immunogenicity of doxorubicin-induced tumor cell death. The Journal of Experimental Medicine, 202(12), 1691-1701. doi:10.1084/jem.20050915

Cavallaro, U., & Christofori, G. (2004). Cell adhesion and signalling by cadherins and ig-CAMs in cancer. Nature Reviews Cancer, 4(2), 118-132. doi:10.1038/nrc1276

Chaffer, C. L., Brueckmann, I., Scheel, C., Kaestli, A. J., Wiggins, P. A., Rodrigues, L. O., . . . Weinberg, R. A. (2011). Normal and neoplastic nonstem cells can spontaneously convert to a stem-like state. Proceedings of the National Academy of Sciences of the United States of America, 108(19), 7950-7955. doi:10.1073/pnas.1102454108

Chandarlapaty, S., Sawai, A., Scaltriti, M., Rodrik-Outmezguine, V., Grbovic-Huezo, O., Serra, V., . . . Rosen, N. (2011). AKT inhibition relieves feedback suppression of receptor tyrosine kinase expression and activity. Cancer Cell, 19(1), 58-71. doi:10.1016 /j.ccr.2010.10.031

Charafe-Jauffret, E., Ginestier, C., Iovino, F., Tarpin, C., Diebel, M., Esterni, B., . . . Wicha, M. S. (2010). Aldehyde dehydrogenase 1–Positive cancer stem cells mediate metastasis and poor clinical outcome in inflammatory breast cancer. Clinical Cancer Research, 16(1), 45-55. doi:10.1158/1078-0432.CCR-09-1630

Cheang, M. C. U., Chia, S. K., Voduc, D., Gao, D., Leung, S., Snider, J., . . . Nielsen, T. O. (2009). Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. Journal of the National Cancer Institute, 101(10), 736-750. doi:10.1093/jnci/djp082

Chen, D. S., & Mellman, I. (2017). Elements of cancer immunity and the cancer-immune set point. Nature, 541(7637), 321-330. doi:10.1038/nature21349

Chiou, S., Yu, C., Huang, C., Lin, S., Liu, C., Tsai, T., . . . Lo, J. (2008). Positive correlations of oct-4 and nanog in oral cancer stem-like cells and high-grade oral squamous cell carcinoma. Clinical Cancer Research, 14(13), 4085-4095. doi:10.1158/1078-0432. CCR-07-4404

Cho, H. S., Mason, K., Ramyar, K. X., Stanley, A. M., Gabelli, S. B., Denney, D. J., & Leahy, D. (2003). Structure of the extracellular region of HER2 alone and in complex with the herceptin fab. Nature, 421(6924), 756-760. doi:10.1038/nature01392

Cioffi, M., D'Alterio, C., Camerlingo, R., Tirino, V., Consales, C., Riccio, A., . . . Scala, S. (2015). Identification of a distinct population of CD133 + CXCR4 + cancer stem cells in ovarian cancer. Scientific Reports, 5(1), 10357. doi:10.1038/srep10357

Clevers, H. (2006). Wnt/beta-catenin signaling in development and disease. Cell, 127(3), 469-80. doi:10.1016/j.cell.2006.10.018

93

Clynes, R. A., Towers, T. L., Presta, L. G., & Ravetch, J. V. (2000). Inhibitory fc receptors modulate in vivo cytotoxicity against tumor targets. Nature Medicine, 6(4), 443-446. doi:10.1038/74704

Coffelt, S. B., Wellenstein, M. D., & De Visser, K. E. (2016). Neutrophils in cancer: Neutral no more. Nature Reviews Cancer, 16(7), 431-446. doi:10.1038/nrc.2016.52

Cortet, M., Bertaut, A., Molinié, F., Bara, S., Beltjens, F., Coutant, C., & Arveux, P. (2018). Trends in molecular subtypes of breast cancer: Description of incidence rates between 2007 and 2012 from three french registries. BMC Cancer, 18(1), 161. doi:10.1186/ s12885-018-4080-8

Costa, D. B., Kobayashi, S., Pandya, S. S., Yeo, W., Shen, Z., Tan, W., & Wilner, K. D. (2011). CSF concentration of the anaplastic lymphoma kinase inhibitor crizotinib. Journal of Clinical Oncology, 29(15), 443. doi:10.1200/JCO.2010.34.1313

Creighton, C. J., Li, X., Landis, M., Dixon, J. M., Neumeister, V. M., Sjolund, A., . . . Chang, J. C. (2009). Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features. Proceedings of the National Academy of Sciences of the United States of America, 106(33), 13820-13825. doi:10.1073/pnas.0905718106

Croce, C. M. (2008). Oncogenes and cancer. New England Journal of Medicine, 358(5), 502-511. doi:10.1056/NEJMra072367

Cross, D. A. E., Ashton, S. E., Ghiorghiu, S., Eberlein, C., Nebhan, C. A., Spitzler, P. J., . . . Pao, W. (2014). AZD9291, an irreversible EGFR TKI, overcomes T790M-mediated resistance to EGFR inhibitors in lung cancer. Cancer Discovery, 4(9), 1046-1061. doi:10.1158/2159-8290.CD-14-0337

Davies, H., Bignell, G. R., Cox, C., Stephens, P., Edkins, S., Clegg, S., . . . Futreal, P. A. (2002). Mutations of the BRAF gene in human cancer. Nature, 417(6892), 949-954. doi:10.1038/nature00766

Day, K. C., Hiles, G. L., Kozminsky, M., Dawsey, S. J., Paul, A., Broses, L. J., . . . Day, M. L. (2017). HER2 and EGFR overexpression support metastatic progression of prostate cancer to bone. Cancer Research, 77(1), 74-85. doi:10.1158/0008-5472.CAN-16-1656

De Visser, K. E., Eichten, A., & Coussens, L. M. (2006). Paradoxical roles of the immune system during cancer development. Nature Reviews Cancer, 6(1), 24-37. doi:10.1038/ nrc1782

DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G., & Thompson, C. B. (2008). The biology of cancer: Metabolic reprogramming fuels cell growth and proliferation. Cell Metabolism, 7(1), 11-20. doi:10.1016/j.cmet.2007.10.002

Della Chiesa, M., Carlomagno, S., Frumento, G., Balsamo, M., Cantoni, C., Conte, R., . . . Vitale, M. (2006). The tryptophan catabolite L-kynurenine inhibits the surface expression of NKp46- and NKG2D-activating receptors and regulates NK-cell function. Blood, 108(13), 4118-4125. doi:10.1182/blood-2006-03-006700

DeNardo, D. G., Brennan, D. J., Rexhepaj, E., Ruffell, B., Shiao, S. L., Madden, S. F., . . . Coussens, L. M. (2011). Leukocyte complexity predicts breast cancer survival and functionally regulates response to chemotherapy. Cancer Discovery, 1(1), 54-67. doi:10.1158/2159-8274.CD-10-0028

94

Denkert, C., von Minckwitz, G., Brase, J. C., Sinn, B. V., Gade, S., Kronenwett, R., . . . Loibl, S. (2015). Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers. Journal of Clinical Oncology, 33(9), 983-991. doi:10.1200/JCO.2014.58.1967

Denkert, C., von Minckwitz, G., Darb-Esfahani, S., Lederer, B., Heppner, B. I., Weber, K. E., . . . Loibl, S. (2018). Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: A pooled analysis of 3771 patients treated with neoadjuvant therapy. The Lancet. Oncology, 19(1), 40-50. doi:10.1016/S1470-2045(17)30904-X

Doebele, R. C., Pilling, A. B., Aisner, D. L., Kutateladze, T. G., Le, A. T., Weickhardt, A. J., . . . Camidge, D. R. (2012). Mechanisms of resistance to crizotinib in patients with ALK gene rearranged non-small cell lung cancer. Clinical Cancer Research, 18(5), 1472-1482. doi:10.1158/1078-0432.CCR-11-2906

Dontu, G., Jackson, K. W., McNicholas, E., Kawamura, M. J., Abdallah, W. M., & Wicha, M. S. (2004). Role of notch signaling in cell-fate determination of human mammary stem/progenitor cells. Breast Cancer Research, 6(6), R605–R615. doi:10.1186/bcr920

Drayton, D. L., Liao, S., Mounzer, R. H., & Ruddle, N. H. (2006). Lymphoid organ development: From ontogeny to neogenesis. Nature Immunology, 7(4), 344-353. doi:10.1038/ni1330

Elster, N., Collins, D. M., Toomey, S., Crown, J., Eustace, A. J., & Hennessy, B. T. (2015). HER2-family signalling mechanisms, clinical implications and targeting in breast cancer. Breast Cancer Research and Treatment, 149(1), 5-15. doi:10.1007/s10549-014-3250-x

Engelman, J. A., Zejnullahu, K., Mitsudomi, T., Song, Y., Hyland, C., Park, J. O., . . . Jänne, P. A. (2007). MET amplification leads to gefitinib resistance in lung cancer by activating ERBB3 signaling. Science, 316(5827), 1039-1043. doi:10.1126/science. 1141478

Esteller, M. (2008). Epigenetics in cancer. New England Journal of Medicine, 358(11), 1148-1159. doi:10.1056/NEJMra072067

Ettinger, D. S., Wood, D. E., Aisner, D. L., Akerley, W., Bauman, J., Chirieac, L. R., . . . Hughes, M. (2017). Non-small cell lung cancer, version 5.2017, NCCN clinical practice guidelines in oncology. Journal of the National Comprehensive Cancer Network, 15(4), 504-535.

Eun, K., Ham, S. W., & Kim, H. (2017). Cancer stem cell heterogeneity: Origin and new perspectives on CSC targeting. BMB Reports, 50(3), 117-125. doi:10.5483/BMBRep. 2017.50.3.222

Feinberg, A. P., & Vogelstein, B. (1983). Hypomethylation distinguishes genes of some human cancers from their normal counterparts. Nature, 301(5895), 89-92. doi:10.1038/ 301089a0

Feldinger, K., & Kong, A. (2015). Profile of neratinib and its potential in the treatment of breast cancer. Breast Cancer, 7, 147-162. doi:10.2147/BCTT.S54414

Fernald, K., & Kurokawa, M. (2013). Evading apoptosis in cancer. Trends in Cell Biology, 23(12), 620-633. doi:10.1016/j.tcb.2013.07.006

95

Finkle, D., Quan, Z. R., Asghari, V., Kloss, J., Ghaboosi, N., Mai, E., . . . Erickson, S. (2004). HER2-targeted therapy reduces incidence and progression of midlife mammary tumors in female murine mammary tumor virus huHER2-transgenic mice. Clinical Cancer Research, 10(7), 2499-2511.

Finnish Breast Cancer Group. (2019). Rintasyövän valtakunnallinen diagnostiikka- ja hoitosuositus. Retrieved from https://rintasyoparyhma.yhdistysavain.fi/hoitosuositus/

Fleischmann, A., Schlomm, T., Köllermann, J., Sekulic, N., Huland, H., Mirlacher, M., . . . Erbersdobler, A. (2009). Immunological microenvironment in prostate cancer: High mast cell densities are associated with favorable tumor characteristics and good prognosis. Prostate, 69(9), 976-981. doi:10.1002/pros.20948

Flynn, N. J., Somasundaram, R., Arnold, K. M., & Sims-Mourtada, J. (2017). The multifaceted roles of B cells in solid tumors: Emerging treatment opportunities. Targeted Oncology, 12(2), 139-152. doi:10.1007/s11523-017-0481-x

Freeman, G. J., Long, A. J., Iwai, Y., Bourque, K., Chernova, T., Nishimura, H., . . . Honjo, T. (2000). Engagement of the PD-1 immunoinhibitory receptor by a novel B7 family member leads to negative regulation of lymphocyte activation. Journal of Experimental Medicine, 192(7), 1027-1034. doi:10.1084/jem.192.7.1027

Fridman, W. H., Pagès, F., Saut`s-Fridman, C., & Galon, J. (2012). The immune contexture in human tumours: Impact on clinical outcome. Nature Reviews Cancer, 12(4), 298-306. doi:10.1038/nrc3245

Fridman, W. H., Zitvogel, L., Sautès-Fridman, C., & Kroemer, G. (2017). The immune contexture in cancer prognosis and treatment. Nature Reviews Clinical Oncology, 14(12), 717-734. doi:10.1038/nrclinonc.2017.101

Gabrilovich, D. I., & Nagaraj, S. (2009). Myeloid-derived suppressor cells as regulators of the immune system. Nature Reviews Immunology, 9(3), 162-174. doi:10.1038/nri2506

Gagliato, D. M., Jardim, D. L. F., Marchesi, M. S. P., & Hortobagyi, G. N. (2016). Mechanisms of resistance and sensitivity to anti-HER2 therapies in HER2+ breast cancer. Oncotarget, 7(39), 64431-64446. doi:10.18632/oncotarget.7043

Gainor, J. F., Dardaei, L., Yoda, S., Friboulet, L., Leshchiner, I., Katayama, R., . . . Shaw, A. T. (2016). Molecular mechanisms of resistance to first- and second-generation ALK inhibitors in ALK-rearranged lung cancer. Cancer Discovery, 6(10), 1118-1133. doi:10.1158/2159-8290.CD-16-0596

Galdiero, M. R., Bianchi, P., Grizzi, F., Di Caro, G., Basso, G., Ponzetta, A., . . . Jaillon, S. (2016). Occurrence and significance of tumor-associated neutrophils in patients with colorectal cancer. International Journal of Cancer, 139(2), 446-456. doi:10.1002/ijc. 30076

Galli, S. J., Grimbaldeston, M., & Tsai, M. (2008). Immunomodulatory mast cells: Negative, as well as positive, regulators of immunity. Nature Reviews Immunology, 8(6), 478-486. doi:10.1038/nri2327

Galon, J., Mlecnik, B., Bindea, G., Angell, H. K., Berger, A., Lagorce, C., . . . Pages, F. (2014). Towards the introduction of the 'immunoscore' in the classification of malignant tumours. Journal of Pathology, 232(2), 199-209. doi:10.1002/path.4287

96

Galon, J., Costes, A., Sanchez-Cabo, F., Kirilovsky, A., Mlecnik, B., Lagorce-Pages, C., . . . Pages, F. (2006). Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science, 313(5795), 1960-1964. doi:10.1126/science. 1129139

García-Martínez, E., Gil, G. L., Benito, A. C., González-Billalabeitia, E., Conesa, M. A. V., García García, T., . . . Ayala de la Peña, Francisco. (2014). Tumor-infiltrating immune cell profiles and their change after neoadjuvant chemotherapy predict response and prognosis of breast cancer. Breast Cancer Research, 16(6), 488. doi:10.1186/s13058-014-0488-5

Garrett, J. T., & Arteaga, C. L. (2011). Resistance to HER2-directed antibodies and tyrosine kinase inhibitors: Mechanisms and clinical implications. Cancer Biology & Therapy, 11(9), 793-800.

Garrett, J. T., Sutton, C. R., Kurupi, R., Bialucha, C. U., Ettenberg, S. A., Collins, S. D., . . . Arteaga, C. L. (2013). Combination of antibody that inhibits ligand-independent HER3 dimerization and a p110α inhibitor potently blocks PI3K signaling and growth of HER2+ breast cancers. Cancer Research, 73(19), 6013-6023. doi:10.1158/0008-5472. CAN-13-1191

Gennari, R., Menard, S., Fagnoni, F., Ponchio, L., Scelsi, M., Tagliabue, E., . . . Costa, A. (2004). Pilot study of the mechanism of action of preoperative trastuzumab in patients with primary operable breast tumors overexpressing HER2. Clinical Cancer Research, 10(17), 5650-5655. doi:10.1158/1078-0432.CCR-04-0225

Geyer, C. E., Forster, J., Lindquist, D., Chan, S., Romieu, C. G., Pienkowski, T., . . . Cameron, D. (2006). Lapatinib plus capecitabine for HER2-positive advanced breast cancer. The New England Journal of Medicine, 355(26), 2733-2743. doi:10.1056/ NEJMoa064320

Ghiringhelli, F., Ménard, C., Terme, M., Flament, C., Taieb, J., Chaput, N., . . . Zitvogel, L. (2005). CD4+CD25+ regulatory T cells inhibit natural killer cell functions in a transforming growth factor-ß-dependent manner. Journal of Experimental Medicine, 202(8), 1075-1085. doi:10.1084/jem.20051511

Ghosh, R., Narasanna, A., Wang, S. E., Liu, S., Chakrabarty, A., Balko, J. M., . . . Arteaga, C. L. (2011). Trastuzumab has preferential activity against breast cancers driven by HER2 homodimers. Cancer Research, 71(5), 1871-1882. doi:10.1158/0008-5472. CAN-10-1872

Gianni, L., Pienkowski, T., Im, Y., Roman, L., Tseng, L., Liu, M., . . . Valagussa, P. (2012). Efficacy and safety of neoadjuvant pertuzumab and trastuzumab in women with locally advanced, inflammatory, or early HER2-positive breast cancer (NeoSphere): A randomised multicentre, open-label, phase 2 trial. The Lancet. Oncology, 13(1), 25-32. doi:10.1016/S1470-2045(11)70336-9

Ginestier, C., Hur, M. H., Charafe-Jauffret, E., Monville, F., Dutcher, J., Brown, M., . . . Dontu, G. (2007). ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell Stem Cell, 1(5), 555-567. doi:10.1016/j.stem.2007.08.014

97

Goldhirsch, A., Gelber, R. D., Piccart-Gebhart, M. J., de Azambuja, E., Procter, M., Suter, T. M., . . . Baselga, J. (2013). 2 years versus 1 year of adjuvant trastuzumab for HER2-positive breast cancer (HERA): An open-label, randomised controlled trial. Lancet, 382(9897), 1021-1028. doi:10.1016/S0140-6736(13)61094-6

Gonzalez-Angulo, A. M., Litton, J. K., Broglio, K. R., Meric-Bernstam, F., Rakkhit, R., Cardoso, F., . . . Hortobagyi, G. N. (2009). High risk of recurrence for patients with breast cancer who have human epidermal growth factor receptor 2-positive, node-negative tumors 1 cm or smaller. Journal of Clinical Oncology, 27(34), 5700-5706. doi:10.1200/JCO.2009.23.2025

Grande, E., Bolós, M., & Arriola, E. (2011). Targeting oncogenic ALK: A promising strategy for cancer treatment. Molecular Cancer Therapeutics, 10(4), 569-579. doi:10.1158/1535-7163.MCT-10-0615

Granot, Z., Henke, E., Comen, E., King, T., Norton, L., & Benezra, R. (2011). Tumor entrained neutrophils inhibit seeding in the premetastatic lung. Cancer Cell, 20(3), 300-314. doi:10.1016/j.ccr.2011.08.012

Greenwald, R. J., Freeman, G. J., & Sharpe, A. H. (2005). The B7 family revisited. Annual Review of Immunology, 23, 515-48. doi:10.1146/annurev.immunol.23.021704.115611

Greger, V., Passarge, E., Höpping, W., Messmer, E., & Horsthemke, B. (1989). Epigenetic changes may contribute to the formation and spontaneous regression of retinoblastoma. Human Genetics, 83(2), 155-158. doi:10.1007/BF00286709

Grigoryan, T., Wend, P., Klaus, A., & Birchmeier, W. (2008). Deciphering the function of canonical wnt signals in development and disease: Conditional loss- and gain-of-function mutations of beta-catenin in mice. Genes & Development, 22(17), 2308. doi:10.1101/gad.1686208

Grivennikov, S. I., Greten, F. R., & Karin, M. (2010). Immunity, inflammation, and cancer. Cell, 140(6), 883-899. doi:10.1016/j.cell.2010.01.025

Gupta, P., Fillmore, C., Jiang, G., Shapira, S., Tao, K., Kuperwasser, C., & Lander, E. (2011). Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell, 146(4), 633-644. doi:10.1016/j.cell.2011.07.026

Hall, J. M., Lee, M. K., Newman, B., Morrow, J. E., Anderson, L. A., Huey, B., & King, M. -. (1990). Linkage of early-onset familial breast cancer to chromosome 17q21. Science, 250(4988), 1684-1689. doi:10.1126/science.2270482

Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: The next generation. Cell, 144(5), 646-674. doi:10.1016/j.cell.2011.02.013

Hanker, A. B., Brewer, M. R., Sheehan, J. H., Koch, J. P., Sliwoski, G. R., Nagy, R., . . . Arteaga, C. L. (2017). An acquired HER2T798I gatekeeper mutation induces resistance to neratinib in a patient with HER2 mutant-driven breast cancer. Cancer Discovery, 7(6), 575-585. doi:10.1158/2159-8290.CD-16-1431

Hegde, G. V., de la Cruz, Cecile C., Chiu, C., Alag, N., Schaefer, G., Crocker, L., . . . Jackson, E. L. (2013). Blocking NRG1 and other ligand-mediated Her4 signaling enhances the magnitude and duration of the chemotherapeutic response of non-small cell lung cancer. Science Translational Medicine, 5(171), 171ra18. doi:10.1126/scitranslmed.3004438

98

Hermans, C., Anz, D., Engel, J., Kirchner, T., Endres, S., & Mayr, D. (2014). Analysis of FoxP3+ T-regulatory cells and CD8+T-cells in ovarian carcinoma: Location and tumor infiltration patterns are key prognostic markers: e111757. PLoS ONE, 9(11) doi:10. 1371/journal.pone.0111757

Hollmén, M., Määttä, J. A., Bald, L., Sliwkowski, M. X., & Elenius, K. (2009). Suppression of breast cancer cell growth by a monoclonal antibody targeting cleavable ErbB4 isoforms. Oncogene, 28(10), 1309-1319. doi:10.1038/onc.2008.481

Horn, L., Gettinger, S., Camidge, D. R., Smit, E. F., Janjigian, Y. Y., Miller, V. A., . . . Groen, H. J. M. (2017). Continued use of afatinib with the addition of cetuximab after progression on afatinib in patients with EGFR mutation-positive non-small-cell lung cancer and acquired resistance to gefitinib or erlotinib. Lung Cancer, 113, 51-58. doi:10.1016/j.lungcan.2017.08.014

Hou, Y., Nitta, H., Wei, L., Banks, P. M., Lustberg, M., Wesolowski, R., . . . Li, Z. (2018). PD-L1 expression and CD8-positive T cells are associated with favorable survival in HER2-positive invasive breast cancer. The Breast Journal, 24(6), 911-919. doi:10. 1111/tbj.13112

Hou, Y., Nitta, H., Wei, L., Banks, P. M., Parwani, A. V., & Li, Z. (2018). Evaluation of immune reaction and PD-L1 expression using multiplex immunohistochemistry in HER2-positive breast cancer: The association with response to anti-HER2 neoadjuvant therapy. Clinical Breast Cancer, 18(2), e23-e244. doi:10.1016/j.clbc.2017.11.001

Hu, J., Yang, D., Zhang, H., Liu, W., Zhao, Y., Lu, H., . . . Cai, L. (2015). USP22 promotes tumor progression and induces epithelial-mesenchymal transition in lung adenocarcinoma. Lung Cancer, 88(3), 239-245. doi:10.1016/j.lungcan.2015.02.019

Hubbard, S. R., & Miller, W. T. (2007). Receptor tyrosine kinases: Mechanisms of activation and signaling. Current Opinion in Cell Biology, 19(2), 117-23. doi:10.1016/j.ceb. 2007.02.010

Hynes, N. E., & Lane, H. A. (2005). ERBB receptors and cancer: The complexity of targeted inhibitors. Nature Reviews Cancer, 5(5), 341. doi:10.1038/nrc1609

Hynes, N. E., & MacDonald, G. (2009). ErbB receptors and signaling pathways in cancer. Current Opinion in Cell Biology, 21(2), 177-184. doi:10.1016/j.ceb.2008.12.010

Ingham, P. W., & McMahon, A. P. (2001). Hedgehog signaling in animal development: Paradigms and principles. Genes & Development, 15(23), 3059-3087. doi:10.1101/ gad.938601

Ingold Heppner, B., Untch, M., Denkert, C., Pfitzner, B. M., Lederer, B., Schmitt, W., . . . Loibl, S. (2016). Tumor-infiltrating lymphocytes: A predictive and prognostic biomarker in neoadjuvant-treated HER2-positive breast cancer. Clinical Cancer Research, 22(23), 5747-5754. doi:10.1158/1078-0432.CCR-15-2338

Inoshima, N., Nakanishi, Y., Minami, T., Izumi, M., Takayama, K., Yoshino, I., & Hara, N. (2002). The influence of dendritic cell infiltration and vascular endothelial growth factor expression on the prognosis of non-small cell lung cancer. Clinical Cancer Research, 8(11), 3480-3486.

99

Ithimakin, S., Day, K. C., Malik, F., Zen, Q., Dawsey, S. J., Bersano-Begey, T. F., . . . Wicha, M. S. (2013). HER2 drives luminal breast cancer stem cells in the absence of HER2 amplification: Implications for efficacy of adjuvant trastuzumab. Cancer Research, 73(5), 1635-1646. doi:10.1158/0008-5472.CAN-12-3349

Iwahara, T., Fujimoto, J., Wen, D., Cupples, R., Bucay, N., Arakawa, T., . . . Yamamoto, T. (1997). Molecular characterization of ALK, a receptor tyrosine kinase expressed specifically in the nervous system. Oncogene, 14(4), 439-49.

Jaiswal, B. S., Kljavin, N. M., Stawiski, E. W., Chan, E., Parikh, C., Durinck, S., . . . Seshagiri, S. (2013). Oncogenic ERBB3 mutations in human cancers. Cancer Cell, 23(5), 603-617. doi:10.1016/j.ccr.2013.04.012

Jaiswal, S., Jamieson, C. H. M., Pang, W. W., Park, C. Y., Chao, M. P., Majeti, R., . . . Weissman, I. L. (2009). CD47 is upregulated on circulating hematopoietic stem cells and leukemia cells to avoid phagocytosis. Cell, 138(2), 271-285. doi:10.1016/j.cell. 2009.05.046

Janjigian, Y. Y., Smit, E. F., Groen, H. J. M., Horn, L., Gettinger, S., Camidge, D. R., . . . Pao, W. (2014). Dual inhibition of EGFR with afatinib and cetuximab in kinase inhibitor-resistant EGFR-mutant lung cancer with and without T790M mutations. Cancer Discovery, 4(9), 1036-1045. doi:10.1158/2159-8290.CD-14-0326

Jänne, P. A., Yang, J. C., Kim, D., Planchard, D., Ohe, Y., Ramalingam, S. S., . . . Ranson, M. (2015). AZD9291 in EGFR inhibitor-resistant non-small-cell lung cancer. The New England Journal of Medicine, 372(18), 1689-1699. doi:10.1056/NEJMoa1411817

Jardé, T., Lloyd-Lewis, B., Thomas, M., Kendrick, H., Melchor, L., Bougaret, L., . . . Dale, T. C. (2016). Wnt and Neuregulin1/ErbB signalling extends 3D culture of hormone responsive mammary organoids. Nature Communications, 7, 13207. doi:10.1038/ ncomms13207

Jensen, H. K., Donskov, F., Marcussen, N., Nordsmark, M., Lundbeck, F., & Von Der Maase, H. (2009). Presence of intratumoral neutrophils is an independent prognostic factor in localized renal cell carcinoma. Journal of Clinical Oncology, 27(28), 4709-4717. doi:10.1200/JCO.2008.18.9498

Jeong, H., Kim, J., Lee, Y., Seo, J. H., Hong, S. R., & Kim, A. (2014). Neuregulin-1 induces cancer stem cell characteristics in breast cancer cell lines. Oncology Reports, 32(3), 1218-1224. doi:10.3892/or.2014.3330

Jiang, F., Qiu, Q., Khanna, A., Todd, N. W., Deepak, J., Xing, L., . . . Katz, R. L. (2009). Aldehyde dehydrogenase 1 is a tumor stem cell-associated marker in lung cancer. Molecular Cancer Research, 7(3), 330-338. doi:10.1158/1541-7786.MCR-08-0393

Jiang, J., Zhang, Y., Chuai, S., Wang, Z., Zheng, D., Xu, F., . . . Chen, Z. (2012). Trastuzumab (herceptin) targets gastric cancer stem cells characterized by CD90 phenotype. Oncogene, 31(6), 671-682. doi:10.1038/onc.2011.282

Joensuu, H., Bono, P., Kataja, V., Alanko, T., Kokko, R., Asola, R., . . . Kellokumpu-Lehtinen, P. (2009). Fluorouracil, epirubicin, and cyclophosphamide with either docetaxel or vinorelbine, with or without trastuzumab, as adjuvant treatments of breast cancer: Final results of the FinHer trial. Journal of Clinical Oncology, 27(34), 5685-5692. doi:10.1200/JCO.2008.21.4577

100

Johansson, A., Rudolfsson, S., Hammarsten, P., Halin, S., Pietras, K., Jones, J., . . . Bergh, A. (2010). Mast cells are novel independent prognostic markers in prostate cancer and represent a target for therapy. American Journal of Pathology, 177(2), 1031-1041. doi:10.2353/ajpath.2010.100070

Jokinen, E., Laurila, N., Koivunen, P., & Koivunen, J. P. (2014). Combining targeted drugs to overcome and prevent resistance of solid cancers with some stem-like cell features. Oncotarget, 5(19), 9295-9307. doi:10.18632/oncotarget.2424

Joyce, J. A., & Fearon, D. T. (2015). T cell exclusion, immune privilege, and the tumor microenvironment. Science, 348(6230), 74-80. doi:10.1126/science.aaa6204

Junttila, T. T., Li, G., Parsons, K., Phillips, G. L., & Sliwkowski, M. X. (2011). Trastuzumab-DM1 (T-DM1) retains all the mechanisms of action of trastuzumab and efficiently inhibits growth of lapatinib insensitive breast cancer. Breast Cancer Research and Treatment, 128(2), 347-356. doi:10.1007/s10549-010-1090-x

Kainulainen, V., Sundvall, M., Määttä, J. A., Santiestevan, E., Klagsbrun, M., & Elenius, K. (2000). A natural ErbB4 isoform that does not activate phosphoinositide 3-kinase mediates proliferation but not survival or chemotaxis. The Journal of Biological Chemistry, 275(12), 8641-8649. doi:10.1074/jbc.275.12.8641

Karachaliou, N., Lazzari, C., Verlicchi, A., Sosa, A. E., & Rosell, R. (2017). HER3 as a therapeutic target in cancer. BioDrugs, 31(1), 63-73. doi:10.1007/s40259-016-0205-2

Karnoub, A. E., & Weinberg, R. A. (2008). Ras oncogenes: Split personalities. Nature Reviews Molecular Cell Biology, 9(7), 517-531. doi:10.1038/nrm2438

Katayama, R., Shaw, A. T., Khan, T. M., Mino-Kenudson, M., Solomon, B. J., Halmos, B., . . . Engelman, J. A. (2012). Mechanisms of acquired crizotinib resistance in ALK-rearranged lung cancers. Science Translational Medicine, 4(120), 120ra17. doi:10.1126/scitranslmed.3003316

Katzmann, D. J., Odorizzi, G., & Emr, S. D. (2002). Receptor downregulation and multivesicular-body sorting. Nature Reviews. Molecular Cell Biology, 3(12), 893-905. doi:10.1038/nrm973

Kavuri, S. M., Jain, N., Galimi, F., Cottino, F., Leto, S. M., Migliardi, G., . . . Bose, R. (2015). HER2 activating mutations are targets for colorectal cancer treatment. Cancer Discovery, 5(8), 832-841. doi:10.1158/2159-8290.CD-14-1211

Kawamoto, T., Sato, J. D., Le, A., Polikoff, J., Sato, G. H., & Mendelsohn, J. (1983). Growth stimulation of A431 cells by epidermal growth factor: Identification of high-affinity receptors for epidermal growth factor by an anti-receptor monoclonal antibody. Proceedings of the National Academy of Sciences of the United States of America, 80(5), 1337-1341.

Kim, M. S., Cho, H. I., Yoon, H. J., Ahn, Y., Park, E. J., Jin, Y. H., & Jang, Y. K. (2018). JIB-04, A small molecule histone demethylase inhibitor, selectively targets colorectal cancer stem cells by inhibiting the wnt/β-catenin signaling pathway. Scientific Reports, 8(1), 6611.

101

Kohler, B. A., Sherman, R. L., Howlader, N., Jemal, A., Ryerson, A. B., Henry, K. A., . . . Penberthy, L. (2015). Annual report to the nation on the status of cancer, 1975-2011, featuring incidence of breast cancer subtypes by race/ethnicity, poverty, and state. Journal of the National Cancer Institute, 107(6), djv048. doi:10.1093/jnci/djv048

Konecny, G. E., Pegram, M. D., Venkatesan, N., Finn, R., Yang, G., Rahmeh, M., . . . Slamon, D. J. (2006). Activity of the dual kinase inhibitor lapatinib (GW572016) against HER-2-overexpressing and trastuzumab-treated breast cancer cells. Cancer Research, 66(3), 1630-1639. doi:10.1158/0008-5472.CAN-05-1182

Köninki, K., Tanner, M., Auvinen, A., & Isola, J. (2009). HER-2 positive breast cancer: Decreasing proportion but stable incidence in finnish population from 1982 to 2005. Breast Cancer Research, 11(3), R37. doi:10.1186/bcr2322

Korkaya, H., Paulson, A., Iovino, F., & Wicha, M. S. (2008). HER2 regulates the mammary stem/progenitor cell population driving tumorigenesis and invasion. Oncogene, 27(47), 6120-6130. doi:10.1038/onc.2008.207

Korkaya, H., Paulson, A., Charafe-Jauffret, E., Ginestier, C., Brown, M., Dutcher, J., . . . Wicha, M. S. (2009). Regulation of mammary stem/progenitor cells by PTEN/akt/beta-catenin signaling. PLoS Biology, 7(6), e1000121. doi:10.1371/journal.pbio.1000121

Kurdi, A. T., Glavey, S. V., Bezman, N. A., Jhatakia, A., Guerriero, J. L., Manier, S., . . . Ghobrial, I. M. (2018). Antibody-dependent cellular phagocytosis by macrophages is a novel mechanism of action of elotuzumab. Molecular Cancer Therapeutics, 17(7), 1454-1463. doi:10.1158/1535-7163.MCT-17-0998

Kurppa, K. J., Denessiouk, K., Johnson, M. S., & Elenius, K. (2016). Activating ERBB4 mutations in non-small cell lung cancer. Oncogene, 35(10), 1283-1291. doi:10.1038/ onc.2015.185

Kwak, E. L., Bang, Y., Camidge, D. R., Shaw, A. T., Solomon, B., Maki, R. G., . . . Iafrate, A. J. (2010). Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. The New England Journal of Medicine, 363(18), 1693-1703. doi:10.1056/NEJMoa1006448

Lee, C. Y. -., Lin, Y., Bratman, S. V., Feng, W., Kuo, A. H., Scheeren, F. A., . . . Diehn, M. (2014). Neuregulin autocrine signaling promotes self-renewal of breast tumor-initiating cells by triggering HER2/HER3 activation. Cancer Research, 74(1), 341-352. doi:10. 1158/0008-5472.CAN-13-1055

Lee, H. H., Kang, H., & Cho, H. (2017). Natural killer cells and tumor metastasis. Archives of Pharmacal Research, 40(9), 1037-1049. doi:10.1007/s12272-017-0951-9

Lee, H. J., Kim, J. Y., Park, I. A., Song, I. H., Yu, J. H., Ahn, J., & Gong, G. (2015). Prognostic significance of tumor-infiltrating lymphocytes and the tertiary lymphoid structures in HER2-positive breast cancer treated with adjuvant trastuzumab. American Journal of Clinical Pathology, 144(2), 278-288. doi:10.1309/AJCPIXUYDVZ0RZ3G

Lee-Hoeflich, S. T., Crocker, L., Yao, E., Pham, T., Munroe, X., Hoeflich, K. P., . . . Stern, H. M. (2008). A central role for HER3 in HER2-amplified breast cancer: Implications for targeted therapy. Cancer Research, 68(14), 5878-5887. doi:10.1158/0008-5472. CAN-08-0380

102

Leibowitz-Amit, R., Templeton, A. J., Omlin, A., Pezaro, C., Atenafu, E. G., Keizman, D., . . . Joshua, A. M. (2014). Clinical variables associated with PSA response to abiraterone acetate in patients with metastatic castration-resistant prostate cancer. Annals of Oncology, 25(3), 657-662. doi:10.1093/annonc/mdt581

Leis, O., Eguiara, A., Lopez-Arribillaga, E., Alberdi, M. J., Hernandez-Garcia, S., Elorriaga, K., . . . Martin, A. G. (2012). Sox2 expression in breast tumours and activation in breast cancer stem cells. Oncogene, 31(11), 1354-1365. doi:10.1038/onc.2011.338

Lemmon, M. A., & Schlessinger, J. (2010). Cell signaling by receptor tyrosine kinases. Cell, 141(7), 1117-1134. doi:10.1016/j.cell.2010.06.011

Lewis Phillips, G. D., Li, G., Dugger, D. L., Crocker, L. M., Parsons, K. L., Mai, E., . . . Sliwkowski, M. X. (2008). Targeting HER2-positive breast cancer with trastuzumab-DM1, an antibody-cytotoxic drug conjugate. Cancer Research, 68(22), 9280-9290. doi:10.1158/0008-5472.CAN-08-1776

Li, D., Ambrogio, L., Shimamura, T., Kubo, S., Takahashi, M., Chirieac, L. R., . . . Wong, K. -. (2008). BIBW2992, an irreversible EGFR/HER2 inhibitor highly effective in preclinical lung cancer models. Oncogene, 27(34), 4702-4711. doi:10.1038/onc. 2008.109

Li, Y., Atkinson, K., & Zhang, T. (2017). Combination of chemotherapy and cancer stem cell targeting agents: Preclinical and clinical studies. Cancer Letters, 396, 103-109. doi:10.1016/j.canlet.2017.03.008

Lin, J. J., Riely, G. J., & Shaw, A. T. (2017). Targeting ALK: Precision medicine takes on drug resistance. Cancer Discovery, 7(2), 137-155. doi:10.1158/2159-8290.CD-16-1123

Lin, J. J., & Shaw, A. T. (2016). Resisting resistance: Targeted therapies in lung cancer. Trends in Cancer, 2(7), 350-364. doi:10.1016/j.trecan.2016.05.010

Lin, W., Karin, M., Pearce, D., & Kleyman, T. R. (2007). A cytokine-mediated link between innate immunity, inflammation, and cancer. Journal of Clinical Investigation, 117(5), 1175-1183. doi:10.1172/JCI31537

Liu, P., Cheng, H., Roberts, T. M., & Zhao, J. J. (2009). Targeting the phosphoinositide 3-kinase pathway in cancer. Nature Reviews Drug Discovery, 8(8), 627-644. doi:10.1038/ nrd2926

Liu, R., Wang, X., Chen, G. Y., Dalerba, P., Gurney, A., Hoey, T., . . . Clarke, M. F. (2007). The prognostic role of a gene signature from tumorigenic breast-cancer cells. The New England Journal of Medicine, 356(3), 217-226. doi:10.1056/NEJMoa063994

Liu, S., Chen, B., Burugu, S., Leung, S., Gao, D., Virk, S., . . . Nielsen, T. O. (2017). Role of cytotoxic tumor-infiltrating lymphocytes in predicting outcomes in metastatic HER2-positive breast cancer: A secondary analysis of a randomized clinical trial. JAMA Oncology, 3(11), e172085. doi:10.1001/jamaoncol.2017.2085

Liu, T. J., Sun, B. C., Zhao, X. L., Zhao, X. M., Sun, T., Gu, Q., . . . Liu, N. (2013). CD133+ cells with cancer stem cell characteristics associates with vasculogenic mimicry in triple-negative breast cancer. Oncogene, 32(5), 544-553. doi:10.1038/onc.2012.85

103

Loi, S., Michiels, S., Salgado, R., Sirtaine, N., Jose, V., Fumagalli, D., . . . Sotiriou, C. (2014). Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: Results from the FinHER trial. Annals of Oncology, 25(8), 1544-1550. doi:10.1093/annonc/mdu112

Lombardi, V. C., Khaiboullina, S. F., & Rizvanov, A. A. (2015). Plasmacytoid dendritic cells, a role in neoplastic prevention and progression. European Journal of Clinical Investigation, 45(s1), 1-8. doi:10.1111/eci.12363

Luen, S. J., Salgado, R., Fox, S., Savas, P., Eng-Wong, J., Clark, E., . . . Loi, S. (2017). Tumour-infiltrating lymphocytes in advanced HER2-positive breast cancer treated with pertuzumab or placebo in addition to trastuzumab and docetaxel: A retrospective analysis of the CLEOPATRA study. The Lancet. Oncology, 18(1), 52-62. doi:10.1016/ S1470-2045(16)30631-3

Lynch, T. J., Bell, D. W., Sordella, R., Gurubhagavatula, S., Okimoto, R. A., Brannigan, B. W., . . . Haber, D. A. (2004). Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. The New England Journal of Medicine, 350(21), 2129-2139. doi:10.1056/NEJMoa040938

Maccalli, C., Parmiani, G., & Ferrone, S. (2017). Immunomodulating and immunoresistance properties of cancer-initiating cells: Implications for the clinical success of immunotherapy. Immunological Investigations, 46(3), 221-238. doi:10.1080/08820139. 2017.1280051

Malanchi, I., Peinado, H., Kassen, D., Hussenet, T., Metzger, D., Chambon, P., . . . Huelsken, J. (2008). Cutaneous cancer stem cell maintenance is dependent on beta-catenin signalling. Nature, 452(7187), 650-653. doi:10.1038/nature06835

Mantovani, A., Allavena, P., Sica, A., & Balkwill, F. (2008). Cancer-related inflammation. Nature, 454(7203), 436-444. doi:10.1038/nature07205

Mantovani, A., Marchesi, F., Malesci, A., Laghi, L., & Allavena, P. (2017). Tumour-associated macrophages as treatment targets in oncology. Nature Reviews Clinical Oncology, 14(7), 399-416. doi:10.1038/nrclinonc.2016.217

Martin, M., Holmes, F. A., Ejlertsen, B., Delaloge, S., Moy, B., Iwata, H., . . . Chan, A. (2017). Neratinib after trastuzumab-based adjuvant therapy in HER2-positive breast cancer (ExteNET): 5-year analysis of a randomised, double-blind, placebo-controlled, phase 3 trial. The Lancet. Oncology, 18(12), 1688-1700. doi:10.1016/S1470-2045(17)30717-9

Marty, M., Cognetti, F., Maraninchi, D., Snyder, R., Mauriac, L., Tubiana-Hulin, M., . . . Extra, J. (2005). Randomized phase II trial of the efficacy and safety of trastuzumab combined with docetaxel in patients with human epidermal growth factor receptor 2-positive metastatic breast cancer administered as first-line treatment: The M77001 study group. Journal of Clinical Oncology, 23(19), 4265-4274. doi:10.1200/JCO.2005.04. 173

Masui, S., Nakatake, Y., Toyooka, Y., Shimosato, D., Yagi, R., Takahashi, K., . . . Niwa, H. (2007). Pluripotency governed by Sox2 via regulation of Oct3/4 expression in mouse embryonic stem cells. Nature Cell Biology, 9(6), 625-635. doi:10.1038/ncb1589

104

Mazzoleni, S., Politi, L. S., Pala, M., Cominelli, M., Franzin, A., Sergi, L. S., . . . Galli, R. (2010). Epidermal growth factor receptor expression identifies functionally and molecularly distinct tumor-initiating cells in human glioblastoma multiforme and is required for gliomagenesis. Cancer Research, 70(19), 7500-7513. doi:10.1158/0008-5472.CAN-10-2353

McCubrey, J. A., Steelman, L. S., Kempf, C. R., Chappell, W. H., Abrams, S. L., Stivala, F., . . . Martelli, A. M. (2011). Therapeutic resistance resulting from mutations in raf/MEK/ERK and PI3K/PTEN/akt/mTOR signaling pathways. Journal of Cellular Physiology, 226(11), 2762-2781. doi:10.1002/jcp.22647

McKay, R. D. G., Kittappa, R., Leker, R. R., Soldner, F., Poser, S. W., Rueger, M. A., . . . Androutsellis-Theotokis, A. (2006). Notch signalling regulates stem cell numbers in vitro and in vivo. Nature, 442(7104), 823-826. doi:10.1038/nature04940

McLendon, R. E., Hjelmeland, A. B., Dewhirst, M. W., Shi, Q., Rich, J. N., Wu, Q., . . . Hao, Y. (2006). Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature, 444(7120), 756-760. doi:10.1038/nature05236

Medema, J. P. (2013). Cancer stem cells: The challenges ahead. Nature Cell Biology, 15(4), 338-344. doi:10.1038/ncb2717

Melzer, C., von der Ohe, J., Lehnert, H., Ungefroren, H., & Hass, R. (2017). Cancer stem cell niche models and contribution by mesenchymal stroma/stem cells. Molecular Cancer, 16(1), 28. doi:10.1186/s12943-017-0595-x

Ménard, S., Balsari, A., Casalini, P., Tagliabue, E., Campiglio, M., Bufalino, R., & Cascinelli, N. (2002). HER-2-positive breast carcinomas as a particular subset with peculiar clinical behaviors. Clinical Cancer Research, 8(2), 520-525.

Mendelsohn, J., Gray, J. W., Howley, P. M., Israel, M. A., & Thompson, C. B. (2014). The molecular basis of cancer: Fourth edition. The molecular basis of cancer: Fourth edition (pp. 1-863)

Metro, G., Lunardi, G., Floridi, P., Pascali, J. P., Marcomigni, L., Chiari, R., . . . Gori, S. (2015). CSF concentration of crizotinib in two ALK-positive non-small-cell lung cancer patients with CNS metastases deriving clinical benefit from treatment. Journal of Thoracic Oncology, 10(5), 26. doi:10.1097/JTO.0000000000000468

Miller, V. A., Hirsh, V., Cadranel, J., Chen, Y., Park, K., Kim, S., . . . Yang, J. C. (2012). Afatinib versus placebo for patients with advanced, metastatic non-small-cell lung cancer after failure of erlotinib, gefitinib, or both, and one or two lines of chemotherapy (LUX-lung 1): A phase 2b/3 randomised trial. The Lancet. Oncology, 13(5), 528-538. doi:10.1016/S1470-2045(12)70087-6

Mishra, R., Hanker, A. B., & Garrett, J. T. (2017). Genomic alterations of ERBB receptors in cancer: Clinical implications. Oncotarget, 8(69), 114371-114392. doi:10.18632/ oncotarget.22825

Mishra, R., Patel, H., Alanazi, S., Yuan, L., & Garrett, J. T. (2018). HER3 signaling and targeted therapy in cancer. Oncology Reviews, 12(1), 355. doi:10.4081/oncol.2018.355

105

Mizuno, E., Iura, T., Mukai, A., Yoshimori, T., Kitamura, N., & Komada, M. (2005). Regulation of epidermal growth factor receptor down-regulation by UBPY-mediated deubiquitination at endosomes. Molecular Biology of the Cell, 16(11), 5163-5174. doi:10.1091/mbc.E05-06-0560

Moilanen, T., Mustanoja, S., Karihtala, P., & Koivunen, J. P. (2017). Retrospective analysis of HER2 therapy interruption in patients responding to the treatment in metastatic HER2+ breast cancer. ESMO Open, 2(3) doi:10.1136/esmoopen-2017-000202

Mok, T. S., Wu, Y., Ahn, M., Garassino, M. C., Kim, H. R., Ramalingam, S. S., . . . Papadimitrakopoulou, V. A. (2017). Osimertinib or platinum-pemetrexed in EGFR T790M-positive lung cancer. The New England Journal of Medicine, 376(7), 629-640. doi:10.1056/NEJMoa1612674

Mok, T. S., Wu, Y., Thongprasert, S., Yang, C., Chu, D., Saijo, N., . . . Fukuoka, M. (2009). Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. The New England Journal of Medicine, 361(10), 947-957. doi:10.1056/NEJMoa0810699

Molina, M. A., Codony-Servat, J., Albanell, J., Rojo, F., Arribas, J., & Baselga, J. (2001). Trastuzumab (herceptin), a humanized anti-Her2 receptor monoclonal antibody, inhibits basal and activated Her2 ectodomain cleavage in breast cancer cells. Cancer Research, 61(12), 4744-4749.

Morath, I., Hartmann, T. N., & Orian-Rousseau, V. (2016). CD44: More than a mere stem cell marker. International Journal of Biochemistry and Cell Biology, 81(Pt A), 166-173. doi:10.1016/j.biocel.2016.09.009

Moscatello, D. K., Holgado-Madruga, M., Godwin, A. K., Ramirez, G., Gunn, G., Zoltick, P. W., . . . Wong, A. J. (1995). Frequent expression of a mutant epidermal growth factor receptor in multiple human tumors. Cancer Research, 55(23), 5536-5539.

Motegi, A., Fujimoto, J., Kotani, M., Sakuraba, H., & Yamamoto, T. (2004). ALK receptor tyrosine kinase promotes cell growth and neurite outgrowth. - PubMed - NCBI. Journal of Cell Science, 117(15), 3319-29.

Munk, M., Memon, A., Poulsen, S. S., Borre, M., Nexo, E., & Sorensen, B. S. (2013). The HER4 isoform JM-a/CYT2 relates to improved survival in bladder cancer patients but only if the estrogen receptor α is not expressed. Scandinavian Journal of Clinical and Laboratory Investigation, 73(6), 503-513. doi:10.3109/00365513.2013.818706

Murthy, R. K., Varma, A., Mishra, P., Hess, K. R., Young, E. J., Murray, J. L., . . . Esteva, F. J. (2012). Impact of adjuvant trastuzumab on outcomes of HER2-positive breast cancer patients treated with HER2-targeted therapy in the metastatic setting. Journal of Clinical Oncology, 30(15), 527. doi:10.1200/jco.2012.30.15_suppl.527

Musolino, A., Naldi, N., Bortesi, B., Pezzuolo, D., Capelletti, M., Missale, G., . . . Ardizzoni, A. (2008). Immunoglobulin G fragment C receptor polymorphisms and clinical efficacy of trastuzumab-based therapy in patients with HER-2/neu-positive metastatic breast cancer. Journal of Clinical Oncology, 26(11), 1789-1796. doi:10.1200/JCO.2007. 14.8957

106

Nakanishi, T., Chumsri, S., Khakpour, N., Brodie, A. H., Leyland-Jones, B., Hamburger, A. W., . . . Burger, A. M. (2010). Side-population cells in luminal-type breast cancer have tumour-initiating cell properties, and are regulated by HER2 expression and signalling. British Journal of Cancer, 102(5), 815-826. doi:10.1038/sj.bjc.6605553

Nami, B., Maadi, H., & Wang, Z. (2018). Mechanisms underlying the action and synergism of trastuzumab and pertuzumab in targeting HER2-positive breast cancer. Cancers, 10(10) doi:10.3390/cancers10100342

Nelson, B. H. (2010). CD20+ B cells: The other tumor-infiltrating lymphocytes. Journal of Immunology, 185(9), 4977-4982. doi:10.4049/jimmunol.1001323

Nikolaev, S. I., Rimoldi, D., Iseli, C., Valsesia, A., Robyr, D., Gehrig, C., . . . Antonarakis, S. E. (2012). Exome sequencing identifies recurrent somatic MAP2K1 and MAP2K2 mutations in melanoma. Nature Genetics, 44(2), 133-139. doi:10.1038/ng.1026

Nishikawa, R., Ji, X. D., Harmon, R. C., Lazar, C. S., Gill, G. N., Cavenee, W. K., & Huang, H. J. (1994). A mutant epidermal growth factor receptor common in human glioma confers enhanced tumorigenicity. Proceedings of the National Academy of Sciences of the United States of America, 91(16), 7727-7731.

Nonomura, N., Takayama, H., Nishimura, K., Oka, D., Nakai, Y., Shiba, M., . . . Okuyama, A. (2007). Decreased number of mast cells infiltrating into needle biopsy specimens leads to a better prognosis of prostate cancer. British Journal of Cancer, 97(7), 952-956. doi:10.1038/sj.bjc.6603962

Obeid, M., Tesniere, A., Ghiringhelli, F., Fimia, G. M., Apetoh, L., Perfettini, J., . . . Kroemer, G. (2007). Calreticulin exposure dictates the immunogenicity of cancer cell death. Nature Medicine, 13(1), 54-61. doi:10.1038/nm1523

Ocana, A., Nieto-Jiménez, C., Pandiella, A., & Templeton, A. J. (2017). Neutrophils in cancer: Prognostic role and therapeutic strategies. Molecular Cancer, 16(1) doi:10. 1186/s12943-017-0707-7

Ogiso, H., Ishitani, R., Nureki, O., Fukai, S., Yamanaka, M., Kim, J., . . . Yokoyama, S. (2002). Crystal structure of the complex of human epidermal growth factor and receptor extracellular domains. Cell, 110(6), 775-787. doi:10.1016/S0092-8674(02)00963-7

Ojesina, A. I., Lichtenstein, L., Freeman, S. S., Pedamallu, C. S., Imaz-Rosshandler, I., Pugh, T. J., . . . Meyerson, M. (2014). Landscape of genomic alterations in cervical carcinomas. Nature, 506(7488), 371-375. doi:10.1038/nature12881

Okazaki, S., Nakatani, F., Masuko, K., Tsuchihashi, K., Ueda, S., Masuko, T., . . . Nagano, O. (2016). Development of an ErbB4 monoclonal antibody that blocks neuregulin-1-induced ErbB4 activation in cancer cells. Biochemical and Biophysical Research Communications, 470(1), 239-244. doi:10.1016/j.bbrc.2016.01.045

Paez, J. G., Jänne, P. A., Lee, J. C., Tracy, S., Greulich, H., Gabriel, S., . . . Meyerson, M. (2004). EGFR mutations in lung cancer: Correlation with clinical response to gefitinib therapy. Science, 304(5676), 1497-1500. doi:10.1126/science.1099314

Pagès, F., Mlecnik, B., Marliot, F., Bindea, G., Ou, F., Bifulco, C., . . . Galon, J. (2018). International validation of the consensus immunoscore for the classification of colon cancer: A prognostic and accuracy study. The Lancet, 391(10135), 2128-2139. doi:10.1016/S0140-6736(18)30789-X

107

Pallini, R., Ricci-Vitiani, L., Banna, G. L., Signore, M., Lombardi, D., Todaro, M., . . . De Maria, R. (2008). Cancer stem cell analysis and clinical outcome in patients with glioblastoma multiforme. Clinical Cancer Research, 14(24), 8205-8212. doi:10.1158 /1078-0432.CCR-08-0644

Pao, W., & Chmielecki, J. (2010). Rational, biologically based treatment of EGFR-mutant non-small-cell lung cancer. Nature Reviews. Cancer, 10(11), 760. doi:10.1038/nrc2947

Pao, W., Miller, V. A., Politi, K. A., Riely, G. J., Somwar, R., Zakowski, M. F., . . . Varmus, H. (2005). Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Medicine, 2(3), e73. doi:10.1371/journal.pmed.0020073

Parikh, F., Duluc, D., Imai, N., Clark, A., Misiukiewicz, K., Bonomi, M., . . . Sikora, A. G. (2014). Chemoradiotherapy-induced upregulation of PD-1 antagonizes immunity to HPV-related oropharyngeal cancer. Cancer Research, 74(24), 7205-7216. doi:10.1158/ 0008-5472.CAN-14-1913

Park, S., Jiang, Z., Mortenson, E. D., Deng, L., Radkevich-Brown, O., Yang, X., . . . Fu, Y. (2010). The therapeutic effect of anti-HER2/neu antibody depends on both innate and adaptive immunity. Cancer Cell, 18(2), 160-170. doi:10.1016/j.ccr.2010.06.014

Payandeh, M., Shahriari-Ahmadi, A., Sadeghi, M., & Sadeghi, E. (2016). Correlations between HER2 expression and other prognostic factors in breast cancer: Inverse relations with the ki-67 index and P53 status. Asian Pacific Journal of Cancer Prevention, 17(3), 1015-1018.

Pernicova, I., & Korbonits, M. (2014). Metformin--mode of action and clinical implications for diabetes and cancer. Nature Reviews. Endocrinology, 10(3), 143-156. doi:10.1038/ nrendo.2013.256

Peters, S., Camidge, D. R., Shaw, A. T., Gadgeel, S., Ahn, J. S., Kim, D., . . . Mok, T. (2017). Alectinib versus crizotinib in untreated ALK-positive non-small-cell lung cancer. The New England Journal of Medicine, 377(9), 829-838. doi:10.1056/NEJMoa1704795

Petricevic, B., Laengle, J., Singer, J., Sachet, M., Fazekas, J., Steger, G., . . . Bergmann, M. (2013). Trastuzumab mediates antibody-dependent cell-mediated cytotoxicity and phagocytosis to the same extent in both adjuvant and metastatic HER2/neu breast cancer patients. Journal of Translational Medicine, 11(1) doi:10.1186/1479-5876-11-307

Phillips, T. M., McBride, W. H., & Pajonk, F. (2006). The response of CD24(-/low)/CD44+ breast cancer-initiating cells to radiation. Journal of the National Cancer Institute, 98(24), 1777. doi:10.1093/jnci/djj495

Pivot, X., Romieu, G., Debled, M., Pierga, J., Kerbrat, P., Bachelot, T., . . . Kramar, A. (2013). 6 months versus 12 months of adjuvant trastuzumab for patients with HER2-positive early breast cancer (PHARE): A randomised phase 3 trial. The Lancet. Oncology, 14(8), 741-748. doi:10.1016/S1470-2045(13)70225-0

Plaks, V., Kong, N., & Werb, Z. (2015). The cancer stem cell niche: How essential is the niche in regulating stemness of tumor cells? Cell Stem Cell, 16(3), 225-238. doi:10.1016/j.stem.2015.02.015

108

Rainusso, N., Brawley, V. S., Ghazi, A., Hicks, M. J., Gottschalk, S., Rosen, J. M., & Ahmed, N. (2012). Immunotherapy targeting HER2 with genetically modified T cells eliminates tumor-initiating cells in osteosarcoma. Cancer Gene Therapy, 19(3), 212-217. doi:10.1038/cgt.2011.83

Rajput, A. B., Turbin, D. A., Cheang, M. C., Voduc, D. K., Leung, S., Gelmon, K. A., . . . Huntsman, D. G. (2008). Stromal mast cells in invasive breast cancer are a marker of favourable prognosis: A study of 4,444 cases. Breast Cancer Research and Treatment, 107(2), 249-257. doi:10.1007/s10549-007-9546-3

Regales, L., Gong, Y., Shen, R., de Stanchina, E., Vivanco, I., Goel, A., . . . Pao, W. (2009). Dual targeting of EGFR can overcome a major drug resistance mutation in mouse models of EGFR mutant lung cancer. The Journal of Clinical Investigation, 119(10), 3000-3010. doi:10.1172/JCI38746

Reim, F., Dombrowski, Y., Ritter, C., Buttmann, M., Häusler, S., Ossadnik, M., . . . Wischhusen, J. (2009). Immunoselection of breast and ovarian cancer cells with trastuzumab and natural killer cells: Selective escape of CD44high/CD24low/HER2low breast cancer stem cells. Cancer Research, 69(20), 8058-8066. doi:10.1158/0008-5472.CAN-09-0834

Ribatti, D., Vacca, A., Ria, R., Marzullo, A., Nico, B., Filotico, R., . . . Dammacco, F. (2003). Neovascularisation, expression of fibroblast growth factor-2, and mast cells with tryptase activity increase simultaneously with pathological progression in human malignant melanoma. European Journal of Cancer, 39(5), 666-674. doi:10.1016/ S0959-8049(02)00150-8

Rier, H. N., Levin, M. D., van Rosmalen, J., Bos, M., Drooger, J. C., de Jong, P., . . . Jager, A. (2017). First-line palliative HER2-targeted therapy in HER2-positive metastatic breast cancer is less effective after previous adjuvant trastuzumab-based therapy. Oncologist, 22(8), 901-909.

Ríos-Luci, C., García-Alonso, S., Díaz-Rodríguez, E., Nadal-Serrano, M., Arribas, J., Ocaña, A., & Pandiella, A. (2017). Resistance to the antibody-drug conjugate T-DM1 is based in a reduction in lysosomal proteolytic activity. Cancer Research, 77(17), 4639-4651. doi:10.1158/0008-5472.CAN-16-3127

Ritter, C. A., Perez-Torres, M., Rinehart, C., Guix, M., Dugger, T., Engelman, J. A., & Arteaga, C. L. (2007). Human breast cancer cells selected for resistance to trastuzumab in vivo overexpress epidermal growth factor receptor and ErbB ligands and remain dependent on the ErbB receptor network. Clinical Cancer Research, 13(16), 4909-4919. doi:10.1158/1078-0432.CCR-07-0701

Rosell, R., Carcereny, E., Gervais, R., Vergnenegre, A., Massuti, B., Felip, E., . . . Paz-Ares, L. (2012). Erlotinib versus standard chemotherapy as first-line treatment for european patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): A multicentre, open-label, randomised phase 3 trial. The Lancet. Oncology, 13(3), 239-246. doi:10.1016/S1470-2045(11)70393-X

Roskoski, R. (2014). The ErbB/HER family of protein-tyrosine kinases and cancer. Pharmacological Research, 79, 34-74. doi:10.1016/j.phrs.2013.11.002

109

Routy, B., Le Chatelier, E., Derosa, L., Duong, C. P. M., Alou, M. T., Daillère, R., . . . Zitvogel, L. (2018). Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science, 359(6371), 91-97. doi:10.1126/science.aan3706

Rupaimoole, R., & Slack, F. J. (2017). MicroRNA therapeutics: Towards a new era for the management of cancer and other diseases. Nature Reviews Drug Discovery, 16(3), 203-221. doi:10.1038/nrd.2016.246

Saadeh, D., Kurban, M., & Abbas, O. (2016). Plasmacytoid dendritic cell role in cutaneous malignancies. Journal of Dermatological Science, 83(1), 3-9. doi:10.1016/j.jdermsci. 2016.05.008

Sabbaghi, M., Gil-Gómez, G., Guardia, C., Servitja, S., Arpí, O., García-Alonso, S., . . . Albanell, J. (2017). Defective cyclin B1 induction in trastuzumab-emtansine (T-DM1) acquired resistance in HER2-positive breast cancer. Clinical Cancer Research, 23(22), 7006-7019. doi:10.1158/1078-0432.CCR-17-0696

Sabir, S. R., Yeoh, S., Jackson, G., & Bayliss, R. (2017). EML4-ALK variants: Biological and molecular properties, and the implications for patients. Cancers, 9(9) doi:10.3390/ cancers9090118

Salgado, R., Denkert, C., Campbell, C., Savas, P., Nuciforo, P., Nucifero, P., . . . Loi, S. (2015). Tumor-infiltrating lymphocytes and associations with pathological complete response and event-free survival in HER2-positive early-stage breast cancer treated with lapatinib and trastuzumab: A secondary analysis of the NeoALTTO trial. JAMA Oncology, 1(4), 448-454. doi:10.1001/jamaoncol.2015.0830

Sasaki, T., Koivunen, J., Ogino, A., Yanagita, M., Nikiforow, S., Zheng, W., . . . Jänne, P. A. (2011). A novel ALK secondary mutation and EGFR signaling cause resistance to ALK kinase inhibitors. Cancer Research, 71(18), 6051-6060. doi:10.1158/0008-5472.CAN-11-1340

Scheuer, W., Friess, T., Burtscher, H., Bossenmaier, B., Endl, J., & Hasmann, M. (2009). Strongly enhanced antitumor activity of trastuzumab and pertuzumab combination treatment on HER2-positive human xenograft tumor models. Cancer Research, 69(24), 9330-9336. doi:10.1158/0008-5472.CAN-08-4597

Schreiber, R. D., Old, L. J., & Smyth, M. J. (2011). Cancer immunoediting: Integrating immunity's roles in cancer suppression and promotion. Science, 331(6024), 1565-1570. doi:10.1126/science.1203486

Schulze, W. X., Deng, L., & Mann, M. (2005). Phosphotyrosine interactome of the ErbB-receptor kinase family. Molecular Systems Biology, 1, 2005.0008. doi:10.1038/ msb4100012

Schwitalla, S., Fingerle, A., Cammareri, P., Nebelsiek, T., Göktuna, S., Ziegler, P., . . . Greten, F. (2013). Intestinal tumorigenesis initiated by dedifferentiation and acquisition of stem-cell-like properties. Cell, 152(1-2), 25-38. doi:10.1016/j.cell.2012.12.012

Seigo Kashimura, Saze, Z., Terashima, M., Soeta, N., Ohtani, S., Osuka, F., . . . Gotoh, M. (2012). CD83+ dendritic cells and Foxp3+ regulatory T cells in primary lesions and regional lymph nodes are inversely correlated with prognosis of gastric cancer. Gastric Cancer, 15(2), 144-153. doi:10.1007/s10120-011-0090-9

110

Senkus, E., Kyriakides, S., Ohno, S., Penault-Llorca, F., Poortmans, P., Rutgers, E., . . . Cardoso, F. (2015). Primary breast cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Annals of Oncology, 26 Suppl 5, 8. doi:10.1093/ annonc/mdv298

Shan, L., Wang, Z., Guo, L., Sun, H., Qiu, T., Ling, Y., . . . Ying, J. (2015). Concurrence of EGFR amplification and sensitizing mutations indicate a better survival benefit from EGFR-TKI therapy in lung adenocarcinoma patients. Lung Cancer, 89(3), 337-342. doi:10.1016/j.lungcan.2015.06.008

Shankaran, V., Ikeda, H., Bruce, A. T., White, J. M., Swanson, P. E., Old, L. J., & Schreiber, R. D. (2001). IFNγ, and lymphocytes prevent primary tumour development and shape tumour immunogenicity. Nature, 410(6832), 1107-1111. doi:10.1038/35074122

Sharma, P., & Allison, J. P. (2015). The future of immune checkpoint therapy. Science, 348(6230), 56-61. doi:10.1126/science.aaa8172

Shaw, A. T., Kim, D., Nakagawa, K., Seto, T., Crinó, L., Ahn, M., . . . Jänne, P. A. (2013). Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. The New England Journal of Medicine, 368(25), 2385-2394. doi:10.1056/NEJMoa1214886

Shaw, A. T., Yeap, B. Y., Mino-Kenudson, M., Digumarthy, S. R., Costa, D. B., Heist, R. S., . . . Iafrate, A. J. (2009). Clinical features and outcome of patients with non-small-cell lung cancer who harbor EML4-ALK. Journal of Clinical Oncology, 27(26), 4247-4253. doi:10.1200/JCO.2009.22.6993

Sherr, C. J., & McCormick, F. (2002). The RB and p53 pathways in cancer. Cancer Cell, 2(2), 103-112. doi:10.1016/S1535-6108(02)00102-2

Shi, F., Telesco, S. E., Liu, Y., Radhakrishnan, R., & Lemmon, M. A. (2010). ErbB3/HER3 intracellular domain is competent to bind ATP and catalyze autophosphorylation. Proceedings of the National Academy of Sciences of the United States of America, 107(17), 7692-7697. doi:10.1073/pnas.1002753107

Shi, Y., Fan, X., Deng, H., Brezski, R. J., Rycyzyn, M., Jordan, R. E., . . . An, Z. (2015). Trastuzumab triggers phagocytic killing of high HER2 cancer cells in vitro and in vivo by interaction with fcγ receptors on macrophages. Journal of Immunology, 194(9), 4379-4386. doi:10.4049/jimmunol.1402891

Shi, Y., Liu, N., Lai, W., Yan, B., Chen, L., Liu, S., . . . Tao, Y. (2018). Nuclear EGFR-PKM2 axis induces cancer stem cell-like characteristics in irradiation-resistant cells. Cancer Letters, 422, 81-93. doi:10.1016/j.canlet.2018.02.028

Shibue, T., & Weinberg, R. A. (2017). EMT, CSCs, and drug resistance: The mechanistic link and clinical implications. Nature Reviews. Clinical Oncology, 14(10), 611-629. doi:10.1038/nrclinonc.2017.44

Shook, D., & Keller, R. (2003). Mechanisms, mechanics and function of epithelial–mesenchymal transitions in early development. Mechanisms of Development, 120(11), 1351-1383. doi:10.1016/j.mod.2003.06.005

Slamon, D. J., Clark, G. M., Wong, S. G., Levin, W. J., Ullrich, A., & McGuire, W. L. (1987). Human breast cancer: Correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science, 235(4785), 177-182. doi:10.1126/science.3798106

111

Slamon, D. J., Godolphin, W., Jones, L. A., Holt, J. A., Wong, S. G., Keith, D. E., . . . Ullrich, A. (1989). Studies of the HER-2/neu proto-oncogene in human breast and ovarian cancer. Science, 244(4905), 707-712.

Soda, M., Choi, Y. L., Enomoto, M., Takada, S., Yamashita, Y., Ishikawa, S., . . . Mano, H. (2007). Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature, 448(7153), 561-566. doi:10.1038/nature05945

Solomon, B. J., Mok, T., Kim, D., Wu, Y., Nakagawa, K., Mekhail, T., . . . Blackhall, F. (2014). First-line crizotinib versus chemotherapy in ALK-positive lung cancer. The New England Journal of Medicine, 371(23), 2167-2177. doi:10.1056/NEJMoa1408440

Soria, J., Ohe, Y., Vansteenkiste, J., Reungwetwattana, T., Chewaskulyong, B., Lee, K. H., . . . Ramalingam, S. S. (2018). Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. The New England Journal of Medicine, 378(2), 113-125. doi:10.1056/NEJMoa1713137

Soussi, T., & Wiman, K. G. (2015). TP53: An oncogene in disguise. Cell Death and Differentiation, 22(8), 1239-1249. doi:10.1038/cdd.2015.53

Souttou, B., Carvalho, N. B., Raulais, D., & Vigny, M. (2001). Activation of anaplastic lymphoma kinase receptor tyrosine kinase induces neuronal differentiation through the mitogen-activated protein kinase pathway. Journal of Biological Chemistry, 276(12), 9526-31.

Stratton, M. R., Campbell, P. J., & Futreal, P. A. (2009). The cancer genome. Nature, 458(7239), 719-724. doi:10.1038/nature07943

Su, S., Zhao, J., Xing, Y., Zhang, X., Liu, J., Ouyang, Q., . . . Song, E. (2018). Immune checkpoint inhibition overcomes ADCP-induced immunosuppression by macrophages. Cell, 175(2), 44-457.e23. doi:10.1016/j.cell.2018.09.007

Sugawa, N., Ekstrand, A. J., James, C. D., & Collins, V. P. (1990). Identical splicing of aberrant epidermal growth factor receptor transcripts from amplified rearranged genes in human glioblastomas. Proceedings of the National Academy of Sciences of the United States of America, 87(21), 8602.

Suire, S., Hawkins, P., & Stephens, L. (2002). Activation of phosphoinositide 3-kinase gamma by ras. Current Biology, 12(13), 1068-1075.

Sung, M., Tan, X., Lu, B., Golas, J., Hosselet, C., Wang, F., . . . Loganzo, F. (2018). Caveolae-mediated endocytosis as a novel mechanism of resistance to trastuzumab emtansine (T-DM1). Molecular Cancer Therapeutics, 17(1), 243-253. doi:10.1158/ 1535-7163.MCT-17-0403

Suzuki, M., Shiraishi, K., Yoshida, A., Shimada, Y., Suzuki, K., Asamura, H., . . . Tsuta, K. (2015). HER2 gene mutations in non-small cell lung carcinomas: Concurrence with Her2 gene amplification and Her2 protein expression and phosphorylation. Lung Cancer, 87(1), 14-22. doi:10.1016/j.lungcan.2014.10.014

Swain, S. M., Baselga, J., Kim, S., Ro, J., Semiglazov, V., Campone, M., . . . Cortés, J. (2015). Pertuzumab, trastuzumab, and docetaxel in HER2-positive metastatic breast cancer. The New England Journal of Medicine, 372(8), 724-734. doi:10.1056/ NEJMoa1413513

112

Swann, J. B., & Smyth, M. J. (2007). Immune surveillance of tumors. The Journal of Clinical Investigation, 117(5), 1137-1146. doi:10.1172/JCI31405

Szczerba, B. M., Castro-Giner, F., Vetter, M., Krol, I., Gkountela, S., Landin, J., . . . Aceto, N. (2019). Neutrophils escort circulating tumour cells to enable cell cycle progression. Nature, doi:10.1038/s41586-019-0915-y

Takada, K., Kashiwagi, S., Goto, W., Asano, Y., Takahashi, K., Takashima, T., . . . Ohira, M. (2018). Use of the tumor-infiltrating CD8 to FOXP3 lymphocyte ratio in predicting treatment responses to combination therapy with pertuzumab, trastuzumab, and docetaxel for advanced HER2-positive breast cancer. Journal of Translational Medicine, 16(1), 86. doi:10.1186/s12967-018-1460-4

Takahashi, H., Sakakura, K., Mito, I., Ida, S., & Chikamatsu, K. (2016). Dynamic changes in immune cell profile in head and neck squamous cell carcinoma: Immunomodulatory effects of chemotherapy. Cancer Science, 107(8), 1065-1071. doi:10.1111/cas.12976

Takebe, N., Harris, P. J., Warren, R. Q., & Ivy, S. P. (2011). Targeting cancer stem cells by inhibiting wnt, notch, and hedgehog pathways. Nature Reviews Clinical Oncology, 8(2), 97-106. doi:10.1038/nrclinonc.2010.196

Tangka, F. K., Trogdon, J. G., Richardson, L. C., Howard, D., Sabatino, S. A., & Finkelstein, E. A. (2010). Cancer treatment cost in the united states: Has the burden shifted over time? Cancer, 116(14), 3477-3484. doi:10.1002/cncr.25150

Templeton, A. J., McNamara, M. G., Šeruga, B., Vera-Badillo, F. E., Aneja, P., Ocaña, A., . . . Amir, E. (2014). Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: A systematic review and meta-analysis. Journal of the National Cancer Institute, 106(6) doi:10.1093/jnci/dju124

Terabe, M., & Berzofsky, J. A. (2004). Immunoregulatory T cells in tumor immunity. Current Opinion in Immunology, 16(2), 157-62. doi:10.1016/j.coi.2004.01.010

Tesone, A. J., Rutkowski, M. R., Brencicova, E., Svoronos, N., Perales-Puchalt, A., Stephen, T. L., . . . Conejo-Garcia, J. R. (2016). Satb1 overexpression drives tumor-promoting activities in cancer-associated dendritic cells. Cell Reports, 14(7), 1774-1786. doi:10.1016/j.celrep.2016.01.056

Thress, K. S., Paweletz, C. P., Felip, E., Cho, B. C., Stetson, D., Dougherty, B., . . . Oxnard, G. R. (2015). Acquired EGFR C797S mutation mediates resistance to AZD9291 in non-small cell lung cancer harboring EGFR T790M. Nature Medicine, 21(6), 560-562. doi:10.1038/nm.3854

Tian, X., Wei, F., Wang, L., Yu, W., Zhang, N., Zhang, X., . . . Ren, X. (2017). Herceptin enhances the antitumor effect of natural killer cells on breast cancer cells expressing human epidermal growth factor receptor-2. Frontiers in Immunology, 8(OCT) doi:10.3389/fimmu.2017.01426

Torkki, P., Leskelä, R., Linna, M., Mäklin, S., Mecklin, J., Bono, P., . . . Karjalainen, S. (2018). Cancer costs and outcomes in the finnish population 2004-2014. Acta Oncologica, 57(2), 297-303. doi:10.1080/0284186X.2017.1343495

Torre, L. A., Bray, F., Siegel, R. L., Ferlay, J., Lortet-Tieulent, J., & Jemal, A. (2015). Global cancer statistics, 2012. CA Cancer Journal for Clinicians, 65(2), 87-108. doi:10.3322/caac.21262

113

Townsend, S. E., & Allison, J. P. (1993). Tumor rejection after direct costimulation of CD8+ T cells by B7-transfected melanoma cells. Science, 259(5093), 368-370. doi:10.1126/science.7678351

Triulzi, T., Forte, L., Regondi, V., Di Modica, M., Ghirelli, C., Carcangiu, M. L., . . . Tagliabue, E. (2019). HER2 signaling regulates the tumor immune microenvironment and trastuzumab efficacy. Oncoimmunology, 8(1), e1512942. doi:10.1080/2162402X. 2018.1512942

Tth, T., Tth-Jakatics, R., Jimi, S., Takebayashi, S., & Kawamoto, N. (2000). Cutaneous malignant melanoma: Correlation between neovascularization and peritumor accumulation of mast cells overexpressing vascular endothelial growth factor. Human Pathology, 31(8), 955-960. doi:10.1053/hupa.2000.16658

Ullrich, A., Coussens, L., Hayflick, J. S., Dull, T. J., Gray, A., Tam, A. W., . . . Seeburg, P. H. (1984). Human epidermal growth factor receptor cDNA sequence and aberrant expression of the amplified gene in A431 epidermoid carcinoma cells. Nature, 309(5967), 418-425. doi:10.1038/309418a0

van den Hoogen, C., van der Horst, G., Cheung, H., Buijs, J. T., Lippitt, J. M., Guzmán-Ramírez, N., . . . van der Pluijm, G. (2010). High aldehyde dehydrogenase activity identifies tumor-initiating and metastasis-initiating cells in human prostate cancer. Cancer Research, 70(12), 5163-5173. doi:10.1158/0008-5472.CAN-09-3806

van Rooijen, J. M., Qiu, S., Timmer-Bosscha, H., van der Vegt, B., Boers, J. E., Schröder, C. P., & de Vries, Elisabeth G. E. (2018). Androgen receptor expression inversely correlates with immune cell infiltration in human epidermal growth factor receptor 2-positive breast cancer. European Journal of Cancer, 103, 52-60. doi:10.1016/ j.ejca.2018.08.001

Väyrynen, J. P., Vornanen, J. O., Sajanti, S., Böhm, J. P., Tuomisto, A., & Mäkinen, M. J. (2012). An improved image analysis method for cell counting lends credibility to the prognostic significance of T cells in colorectal cancer. Virchows Archiv, 460(5), 455-465. doi:10.1007/s00428-012-1232-0

Väyrynen, J. P., Tuomisto, A., Klintrup, K., Mäkelä, J., Karttunen, T. J., & Mäkinen, M. J. (2013). Detailed analysis of inflammatory cell infiltration in colorectal cancer. The British Journal of Cancer, 109(7), 1839-1847. doi:10.1038/bjc.2013.508

Veglia, F., & Gabrilovich, D. I. (2017). Dendritic cells in cancer: The role revisited. Current Opinion in Immunology, 45, 43-51. doi:10.1016/j.coi.2017.01.002

Verma, S., Miles, D., Gianni, L., Krop, I. E., Welslau, M., Baselga, J., . . . Blackwell, K. (2012). Trastuzumab emtansine for HER2-positive advanced breast cancer. The New England Journal of Medicine, 367(19), 1783-1791. doi:10.1056/NEJMoa1209124

Vermi, W., Micheletti, A., Finotti, G., Tecchio, C., Calzetti, F., Costa, S., . . . Cassatella, M. A. (2018). Slan+ monocytes and macrophages mediate CD20-dependent b-cell lymphoma elimination via ADCC and ADCP. Cancer Research, 78(13), 3544-3559. doi:10.1158/0008-5472.CAN-17-2344

Vesely, M. D., Kershaw, M. H., Schreiber, R. D., & Smyth, M. J. (2011). Natural innate and adaptive immunity to cancer. Annual Review of Immunology, 29(1), 235-271. doi:10.1146/annurev-immunol-031210-101324

114

Vétizou, M., Pitt, J. M., Daillère, R., Lepage, P., Waldschmitt, N., Flament, C., . . . Zitvogel, L. (2015). Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science, 350(6264), 1079-1084. doi:10.1126/science.aad1329

Vlashi, E., & Pajonk, F. (2014). Cancer stem cells, cancer cell plasticity and radiation therapy. Seminars in Cancer Biology, 31, 28-35. doi:10.1016/j.semcancer.2014.07.001

Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz Jr., L. A., & Kinzler, K. W. (2013). Cancer genome landscapes. Science, 340(6127), 1546-1558. doi:10.1126/ science.1235122

Von Minckwitz, G., Du Bois, A., Schmidt, M., Maass, N., Cufer, T., De Jongh, F. E., . . . Loibl, S. (2009). Trastuzumab beyond progression in human epidermal growth factor receptor 2-positive advanced breast cancer: A german breast group 26/breast international group 03-05 study. Journal of Clinical Oncology, 27(12), 1999-2006. doi:10.1200/JCO.2008.19.6618

Wang, A., Qu, L., & Wang, L. (2017). At the crossroads of cancer stem cells and targeted therapy resistance. Cancer Letters, 385, 87-96. doi:10.1016/j.canlet.2016.10.039

Wang, J., Jia, Y., Wang, N., Zhang, X., Tan, B., Zhang, G., & Cheng, Y. (2014). The clinical significance of tumor-infiltrating neutrophils and neutrophil-to-CD8+ lymphocyte ratio in patients with resectable esophageal squamous cell carcinoma. Journal of Translational Medicine, 12(1) doi:10.1186/1479-5876-12-7

Wang, J., Yin, J., Yang, Q., Ding, F., Chen, X., Li, B., & Tian, X. (2016). Human epidermal growth factor receptor 4 (HER4) is a favorable prognostic marker of breast cancer: A systematic review and meta-analysis. Oncotarget, 7(47), 76693-76703. doi:10.18632/ oncotarget.12485

Wang, L., Wu, C., Rajasekaran, N., & Shin, Y. K. (2018). Loss of tumor suppressor gene function in human cancer: An overview. Cellular Physiology and Biochemistry, 51(6), 2647-2693. doi:10.1159/000495956

Wang, X. -., Wang, H. -., Wang, H., Zhang, F., Wang, K. -., Guo, Q., . . . Du, J. (2014). The role of indoleamine 2,3-dioxygenase (IDO) in immune tolerance: Focus on macrophage polarization of THP-1 cells. Cellular Immunology, 289(1-2), 42-48. doi:10.1016/ j.cellimm.2014.02.005

Williams, C. S., Bernard, J. K., Demory Beckler, M., Almohazey, D., Washington, M. K., Smith, J. J., & Frey, M. R. (2015). ERBB4 is over-expressed in human colon cancer and enhances cellular transformation. Carcinogenesis, 36(7), 710-718. doi:10.1093/ carcin/bgv049

Wilson, F. H., Johannessen, C. M., Piccioni, F., Tamayo, P., Kim, J. W., Van Allen, E. M., . . . Garraway, L. A. (2015). A functional landscape of resistance to ALK inhibition in lung cancer. Cancer Cell, 27(3), 397-408. doi:10.1016/j.ccell.2015.02.005

Won, J., Yang, H. W., Shin, S., Lee, J. H., Heo, W. D., & Cho, K. (2012). The crossregulation between ERK and PI3K signaling pathways determines the tumoricidal efficacy of MEK inhibitor. Journal of Molecular Cell Biology, 4(3), 153-163. doi:10.1093/jmcb/mjs021

115

Wood, E. R., Truesdale, A. T., McDonald, O. B., Yuan, D., Hassell, A., Dickerson, S. H., . . . Shewchuk, L. (2004). A unique structure for epidermal growth factor receptor bound to GW572016 (lapatinib): Relationships among protein conformation, inhibitor off-rate, and receptor activity in tumor cells. Cancer Research, 64(18), 6652-6659. doi:10.1158/0008-5472.CAN-04-1168

Wooster, R., Neuhausen, S. L., Mangion, J., Quirk, Y., Ford, D., Collins, N., . . . Stratton, M. R. (1994). Localization of a breast cancer susceptibility gene, BRCA2, to chromosome 13q12-13. Science, 265(5181), 2088-2090. doi:10.1126/science.8091231

Wright, M. H., Calcagno, A. M., Salcido, C. D., Carlson, M. D., Ambudkar, S. V., & Varticovski, L. (2008). Brca1 breast tumors contain distinct CD44+/CD24 and CD133+ cells with cancer stem cell characteristics. Breast Cancer Research, 10(1), R10. doi:10.1186/bcr1855

Yan, Q., Guo, K., Feng, G., Shan, F., Sun, L., Zhang, K., . . . Ruan, S. (2018). Association between the overexpression of Her3 and clinical pathology and prognosis of colorectal cancer: A meta-analysis. Medicine, 97(37), e12317. doi:10.1097/MD.00000000000 12317

Yarden, Y., & Pines, G. (2012). The ERBB network: At last, cancer therapy meets systems biology. Nature Reviews. Cancer, 12(8), 553-563. doi:10.1038/nrc3309

Yonesaka, K., Zejnullahu, K., Okamoto, I., Satoh, T., Cappuzzo, F., Souglakos, J., . . . Jänne, P. A. (2011). Activation of ERBB2 signaling causes resistance to the EGFR-directed therapeutic antibody cetuximab. Science Translational Medicine, 3(99), 99ra86. doi:10.1126/scitranslmed.3002442

Yun, C., Boggon, T. J., Li, Y., Woo, M. S., Greulich, H., Meyerson, M., & Eck, M. J. (2007). Structures of lung cancer-derived EGFR mutants and inhibitor complexes: Mechanism of activation and insights into differential inhibitor sensitivity. Cancer Cell, 11(3), 217-227. doi:10.1016/j.ccr.2006.12.017

Zhang, S., Ma, X., Zhu, C., Liu, L., Wang, G., & Yuan, X. (2016). The role of myeloid-derived suppressor cells in patients with solid tumors: A meta-analysis. PLoS ONE, 11(10) doi:10.1371/journal.pone.0164514

Zhang, X., Gureasko, J., Shen, K., Cole, P. A., & Kuriyan, J. (2006). An allosteric mechanism for activation of the kinase domain of epidermal growth factor receptor. Cell, 125(6), 1137-1149. doi:10.1016/j.cell.2006.05.013

Zhao, W., Zhuang, C., Xu, J., Wang, M., Zhang, Z., Tu, L., . . . Zhang, Z. (2014). HER4 is a novel prognostic biomarker in gastrointestinal stromal tumor specifically originated from stomach. American Journal of Cancer Research, 4(6), 838-849.

Zhou, C., Wu, Y., Jiang, L., Li, Z., Diao, P., Wang, D., . . . Yang, J. (2018). Density and location of CD3+ and CD8+ tumor‐infiltrating lymphocytes correlate with prognosis of oral squamous cell carcinoma. Journal of Oral Pathology & Medicine, 47(4), 359-367. doi:10.1111/jop.12698

Zhu, F., Qian, W., Zhang, H., Liang, Y., Wu, M., Zhang, Y., . . . Li, Y. (2017). SOX2 is a marker for stem-like tumor cells in bladder cancer. Stem Cell Reports, 9(2), 429-437. doi:10.1016/j.stemcr.2017.07.004

116

Zimmerli, D., Cecconi, V., Valenta, T., Hausmann, G., Cantù, C., Restivo, G., . . . van den Broek, M. (2018). WNT ligands control initiation and progression of human papillomavirus-driven squamous cell carcinoma. Oncogene, 37(27), 3753-3762. doi:10.5167/uzh-151334

Zlobec, I., Terracciano, L., Jass, J. R., & Lugli, A. (2007). Value of staining intensity in the interpretation of immunohistochemistry for tumor markers in colorectal cancer. Virchows Archiv, 451(4), 763-769. doi:10.1007/s00428-007-0466-8

Zou, H. Y., Friboulet, L., Kodack, D. P., Engstrom, L. D., Li, Q., West, M., . . . Smeal, T. (2015). PF-06463922, an ALK/ROS1 inhibitor, overcomes resistance to first and second generation ALK inhibitors in preclinical models. Cancer Cell, 28(1), 70-81. doi:10.1016/j.ccell.2015.05.010

Zou, H. Y., Li, Q., Lee, J. H., Arango, M. E., McDonnell, S. R., Yamazaki, S., . . . Christensen, J. G. (2007). An orally available small-molecule inhibitor of c-met, PF-2341066, exhibits cytoreductive antitumor efficacy through antiproliferative and antiangiogenic mechanisms. Cancer Research, 67(9), 4408-4417. doi:10.1158/0008-5472.CAN-06-4443

117

Original publications

I Honkanen, T.J.*, Moilanen, T.*, Karihtala, P., Tiainen, S., Auvinen, P., Väyrynen, J.P., Mäkinen, M. & Koivunen, J.P. (2017). Prognostic and predictive role of spatially positioned tumour infiltrating lymphocytes in metastatic HER2 positive breast cancer treated with trastuzumab. Scientific Reports, 7(1), 18027.

II Honkanen, T.J., Tikkanen, A., Karihtala, P., Mäkinen, M., Väyrynen, J.P. & Koivunen, JP. (2019). Prognostic and predictive role of tumour-associated macrophages in HER2 positive breast cancer. Scientific Reports, 9(1), 10961.

III Honkanen, T., Wilenius, E., Koivunen, P., & Koivunen, J.P. (2017). HER2 regulates cancer stem-like cell phenotype in ALK translocated NSCLC. International Journal of Oncology, 51(2), 599-606.

IV Honkanen T.J. & Koivunen J.P. (2019). HER3 regulates cancer stem-like cell properties in ALK translocated NSCLC. Manuscript.

Reprinted with permission from Springer Nature (I, II) and Spandidos Publications

(III).

Original publications are not included in the electronic version of the dissertation.

118

A C T A U N I V E R S I T A T I S O U L U E N S I S

Book orders:Granum: Virtual book storehttp://granum.uta.fi/granum/

S E R I E S D M E D I C A

1513. Ollila, Meri-Maija (2019) The role of polycystic ovary syndrome (PCOS) andoverweight/obesity in women’s metabolic and cardiovascular risk factors andrelated morbidities

1514. Kotiaho, Antti (2019) Radiation dose determination using MOSFET and RPLdosimeters in x-ray imaging

1515. Hänninen, Sandra Lynn (2019) Transcriptional control of muscle cell excitation-contraction coupling : the role of activity and mitochondrial function

1516. Väyrynen, Otto (2019) Factors affecting aggressive oral tongue cancer invasionand development of in vitro models for chemoradiotherapy assay

1517. Euro, Ulla (2019) Risk factors for sciatica

1518. Lotvonen, Sinikka (2019) Palvelutaloon muuttaneiden ikääntyneiden fyysinentoimintakyky, sen muutos ja toimintakykyyn yhteydessä olevat tekijät ensim-mäisen asumisvuoden aikana

1519. Tuomikoski, Anna-Maria (2019) Sairaanhoitajien opiskelijaohjausosaaminen jaohjaajakoulutuksen vaikutus osaamiseen

1520. Raza, Ghulam Shere (2019) The role of dietary fibers in metabolic diseases

1521. Tiinanen, Suvi (2019) Methods for assessment of autonomic nervous systemactivity from cardiorespiratory signals

1522. Skarp, Sini (2019) Whole exome sequencing in identifying genetic factors inmusculoskeletal diseases

1523. Mällinen, Jari (2019) Studies on acute appendicitis with a special reference toappendicoliths and periappendicular abscesses

1524. Paavola, Timo (2019) Associations of low HDL-cholesterol level and prematurecoronary heart disease with functionality and phospholipid composition of HDLand with plasma oxLDL antibody levels

1525. Karki, Saujanya (2019) Oral health status, oral health-related quality of life andassociated factors among Nepalese schoolchildren

1526. Szabo, Zoltan (2019) Modulation of connective tissue growth factor and activinreceptor 2b function in cardiac hypertrophy and fibrosis

1527. Karttunen, Markus (2019) Lääkehoidon turvallinen toteuttaminen ikääntyneidenpitkäaikaishoidossa hoitohenkilöstön arvioimana

UNIVERSITY OF OULU P .O. Box 8000 F I -90014 UNIVERSITY OF OULU FINLAND

A C T A U N I V E R S I T A T I S O U L U E N S I S

University Lecturer Tuomo Glumoff

University Lecturer Santeri Palviainen

Senior research fellow Jari Juuti

Professor Olli Vuolteenaho

University Lecturer Veli-Matti Ulvinen

Planning Director Pertti Tikkanen

Professor Jari Juga

University Lecturer Anu Soikkeli

Professor Olli Vuolteenaho

Publications Editor Kirsti Nurkkala

ISBN 978-952-62-2343-8 (Paperback) ISBN 978-952-62-2344-5 (PDF) ISSN 0355-3221 (Print)ISSN 1796-2234 (Online)

U N I V E R S I TAT I S O U L U E N S I S

MEDICA

ACTAD

D 1528

AC

TAT

iia Honkanen

OULU 2019

D 1528

Tiia Honkanen

MORE EFFICIENT USE OFHER TARGETING AGENTSIN CANCER THERAPY

UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF MEDICINE;MEDICAL RESEARCH CENTER OULU;OULU UNIVERSITY HOSPITAL