These slides were released by the speaker for internal use by Novartis

1. These slides were released by the speaker for internal use by Novartis

2. Tools for assessing risk of relapse in individuals • Peter Ravdin • (University of Texas Health Science Center at San Antonio, TX, USA)

3. Adjuvant Guidelines (Never A Mention Of Numbers) A Relic Of The Empire ! Clin Pract Guide Oncol. v.1.2006. Breast Cancer. http://www.nccn.org; Goldhirsch et al.Ann Oncol 2005;16:1569–83

4. What Is Missing From These Guidelines? Quantitative numerical estimates of benefit gained or given up…. Ravdin et al.Lancet 2002;359:2126–7.

5. Change In The Goals Of Prognostication Effective Adjuvant Therapy! But Is A Given Adjuvant Therapy Worth It? • Chemo: Leukemia 0.3% mortality, Sepsis 0.1%, CHF 0.1% ? • Tamoxifen about a 0.2 % mortality (Thrombosis/Uterine CA) Cost, Toxicity X % OS Benefit

6. How Much Of A Reduction In Breast Cancer Would Make The Adjuvant Worthwhile? Bimodal Distribution Of Answers < 0.5 1–2 5–10 < 20 0.5–1.0 2–5 10–20 % Reduction Breast Cancer Mortality Ravdin et al. J Clin Oncol 1998;16:515–21

7. First Widely Used Tool For Prognostic Estimates: Nottingham Prognostic Index 0.2 * Tumor size in centimeters + Stage of lymph nodes (1 to 3 by level ) + Histologic grade (SBR, 1-3) ______________________________________ Nottingham Prognostic Index Group Score 15 yr BCSS Excellent < 2.4 15 % Good < 3.4 20 % Moderate 3.41–5.4 58 % Poor > 5.4 87 % For NN Patients = Scores from 2.0–5.0 NN 2.0 cm Grade 2 = 3.4 (Good) Galea et al. Breast Cancer Res Treat 1992;22:207

8. Tools For Prognostic Assessment And Decision Making Adjuvant! Whelan Decision Boards Nottingham Index Finn Prog MSKCC Mayo Model

9. Adjuvant! A program for aiding health professionals in making estimates of outcome of patients with invasive cancer who have undergone definitive local therapy (without prior radiation or systemic therapy) and who are now deciding on whether to get systemic adjuvant therapy Ravdin et al. J Clin Oncol 2001;19:980

10. Mainscreen

11. Information Input Natural Mortality Tx Efficacy Br Ca Mortality

12. Age and Average Non-Breast Cancer Mortality (at 10 years Follow-up)

13. Risk Estimates In Adjuvant! Derived From SEER for N0T1c Cases Breast Cancer Deaths at 10 Years SEER 2001

14. But It Has Not Included A Variable That Is Important What About Her2? What About Tumor Detection Method? In areas where there is controversy you make the choice With some assistance of the help files

15. Logically Using Additional Prognostic Information

16. Her2 Prognostic Review in Help Files Largest Study: From Slamon’s Lab 589 untreated node-negative patients Vysis FISH used Published JCO 2000 18:86–96 Her2 was a weak independent variable Pauletti et al. J Clin Oncol 2000;18:3651

17. Using Her2

18. Mammographically Detected Tumors Combined analysis of 3 randomized trials 1927 breast cancer cases – most without adjuvant Shen et al. J Natl Cancer Inst 2005;97:1195–203 Detection method was a weak independent variable Finnish non-randomized study found a RR of 1.9

19. ShouldState Evidence / Assumptions

20. Setting for Validation Study • BC Cancer Agency • Population about 4 million • 2600 new breast cancers/year • 75–85% referred to BC Cancer Agency • Breast Cancer Outcomes Unit • Systemic therapy indications • 1989–92 • Node positive • pN0 if LVI + • pN0 if T>2cm and ER negative • If age >65 years, not given chemo Olivotto et al. J Clin Oncol 2005;23:2716

21. Results: Overall effect: N=4083 All p = NS

22. Breast Cancer-Specific SurvivalSlopes of a perfect fit line (red) and a line fitted to the observed data (blue) were not different BCOU Observed BCSS Adjuvant! predicted BCSS

23. 10-year BCSS: Age Age <35: Adjuvant! was 1.5X optimistic Age >75: Adjuvant! was 1.3X optimistic *p<0.05

24. Weaknesses of Adjuvant! • Input Variable Issues • Categorical use of T and N subgroups • Use of histologic grade as a categorical variable with “errors” at interfaces • Possible drift in variables such as nodal status miscalled in about 10% of NN patients who have SLNB • These non-idealities affect both guideline and tool-based decisions

25. Weaknesses of Adjuvant! • Limited knowledge about treatment regimens • Mid range follow-up on new regimens? • Equally effective in all patient subsets? • These non-idealities effect both guideline and tool-based decisions

26. Making Efficacy Estimates Selecting Options for Therapy (Proportional Risk Reductions)

27. 2000 Overview Effectiveness Of Adjuvant Therapy TamoxifenChemoCombined < 50 ER+ 32 % 30 % 48 % ER- 0 % 30 % 30 % > 50 ER+ 32 % 10 % 39 % ER- 0 % 18 % 18 % Anonymous. Lancet 1998351:1451–67 Anonymous: EBCTCG Lancet 1998 352:930–42

28. The Generations: Hormonal Therapy (Lineages and Chains of Inference) Tamoxifen Ovarian Strategies Aromatase ? Inhibitor Strategies

29. The Generations (Lineages and Chains of Inference) CMF CMF FE(50)C CA * 4 CAF, FAC FE(100)C CA*4+P*4 CEF DAC FEC*3+D3 CA*4+P*4 q2w FEC*4+P*8 CA*4 +P*12qw) P = paclitaxel; D = docetaxel; A = doxorubicin; E = epirubicin

30. The Generations Trials Comparing Regimens CMF CMF FE(50)C CA * 4 CAF, FAC FE(100)C CA*4+P*4 CEF DAC FEC*3+D3 Q2W (?) (CA*4+P*4) P = paclitaxel; D = docetaxel; A = doxorubicin; E = epirubicin

31. Selecting A Treatment

32. Its Flexible!States Assumptions/Data!

33. Chemotherapy Has Less Almost No Late Effect In Older Women 50 + 36% 1 %* 16 %* 8 % EBCTCG. Lancet 2005;365:1687–717

34. What Probably Will Not Be Part Of The 2006 Overview But Still Is A Hot Topic Adjuvant trastuzumab Some US Clinicians State “All Her2 Positive Patients Should Get trastuzumab” Is this reasonable and what does Adjuvant! say about this??

35. Combined Analysis for OS of NSABP B-31 / NCCTG – N9831 ACTH 94% 91% ACT 92% 87% N Deaths ACT 1679 92 ACTH 1672 62 HR=0.67, 2P=0.015 Years From Randomization B31/N9831 Romond et al. N Engl J Med 2005;353:1673–84

36. Cardiac Monitoring Age and Post AC LVEF were predictors of the risk of developing CHF In both age groups about 10% of the patients had a LVEF of 50-54, about 50% of the patients had a LVEF of 55-64, and 35% had a LVEF of > 65%. Average risk of early CHF for patient younger than 50 is 2% and older than 50 is ~ 5%

37. So Is Adjuvant Herceptin For All Breast Cancer Patients? Informed Speculation ! 60 Year Old Women: ER +, Her2 +, average comorbidity Competeing mortality about 8%: To Get Tam + CA * 4, T * 4q3w Her2 FISH +: Additional RR conferred by Her2 1.5 Risk of developing CHF ~5%, 2/3 have symptoms resolve in 6 months. Cardiac status at 10 years??

38. The Crucial Question Is Not Which Regimen Is Best… The Real Question Is, Can We Tell Which Patient Would Most Benefit From Which Regimen? Do ER Level / Her2 Expression / Specific Genomic Profiles Predict Responsiveness

39. Emerging Picture Of Breast Cancer Subtypes And Treatment Efficacy ( St Gallen Guidelines ) ER - ER + Endocrine Response Uncertain Endocrine Non-Responsive Endocrine Responsive

40. St Gallen Guidelines Definition Of Endocrine Responsiveness Uncertain An Interesting Mixed Bag Of Features Low ER No PgR Her2 + (for Tamoxifen) Large Number of Nodes The exact boundary between “endocrine responsive” and “ endocrine response uncertain” is unknown

41. What Is New: Genomic Profiles An example of what should be better. Oncotype Dx Excellent Standardization Multiple quantitative measured variables Continuous rather then categorical Uses well defined data sets Ravdin 2005

42. Adjuvant! Genomic Variant

43. Print Schemata and Side-Effects Information

45. Conclusions Decision tools have powerful advantages over guidelines Decision making depends on integrating increasingly complex information about: Prognosis, treatment efficacy, toxicity and competing mortality And communicated this information in an intelligible manner