Biologic Subtypes and Prognos5c Factors. Claudine Isaacs, MD Georgetown University

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1 Biologic Subtypes and Prognos5c Factors Claudine Isaacs, MD Georgetown University

2 Prognos5c Factor Defini5on Predicts outcome in absence of systemic therapy Thus tell us when (not how) to treat a pa5ent Reflect biological features of the tumour such as ability to proliferate, invade, and induce angiogenesis

3 Predic5ve Factor Indicate tumour s responsiveness to a par5cular therapy

4 Classic Prognos5c Factors Stage Lymph node status Tumour size Histologic grade and subtype ER/PgR status HER2 status Lympha5c and vascular invasion Prolifera5ve status Classic factors limited in ability to more finely dis5nguish prognoses

5 Clinical Tool for Es5ma5ng Prognosis and Predic5ng Benefit of Therapy Adjuvant! Online Prognos5c value validated in BC Cancer Outcome Unit database (4083 women with T1-2, N0-1, M0) Poorer performance in women < 40 or > 65; HER2 posi5ve, non- ductal non- lobular histo Overall Survival Engelhardt JCO 2014:32:238 OlivoPo et al. JCO 2005;23:2716

6 Gene Expression Profiles

7 Gene Expression Analysis Studies of gene expression analysis focused on two different situa5ons Defining dis5nct subtypes of breast cancer U5lizing gene expression analysis to predict clinical outcome In both situa5ons, this technology demonstrates known heterogeneity of breast cancers

8 Breast Cancer Subtypes ER+ HER2+ Basal ER+ Basal HER2+ Sorlie T PNAS 2003;100:8418

9 Claudin- low Least frequent subtype High mesenchymal features Stem- cell like picture Mostly high grade ER- /PR- / HER2- but ~ 15-25% HR+ Prat & Perou Mol Oncol 2011;5:5-23

10 Carey L et al. JAMA 2006;295:2492 Breast Cancer Subtypes Correspond to Clinically Recognized Subtypes

11 Prat & Perou Mol Oncol 2011;5 Intrinsic Subtypes Heterogeneous ER+ TNBC ER+/HER2+ TNBC ER+/HER2+ TNBC ER+/HER2+

12 Breast Cancer Subtypes Luminal Breast Cancers Accounts for majority of breast cancers (pop n based studies ~ 67% of cases) Includes two separate subtypes of HR+ disease with differing characteris5cs and prognoses Luminal A vs Luminal B Higher expression ER related genes Lower expression of prolifera5on genes than luminal B Histopathologically, lower grade Luminal A associated with beper prognosis In keeping with clinical data regarding heterogeneity of hormone receptor posi5ve disease

13 Carey L et al. JAMA 2006;295:2492 Yang et al. CEBP 2007;16:439 Prat & Perou Mol Oncol 2011;5 Hudis C. NEJM 2007 Breast Cancer Subtypes HER2 Array Group Many tumours that are clinically HER2 posi5ve (by IHC/FISH) will be in this group However, those tumours that are HER2 posi5ve (by IHC/FISH) and ER/ PgR posi5ve omen fall in luminal group In studies, using IHC correlates of subtypes, HER2 subtype found in about 6-20% of pts Associated with poor prognosis in absence of HER2 targeted therapy

14 Breast Cancer Subtypes Basal Group Mimic expression papern of normal myoepithelial cells of the breast and basal epithelial cells of other parts of the body Lack of expression of ER & ER- related genes Low expression of HER2 Strong expression basal cytokera5ns CK5, CK6, and CK17 Frequently high grade Low expression of BRCA1 More common in African Americans (par5c pre- menopausal) and BRCA1 carriers Associated with poor prognosis Not synonymous with triple nega5ve breast cancer Heterogeneous group of tumors Carey L JAMA 2006;295:2492; Rouzier Clin Can Res 2005:11:5678; Carey L Clin Can Res 2007;13:2329; Yang CEBP 2007;16:439

15 TNBC - Heterogeneous Analysis of 21 different data sets including 587 TNBC Cluster analysis revealed 6 TNBC subtypes Basal- like 1 and 2 (BL- 1; BL- 2) Immunomodulatory (IM) Mesenchymal (M) Mesenchymal stem- like (MSL) Luminal androgen receptor (LAR) Different prognoses Associated with different muta5ons and poten5al targets for therapy Lehmann, Pietenpol et al. J Clin Invest 2011

16 Gene Expression Profiles Predictor of Clinical Outcome

17 Gene Expression Profile RoPerdam Study 70- Gene Profile Prognos5c in both node nega5ve (n=151) and node posi5ve (N = 144) Validated in mul5- ins5tu5on study in untreated node nega5ve pa5ents (N=307) and added to clinicopath factors van de Vijver, et al. NEJM 2002;347:1999; Buyse et al for TRANSBIG. JNCI 2006;98:1183

18 70- Gene Expression Signature Predictor of chemotherapy benefit? Retrospec5ve pooled analysis of 541 pa5ents with LNN or LNP disease receiving hormone therapy +/- chemo ET = endo therapy; CT = chemotherapy Knauer et al. Breast Cancer Res and Treat 2010;120:655

19 MINDACT EORTC Prospec5ve RCT LN nega5ve

20 Recurrence Score

21 21- gene Recurrence Score Mul5gene RT PCR PROLIFERATION Ki- 67 STK15 Survivin Cyclin B1 MYBL2 ESTROGEN ER PR Bcl2 SCUBE2 INVASION Stromolysin 3 Cathepsin L2 RS = x HER2 group score x ER group score x prolifera5on group score x invasion group score x CD x GS TM x BAG1c GSTM1 CD68 HER2 GRB7 HER2 BAG1 REFERENCE Beta- acpn GAPDH RPLPO GUS TFRC Category Low risk RS (0 100) RS < 18 Int risk RS 18 and < 31 High risk RS 31

22 21- gene Recurrence Score Valida5on Study B- 14 Addi5on of standard clinicopath factors refined assessment of distant recurrence risk (RS Path- Clinical RSPC) and decreased numbers with intermediate score. Calculator available includes age, tumor size, grade Paik et al. NEJM 2004;351:2817; Tang JCO 2011;29:4365

23 Recurrence Score Predictor of CTX Benefit DDFS 97% 96% DDFS 89% 91% DDFS 60% 88% Paik S et al, JCO 2006;24:3726

24 Intergroup TAILORX Trial Design Node Nega5ve ER/PR posi5ve 21- gene Recurrence Score Assay RS < 11 HT Registry RS RANDOMIZE RS > 25 CTX +HT Registry Hormone Tx Hormone Tx + CTX N= 4400 for randomiza5on arm

25 SWOG 8814: Tamoxifen Arm RS Prognos5c for DFS and OS Disease Free Survival Overall Survival 10- yr DFS: 60%, 49%, 43% 10- yr OS: 77%, 68%, 51% Breast Cancer Specific Survival secondary endpoint low recurrence group ~80-85% Albain, K et al. Lancet Oncol 2010; 11:

26 SWOG 1007 RxPONDER Trial ER/PR posi5ve 1-3 LN posi5ve 21- gene Recurrence Score Assay RS < 25 RANDOMIZE RS > 25 Discuss other Tx Hormone Tx Hormone Tx + CTX N= 4000

27 PAM50 Intrinsic Subtypes 50- gene classifier for ER- and ER+ disease for clinical use Performed on paraffin blocks Divides tumors into intrinsic subtypes Basal- like, HER2- enriched, Luminal A, Luminal B Parker J JCO 2009;27:1160

28 PAM50 Prognos5c Factor PAM50 subtypes prognos5c for relapse- free survival From test set of 710 untreated, node nega5ve pts Parker J JCO 2009;27:1160

29 Predictor of Late Recurrence in ER+ Tumors Dignam et al. BCRT 2009; 116: 595

30 Predictor of Late Recurrence Breast Cancer Index Tumor blocks from 665 pts from TransATAC study with ER+, node nega5ve disease on whom already had 21- gene RS and IHC4 BCI assay consists of 2 independently developed gene expression biomarkers Molecular grade index (MGI) HOXB13/IL17BR BCI, RS, and IHC4 all predicted early recurrence (yr 0-5) but only BCI significant predictor of late (yr 5-10) recurrences Breast Cancer Index Sgroi et al. Lancet Oncol 2013;14:1067

31 Does BCI Predict Benefit from Extended Adjuvant Endocrine Therapy? MA17 Reduc5on in risk of recurrence P = 0.35 P = Sgroi et al. JNCI 2013;105:1036

32 Summary Gene expression profiles iden5fy number of breast cancer subtypes Newer factors provide advantage over prior methods focusing on role of single factors Provide overall profile of tumour Important step towards more tailored therapy Ongoing prospec5ve randomized clinical trials evalua5ng u5lity of these factors

33 Additional Data Slides

34 What do standard factors add to Recurrence Score? RS-Pathology-Clinical (RSPC) assessment Inclusion of standard factors (grade, size, pt age, nodal status (0, 1-3, 4+ nodes), treatment (tam vs anastrozole) Low Risk Int Risk High Risk RS RSPC 19% 18% 27% 54% 18% 64% n=1444 from B14, TransATAC RSPC Classifies Fewer Patients as Having Intermediate Risk Tang et al. ASCO 2010; abs 509

35 Recurrence Score - - ECOG 2197 What does it add to classical factors? RCT AC vs AT for 0-3 LNP analysis focused on those with HR+ disease Classical factors defined by 5 yr version of Adjuvant! (aka Integrator) RS added further information for all Adjuvant! Groups Classical factors added further information only for low RS group Goldstein L et al. JCO 2008;26:4063

36 Concordance of Gene Expression Based Predictors Fan C et al. NEJM 2006;355:560

37 IHC4 Score Weighted ER, PR, HER2, Ki67 IHC centrally performed on specimens from TransATAC Patients receiving endo tx alone IHC4 and GHI-RS added to clinical score IHC4 score provided similar prognostic info as GHI-RS Little added prognostic value if add GHI-RS to IHC4 + Clinical Validation of prognostic value of IHC4 score in independent data set HOWEVER, done centrally issues expanding to local lab IHC assays Cuzick, Dowsett et al. JCO 2011;epub Oct 11, 2011

38 Microarray- Based Gene Expression Profile Studies and Cancer Outcomes Caveats Review of methodology of 90 studies, many of which published in high- impact journals 50% had at least 1 of 3 basic flaws In outcome- related gene finding unstated, unclear, or inadequate control for mul5ple tes5ng In class discovery incorrect statement of correla5on between clusters and outcomes, when used gene related to outcome to generate cluster In supervised predicfon biased es5mate of predic5on accuracy due to faulty cross- valida5on process Dupuy and Simon. JNCI 2007;99:147

39 70 Gene Signature MammaPrint 70- gene signature dividing into low and high risk group BluePrint Gene expression profile that divides tumors into intrinsic subtypes TargetPrint Microarray based GEP providing quan5ta5ve assessment of levels of ER, PR and HER2 overexpression Recent study of 125 matched FFPE and fresh frozen 5ssue showed high overall equivalence between the two techniques Sapino J Mol Diagnos5cs :16:190

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