highlights in the management of breast cancer roma, 10 maggio 2013 molecular tools for decision...

Highlights in the Management of Breast CancerRoma, 10 Maggio 2013

Molecular tools for decision making in adjuvant therapy

Enrico Ricevuto & Valentina Cocciolone Oncologia Medica

Ospedale San Salvatore Università degli Studi di L’Aquila

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Adjuvant Breast Cancer TreatmentKey issues Patients selection according to biomarkers

Selection of appropriate treatment

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Adjuvant Breast Cancer TreatmentMolecular Tools for decision-making Biomarkers

ER/PR HER2

Topoisomerase II BRCA1/BRCA2 KI67

Multi-Genes expression profiles (GEPs) Circulating Tumoral Cells (CTCs)

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BC Prevalence according to ER/PR and HER2

ER/PR HER2

Total +++ Negative

Negative 35-40 15-20 15-20

Positive 60-65 10-15 50-55

Total 100 30 70

5

Hormone receptor status: prognostic potential

• Patients with ER-positive/PgR-positive and ER-positive/PgR-negative BC had significantly better prognoses than patients with ER-negative/PgR-negative disease.

• Patients with ER-positive/PgR-negative tumors tended to have slightly worse disease-free and overall survival than patients with ER-positive/PgR-positive tumors, but the differences did not achieve statistical significance (P .05)

Bordou et al. J Clin Oncol 2003; 21:1973-1979

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Hormone receptor status: predictive potential• multivariate analyses confirmed that both ER and PgR are independent significant predictors of DFS and OS among patients who received adjuvant endocrine therapy;

• the reduction in RR of recurrence was 53% for ER-positive/PgR-positive patients and 25% for ER-positive/PgR-negative patients (P .0001);

• patients whose tumors are positive for both receptors have the greatest reduction of RR of death compared with patients whose tumors are ER-negative and PgR-negative.

Bordou et al. J Clin Oncol 2003; 21:1973-1979

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Her2 status: prognostic potential

Pritchard et al. N Engl J Med 2006;354:2103-11

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Her2 status: predictive potential

Pritchard et al. N Engl J Med 2006;354:2103-11

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BC Prevalence according to ER/PR and HER2

ER/PR HER2

Total +++ Negative

Negative 35-40 15-20 15-20

Positive 60-65 10-15 50-55

Total 100 30 70

10

Topoisomerase II and responsiveness to adjuvant Anthracyclines

O’Malley et al. J Natl Cancer Inst 2009;101:644-650

CEF

CMF

CEFCMF

amplified or deleted TOPO2A

normal TOPO2A

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amplified or deleted TOPO2A

normal TOPO2A


CEF

CEF

CMF

CMF

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TOPO2A HR 0.53 for RFS (p 0.09) HR 0.38 for OS (p 0.02)


HER2 HR 0.40 for RFS (p 0.008) HR 0.44 for OS (p 0.02)

Adjusted test for interaction:


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Slamon et al. N Engl J Med 2011;365-1273-83

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BRCA1-ness in TNBC

ER/PR HER2

Total +++ Negative

Negative 35-40 15-20 BRCA1+ 11%“BRCA1-ness”>50%

Positive 60-65 10-15 50-55

Total 100 30 70

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BRCA1, BRCA2 predisposition carriersBreast Cancer prognosis

BRCA1+ BRCA2+ Non carriers

Pts 93 71 1550Risk recurrence (CI) 1.19 (.74-1.89) 1.63 (1.02-2.60)

p .47 .04Risk death (CI) 1.43 (.91-2.23) 1.81 (1.15-2.86)

p .12 .01

Goodwin et al, JCO’12: 30; 19-26

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Ki67: prognostic role in EBC

de Azambuja et al. Br J Cancer 2007;96:1504-13

Despite some limitations, this meta-analysis supports the

prognostic role of Ki-67 in early BC,

by showing a significant association between its

expression and the risk of

recurrence and death in all populations

considered and for both

outcomes, DFS and OS.

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Two different gene sets: first, a set of 476 cDNA

clones previously selected to reflect intrinsic

properties of the tumors and, second, a gene set

that highly correlated with patient outcome.

Breast Cancer Genomics and Clinical Classification

Sørlie et al. Proc Natl Acad Sci USA 2001;98:10869-74

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Gene expression profiling predicts clinical outcome of breast cancer

Van de Vijver et al. N Engl J Med 2002;347:1999-2009

Panel A shows the pattern of expression of the 70 marker genes in a series of 295 consecutive patients with breast carcinomas. Each row

represents the prognostic profile of the 70 marker genes for one tumor, and each column represents the relative level of expression of one gene. The tumors are numbered from 1 to 295 on the y axis,

and the genes are numbered from 1 to 70 on the x axis. Red indicates a high level of expression of messenger RNA (mRNA) in the

tumor, as compared with the reference level of mRNA, and green indicates a low level of expression. The dotted line is the determined threshold between a good-prognosis signature and a poor-prognosis

signature.

Panel B shows the time in years to distant metastases as a first event for those in whom this occurred, and the total duration of

follow-up for all other patients.

Panel C shows the lymph-node status (blue marks indicate lymph-node–positive disease, and white lymph-node–negative disease), the

number of patients with distant metastases as a first event (blue marks), and the number of patients who died (blue marks).

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Gene-expression signature is a predictor of survival in breast cancer

ASSOCIATION BETWEEN CLINICAL CHARACTERISTICS

AND THE PROGNOSIS SIGNATURE:

the prognosis profile was significantly associated with:

- the histologic grade of the tumor (P<0.001);- the estrogen-receptor status (P<0.001);

-age (P<0.001)

but not with:- the diameter of the tumor;

- the extent of vascular invasion;- the number of positive lymph nodes

- treatment


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Gene-expression signature is a predictor of DFS and OS in breast cancer


Among the overall population:

HR for distant metastases as a firstevent was 5.1

(95% confidence interval, 2.9 to 9.0; P<0.001);

HR for overall survival was 8.6(95 %confidence interval, 4 to 19;

P<0.001).

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The St. Gallen and NIH criteria classify patients as at low risk or high risk on the basis of various histologic and clinical characteristics.

This comparison shows that the prognosis profile assigned many more patients with lymph-node–negative disease to the low-risk (good-prognosis signature) group than did the traditional methods (40 percent, as compared with 15 percent according to the St. Gallen criteria and 7 percent according to the NIH criteria).

Comparison with St. Gallen criteria and NIH Consensus Criteria


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Histologic grade

Simpson et al. J Clin Oncol 2000; 18:2059-2069

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Molecular basis of histologic grade

Sotiriou et al. J Natl Cancer Inst 2006; 98:262-272

Most genes are overexpressed in grade 3 tumors (high expression is RED) and have functions

that have been previously associated with cell cycle

progression and proliferation.

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Oncotype DX 21 Gene Recurrence Score (RS) Assay For ER-positive patients

Category RS (0 – 100)Low risk RS < 18

Intermediate risk

RS ≥ 18 and < 31

High risk RS ≥ 31

N Events 117 13 47 18

Paik S, et al. Breast Cancer Res Treat 88 (S1):A24, 2004

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Validation in tamoxifen-treated patients with node-negative, ER–positive breast cancer (NSABP-B14)

Paik et al. N Engl J Med 2004;351:2817-26

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Validation in tamoxifen-treated patients with node-negative, ER–positive breast cancer


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Percentage of patients on tamoxifen with larger N0 tumors

free of recurrence at 10 years varies by Recurrence Score in

NSABP B14

Percentage of patients on tamoxifen with moderately/poorly

differentiated tumors free of recurrence at 10 years varies by Recurrence Score in NSABP B14

Validation in tamoxifen-treated patients with node-negative, ER–positive breast cancer


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Prognostic disease-free survival and overall survival analyses by Recurrence Score group in patients treated with TAMOXIFEN ALONE

Validation in tamoxifen-treated patients with node-positive, ER–positive breast cancer

Albain et al. Lancet Oncol 2010; 11:55–65

30Dowsett et al. J Clin Oncol 2010; 28:1829-1834

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Many patients live normal life expectancy free of breast cancer recurrence after surgical treatment alone

1.3% of recurrences occurred after 20 years

(3.7% of the 20-year survivors)

Albain KS. Presented at SABCS 2012

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10-Year Survival Rate by axillary node status for patients treated with radical mastectomy

Study 1-3+ Nodes 4+ Nodes

Valagussa, 1978 50% 24%

Haagensen, 1986 63% 27%

Fisher B, 1975 38% 13%

Ferguson, 1982 52% 27%

Albain KS. Presented at SABCS 2012

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Prediction of recurrence in NSABP-B20 (Tam vs Tam + CMF)

Paik et al. J Clin Oncol 2006; 24:3726-3734

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NSABP-B20

Paik et al. J Clin Oncol 2006; 24:3726-3734

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Prediction of anthracycline-based chemotherapy benefit by RS: DFS


The RS was a strong predictive factor of CAF

benefit for DFS, but degree of CAF benefit depended on

the RS:

•NO apparent benefit for scores <18 (p=0.97; HR=1.02) or 18–

30 (p=0.48; HR=0.72);

•SIGNIFICANT advantage for CAF-T compared to tamoxifen alone for patients with RS ≥31

(p=0.033; HR=0.59)

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Impact on clinical practice (where available)

Albain et al. The Breast 2009;18: S141–S145

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Available tests and prospective ongoing clinical trials

Goncalves and Bose. J Natl Compr Canc Netw 2013;11:174-182

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Theoretical spectrum of sensitivity to adjuvant systemic therapy

Hayes D. J Clin Oncol 2012; 30:1264-1267

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TAILORx trial


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RxPONDER trial


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MINDACT trial


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Adjuvant Breast Cancer TreatmentKey questions Patients selection according to biomarkers

Selection of appropriate treatment no adjuvant treatment Chemotherapy Hormonal therapy

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Circulating Tumoral Cells in Adjuvant Therapy

Three typical patterns of response observed:Decrease >10xMarginal ChangeIncrease >10x

Pachmann et al. J Clin Oncol 2008; 26:1208-1215

Peripherally circulating tumor cells are influenced by systemic CT

An increase (even after initial response to therapy) of 10-fold or more at the end of therapy is a strong predictor of relapse and a surrogate marker for the aggressiveness of the tumor cells

http://jco.ascopubs.org/content/vol26/issue8/images/large/zlj0080869580004.jpeg

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Circulating Tumoral Cells in Adjuvant Therapy

Tamoxifene

Trastuzumab

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Metastatic Tumors Evolution of medical treatment

Markers None “One fit (unfit) all” Clinical “One fit some” (>10%) Monogene “One fit few” (<10%) Multigenes “One fit one” (<1%)

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Early Breast carcinoma Evolution of medical treatment

Markers None “One fit (unfit) all” Clinical “One fit some” (>10%) Monogene “One fit few” (<10%) Multigenes “More fit many” (>30%)

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Renal cell carcinoma Evolution of medical treatment

Parameters None “One fit (unfit) all” Bio-Clinical “One fit some” (>10%)

Patient fitness (age, comorbidities) Tumor prognostic risk Drugs prediction (safety/toxicity, efficacy)

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Renal cell carcinoma Evolution of medical treatment

Markers None “One fit (unfit) all” Clinical “One fit some” (>10%) Monogene “One fit few” (1-10%)

Other genetic alterations Heterogeneity (tumor/metastasis)

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Tailor therapy for individual patients Unanswered questions Are CTCs detected in the peripheral blood

released from existing micrometastases or are they the source of distant metastases after “seeding” organ sites?

Are there stem cells capable of “seeding”new tumor sites in the CTC?

Could theCTCs be used to assess sensitivity of resistant tumor cells to alternative agents?

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Adjuvant Breast Cancer TreatmentKey questions Selection of appropriate treatment

Chemotherapy anthracycline-based taxane-based +Trastuzumab +Hormonetherapy A/T-based

Hormonetherapy SERMs Aromatase Inhibitors

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Multi-Gene Predictors in Breast Cancer (1)

ClassificationPerou

Grading Sotiriou

OncotypeDx Genomic Health

MammaPrintAgendia

Number of Genes 427/33,000 97 21 70

Indication Tumor Subtypes Molecular Grading PrognosisTamox/CMF

Prognosis

Guide to Specific Therapy

No No YesCMF

ER/HER2

No

Platform Stanford/ Affymetrix Affymetrix RT-PCR GE/Agilent

FormalinParaffin

In Development No Yes No

FDA Approval No No ? Pending Yes

Commercial Status

None None On the Market On the Market

53

Breast Cancer Prevalence according to ER and HER2 status

Total +++ Negative

Negative 35-40 15-20 15-20

Positive 60-65 10-15 50-55

Total 100 30 70

HER 2

ER/PR

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Breast Cancer Prevalence according to ER and HER2 status

Total +++ Negative

Negative 35-40 15-20 15-20

Positive 60-65 10-15 50-55

Total 100 30 70

HER 2

ER/PR

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CTCs Identification and Detection Semiquantitative determination Prognostic/Predictive factor Molecular Characterization

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CTCsDetection Methods Immunofluorescent staining (CellSearch System,

Veridex): FDA approved Immunomagnetic separation Fluorescent staining

Cristofanilli M et al, NEJM’04

Harris et al, JCO’07: 25; 5287

RT-PCR Xenidis et al, JCO’06

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CTCsCell Search System (Veridex)

Cut-off>5 CTCs/7.5 ml blood Metastatic BC>1 CTC/7.5 ml blood Early BC

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Pharmaco-genetics Modulations of activity dependent from genetic

alterations (DNA) structural

mutations (point- or rearrangements) constitutive somatic

Allelic variants (constitutive) Functional (methylation)

Pharmaco-genomics Modulations of activity dependent from or inducing

alterations of gene expression (RNA, protein)

Pharmaco-Genetics and -Genomics

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Breast Cancer Genomics and Clinical Classification

Gene Expression Profile Breast Cancer

“Basal-like” “Triple-negative” (ER/PR/HER2-negative)

HER2-positive HER2-postive (IHC+++/FISHampl)

Luminal A/B/C ER and/or PR-positive

Sørlie et al. Proc Natl Acad Sci USA 2001;98:10869-74

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Ki67: predictive role in EBC

There are not robust evidence

that Ki67 can serve as a

tool to identify

patients who will benefit

from a specific

chemotherapy or

endocrine treatment.

Yerushalmi et al. Lancet Oncol 2010; 11:174–83

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Gene expression profiling

Unfixed samples of tumor tissue obtained during surgery are the

starting material for gene-expression profiling.

The expression levels of a set of prognostically relevant genes are determined by DNA-microarray

analysis.

The resulting molecular signatures allow the patients to be classified into

groups with a poor prognosis or a good prognosis, thus facilitating

therapeutic decision making.

Van’t Veer et al. J Clin Oncol 2005; 23:1631-1635

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Red= upregulationGreen =Down-regul

Black = constiutive exp

Gene expression profiling

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Thus, are there tumors with favorable prognosis which

don’t benefit from chemotherapy?

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•Of 89 assessable patients, 11 (12%) had a pCR;

•86 genes correlated with pCR (unadjusted P 0.05);

•pCR was more likely with higher

expression of proliferation-related genes and immune-related genes, and with

lower expression of ER–related genes;

•The Recurrence Score was positively associated with the likelihood of pCR (P

0.005), suggesting that the•patients who are at greatest recurrence

risk are more likely to have chemotherapy benefit

Gianni et al. J Clin Oncol 2005; 23:7265-7277

66Chang et al. Breast Cancer Res Treat 2008; 108:233-240

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Single gene/protein predictors of treatment tailoring

Bedard et al. The Breast 2009; 18:S25–S30

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Cancer Genetics and Genomics

Genetics → genetic alterations (DNA) structural



Genomics → alterations of gene expression (RNA, protein)

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Genomics

Identification of biomarkers Response Prediction Target identification

Between patients

Within patients during treatment between primary and

mets

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Breast cancerPrognostic/predictive factors “Simple markers”

HR NG Tumor size N status

GEP CTCs

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Candidate targets and pathways in TNBC

Berrada et al. Ann Oncol 2010; 21(S7): vii30-vii35

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TNBC paradox

Tang et al. J Translational Medicine 2012, 10(Suppl 1):S4

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EGFR and pCR in TNBC

Tang et al. J Transl Med 2012, 10(Suppl 1):S4

76

Cancer Genetics and Genomics

Genetics → genetic alterations (DNA) structural



Genomics → alterations of gene expression (RNA, protein)

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Microtubule-Associated Protein-tau is a bifunctional predictor of endocrine sensitivity and chemotherapy resistance in ER-positive BC

Andrè et al. Clin Cancer Res 2007;13:2061-67

No adjuvant therapyNeoadjuvant T/FAC

Adjuvant tamoxifen

78

Prognostic and predictive value of tau-mRNA expression

• Higher tau-mRNA expression showed borderline non significant association with better prognosis in the absence of systemic adjuvant therapy

• Higher tau-mRNA expression was significantly associated with no recurrence (at 5 and10 years, P =0.005 and P = 0.05, respectively) in patients treated with tamoxifen, indicating a predictive value for endocrine therapy

• Tau expression was significantly lower in patients who achieved pCR to paclitaxel/FAC chemotherapy (P < 0.001)

Andrè et al. Clin Cancer Res 2007;13:2061-67

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C-Myc alterations and association with benefit from adjuvant trastuzumab

DFS (median follow-up, 4.0 y):

MYC:CEP8 ratio ≤ 2.2↓

HR 0.46 (p 0.001)

MYC:CEP8 ratio > 2.2↓

HR 0.67 (p 0.33)

interaction p 0.38

MYC copies/nucleus ≤ 5.0↓

HR 0.52 (p 0.002)

MYC copies/nucleus > 5.0↓

HR 0.48 (p 0.02)

interaction p 0.94

Perez et al. J Clin Oncol 2011; 29:651-659

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C-Myc alterations and association with benefit from adjuvant trastuzumab

MYC:CEP8 ratio < 1.3 with normal chromosome 8 copy number

↓HR 0.66 (p 0.28)

MYC:CEP8 ratio ≥ 1.3 or < 1.3 with polysomy 8

↓HR 0.44 (p 0.001)

interaction p 0.23

Perez et al. J Clin Oncol 2011; 29:651-659

MYC copies/nucleus < 2.5 ↓

HR 1.07 (p 0.87)

MYC copies/nucleus ≥ 2.5 ↓

HR 0.42 (p 0.001)

interaction p 0.05

81Fumagalli, J Natl Cancer Inst 2011;

How to improve the development of predictive tools

82

Gene-expression signature is a predictor of DFS and OS in breast cancer


83



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Prognostic utility of Recurrence Score compared to clinicopathologic features

The 21-gene assay was a more accurate predictor of

relapse than standard clinical features for

individual patients with HR-positive operable breast

cancer treated with chemohormonal therapyand provides information that is complementary to features typically used in

anatomic staging,such as tumor size and

lymph node involvement.

Goldstein et al. J Clin Oncol 2008; 26:4063-4071

85Paik et al. J Clin Oncol 2006; 24:3726-3734

NSABP-B20

86

Prediction of anthra-based CT benefit by RS: OS


There was no statistically significant benefit to CAF for the low (p=0.63) or

intermediate (p=0.85) RS groups. However, there was a significant CAF

benefit in the high RS group (p=0.0271), which did not vary by age.

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Prediction over time


Increasing involvement of axillary lymph nodes was prognostic.

At 10 years, the treatments start diverging at approximately RS=10, though any clinically significant CAF benefit is not evident until much higher RS.

RS has better short- than long-term prediction: the treatments are equivalent up to approximately RS=20, but diverge at higher RS values

highlights in the management of breast cancer roma, 10 maggio 2013 molecular tools for decision...

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