Identifying Tumors Expressing Predictive Markers: Lessons Learned From HER2

1. Identifying Tumors Expressing Predictive Markers: Lessons Learned From HER2 Dennis J. Slamon, MD, PhD TRIO Chairman Chief, Division of Hematology/Oncology David Geffen School of Medicine at UCLA Los Angeles, California

2. Faculty Disclosure Dennis J. Slamon, MD, PhD, Speakers Bureau: Genentech/Roche, GSK, sanofi-aventis Advisory Board: Novartis

3. Molecular Diversity of Human Cancers: Biologic and Therapeutic Implications BRCA1 HER2

4. Paradigm Changes from Human Breast Cancers

5. Lymph. infiltrate STAGE invasive low In situ Well- Nuclear Grade Margins Differentiation Poorly- high infiltrating “single-file” “pushing” Human Breast Cancer Is Highly Heterogeneous Can we decipher new molecular genetic information for these complex and variable tumors and establish a new classification with real therapeutic impact.

6. THE PAST

7. The “One-Size-Fits-All” Approach to Cancer

8. TDLU K18 Cell Type and Phenotype K14

9. CALGB 9344: Overall Survival 9 Henderson, et al. J Clin Oncol. 2003;21:976-83.

10. Breast Cancer Subtypes are associated with disease outcome Sørlie et. al. PNAS 2003

11. 15-18% 20-25% 60-65% CURRENT THERAPEUTIC BREAST CANCER SUBTYPES

12. Triple-Negative Breast Cancers: Some Potential Therapeutic Targets Cetuximab EGFRTyrosine Kinase MET tyrosine kinase MET mab MAP Kinase Pathway Akt Pathway MAPK inhibitors; NOTCH inhibitors Transcriptional Control PARP inhibitors Anti-Angiogenesis DNA Repair pathways Cell Cycle Bevacizumab After Cleator S et al. Lancet Oncol. 2006:8:235-244 Cell Death

13. Can We Do Better? The Hope - Clinical Translation of Biologically Relevant Molecular Information Should Lead to More Effective and Less Toxic Therapeutic Approaches

14. CURRENT TRANSLATIONAL RESEARCH PROCESS HypothesisGeneration Tissue Specimens TRANS CLINICAL TEAMS: Protocol Development BASIC SCIENCE LABORATORIES BASIC SCIENCE LABORATORIES HypothesisGeneration Data and Data Processing/ Analyses Specimen/Sample

15. The HER2 Alteration Southern Northern Western IHC Slamon et al. Science 1989

16. Breast Cancer HER-2 OncogeneAmplification HER-2 OncoproteinOverexpression Shortened Survival Median Survival from First Diagnosis HER-2 overexpressing 3 yrsHER-2 normal 6 - 7 yrs Slamon et al, Science 1987

17. Target Validation - A

18. Biologic Effects of HER-2/neuAmplification/Overexpression in Human Breast Cancer Cells ­DNA Synthesis ­Cell Growth HER2+ Breast Cancer Cell Lines HER2- Breast Cancer Cell Lines HER-2 Transfection ­Growth inSoft Agar ­Tumorigenicity ­MetastaticPotential E2 Response, ­ Tam Resist.

19. Target Validation - B

20. Preclinical Impact of Trastuzumab on Tumor Growth Effect of Trastuzumab Treatment on HER2+ Breast Cancer Xenografts 2000 Control 1500 Trastuzumab 1000 Tumor volume (mm3) Trastuzumabwithdrawn 500 0 0 10 20 30 40 50 60 70 Treatment day Pietras et al. Oncogene. 1998;17:2235.

21. Trastuzumab in Combination with Chemotherapy • Primary • Time to disease progression (REC) • Safety • Secondary • Overall response rates • Durations of response • Time to treatment failure • 1-year survival • Quality of life Objective - Combination Compared to Chemotherapy Alone

22. Summary: Phase III Clinical Trial Comparing Best Available Chemotherapy to Chemotherapy+Trastuzumab Enrolled 469 pts RR Resp Duration TTP H +CT 235 pts 49% (^53%) 9.3M (^59%) 7.6M (^68%) CT 234 pts 32% 5.9 M 4.6M

23. The HER2 Alteration Southern Northern Western IHC Slamon et al. Science 1987,1989

24. Disease-Free Survival 100 100 90 90 80 80 70 70 60 60 50 50 0 1 2 3 4 5 0 1 2 3 4 5 B-31 N9831 ACTH ACTH 87% 87% 85% ACT 86% ACT 78% 74% % 66% 68% N Events N Events ACT 807 90 ACT 872 171 ACTH 808 51 ACTH 864 83 HR=0.55, 2P=0.0005 HR=0.45, 2P=1x10-9 Years From Randomization

25. Lessons from the HER2 Story • 1.) Target Identification • 2.) Target Validation • 3.) Preclinical Confirmation • 4.) Determintion of Potential Usage Preclinically • 5.) Clinical Translation - Proof of Concept • 6.) Clinical Optimization

26. Other Lessons Learned: What we are learning about already established agents

27. The META-Analysis

29. How Did The Current Chapter Start ? Attempts to explain the differential prognosis of HER2 positive breast cancers

30. The HER-2 Gene: encodes a 185kd protein that is a member of the type I receptor tyrosine kinase family which also contains EGFR, HER-3 and HER-4 Functions When Altered: 1.) Growth and proliferation - increased 2.) Differentiation - decreased 3.) Cell survival - increased 4.) Motility - increased 5.) Neoangiogenesis - increased 6.) Reduced dependency on estrogen and insensitivity to hormonal blockade

31. HER-2 neg MA-5 TRIAL HER-2 pos Pritchard, NEJM 354:2103, 2006

32. HER2 positive HER2 negative Disease Free Survival Study HR 95% CI anthra better non anthra better NSABP B11 0.44 - 0.82 0.75 - 1.23 0.60 0.96 NSABP B15 0.84 1.02 0.65 - 1.08 0.86 - 1.20 Brussels 0.65 1.35 0.34 - 1.27 0.93 - 1.97 Milan 0.83 1.22 0.46 - 1.49 0.91 - 1.64 DBCCG-89-D 0.75 0.79 0.53 - 1.06 0.60 - 1.05 NCIC MA-5 0.52 0.91 0.34 - 0.80 0.71 - 1.17 Total 0.82 - 0.98 0.90 p = 0.01 p < 0.0001 Overall 0.71 1.00 0.61 - 0.83 0.90 - 1.11 p = 1.0 heterogeneity c25 = 5.3, p = 0.38 heterogeneity c25 = 7.6, p = 0.18 0.4 0.9 0.6 2 5 1 Test for interaction chi2 = 13.7 p < 0.001 A. Gennari, JNCI 2007

33. Overall Survival HER2 positive HER2 negative anthra better non anthra better HR 95% CI Study 0.66 0.90 0.47 - 0.92 0.69 - 1.18 NSABP B11 0.82 1.07 0.63 - 1.06 0.88 - 1.30 NSABP B15 0.85 1.64 GUN 3 0.27 - 2.69 0.85 - 3.15 0.61 1.26 0.32 - 1.16 0.89 - 1.79 Milan 0.73 0.82 0.50 - 1.05 0.59 - 1.13 DBCG-89-D 0.65 1.06 0.42 - 1.01 0.80 - 1.40 NCIC MA-5 0.91 0.83 - 1.00 Total p = 0.056 p < 0.0001 Overall 0.73 1.03 0.62 - 0.85 0.92 - 1.16 p = 0.86 heterogeneity c25 = 5.2, p = 0.39 heterogeneity c25 = 5.5, p = 0.36 0.4 0.9 0.6 2 5 1 Test for interaction chi2 = 12.0, p < 0.001 A. Gennari, JNCI 2007

34. The Topoisomerase IIa Gene: encodes an enzyme which is critical in DNA replication and function including RNA transcription Functions: 1.) Resolves topological problems in DNA 2.) Is critical in RNA transcription from DNA 3.) Makes transient protein-bridged DNA breaks on one or both DNA strands during replication 4. Plays critical roles in segregation, condensation and superhelicity

35. The Topo IIa protein is a major target of the anthracyclines

36. Can We Do Even Better? The Hope - Further Clinical Translation of Biologically Relevant Molecular Information Should Lead to Even More Effective and Less Toxic Therapeutic Approaches

37. CURRENT TRANSLATIONAL RESEARCH PROCESS HypothesisGeneration Tissue Specimens TRANS CLINICAL TEAMS: Protocol Development BASIC SCIENCE LABORATORIES BASIC SCIENCE LABORATORIES HypothesisGeneration Data and Data Processing/ Analyses Specimen/Sample

38. Clinical Outcome in Primary Papillary Serous Carcinoma Disease Free Survival ≈ 60% recur within 2 years ≈ 75% recur within 3 years Overall Survival ≈ 20% mortality within 2 years ≈ 40% mortality within 3 years uncensored: 83 ( 83.00%) censored: 17 ( 17.00%) uncensored: 57 ( 55.34%) censored: 46 ( 44.66%)

39. Goals Identify molecular subtypes of ovarian tumors that may have clinical and biological relevance for disease initiation and progression Utilize these data to generate and test therapeutic hypotheses Build on the work done in other programs

40. Cedars-Sinai/UCLA Ovarian Cohort • 225 ovarian samples have been received from Dr. Beth Karlan of Cedar Sinai, profiled and imported into Rosetta analysis software • Samples collected between 1989 and 2005 • RNA quality measured using Agilent BioAnalyzer • RNA Integrity Number (RIN) average = 9.16 • All samples were profiled using Agilent Human 1A V2 chip • Reference is an equal mixture of the first 106 ovarian samples profiled • Detailed clinical outcome is available on 90% of the samples • UCLA has completed FISH analysis and/or Northerns for a number of genes including HER2, EGFR, Periostin (POSTN, PN)

41. UCLA/Cedar Sinai Ovarian Tumor Study: Papillary Serous NED: No evidence of disease

42. Hierarchical Cluster of Ovarian Samples across 6165 Genes Normal samples (n=14) show a very similar pattern of gene expression Unsupervised clustering does not group remaining samples into clear subtypes

43. Refine Analysis to Discover Ovarian Subtypes • Unsupervised hierarchical clustering clearly defines only a normal & “normal-like” subtype • Clinical outcome does not define subgroups • ANOVA based on overall survival finds 0 differentially expressed genes (DEG) • Consider other markers to distinguish ovarian subgroups • Periostin (POSTN, PN) & TGFβ Induced (TGFβI) • Hormone receptor markers: AR, PGR, ER • CA125 (MUC16)

44. Refine Analysis to Discover Ovarian Subtypes • Unsupervised hierarchical clustering clearly defines only a normal & “normal-like” subtype • Clinical outcome does not define subgroups • ANOVA based on overall survival finds 0 differentially expressed genes (DEG) • Consider other markers to distinguish ovarian subgroups • Periostin (POSTN, PN) & TGFβ Induced (TGFβI) • Hormone receptor markers: AR, PGR, ER • CA125 (MUC16)

45. 225 Ovarian Samples Clustered across 2830 Genes identifies three major subtypes Normal POSTN ER

46. NORMAL AR PR POSTN TGFβI CA125 ER

47. Clinical Outcome in Primary Papillary Serous Carcinoma Disease Free Survival ≈ 60% recur within 2 years ≈ 75% recur within 3 years Overall Survival ≈ 20% mortality within 2 years ≈ 40% mortality within 3 years uncensored: 83 ( 83.00%) censored: 17 ( 17.00%) uncensored: 57 ( 55.34%) censored: 46 ( 44.66%)

48. POSTN Signature Related to Clinical Outcome in Primary Ovarian Samples Disease Free Survival Overall Survival

49. NORMAL AR PR POSTN TGFβI CA125 ER

50. POSTN Signature Related to Clinical Outcome in Primary Ovarian Samples Disease Free Survival Overall Survival