Gene Profiling in Clinical Oncology - Slide 9 - F. André - Genomic evaluation: what are the data?

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Gene Profiling in Clinical Oncology - Slide 9 - F. André - Genomic evaluation: what are the data?

  1. 1. Genomic predictors: what are the data ? Fabrice ANDRE, MD PhD Institut Gustave Roussy, Villejuif, France
  2. 2. Outline <ul><li>Genomic predictors for the decision of adjuvant chemotherapy </li></ul><ul><ul><li>Data </li></ul></ul><ul><ul><li>Do they provide additional information as compared to standard clinical parameters ? </li></ul></ul><ul><li>Perspectives: </li></ul><ul><ul><li>How to improve performances of prognostic signatures ? </li></ul></ul><ul><ul><li>Genomic predictors for treatment efficacy </li></ul></ul><ul><ul><li>Models of implementations in the academic setting </li></ul></ul>
  3. 3. Genomic predictors for prognostic purpose <ul><li>Recurrence score: target : ER+/Her2-negative BC </li></ul><ul><li>70 genes signature </li></ul><ul><li>Genomic grade </li></ul>
  4. 4. 8 8 RS = + 0.47 x HER2 Group Score - 0.34 x ER Group Score + 1.04 x Proliferation Group Score + 0.10 x Invasion Group Score + 0.05 x CD68 - 0.08 x GSTM1 - 0.07 x BAG1 Onco type DX ™ 21 - Gene Recurrence Score (RS) Assay PROLIFERATION Ki - 67 STK15 Survivin Cyclin B1 MYBL2 ESTROGEN ER PR Bcl2 SCUBE2 INVASION Stromelysin 3 Cathepsin L2 HER2 GRB7 HER2 BAG1 GSTM1 REFERENCE Beta - actin GAPDH RPLPO GUS TFRC CD68 16 Cancer and 5 Reference Genes From 3 Studies RS > 31 High risk RS > 18 and <31 Int risk RS <18 Low risk RS (0 - 100) Category
  5. 5. Oncotype DX: Validation I: ER-positive disease (NSABP-B14) Paik NEJM 2004
  6. 6. Oncotype DX: Validation II (NSABP-B20) Paik, JCO, 2006 RS<18 18<RS<31 RS>31
  7. 7. Predictive value for efficacy of adjuvant chemotherapy Concordant data with SWOG 8814 (Albain, Lancet Oncol, 2009) Paik, JCO, 2006 Interaction test, p=0.038 NSABP-B20 trial
  8. 8. Oncotype: Summary of data <ul><li>>3000 patients analyzed (4 from randomized trials) </li></ul><ul><ul><li>NSABP-B14 (NEJM, 04), NSABP-B20 (JCO, 06), Kaiser studies (Breast Cancer Res), SWOG-8814 (Lancet Oncol, 2009), Trans ATAC (SABCS, 08), Japanese study (ASCO breast, 09) </li></ul></ul><ul><li>Concordant data: </li></ul><ul><ul><li>Prognostic parameter </li></ul></ul><ul><ul><li>Risk of metastatic relapse <10% if RS<18 and N0 </li></ul></ul><ul><ul><li>Predictive parameter for the efficacy of suboptimal chemotherapy </li></ul></ul><ul><li>Missing data: </li></ul><ul><ul><li>Comparison with optimal IHC score </li></ul></ul><ul><ul><li>Efficacy of taxanes in RS<18 not evaluated </li></ul></ul><ul><ul><li>Prospective validation (ongoing TAILORx, accrual done) </li></ul></ul>
  9. 9. Mammaprint 70 genes signature Associated with high risk of metastatic relapse
  10. 10. Performances Mammaprint: Validation I Node-negative Van de Vijvers, NEJM, 2002
  11. 11. Performances Mammaprint: Validation II Buyse, JNCI, 05
  12. 12. Mammaprint: Summary <ul><li>Retrospective cohorts outside clinical trials </li></ul><ul><li>Significant prognostic parameter </li></ul><ul><li>Hazard ratio: 3-5 </li></ul><ul><li>Good predictor for the 5st years </li></ul><ul><li>No data regarding the predictive value for the efficacy of adjuvant chemotherapy </li></ul><ul><li>Identifies a group of 30-40% of breat cancer patients who could be spared of chemotherapy </li></ul><ul><li>Ongoing prospective validation </li></ul>
  13. 13. Genomic grade 97 genes
  14. 14. Genomic Grade: Validation I
  15. 15. Genomic Grade: validation II HR:2.5 (1.2-4.9)
  16. 16. Genomic grade: summary <ul><li>Prognostic value in at least 6 different datasets </li></ul><ul><li>Predictive for the efficacy of neoadjuvant chemotherapy (Liedtke, JCO, 2009) </li></ul>
  17. 17. Overall summary <ul><li>1st generation genomic signature: </li></ul><ul><ul><li>Robust / reproducible </li></ul></ul><ul><ul><li>Prognostic value consistant across studies </li></ul></ul><ul><ul><li>Do not capture specific pathway (ER/Her2/proliferation) </li></ul></ul><ul><li>3 questions: </li></ul><ul><li>What level of evidence ? </li></ul><ul><li>Which add-value as compared to </li></ul><ul><li>standard pathological characteristics ? </li></ul><ul><li>How to improve performance ? </li></ul>
  18. 18. Levels of evidence (Simon-Hayes) Consistent retrospective data from prospective clinical studies could define a level I evidence…. pending a prospective validation, like observational cohort (speaker’s opinion)
  19. 19. Does Recurrence score add to conventional parameters ? I: adjuvant online Recurrence score is adding information to AOL Tang, BCRT
  20. 20. Does Recurrence score add to conventional parameters ? I: « optimal » IHC score (ER, PR, Her2, KI67) Since RS includes ER, PR, Her2 and KI67, does it provide additional information ?
  21. 21. Correlation between RS and (ER, PR, Her2, proliferation) RS correlates with standard pathological parameters but… the level of correlation is not high
  22. 22. Comparison between RS and IHC4
  23. 23. Does RS provide additional information as compared to standard parameters ? Comparison with AOL: Yes Comparison with ER/PR/Her2/KI67 : this is NOT the righ question since: the level of evidence for KI67 is not I, at least for the prediction to adjuvant chemotherapy (speaker’s opinion) Current status : the equivalency between RS and (ER,PR,Her2,KI67), together with the predictive value of KI67 are still research hypotheses
  24. 24. Outline <ul><li>Genomic predictors for the decision of adjuvant chemotherapy </li></ul><ul><ul><li>Data </li></ul></ul><ul><ul><li>Do they provide additional information as compared to standard clinical parameters ? </li></ul></ul><ul><li>Perspectives: </li></ul><ul><ul><li>How to improve performances of prognostic signatures ? </li></ul></ul><ul><ul><li>Genomic predictors for treatment efficacy </li></ul></ul><ul><ul><li>Models of implementations in the academic setting </li></ul></ul>
  25. 25. Potential solutions to capture specific information that would encompass ER, He2, Ki67 <ul><li>Higher number of events in training set (Michiels, Lancet, 2005) </li></ul><ul><li>Population more homogenous regarding clinical/molecular characteristics (Andre, Pusztai, Nat Clin Pract Oncol, 2006) : develop predictors within molecular classes </li></ul><ul><li>Tests developed to provide additional information to clinico-pathological characteristics </li></ul><ul><li>New technologies (+ complementarity) (Desmedt, EJC, 2009) </li></ul><ul><li>Supported by functional studies (Ioannidis, PLoS Med, 2005) </li></ul>
  26. 26. Illustration: Identification of prognostic signature within Her2+ BC Staaf, JCO, 2010 <ul><li>Predictor developed: </li></ul><ul><li>Within a molecular class </li></ul><ul><li>to identify a functional pathway </li></ul><ul><li>With a significant nb of samples for training set </li></ul>
  27. 27. Outline <ul><li>Genomic predictors for the decision of adjuvant chemotherapy </li></ul><ul><ul><li>Data </li></ul></ul><ul><ul><li>Do they provide additional information as compared to standard clinical parameters ? </li></ul></ul><ul><li>Perspectives: </li></ul><ul><ul><li>How to improve performances of prognostic signatures ? </li></ul></ul><ul><ul><li>Genomic predictors for treatment efficacy </li></ul></ul><ul><ul><li>Models of implementations in the academic setting </li></ul></ul>
  28. 28. Genomic predictors for treatment efficacy: Conventional treatment Predictor Treatment Reference Stroma metagene chemotherapy Farmer, Nat Med TOP2A metagene anthracyclines Desmedt, ASCO DLD30 paclitaxel > FAC Hess, J Clin Oncol SET index endocrine therapy Symmans, J Clin Oncol MX1 metagene anthracyclines Andre, ASCO
  29. 29. Genomic predictors for treatment efficacy: targeted therapies Functional pathways Target detection Quantification of PI3K activation / GE array Loi, PNAS, 2010 B. Hennessy, CCR, 09
  30. 30. Outline <ul><li>Genomic predictors for the decision of adjuvant chemotherapy </li></ul><ul><ul><li>Data </li></ul></ul><ul><ul><li>Do they provide additional information as compared to standard clinical parameters ? </li></ul></ul><ul><li>Perspectives: </li></ul><ul><ul><li>How to improve performances of prognostic signatures ? </li></ul></ul><ul><ul><li>Genomic predictors for treatment efficacy </li></ul></ul><ul><ul><li>Models of implementations in the academic setting </li></ul></ul>
  31. 31. Current model Academic hospital Tumor block Multiplex assay: CLIA lab or biomarker company Limitation: multiplicity of multigene assays will make non feasible the outsoursing Solution: run whole genome arrays in academic centers (with reimbursement of IP)
  32. 32. Are whole genome arrays feasible in the context of daily practice ?
  33. 33. Genomic driven chemotherapy: REMAGUS 04 trial Breast adenocarcinoma Conservative surgery not feasible FEC > Docetaxel Primary endpoint: pCR Secondary endpoint : feasibility of whole genome array in academic centers in the context of daily practice Funding: French NCI DLD30+: Paclitaxel >FEC DLD30-/TOP2A+: FEC>docetaxel DLD30-/TOP2A-: Docetaxel / cape RNA extraction, QC, hybridization Affy U133plus2, bioinformatics If QC genomic: inclusion
  34. 34. Remagus04 Trial : January-September 2009 <10% if biopsy is guided by ultrasonography Necessary ? Current status: N=200 Interim analysis
  35. 35. Robustness: comparison between ER status and ESR1 expression 205225_at Gene expression array is accurate to define ER status in the context of daily practice MD Anderson correlation Remagus04 Correlation Oestrogen receptor:ESR1 Receptor status Probeset Receptor status Probeset 205225_at 0,77 1·00 0,80 1,00 211233_x_at 0,53 0,78 0,60 0,71 211234_x_at 0,38 0,62 0,56 0,62 211235_s_at 0,55 0,79 0,50 0,63 211627_x_at 0,17 0,05 0,07 0,14 215551_at – 0,12 – 0,06 0,49 0,61 215552_s_at 0,62 0,8 0,54 0,70 217163_at – 0,21 – 0,14 0,18 0,21 217190_x_at 0,47 0,61 0,48 0,58
  36. 36. SAFIR01 trial Phase I/II FGFR1 inh Rare events Phase I Phase II NOTCH inh Started or to start soon Under discussion Prospective evaluation of Integrated biology for treatment decision Cooperative group (FNCLCC) Biopsy of metastatic sites Frozen sample CGH / hot spot mutations (PIK3CA/AKT) n=400 PAK inh PAK1 amp Molecular screening: Which candidate target ? Clinical trials: Is the target relevant ? FGFR1 FGFR2 FGF4 amp Phase II PI3K inh Or everolimus NOTCH amp Phase II CBDCA +/- BSI201 Genetic instability PAK1 ampli PIK3CA / AKT / PTEN alteration bevacizumab VEGFA amplification Others Funding: French NCI
  37. 37. SAFIR01 trial: logistics Patient inclusion DNA extraction Hybridization Hot spot mutations Target identification Quantification genetic instability Weekly tumor board Investigation center Genomic unit Curie (Affy 6.0) Genomic unit Gustave roussy (Agilent 4*44) Genomic unit Lyon (Affy 6.0) Bioinformatics Gustave Roussy
  38. 38. Conclusion <ul><li>Multiplex assay allow a robust evaluation of gene expression </li></ul><ul><li>RS has reached level I evidence for prognostic value and could be predictive for the efficacy of adjuvant chemotherapy </li></ul><ul><li>Several multiplex assays are being developed in order to predit treatment efficacy and to improve prediction of 1st generation signaturess </li></ul><ul><li>Whole genome array analyses are feasible in the context of daily practice and could save cost by avoiding multiplicity of bioassays </li></ul>

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