From the Gross Room to the Proteome: Prognostic and Predictive Markers of Breast Carcinoma

1. From the �Gross Room� to the Proteome: Prognostic and Predictive Markers of Breast Carcinoma Omar Hameed Associate Professor of Pathology and Oncologic Surgery Director of Surgical Pathology Vanderbilt University Medical Center

2. Overview Background Prognostic versus predictive markers Established markers Newer markers Gene expression profiles Proteomics

3. Breast Carcinoma Worldwide

4. Breast Carcinoma in the US

5. Survival

6. Question What factors are associated with worse overall outcome and suggest the need for more aggressive therapy?

7. Management of breast carcinoma Biopsy Surgery Radiotherapy Adjuvant drug therapy Hormonal Rx Chemotherapy Herceptin�

8. Question Are there factors that might determine a particular response to a specific therapy and help guide management?

9. What are tumor (bio)markers? Tumor characteristics that may imply some future behavior/outcome; used for: Screening Diagnosis Classification Prognostication Prediction of therapeutic response

10. Prognostic Markers Associated with outcome independent of treatment Reflect the inherent ability of tumors to proliferate, invade and metastasize

11. Predictive Markers Predict response (or sometimes lack of response) to a particular therapy

12. Many are both

13. How do new markers become part of clinical practice?

14. How do new markers become part of clinical practice? Study issues (LOE) Prospective versus retrospective Untreated versus untreated versus mixed population Analytic issues Ease of measurement Accuracy and precision Reproducibility Presence or absence of effective therapy Clinical/statistical significance and strength Cost/reimbursement issues

15. Established Pathologic Prognostic and Predictive Markers

16. Strong Prognostic Markers (RR>2) Pathological (TNM) stage Tumor size Lymph node (LN) status Presence/absence of metastasis

17. Strong Prognostic Markers (RR>2) Pathological (TNM) stage

18. Moderate prognostic markers (RR 1.5-2) Histologic type Histologic grade Lymphovascular invasion

19. Survival associated with invasive breast cancer according to tumour gradeSurvival associated with invasive breast cancer according to tumour grade

20. Weak prognostic markers (RR<1.5) Estrogen receptor status Progesterone receptor status Human epidermal growth factor receptor-2 (HER2) status MUCH STRONGER PREDITIVE MARKERS

21. Others-not established (�YAPIs�) Cathepsin D Cyclin D S-phase fraction and DNA ploidy p53 bcl-2 TGF-a EGFR Tumor angiogenesis Micrometastasis

22. How are these markers used? Indications for hormonal therapy: Estrogen receptor expression Progesterone receptor expression Indications for HER2 antagonists [e.g. tratsuzumab (herceptin)]: HER2 protein overexpression HER2 gene amplification

23. How are these markers used? Indications for chemotherapy: Positive axillary LNs Negative LNs but with other features such as any of: Larger tumor size (> 2 cm) Higher histological grade Negative hormone receptors

24. What might this mean? We could be treating 90% of patients with LN-negative disease However, without treatment only 30% of those patients would relapse A significant number of patients are being overtreated

25. The need for individualized management Identify pts more likely to have recurrences or relapses Individualized counseling Label those pts for more aggressive Rx Identify pts more likely to respond to a particular therapeutic modality

26. Methods of identifying new (molecular) markers Genome-wide screening Gene expression screening Protein screening Educated guessing

27. Breast Biomarkers >23,000 citations in Pubmed for �breast cancer� AND �biomarker� Most of these are just �YAPIs� Focus on Gene expression profiling Proteomic methods

28. Gene Expression Profiling (GEP) A technique in which the expression of hundreds or thousands of genes can be measured at once In contrast to whole genome sequencing, GEP only measures the active genes, i.e. the ones being transcribed into mRNA

29. GEP of Breast Carcinoma Define biological subtypes of BC (type, ER, HER-2) Perou, PNAS 1999 Perou, Nature 2000 Martin, Cancer Res 2000 Predict clinical status West, PNAS 2001 (LN involvement) Woelfe, Cancer Res 2003 (BM micrometastasis) Predict outcome v�ant Veer, Nature 2002 van de Vijer, NEJM 2002 Predict response to Rx Chang, Lancet 2003 Pustzai, Proc Am Soc Clin Oncol 2003

30. GEP predicts Survival Patients, Methods N = 98 (34 with mets, 44 met-free within 5 yrs, 20 controls) RNA in vitro transcribed? cRNA hybridized? microarray Results 70 genes (cell cycle, inv, angio and mets) correctly predicted outcome in 83% of pts OR for mets within 5yrs = 18 (CI 3.3-94), P = 0.0001

32. GEP predicts survival Patients and Methods N = 295 (<52 yr; 61 part of Nature 2002 study) Median FU = 6.7 yrs Results HR = 4.6 (CI 2.3-9.2), P < 0.001 Was able to specifically classify pts with LN-ve disease

34. 21 genes can predict outcome

35. 21 genes can predict outcome Patients and Methods Identified 250 candidate genes Tested 447 pts and identified 16 robust marker genes Validated on NSABP B-14 (LN-, ER+, Tamoxifen; N = 668; median FU, 14.1 yrs) Developed a formula to calculate a recurrence score (0-100) based on weights of genes on prognosis Low (<18) Intermediate (18-30) High (>30)

36. 21 gene assay

37. Oncotype assay

38. Commercially-available GEP-based genomic tests 70 gene expression (MAMMAPRINT) 21 gene expression (ONCOTYPE DX) 2 gene expression ratio (H:I) 5 gene expression (MOLECULAR GRADE INDEX)

39. GEP-based assays

40. Pros and Cons of GEP Pros Impressive results Combining multiple markers Excellent screening tool Cons Study issues Not widely available and expensive Tumor vs. stroma vs. normal Need for fresh tissue

41. The proteome: a new player? Large scale global analysis of expressed proteins Various reports in BC Identification of serum biomarkers/proteomic patterns Identification of proteomic patterns in nipple aspiration fluid Identification of characteristic proteomic patterns in benign/premalignant/malignant tissues Shares many of the GEP cons Has not reached the status of GEP

42. Can we combine established markers? Pathological stage (tumor size and LN status) Nottingham Prognostic Index Adjuvant Online

43. Nottingham Prognostic Index (NPI) Described in early 1980�s NPI = 0.2 x tumor size + LN stage (NEG = 1; 1-3 POS = 2; >3 POS = 3) + HG (1-3) Stratifies patients into 3 (or 6) prognostic groups Has been validated as an excellent prognostic marker in >10,000 patients

44. Nottingham Prognostic Index (NPI)

45. Nottingham Prognostic Index (NPI)

46. Adjuvant Online! Described in 2000 SEER actuarial survival data were used to develop a computer program to predict outcomes Validated in a population of >4,000 patients

47. Adjuvant Online!

48. Adjuvant Online

49. Can we do any better? Combine traditional and molecular markers?

50. Summary Breast carcinoma is heterogenous Established prognostic and predictive markers include tumor size, LN status, stage, histological grade, hormone receptor status Gene expression profiling is emerging as a powerful prognostic and predictive tool The role of proteomics in prognostication, if any, is yet to be determined Combining markers has a great potential in further increasing the prognostic/predictive power

51. Final note There is significant overlap between molecular and traditional markers, just different methods of measurement