Precision Medicine for TNBC Patients using a Systems Biology Approach

Transcription

1 Precision Medicine for TNBC Patients using a Systems Biology Approach Charles M. Perou, Ph.D. Departments of Genetics and Pathology Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill

2 Lobular/Normal-like Claudin-low HER2-enriched Luminal B Basal-like Luminal A Estimated Cancer Deaths per year in the USA 10% Breast Luminal 5% Triple-Negative BC (ER-, PR-, HER2-)

3 Molecular Characterization of Basal-Like and Non-Basal-Like Triple-Negative Breast Cancers. Prat et al., The Oncologist, 2013 (PMID: ) All Patient (n=1005) 78.6% 68.5%

4 TCGA Breast mrnaseq Data 1101 tumors and 97 normals 177 TNBC studied Collaborative bioinformatics analysis of TCGA data with: Katie Hoadley and Chris Fan of University of North Carolina = (PAM50 + Claudin-low subtyping) Brian Lehmann and Jennifer Pietenpol of Vanderbilt University = (TNBCtype-4) Powel Brown (MDACC) and Susan Hilsenbeck (Baylor College of Medicine) = (MDA/BCM TNBC-4) Carlos Caldas and Oscar Rueda of University of Cambridge = (IntClust)

5 TCGA Breast mrnaseq Data 1101 tumors and 97 normals 177 TNBC studied PAM50 + Claudin-low IntClust MDA/BCM TNBC-4 TNBCtype-4

6 PAM50 + CLOW Int_1 Int_2 Int_3 Int_4 Int_7 Int_8 Int_9 Int_10 TNBCType-4 Vanderbilt PAM50 + CLOW PAM50 + CLOW TNBCtype-4 Vanderbilt LAR BL1 BL2 MES No Class Basal CLOW HER2E LumA LumB Normal TNBC-4 MDA/BCM LAR BLIS BLIActive MES unclassified Basal CLOW HER2E LumA LumB Normal TNBC-4 MDA/BCM LAR BLIS BLIActive MES unclassified BL BL LAR MES No Class TCGA Breast mrnaseq Data 1101 tumors and 97 normals 177 TNBC IntClust from Cambridge Basal CLOW HER2E LumA LumB Normal

7 How can Triple Negative Breast Cancers be stratified TNBC 20-30% 70-80% Luminal/AR Basal Luminal A+B HER2-Enriched Claudin-low / Mesenchymal Basal-like Possible AR Dependency Lapatinib-Sensitivity Chemo-Sensitivity

8 Molecular Characterization of Basal-Like and Non-Basal-Like Triple-Negative Breast Cancers. Prat et al., The Oncologist, 2013 (PMID: )

9 Overall Survival (OS) from the Phase 2 Study of Enzalutamide, an Androgen Receptor (AR) Signaling Inhibitor, in AR+ Advanced Triple- Negative Breast Cancer (atnbc) Javier Cortes, John Crown, Ahmad Awada, Peter Schmid, Luca Gianni, Laura Garcia-Estevez, Noelia Martinez-Janez, Stephen Chan, Joyce L. Steinberg, Martha Blaney, Iulia Cristina Tudor, Hirdesh Uppal, Amy Peterson, Kathy Miller, Denise A. Yardley, Clifford A. Hudis, Tiffany A. Traina Javier Cortes et al. ECCO 2015 Study Schema (MDV ) A Phase 2, Single Arm, Open Label, Multicenter Study of the Clinical Activity and Safety of Enzalutamide in Patients With Advanced, Androgen Receptor Positive, Triple Negative Breast Cancer ClinicalTrials.gov Identifier:NCT Eligibility AR positive advanced TNBC* ECOG-PS 1 Any number of prior therapies permissible Evaluable bone-only disease allowed No CNS metastases Sufficient tissue to enable biomarker discovery Statistical Considerations Primary Endpoints Clinical Benefit Rate at 16 (CBR16) Other Key Endpoints CBR24 Response rate PFS OS Safety AR biomarker discovery Treatment Enzalutamide 160 mg/day orally Stage 1 3 of 26 Evaluable have CBR16 Go to Stage 2 Stage 2 9 of 62 Evaluable have CBR16 Rejection of H 0 85% power to detect true CBR16 = 8% tested against 1-sided alternative (CBR16 20%); alpha = 5%

10 Clinical benefit of enzalutamide in biomarker -/+ groups PREDICT AR (n = 62) PREDICT AR+ (n = 56) PREDICT AR PREDICT AR+ Total, n (%) 62 (53%) 56 (47%) CBR16, % (95% CI) n 11 (5, 21) n = 7 39 (27, 53) n = 22 CBR24, % (95% CI) n CR or PR, % n 6 (2, 16) n = 4 3 n = 2 36 (24, 49) n = 20 9 n = prior regimens 2+ prior regimens Active Confirmed CR or PR Time to PFS event* (weeks) Median Treatment Duration 8 weeks (range 1 81) CBR 16: Clinical benefit rate (CR + PR + SD for 16 weeks) CBR 24: Clinical benefit rate (CR + PR + SD for 24 weeks ) Time to PFS event*(weeks) Median Treatment Duration 15 weeks (range 1 79) Data cutoff 01 July * Censoring applies PFS = progression free survival, AR = androgen receptor Javier Cortes et al. ECCO 2015

11 R Letrozole + placebo Letrozole + lapatinib Phase III clinical trial 1,286 patients with HR+ metastatic disease - No benefit of lapatinib in HR+/HER2-negative disease - Survival benefit of lapatinib in HR+/HER2+ disease (PMID: )

12 Aleix Prat, Hospital Clínic de Barcelona, Universitat de Barcelona, Barcelona, Spain HER2-Enriched subtype and benefit from lapatinib in HR+/HER2-negative disease Prat et al., JAMA Oncology 2016 (PMID: ) Luminal A Luminal B HER2-E Basal-like

13 How can Triple Negative Breast Cancers be stratified TNBC 20-30% 70-80% Luminal/AR Basal Luminal A+B HER2-Enriched Claudin-low / Mesenchymal Basal-like Possible AR Dependency Lapatinib-Sensitivity Chemo-Sensitivity

14 Breast Cancer Statistics, 2015: Convergence of Incidence Rates Between Black and White Women. DeSantis et al., Cancer J Clin., 2015 (PMID: ) Trends in female breast cancer incidence and mortality by race/ethnicity

15 Incidence of breast and gynaecological cancers by ethnic group in England, : a descriptive study. Shirley et al., BMC Cancer Dec 18;14:979. doi: / Breast cancer incidence, stage, treatment and survival in ethnic groups in South East England. Jack et al., Br J Cancer Feb 10;100(3): doi: /sj.bjc

16 Carolina Breast Cancer Study (CBCS) Population-based case-control study ROBERT MILLIKAN % African-American / 60% White 50% under the age of 50 at diagnosis 1424 cases with FFPE materials/tumors

17 Epidemiology of basal-like breast cancer Millikan et al., Breast Cancer Research and Treatment, 2008 (PMID: ) Distribution of breast cancer intrinsic subtypes by IHC according to race and menopausal status using 1424 cases: invasive (1000) and in-situ (424) breast cancers Breast cancer subtype African-American Premenopausal N (%) African-American Postmenopausal N (%) White Premenopausal N (%) White Postmenopausal N (%) Luminal A (HR+/HER2-) Basal-like (ER-/PR-/HER2-, CK5/6+ or EGFR+) HER2-Enriched (HR-/HER2+) Luminal B (HR+/HER2+) 108 (41.4%) 179 (56.3%) 216 (57.4%) 293 (66.5%) 70 (27.2%) 52 (16.0%) 54 (14.5 %) 49 (9.3%) 22 (8.4%) 26 (7.7%) 24 (5.6%) 44 (6.0%) 19 (7.3%) 26 (8.7%) 46 (12.4%) 46 (10.7%) Unclassified 41 (15.7%) 38 (11.3%) 38 (10.1%) 33 (7.5%) Total: 1424 P < (100%) 321 (100%) 378 (100%) 465 (100%)

18 Racial Differences in PAM50 Subtypes in the Carolina Breast Cancer Study. Troester et al, JNCI 2017 (PMID: ) Distribution of breast cancer intrinsic subtypes by PAM50 gene expression according to race and menopausal status using 980 cases of invasive breast cancer Melissa Troester Department of Epidemiology UNC Black women of all ages have higher frequency of Basal-like and HER2-Enriched and lower frequency of Luminal Subtypes.

19 Comparison of Breast Cancer Molecular Features and Survival by African and European Ancestry in The Cancer Genome Atlas. Huo, Hu, et al., JAMA Oncology, 2017 (PMID: )

20 Comparison of Breast Cancer Molecular Features and Survival by African and European Ancestry in The Cancer Genome Atlas. Huo, Hu, et al., JAMA Oncology, 2017 (PMID: ) Dezheng Huo (Univ. of Chicago) while most molecular differences were eliminated after adjusting for intrinsic subtype, the study found 16 DNA methylation probes, 4 DNA copy number segments, 1 protein, and 142 genes that were differentially expressed

21 Population Differences in Breast Cancer: Survey in Indigenous African Women Reveal Overrepresentation of Triple Negative Breast Cancer. Huo et al., JCO 2009 (PMID: ) Funmi Olopade University of Chicago

22 Cancer 2008 (PMID: )

23 A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer Haiman et al., Nature Genetics, 2012 (PMID: )

24 A common variant at the TERT-CLPTM1L locus is associated with triple-negative breast cancer Haiman et al., Nature Genetics, 2012 (PMID: ) African American (AABC) European ancestry (TNBCC) Risk allele frequency (RAF) in Asians (Chinese, Korean, Japanese) is Zheng et al., Human Molecular Genetics, 2013 (PMID: )

25 Genetic susceptibility to triple-negative breast cancer. Stevens et al. Cancer Research, 2013 (PMID: )

26 Epidemiology of basal-like breast cancer Millikan et al., Breast Cancer Research and Treatment, 2008 (PMID: ) Luminal A N=796 Basal-like N=225 Menarche < ( ) 1.4 ( ) > 3 children 0.7 ( ) 1.9 ( ) First birth < ( ) 1.9 ( ) Breastfeeding > 4m 0.9 ( ) 0.7 ( ) Parity > 3 and No breastfeeding 0.7 ( ) 1.9 ( ) Waist:Hip > ( ) 2.3 ( ) Adjusted ORs (95% CI) N = 1424 cases and 2022 controls

27 How can Triple Negative Breast Cancers be stratified TNBC 20-30% 70-80% Luminal/AR Basal Luminal A+B HER2-Enriched Claudin-low / Mesenchymal AR Expression Basal-like Low Immune High Gene Expression or TILs Lapatinib-Sensitivity Chemo-Sensitivity Proliferation Chemo-Sensitivity

28 Neoadjuvant pathological complete response (pcr) to anthracycline/taxane-based (TFAC) regimen according to PAM50 subtype, and clinical markers (n=441 1, no trastuzumab) Usary et al., Clinical Cancer Research 2013 (PMID: ) Classification pcr rate # of patients MVA 2 OR (95% C.I.) p-value All Patients 84 (19%) 441 PAM50 subtype ER PR Luminal A 4 (3%) Luminal B 9 (14%) ( ) Normal-like 4 (15%) ( ) HER2-Enriched 4 (17%) ( ) Basal-like 48 (38%) ( ) <0.001 Claudin-low 15 (22%) ( ) ER+ 27 (10%) ER- 57 (33%) ( ) PR- 62 (27%) PR+ 22 (10%) ( ) Microarray and Clinical Neoadjuvant Data taken from Hatzis et al., JAMA, MVA model also included grade, clinical T status, and nodal status measured at baseline

29 Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers. Carsten Denkert et al., JCO 2015 (PMID: )

30 Michael Iglesia Prognostic B-cell signatures using mrna-seq in patients with subtype-specific Breast and Ovarian Cancer. Iglesia et al., Clinical Cancer Research PMID:

31 Prognostic B-cell signatures using mrna-seq in patients with subtype-specific Breast and Ovarian Cancer. Iglesia et al., Clinical Cancer Research PMID:

32 Impact of the Addition of Carboplatin and/or Bevacizumab to Neoadjuvant Once-per-Week Paclitaxel Followed by Dose-Dense Doxorubicin and Cyclophosphamide on Pathologic Complete Response Rates in Stage II to III Triple-Negative Breast Cancer: CALGB (ALLIANCE), Sikov et al., JCO 2014 (ClinicalTrials.gov identifier: NCT ) pcr Rate 42% 50% 53% 67%

33 San Antonio Breast Cancer Symposium Cancer Therapy and Research Center at UT Health Science Center December 9-13, 2014 CALGB Gene Expression Immune Cell Signatures Tested Signatures tested 1 pcr predictive p-value All samples Basal-like only B Cell Signature CD8 Signature T Cell Signature IGG Signature General Immune Cell Signatures tested taken from Iglesia et al., Clinical Cancer Res, 2014 (PMID: ) Similar results reported from GeparSixto using TILs (Denkert et al, JCO 2015, PMID: ) No association between these signatures and greater pcr benefit for carboplatin or bevacizumab (p-values for interaction all >0.2) This presentation is the intellectual property of the author/presenter. Contact him at wsikov@wihri.org for permission to reprint or distribute.

34 Pembrolizumab in Patients With Advanced Triple-Negative Breast Cancer: Phase Ib KEYNOTE-012 Study, Nanda et al., JCO 2016 (PMID: )

35 Cluster of 632 Gene Expression Signatures in 1100 Breast Cancer Patients (300 signatures from Perou Lab and 332 from publications) TCGA Breast Cancer RNAseq Data Immune System Features Adaptive, Innate, IFN, etc. ~130 signatures RTK signatures HER2 Basal Prosigna ROR, OncotypeDX, Mammaprint Proliferation Apoptosis Fibroblasts/Stromal Cells Luminal Estrogen-regulated

36 Multivariate Computational Predictors built using Elastic Net α = 0 ---> Ridge regression α = 1 ---> LASSO 0 < α < 1 ---> Elastic Net ELASTIC NET is a modeling approach that can be used to perform both feature selection (from multiple data types) and parameter estimation. It is a hybrid of Ridge Regression and Least Absolute Shrinkage and Selection Operator (LASSO) Regression. Like the LASSO, ELASTIC NET performs automatic feature selection and shrinkage to produce sparse models with high prediction accuracy. LASSO sometimes fails to do grouped feature selection, and it tends to select one feature from a group of correlated features and ignore the others. ELASTIC NET does not have this limitation, and seems to strike a good balance between selecting just one correlated feature versus selecting all correlated features (Hastie, )

37 San Antonio Breast Cancer Symposium, December 6-10, 2016 Multivariate Analysis of Subtype and Gene Expression Signatures Predictive of Pathologic Complete Response (pcr) in Triple-Negative Breast Cancer (TNBC): CALGB (Alliance) Katherine A. Hoadley, Terry Hyslop, Cheng Fan, Donald A. Berry, Olwen Hahn, Sara M. Tolaney, William M. Sikov, Charles M. Perou, Lisa A. Carey Katie Hoadley Lisa Carey Elastic Net Model using 632 Signatures and all 4 Treatment Arms (n=400 patients)

38 Triple Negative Breast Cancer Conclusions 1. Basal-like and Luminal A subtypes vary in frequency according to race and age. There are both genetic, and non-genetic life history features, that contribute to these differences in subtype frequency 2. Response to chemotherapy in TNBCs is multi-factorial and includes genetic features of the tumor (amplicons), pathway-based features (apoptosis), and features of the microenvironment (immune cells) 3. There are many promising new anti-tnbc drugs that are being tested, most of which will require a biomarker, including: -Chemotherapeutics (carboplatin, capecitabene) -AR antagonists for AR+ TNBCs -Lapatinib for HER2-Enriched/HER2-negative -Epigenetic Inhibitors (anti-brd4, see PMID: ) -PARP Inhibitors for BRCA1 and BRCA2 (now FDA approved) -Immune checkpoint inhibitors and other immune system drugs

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40 Perou Lab Current Members Kevin Mott Xiaping He Joe Garay Susana Recio Garcia Dan Hollern Aatish Thennavan Koby Amankwah Youli Xia Maki Tanioka Jonathan Shepherd Lynn Chollet Hinton Marni Siegel Aranzazu Fernandez Martinez Collaborating Past Perou Lab Members Kin Yau Wong (Hong Kong Polytechnic University) Michael Iglesia (Washington University) Katie Hoadley (Department of Genetics, UNC) Jason Herschkowitz (University at Albany-SUNY) Michael Gatza (Rutgers University) J. Chuck Harrell (Virginia Commonwealth University) Maggie Cheang (The Institute for Cancer Research, Sutton, UK) Aleix Prat (Hospital Clínic de Barcelona, Universitat de Barcelona, Spain) Melissa Troester (Department of Epidemiology, UNC) UNC Collaborators Shelley Earp (LCCC) Gary Johnson (Pharmacology) Steve Marron, Andrew Nobel (Statistics) Danyu Lin, Michael Kosorok (Biostatistics) Corbin Jones and members of the High Throughput Sequencing Facility (HTSF) Joel Parker, Chris Fan, Sai Balu and members of the LCCC Bioinformatics Group Lisa Carey, Carey Anders, Neil Hayes, Jon Serody, Ben Vincent, Hy Muss (Oncology) Other Universities Collaborators Baylor College of Medicine Jeff Rosen Lab Matthew Ellis Lab Nationwide Children's Hospital Elaine Mardis Lab The Royal Marsden Hospital and ICR Mitch Dowsett The University of Edinburgh Mike Dixon The Institute for Cancer Research Andrew Tutt Clinical Trial Groups ALLIANCE for Clinical Trials in Oncology Translational Breast Cancer Research Consortium (TBCRC) Miguel Martin and GEICAM Funding Sources National Cancer Institute V Foundation for Cancer Research Susan G Komen Scholar Breast Cancer Research Foundation All Individuals who donate their tissues to medical research