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Molecular Predictors in Clinical Decision Making for Breast Cancer

Explore the role of molecular predictors in clinical decision-making for ER-positive, lymph-node negative breast cancer. Understand the challenges, benefits, and limitations of gene arrays in predicting treatment outcomes.

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Molecular Predictors in Clinical Decision Making for Breast Cancer

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  1. Molecular Predictors in Clinical Decision Making for Breast Cancer George W. Sledge, Jr. M.D. Indiana University Simon Cancer Center

  2. 2000: The Problem of ER-Positive, Lymph-Node Negative Breast Cancer • Common: ~ 137,000 diagnosed annually • Significant benefit fromhormonotherapy • Small absolute benefit (~3-5%) from chemotherapy • Chemotherapy recommended for all with T >1cm

  3. Breast Cancer: not one disease, but criminals sharing the same house

  4. Questions Regarding Gene Array Utility How good are current clinical predictors? Do gene arrays add to current clinical predictors of benefit? Do gene arrays replace current clinical predictors of benefit? Lymph node positive tumors Where Aren’t They Useful? Future directions and challenges

  5. How Good Are Current Clinical Predictors?

  6. Node-Negative Breast Cancer Grows in 5mm Increments! (SEER 1995, 1996) % of cases Tumor Size (in mm)

  7. Tumor Grade Rakha et al. Breast Cancer Res 2010, 12: 207.

  8. Reproducibility of tumor histological grade Kappa: 0.43-0.83 for inter-observer variability Despite the objective improvements that have been made to breast cancer grading methods, any assessment of morphological characteristics inevitably retains a subjective element and is heavily dependent on the pre-analytical parameters. Rakha et al. Breast Cancer Res 2010, 12: 207.

  9. How Good are Current Clinical Predictors of Benefit? • They aren’t useless (i.e., stage and grade mean something) • But they lack reproducibility • And they aren’t perfect predictors

  10. Do gene arrays add to current clinical predictors of benefit?

  11. Gene Arrays All Measure the Same Thing all patients from the NKI 295 data set (results are identical if only ER+ patients are used)

  12. Mammaprint:n = 295 patients Kaplan-Meier Survival Curves survival metastases-free time (years) time (years)

  13. Does 70-gene Signature have Independent Prognostic Value? • Gene signature adds independent prognostic information to that provided by various risk classifications • The signature remained a statistically significant prognostic factor for time to distant metastases & OS even after adjustment for various risk classifications (HR 2.15 & 2.15, respectively) Buyse, M. J of NCI, 2006.

  14. Oncotype DX 21 Gene Recurrence Score (RS) Assay 16 Cancer and 5 Reference Genes From 3 Studies PROLIFERATION Ki-67 STK15 Survivin Cyclin B1 MYBL2 ESTROGEN ER PR Bcl2 SCUBE2 GSTM1 BAG1 INVASION Stromolysin 3 Cathepsin L2 CD68 REFERENCE Beta-actin GAPDH RPLPO GUS TFRC HER2 GRB7 HER2

  15. Levels of Gene Expression Determine Recurrence Score 21-gene assay = 16 outcome-related genes + 5 reference genes Higher expression levels of “favorable” genes = ↓ RS Higher expression levels of “unfavorable” genes = ↑ RS Cutoff points chosen based on Results of NSABP trial B-20 A risk score is calculated from 0 -100 Sparano, J & Paik, S. JCO, 2008.

  16. 40 35 95% C.I. 30 Recurrence Rate 25 20 Rate of Distant Recurrence at 10 years 15 10 5 0 0 5 10 15 20 25 30 35 40 45 50 Recurrence Score Recurrence Score and Distant Recurrence-Free Survival Low RS < 18 Rec. Rate = 6.8% C.I. = 4.0% - 9.6% Intermediate RS 18 - 31 Rec. Rate = 14.3% C.I. = 8.3% - 20.3% High RS  31 Rec. Rate = 30.5% C.I. = 23.6% - 37.4% Paik .S. et al. N Engl J Med 2004;351:2817-26

  17. B-20: Absolute % Increase in DRFS at 10 Years • Benefit of Chemo Depends on RS n = 353 Low RS<18 n = 134 Int RS18-30 n = 164 High RS≥31 0 10% 20% 30% 40% % Increase in DRFS at 10 Yrs (mean ± SE)

  18. 21-Gene Array adds to St. Gallen Risk Groups

  19. Do Gene Arrays Add To Standard Predictors? • YES • Highly reproducible, robust assays • Among ER-positive node-neg. patients, they: • are prognostic for recurrence • are predictive of chemotherapy benefit

  20. Do gene arrays replace current clinical predictors of benefit?

  21. Value of Gene Arrays and Classic Pathologic Features (Treated Patients) Wirapati et al. Breast Cancer Res 10:R65 (doi:10.1186/bcr2124), 2008

  22. TransATAC Study: Recurrence Score Vs Adjuvant! Online Prediction of Relapse Rate: Multivariate model Correlation 40 30 RS Risk (%)* 20 10 0 0 10 20 30 40 Adjuvant! Risk Using Central Grade (%)** • RS and Adjuvant! are each: • Highly significant • Independent • Predict relapse in both node negative and node positive patients • Similar results for mortality RS and Adjuvant! correlate weakly (Spearman R=0.234) Dowsett M, et al. SABCS 2008. Abstract 53.

  23. Nodal Status and Recurrence Score:TransATAC Dowsett, M et al. J ClinOncol 28: 1829-34, 2010

  24. Trans ATAC: Multivariate Analysis in Node-Positive ER-Positive Patients Dowsett, M et al. J ClinOncol 28: 1829-34, 2010

  25. Phase III SWOG 8814 (TBCI 0100) Postmenopausal, N+, ER+ RANDOMIZE n = 1477 tamoxifen x 5 yrs CAF x 6, then tamoxifen CAF x 6, with concurrent tam (n = 361) (n = 550) (n = 566) Superior Disease-Free Survival (DFS) and Overall Survival (OS) over 10 Years Albain, et al. Breast Cancer Res Treat 2005

  26. SWOG 8814/TBCI 0100 : 10-Year DFS Point Estimates Outcome Based on Recurrence Score: Hormonal Therapy +/- Chemotherapy N.S. P < 0.05 P < 0.05 K Albain et al: Lancet Oncology 2010

  27. Node Negative, Both arms (n=872) Tamoxifen (n=432) Anastrozole (n= 440) Node Positive, Both arms (n=306) Tamoxifen (n=152) Anastrozole (n=154) ATAC: Hazard Ratios for Recurrence Score by Nodes and Treatment Arm All(n=1231) Size of symbol proportional to number of events 4.35 0.1 1.0 10.0 100.0 Hazard Ratio* *Hazard ratio for RS/50 adjusted for tumour size, grade and age Dowsett et al. SABCS 2008 #53

  28. Association between 70 gene assay test recurrence score and pathologic CR in a neoadjuvant T-AT study Cases with pathologic CR after paclitaxel / paclitaxel-doxorubicin Cases with residual cancer after paclitaxel / paclitaxel-doxorubicin Almost all pCR occurred in the high RS group. Most patients with high RS did not have a pCR. >31 high RS <18 low RS L Gianni et al., JCO 23:7265-77, 2005

  29. Pre-Op Chemotherapy: Relationship between prognostic predictors and first generation chemotherapy sensitivity predictors Predictors of Pathologic CR High Recurrence Score High tumor grade ER-negative cancer Younger age HER-2 amplification These very same variables predict for worse survival ! (even after chemotherapy) This is because even if most of pCRs occur in these poor prognosis groups, most patients do not achieve pCR and their prognosis brings down the overall survival of the entire group.

  30. An inconvenient truth about biomarkers • Survival of individual patients with stage I-III breast cancer depends on • Baseline prognosis • Efficacy of chemotherapy • Efficacy of endocrine therapy Many known biomarkers interact separately with each of these outcome variables!

  31. Gene Arrays in Node-Positive ER-Positive Patients • Size and lymph node status matter: big tumors and multiple nodes kill • Gene arrays define a high-risk population that does not benefit from chemotherapy • Gene arrays DO NOT replace standard predictors

  32. Gene Arrays Predict Early Recurrence and Chemotherapy Benefit Hazard Ratios over Time for High vs. Low RS 2y 17 (8.4-34) 5y 4.0 (2.0-8.0) 10y 1.7 (.84-3.4) Lau, KF. J ClinOncol 27:15s, 2009 (suppl; abstr 11085) Blows, FM et al. PLOS Med 7(5): e1000279, 2010 SWOG 8814: “The effect of the RS on treatment is not constant over time. In the first five years, RS predicts chemotherapy benefit (interaction p=0.029), but not after five years (p=0.58).” Albain et al. Lancet Oncol 11: 55-65, 2010

  33. Gene Arrays Predict Early Recurrence and Chemotherapy Benefit Hazard Ratios over Time for High vs. Low RS ER+ 2y 17 (8.4-34) 5y 4.0 (2.0-8.0) 10y 1.7 (.84-3.4) Lau, KF. J ClinOncol 27:15s, 2009 (suppl; abstr 11085) Blows, FM et al. PLOS Med 7(5): e1000279, 2010 SWOG 8814: “The effect of the RS on treatment is not constant over time. In the first five years, RS predicts chemotherapy benefit (interaction p=0.029), but not after five years (p=0.58).” Albain et al. Lancet Oncol 11: 55-65, 2010

  34. Gene Arrays Predict Early Recurrence and Chemotherapy Benefit Hazard Ratios over Time for High vs. Low RS 2y 17 (8.4-34) 5y 4.0 (2.0-8.0) 10y 1.7 (.84-3.4) Lau, KF. J ClinOncol 27:15s, 2009 (suppl; abstr 11085) Blows, FM et al. PLOS Med 7(5): e1000279, 2010 SWOG 8814: “The effect of the RS on treatment is not constant over time. In the first five years, RS predicts chemotherapy benefit (interaction p=0.029), but not after five years (p=0.58).” Albain et al. Lancet Oncol 11: 55-65, 2010

  35. Risk of Breast Cancer Recurrence: Two Cell Populations 0.3 1)Proliferating Micromets ER/PgR+ ER/PgR– 0.2 2)Relapsing Dormant Cells Recurrence hazard rate 0.1 0 0 1 2 3 4 5 6 7 8 9 10 11 12 Years PgR = progesterone receptor. Saphner et al. J Clin Oncol. 1996;14:2738.

  36. Future Directions:Integration of Gene Arrays and Standard Predictors • Tang et al. (ASCO 2010 Abstract 509) combined RS with pathologic and clinical information (RSPC Index) • RSPC risk index is superior to RS alone at predicting 10-year distant recurrence rates • RSPC risk index reduces patients in the intermediate category (18% vs 26%, p = 0.001)

  37. Where Aren’t They Useful? • “Special Type” Cancers • Most have excellent prognosis • Some have specific mutational events • ER-Negative and HER2-Positive cancers • Most require treatment • Inflammatory cancer

  38. Inflammatory Breast Cancer:Not a Genomically Homogenous Population Bertucci et al., Cancer 116: 2783-93, 2010

  39. Future Directions

  40. Base power Proportion of patients in low RS categoryaffects the power of adjuvant chemotherapy trials in ER-positive breast cancer Pusztai et al JCO 26:4679-4683, 2008

  41. Prospective Validation of Mammaprint: The MINDACT Trial Risk assessed via Clinicopathological Factors (adjuvant) + Mammaprint 6,000 LN- patients High risk  Chemo + Endocrine Low risk  Endocrine Discordant cases: random assignment to follow genomic vs clinicopathologic result Accrual started 2/07 and is expected to be finished within 3 years Cardoso, F. JCO, 2008.

  42. Prospective Validation of Oncotype DX: The TAILORX Trial 11,248 ER+/LN- patients Low RS: Hormonal Therapy High RS: Chemo +Hormonal Therapy Chemo + Hormonal Hormonal Therapy Dowsett, M. & Dunbier, A. Clin Cancer Res, 2008.

  43. E2100: Role of Anti-angiogenic Therapy in Metastatic Breast Cancer RANDOMI ZE Eligibility: - No prior Rx for mets - Adjuvant taxane if >12 mos. Exclusion: - Her-2 + - CNS mets - Proteinuria - Uncontrolled HTN Arm A: Paclitaxel (q wk) + rhuMAb VEGF Arm B: Paclitaxel (90 mg/m2 q wk)

  44. 1.0 Pac. + Bev. 11.8 months Paclitaxel 5.9 months 0.8 0.6 HR = 0.60 Log Rank Test p<0.001 0.4 0.2 0.0 0 6 12 18 24 30 36 42 48 54 Progression Free Survival DFS Proportion Months Patients at risk: P+B 347 323 167 100 53 25 14 7 2 1 P 326 159 89 47 20 12 6 2 0 0

  45. p=0.047 p=0.035 46.5 mo 25.2 mo 25.2 mo 37.0 mo VEGF -2578 AA & -1154 AA genotypes in combination arm outperformed control Median OS Control arm=25.2 mo Combination arm=26.7 mo Combination arm AA=37.0 mo Median OS Control arm=25.2 mo Combination arm=26.7 mo Combination arm AA=46.5 mo

  46. Challenges for Gene Arrays • T1a,b ER-positive tumors: how low do you go? • ER-negative, node-negative tumors: who doesn’t need chemotherapy? • Prediction of late relapse in ER-positive patients (tumor dormancy) • Prediction of benefit for specific drugs or regimens

  47. Conclusions Multi-gene assays provide consistent prognostic results and probably measure the same biology Arrays add to but do not replace current clinical predictors Much work remains .

  48. Thank You!

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