muntasir mamun majumder: improving precision in …
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Improving Precision in Therapies for Hematological Malignancies
MUNTASIR MAMUN MAJUMDER
dissertationes scholae doctoralis ad sanitatem investigandam universitatis helsinkiensis 66/2018
66/2018
Helsinki 2018 ISSN 2342-3161 ISBN 978-951-51-4552-9
MU
NTA
SIR M
AM
UN
MA
JUM
DE
R Im
proving P
recision in
Therapies for Hem
atological Malign
ancies
Recent Publications in this Series
45/2018 Jonni HirvonenSystems-Level Neural Mechanisms of Conscious Perception in Health and Schizophrenia46/2018 Panu LuukkonenHeterogeneity of Non-Alcoholic Fatty Liver Disease – Genetic and Nutritional Modulation of Hepatic Lipid Metabolism47/2018 Henriikka KentalaORP2 – A Sterol Sensor Controlling Hepatocellular Bioenergetics and Actin Cytoskeletal Functions48/2018 Liisa PelttariGenetics of Breast and Ovarian Cancer Predisposition with a Focus on RAD51C and RAD51D Genes49/2018 Juha GogulskiPrefrontal Control of the Tactile Sense50/2018 Riku TurkkiComputer Vision for Tissue Characterization and Outcome Prediction in Cancer51/2018 Khalid SaeedFunctional Testing of Urological Cancer Models by RNAi and Drug Libraries52/2018 Johanna I. KiiskiFANCM Mutations in Breast Cancer Risk and Survival 53/2018 Jere WeltnerNovel Approaches for Pluripotent Reprogramming54/2018 Diego Balboa AlonsoHuman Pluripotent Stem Cells and CRISPR-Cas9 Genome Editing to Model Diabetes55/2018 Pauli PöyhönenCardiovascular Magnetic Resonance Evaluation and Risk Stratification of Myocardial Diseases56/2018 Pyry N. SipiläDissecting Epidemiological Associations in Alcohol Drinking and Anorexia Nervosa57/2018 Elisa LahtelaGenetic Variants Predisposing to Prognosis in Pulmonary Sarcoidosis 58/2018 Ilari SireniusLääkkeisiin ja lääkkeeen kaltaisiin tuotteisiin liittyvät toiveet ja illuusiot – psykodynaaminen näkökulma59/2018 Nuno NobreQuality of Life of People Living with HIV/AIDS in Finland60/2018 Pedro Miguel Barroso InácioThe Value of Patient Reporting of Adverse Drug Reactions to Pharmacovigilance Systems61/2018 Taru A. MuranenGenetic Modifiers of CHEK2-Associated and Familial Breast Cancer 62/2018 Leena Seppä-LassilaAcute Phase Proteins in Healthy and Sick Dairy and Beef Calves and Their Association with Growth63/2018 Pekka VartiainenHealth-Related Quality of Life in Patients with Chronic Pain64/2018 Emilia GalliDevelopment of Analytical Tools for the Quantification of MANF and CDNF in Disease and Therapy65/2018 Tommi AnttonenResponses of Auditory Supporting Cells to Hair Cell Damage and Death: Cellular Stress Signalling and Epithelial Repair
INSTITUTE FOR MOLECULAR MEDICINE FINLANDFACULTY OF BIOLOGICAL AND ENVIRONMENTAL SCIENCESDOCTORAL PROGRAMME IN BIOMEDICINEUNIVERSITY OF HELSINKI
Group I
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AML, ALL, Others
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CD3T-PLL
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CD138
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Simultaneous Monitoring of Drug Responses in 11 Hematopoeitic Cell Populations
Signalome and Proteome Profiling of Blood Cells
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Pimasertib (M
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Tram
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Omipalisib (P
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PF.04691502 (PI3K/mTOR)
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Precision Therapy
Luminespib
Tanespimycin
Alvespimycin
Idarubicin
Doxorubicin
Panobinostat
Vorinostat
Quisinostat
Trametinib
Pimasertib
Refametinib
Bortezomib
Ixazomib
Carfilzomib
Alvocidib
Dinaciclib
AZD2014
AZD8055
Pictilisib
Omipalisib
Bryostatin 1
Venetoclax
Navitoclax
Methylprednisolone
Dexamethasone
Pomalidomide
Lenalidomide
0 20sDSS
Color Key
Immunomodulators
BCL2 Inhibitors
CDK Inhibitors
Proteasome Inhibitors
MEK Inhibitors
HDAC Inhibitors
Anthracyclines
PKC Modulator
Group -IV Group -III Group -II Healthy Group -I
Pat
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Drug
Ex vivo Drug Response Somatic Mutation
Gene Expression Integrative Analysis
MultiplexedFlow Cytometry
IMPROVING PRECISION IN THERAPIES FOR HEMATOLOGICAL MALIGNANCIES
Muntasir Mamun Majumder Institution for Molecular Medicine Finland
Helsinki Institute for Life Sciences Faculty of Biological and Environmental Sciences
Doctoral Programme in Biomedicine University of Helsinki, Helsinki, Finland
ACADEMIC DISSERTATION To be presented, with the permission of the Faculty of Biological and
Environmental Sciences of the University of Helsinki, for public examination in Haartman Institute, Lecture Hall 2, Haartmaninkatu 3,
on October 12th 2018, at 13 o’ clock.
Helsinki 2018
Supervised by and
Caroline Heckman, Ph.D. Group leader and Principal Investigator Institute for Molecular Medicine Finland (FIMM) Helsinki Institute for Life Sciences, HiLIFE University of Helsinki, Helsinki, Finland
Professor Krister Wennerberg Biotech Research and Innovation Centre, BRIC University of Copenhagen, Copenhagen, Denmark and Institute for Molecular Medicine Finland (FIMM) Helsinki Institute for Life Sciences, HiLIFE University of Helsinki, Helsinki, Finland
Reviewed by and
Adjunct Professor Eeva Marjaana Säily Department of Hematology Oulu University Hospital Oulu, Finland Assistant Professor Evren Alici Department of Medicine Karolinska Institute Stockholm, Sweden
Opponent Associate Professor Alf Grandien Dr. en Sciences de la vie, Dr. Med. Sci. Center for Hematology and Regenerative Medicine Department of Medicine Karolinska Institute Stockholm, Sweden
Custos
Professor Liisa Holm Faculty of Biological and Environmental Sciences University of Helsinki Helsinki, Finland
Dissertationes Scholae Doctoralis Ad Sanitatem Investigandam Universitatis Helsinkiensis 66/2018 ISSN 2342-3161 (print) / ISSN 2342-317X (online) ISBN 978-951-51-4552-9 (paperback) / ISBN 978-951-51-4553-6 (PDF) Cover layout by Anita Tienhaara Painosalama, Turku, 2018
‘‘When something is important enough, you do it even if the odds are not in
your favor’’ Elon Musk
Dedicated to my family
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TABLE OF CONTENTS ABBREVIATIONS ................................................................................................... 6 ORIGINAL PUBLICATIONS ................................................................................. 8
RELATED PUBLICATIONS ................................................................................ 9 ABSTRACT ............................................................................................................ 11 1 INTRODUCTION ............................................................................................... 13 2 LITERATURE REVIEW .................................................................................... 14
2.1 Hematopoiesis and hematological malignancies .............................................. 14 2.2 Plasma cells ................................................................................................... 14
2.2.1 Origin of plasma cells .......................................................................................... 15 2.3 Multiple myeloma .......................................................................................... 15
2.3.1 Epidemiology ...................................................................................................... 15 2.3.2 Risk factors.......................................................................................................... 15 2.3.3 Onset and disease progression of myeloma ........................................................... 16 2.3.4 Clinical presentation and diagnosis ....................................................................... 17 2.3.5 Risk stratification and prognostic factors............................................................... 18 2.3.6 Genomic landscape and clonal evolution ............................................................... 20 2.3.7 DNA repair and genomic instability in myeloma ................................................... 21 2.3.8 Therapeutic landscape .......................................................................................... 21
2.3.8.1 Proteasome inhibitors ................................................................................... 22 2.3.8.2 Immunomodulatory agents ........................................................................... 23 2.3.8.3 Histone deacetylase inhibitors ....................................................................... 24 2.3.8.4 Monoclonal antibodies ................................................................................. 25 2.3.8.5 Emerging therapies in myeloma .................................................................... 26
2.4 Precision oncology ....................................................................................... 28 2.4.1 Patient stratification ............................................................................................. 28 2.4.2 Biomarkers for precision medicine ....................................................................... 29 2.4.3 Tools for achieving precision in oncology ............................................................. 30
2.4.3.1 Next generation sequencing .......................................................................... 30 2.4.3.2 Functional assays ......................................................................................... 30 2.4.3.3 Systems biology ........................................................................................... 31
3 AIMS OF THE STUDY ....................................................................................... 32 4 MATERIALS AND METHODS ......................................................................... 33
4.1 Patient materials and ethical compliance ......................................................... 33 4.1.1 Sample processing ............................................................................................... 33
4.2 Drug sensitivity and resistance testing (DSRT)................................................ 33 4.2.1 Small molecule library ......................................................................................... 33 4.2.2 Cell viability assay ............................................................................................... 34
4.2.2.1 Ex vivo drug sensitivity data analysis ............................................................ 34 4.2.3 High throughput flow cytometry (HTFC) .............................................................. 35
4.2.3.1 Antibodies ................................................................................................... 35 4.2.3.2 No wash multiplexed HTFC ......................................................................... 35
4.3 Next generation sequencing ............................................................................ 36 4.3.1 DNA sequencing .................................................................................................. 36
4.3.1.1 Driver alteration analysis .............................................................................. 36 4.3.2 RNA sequencing .................................................................................................. 36
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4.4 Network and pathway analysis........................................................................ 37 4.5 Proteome analysis .......................................................................................... 37 4.6 Mass cytometry (CyTOF) ............................................................................... 38 4.7 Statistical and machine learning approaches for analyzing genome–response associations.......................................................................................................... 38
5 RESULTS ............................................................................................................ 41 5.1 Developing a comprehensive platform for precision medicine ......................... 41 5.2 Precision therapies for multiple myeloma ....................................................... 42
5.2.1 Identification of four chemosensitivity subgroups of myeloma with varied clinical outcome ....................................................................................................................... 42 5.2.2 Genomic landscape of multiple myeloma ............................................................. 45 5.2.3 Genetic and transcriptomic signature of chemosensitivity subgroups ..................... 47 5.2.4 Alterations in DNA damage repair genes are mutually exclusive and predict poor prognosis in myeloma patients ...................................................................................... 48 5.2.5 Distinct transcriptional signatures and drug responses are associated to mode of alterations in TP53 suggests a divergent role in disease progression ............................... 49 5.2.6 Impact of clonal heterogeneity on drug responses in MM ...................................... 51 5.2.7 Identification of biomarkers for predicting response to therapies ........................... 53 5.2.8 Identification of new candidates for drug repositioning in MM .............................. 56 5.2.9 Data integration with machine learning approach .................................................. 57 5.2.10 Ex vivo drug responses recapitulate the in vivo response ...................................... 60
5.3 Exploiting innate sensitivity of hematopoietic cells to achieve precision in therapies .............................................................................................................. 62
5.3.1 Understanding differences in proteome profiles in hematopoietic cell subsets ........ 62 5.3.2 Basal signaling profiles of hematopoietic cell subsets ........................................... 62 5.3.3 Hematopoietic cell populations show distinct drug response profiles associated to cellular lineages ........................................................................................................... 64 5.3.4 Comparing innate sensitivity of cell subsets to malignant counterparts in hematological malignancies .......................................................................................... 68
6 DISCUSSION ...................................................................................................... 70 6.1 Precision therapies for myeloma ..................................................................... 70 6.2 Characterizing innate drug sensitivity to improve precision in therapies .......... 76
7 CONCLUSIONS AND FUTURE PERSPECTIVE ............................................. 79 ACKNOWLEDGEMENTS .................................................................................... 80 REFERENCES ....................................................................................................... 84
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ABBREVIATIONS
AML Acute myeloid leukemia ANOVA Analysis of variance ATP Adenosine triphosphate AUC Area under the curve BM Bone marrow CCF Clonal cell fraction CD Cluster of differentiation CLL Chronic lymphocytic leukemia CML Chronic myeloid leukemia CMML Chronic myelomonocytic leukemia CNA Copy number alteration DNA Deoxyribonucleic Acid DSRT Drug sensitivity and resistance testing DSS Drug sensitivity score ECM Extracellular matrix FISH Fluorescence in-situ hybridization GEP Gene expression profiling GFA Group factor analysis HD Hyperdiploid HDAC Histone deacetylases HTFC High throughput flow cytometry HSC Hematopoietic stem cell IMiD Immunomodulatory drugs mAB Monoclonal antibody LDH Lactate dehydrogenase MDS Myelodysplastic syndrome MGUS Monoclonal gammopathy of undetermined significance MM Multiple myeloma NDMM Newly diagnosed multiple myeloma NGS Next generation sequencing NK Natural killer cell ORR Overall response rate OS Overall survival PB Peripheral blood
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PAM Protein affecting mutations PFS Progression free survival PCL Plasma cell leukemia RNA Ribonucleic acid RRMM Relapsed/refractory multiple myeloma sDSS Selective drug sensitivity score SMM Smoldering multiple myeloma T-ALL T cell-acute lymphoblastic leukemia T-PLL T cell-prolymphocytic leukemia VAF Variant allele frequency
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ORIGINAL PUBLICATIONS This thesis is based on following original publications I. Muntasir Mamun Majumder*, Raija Silvennoinen*, Pekka Anttila,
David Tamborero, Samuli Eldfors, Bhagwan Yadav, Riikka Karjalainen, Heikki Kuusanmäki, Juha Lievonen, Alun Parsons, Minna Suvela, Esa Jantunen, Kimmo Porkka, Caroline A. Heckman (2017). Identification of precision treatment strategies for relapsed/refractory multiple myeloma by functional drug sensitivity testing. Oncotarget 8, 56338-56350. *Equal contributions.
II.
Muntasir Mamun Majumder, Muhammad Ammad-ud-din, David Tamborero, Pekka Anttila, Raija Silvennoinen, Samuli Eldfors, Ashwini Kumar, Juha Lievonen, Alun Parsons, Minna Suvela, Pekka Martinen, Esa Jantunen, Samuel Kaski, Kimmo Porkka, Caroline A. Heckman. Pharmacogenomic interactions modulating drug responses in multiple myeloma. (Manuscript)
III.
Muntasir Mamun Majumder, Aino-Maija Leppä, Monica Hellesøy, Paul Dowling, Alina Malyutina, Despina Bazou, Emma Andersson, Alun Parsons, Jing Tang, Satu Mustjoki, Peter O Gorman, Krister Wennerberg, Kimmo Porkka, Bjørn T. Gjertsen, Caroline A. Heckman. High content multi-parametric single cell assay defines distinct drug effects in healthy hematological cell lineages that are retained in malignant counterparts. (Manuscript)
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RELATED PUBLICATIONS The following related publications resulted from work done during the thesis time period
I. Sundin I, Peltola T, Micallef L, Afrabandpey H, Soare M, Mamun Majumder M, et al. Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge. Bioinformatics 2018 Jul 1; 34(13): i395-i403.
II.
Alzrigat M, Parraga AA, Majumder MM, Ma A, Jin J, Osterborg A, et al. The polycomb group protein BMI-1 inhibitor PTC-209 is a potent anti-myeloma agent alone or in combination with epigenetic inhibitors targeting EZH2 and the BET bromodomains. Oncotarget 2017 Nov 28; 8(61): 103731-103743.
III Karjalainen R, Pemovska T, Popa M, Liu M, Javarappa KK, Majumder MM, et al. JAK1/2 and BCL2 inhibitors synergize to counteract bone marrow stromal cell-induced protection of AML. Blood 2017 Aug 10; 130(6): 789-802.
IV
Eldfors S, Kuusanmaki H, Kontro M, Majumder MM, Parsons A, Edgren H, et al. Idelalisib sensitivity and mechanisms of disease progression in relapsed TCF3-PBX1 acute lymphoblastic leukemia. Leukemia 2017 Jan; 31(1): 51-57.
V Kontro M, Kumar A, Majumder MM, Eldfors S, Parsons A, Pemovska T, et al. HOX gene expression predicts response to BCL-2 inhibition in acute myeloid leukemia. Leukemia 2017 Feb; 31(2): 301-309.
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ABSTRACT
Unlike the traditional trial and error approach, precision medicine strategies exploit specific genetic defects and deregulated signaling pathways within cancer cells and may target unique cellular phenotypes to match therapies to patients. Multiple myeloma displays enormous genetic complexity and heterogeneity, which may be ascribed to the diversity of responses observed in patients sharing identical prognostic markers or disease stage. An ambitious aim has been to identify predictive biomarkers that forecast the treatment outcome and detect patient subgroups that are likely to respond. Additionally, characterizing the diverse effects of small molecules on nonmalignant hematopoietic cells is required to understand potential off-target effects and drug interactions, which could further improve the precision of treatment and thus the outcome in patients. In this study, we comprehensively assessed responses to 142 anticancer therapies in 100 patient samples and integrated their responses with genomic, transcriptomic, and clinical profiles, generating a rich pharmacogenetic resource for connecting myeloma genotype to drug responses. An unsupervised clustering of drug responses identified four therapeutically relevant myeloma patient subgroups with a significant variation observed in their ex vivo sensitivity to therapies, genomic composition, and clinical outcome. An acquired sensitivity to signaling inhibitors was associated with a clinically aggressive disease and a higher mutational load in those patients. Fourteen percent of patients displayed cross resistance to nearly all tested drugs and were characterized by a higher expression of the drug resistance transporter genes ABCB1 and ABCC3, genes participating in cell adhesion, and cytokines. Alterations in CRBN, a key molecular target of immunomodulatory drugs, were detected in 8% of patients resistant to these agents. Mutations in DNA-repair genes predicted poor prognosis and conferred sensitivity to PI3K-mTOR and HDAC inhibitors. Utilizing matched multi-assay data from myeloma patient derived cells, we elucidated indicators of drug sensitivity and identified approved drugs that could potentially be repurposed for myeloma. Midostaurin sensitivity was detected in 43% of relapsed patients harboring mutations in TP53 and FAM46C. Four patients who were treated with tailored therapies based on preclinical evidence from this study achieved meaningful and objective responses, providing clinical evidence that ex vivo responses could reflect treatment outcome in patients. Signaling and molecular heterogeneity in cell lineages are incurred during hematopoiesis, which dictates the phenotype of blood cells and thus influences
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their cellular response to drug treatments. We developed and utilized a high-content, multi-parametric flow cytometry assay to determine the diversity of responses in 11 hematopoietic cell types to 71 small molecules and compared their basal signaling state and protein abundances. We discovered that hematological cell populations exhibit distinct drug responses that are tied to their cellular lineages. For instance, dexamethasone, venetoclax, and midostaurin showed higher sensitivity in B/CD19+ and natural killer (NK)/CD56+ cells compared to other lineages. Venetoclax exhibited dose-dependent cell selectivity toward lymphocyte lineages. From a comparison of sensitivity profiles in healthy and malignant cells in 281 patient samples from diverse hematological malignancies, we found that drug response detected in the cell of origin is predictive of its response in its malignant state. We provide evidence that cataloging cell type–specific response is beneficial to predict off-target effects, imminent drug interactions, and lineage-specific anticancer therapies. The findings presented in this thesis demonstrate that deep molecular profiling and functional testing provides powerful tools that are complementary to each other for stratifying patients into subgroups, generating mechanistic biomarkers, and individualizing treatments in patients, which lies at the heart of precision medicine. The knowledge gained from these analyses can be best applied to match therapies to individual genomic alterations, anticipate off-target effects, design informed clinical trials, and, most importantly, improve the quality of life of patients.
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1 INTRODUCTION
A shared goal in cancer therapies has been to achieve precision by identifying strategies that promise to deliver selective eradication of cancer cells and, at the same time, spare the non-malignant cells. Technological advances in genomics, transcriptomics, and proteomics together with advances in systems biology is shaping the management strategies for cancer patients and is also making a difference in how new drugs are developed. During last decades, increasingly safe and molecularly targeted treatments have been adopted to treat hematological malignancies. As such, there is a growing demand to match these therapies to individual patients with actionable genetic vulnerabilities. Following the sequencing effort directed towards characterizing normal and cancer genomes in humans, a renewed enthusiasm in genome-based precision medicine has evolved. Success observed with imatinib in chronic myeloid leukemia (CML) and trastuzumab in breast cancer, among others, has shown that the promises of precision oncology can be realized to achieve durable, safe, and effective treatments. The plummeting sequencing cost and robust high-throughput sequencing has promised to make genomic medicine a clinical reality. Despite all the potential, the excitement for precision medicine strategies has largely been confined to the academic sphere, and much needs to be done to make these strategies appropriate for patient care. As we are beginning to understand the molecular complexity and heterogeneity in individual cancer patients, it has become increasingly clear that the stratification of both disease and patients is needed to provide more accurate prediction of response to therapies and disease progression. Additionally, reliable biomarkers are needed to aid the identification of responding subsets or individual patients to apply treatments based on characterized genetic alterations. Arguably, such strategies are best studied in patient-derived cells and facilitated by the generation of a pharmacogenomic dataset where a meaningful link between cancer genotype and treatment responses can be established. Importantly, the comprehensive characterization of diverse effects exerted by small molecules on hematopoietic cell subsets is required to understand the selective as well as off-target effects of individual compounds. The study is focused on multiple myeloma (MM), an incurable disease with an unmet need for new therapies. The current thesis addresses some key aspects to accelerate the adoption of precision medicine practices in myeloma and extend evidence for other hematological malignancies by i) providing a deeper understanding of patient stratification, ii) identifying predictive and prognostic biomarkers, iii) detecting off-target effects of small molecules, and iv) validating the clinical utility of individualized and tailored treatment strategies to improve clinical outcome in patients.
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2 LITERATURE REVIEW
2.1 Hematopoiesis and hematological malignancies
Hematopoiesis is considered as a hierarchical process that originates from multipotent hematopoietic stem cells (HSCs) and through multiple intermediary progenitor cells, produces phenotypically distinct mature blood cells1-3. Distinct gene expression modules operate in a coordinated manner to define the cell fate decision and generate more mature cell progenies3. Adult hematopoietic system constitutes more than 10 types of differentiated cells with distinct molecular functions including T, B and natural killer (NK) cells, monocytes plus granulocytes, which can be further subdivided. For instance, leukocytes represent cell types involved in innate and acquired immunity; erythrocytes transport oxygen and carbon dioxide and platelets take part in hemostasis. Major site of hematopoiesis in adults is bone marrow. Hematological malignancies (HM) are diverse group of diseases affecting blood, bone marrow, lymph and lymphatic systems, which varies with respect to prognosis and pathobiology. Historically hematological malignancies have been categorized in three major subgroups based on the organs which are involved: leukemia, lymphomas and myeloma. Lymphomas were further subdivided into Hodgkin and non-Hodgkin lymphoma based on the pattern of lymph node involved. In 2008, World Health Organization updated its previously proposed classification of hematological malignancies based on the cell of origin, clinical and genetic parameters. The classification is incorporated in the International Classification of Diseases for Oncology (ICD-O-3) classifying hematological malignancies in 60 disease subtypes. In Europe, the age adjusted incidences of lymphoid and myeloid malignancies are 25 and 8 per 100000 individuals respectively4. HMs are more prevalent in man compared to women.
2.2 Plasma cells
Plasma cells synthesize and secrete antibodies (immunoglobulin), are terminally differentiated B cells, essential for humoral immune response. Differentiation process occur in multiple stages and characterized by the gain of secretory capacity with a loss of capacity to present antigens, cell-cycle exit and changes in surface antigen expression pattern. Mature plasma cells express CD38 and CD138 (Syndecan-1). This is accompanied by a reduced expression of CD19, CD20 and CD45RA, which are present on surface of B cells. Plasma cells also express CD54, CD229 and CD319 aiding their identification5. Paiva et al. further divided normal bone marrow plasma cells into three subsets based on their
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expression levels for CD19 and CD81 reflecting their differentiation stage: less differentiated (CD19+/CD81+), intermediate-differentiated (CD19-/CD81+), and fully differentiated CD19-/CD81-)6.
2.2.1 Origin of plasma cells
Upon exposure to antigens, resting B cells become activated in T cell dependent or independent manner and undergoes a finite number of cell division. In T cell dependent pathway, activated B cells with neighboring T and dendritic cells form the germinal center7 in the B cell follicles of secondary lymphoid tissues. They undergo affinity maturation by somatic hypermutation to increase antigen affinity and subjected to clonal selection. During somatic hypermutation, variable domain of B cell receptor (BCR) is randomly mutated by the enzyme activation-induced cytidine deaminase. A class switch recombination ensues to rearrange the constant region in the immunoglobulin heavy chain locus to switch expression of one class of immunoglobulin to another, mostly immunoglobulins A, G and M. This increases antigen affinity without compromising specificity. B cells exit the germinal center as memory B cells and high-affinity plasma cells. Plasma cells subsequently migrate to bone marrow and reside as long-lived plasma cells indefinitely to provide immunologic memory.
2.3 Multiple myeloma
2.3.1 Epidemiology
Multiple myeloma (MM) is a clonal plasma cell malignancy8, 9 characterized by infiltration and growth of plasma cells in bonemarrow10. Myeloma accounts for approximately 1% of all neoplasms and 13% of hematological malignancies diagnosed per yer11. Median age of patients at diagnosis is 65-70 years with a worldwide annual incidence of 6–7 cases per 100 000 persons12. Myeloma is rare in individuals below 30 years of age and is slightly more frequent in man than women13. Introduction of novel agents, notably, the immunomodulatory agents (IMiDs) and proteasome inhibitors (PIs) have significantly increased the median overall survival in patients less than 65 years of age14.
2.3.2 Risk factors
Risk factors for the development of myeloma are poorly understood. Several factors have been documented to increase risk of developing myeloma, which includes previous history of monoclonal gammopathy of undetermined
16
significance (MGUS), increasing age, and male sex. Additionally, environmental and occupational exposure to ionizing radiation, pesticides and petroleum products has been recognized as risk factors. Based on evidences a premalignant condition MGUS or smoldering myeloma (SMM) precedes the development of MM. The progression rate from MGUS to malignant transformation to myeloma is approximately 1% per annum15, 16. Although myeloma is not considered as a genetic disease, a family history of MGUS, SMM, MM, or other B cell malignancies were described previously13. Additionally, germline variation at 2p23.3, 3p22.1 and 7p15.3 locus has been reported to predispose individuals to MGUS17.
2.3.3 Onset and disease progression of myeloma
Myeloma is considered to be originating from long-lived plasma cells in bone marrow. During B cell development, somatic hypermutation and class-switch recombination that are required for affinity maturation involve the generation of DNA double-strand breaks and genomic rearrangements in the loci encoding immunoglobulin. Primary translocations and genetic alterations that lead immortalization of myeloma initiating cells are presumed to occur during this process and considered to be the key initiating events for myeloma (Figure 1).
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Figure 1. Key genetic events for initiation and progression of myeloma. Myeloma is preceded by premalignant conditions MGUS and SMM that lacks clinical features of myeloma. At advanced stage, myeloma cells are found in extramedullary sites and not only confined to the bone marrow. The transformation from premalignant to malignant stages are driven by genetic lesions and complex clonal evolutionary processes. At the end of the disease spectrum, at plasma cell leukemia (PCL) stage the cells are independent of bone marrow microenvironment and capable of surviving in the blood. The precursor clones may be replaced by more aggressive clones giving rise to drug resistance. In the context of evolution, PCL could be considered to be a migration of the founding clones and selection of the clones able to propagate in the peripheral blood. Adapted from Morgan et al, Nature Reviews Cancer, 201218. Therefore, mistakes in DNA repair arising during a normal physiological process can lead towards myeloma and may explain the prevalence of MGUS in more than three percent of individuals over the age of 6019. Along with the primary genetic events, bone marrow microenvironment play a critical role in nurturing and supporting the growth of myeloma propagating cells18. Secondary genetic events mediated through loss of heterozygosity (LOH), gene amplification, mutation or epigenetic changes are required to further deviate from normal plasma cell biology and to exhibit features characterizing multiple myeloma. Genetic changes acquired during the disease progression allows plasma cells to ultimately progress from premalignant MGUS to myeloma and to a leukemic phase in many patients.
2.3.4 Clinical presentation and diagnosis
Myeloma patients generally present with fatigue and continual bone pain, recurrent infections and radiculopathy. The diagnostic hallmarks of the disease are the development of hypercalcemia, renal disease, anemia and osteolytic bone disease (CRAB criteria)20. An updated diagnostic criterion was proposed by the International Myeloma Working Group (IMWG) in 2014, which included of patients without the CRAB criteria but presenting with more than 60 % of clonal bone marrow plasma cells, serum free light chain ratio greater than100, or more than one focal bone lesion detected in MRI of at least 5 mm in diameter. Diagnostic criteria for MGUS, SMM and myeloma are summarized in Table 1
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2.3.5 Risk stratification and prognostic factors
Myeloma patients are categorized using chromosomal aberrations and/or applying risk stratification models. Durie-Salmon staging was introduced approximately 30 years before that aimed at measuring tumor burden by assessing calcium, renal function, hemoglobin and bone lesions. In 2003, IMWG Table 1. Disease definition for MGUS, SMM and myeloma21-23
Disorder Disease definition MGUS All criteria must be fulfilled
-Serum monoclonal protein <30 g/L -Clonal bone marrow plasma cells <10% -Absence of end-organ damage (hypercalcemia, renal insufficiency, anemia, and bone lesions (CRAB) or amyloidosis)
Smoldering multiple myeloma
Both criteria must be met: -Serum monoclonal protein (IgG or IgA) 30 g/L, or urinary monoclonal protein 500 mg per 24 h and/or clonal bone marrow plasma cells 10 -60% - Absence of myeloma defining events or amyloidosis
Multiple Myeloma
-Clonal bone marrow plasma cells >10% or biopsy-proven bony or extramedullary plasmacytoma -Any one or more of the following myeloma defining events: Evidence of end organ damage that can be attributed to the underlying plasma cell proliferative disorder, specifically: Hypercalcemia: serum calcium >0.25 mmol/L (>1 mg/dL) higher than the upper limit of normal or >2.75 mmol/L (>11 mg/dL) Renal insufficiency: creatinine clearance <40 mL per minute or serum creatinine >177 lmol/L (>2 mg/dL) Anemia: hemoglobin value of >20 g/L below the lower limit of normal, or a hemoglobin value <10 g/L Bone lesions: one or more osteolytic lesions on skeletal radiography, computed tomography (CT), or positron emission tomography-CT (PET-CT) Presence of additional biomarkers of malignancy Clonal bone marrow plasma cell percentage > 60% Involved: uninvolved serum free light chain (FLC) ratio ³100 >1 focal lesion on magnetic resonance imaging (MRI) studies
Reproduced from Kyle et al. 200921, Rajkumar, 201623
updated the system by including more sensitive MRI and PET/CT and excluding hemoglobin measurements24. A more clinically relevant and simplistic
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international staging system (ISS) was proposed based on measurement of serum β2-microglobulin and serum albumin classifying myeloma in three stages25. However, these two classifications did not incorporate karyotype information that is considered one of the most important prognostic factors in myeloma (Table 2). In 2015, a revision of ISS was performed by IMWG to formulate the revised ISS (RISS). Stage I includes patients with ISS stage I with no high-risk chromosomal aberrations (del (17p) and/or t(4;14) and/or t(14;16)), and normal lactate dehydrogenase (LDH) level. Stage III includes patients with ISS stage III and either high-risk cytogenetics or high LDH level. Other patients excluded by these two categories were categorized as stage II26. It should be stressed that these classifications have prognostic value, however have little utility in making therapeutic decisions. Based on chromosomal aberrations detected by fluorescence in situ hybridization (FISH), myeloma is classified into two prognostic groups. High risk and poor prognosis group is defined by presence of del(17p), t(4;14) or t(14;16). Absence of high-risk features was considered as standard risk. Table 2. Standard prognostic factors for myeloma27
Prognostic factors
ISS Stage I Serum b2-microglobulin < 3.5 mg/L
Serum albumin ³ 35 g/L II Not ISS stage I or III III Serum b2-microglobulin ³ 5.5 mg/L
Chromosomal abnormalities by FISH High-risk Presence of del(17p) and/or translocation t(4;14) and/or translocation
t(14;16) Standard Risk No high-risk cytogenetic aberrations
Serum LDH Normal Serum LDH < the upper limit of normal
High Serum LDH > the upper limit of normal RISS
I ISS stage I and standard-risk chromosomal aberrations by iFISH and normal LDH
II Not RISS stage I or III III ISS stage III and either high-risk chromosomal aberrations by iFISH
or high LDH Adapted from Palumbo et al. 2015
Application of gene expression profiling (GEP) has also been investigated by two large studies. Both of these studies report two sets of non-overlapping genes,
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which can identify patients with poor prognosis. University of Arkansas for Medical Sciences (UAMS) has provided a 70-gene model and later reduced to a 17-gene model28. Majority of those genes were mapped to chromosome 1. The Intergroupe Francophone du Myélome (IFM) proposed a 15-gene model to identify high-risk patients29. Although, GEP based technique showed promise to separate high-risk from low risk patients, their application is hampered by the availability of the infrastructure and data analysis challenges30.
2.3.6 Genomic landscape and clonal evolution
Based on primary genetic events, myeloma is divided into hyperdiploid and non-hyperdiploid types. Hyperdiploidy is observed in more than half of the myeloma patients. Hyperdiploid patients carry 48 to 75 chromosomes and involves multiple trisomy in odd numbered chromosomes 3, 5, 7, 9, 11,15,19 and 2116, 31,
32. Co-existing immunoglobulin heavy chain (IgH) translocations are present only in 10% hyperdiploid myeloma. In contrast, more than 70% of non-hyperdiploid myeloma is detected with primary 14q32 translocation33. Technological advances in next generation sequencing (NGS) have enabled sequencing transcribed genome (whole exome) in myeloma. First cohort-based sequencing effort was described by Chapman et al. in 2011. Authors reported 10 statistically significant mutations in protein coding genes including NRAS, KRAS, FAM46C, DIS3, TP53, CCND1, PNRC1, ALOX12B, HLA-A, and MAGED134. Later several large-scale sequencing efforts were conducted to define the genomic landscape of myeloma 35, 36. Walker et al. reported the largest study in 463 myeloma patients, which identified somewhat overlapping list of 15 mutated genes and included IRF4, HIST1H1E, RB1, EGR1 and LTB37. Based on these studies, RAS (NRAS, KRAS and BRAF) and NF-kB signaling pathways were found recurrently affected by mutations reassuring their critical role in myeloma38. Intriguingly co-occurring mutations in KRAS, NRAS or BRAF was observed in a single patient, which are involved in RAS pathway35. Myeloma genome presents widespread heterogeneity, which is further complicated by intraclonal heterogeneity observed in patients39. Lohr et al. reported presence of 3-7 subclones in most of the patients and highlighted the complexity of the disease36. During progression of disease clonal composition changes may follow a linear acquisition of mutations where the cells accrues new alterations in stepwise manner. Heterogeneity observed at molecular level suggest clonal evolution is rather occurring via branching pathways implicating a Darwinian evolution taking place where different clones compete for dominance18, 34, 35. Clonal heterogeneity was not only limited to single
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nucleotides but also observed at copy number level. Additionally, subclonality was also detected for driver mutations (i.e. KRAS, NRAS and BRAF) suggesting a dynamic mutational landscape, and mutations in these driver genes may appear at later stage of the disease. Importantly, analysis with sequential samples from individual patients with preceding smoldering myeloma stage and full-blown myeloma samples has shown the presence of clonal diversity at premalignant stage40. Clonal heterogeneity poses significant challenges in relapsed refractory patients who bear a higher number of clones. Such clonal heterogeneity has profound clinical and therapeutic significance. Patients with multiple clones might benefit from combination strategies, which may affect multiple clones simultaneously or no selectively. Again this clonal tide may render previously sensitive clones to reemerge and provides opportunities to challenge with previously applied treatments intermittently41.
2.3.7 DNA repair and genomic instability in myeloma
Similar to many other cancers myeloma displays remarkable genomic instability42. Majority of 14q32 translocations occur at switch region of the immunoglobulin gene, suggesting deletion occurring during class switch recombination may play an important role in generation of these chromosomal translocations. Recurrent aneuploidy and chromosomal translocation are present at premalignant MGUS or SMM stage. These evidences reflect aberrant DNA repair is manifested at onset of the disease and also involved in disease progression. Compared to healthy plasma cells, an increased load of DNA double strand breaks has been observed in primary myeloma patients and in myeloma cell lines. Repair of these double strand breaks requires functional DNA repair pathways including non-homologous end joining (NHEJ) and homologous recombination (HR). Additionally, alkylating agents such as melphalan inflicts crosslinks between two strands of DNA, which are candidates for nucleotide excision repair (NER). Increased HR activity has been reported to be linked to an increased expression of genes which contribute to HR (RAD50 and RAD51)43,
44, which in turn contributes to in an increase in somatic mutation rate and loss of heterozygousity43. Translocations and genetic alterations in ATM, ATR and TP53 involved in DNA repair pathway are present in myeloma and negatively impacts survival.
2.3.8 Therapeutic landscape
Early treatments for myeloma have reported the use of rhubarb, leeches and quinine45. First drug to be used for treating myeloma patients was Urethane by
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Alwall in 1947. After being a standard therapy for 15 years it was later found to be ineffective in clinical trials. Median overall survival (OS) for myeloma patients in that era was only 6 months46. Blokhin et al. in 1958 tested melphalan in small group of patients and reported its clinical benefit47 which was confirmed in subsequent study by Bergsagel et al.48. Combination of melphalan and prednisolone showed additional survival benefit of 6 months compared to melphalan monotherapy with an increase in overall survival to 3-4 years48, 49. This remarkable clinical benefit led widespread use of the combined regimen for decades. Treatment landscape of myeloma has dramatically changed over the years with introduction of multiple novel agents and immunotherapeutic approaches.
2.3.8.1 Proteasome inhibitors
PIs are one of the most important classes of novel agents for the treatment of multiple myeloma, and are used as a backbone of myeloma treatment in both newly diagnosed and relapsed patients. Several reversible and irreversible inhibitors targeting one or more of the subunits of the 20S are currently approved (bortezomib, carfilzomib and oral PI ixazomib) or under clinical investigation (marizomib and oprozomib) for the treatment of myeloma. Sensitivity of myeloma cells to PIs stem from the inhibition of the 26S proteasome, which is responsible for the processing of 70-80% of intracellular proteins50. Proteasome inhibition results in accumulation of proteins, endoplasmic reticulum stress, and consequent induction of cell death51. Furthermore, inhibition of NF-κB signaling is a key consequence of proteasome inhibition, which is known to be constitutively active in MM52, 53. Bortezomib was the first in class to be approved for myeloma in 2004. Data from two Phase II clinical trials (CREST54 and SUMMIT55) was instrumental for its accelerated approval for previously treated myeloma. Bortezomib is a peptide boronic acid and reversible inhibitor of the β5 catalytic subunit56, 57. Common side effects include gastrointestinal disturbances, fever, infections, thrombocytopenia, dizziness and peripheral neuropathy58. Subcutaneous administration was associated to less degree of adverse events compared to intravenous route. To overcome the limitations of bortezomib, a more potent second-generation proteasome inhibitor carfilzomib was introduced. Clinical data supporting its approval was carried out in relapsed refractory setting. Response rate in bortezomib naive and pretreated patients was 64% and 37% respectively59.
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Carfilzomib is a peptide epoxy ketone that irreversibly binds to β5 subunit. It has been proven as more selective towards proteasome, has a better toxicity profile60 and effective in bortezomib resistant patients. Reduced incidences of peripheral neuropathy are associated with carfilzomib compared to bortezomib (19% versus 52%). Common side effects were fatigue, hypertension, nausea, anemia, and thrombocytopenia and rare pulmonary hypertension61. Ixazomib is the first orally administered proteasome inhibitor which is quite similar to bortezomib in respect of the mechanism of action. However, the chemical structure and pharmacology is different from bortezomib62. Its long half-life allows convenient once a week dosing63, 64 and provides the advantage of oral administration. Ixazomib is approved in Europe to be used in combination with lenalidomide and dexamethasone for patients who received at least one prior line of therapy. Compared to placebo groups the overall response rate was 78% and very good partial response (VGPR) was recorded in 48% of patients. Clinical evidences suggest its potential benefit in overcoming negative impact of high-risk cytogenetic aberrations62.
2.3.8.2 Immunomodulatory agents
IMiDs (thalidomide, lenalidomide and pomalidomide) constitute a new class of anti-inflammatory and antineoplastic agents that have revolutionized the treatment outcome of myeloma patients65-68. IMiDs has made a remarkable impact on improving overall response rate and survival of both newly diagnosed and relapsed/refractory myeloma patients69, 70. Newer thalidomide analogues lenalidomide and pomalidomide share functional similarities with the parent compound. In addition to antiangiogenic properties antiproliferative, apoptotic71-
73 and direct antitumor effect has been demonstrated. Cereblon (CRBN), a protein substrate receptor for the E3 ubiquitin ligase complex CRL4CRBN, has been identified as the key target of the IMiDs (thalidomide, lenalidomide and pomalidomide)74-76. Expression of CRBN is required for their activity65, 77, 78. The CRL4CRBN complex consists of core scaffolding protein CUL4 that binds to RBX1 and DDB1. DDB1 offers a binding site for CRL4 substrate receptor proteins, which then recruits substrates to the ubiquitin ligase complex for degradation by 26S ribosome. Binding of IMiDs to CRBN enhances interaction with the lymphoid transcription factors Ikaros (IKZF1) and Aiolos (IKZF3), resulting in their ubiquitination and degradation, that induces a subsequent reduction of cMYC and IRF4 levels in MM cells79, 80. The immunomodulatory properties are attributed to its wide range of effect on immune cells which includes stimulatory properties on T and natural killer T cells and suppressive
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effect on B and monocyte lineages66. A lower expression and mutation in CRBN have been demonstrated as mechanisms driving resistance in patients74, 77, 81. Thalidomide was approved in 2006 in combination with dexamethasone for the treatment of newly diagnosed MM. Lenalidomide, a second generation IMiD, was introduced in Europe during 2008 based on data presented in MM-00968 and MM-01082 studies. Both of the studies evaluated superior effect of lenalidomide and dexamethasone combination compared to dexamethasone as a single agent. Lenalidomide is administered as 25mg daily dose for 1-21 days of 28 days cycle. Grade 3 or 4 adverse events associated with lenalidomide were neutropenia, thrombocytopenia and venous thromboembolism. Other untoward effect includes liver failure, neutropenia, rash, fatigue and constipation. Risk of developing secondary primary malignancies such as myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) has been reported83, 84. Pomalidomide has recently been approved in Europe for myeloma. It is more potent compared to other analogues and effective in lenalidomide refractory patients. Neutropenia and infections have been reported as common grade 3 side effects. Cost effectiveness of pomalidomide has been questioned. Compared to dexamethasone alone, cost of per years of life gain has been estimated to be €39 91185.
2.3.8.3 Histone deacetylase inhibitors
Aberrant histone modifications play an important role in development of myeloma86. Histone deacetylases (HDACs) are responsible for the regulation of gene transcription, cellular differentiation, cell-cycle progression, and apoptosis. Deregulated expression of HDACs is associated with poor outcome in myeloma patients87. HDACs catalyze the removal of acetylation on lysine residues on its target proteins, leading to a closed chromatin conformation88, 89. This results in transcriptional repression. HDAC inhibitors induce cell-cycle arrest by upregulation of cyclin-dependent kinase inhibitor CDKN1A which ultimately leads to cell death. Upregulation of proapoptotic proteins has been also demonstrated. First report on efficacy of a HDAC inhibitor (Vorinostat) was demonstrated by Dimopoulos et al90. A phase I study panobinostat plus bortezomib combination reported an overall response rate of 73% in relapsed or refractory patients. However, treatment was associated with grade 3 thrombocytopenia in about 90% of patients. PANORAMA 1 and 2 evaluated its benefit in combination with bortezomib and dexamethasone in relapsed refractory patients. Bortezomib refractory patients had a response rate of 35%. Panobinostat is approved by the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) for use in combination with bortezomib and
25
dexamethasone for treating myeloma patients who have received more than two prior lines of treatments91. Severe side effects of panobinostat including grade 3 diarrhea, thrombocytopenia and fatigue which was found to be dose dependent92,
93. Selective inhibitors of HDAC6, such as ricolinostat are being studied. In preclinical studies synergy has been observed when used with lenalidomide and pomalidomide. Combination of ricolinostat with bortezomib and dexamethasone has been tested in a phase 1 trial and showed an overall response of 37%94.
2.3.8.4 Monoclonal antibodies
Cell surface antigen CD38 was first identified in 1981and is structurally similar to human major histocompatibility (HLA) antigen95. It is a single chain transmembrane type II glycoprotein expressed in 20% of all human bone marrow cells96 including plasma cells, thymocytes, myeloid and subset of T cells. Expression is detected in 90% of normal plasma cells in peripheral blood and frequently expressed in malignant plasma cells in MGUS and myeloma patients. CD38 ligation can stimulate normal mature lymphocytes97. It has enzymatic activity and participates in cell adhesion and signal transduction98. Three anti-CD38 antibodies have shown clinical and preclinical activity in myeloma, namely daratumumab, isatuximab and MOR202. They target different epitopes on CD38 and thus are mechanistically distinct. Daratumumab is a humanized immunoglobulin G Kappa (IgGk) anti-CD38 monoclonal antibody (mAb). It mediates potent effect on myeloma cells by antibody-dependent cell-mediated cytotoxicity (ADCC), complement-dependent cytotoxicity (CDC) and antibody-dependent cellular phagocytosis (ADCP). In vivo and in vitro response was found to correlate with CD38 expression intensity on the MM cells99. In clinical trials daratumumab has been shown to be effective both as single agent and in combination in pretreated patients. Another study reported an overall response rate of 36% as monotherapy in a patient cohort where 64% are heavily pretreated and refractory to treatment100. Elotuzumab is a humanized monoclonal antibody, which targets signaling lymphocytic activation molecule family member 7 (SLAMF7) also known as CS1. SLAMF7 is a glycoprotein that is expressed at a high-level on cell surface of normal and malignant plasma cells. Expression has also been detected on other lymphocyte subsets that include dendritic cells, NK and T cells101. Elotuzumab induced cytotoxicity is attributed to ADCC and its ability to stimulate NK cells. Initial Phase 1 trial data were disappointing as no clinical activity was detected as single agent102. When combined with lenalidomide and dexamethasone it showed a significant benefit in terms of objective clinical response (86%) and progression free survival (PFS)103, 104. Several other targets have been investigated as potential treatments for myeloma
26
and are summarized in Table 3. Table 3. Monoclonal antibodies investigated for myeloma98
Target Antibody Clinical trials
Cell surface targets
CD38 Daratumumab* Effective as single agent, in combination with IMiDs and PIs, approved for clinical use
Isatuximab
In clinical trials, effective as single agent, in combination with IMiDs
MOR202 In clinical trials SLAMF7
Elotuzumab* Effective in combination with IMiDs and PIs, approved
for clinical use CD138 Indatuximab
ravtansine (BT062)
In clinical trials for MM, as single agent, the ORR was 4% and in combination with lenalidomide, ORR was 78%
CD56 Lorvotuzumab In clinical trials for MM, as single agent, the ORR was 7% and in combination with lenalidomide and dexamethasone, ORR was 56%
CD40 Dacetuzumab (SGN40) and lucatumumab
In clinical trials for MM no responses with single agents
CD74 Milatuzumab (hLL1)
In phase I trial, no objective responses; combination trials ongoing
ICAM-1 BI-505 In clinical trial KIR IPH2101 Stable disease seen in relapsed MM Cytokine/growth factor targeted IL6 Siltuximab* No clinical efficacy in MM, approved for treatment of
Castleman disease VEGF Avastin* No clinical efficacy in MM
BAFF Tabalumab (LY2127399)
In a phase I study in relapsed MM, combination with bortezomib and dexamethasone had an ORR of 46%
DKK1 BHQ880 Bone beneficial effects seen in early trials CXCR4 Ulocuplumab ORR was 55% in combination with lenalidomide-
dexamethasone and 40% in combination with bortezomib-dexamethasone
* FDA approved drugs
Adapted from Kumar et al. 2016
2.3.8.5 Emerging therapies in myeloma
Treatment of myeloma experiences a rapidly evolving treatment landscape with recent approval of panobinostat, ixazomib, daratumumab and elotuzumab. Regardless, the disease remained incurable by the approved agents and
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continuous effort is driven to identify new treatment options. Several drugs are being investigated in clinical and preclinical setting, which are discussed below. Two new proteasome inhibitors are being developed. Oprozomib, is an irreversible peptide epoxyketone and is currently under investigation. In vitro data suggesting comparable efficacy like intravenously administered carfilzomib105. Marizomib, unlike other members of this class, inhibits several catalytic sites within the 20S proteasome106. Venetoclax, formerly known, as ABT-199 is a potent and selective inhibitor of Bcl-2, which has shown significant activity against various Bcl-2 dependent hematologic malignancies such as B cell malignancies107-113. As a single agent, effect of venetoclax in myeloma has been investigated in a phase I study with 300-1200 mg daily doses. In heavily pretreated patients (> 5 lines of treatment) ORR was 21% and a higher response rate of 40% was documented in patients with t(11:14)114. Objective response with a PFS benefit of 5.5 months was noted in t(11:14 ) patients. A higher BCL2 mRNA expression compared to BCL2L1 or MCL1 was considered beneficial to identify the responding patients. Moreau et al. reported an overall response rate of 67% when combined with bortezomib and dexamethasone115. Selinexor, a selective inhibitor of exportin-1 (CRM1/XPO1), targets nuclear export of tumor suppressor, glucocorticoid receptor and several oncoproteins (MYC and BCL-2) represent a promising novel agent in myeloma116. An elevated expression of XPO1 is detected in bortezomib resistant patients. Clinical investigation of selinexor with low dose dexamethasone in patients who are refractory to bortezomib, carfilzomib, lenalidomide and pomalidomide and/or daratumumab showed an overall response rate of 21% with an improved PFS and OS of 2.3 and 5.5 months, respectively116. Filanesib (ARRY-520) targeting kinesin spindle protein (KSP), a component of microtubule-based protein, exhibits antimyeloma effect primarily through promoting apoptosis by degrading anti-apoptotic protein MCL1117. As a single agent or in combination with dexamethasone, filanesib showed an overall response of 16% and 15% respectively118. A lower expression of α1-acid glycoprotein is associated with better response to filanesib. Dinaciclib, a cyclin dependent kinase (CDK1, CDK2, CDK5 and CDK9) inhibitor, has shown well tolerated in a dose escalation trial as a single agent and showed an objective response in 19% of patients119.
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2.4 Precision oncology
Every treatment regimen prescribed to treat patients are aspired to achieve precision. However, therapeutic modalities effective in some patients may be associated with lack of response and toxicities in others. Precision medicine approach utilizes comprehensive genomic and diagnostic characterization of individual patients to tailor treatments in a single or subgroup of patients based on their defined druggable targets. The clinical success observed with imatinib120,
121, trastuzumab122 and vemurafenib in CML, breast cancer and melanoma, respectively has spurred a new paradigm of genomic-driven precision therapies. Consequently, in the field of oncology, precision medicine is frequently referred to as genome-guided therapies123. Staggering advances in NGS technologies has helped genomic characterization of a wide variety of malignancies18, 124-151. However, there has not been a proportionate progress in identification of molecules showing similar durable and effective response rates matched to these genetic alterations. Providing the right drug to the right patient and at the right time may not only be achieved by a single technology and can be addressed by a combination of molecular and functional tools. It also argues to be dynamic in nature where one needs to assess of patients molecular and pharmacological status before making each therapeutic decision. Making precision medicine a reality critically depends on building an ecosystem ensuring participation of the patient, physicians, researchers, and pharmaceutical industries, which can together address scientific, societal and regulatory challenges to bring precision oncology in clinical practice.
2.4.1 Patient stratification
Stratification may be achieved at patient or disease level. Molecular profiles such as mutational profiles and gene expression data has been applied to identify subset of myeloma152, breast cancer and prostate cancer patients153. Similarly, myeloma patients may be classified based on risk categories, which are guided, by their karyotype and different staging systems available to date24-26. Currently available stratification techniques are mostly relevant to predict prognosis or monitor disease progression but lacks their role in predicting treatment responses in individual patients. Heterogeneity in treatment outcome for a given anti-cancer drug in patient populations has long been recognized. This is due to their inherent differences in etiology, environmental factors, co-morbidities, or genetics. Spatial and clonal heterogeneity further contributes to this problem. There is also a significant gap in mechanistic understanding of contribution of genotype on phenotype.
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Figure 2. Cartoon illustrating how drug sensitivity assessment and molecular profiling can identify patient subgroups and match therapies to patients. As such, applying a molecularly targeted therapy based on genomics alone may not always lead to a clinical response in all patients and obscure the benefit in the responding subset of patients. Therefore, ex vivo drug response assessment could complement to identify responding population. This can be equally applicable to broadly acting drugs where patients can experience suboptimal or no responses to therapies due to resistance to the drug in question. It is therefore critical to identify those responding patients in advance and apply treatments in an informed population (Figure 2). This will ensure the likelihood of improved treatment outcome and reduce ineffective and toxic treatment to patients. In the context of drug development, if responding patient subset could be identified prior to enrollment in clinical trials, efficacy and response rates could be improved.
2.4.2 Biomarkers for precision medicine
Biological markers or biomarkers are objective and accurately quantifiable characteristics, which can be used as an indicator for biological processes, disease outcome and response to therapeutic interventions154. Each proposed biomarker is required to be sensitive, specific, robust and validated before it is integrated in clinical decision-making. In complex diseases involving multiple genetic pathways, such as cancer, cellular phenotype may be the coordinated contribution from multiple molecular disease driving mechanisms. Omics technologies has opened a new avenue (i.e. genomic, transcriptomic, epigenetic and proteomic) to identify these molecular correlates which can predict individuals responses or aid in monitoring disease progression155. This has been
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accompanied by availability of powerful systems biology approaches to enable deciphering biological complexity of disease156.
2.4.3 Tools for achieving precision in oncology
2.4.3.1 Next generation sequencing
Following completion of large-scale cancer genome sequencing projects such as cancer genome project157 and the cancer genome atlas (TCGA)158, it has raised the enthusiasm for applying NGS in precision oncology. DNA sequencing in tumor samples provides an effective strategy to capture a large amount of genomic information about the tumor and identify actionable mutations, which can be potentially matched with targeted therapies. However, the functional impact of majority of these mutations are poorly defined. Evidences suggest that the presence of few mutations referred, as ‘driver mutations’ are important for supporting tumor growth and maintenance and therefore represent candidates for cancer-specific targeted therapies159. Remarkable success with inhibition of BCR-ABL tyrosine kinase oncogene with imatinib marks the beginning of a new era for genomics-based precision medicine. Several other examples targeting specific mutations include vemurafenib in mutated BRAF (V600E) melanoma160, gefitinib in epidermal growth factor receptor (EGFR) mutant non-small-cell lung cancer (NSCLC)161 and crizotinib in anaplastic lymphoma kinase (ALK)-mutant NSCLC162. In addition, transcriptome sequencing has been applied to identify molecular subsets of cancer patients152, 163. Due to availability and technological advances DNA sequencing methods are gaining acceptance into standard clinical practice. New therapies, which targets somatically mutated genes are detected by clinical NGS, are gaining approval from regulatory authorities164. NGS has lead success in identification of targeted therapies. Nevertheless, it has to be stressed that none of these newer targeted therapies produce a durable response as seen with imatinib.
2.4.3.2 Functional assays
Cancer is a complex disease, which harbors multiple genetic alterations in a single individual. Many cellular effects of DNA alterations are often lineage specific often poses challenges to directly match therapies to somatic mutations. For instance, vemurafenib effective for mutant BRAF (V600E) in melanoma lacks a strong response in colorectal cancer165, 166. The combinatorial effect of multiple genomic alterations on drug responses is poorly understood. Also only a handful of the thousands of mutations identified in a particular cancer have
31
functional annotation to match with targeted therapies123. These challenges can be addressed by applying functional assays, which can directly assess effect of anticancer drugs on patient’s tumor cells and identify effective therapies best suited for that individual. Several chemosensitivity assays have been developed over the years that include target and pathway profiling methods (i.e. kinase substrate activity, phosphoflow), those measuring direct cytotoxicity (drug sensitivity and resistance testing, dynamic BH3 profiling) and in vivo patient derived xenograft mouse models. Most commonly used ex vivo cell viability assays utilize metabolic tetrazolium dye (MTT), sulforhodamine B protein (SRB) and cellular ATP content based read outs. These assays capture cytotoxic or cytostatic effects of drug molecules by measuring decrease in metabolic activity (MTT), reduction in cellular biomolecules (ATP) or cell mass (SRB), which provide a mean to estimate cell viability. They vary in terms of their sensitivity. ATP assay exhibits the highest sensitivity and preferably applied for screening primary samples159. Several studies have now evaluated their predictive potential in multiple cancer types including hematological malignancies167-174.
2.4.3.3 Systems biology
The growth of large scale multi-omics datasets to comprehensively assess DNA, RNA, protein, and metabolites in patient tumors is fueled by continuing development of high-throughput technologies. During last decades, NGS technologies had produced deep sequencing of genomic and transcriptomic data in large patient cohorts and being provided to the researcher as a tool to study molecular complexity of healthy as well as human cancer biology. Data-driven science has transformed the field of oncology to move away from reductionist and hypothesis driven research to approach complex diseases with a systems level understanding156. Systems biology, which studies complex interactions in biological systems, has emerged as an important component to address the data analysis tasks. Methodologically such approach does not only focus on a set of molecular components and instead provides a comprehensive understanding how interconnected molecular elements in a system interact with each other in concert to define a pulpable phenotype by utilizing complex and multiple system level datasets. Such systems biology models convert the information contained in multidimensional data sets to enable identification of biomarkers, provides patient stratification based on genomic, transcriptomic and pharmacological profiles and demystify complex disease biology. Resultantly, this approach facilitates the translatability of preclinical knowledge into clinical practices.
32
3 AIMS OF THE STUDY
The goal of the current study was to identify individualized treatment decisions and to facilitate drug discovery efforts for multiple myeloma. The specific aims of the study are as follows:
1. Implement a precision medicine platform by integrating ex vivo drug sensitivity assessment with deep molecular profiling to stratify myeloma patients in molecular subgroups and identify individualized therapies for relapsed/refractory multiple myeloma patients
2. Identify functional oncogenic alterations in myeloma and understand
their contribution to drug response
3. Discover multi-omics gene signatures associated with individual drug responses, enabling the identification of the patients likely to benefit
4. Identify lineage-specific drug responses in hematological cell
populations to predict off-target effects and imminent drug interactions
33
4 MATERIALS AND METHODS
4.1 Patient materials and ethical compliance
Bone marrow and blood aspirates from patients were obtained during their standard diagnostic procedures following written informed consent (Study number 239/13/03/00/2010 and 303/13/03/01/2011) in compliance with the Declaration of Helsinki. Matched skin biopsies were collected from patients to serve as a germline control for identifying cancer specific mutations. Repeated biopsies were procured at successive relapses when permitted. Healthy donor derived samples were collected from 20 individuals. Sample identities were encoded and generated data were stored in a secured server to ensure data privacy and anonymity of the donors.
4.1.1 Sample processing
Samples were processed in a biosafety level 2 equipped cell culture facility. Mononuclear cells were prepared from marrow or blood aspirates by Ficoll-Paque density gradient centrifugation (Ficoll-Paque PREMIUM; GE Healthcare). Freshly isolated mononuclear cells were used for successive experiments unless cell enrichment was opted. All myeloma samples were subjected to CD138+ enrichment to isolate plasma cells. CD3+, CD14+, CD19+, CD34+ and CD138+ cell enrichments were performed from the mononuclear cell fraction using the EasySep™ positive selection kits from Stem Cell Technologies. Remaining cells were viably frozen. Non-essential hazardous fractions were treated with virkon and successively discarded to prevent spread of blood borne diseases.
4.2 Drug sensitivity and resistance testing (DSRT)
4.2.1 Small molecule library
Chemical library used in current study composed of US Food and Drug Authority (FDA)/ European Medicines Agency (EMA) approved and investigational oncology compounds targeting multiple signaling networks and molecular machineries. Compounds were either purchased from commercial vendors e.g. Active Biochem, Axon Medchem, Cayman Chemical Company, Chemie Tek, ENZO life Sciences, LC Laboratories, Santa Cruz Biotechnology, Selleck, Sequoia Research products, Sigma Aldrich and Tocris biosciences or acquired from National Cancer Institute drug testing program. Dissolved
34
compounds in dimethyl sulfoxide (DMSO) or water were stored in desiccators according to manufacturer’s instruction. Assay ready 96 or 384 well plates were prepared by transferring each compound in five dilutions ranging from 1-10,000 nM, with exceptions, using an acoustic liquid handler (ECHO 550; Labcyte Inc.) and stored in nitrogen pressurized storage pods (Roylan Developments Ltd.) before they were used. The study described in paper 1 included a library of 308 small molecules. Subsequent studies used a smaller library containing142 and 71 respectively.
4.2.2 Cell viability assay
5µl of cell culture medium comprised of RPMI 1640 medium (Lonza) supplemented with 10% fetal bovine serum, 2 mM L-glutamine, penicillin (100 U/ml), streptomycin (100 µg/ml) and 25% conditioned medium175 from the HS-5 human BM stromal cell line was added to 384 well drug plates and shaken for 5 min to dissolve the compounds. Cells were diluted to a desired density in culture medium and 20µl of the cell suspension was transferred to each well using a MultiDrop Combi peristaltic dispenser (Thermo Scientific). The plates were incubated in a humidified environment at 37°C and 5% CO2. Cell viability was measured after 72 h using the CellTiter-Glo assay (Promega) with a PHERAstar® microplate reader (BMG-Labtech) measuring luminescence. The data was normalized to negative (DMSO only) and positive control wells (containing 100 µM benzethonium chloride).
4.2.2.1 Ex vivo drug sensitivity data analysis
Luminescence intensity per well derived from the plate reader was used as input for Dotmatics software (Dotmatics Ltd.) to generate dose response curves of individual drugs. A four parameter (maximum and minimum response, slope and IC50) logistic regression was applied to fit the sigmoidal dose response curves. Curve fitting parameters were further integrated to quantify drug responses with a drug sensitivity score (DSS) as described by Bhagwan et al. and elsewhere176-
178. DSS is a modified form of the area under the curve calculation that considers all four curve fitting parameters of a non-linear response model, generating a single response metric. Higher DSS corresponds to higher sensitivity ranging values from 0 to 50. Selective drug sensitivity scores (sDSS) indicating tumor specific sensitivity to the drugs were obtained by calculating the mean differences in DSS values between the tested BM cells from healthy individuals and the patient samples.
35
4.2.3 High throughput flow cytometry (HTFC)
4.2.3.1 Antibodies
The following monoclonal antibodies were purchased from BD Biosciences: APC anti-CD3 (clone SK7), BV421 anti-CD4 (clone RPA-T4), BV510 anti-CD19 (clone SJ25C1), BV786 anti-CD45 (clone HI30), PE-Cy7 anti-CD34 (clone 8G12), APC anti-CD138 (clone MI15), APC-H7 anti-CD9 (clone M-L13), BV786 anti-CD14 (clone M5E2), PE Annexin-V and 7-amino-actinomycin (7-AAD). The FITC anti-CD38 (clone LD38) and the PE-Vio770 anti-CD56 (clone REA196) was purchased from Cytogonos and Miltenyi Biotec. Panels of antibodies were combined following dilution optimization and compensation using primary cells. Annexin-V and 7-AAD were used to distinguish live cell populations from apoptotic and dead cells.
4.2.3.2 No wash multiplexed HTFC
A multiplexed no wash assay using a high throughput flow cytometer (iQue®Screener PLUS) was optimized during the study period that allowed us to simultaneously monitor drug responses in immune subsets. Optimization was carried out to identify optimal, i) cell density, ii) antibody dilutions, iii) incubation time and finally to compare staining performances with and without washing. Flow cytometric analysis of drug responses was performed in both 384 well (n=3, 71 drugs) and 96 well plate format (n=26) to study the effects of drugs in 5 dilutions (1-10,000nm). Briefly, homogeneous cell suspension at a density of 2 million/ml was prepared in RPMI 1640 medium (Lonza) supplemented with 10% fetal bovine serum, 2 mM L-glutamine, penicillin (100 U/ml), streptomycin (100 µg/ml) and 25% conditioned medium from the HS-5 human BM stromal cell line. Cells were dispensed into each well and shaken for 5 minutes to dissolve the compounds. The plates were incubated in a humidified environment at 37°C and 5% CO2. After 72 hours, antibody cocktails were added into the wells and incubated for 60 minutes before the plates were read with the iQue® Screener PLUS instrument (Intellicyt®). The data was analyzed using ForeCyt Software (Intellicyt). Counts for each cell types derived from the HTFC were processed and analyzed as described in 3.2.2.1
36
4.3 Next generation sequencing
4.3.1 DNA sequencing
DNA was isolated with the DNeasy Blood and Tissue kit (Qiagen). DNA samples were preprocessed using the NEB. Next DNA Sample Prep Master Mix protocol (New England Biolabs). Subsequent Exome capture was performed using the SeqCap EZ MedExome kit (Roche Nimblegen), SureSelect Clinical Research Exome kit or the SureSelect Human All Exon V5 kit (Agilent Technologies). Sequencing was performed with the HiSeq 1500 platform (Illumina). For the matched skin samples 4×107 and 10×107 2×100 base pair paired-end reads were sequenced from CD138+ cells derived from 32 newly diagnosed and 62 relapsed/refractory patients. Reads were processed and aligned to the GRCh37 reference-genome. Copy number estimates were derived from exome sequence data. Somatic mutations were identified using the VarScan2 somatic algorithm179, 180.
4.3.1.1 Driver alteration analysis
Cancer genome interpreter (CGI) pipeline was deployed to analyze (https://www.cancergenomeinterpreter.org/home) driver alteration landscape. Briefly, genomic alterations that are clinically or experimentally validated to drive tumor phenotypes described previously in several public sources are identified by the CGI. Mutations, the variants of unknown significance are analyzed by using a method (OncodriveMUT) to identify oncogenic potential of the alteration. Briefly, this tool combines mutation-centric measurements (such as the functional impact score) with features characterizing the genes (or regions within genes) where that mutation occurs (such as the location of hotspots of somatic mutations or regions of the protein depleted by functional germline variants). This knowledge is retrieved from analyses of large cohorts of tumors (6,792 samples across 28 cancer types)181 and samples from healthy donors (60,706 unrelated individuals)182.
4.3.2 RNA sequencing
Total RNA was extracted from freshly isolated plasma cells using Qiagen AllPrep kit. Ribosomal RNA is depleted from total RNA using Ribo Zero rRNA Removal Kit (Epicenter), which is then transcribed to double-stranded cDNA with random hexamers. Sequencing libraries are prepared using Scriptseq technology (Illumina) and then size-selected and purified by agarose gel
37
electrophoresis. Sequencing was performed on the Illumina HiSeq 1500 or 2500 instruments. Sequence reads were filtered and then aligned to the reference genome version GRch38. Read counts for each features (gene) was derived using software program featureCounts183 in subread package184. Gene expression analysis is performed on processes of raw counts from aligned RNA sequencing data. Differentially expressed genes between two conditions i.e. sensitive versus resistant to a specific drug, or between chemosensitivity subgroups were identified using deseq2 185 or edgeR 186 package.
4.4 Network and pathway analysis
Network and pathway analyses were performed using the commercial Ingenuity pathway analysis (IPA) tool. For data integration, the primary networks were drawn from gene expression data. Genomic and drug sensitivity information is then overlaid on the existing network. Apart from IPA, STRING database187 was used for identification of protein interactions and Cytoscape®188 were often used for visualization and network analyses.
4.5 Proteome analysis
Whole cell protein lysates were prepared from purified CD3, CD19 and CD14 fractions derived from six BM samples from healthy (n=2) and MM (n=4) using radio immunoprecipitation assay (RIPA) buffer supplemented with protease and phosphatase inhibitors. 10ug of cell lysates were digested and subsequently 500 ng of each digested sample was loaded onto a Q-Exactive (ThermoFisher Scientific) high-resolution accurate mass spectrometer (MS) connected to a Dionex Ultimate 3000 (RSLCnano) chromatography system (ThermoFisher Scientific). Peptides were separated using a 2% to 40% gradient of acetonitrile on a Biobasic C18 Picofrit column (ThermoFisher Scientific) (100mm length, 75mm ID) over 65 min at a flow rate of 250nl/min. A full MS scan at 140,000 resolution and a range of 300–1700 m/z was followed by an MS/MS scan, resolution 17,500 and a range of 200–2000 m/z, selecting the 10 most intense ions prior to MS/MS. Protein identification and label-free quantification (LFQ) normalization of MS/MS data was performed using MaxQuant v1.5.2.8 (http://www.maxquant.org). MaxQuant used the Andromeda search algorithm incorporated in the MaxQuant software to correlate MS/MS data against the Homo sapiens Uniprot reference proteome database and a contaminant sequence set was provided. Perseus v.1.5.6.0 (www.maxquant.org/) was used for data analysis, processing and visualization. Normalized LFQ intensity values were
38
used as the quantitative measurement of protein abundance for subsequent analysis.
4.6 Mass cytometry (CyTOF)
PB and BM samples from healthy volunteers was collected using EDTA and Heparin as anticoagulants, respectively. PB was also collected from newly diagnosed AML and B-ALL patients admitted to the hematological ward at Haukeland University Hospital and Oslo University Hospital (Rikshospitalet) using anticoagulant within a maximum of 1 hour after sampling. The primary materials were fixed and erythrocytes lysed using BD Lyse/Fix buffer (BD Phosflow), according to the manufacturer’s instructions, in local hospital laboratories. Samples were subsequently frozen in saline for shipping and long-term storage at -80°C. To assure comparability of signaling across samples from different donors, the samples were barcoded using a commercially available metal barcoding kit (Fluidigm) according to the manufacturer’s instructions. Subsequently, the 14 samples were pooled into a single sample and stained with the antibody panels following the MaxPar Phospho-Protein Staining Protocol (Fluidigm). Acquisition of samples was done using a Helios mass cytometer (Fluidigm) at the Flow Cytometry Core Facility, University of Bergen. Data analysis was performed using FlowJo v.10.2 software (FlowJo LCC) and the cloud-based analysis platform Cytobank (Cytobank Inc.).
4.7 Statistical and machine learning approaches for analyzing genome–response associations
To study the relative contribution of individual oncogenic alterations on drug responses we applied three computational approaches, each for a different data analysis task. First, analysis of variance (ANOVA) was used to identify the individual drug responses that were statistically different across the mutant and non-mutant patients for a particular mutation. Secondly, we analyzed transcriptomic correlates by computing pairwise correlation estimates between gene expressions and drug responses. Finally, we identified multivariate associations between multi-omics features and drug responses using an integrative machine learning approach. Resultantly, the statistical data analysis not only unveiled known and novel relationships from the current datasets, but also provided biological findings, which could be applied to new datasets. The motivation for introducing the multivariate analysis was the clearly visible distinct drug response patterns spanning across multiple patient samples from
39
our screening results (Figure 4). We hypothesized that the variation in the response pattern is linked to multiple groups of genomic and transcriptomic features, which are involved in several biological processes. In other words, there exist multiple factors in the drug response dataset, which are linked to more than one factor (groups of features) in the multi-omics datasets. Therefore, we needed an integrative machine learning approach such as Group Factor Analysis (GFA) 189, to jointly cover the complex variation across multi-omics and drug response datasets. GFA is a Bayesian data factorization approach and assumes that the observed datasets (here genomic, transcriptomic and drug response data) have originated from a set of low-dimensional latent factors (also called ‘components’). These latent factors capture the associations between the multi-omics features (here mutations, genes and cytogenetics) and drug responses, which are estimated from the observed datasets. This assumption of a latent factor model is consistent with the above biological hypothesis. In drug response analysis, GFA has been widely used for instance to identify drugs’ structural features associated with genome-wide gene expression 190 and for drug sensitivity prediction 191 . To give further empirical validation for the analysis, we compared its accuracy with the frequently used multivariate regularized linear regression (LR) 192 in the task of predicting drug responses to new patient samples. Additionally, the mean of the drug response data was used as the baseline in a cross-validation setting. Table 4 illustrates the prediction accuracies for the three approaches.
Table 4. Drug-wise cross-validated predictive accuracy (Mean ± 1-SE). For each drug, accuracy was measured using the predicted and observed responses across patient samples. The number denotes the mean of the predictive accuracies across drugs ± 1 standard error of the mean (SEM). GFA provided better predictions as compared to LR (elnet: elastic net, lasso, ridge) and baseline approaches.
As the first observation, the genomic and transcriptomic datasets are predictive of the response; this can be noticed from GFA and LR accuracies, which clearly outperform the baseline predictions (obtained without using any genomic or transcriptomic data). The negative correlations in Table 4 represent poor
nRMSE Sp.Cor Pr.Cor aMCC GFA 0.959 ± 0.011 0.211 ± 0.03 0.217 ± 0.037 0.39 ± 0.023 LRelnet 0.977 ± 0.002 0.118 ± 0.039 0.097 ± 0.039 0.322 ± 0.029 LRlasso 0.978 ± 0.002 0.091± 0.043 0.078 ± 0.043 0.309 ± 0.030 LRridge 0.996 ± 0.0004 -0.508 ± 0.036 -0.578 ± 0.035 -0.017 ± 0.010 Baseline 1 ± 0 -1 ± 0 -1 ± 0 -0.043 ± 0
40
accuracies that are equivalent to random predictions, since in the specific case of LOOCV, the mean prediction is known to yield a correlation of -1193. Second, the results validate our hypothesis that the integrative machine learning approach GFA further improves the predictions. The standard linear regression fails to learn the underlying factor-based structure from the data accurately, resulting in the lower prediction accuracies. Moreover, the regression approach does not seem to benefit from the joint analysis of multiple datasets mainly due to the lack of systematic data integration approach. Having achieved confidence from the cross-validation results, we next trained the GFA model on the full data. We adopted the similar experimental settings that were used in cross-validation. The goal here was to learn a model of the full data, which can be used to interpret associations between multi-omics features and drug responses, and which were inferred in the form of GFA components. We discussed clinically relevant findings from these components in the main text.
41
5 RESULTS
5.1 Developing a comprehensive platform for precision medicine
Identification of patient subgroups with distinct therapeutic response profiles, associated predictive biomarkers and minimizing off-target toxicity lies at the core of precision medicine. Present study addresses these needs by i) identifying pharmacogenomic interactions using myeloma as a disease model and ii) dissecting diverse drug responses in hematopoietic cell populations to identify lineage specific cellular vulnerabilities. A graphical abstract illustrating the concept is provided in figure 3.
Figure 3. Schematic representation of an overview of the concept, methods applied and generated datasets to develop and implement precision therapies for patients. Utilizing drug response profiles for 142 anticancer drugs and integrating with genomic, transcriptomic and clinical profiles of 100 myeloma patient derived samples, we generated a unique resource to establish link between myeloma genotype to phenotype and identify pharmacogenetic interactions that have profound implication in designing precision therapies for myeloma. Comparison of drug response profiles in a large number of samples revealed insight into therapeutic subgrouping of patients. Linking clinical profiles of the patients with molecular features prognostic markers were identified. Analyzing sequential samples from single patients during disease progression revealed mechanism of acquired drug resistance and identified driver events for disease progression. Integrating multi-assay datasets molecular signatures for predicting responses to certain drugs were identified. Presented biomarkers could be exploited to match
Group I
Genes
5163
_347
12_3
973_
265
6_4
2143
_152
60_3
3001
_388
2_3
911_
557
2_6
3001
_15
4398
_320
97_3
4708
_313
80_1
123
83_7
2383
_335
14_3
982_
331
77_3
861_
330
01_1
230
01_9
4691
_329
79_6
899_
6
Group II
899_
1213
80_5
3595
_334
97_3
3647
_335
86_3
3129
_239
01_3
921_
322
49_2
2235
_263
62_3
6348
_364
25_3
911_
291
1_7
2979
_234
34_3
4692
_343
50_3
4346
_381
0_3
4349
_322
35_6
917_
227
00_2
Group III
6463
_363
50_3
920_
228
55_2
1878
_393
3_3
2777
_213
54_3
1151
_315
79_3
4637
_326
84_3
4787
_338
86_3
4011
_3
Group IV
MCM2MCM10FEN1PCNATOP2ABIRC5BUB1BEZH2PRC1CCNB1KIF14UBE2CTYMSRRM2TTKSOX2NODALBMP4NTRK1NTRK3PRKCBPRKG1ITGA1ITGA2ITGA5ITGA9ACTN1PARVGVCLIL1BCCL5ABCC3ABCB1
4369
_347
05_3
4317
_327
00_5
584_
343
65_3
1193
_337
67_3
Hea
lthy
and
Patie
nt S
ampl
es Patie
nt S
tratif
icat
ion
Pred
ictiv
e Bi
omar
ker
Indi
vidu
aliz
ed T
reat
men
tR
apid
Clin
ical
Tra
nsla
tion
A G A C T A T A T G C A G C T C G T A C T A C T G A A T A
Healthy PB and BM
Multiple Myeloma
AML, ALL, Others
0 nM
0.1 nM 1 n
M10
nM10
0 nM
1000
nM
0
20
40
60
80
100
pErk p4E-BP1 pSTAT3
Granulocytes
B-cells
Monocytes
CD8 T-cells
CD4 T-cells
CD25 T-cells
NK cells
pDCs
Blasts/CD34+ CD38+
Venetoclax
CD34CD14
MDSCMML
D-CML
R-CML
D-AML
R-AML
CD19D-C
LLR-C
LLD-A
LLR-A
LLB-PLL
CD3T-PLL
T-ALL
CD138
D-MM
R-MM
0
10
20
30
40
DSS
Myeloid Lineage Lymphoid Lineage
0 nM
0.1 nM 1 n
M10
nM10
0 nM
1000
nM
0
20
40
60
80
100
0 nM
0.1 nM 1 n
M10
nM10
0 nM
1000
nM
0
20
40
60
80
100
Simultaneous Monitoring of Drug Responses in 11 Hematopoeitic Cell Populations
Signalome and Proteome Profiling of Blood Cells
Line
age
Spec
ific
Dru
g R
espo
nse
Cel
lula
r Phe
noty
peO
ff Ta
rget
Effe
ctD
rug
Rep
ositi
on
Healthy BM
Multiple Myeloma
+ SEPT6+ SEPT9+ YY1+ CD79B + PCM1 + EZR + KLC1 + TFG + MAML2 + DICER1 + SBDS + CCND3 + NCOR1 + TRIP11 + MALT1 + TRAF3 + BCOR + TGFBR2 + TET1 + GOLGA5
+ CCND1+ HSP90AA1
+ BCR + KIAA1524+ SETD2
+ TFRC + AXIN2+ CHEK2
- BRD4
- PTPRK
- CRTC3
- NCOA2
- LPP - S
TAT3- A
GTRAP
- WHSC1L1
- GMPS
- CDKN1B
- MLF
1- B
CL11A
- ABI1
- ERC1
- ARID
2
- CCND2
- SORBS2
- BCL9
- TET
2
- KIA
A154
9
- ATR - FOXO
3
- PIK
3CA
- LRI
G3
- GPH
N
- ETV
1
- SET
BP1
- HEY
1
+ SM
ARCB
1
+ SD
HA
+ EW
SR1
- ESR
P1
- MAP
3K1
- ESR
1- d
el.1
7p.
- X1q
.gai
n
- del
.14q
.
+ t.1
1.14
.+
t.14.
16.
+ DI
S3- K
DM5A
- NCO
R1+
NRAS
+ NS
D1+
TSC2
+ PA
X7+
MAX
+ SS
X1- K
RAS
- CBL
- TRI
M33
- RAF
1+
TP53
+ AR
+ FA
M46
C+
PTPN
13+
RHO
H+
PRDM
1+
TPM
4+
BCL2
+ NF
KBIE
+ BR
CA2
+ ER
BB4
+ RB
1+
NDRG
1+
CHIC
2+
AKT1
+ CA
RS+
ACTN
4+
SOX2
+ NO
TCH1
+ NA
B2+
RNF2
13+
SFPQ
+ AC
SL3
+ SR
SF2
+ COL1
A2
+ NFE
2L2
+ POU2A
F1
+ TFP
T+ R
AC1+ W
AS
- FANCF
- FLI1
- SEPT5
- NOTCH2
- CXCR4
- MLLT4
- FLT3
- MET
- TOP3A
- GNAQ
- FANCG
- NF1
- NACAP1
- CBLB
- PTPRC
- NCOA4
- BCL11B
- PRF1
- SPOP
- REL- MLLT10
- HERPUD1
- STAT5B
- MYCN
- FNBP1
- VCL- Monosomy.13
+ t.4.14.
+ X1q.gain
+ NF1+ UBR5+ BRAF- RPL5- FRYL- FOXA1- MAML2- KAT6A- XPO1- NRAS
Linsitinib (IGF1R)
Methylprednisolone (Glucocorticoid)
Pimasertib (M
EK1/2)
Refameti
nib (M
EK1/2)
Tram
etin
ib (M
EK1/
2)
Vene
tocla
x (B
CL-2
)
Borte
zom
ib (P
rote
asom
e)
Carfi
lzom
ib (P
rote
asom
e)
Dactin
omyc
in (R
NA and D
NA synth
esis)
Omipalisib (P
I3K/mTOR) Panobinostat (HDAC)
PF.04691502 (PI3K/mTOR)
1G M C
2G M C
Compare Drug Responses in Healthy and Malignant Cells
Precision Therapy
Luminespib
Tanespimycin
Alvespimycin
Idarubicin
Doxorubicin
Panobinostat
Vorinostat
Quisinostat
Trametinib
Pimasertib
Refametinib
Bortezomib
Ixazomib
Carfilzomib
Alvocidib
Dinaciclib
AZD2014
AZD8055
Pictilisib
Omipalisib
Bryostatin 1
Venetoclax
Navitoclax
Methylprednisolone
Dexamethasone
Pomalidomide
Lenalidomide
0 20sDSS
Color Key
Immunomodulators
BCL2 Inhibitors
CDK Inhibitors
Proteasome Inhibitors
MEK Inhibitors
HDAC Inhibitors
Anthracyclines
PKC Modulator
Group -IV Group -III Group -II Healthy Group -I
Pat
ient
Drug
Ex vivo Drug Response Somatic Mutation
Gene Expression Integrative Analysis
MultiplexedFlow Cytometry
42
therapies to individual patients. In addition, we assessed cellular effects of several drug molecules on 11 hematopoietic cell subsets simultaneously to catalogue cell specific responses. By correlating with signaling profiles and protein abundance we reveal molecular cues that explain the cellular phenotype and their impact on drug response. This analysis is extremely valuable in identifying new indication of approved drug molecules and also reveal off-target effects due to their selectivity towards multiple cell types. These approaches together provide understanding drug responses with unprecedented resolution, identify options for precision therapies, detect drug repositioning opportunities and better design informed clinical trials for drug discovery.
5.2 Precision therapies for multiple myeloma
5.2.1 Identification of four chemosensitivity subgroups of myeloma with varied clinical outcome
In order to identify distinct subsets of myeloma patients based on their drug response profiles, unsupervised hierarchical clustering was performed on 100 samples from myeloma patients using their selective response to 142 drug molecules. We discovered patients were segmented in four subgroups (Group I-IV), which were further validated using 10000 bootstrapping using R package pvclust194. The observed responses were broadly categorized as sensitive (group I), moderately sensitive (group II), resistant (group III) and highly resistant (group IV) to signaling inhibitors (Figure 4). Conventional chemotherapeutics and proteasome inhibitors displayed non-selective responses when compared between the samples derived from myeloma patients and healthy donors. Significant differences in response to signal transduction inhibitors were observed between the subgroups. Samples in group I (n=33), showed highest sensitivity to several signal transduction inhibitors including those targeting IGF1R-PI3K-mTOR, HDAC, MEK, CDK and HSP90. Majority (90%) of samples in this subgroup were derived from relapsed refractory myeloma patients. A modest sensitivity to these inhibitors were detected in group II samples (n=37) responded to many of the same drugs, in particular to MEK, HDAC and HSP90 inhibitors. In contrast, group III samples (n=17) were relatively insensitive to these targeted therapies when compared to healthy controls. Group IV (n=11) patients displayed cross-resistance to most of the drugs tested that are structurally unrelated and that did not share common targets. Although this subset of patients showed multidrug resistance (MDR), remarkable sensitivity was seen for Navitoclax and PKC modulator Bryostain1.
43
840_
319
94_3
3767
_337
17_3
1193
_358
4_3
4365
_343
17_3
2700
_543
69_3
4705
_328
55_2
1878
_393
3_3
2777
_292
0_2
6350
_364
63_3
1151
_340
35_3
1354
_338
00_3
3886
_340
11_3
4787
_346
37_3
2684
_332
14_6
1579
_392
1_3
3906
_322
49_2
156_
339
01_3
3129
_231
29_5
1862
_390
8_3
3586
_325
18_3
1380
_513
80_8
3595
_334
97_3
3647
_389
9_12
899_
963
48_3
6425
_363
62_3
2235
_629
79_2
3434
_634
34_3
911_
791
1_2
4692
_343
50_3
4596
_343
46_3
810_
343
49_3
2235
_227
00_2
917_
2H_
3686
H_15
19H_
4032
H_17
77H_
1775
H_25
03H_
3685
H_22
01H_
2200
H_20
71H_
2503
H_25
0113
80_2
3481
_346
55_6
H_15
18H_
1520
5163
_347
12_3
2757
_627
57_3
973_
265
6_4
2143
_152
60_3
3001
_388
2_3
911_
539
66_3
572_
630
01_1
529
26_3
3001
_930
01_1
246
91_3
2979
_643
98_3
2097
_347
08_3
4011
_613
80_1
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83_7
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_393
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3514
_386
1_3
899_
698
2_3
3177
_3
LestaurtinibMidostaurin
EverolimusTemsirolimus
Vincristine
IdarubicinDoxorubicin
TanespimycinAlvespimycin
PimasertibSCH772984Selumetinib
Trametinib
Bryostatin 1
WEHI−539
Vemurafenib
DexamethasonePrednisolone
Methylprednisolone
VenetoclaxNavitoclax
PlicamycinDactinomycin
I−BET151(+)JQ1
AlvocidibDinaciclib
Panobinostat
GDC−0068AZD8055
INK128DactolisibAZD2014
PF−04691502Pictilisib
Omipalisib
BortezomibIxazomib
CarfilzomibOprozomib
PomalidomideLenalidomide
BMS−754807
IMiDs and Corticosteroids
BCL2/BCL-xL Inhibitors
HSP90 Inhibitors
Others
Proteasome Inhibitors
Bromodomain InhibitorsMEK/ERK Inhibitors
CDK InhibitorsRNA/DNA Synthesis Inhibitors
Topoisomerase II Inhibitors
IGF1R-PI3K-mTOR Inhibitors
Group IGroup IIGroup IIIGroup IV Healthy
−20 0 20sDSS
Color Key
44
Figure 4. Unsupervised hierarchical clustering identified four subgroups of myeloma patients (Group I-IV) with unique drug response profile. Significant variation in response to signal transduction inhibitors were noted between subgroups. Samples clustered in Group I displayed a higher sensitivity to signaling inhibitors. Samples in group IV were cross resistant to diverse collection of chemotherapeutics as well as signaling inhibitors. To investigate if the signaling inhibitor responses were specific to CD138+ cells, we compared the drug responses between CD138+ cells and CD138 negative fractions in nine patient specimens, majority of which are presumably healthy cells. As expected, sensitivity to signal transduction inhibitors (PI3K-AKT-mTOR, HDAC, MAPK, and IGF1R, HSP90 and BCL2 inhibitors) were detected in CD138+ plasma cell fraction (Figure 5A) compared to CD138 negative cells, which exhibited similar response as healthy BM samples. Variation in the drug response profiles was also reflected on survival for these patients clustered in four subgroups. Patients in group I displayed a clinically aggressive disease with shorter overall survival (HR 6.48, 95% CI 2.4-17.4). Mortality rate for patients clustered in Group I was highest (56%) followed by Group IV (27%), Group II (18%) and Group III (7%) (Figure 5B).
Figure 5. (A) Comparison of mean ex vivo responses to all tested drugs in paired CD138+ and remaining CD138- cells for 9 individual MM patients. Acquired sensitivity to signaling inhibitors were enriched in CD138+ plasma cell fraction. (B) Variation in overall survival between patients clustered in four chemosensitivity subgroups. Group I patients had very aggressive disease with shorter overall survival compared to other patients.
0 10 20 30 400
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sitive
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Auranofin
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Cladribine
Clofarabine
DocetaxelFludarabine Gemcitabine
GSK2126458
GSK-J4
Navitoclax
Paclitaxel
PF-04691502
Pictilisib
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Survival (months)
Cum
ulat
ive S
urviv
al
Group I (n=27)Group II (n=27)
Group III (n=17)Group IV (n=10)
<0.0001p HR(Log rank) , Gr I vs Others , 6.8
A B
45
5.2.2 Genomic landscape of multiple myeloma
The present MM cohort of 98 samples exhibited a median of 80 protein affecting mutations per sample (ranged from 18 to 921), as well as numerous gene copy number alterations (CNAs), most of them subsequent to chromosomal aneuploidies but also as a result of focal events. The mutational landscape is summarized in Figure 6. Overall, the most frequently mutated gene in our cohort was KRAS (29% of the samples) and NRAS (18%), and other genes such as BRAF and NF1, accounting for a total of 56% of the samples that exhibited driver alterations in the RAS-RAF-MAPK- pathway. Cell cycle genes also accumulated a large number of alterations, mainly due to RB1 monoallelic losses subsequent to chromosome 13q deletions (36% of the samples), and also acquired CNAs in other genes as CKS1B, CDKN2A, CDKN2C, CDKN1B and CCND1. Deletions in 13q also affects TNFSF13B, a B-cell activating factor acting via the NF-kB alternate pathway. Other genes frequently targeted in the present cohort and involved in NF-kB signaling by mutations and/or gene losses were CYLD, TRAF3, BIRC2 and BIRC3. Well-known MM altered genes related to RNA editing (DIS3 and FAM46C) also exhibited driver events in a large percentage of our cohort.
The detection of these lowly recurrent events provides a comprehensive portrait of the putative genomic drivers acting in each tumor. Of note, 94% of the mutant alleles found in the exome sequencing and in silico stated as drivers were transcribed, stressing their relevance as functional events. Overall, the RRMM samples exhibited a larger number of mutations, including the driver alterations, and also trended to harbor more CNAs (Mann Whitney p values of 1.9e-05 and 0.09, respectively) as compared to the NDMM group. Taken together, these results support the notion that the progression of the disease is driven by the acquisition of novel mutations that may construct a more complex clonal landscape. TP53, FAM46C and CYLD mutations were more prevalent in relapsed samples (NDMM/RRMM, 2/10, 0/7 and 2/6 respectively).
To reveal an insight into processes associated with disease progression in patients, we compared mutational and drug response profiles in 9 individual patients where repeated sampling was performed prospectively. Analysis of sequential samples revealed that most patients trended to acquire additional mutations during progression from NDMM to RRMM, which was also evident in relapsed diseases between successive samples (Figure 7) from same individuals. Often those mutations were acquired in linear fashion. Drug response profiles were also significantly altered.
46
Figure 6. Landscape of driver alterations in the m
ultiple myelom
a samples
grouped according to their biological module. Relevant annotations for each
sample are provided at the top of the figures. Bar plot at the right side show
s the frequency of individual alterations in the cohort.
KRASNRASBRAFNF1PTPN11TP53TP73ATMTP53BP1BAXMSH3MDM4DIS3FAM46CPOLR2BSF3B1CYLDTRAF3NCOR1SETD2ARID1ASMARCA4CREBBPKDM6APIK3CAPIK3CBPIK3CDPIK3R1TSC2FAT3FAT4ALKERBB2UBR5EIF2AK3
GenderHeavy chainLight chainLines of treatmentDisease stage
Light chainLambdaKappa
Lines of treatment < 3≥ 3Untreated
Age< 65≥ 65
del(17p)t(4;14)t(11;14)t(14;16)del13q1q Gaint(14;20)
Hyperdiploid
Age
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_347
12_3
973_
265
6_4
2143
_152
60_3
3001
_388
2_3
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557
2_6
3001
_15
4398
_320
97_3
4708
_313
80_1
123
83_7
2383
_335
14_3
982_
331
77_3
861_
330
01_1
230
01_9
4691
_329
79_6
899_
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9_12
1380
_535
95_3
3497
_336
47_3
3586
_331
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3901
_392
1_3
2249
_222
35_2
6362
_363
48_3
6425
_391
1_2
911_
729
79_2
3434
_346
92_3
4350
_343
46_3
810_
343
49_3
2235
_627
00_2
6463
_363
50_3
920_
228
55_2
1878
_393
3_3
2777
_213
54_3
1151
_315
79_3
4637
_326
84_3
4787
_338
86_3
4011
_343
69_3
4705
_343
17_3
2700
_558
4_3
4365
_311
93_3
3767
_3
Mut
atio
ns a
nd C
NAs
Kary
otyp
e
Drug sensitivity subgroupsGr-IGr-II
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Subgroups
Delet
ionM
utat
ion +
Dele
tion
Mut
ation
GenderMaleFemale
Heavy chainIgAIgG
NDMMRRMM
Disease stage
MAPK Pathway
DNA Damage Response Pathway
DNA/RNA Processing
NF-kB Pathway
Epigenomic Modifiers
PI3K-mTOR Pathway
Others
0 10 20 30
NDMMRRMM
Frequency (%)
47
To understand patterns of drug response changes that occur during relapse, we compared sequential samples at diagnosis and relapse in three patients (Figure 7). The comparison revealed that acquired responses to targeted therapies were common at relapse. However, it might follow an opposite trend (Patient 2700) where patients may acquire cross-resistance to most tested drugs.
Figure 7. Changes in mutation burden and drug sensitivity patterns during relapse. Left panel showing comparison of somatic mutation count between serial samples. Comparison of drug responses at diagnosis (D) and at relapse (R) showing acquired sensitivity in signaling inhibitor response in first two patients (2979 and 4011). Patient 2700 acquired cross resistance to therapies during progression.
5.2.3 Genetic and transcriptomic signature of chemosensitivity subgroups
To investigate the molecular basis of observed drug response heterogeneity within chemosensitivity subgroups, we next mapped genomic, cytogenetic and transcriptional profiles onto the drug response subgroups. A significantly higher mutational load was present in group I patients compared to other samples in our cohort. Median protein affecting mutation count in group I, II, III and IV was 107, 75, 67 and 46 respectively. Importantly, mutational signature enriched in subgroup group I included TP53, FAM46C, ARID1A, SETD2 and PIK3CA. Overall mutational load corresponded to a higher sensitivity to signal transduction inhibitors. Gene expression profiling (GEP) between subgroups indicated a higher level of genes contributing to cell proliferation and self-
Pre
Post
1
51
101
151
300
500
700
900
Count of som
atic m
uta
tions
899
9111380
22352383
2700
2979
3001
31293434
4011
2979 (D)
2979 (R)
0
10
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30
DS
S
Omipalisib
BMS-754807
Venetoclax
Panobinostat
(+)JQ1
Bortezomib
Carfilzomib
Dactinomycin
Pomalidomide
Dexamethasone
Vincristine
4011
(D)
4011
(R)
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10
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DS
S
Bortezomib
Carfilzomib
BMS-754807
(+)JQ1
Omipalisib
Dactinomycin
Pomalidomide
Dexamethasone
Panobinostat
Venetoclax
Vincristine
2700 (D)
2700 (R)
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S
Bortezomib
Carfilzomib
Bortezomib
Carfilzomib
BMS-754807
(+)JQ1
Omipalisib
Dactinomycin
Pomalidomide
Dexamethasone
Panobinostat
Venetoclax
Vincristine
48
renewal (e.g. MCM2, FEN1, BUB1B, TOP2A, BMP4, NODAL and SOX2) in Group I patients (Figure 8).
Figure 8. Gene expression signature associated with chemosensitivity subgroups. Patient samples in group I had higher expression of cell proliferation associated genes. Group III and IV patient displayed elevated expression of ABCB1 and ABCC3. Intriguingly, by comparing the gene expression profiles to previous studies by Zhan et al., we identified a similar GEP profile in group I patients described to characterize highly progressive patients (designated as PR subtype)152. In contrast, heightened expression of drug efflux transporters ABCB1 (MDR1) and ABCC3 (MRP3) characterized more resistant samples (Groups III and IV). Moreover, these patients co-expressed genes coding for proteins contributing to extracellular matrix (ECM) interaction, including integrin signaling molecules, Rho family GTPases. Several cytokines (IL1A, IL1B) and chemokine (CCL5, CCL2) were also found to be upregulated in resistant samples which suggests their dependence on microenvironment.
5.2.4 Alterations in DNA damage repair genes are mutually exclusive and predict poor prognosis in myeloma patients
To understand mutational pattern in genes involved in DNA damage sensing or repair (DDR), we investigated the pattern of somatic alterations detected in myeloma patients. Hemizygous loss of TP53 due to deletion of 17p13 was present in 21% (n=17/83) of patients. 12 patients harbored mutation in TP53 and
MCM2MCM10FEN1PCNATOP2ABIRC5BUB1BEZH2PRC1CCNB1KIF14UBE2CTYMSRRM2TTKSOX2NODALBMP4NTRK1NTRK3PRKCBPRKG1ITGA1ITGA2ITGA5ITGA9ACTN1PARVGVCLIL1BCCL5ABCC3ABCB1
−3 0 3Log2 Fold Change
Color Key Prol
ifera
tion
and
Self
Rene
wal
ECM
Inte
ract
ion
and
Dru
g Re
sista
nce
GenderHeavy chainLight chainLines of treatmentDisease stage
Light chainLambdaKappa
Lines of treatment < 3≥ 3Untreated
Age< 65≥ 65
Age
5163
_347
12_3
973_
265
6_4
2143
_152
60_3
3001
_388
2_3
911_
557
2_6
3001
_15
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_320
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_313
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83_7
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_335
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982_
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_329
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689
9_12
1380
_535
95_3
3497
_336
47_3
3586
_331
29_2
3901
_392
1_3
2249
_222
35_2
6362
_363
48_3
6425
_391
1_2
911_
729
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3434
_346
92_3
4350
_343
46_3
810_
343
49_3
2235
_627
00_2
6463
_363
50_3
920_
228
55_2
1878
_393
3_3
2777
_213
54_3
1151
_315
79_3
4637
_326
84_3
4787
_338
86_3
4011
_343
69_3
4705
_343
17_3
2700
_558
4_3
4365
_311
93_3
3767
_3
Drug sensitivity subgroupsGr-IGr-II
Gr-IIIGr-IV
Subgroups
GenderMaleFemale
Heavy chainIgAIgG
NDMMRRMM
Disease stage
49
in more than half of these patients both alleles (n=7) were affected due to either del(17p) or a focal deletion in chromosome 17. Mutations other than TP53 in DDR pathway were detected in ATM, PPM1D, MSH3, BAX, TP53BP1, and TP73 genes in 13 additional patients. ATM, MSH3, TP73 and TP53 mutations were present in mutually exclusive manner (Figure 9A) stressing their similar downstream effects and accumulating multiple mutations in this pathway does not provide additional benefit for the MM tumorigenesis. Altogether approximately 27% of patients harbored mutations in DDR genes. Importantly, prevalence of DDR alterations was in Group-I patients with a higher mutational load and was not restricted only to relapsed patients. When compared the survival outcome of patients with or without DDR alterations, we noted presence of DDR alterations independently predicted poor prognosis with a hazard ratio of 11.7, 95% CI 3.6-35 (Figure 9B). Median survival was 8 months for these patients from the date of sampling and was independent of concurrent presence of del(17P). Figure 9. (A) Mutation in DNA damage repair signaling pathway often occur in mutually exclusive manner. (B) Alteration in DDR gene confers poor prognosis.
5.2.5 Distinct transcriptional signatures and drug responses are associated to mode of alterations in TP53 suggests a divergent role in disease progression
Genomic alteration in TP53 gene in myeloma occurred primarily by, i) heterozygous deletion associated with del(17p) (WT/-), ii) mutations affecting a single allele (mut/WT) and iii) bilalleleic inactivation due to mutation in one allele and the other allele involved del(17p) or focal deletion (mut/-). We investigated whether TP53 is affected by mutation of deletion might influence cellular phenotype and response to therapies. We first compared gene expression profiles between these molecular subsets to identify uniquely expressing genes linked to each group. Both WT/- and mut/- samples showed higher expression of IL21R, CCL5, CCR7, REL and PDGFC compared to mut/WT samples.
40
35
_3
40
11
_3
27
57
_6
47
08
_3
13
80
_5
63
50
_3
22
49
_2
30
01
_1
5
23
83
_7
40
11
_6
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25
_3
93
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6
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3
35
14
_3
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12
_3
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3
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3
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_3
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_3
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_1
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_3
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_6
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_3
19
94
_3
57
2_
6
11
93
_3
15
6_
3
34
34
_3
39
66
_3
22
35
_2
46
92
_3
37
17
_3
25
18
_3
21
43
_1
del (17p)
TP53
TP73
ATM
MSH3
TP53BP1
MDC1
BAX
NDMM RRMM Focal deletion 0 10 20 300
0.5
1
Survival (months)
Cum
ula
tive S
urv
ival
DDR (n=19)
Others (51)
p <0.0001
HR 11.7, 95%CI 3.6 to 35
A B
50
Reduced expression of genes involved in interferon signaling (IFIT1, IFIT3 TNFSF10) were particularly noted in samples where both alleles were disrupted (mut/-) compared to WT/- or mut/WT samples (Figure 10A). A significant difference in ex-vivo drug responses was also detected between these subsets. However, response profiles for mut/WT and mut/- subgroups were more comparable compared to WT/- samples. WT/- samples were comparatively insensitive to signal transduction inhibitors and also exhibited reduced sensitivity to approved anti myeloma therapies. Notably, mut/WT and mut/- subgroups were particularly sensitive to PI3K-mTOR and HDAC inhibitors (Figure 10B). None of the patients containing bi-allelic inactivation of TP53 showed response to dexamethasone. We also performed a prospective analysis in two patients who
acquired TP53 mutations to investigate the association in more detail.
Figure 10. (A) Gene expression and (B) drug response profiles differ whether TP53 gene is affected by mutation, deletion or biallelic inactivation. Mutation in TP53 is associated to PI3K-mTOR and HDAC inhibitors. No response to dexamethasone was detected in samples with biallelic loss of TP53. While, the first patient (1380) who acquired both del(17p) and TP53 mutation, the second patient (2235) was del(17p) positive and further acquired a TP53 mutation. In both cases, patients showed higher efficacy to these inhibitors compared to previous samples without these alterations. Based on our
11934346
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RUFY
4KI
TTN
FSF1
0D
MRT
2G
OLG
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ROBO
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PLO
D2
HR
ASLS
2PL
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PHLP
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D24
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109
CC
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GC
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NKG
7EN
C1
SLC
25A2
7PH
LDA1
KLR
D1
STXB
P5L
PDG
FCS1
PR5
IGLV3−9
IGHV4−61
CAL
N1
UN
C13
CN
LRP7
RIM
S2M
YO1E
MT−
TL1
SHRO
OM
3R
ND
3D
GAT
2D
PEP3
NPA
S3C
CD
C14
4CP
NTR
K2EM
ILIN
1N
FIA
AFF1
FER
CN
OT6
LG
PCPD
1TA
B2PI
K3R
1EP
AS1
IKZF
2SN
OR
A22
ZNF2
14G
PR17
4TM
EM2
C12
orf7
5ST
AP1
TLR
10
del(17p) TP53 del(17p)+TP53
−3 0 3Log2 Fold Change
Color Key
Omipalisib
Health
y
TP53 (W
T/-)
TP53 (m
ut/W
T)
TP53 (m
ut/-)
Others
0
10
20
30
DS
S
****
*AZD-2014
Health
y
TP53 (W
T/-)
TP53 (M
utant)
TP53 (-
/-)
Others
0
10
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30
DS
S
*** *
Panobinostat
Health
y
TP53 (W
T/-)
TP53 (m
ut/W
T)
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ut/-)
Others
0
10
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DS
S
*
Dexamethasone
Health
y
TP53 (W
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T)
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ut/-)
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DS
S
**
**
A
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51
observation, whether TP53 is affected by mutation or deletion appeared to be phenotypically distinct. Importantly, the presence of additional alterations in DDR conferred more aggressive disease compared to del(17p) background and offers novel vulnerabilities to be exploited.
5.2.6 Impact of clonal heterogeneity on drug responses in MM
Myeloma patients exhibit both inter and intra-patient heterogeneity, which has been shown to be a commonplace event occurring in either linear or branching fashion18, 195, 196. Irrespective of the mechanisms, clonal heterogeneity plays a big role in determining outcome of applied treatment if they are ineffective against more aggressive sub clones driving the disease progression. We observed mutation in mitogen-activated protein kinase (MAPK) pathway (NRAS, KRAS, NF1 and BRAF), which occurs in more than half (56%) of the myeloma patients, is present in higher clonal frequencies in relapsed patients (Figure 11A). This observation may suggest, i) the anti-myeloma drugs approved for treating patients are ineffective against the cells containing the mutated proteins and importantly ii) these genomic events are positively selected during the progression of the disease and provides a therapeutic opportunity to explore with specific inhibitors (i.e. MEK or BRAF inhibitors) in these subgroups of patients. We evaluated the ex vivo response for these inhibitors in our cohort and observed a more complex pattern of response than expected. Only 55% of patients with clonal NRAS, KRAS showed response to trametinib, which provides inhibition of MEK1/2 in the RAS-MAPK cascade (Figure 11B). The effect could not be explained by changes in amino acid residues associated to these mutations. Similarly, out of two clonal BRAF V600E mutant patients, a single patient displayed response to vemurafenib ex vivo. Although, a measurable response could be achieved in patients with these targeted agents, importance on functional screens to identify the responding fraction of patients cannot be ignored. Acquired mutations often modulate the response to treatments and mediate resistance. CRBN, a protein substrate receptor for the E3 ubiquitin ligase complex CRL4CRBN, has been identified as the key target of the IMiDs (thalidomide, lenalidomide and pomalidomide)74-76. We identified acquired nonsynonymous mutations to the CRBN coding region: S77R (exon 3), P98A (exon 3) and T361I (exon 10) in three patients (Figure 11C). Additionally, focal deletion in distal arm of chromosome 3 was observed in a patient that conferred loss of CRBN (Figure 11D). While the S77R and T361I variants were subclonal
52
(Clonal cell fractions (CCF) were < 40%), P98A mutation showed CCF of 82% and was present in the dominant clone (Figure 11E).
Figure 11. Clonal burden impacts drug responses in myeloma. (A) Compared to newly diagnosed patients, higher clonal frequency of RAS pathway mutations was detected in relapsed patients. (B) Samples bearing clonal RAS mutations trended to be more sensitive to MEK inhibition. (C) Protein domain affected by detected CRBN mutations. (D) Focal deletion at CRBN locus in chromosome 3 was detected in a lenalidomide refractory patient. (E) Shift in clonal burden for CRBN mutation upon withdrawal of treatment. (F) CRBN mutated patients are refractory to IMiD treatment.
C
A
T361I: Lenalidomide refractory
P98A: Thalidomide/Lenalidomide refractory
S77R: Lenalidomide refractory
Len maintenance1 mo
Clinicalrelapse
LLD76 318/319
TBD
S77RP98A
T361I
DDB1 binding motif188-248
IMiD compound pocket380-400
1 442
22% 12%3 mo
Vcr/Doxo/Dex
PDDeath <1 mo
Bor/Len/Dex2 mo
Bor/Len/Dex2 mo
Pom/Dex
Pom/Dex
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umbe
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utatio
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nal R
AS muta
tions
0
10
20
30
DSS
B
D
F
53
All of these patients were either refractory to IMiDs and/or acquired resistance during lenalidomide treatment (Figure 11F). Analysis of posterior sample in one patient showed clone bearing CRBN mutation was present in lower variant allele frequency (VAF) after treatment discontinuation (Figure 11E), suggesting resistance-causing mutation may disappear once the selection pressure is withdrawn. Thus, stressing the opportunity to exploit intermittent treatment with treatment modules to avoid treatment resistance and restore sensitivity in the patients.
5.2.7 Identification of biomarkers for predicting response to therapies
To address inter-patient variability and predict therapeutic response in individual patients, reliable predictive biomarkers are needed. Cellular response to therapies in most cases is influenced by intricate contribution of genomic, transcriptomic and epigenetic events, and challenging to be defined by a single genomic marker197, 198. To reveal multifeatured predictive signatures by combining events in different layers of biology, we applied two computational approaches, each serving a distinct analytical task. First, ANOVA was used to identify significant interaction between individual drug responses and cytogenetic or somatic mutations. Next, we analyzed transcriptomic correlates by computing pairwise correlation estimates between gene expressions and drug responses. Ex vivo response to bortezomib was observed in most samples (89%) except for those samples showed cross-resistance to most tested drugs and clustered in drug sensitivity subgroup IV. No significant association was observed between chromosomal and genomic alteration and bortezomib response, partly due to lack of variability in drug sensitivity scores between sensitive and resistant samples. A similar challenge was also applicable for panobinostat and could be mentioned as a potential challenge to derive biomarkers for broadly acting drugs using such approach. However, gene expression analyses in samples resistant to these molecules show an elevated expression of drug efflux genes (ABCB1 and ABCC3). Genes characterizing group IV patients including members of canonical integrin and RHO signaling pathways were associated to bortezomib resistance. A higher sensitivity to carfilzomib was noted in these patients compared to bortezomib. Dexamethasone efficacy as a single agent was detected in 24% of the tested MM sample. A significant association was noted between dexamethasone response and del(17p), mutations in ARID1A and DIS3 (Figure 12A). Samples presenting with a hyperdiploid (HD) karyotype without co-occurring high-risk features
54
(del(17p) and t(4;14)), showed significantly higher response to glucocorticoids compared to other samples. Pathway analysis (IPA®) of 142 genes, which were found to significantly correlate (p < 0.05) with dexamethasone response, was enriched for genes involved in interferon signaling (IFIT1, IFITM1, ISG15, OAS1, TNFSF10, PSMB8, PSME1 and PSME2), among others. Expression of these genes positively correlated to ex vivo response of dexamethasone. Comparing to GEP profiling reported by Zhan et al. we noted these genes were linked to molecular subgroup HY characterizing hyperdiploid karyotype. Notably, samples from patients with TP53 biallelic inactivation had lower expression of these genes (Figure 10B) and lacked sensitivity to glucocorticoids. Cytotoxic effect of pomalidomide was observed in 24% (n=18/74) of all samples tested that included 17% (n=5/29) of NDMM and 29% (n=13/45) of RRMM patients. Ex vivo responses to lenalidomide correlated to pomalidomide (p< 0.001) suggesting shared mechanisms explaining IMiD activity is active in those patients. We identified 71% of FAM46C mutated samples showed higher response (p=0.03) compared to other samples in cohort (Figure 12A). In contrast to 20% (n=2/10) of samples with del (17p) and 16% (n=1/6) samples with bialleleic loss of TP53(mut/-), 75% (4/6) patients who presented with only TP53 mutations were sensitive. Mutations in CRBN, a key target of the IMiDs74-76, was detected in three patients. All patients were clinically resistant to IMiDs and no ex vivo response was detected (Figure 11F). Additionally, samples harboring t(11;14) and t(14;16) completely lacked ex vivo response. Gene expression profiling and subsequent regression analyses identified 130 genes that correlated to IMiD response. Pathway centric analyses (Ingenuity pathwayIPA®) revealed that up regulated genes detected in the responding patients was enriched in nuclear excision repair (NER) (POLR2I, RAD23B) and protein ubiquitination pathway (PSMB7, PSME2 and UBE2L6). GSPT2 encoding eukaryotic release factor 3 (eRF3b) was found significantly elevated in the responding patients (Figure 12C). While, no substrate specificity of lenalidomide or pomalidomide has been documented for GSPT1 (eRF3a)199, effect of CRBN on its paralogue GSPT2 requires further experimental investigations. In contrast, over expression of genes involved in extracellular matrix interaction (MAPK8, PDGFA, SEPT4, BAIAP2, VCL, PIKFYVE and MMP8) contributed to poor response to IMiDs. This agrees with previous studies demonstrating role of tumor microenvironment on IMiD resistance200-202. Importantly, several genes reported to be associated with drug resistance in other malignancies e.g. S100P203 and ADAM12204 were amplified in the resistant patients.
55
Carfilzomib
Health
yNRAS
KRASBRAF
NF1DIS
3
FAM46
CCYLD
LRP1B
FAT3
ARID1A
PCLO
TRAF3
SETD2
FGFR
3
TP53
BP1ATM
TP53
(wt/m
ut)
TP53
(-/m
ut)
TP53
(-/w
t)
t(4;14
)
t(11;1
4)
t(14;1
6) +1q -13
Hyper
diploi
dGr-I
Gr-II
Gr-III
Gr-IV
010203040
DS
S
Bortezomib
Health
yNRAS
KRASBRAF
NF1DIS
3
FAM46
CCYLD
LRP1B
FAT3
ARID1A
PCLO
TRAF3
SETD2
FGFR
3
TP53
BP1ATM
TP53
(wt/m
ut)
TP53
(-/m
ut)
TP53
(-/w
t)
t(4;14
)
t(11;1
4)
t(14;1
6) +1q -13
Hyper
diploi
dGr-I
Gr-II
Gr-III
Gr-IV
010203040
DS
S
Dexamethasone
Health
yNRAS
KRASBRAF
NF1DIS
3
FAM46
CCYLD
LRP1B
FAT3
ARID1A
PCLO
TRAF3
SETD2
FGFR
3
TP53
BP1ATM
TP53
(wt/m
ut)
TP53
(-/m
ut)
TP53
(-/w
t)
t(4;14
)
t(11;1
4)
t(14;1
6) +1q -13
Hyper
diploi
dGr-I
Gr-II
Gr-III
Gr-IV
0
5
10
15
DS
S
Pomalidomide
Health
yNRAS
KRASBRAF
NF1DIS
3
FAM46
CCYLD
LRP1B
FAT3
ARID1A
PCLO
TRAF3
SETD2
FGFR
3
TP53
BP1ATM
TP53
(wt/m
ut)
TP53
(-/m
ut)
TP53
(-/w
t)
t(4;14
)
t(11;1
4)
t(14;1
6) +1q -13
Hyper
diploi
dGr-I
Gr-II
Gr-III
Gr-IV
0
5
10
15
DS
S
Panobinostat
Health
yNRAS
KRASBRAF
NF1DIS
3
FAM46
CCYLD
LRP1B
FAT3
ARID1A
PCLO
TRAF3
SETD2
FGFR
3
TP53
BP1ATM
TP53
(wt/m
ut)
TP53
(-/m
ut)
TP53
(-/w
t)
t(4;14
)
t(11;1
4)
t(14;1
6) +1q -13
Hyper
diploi
dGr-I
Gr-II
Gr-III
Gr-IV
0
10
20
30
DS
S
Venetoclax
0
10
20
DS
S
Dexamethasone
Sens
itive
Res
ista
nt
Karyotype Mutation Gene Expression DSS
3177_32979_2982_3882_3
3514_32097_32249_22700_5899_6
2143_1
*del
(17p
)t(4
;14)
t(11;
14)
del(1
3q)
+1q
HD
NR
ASKR
ASFA
T4*A
RID
1A*D
IS3
TP53
FAM
46C
MSH
3BA
XFG
FR3
TRAF
3TP
73PC
LOSP
140
DN
AJA4
SLC
2A1
DN
AH17
MM
P14
ADAD
2C
ISH
TAF1
CPI
GA
CAR
D11
SMIM
5PS
ME2
PSM
B8BN
IP1
TNFS
F10
IFIT
1IF
ITM
1IS
G15
OAS
1U
BE2L
6AT
G3
3177_34712_33901_3899_6
5163_32700_53767_34317_34369_34705_3
*HD
*t(1
1;14
)t(
4;14
)+1
qde
l(13q
)
Mutations
NR
AS
GS
PT
2B
RA
FA
RID
2N
CO
R1
TP
53K
RA
SF
GF
R3
SP
140
*FA
M46
CS
ET
D2
PC
LOFA
T3
DIS
3
Genes Expression
CC
R7
CR
YM
PD
GFA
LYN
X1
MS
4A4E
S10
0PM
MP
8C
RIS
P3
SH
RO
OM
4P
LK3
GS
PT
2D
US
P13
CIIT
AS
PC
S2
ISG
20N
DU
FC
2P
SM
B7
UB
E2L
6P
DC
LP
SM
E2
DS
S
Pomalidomide
Sens
itive
Res
ista
nt
Karyotype
A
B C
Health
yNRAS
KRASBRAF
NF1DIS
3
FAM46
CCYLD
LRP1B
FAT3
ARID1A
PCLO
TRAF3
SETD2
FGFR
3
TP53
BP1ATM
TP53
(wt/m
ut)
TP53
(-/m
ut)
TP53
(-/w
t)
t(4;14
)
t(11;1
4)
t(14;1
6) +1q -13
Hyper
diploi
dGr-I
Gr-II
Gr-III
Gr-IV
56
Figure 12. Impact of genomic alterations on approved myeloma agents. (A) A simplified drug activity profiles for individual genetic conditions along with the variation observed in chemosensitivity subgroups are presented here. Each dot represents mean value for drug sensitivity scores (DSS) for those samples indexed genetic alterations were present. (B, C) Shows gene expression signatures and molecular features in sensitive and resistant patients to indexed drugs.
5.2.8 Identification of new candidates for drug repositioning in MM
Unbiased testing of large set of approved drugs for other disease indications allowed us to identify potential drugs, which could be repurposed to treat myeloma patients to achieve clinical benefit. Efficacy of venetoclax as monotherapy114 or in combination115 with bortezomib has shown ORR of 21% and 68% respectively in heavily pretreated myeloma patients. Higher response rate as monotherapy has been associated with presence of t(11;14), leading to an overexpression of CCND1. Although it is considered as first compound to target any specific genetic alteration in MM, evidences from clinical trials indicate a significant benefit observed in patients without harboring this alteration. Ex vivo response to venetoclax was detected in 48% of samples tested. All samples with t(11;14) samples showed ex vivo sensitivity to venetoclax. Ex vivo venetoclax sensitivity also correlated to elevated CCND1 expression (p< 0.001) due to presence of t(11;14). We detected several additional molecular features explaining venetoclax response in our patient cohort (Figure 11A). We noted a higher expression of BMI1 was detected in the responding subset of samples. Moreover, genes encoding proteins involved in protein homeostasis (e.g. HSP90AA1, HSPB1, DNAJB5), cell cycle (e.g. CCND1, BTG1), was enriched in the sensitive samples (Figure 11 A). In contrast, expression of CCNE2 and BCL2L1, among other genes, predicted resistance. Interestingly, presence of t(4;14) and/or mutations in FGFR3 attenuated venetoclax response in those samples. While identification of markers for sensitivity guides to match drugs to right patients, markers predicting resistance could also benefit by avoiding treating patients who are not likely benefit to an individual drug. Midostaurin response was detected in 36% of all patients and that was increased to 43% when considered only relapsed patients. The patient subpopulation with mutated TP53 and FAM46C were significantly more sensitive compared to other patients. In addition, the responding patients displayed an elevated expression of AURKA, NTRK1 and NTRK3 (Figure 13B). Importantly, the responding subset of patients clustered in group I, which was identified based on drug response
57
profiles. In addition to molecular signatures DSRT subgrouping was also informative to segment the midostaurin responding patients. Chemotherapeutic agent such as dactinomycin (90%) and plicamycin (70%) showed potent response in large proportion of patient samples (Figure 13C). Among drugs that have selective molecular targets, trametinib, ruxolitinib, vorinostat and temsirolimus sensitivity is particularly worth noting. Response to these drugs was detected in approximately 30% of tested patients. Selective response to dasatinib was noted in 11% of relapsed patients.
Figure 13. (A, B) Multifeatured signature predictive of response to venetoclax and midostaurin in myeloma. (C) Percentage of patients responding to the selected approved and investigational compounds in our assay. A DSS cutoff of 10 was used oo9to define a measurable response.
5.2.9 Data integration with machine learning approach
Since drug responses in individual patients can rarely be explained by a single genetic event197, 198, and rather involves a coordinated contribution from cooperating genomic and transcriptomic features, we next sought associations between multi-omic features and drug responses using an integrative machine learning approach. We applied Group Factor Analysis (GFA) 189, 205 to jointly capture the complex variation across multi-assay datasets and drug response data. GFA is a Bayesian data factorization approach and assumes that the datasets have originated from a set of low-dimensional latent factors (also termed ‘components’). These latent factors capture the associations between the multi-omics features (mutations, genes and cytogenetics) and drug responses, which
Venetoclax
Karyotype Mutation Gene ExpressionDS
S
2097_34346_34369_34705_3917_2
2249_21380_22855_2920_2
1380_5
del(1
3q)
*t(11
;14)
+1q
*t(4;
14)
HDde
l(17p
)AR
ID2
BRAF
TP53
FAT3
DIS3
MSH
3FA
T4AR
ID1A
LRP1
BKR
ASRO
R2SU
LF2
CYP2
J2PE
RPHO
XB7
CCNE
1BC
L2L1
IDH2
ULK3
PMAI
P1TL
R7CC
ND1
DDX2
4HS
P90A
A1BT
G1
CCNE
2BM
PR2
BMI1
KLF1
0TN
FRSF
18
Sens
itive
Resis
tant
Karyotype Mutation Gene Expression
DSS
4712_3973_2
5260_3656_4882_3
4692_33901_31354_31878_33767_3
del(1
7p)
t(4;1
4)t(1
1;14
)t(1
4;20
)de
l(13q
)+1
qHD
*NRA
SKR
AS NF1
BRAF
SETD
2CY
LD*T
P53
*FAM
46C
BAX
PCLO
APO
EFO
XO1
CDKN
1AA2
MPR
KCE
TNFS
F13
SAV1
TLR1
0M
S4A1
CXCR
4CX
CR4
NTRK
1AU
RKA
MCM
2RA
D51
BIRC
5FE
N1CD
C25A
BUB1
BCE
NPE
MidostaurinSe
nsitiv
eRe
sista
nt
0 20 40 60 80 100Vemurafenib
Everolimus
Idelalisib
Vincristine
Temsirolimus
Dexamethasone
Pomalidomide
Lenalidomide
Trametinib
Vorinostat
Ruxolitinib
Doxorubicin
Midostaurin
Bryostatin 1
Tanespimycin
Venetoclax
(+)JQ1
AZD2014
BMS-754807
Plicamycin
Omipalisib
Dinaciclib
Ixazomib
Panobinostat
Bortezomib
Navitoclax
Dactinomycin
Carfilzomib
Percentage of Responders
A
B
C
58
are estimated from the analyzed datasets. GFA also achieved a higher predictive value in a cross-validation setting when compared to widely used multivariate linear regression models that were trained using ridge, lasso and elastic-net penalties. Analysis with GFA identified 21 significant components explaining features related to all small molecules. Four components are visualized here that captured features related to approved and investigational compounds which showed preclinical efficacy in our screens for multiple myeloma. Features relevant to PI3K-mTOR inhibitor response are captured in component 1 and 3. They reveal an association between mutations in TP53, PIK3CD, FAM46C, SETD2 and ex vivo response (Figure 14). While expression of genes detected in component 1 relates to cell proliferation (i.e. CCNB2, KIF23, KIF2C, PLK4, CDK1, SKA1, CENPI, CDC25C), component 3 identified expression of proto-oncogenes (RET, ROS1) and several protein phosphatases (PTPN13, DUSP8) to be predictive of response (Figure 14). Response to immunomodulatory drugs and glucocorticoids were explained by genes contributing to adaptive immune function (i.e. IFIT1M, OAS2, IRF7, ISG15, STAT1) and the ubiquitin proteasome system (i.e., UBE2L6, USP18) that was captured in component 2. Component 4 reveals features linked to responses to proteasome or HDAC inhibitors and identifies factors associated with resistance to BCL2 inhibitors (Figure 14). Although, a few components and features are highlighted here, we identified a repertoire of novel features, which needs further functional exploration and validation to assess their benefit in a larger patient setting.
59
Figure 14. Analyzing the multi-omics feature—drug response associations with multivariate analytical approach: Group Factor Analysis (GFA). The GFA factors (Components; in the middle) connect groups of drugs (on the left) and groups of multi-omics features (on the right). The features derived from individual ‘–omics’ datasets are represented in different colors within each factor. C, M and G denote cytogenetics, mutations and gene expression datasets, respectively. For each component, the links with highest mean absolute values (expressed by the thickness of the lines) are shown.
60
5.2.10 Ex vivo drug responses recapitulate the in vivo response
To evaluate whether ex vivo response may help predicting treatment outcome in patients, we prospectively tailored treatment decisions in relapse/refractory patients using both approved and off label drugs. Figure 15. Clinical follow up in four patients receiving precision therapies based on ex vivo results presented for (A) patient 1862, (B) patient 899, (C) patient 2757 and (D) for patient 5260. Dots present myeloma serum protein concentrations indicating disease burden. Duration of the treatment is highlighted using broken lines. Ex vivo response (DSS) for the approved and off-label treatments are displayed on the left side of the figure.
0 10 20 30
Healthy
MM
Drug Response (DSS)
Pomalidomide
899
1862
0 10 20 30 40
Healthy
MM
Drug Response (DSS)
Dexamethasone
899
1862
02/01
/12
22/05
/12
10/10
/12
28/02
/13
19/07
/13
07/12
/13
27/04
/14
27/08
/14
06/10
/14
03/11
/14
22/12
/14
20/02
/15
17/04
/15
10/06
/15
06/08
/150
10
20
30
40
S-M
-Com
pone
nt (g
/L)
TD VD
DSRT
Pomalidomide +DexamethasoneASCT
Pomalidomide +Dexamethasone+
Cyclophosphamide
1862
17/09
/07
28/03
/08
12/05
/08
25/09
/08
24/03
/09
31/08
/10
10/12
/10
17/10
/11
12/12
/12
04/03
/14
14/08
/14
04/12
/14
26/02
/15
25/03
/15
22/04
/15
28/05
/15
23/07
/150
10
20
30
40
50
S-M
-Com
pone
nt (g
/L)
CTD VD
DSRT Death
Pomalidomide +DexamethasoneASCT RD RVDC
899
18/07
/13
24/07
/13
19/08
/13
09/09
/13
17/10
/13
13/11
/13
19/02
/14
19/06
/14
08/09
/14
08/10
/14
28/10
/14
13/11
/14
01/12
/14
16/12
/14
07/01
/15
19/01
/15
27/01
/15
10/02
/15
10/03
/15
13/04
/150
2000
4000
6000
S-lg
LCL
(mg/
L)
RVD G-CSFMelphalan
RVD
DSRT
DR-PACE
Death
LENBortezomib+ Temsirolimus
2757
12.9.
16
26.9.
16
3.10.1
6
17.10
.16
24.10
.16
9.11.1
6
23.11
.16
7.12.1
64.1
.17
16.1.
177.2
.17
13.3.
17
18.4.
17
24.4.
17
18.5.
17
20.6.
17
17.7.
17
28.7.
17
28.8.
174.9
.170
10
20
30
40
50
60
S-M
-Com
pone
nt (g
/L)
DSRT
VemurafenibVemurafenib + Trametinib
DR-PACE KRD Death
0 10 20 30 40
Healthy
MM
Drug Response (DSS)
Temsirolimus
2757_3
010
20
30
40
Healthy
MM
Drug Response (DSS)
Trametinib
5260
0 10 20 30
Healthy
MM
Drug Response (DSS)
Vemurafenib
5260
A
B
C
D
Patient 1862
Patient 899
Patient 2757
Patient 5250
61
Evaluating ex vivo drug responses, we noted a subset of myeloma patients bearing t(4;14) translocation showed good sensitivity to pomalidomide. Off note, cost of pomalidomide treatment was not reimbursed by the health care system in Finland before August 2018 and therefore only were administered in compassionate setting at treating physician’s discretion. Two lenalidomide refractory t(4;14) patients, who were sensitive to pomalidomide in ex vivo has subsequently been treated with a combination of dexamethasone. Pomalidomide was used at 4 mg/day on days 1-21 and dexamethasone 40 mg weekly of each 28-day cycle. The treatment resulted in a minimal response for 32 weeks with eight cycles for the first patient and partial response (PR) for 16 weeks with four cycles for the other patient (Figure 15A and 15B). Addition of oral cyclophosphamide 450 mg weekly to the second patient resulted in sustained PR after six cycles, 24 weeks. Importantly, both samples lacked ex vivo dexamethasone sensitivity, which suggests that a measurable benefit in reducing the disease burden in vivo could be due to pomalidomide treatment in those patients. Temsirolimus and everolimus are approved for the treatment of renal cell carcinoma206, 207. Ex vivo response to temsirolimus is observed in 21% (n=18/83) patient samples, which were primarily derived from patients in group I presenting an aggressive clinical course and treatment failure. Exceptional sensitivity for temsirolimus was observed for a t(11;14) harboring patient sample. As the patient was refractory to standard care treatment regimens, temsirolimus was combined with bortezomib. A safe dose of temsirolimus was determined based on prior evidences presented in a phase II study, which evaluated efficacy and safety of temsirolimus in RRMM208. The patient experienced an encouraging response with significant reduction of serum free light chain concentration from 1550 mg/L to 343 mg/L within two weeks. A PFS of 84 days was achieved before the patient eventually relapsed (Figure 15C). Comparison of ex vivo response in pre and post temsirolimus treated samples clearly revealed an acquisition of resistance in the later sample stressing the importance of functional assay to capture resistance to therapies in patients. Although mutation in BRAF gene is present in 10% (n=8) of patients, canonical V600E mutation was detected in two patients. Vemurafenib, a selective inhibitor effective against BRAF V600E variant, represents a viable therapeutic option for these patients. One of these patients with a clonal V600E mutant showed remarkable sensitivity to vemurafenib and trametinib ex vivo. Patient responded to vemurafenib as a single agent for six weeks and trametinib was combined to
62
treatment regimen, which could prolong the response for additional four weeks (Figure 15D). These results provide encouraging evidences that ex vivo response can be predictive of in vivo responses and can be useful as a functional decision-making tool to tailor therapies in patients.
5.3 Exploiting innate sensitivity of hematopoietic cells to achieve precision in therapies
5.3.1 Understanding differences in proteome profiles in hematopoietic cell subsets
To study the differences in protein expression patterns between healthy marrow-derived purified B (CD19), T (CD3) and monocytes (CD14), mass spectrometry based quantitative proteomics was utilized. We applied two analytical strategies to, i) characterize uniquely detected proteins and ii) compare relative abundances of commonly detected proteins. Analysis of whole cell lysates allowed detection of 1060 proteins. Expression of 163, 131 and 13 proteins were detected uniquely in CD3, CD14 and CD19 lysates, respectively. These proteins were associated with specific molecular functions and biological processes that are exhibited by these cell types (Figure 16A-C). Such as, monocytes exhibited proteins contributing to phagosome maturation and autophagy. Similarly, T cell specific proteins were involved in granzyme signaling and T cell receptor signaling. When commonly detected proteins were compared between mononuclear cells, differences were more pronounced between monocytes and lymphocyte. Several enzymes i.e. isocitrate dehydrogenase 1(IDH1), catalase (CAT), liver carboxylesterase 1(CES1) and glutathione peroxidase 1(GPX1) were expressed at a significantly higher level in CD14+ cells compared to B or T cells (Figure 16D). Monocytes also displayed higher expression of S100A8 and S100A9, which form anti-inflammatory protein calprotectin as a dimer. Role of S100A8/9 and CAT in dexamethasone resistance has been documented before in both solid and hematological malignancies209, 210. Comparing cell types derived from patients and healthy bone marrow samples revealed a similar expression pattern for the aforementioned proteins.
5.3.2 Basal signaling profiles of hematopoietic cell subsets
Next, we compared basal signaling activity in cell subsets using phosphospecific mass cytometry (CyTOF), which allowed a direct comparison between samples derived from multiple donors 211. Basal activities of 9 phosphoproteins involved in MAPK, JAK-STAT, NF-κB, and PI3K-AKT-mTOR signaling were
63
monitored in a cohort of 11 samples from patients admitted to the hematological ward at Haukeland University Hospital and Oslo University Hospital (Rikshospitalet).
Figure 16. Canonical pathways enriched for uniquely expressed molecules in healthy (A) CD 14+, (B) CD19+ and (C) CD3+ cells. The height of the bars indicates the significance of the overlap of the molecules in our dataset to the pathways in the Ingenuity pathway knowledge base. Significance values calculated based on the Fisher’s right tailed exact test and the -log(p-value) are displayed on the y-axis of the bar chart. (D) Proteins showing significantly higher expression in monocytes compared to other cell types. (E) Basal phosphorylation of six signaling proteins marked in the figure across the cell types.
Uniquly Enriched Proteins in CD14+ Cells
Uniquly Enriched Proteins in CD19+ Cells
Uniquly Enriched Proteins in CD3+ Cells
0.5
1.0
1.5
2.0
2.5
3.0
Granulocytes
CD14+/Monocytes
CD19+/B-cells
CD3+CD4+/ T Helper-cells
CD3+CD8+/ Cytotoxic T Cells
CD3-CD56+/NK cells
CD34+CD38+/Blasts
0 1 2 3
Healthy BM
Healthy PB
AML
BALL
pNF-κB p4E-BP1
0.0
0.5
1.0
1.5
2.0
2.5
pPLC-γ1
0.0
0.2
0.4
0.6
pAKT
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PB from healthy donors (3), acute myeloid leukemia (AML, n=6), B cell acute lymphoid leukemia (B-ALL, n=2) and matched BM samples from the same healthy donors were tested. Results are summarized in figure 16E. Although, comparable level of NF-κB phosphorylation (pNF-κB) was present in most of the healthy cell types, a higher pNF-κB activity was noted in CD3/T cell types. Higher p4E-BP1 and pPLC-γ1, indicative of mTOR signaling, was associated with CD34+CD38+, CD14+/monocytes, granulocytes and B cells. AKT phosphorylation followed similar trend in those cell types. Additionally, CD34+CD38+ displayed high level of ERK phosphorylation compared to other cell types. Elevated level of pSTAT3 was detected in monocytes and granulocytes, which were undetectable in B and NK cells. Notably, pSTAT3 level differed between CD3+CD4+ and CD3+CD8+ cells. Signaling events characterizing the healthy PB cell subsets was similar to those derived from healthy BM or corresponding leukemic cells with identical cell surface antigen expression (Figure 16E).
5.3.3 Hematopoietic cell populations show distinct drug response profiles associated to cellular lineages
To characterize diversity in cellular responses to a large set of oncology compounds (n=71), we tested their effect simultaneously on six hematopoietic cell subsets in peripheral blood mononuclear cells from three healthy donors. A multiplexed flow cytometer-based assay was utilized which was optimized during the study. The cell subsets analyzed in the primary screen included B/CD19+, natural killer (NK, CD56+), T helper cells (THC, CD3+CD4+), cytotoxic T lymphocytes (CTL, CD3+CD8+), natural killer T (NK-T, CD3+CD56+) and monocytes (CD14+). We performed an unsupervised hierarchical clustering with their drug response profiles, which is presented as drug sensitivity scores (DSS). Three major clusters were derived that was driven solely by their cell lineages (Figure 17A). Sensitivity to MEK/ERK inhibitors (trametinib, refametinib and SCH772994) and SRC inhibitor dasatinib was detected in monocyte, which formed a distinct cluster. Except for NK-T cells from one donor all T cell subsets showed similar drug response profiles and were grouped together. B and NK cells were segregated in a single cluster. A higher response to venetoclax, midostaurin and dexamethasone was detected in B and NK cells. Based on the findings from the initial screen on healthy blood samples we further tested six molecules in 16 additional samples, which were procured from ten
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Figure 17. (A) Unsupervised hierarchical clustering the drug sensitivity scores (DSS) of 71 small molecules identified lineage specific clusters for drug sensitivity. (B) Shows dose response curves for each cell types for the indexed
Samples
GSK269962Vemurafenib
PictilisibLinsitinibIdelalisib
DoxorubicinPlicamycinPimasertib
EnzastaurinTosedostat
IxazomibVinblastine
AlvespimycinPanobinostat
(+)JQ1Ruxolitinib
SGC−CBP30Dexamethasone
DactinomycinMidostaurin
GDC−0068SGI−1776
ONX−0914VenetoclaxNavitoclax
ClofarabineARRY−520
LenalidomideLestaurtinib
AGI−5198PS−1145Ibrutinib
TanespimycinNVP−AEW541
PHA 408Carfilzomib
BMS−754807PF−04691502
OmipalisibQuisinostat
APR−246Dasatinib
RefametinibCladribine
KX2−391PentostatinBortezomib
AZD8055Omacetaxine
QuizartinibWEHI−539
DoramapimodOprozomibRocilinostat
TemsirolimusAuranofinDactolisib
MethylprednisoloneBryostatin 1
TrametinibPomalidomide
TPCA−1SCH772984VGX−1027
DabrafenibGSK−1838705A
EPZ−5676GSK−J4
AlvocidibAZD2014
Daporinad
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drugs. Each dot in the dose response curve showing mean response for each concentration derived from analyzing 16 primary samples. myeloma, three AML and three healthy peripheral blood samples. The studied molecules included compounds that exhibited no cell selectivity (bortezomib and clofarabine) and those exhibited selective effect on a single or few cell types (dexamethasone, venetoclax, navitoclax and omipalisib).
Figure 18. Dose dependent effect of venetoclax on lymphocytic subpopulations The observed drug responses agreed with what was observed in our primary screen. However, a higher resolution allowed investigating drug responses in more rare cell populations and also dissect drug responses between closely related in hematopoietic hierarchies. Although, bortezomib were active against many of the tested cell types, CD138+CD38- plasma cells were found to be resistant compared to CD138+CD38+ and other cell types. CD34+CD38+ cells were more sensitive to clofarabine compared to CD34+CD38-/HSC cells. CD14+/monocytes displayed intrinsic resistance to dexamethasone. Additionally, dose dependent rise in monocyte count was noted. Both CD19+/B and CD56+/NK cells were sensitive to dexamethasone exposure. Venetoclax
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were found to affect primarily the lymphocyte lineages (Figure 17B). A dose dependent selectivity towards a range of cell populations was observed (Figure 18). B cells were sensitive to venetoclax with a median half maximal inhibitory concentration (IC50) of less than 1nM. Effect on CD3+CD4-/ cytotoxic T cells was recorded at approximately ten-fold higher concentration (IC50, 8 to 140 nM). Reduced activity on other lymphocytic cells (CD56+, CD3+CD4+ and CD3+CD56+) were detected (Figure 18) at a concentration range of 100nM to 1000nM). Both CD14+/monocyte and CD45+/SSC++/granulocytes displayed no response to venetoclax. The observed effect tended to be independent of whether samples were from healthy or malignant origin. Midostaurin displayed selective effect on healthy B and NK cells. To provide a comparison of B cell specific response to FLT3-ITD mutated AML samples and to investigate if the response could be retained in malignant B cells such as in chronic lymphoid leukemia (CLL), we tested midostaurin in additional two healthy BM, five FLT3-ITD mutated AML and three CLL patient samples. As expected, B cells from all tested samples were equally sensitive including three samples from CLL patients (Figure 19). FLT3-ITD positive CD34+CD38+/blast cells from five AML samples were sensitive (Figure 19). However, a lower IC50 was observed in B cells compared to CD34+CD38+ cells from FLT3-ITD mutated AML (314nM vs 554 nM). In comparison, one of the three AML samples with wild type FLT3-ITD showed sensitivity to midostaurin (Figure 19).
Figure 19. Dose response effect of midostaurin on CD34+CD38-, CD34+CD38+ and CD19+ cells. HC denotes healthy control samples. Importantly, many of the drug responses could be explained by the basal phosphorylation level of the signaling proteins. For instance, pSTAT3 level negatively correlated to ex vivo sensitivity to venetoclax. A higher pSTAT3 level was detected in monocyte or granulocyte was associated with resistance to venetoclax and sensitive cell populations (B and NK cells) had reduced pSTAT3. Similarly, mTOR activity measured by p4E-BP1 and pPLC-γ1 predicted varied
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68
sensitivity to PI3K-mTOR inhibitors across cell types. However, an increased sensitivity exhibited by monocytes could not be explained by baseline ERK1/2 phosphorylation.
5.3.4 Comparing innate sensitivity of cell subsets to malignant counterparts in hematological malignancies
We also compared the ex vivo response for six molecules in a cohort of 281 samples from hematological malignancies to evaluate whether cell specific innate drug sensitivities could be also observed in their malignant state (Figure 20).
Figure 20. Ex vivo drug responses presented as drug sensitivity score (DSS) of healthy cell types were compared to malignant counterparts in a cohort of 281 primary samples for bortezomib, clofarabine, dexamethasone, omipalisib, venetoclax and navitoclax. Samples included both published and unpublished datasets from CML212, 213 (sample, n=13), CMML (n=11)214, MDS (n=4), AML177, 214 (n=145), B-ALL215 (n=14), CLL214 (n=4), T-PLL216 (n=40), MM178 (n=50) and other hematologic malignancies (n=6). AML and MM samples were subdivided depending on whether they were derived from newly diagnosed (D) and relapsed (R) samples. Results show that response in cell-of-origin is reflected in the malignant counterparts.
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The analyses revealed expected trends reflecting the sensitivity observed in the healthy cell of origin. Broader sensitivity of bortezomib detected on multiple cell types was also visible by activity in most malignancies compared in the cohort. Samples from myelodysplastic syndrome (MDS), chronic myelomonocytic leukemia (CMML), and myeloma samples displayed reduced sensitivity to clofarabine. A higher sensitivity to clofarabine was registered in B cell malignancies (CLL and ALL). In comparison to T-ALL, T-PLL samples showed higher clofarabine sensitivity. Highest sensitivity to dexamethasone was detected in ALL and CLL samples. Disease specific acquisition of dexamethasone was noted in AML and T-PLL samples which were not observed in the healthy CD34+ or CD3+ cells. T cell malignancies, similar to healthy T cells, showed no response to omipalisib and revealed a reduced mTOR signaling activity (p4E-BP1 and pPLC-γ1). A higher response to venetoclax was detected in most malignancies tested with B cell phenotype. Venetoclax response agreed with navitoclax responses in diseases affecting B cells. An increased sensitivity to navitoclax compared to venetoclax was detected in CML, T-ALL and MM samples. B cell specific response to midostaurin was detected in CLL and ALL samples (Figure 20).
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6 DISCUSSION
6.1 Precision therapies for myeloma
Precision medicine holds the promise of selectively and efficiently eradicating malignant cells by targeting the mechanisms driving the key phenotype of a disease and addressing the individual variation in drug responses. However, the application of precision approaches critically depends on the availability of biomarkers that can identify a sensitive subset of patients by using predictive biomarkers. Remarkable advances in both sequencing technologies and cost-effectiveness has made the stride to make genomic analysis essential in clinical research217-221. Consequently, the recurrent mutations associated with the majority of hematological malignancies have been catalogued. Even so, matching approved therapies based on the presence of these mutations, which has been clinically validated, are available for less than 10% of cancer patients123. This is attributed to the complexity of the biological system and also our lack of understanding of the mechanistic relationship between cellular phenotype and rapidly evolving cancer phenotypes123, 159. It is clear that an alternative strategy is needed to identify cancer vulnerabilities suitable to individual patients and annotate the genomic impact on drug response by directly linking drug response profiles to molecular alterations. In this thesis, we present a precision medicine platform for multiple myeloma. In this approach, we utilized malignant plasma cells (CD138+) from myeloma patients to test their sensitivity to a large set of approved and investigational oncology drugs, which allowed us to identify new therapies for refractory patients with limited treatment options and also to tailor the timing and sequence for applying approved myeloma drugs. Drug sensitivity profiles were provided to the treating physicians within four days of sampling to enable an informed treatment decision and rapid translation. In addition, matched genomic, transcriptomic, and clinical data were compared to drug response profiles in a cohort of 100 myeloma samples to create a knowledge base for identifying prognostic and predictive biomarkers for disease progression, clinical outcome, and response to anticancer therapies. In contrast to the current paradigm of precision medicine where genomic information drives the selection of appropriate therapies, we exploited a chemosensitivity assay as complementary to genomic alterations to achieve a new level of precision. Furthermore, by tailoring individualized therapies in patients, we evaluated the benefits of our approach to improve treatment outcomes in patients.
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Although cell lines and animal models provide important biological information regarding individual mutations, it should be stressed that these models rarely recapitulate the molecular complexity existing in patients. Additionally, generating such preclinical models requires allocating resources and is time-consuming. Preserving the molecular landscape that modulates drug responses and cellular diversity is required to identify relevant gene-drug interactions that could be used as predictive biomarkers. The study utilized freshly processed patient-derived materials to be used in profiling to ensure the molecular snapshot was retained, which would provide a link between ex vivo response and small molecules. Previous studies at such scale and scope were primarily performed with cell lines197, 222, 223. Limited insights could be derived from cell lines regarding disease progression and patient heterogeneity. Furthermore, the utility of biomarkers identified from such data sets are not beyond criticism. The generation of a pharmacogenomic resource during the study for biomarker discovery is noteworthy. Such a unique resource would be highly valuable to the research community and may therefore facilitate drug discovery efforts. Additionally, linking drug response data and molecular profiling data to clinical profiles provides a unique opportunity to identify both predictive and prognostic biomarkers for myeloma. Several risk stratification models are available for routine use, including the Durie-Salmon staging system (DSS), the international staging system (ISS), and the revised international staging system (RISS). These prognostic factors and staging systems have been designed to predict the disease outcomes26. Molecular classification of myeloma has been attempted based on the gene expression profiles (GEP) 224-227. These studies defined eight224, later refined to seven152, GEP-based groups, which were strongly influenced by the presence of chromosomal translocations or ploidy30. As such, they act as a confirmation for the cytogenetic analysis to detect chromosomal abnormalities. The generation of these models usually involves using data from treatment-naive myeloma at diagnosis, and their importance in RRMM is unclear. However, a shared limitation of these models may be their limited ability to predict response to individual anticancer therapies. One important goal of this study was to design a strategy to stratify myeloma patients based on their chemosensitivity profiles, which is informative for predicting the cellular response to therapies at each stage of the disease and assessing dynamic changes in the therapeutic landscape at multiple time points in the disease. An analysis of the drug-response profile identified four sub entities of myeloma patients with distinct drug-response profiles, providing a framework for stratified medicine in myeloma. The
72
subgrouping enabled the identification of patients who exhibited cross-resistance to novel myeloma agents. Stark differences were observed between the subgroups for molecularly targeted therapies, which included inhibitors of IGF1R, PI3K, AKT, mTOR, MEK, CDK, and HSP90. The samples within these subgroups not only differed in their drug response profiles but also in their survival outcomes and were informative for predicting prognosis. Chemosensitivity subgroups may reflect clinical heterogeneity observed in patients in response to treatment. The clustering of patients in these subsets was independent of chromosomal alterations. Despite the fact that 90% of the samples in group I were derived from relapsed patients, overall, the heterogeneity could not only be explained by disease stage alone. Such variation in drug responses rather reflected their underlying differences in mutational and gene expression signatures. Samples from the most clinically progressive patients (group I) displayed a higher mutational load with characteristic alterations in genes participating in maintaining DNA integrity (TP53, ATM, ATR, MSH3). One might speculate that the higher mutation rate might stem due to compromised DNA repair in those cells. It may also be likely that such an unstable genome may pave ways to acquire DNA repair alteration, which may cause clonal fitness to evolve as described by Darwin. Regardless of the mechanism, alterations in these genes conferred a poor prognosis in myeloma patients, which has been similarly documented in other studies. Gene expression differences were observed between these subgroups, highlighting differences in cellular phenotypes. For instance, it was found that subgroup I patients had elevated expressions of genes for which gene products are involved in initiating DNA replication (MCM2-7, TOP2A, and PCNA), aberrant cell proliferation228. In addition, the expression of BMP4, NODAL, and SOX2, which contribute to promoting self-renewal and stemness229, 230, were also noted in this subgroup. In contrast to healthy plasma cells, which show a low proliferative index231, the expression of these molecules characterizes a highly proliferative disease152. Increased cell proliferation may be a result of the oncogenic activation of proliferative survival pathways, such as PI3K-AKT-mTOR signaling. Targeting this survival pathway with signaling inhibitors was highly active using a single agent and would likely provide a beneficial effect in combination. In contrast, samples displaying more resistance to ex vivo sensitivity to small molecules (groups III and IV) largely showed lower expressions of gene sets discovered in group I. Signature genes included gene encoding for integrin receptors (ITGA2, ITGA5, and ITGA9), actin, and adapter proteins (TLN1 and VCL) involved in relaying integrin signaling. The binding of individual integrins to a ligand
73
triggers a talin-mediated interaction between the cytoskeleton and the extracellular matrix. A gene expression signature corresponding to this subset pointed to an increased dependence on the bone marrow microenvironment and soluble factors supporting their growth and offering cytoprotection from therapies. Resistance to therapies is a major hurdle to overcome in myeloma, which may prevail at disease onset or may emerge during treatment. De novo resistance mechanisms in myeloma have been proposed to occur by cell adhesion–mediated drug resistance (CAM-DR) or by the secretion of soluble factors232 201, 233. CAM-DR is elicited by the interaction of malignant plasma cells to the extracellular matrix (ECM)234. Clonal plasma cells may express many cell adhesion molecules, which can mediate interaction with neighboring cells or with the ECM via counter receptors. One important mediator is the integrin-signaling pathway202, 235. The adhesion of integrin to fibronectin promotes myeloma cell survival and blocks apoptosis236. In our study, patients who exhibited resistant profiles (groups III and IV) in ex vivo screens were enriched for the expression of genes associated with ECM adhesion and interaction, which agrees with previous studies. Additionally, these patients also co-expressed the ABCB1 and ABCC3 genes, which encode for transporter proteins p-glycoprotein and MRP3, proteins that are notorious for mediating multidrug resistance. However, their expression was not limited to relapsed patients and was also detected in treatment-naive patients. The scenario with acquired drug resistance may be more complex, involving, but not limited to, an increase in clonal complexity, the activation of survival pathways, and acquired mutations in drug targets. Mutations in drug targets remain a key challenge in acquiring resistance to anticancer therapies. CRBN, the primary target for immunomodulatory agents, was found to be mutated in three lenalidomide-refractory patients. Although most of the patients treated with immunomodulatory agents eventually become refractory, mechanisms that mediate the resistance of these agents remain poorly understood and may vary between patients. Genetic alterations in CRBN are more common in relapsed and IMiD-treated patients (9%), which is less than described by Kortum et al. (12%) who evaluated only IMiD-pretreated patients. A T361I mutation resides close to IMiD-binding pocket, is likely to affect the integrity of the drug binding to CRBN, and conferred resistance to pomalidomide both ex vivo and in vivo. Although the S77R or P98R variants do not directly reside in the thalidomide-binding domain, they conferred IMiD resistance in patients. The mutations reside in the LON domain but not in DDB1 interacting
74
sites. They may have no direct effect on IMiD binding but rather can affect interaction with the DCAF-binding partners, which forms a CRL4 ligase complex. It might be the likely mechanism for mediating the resistance in those patients. Further studies are required to demonstrate the hypothesis. Sensitivity to several PI3K, AKT, and mTOR inhibitors in subset of patients is noteworthy. The PI3K-AKT-mTOR pathway is a central signaling cascade that promotes the growth and viability of myeloma and likewise in other cancers. The primary mechanism of activation in several cancers involves oncogenic mutations in PI3K subunits or loss-of-function mutations in PTEN, which acts as a negative regulator. Mutations in the PI3K isoforms PIK3CA, PIK3CB, and PIK3CD, which code for catalytic subunits p110α, p110β, and p110γ, were present in less than 5% of patients. Irrespective of oncogenic mutations, sensitivity to ATP-competitive, dual PI3K-mTOR or mTORC1/2 inhibitors was observed in 75% and 63% of patients, respectively. In contrast, inhibition of only mTORC1 by temsirolimus was effective in 22% of patients, suggesting that the dual inhibition of mTORC1 and 2 provides a more effective inhibition of the signaling cascade in myeloma. Interestingly, the response pattern of IGF1R, PI3K, and mTOR inhibitors were very similar and were detected in the same set of patients, suggesting that the activation of these molecules may be interdependent and therefore inhibiting downstream proteins such as mTOR may provide a similar benefit to blocking the upstream molecules, and more robust inhibition may be achieved if they are applied in combination. A lack of frequent mutations in the PI3K-mTOR pathway and its aberrant activation in a large portion of myeloma patients suggest activation may occur via multiple mechanisms. Efficacy in the TP53 mutated subset was particularly intriguing. Evidence exists supporting an intricate coordination between p53 and the PI3K-AKT-mTOR signaling axis, with p53 playing a role as repressor. However, the detailed mechanism is poorly understood. Similarly, FAM46C and mutations in other DNA-repair pathway genes corresponded to sensitivity and may act as a companion biomarker for IGF1R-PI3K-mTOR inhibitors. Unbiased functional screens identified several potential candidate drugs to be used for myeloma. In addition to chemotherapeutic agents, which are not used in myeloma, several kinase inhibitors showed preclinical activity in the chemosensitivity assay. This included midostaurin, dasatinib, and HSP90 and CDK inhibitors. HSP90 and CDK inhibitor response had been reported before in relapsed refractory MM. However, it should be noted that disease stage alone was not predictive of an ex vivo response to these agents. Rather, a subset of
75
relapsed patients (group I) were particularly sensitive to these drugs. In highly progressive patients who present with a higher mutation burden and response to multiple signaling inhibitors, exploiting a multitarget kinase inhibitor in combination could be a potentially attractive strategy. Although the cells during the drug sensitivity assessment were incubated in conditioned media that recreated soluble cytokine composition, the cells still lacked mechanical contact with the ECM, which can influence the observed drug responses in an ex vivo setting. Since only enriched plasma cells were used for the assay, the indirect cytotoxicity mediated by other immune cells could not be captured. While the sample size appeared to be enough to identify different subgroups, a larger cohort size would be beneficial for determining the impact of genetic alterations on drug responses, enabling the assessment of their contribution in larger frequency. It should also be stressed that significant associations to drug responses were made for genomic alterations present in more than 5% of patients. Considering an average myeloma patient can harbor more than hundred somatic alterations, the contribution of those minimally recurrent mutations remains unknown. The exponential number of alterations poses a significant challenge to functionally validating each individually. Analytical tools predicting their functional contribution to driver alterations needs to be developed. External validation of the discovered pharmacogenomic associations needs to be done. However, such a multi-assay dataset with tandem drug response data is rarely available for myeloma and continues to be a bottleneck for the transition of the gained knowledge into clinical practices. The application of precision treatment strategies in routine clinical practice is still in its infancy. While molecular profile–based prediction of the response to therapies is more exploratory, tailoring treatments using data from chemosensitivity assays in various forms have already been demonstrated to improve treatment outcome177, 178, 237. Comparing in vitro results with clinical outcome data has shown a predictive accuracy of 57%–83% for drug sensitivity and 90% for drug resistance238. Snider et al. has recently shown significant gain in PFS in patients with relapsed refractory hematological malignancies237. Similarly, IMPACT, a precision medicine trial from MD Andersson, showed a higher complete response rate (PFS and OS) was achieved in patients receiving precision therapies. In contrast, in the SHIVA239 study, patients did not show significant benefits (PFS) from molecular target–based selection of therapies. Although tested in a limited number of patients in our study, a reflection of ex vivo response agreed with clinical responses observed in patients for the tailored
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treatment. Even though several clinical anecdotes and results from clinical trials showed the potential benefits of precision medicine, it yet lacks wider acceptance among health care professionals. The lack of standardization and reproducibility of ex vivo assays for prediction is considered to be a large hindrance. Again, there is no consensus on the guidelines and requirements for carrying out such practices. The cost of setting up the infrastructure and overwhelming complexity of the large volume of data generated from high throughput platforms may further deter clinicians from making it a routine practice on top of their daily work schedules. As the knowledge base in this area is continuing to grow, we envisage precision medicine as a field will become integrated in routine patient care in near future and to be considered to be an integral part in drug development process.
6.2 Characterizing innate drug sensitivity to improve precision in therapies
After being administered, the drugs are distributed throughout the body and are exposed to a multitude of cell types that it comes into contact with. As a result, diverse cellular effects are anticipated from a single molecule. Assessing in vivo response or ex vivo effects on bulk cell content provides a measurement of the sum total of all cellular effects, and cell-specific responses are largely missed as a nature of assessment. The current treatment paradigm in hematological malignancies are rapidly moving toward utilizing more targeted therapies. Assessing the contribution of cellular effects from each cell type is critical to improve the precision by reducing the off-target effects of therapies and predicting imminent pharmacodynamic interactions. Although a comprehensive assessment on all cell types present in the body may provide a deeper understanding of such effects, the scope of the study was limited to characterize responses in hematopoietic cell types only. During the process of hematopoiesis, mature blood cells are generated from hematopoietic stem cells and progenitor cells. The cell fate decision is governed by the activation of distinct gene/protein expression modules that are necessary to carry out the diverse molecular functions associated with these cell types. Additionally, signaling heterogeneity between blood cell types are well appreciated and described to be tied with cellular lineages. A logical consequence would be that their innate sensitivity to treatment modalities would also differ due to inherent differences in cellular phenotype. Our results reveal that cell subtypes exhibit distinct drug-response patterns toward a diverse collection of anticancer agents and corresponded to their macromolecule
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abundance and signaling profiles. Therefore, this study provides a comprehensive portrait of the drug-sensitivity landscape in hematological cell subsets and reveals drug responses that are tied to cell lineages. By comparing drug responses between healthy and malignant hematopoietic cells, we found that the cell-specific sensitivity and resistance mechanisms were clearly reflected in their malignant state. Much of the signaling activity was detected similarly between healthy cells of origin and malignant cells. For instance, mTOR signaling, as measured by p4E-BP1 and pPLC-γ1, was significantly higher in CD34+CD38+ cells and corresponding CD34+CD38+/blast cells from AML. Likewise, pERK1 signaling showed the same trend. The identification of such shared signaling nodes highlight the fact that the core cellular state of the cell of origin might be preserved during its malignant state and may provide a vulnerability to be exploited. Signaling behavior was often correlated with drug response variation between cell types. The basal state of pSTAT3 could explain the diversity of venetoclax responses observed between healthy cell populations and even subtle differences observed between CD3+CD4+ and CD3+CD8+ cells. Cell-specific drug responses were informative in identifying new disease indications to repurpose drugs. The B cell–specific effect of venetoclax is likely a response mechanism in CLL and also in other B cell malignancies (i.e., Hodgkin’s lymphoma). It could be also exploited in conditions where the depletion of B cells is considered to be beneficial to alleviate disease symptoms. Midostaurin, which is approved for the treatment of FLT3-mutated AML and systemic mastocytosis240, 241, showed efficacy toward healthy B cells and subsequently in CLL and ALL samples. The preclinical efficacy detected in this study suggests potential clinical benefit in these diseases. In our study, few inhibitors were selective for a single cell type and rather targeted multiple cell types, often in a dose-dependent manner. Such a dose-dependent selectivity for cell types is an important consideration in elderly patients with poor metabolic activity. Drug retention can alter the expected pharmacological effects and may result in affecting other cell types to give undesired effects. Cancer immunotherapies and immunomodulatory drugs are increasingly being adopted for the treatment of hematological and solid tumors66, 242. Preserving immune effector cells are crucial for their efficacy and achieving durable clinical benefit. For example, dexamethasone and midostaurin depleted CD19+/B and CD56+/NK cells. Similarly, venetoclax depleted CD3+CD4-/cytotoxic T cells,
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among other cell types. The depletion of immune effector cells, particularly cytotoxic T cells and NK cells, are relevant due to their key role in cancer immunosurveillance and immunotherapy243 that executes cytotoxic cellular responses. The simultaneous administration of immune therapies and those drugs that negatively impact the immune system can potentially compromise the efficacy of immunotherapies. Therefore, monitoring the effect of drug molecules on immune cells would be beneficial at preclinical drug development stages to reveal potential drug interactions. In summary, the findings presented here indicate that the identification of intrinsic drug responses in hematological cell lineages could serve as an invaluable tool to reveal the full spectrum of cellular effects, and to predict the off-target effects of small molecules. In addition, the assessment of cell lineage–specific drug responses in preclinical drug development holds great promise to identify drug reposition opportunities and improve precision in therapies for leukemia.
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7 CONCLUSIONS AND FUTURE PERSPECTIVE
We developed a precision medicine platform for multiple myeloma that enabled us to stratify myeloma patients in four therapeutic subgroups. Chemosensitivity subgroups showed distinct drug response profiles and genomic alteration patterns and were informative for predicting prognosis. Functional classification will be highly valuable to preselect patient subgroups to conduct informed clinical trials and individualize treatment options for individual patients. By linking the clinical profiles of the patients with molecular features, prognostic markers were identified. Alteration in DNA repair genes were mutually exclusive and associated with clinically aggressive disease. Leveraging a multi-assay dataset and integrative analysis, mechanistic biomarkers for individual drugs were identified which could be used to select patients likely to respond from treatment. In addition, the mechanism of de novo and acquired resistance to antimyeloma drugs are detailed. Unbiased screening of a large set of oncology compounds identified drug candidates to be repurposed and guided the off-label use of approved molecules. Ex vivo drug responses can be rapidly implemented in patients within four days and potentially be useful for deciding the timing and sequence of approved myeloma treatments. Furthermore, the generated multi-omics dataset will serve as an invaluable resource for drug discovery and development efforts in myeloma. By simultaneously monitoring drug responses in hematological cell subsets, lineage-specific drug responses were discovered that often corresponded to basal signaling activity and protein expression in those cell types. Drug responses tied to the cell of origin was mirrored in its malignant state. These complementary approaches together provide an understanding of drug responses with unprecedented resolution, identify individualized treatment options, detect drug-repositioning opportunities, and improve precision in therapies for myeloma and other hematological malignancies. Since most patients receive therapies in combination with other treatment regimens, in the future, it will be necessary to investigate strategies to predict individualized combinations of treatments based on the patient’s genetic makeup. The current functional assay platform needs to be developed to such an extent that it enables assessing the contribution of the microenvironment and neighboring cells to more realistically recapitulate the native microenvironment. A path forward from here leads to the validation of the predicted biomarkers and gene drug interactions retrospectively in a larger cohort of patients and ultimately, to the design of a prospective, biomarker-driven umbrella trial that is complemented with ex vivo drug responses. Such an evaluation will assess the clinical reality of the promises offered by precision medicine and accelerate its integration in routine clinical practices.
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ACKNOWLEDGEMENTS
This study was carried out at the Institute for Molecular Medicine Finland, FIMM. It has always amazed me how all the wonderful people coincidently gathered and made it what I call ‘FIMMLand’, an identity of its own. It was a great privilege to be recruited as the first FIMM/EMBL doctoral candidate which allowed me to take part in research rotation in three individual groups. I truly believe the experiences I gathered during the rotation program and people I met, truly helped me to mature scientifically, more importantly to build a strong network before I embarked my long journey as a PhD student. I sincerely thank Doctoral Programme in Biomedicine (DPBM) and Celgene for providing financial support for the study. I owe my deepest gratitude to my supervisors, Dr Caroline Heckman and Professor Krister Wennerberg, for sharing their knowledge, experience and enthusiasm. Caroline, I still miss the early days in lab when you were working side by side. I have always admired that you kept your door open and encouraged me to chase all my wild ideas. Your trust and support had been the key for me to become an independent scientist. Your role in handling the challenges I faced during arranging my public defense was absolutely fantastic! Krister, I thank you for infecting me with your scientific skepticism and teaching how to be critical about my results. You had been an amazing mentor with good sense of humor, always imaginative and pointing at the right scientific directions. Although it was a quick divorce from your group, I have always considered myself a part your team. I am really grateful for your support in and out of the lab. Professor Satu Mustjoki and Docent Outi Monni are kindly acknowledged for being my thesis committee members during the past years. Adjunct professor Eeva Marjaana Säily and Assistant Professor Evren Alici are warmly thanked for reviewing my thesis and providing their constructive comments. Marjaana, you are such a wonderful soul and full of fun. I promise not tell anyone but everyone what happened in Delhi with your taxi rideJ. I would like to warmly thank Dr. Gretchen Repasky. Words are few to describe your role behind this book and dragging me to this far end of my PhD. Without you I would have been still struggling right now. Thanks for keeping me in track and guiding me through the end of the PhD tunnel. You have been a constant source of inspirations for all the PhD students and importantly to us ‘the FIMM/EMBL PhD student clan’. I feel privileged to work with Professor Kimmo Porkka, Professor Olli Kallioniemi and Professor Jonathan Knowles who gave us a vision and leadership to build the personalized medicine platform
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as it is now. Your vast knowledge and inspiration were indispensable for my projects as is true for many other students. Thanks to all the PIs and team members of PM project to make it a reality. I owe a special thanks to Jonathan for all the encouragements, creating a positive energy field around you and being my lighthouse. I would like to acknowledge all my coauthors and collaborators for your valuable contributions. Your help has been absolutely essential to the projects. This book would have been empty pages without kind contributions of the patients and some of the finest doctors who had given extra effort on top of their super busy schedules at work to provide research materials. I would like to thank Pekka Anttilla, MD for providing most of the myeloma samples and sincerely trying to translate the research findings to patient care. I really admire your enthusiasm professional integrity and your dedication for providing the best care for your patients. I would also like the thank the super lady Dr. Raija Silvennoinen who I envy the most. I still have to ask your secret recipe for the eternal source of energy, enthusiasm and indomitable motivation. You have not only been my closest collaborator rather I found a friend for lifetime. I would like to thank Juha Lievonen for your critical contribution by providing the clinical data. Although you did not continue with your PhD I truly believe that you are a gifted scientist. I warmly thank Sir Mika Kontro for great ideas and as a key collaborator for many projects. You proved the saying wrong ‘An apple per day keeps the doctors away’. It was really great to have you at our office. Professor Samuel Kaski and Assistant Professor Pekka Marttinen are thanked for their help with machine learning methods and providing critical comments on the manuscript. Although it had been a lengthy collaboration, I learned a lot from both of you and enjoyed all the discussions we had. My sincere thanks to Professor Peter O Gorman and Dr Paul Dowling, Dr Jing Tang for extending their help with proteomic analyses. I am especially grateful to David, Ammad, Samuli, Monica, Alina and my trusted hand Maija for playing key roles for all my papers. Maija, I once told your mom that I adopted you as my little sister and will remain like that forever. Looking forward to see you grow as a great scientist. My gratitude toward current and former drug dealers (Anna, Laura, Jani, Tintti, Katja, Karkki, Evgeny and Maria) of Don Krister Corleone cartel at HTB for supporting my addiction for the drug plates and aliquots. JP, Dima and Swapnil are thanked for setting up and helping with the Breeze platform. I wish to thank present and former members of the Heckman, Wennerberg and neighbor groups for your company and active support during my PhD life in and
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outside the lab. Alun, Minna and Siv for processing the samples to support all the personalized medicine projects. Thanks, Siv! for tolerating my bad Swedish and for a great friendship. Alun although we had not got a chance to chat for a while it was always fun to go out with you. Jarno, Heikki, Vesa and Dimitrios for being partners in crime at office. Heikki you are passionate about science and very helpful to all of us. You deserve the best!! I would like to extend my gratitude to Susanna Rosas. Our Finnish Mom who had literally taken care of me and many others when I arrived in Helsinki. I would like to thank all my friends who had shared the good, bad and ugly days of my PhD life. Thanks to my Friday after work mates: Arjan, Anna, Manu, Jenny, Susanne, Mia, Rita, Riikka, Tea, Daivi, Yuxei, Kamila, Martynas, Aga, Kallina, Jannick, Montse, Rickard, Luca, Anna, Michi and many others for sharing the fun times of my PhD. You guys are simply awesome. Thanks to Elsa for being such a good and friend and all the stupidJ therapeutic nagging we did together. It certainly helped both of us to get a good night’s sleep. Before having my family, the two people I had spent almost every day of my first two years life in Helsinki are Tea and Riikka. We have shared our ups and downs together. I still miss our Friday dinners in Habibi and all those evenings when we walked almost the whole Helsinki and still could not decide where to eatJ. We need to put our heads together more often. I had spent memorable moments with my coffee break partners Tiny, Anja, Vanessa among others. I like to thanks Rita, Justina and Anibal De Nero for adopting me as your family. Ele, you are my best friend since Stockholm days. Thanks for always being there. Ashwini, Disha, Vishal and Shweta you had been amazing friends for the whole time. Prson and Alok for being a real friend up in the Norwegian mountain when I had a panic attack. You still owe me a big thanks for a free helicopter ride over the fjords and should delete those fake picturesJ. I would like to thank my fellow FIMM/EMBL PhD students Poojitha, Himanshu, Meri, Anna, Alok, Sarang, Janicka, Ella, Bala, Alina, Dima, Pu, Shabbeer and other new members for sharing all the amazing trips to Heidelberg. Thanks to all my pre-PhD life friends with whom I spent the happiest part of my life. I would like to mention Sabuz, Joy, Fahim, Azim, Chutty, Shipon, Sujon, Togor, Tripty, Nitu, Nily, Uzzal, Tuhin, Achyut, Jami, Dolon, and many more for being best buddies and shaping me who I am today. You are the reason why I started this journey. I know that you are still watching over me every single day. Thanks for being the greatest dad and the finest man I know. I wish you were here. My deepest gratitude goes to my mom, brother,
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sister and to all the little chipmunks - Aquif, Rafid, Rayn, Tasnim, Ramin, Rafa, Raiyat, Ridha for their continuous love, support and never-ending belief in me. I owe special thanks to my Khala and Khalu, who have been always there behind all my educational endeavors and inspired me towards the path of being a scientist. Tisha and Mamun vai, you had been the surrogate parents for my kids and have become part of our family over the years. I want to thank my lovely wife Diana for supporting me throughout the whole time with incredible patience. It is not easy to be a scientist’s wife who are notorious for their poor mental health and high level of stress or simply called ‘The Nerds’. You had shared all my everyday struggles without even getting a PhD. Your love and dedication, super power to keep me sane and your great sense of humor simply makes you the best partner I could ever wish for! Last but not the least, I want to thank my two little angels Vincent and Alyssa, who with their magical power of silliness have filled my life with joy and happiness. You are indeed the best thing that happened to my life!! Helsinki, 2018 Muntasir Mamun Majumder
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218. Kim TM, Lee SH, Chung YJ. Clinical applications of next-generation sequencing in colorectal cancers. World J Gastroenterol 2013 Oct 28; 19(40): 6784-6793.
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Improving Precision in Therapies for Hematological Malignancies
MUNTASIR MAMUN MAJUMDER
dissertationes scholae doctoralis ad sanitatem investigandam universitatis helsinkiensis 66/2018
66/2018
Helsinki 2018 ISSN 2342-3161 ISBN 978-951-51-4552-9
MU
NTA
SIR M
AM
UN
MA
JUM
DE
R Im
proving P
recision in
Therapies for Hem
atological Malign
ancies
Recent Publications in this Series
45/2018 Jonni HirvonenSystems-Level Neural Mechanisms of Conscious Perception in Health and Schizophrenia46/2018 Panu LuukkonenHeterogeneity of Non-Alcoholic Fatty Liver Disease – Genetic and Nutritional Modulation of Hepatic Lipid Metabolism47/2018 Henriikka KentalaORP2 – A Sterol Sensor Controlling Hepatocellular Bioenergetics and Actin Cytoskeletal Functions48/2018 Liisa PelttariGenetics of Breast and Ovarian Cancer Predisposition with a Focus on RAD51C and RAD51D Genes49/2018 Juha GogulskiPrefrontal Control of the Tactile Sense50/2018 Riku TurkkiComputer Vision for Tissue Characterization and Outcome Prediction in Cancer51/2018 Khalid SaeedFunctional Testing of Urological Cancer Models by RNAi and Drug Libraries52/2018 Johanna I. KiiskiFANCM Mutations in Breast Cancer Risk and Survival 53/2018 Jere WeltnerNovel Approaches for Pluripotent Reprogramming54/2018 Diego Balboa AlonsoHuman Pluripotent Stem Cells and CRISPR-Cas9 Genome Editing to Model Diabetes55/2018 Pauli PöyhönenCardiovascular Magnetic Resonance Evaluation and Risk Stratification of Myocardial Diseases56/2018 Pyry N. SipiläDissecting Epidemiological Associations in Alcohol Drinking and Anorexia Nervosa57/2018 Elisa LahtelaGenetic Variants Predisposing to Prognosis in Pulmonary Sarcoidosis 58/2018 Ilari SireniusLääkkeisiin ja lääkkeeen kaltaisiin tuotteisiin liittyvät toiveet ja illuusiot – psykodynaaminen näkökulma59/2018 Nuno NobreQuality of Life of People Living with HIV/AIDS in Finland60/2018 Pedro Miguel Barroso InácioThe Value of Patient Reporting of Adverse Drug Reactions to Pharmacovigilance Systems61/2018 Taru A. MuranenGenetic Modifiers of CHEK2-Associated and Familial Breast Cancer 62/2018 Leena Seppä-LassilaAcute Phase Proteins in Healthy and Sick Dairy and Beef Calves and Their Association with Growth63/2018 Pekka VartiainenHealth-Related Quality of Life in Patients with Chronic Pain64/2018 Emilia GalliDevelopment of Analytical Tools for the Quantification of MANF and CDNF in Disease and Therapy65/2018 Tommi AnttonenResponses of Auditory Supporting Cells to Hair Cell Damage and Death: Cellular Stress Signalling and Epithelial Repair
INSTITUTE FOR MOLECULAR MEDICINE FINLANDFACULTY OF BIOLOGICAL AND ENVIRONMENTAL SCIENCESDOCTORAL PROGRAMME IN BIOMEDICINEUNIVERSITY OF HELSINKI
Group I
Genes
5163
_347
12_3
973_
265
6_4
2143
_152
60_3
3001
_388
2_3
911_
557
2_6
3001
_15
4398
_320
97_3
4708
_313
80_1
123
83_7
2383
_335
14_3
982_
331
77_3
861_
330
01_1
230
01_9
4691
_329
79_6
899_
6
Group II
899_
1213
80_5
3595
_334
97_3
3647
_335
86_3
3129
_239
01_3
921_
322
49_2
2235
_263
62_3
6348
_364
25_3
911_
291
1_7
2979
_234
34_3
4692
_343
50_3
4346
_381
0_3
4349
_322
35_6
917_
227
00_2
Group III
6463
_363
50_3
920_
228
55_2
1878
_393
3_3
2777
_213
54_3
1151
_315
79_3
4637
_326
84_3
4787
_338
86_3
4011
_3
Group IV
MCM2MCM10FEN1PCNATOP2ABIRC5BUB1BEZH2PRC1CCNB1KIF14UBE2CTYMSRRM2TTKSOX2NODALBMP4NTRK1NTRK3PRKCBPRKG1ITGA1ITGA2ITGA5ITGA9ACTN1PARVGVCLIL1BCCL5ABCC3ABCB1
4369
_347
05_3
4317
_327
00_5
584_
343
65_3
1193
_337
67_3
Hea
lthy
and
Patie
nt S
ampl
es Patie
nt S
tratif
icat
ion
Pred
ictiv
e Bi
omar
ker
Indi
vidu
aliz
ed T
reat
men
tR
apid
Clin
ical
Tra
nsla
tion
A G A C T A T A T G C A G C T C G T A C T A C T G A A T A
Healthy PB and BM
Multiple Myeloma
AML, ALL, Others
0 nM
0.1 nM 1 n
M10
nM10
0 nM
1000
nM
0
20
40
60
80
100
pErk p4E-BP1 pSTAT3
Granulocytes
B-cells
Monocytes
CD8 T-cells
CD4 T-cells
CD25 T-cells
NK cells
pDCs
Blasts/CD34+ CD38+
Venetoclax
CD34CD14
MDSCMML
D-CML
R-CML
D-AML
R-AML
CD19D-C
LLR-C
LLD-A
LLR-A
LLB-PLL
CD3T-PLL
T-ALL
CD138
D-MM
R-MM
0
10
20
30
40
DSS
Myeloid Lineage Lymphoid Lineage
0 nM
0.1 nM 1 n
M10
nM10
0 nM
1000
nM
0
20
40
60
80
100
0 nM
0.1 nM 1 n
M10
nM10
0 nM
1000
nM
0
20
40
60
80
100
Simultaneous Monitoring of Drug Responses in 11 Hematopoeitic Cell Populations
Signalome and Proteome Profiling of Blood Cells
Line
age
Spec
ific
Dru
g R
espo
nse
Cel
lula
r Phe
noty
peO
ff Ta
rget
Effe
ctD
rug
Rep
ositi
on
Healthy BM
Multiple Myeloma
+ SEPT6+ SEPT9+ YY1+ CD79B + PCM1 + EZR + KLC1 + TFG + MAML2 + DICER1 + SBDS + CCND3 + NCOR1 + TRIP11 + MALT1 + TRAF3 + BCOR + TGFBR2 + TET1 + GOLGA5
+ CCND1+ HSP90AA1
+ BCR + KIAA1524+ SETD2
+ TFRC + AXIN2+ CHEK2
- BRD4
- PTPRK
- CRTC3
- NCOA2
- LPP - S
TAT3- A
GTRAP
- WHSC1L1
- GMPS
- CDKN1B
- MLF
1- B
CL11A
- ABI1
- ERC1
- ARID
2
- CCND2
- SORBS2
- BCL9
- TET
2
- KIA
A154
9
- ATR - FOXO
3
- PIK
3CA
- LRI
G3
- GPH
N
- ETV
1
- SET
BP1
- HEY
1
+ SM
ARCB
1
+ SD
HA
+ EW
SR1
- ESR
P1
- MAP
3K1
- ESR
1- d
el.1
7p.
- X1q
.gai
n
- del
.14q
.
+ t.1
1.14
.+
t.14.
16.
+ DI
S3- K
DM5A
- NCO
R1+
NRAS
+ NS
D1+
TSC2
+ PA
X7+
MAX
+ SS
X1- K
RAS
- CBL
- TRI
M33
- RAF
1+
TP53
+ AR
+ FA
M46
C+
PTPN
13+
RHO
H+
PRDM
1+
TPM
4+
BCL2
+ NF
KBIE
+ BR
CA2
+ ER
BB4
+ RB
1+
NDRG
1+
CHIC
2+
AKT1
+ CA
RS+
ACTN
4+
SOX2
+ NO
TCH1
+ NA
B2+
RNF2
13+
SFPQ
+ AC
SL3
+ SR
SF2
+ COL1
A2
+ NFE
2L2
+ POU2A
F1
+ TFP
T+ R
AC1+ W
AS
- FANCF
- FLI1
- SEPT5
- NOTCH2
- CXCR4
- MLLT4
- FLT3
- MET
- TOP3A
- GNAQ
- FANCG
- NF1
- NACAP1
- CBLB
- PTPRC
- NCOA4
- BCL11B
- PRF1
- SPOP
- REL- MLLT10
- HERPUD1
- STAT5B
- MYCN
- FNBP1
- VCL- Monosomy.13
+ t.4.14.
+ X1q.gain
+ NF1+ UBR5+ BRAF- RPL5- FRYL- FOXA1- MAML2- KAT6A- XPO1- NRAS
Linsitinib (IGF1R)
Methylprednisolone (Glucocorticoid)
Pimasertib (M
EK1/2)
Refameti
nib (M
EK1/2)
Tram
etin
ib (M
EK1/
2)
Vene
tocla
x (B
CL-2
)
Borte
zom
ib (P
rote
asom
e)
Carfi
lzom
ib (P
rote
asom
e)
Dactin
omyc
in (R
NA and D
NA synth
esis)
Omipalisib (P
I3K/mTOR) Panobinostat (HDAC)
PF.04691502 (PI3K/mTOR)
1G M C
2G M C
Compare Drug Responses in Healthy and Malignant Cells
Precision Therapy
Luminespib
Tanespimycin
Alvespimycin
Idarubicin
Doxorubicin
Panobinostat
Vorinostat
Quisinostat
Trametinib
Pimasertib
Refametinib
Bortezomib
Ixazomib
Carfilzomib
Alvocidib
Dinaciclib
AZD2014
AZD8055
Pictilisib
Omipalisib
Bryostatin 1
Venetoclax
Navitoclax
Methylprednisolone
Dexamethasone
Pomalidomide
Lenalidomide
0 20sDSS
Color Key
Immunomodulators
BCL2 Inhibitors
CDK Inhibitors
Proteasome Inhibitors
MEK Inhibitors
HDAC Inhibitors
Anthracyclines
PKC Modulator
Group -IV Group -III Group -II Healthy Group -I
Pat
ient
Drug
Ex vivo Drug Response Somatic Mutation
Gene Expression Integrative Analysis
MultiplexedFlow Cytometry