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NY Prostate Cancer Conference - M.H. Hussain - Session 7: Role of predictive biomarkers as a measure of individualized medicine
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NY Prostate Cancer Conference - M.H. Hussain - Session 7: Role of predictive biomarkers as a measure of individualized medicine

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  • Novel agents pose new challenges. Rather than conventional histological criteria for diagnosis, we may need to re-define how we classify cancers based on the mechanism of pathogenesis and therapy. This may allow greater efficiency in the clinical trial process by selecting populations with a greater liklihood of responding to treatments. Novel surrogates will need validation and acceptance by the scientific community. Starting doses of agents may need to be aimed at inhibiting or interacting with a target rather than the MTD. Dose ranging studies, accepted as a norm in most other therapeutic areas, may be accepted by oncologists to avoid toxicities and optimize the therapy’s interaction with targets.
  • This slide emphasizes why selecting patients for EGFR TKI therapy by clinical characteristics may be less successful.
  • In addition to our phase I PARPi + RT study, we are also in the process of initiating a multi-center clinical trial stratifying and treating patients by their prostate cancer gene fusion status. Maha – clinical trial Me – translational component

NY Prostate Cancer Conference - M.H. Hussain - Session 7: Role of predictive biomarkers as a measure of individualized medicine NY Prostate Cancer Conference - M.H. Hussain - Session 7: Role of predictive biomarkers as a measure of individualized medicine Presentation Transcript

  • Role of Predictive Biomarkers as a Measure of Individualized Medicine Maha Hussain, M.D., FACP Professor of Medicine & Urology Associate Director for Clinical Research University of Michigan Comprehensive Cancer Center
  • Identification of “ active” drugs Appropriate Phase III studies Identification of active and safe combinations Adjuvant Neoadjuvant Overarching Objective “ Impact and Change the Standards of Care” This = Decades & $$$$$$
  • Nine FDA Approvals for Metastatic Castration Resistant Prostate Cancer in 15 Years
    • Survival
      • Docetaxel, Provenge, Cabazitaxel
      • “ abiraterone”
    • Pain
      • Mitoxantrone, Strontium,
      • Samarium
    • Skeletal related events
      • Zoledronic acid, Denusomab
  • Minimize the Number of Negative Trials
    • Nine Negative Phase III trials over the last 6 years
    • Atrasentan (X2)
    • GVAX (X2)
    • Taxotere +/- DN101
    • Satraplatin
    • Taxotere +/- Bevacizumab
    • Taxotere +/- Sunitinib
    • Zibotenan
  • Plethora of Targets and Agents in Prostate Cancer What to Pick and How Best to Test? Pathway Target Agents Angiogenesis PDGF receptor Olaratumab Unknown Tasquinimod VEGF Aflibercept VEGF receptor Ramucirumab Androgen Androgen receptor ARN-509, MDV3100 CYP17 Abiraterone, Orteronel Apoptosis BCL-2 AT-101 Clusterin Custirsen Cell division Microtubules Eribulin, nab-Docetaxel DNA repair PARP Veliparib Endothelin Endothelin receptor Atrasentan, Zibotentan Histone acetylation HDAC Vorinostat Immune modulation CTLA-4 Ipilimumab Multiple Lenalidomide Insulin-like growth factor IGF-1R Cixutumumab Other mTOR Everolimus, Temsirolimus Multiple, including Src Dasatinib Multiple, MET/VEGFR2 XL184
  • Key Validated Targets Based on “Clinical Benefit”
    • Androgen signaling (Inhibition of androgen synthesis pathways & AR)
      • PSA Response rate: 90% - 38%
    • Microtubule
      • Measurable disease response rate (PSA):
        • Docetaxel 12% (45%), Cabazitaxel 14.4% (39%)
    • Immune system
      • Provenge: No response
    • Bone:
      • Zoledronic acid, Denosumab: No response
  • TAX327 Overall Survival, 2004 Median survival Hazard (mos) ratio P-value Combined: 18.2 0.83 0.03 D 3 wkly: 18.9 0.76 0.009 D wkly: 17.3 0.91 0.3 Mitoxantrone 16.4 – – Months Probability of Surviving 0 6 12 18 24 30 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Docetaxel 3 wkly Docetaxel wkly Mitoxantrone Tannock et al, N Engl J Med, 2004
  • Kantoff et al, NEJM 363:411-22, 2010 Median Survival 25.8 m vs 21.7 m Sipuleucel-T vs Control Overall Survival , 2010
  • Cabazitaxel vs Mitoxantrone Overall Survival, 2010 De Bono et al, Lancet 2010 Median Survival: 15.1 vs 12.7 m )
  • COU-AA-301: Abiraterone Acetate Overall Survival, 2011 HR = 0.646 (0.54-0.77) P < 0.0001 Placebo: 10.9 months (95%CI: 10.2, 12.0 ) 0 100 200 300 400 500 600 700 0 20 40 60 80 100 Survival (%) Days from Randomization Abiraterone acetate: 14.8 months (95%CI: 14.1, 15.4) 2 Prior Chemo OS: 1 Prior Chemo OS 14.0 mos AA vs 10.3 mos placebo 15.4 mos AA vs 11.5 mos placebo AA 797 728 631 475 204 25 0 Placebo 398 352 296 180 69 8 1
  • Prognosis and Prediction
    • A Biomarker is:
    • Prognostic: correlates with outcome, independent of treatment effects ( PS, stage, Gleason’s score, PSA )
    • Predictive: Provides evidence about the probability of benefit or toxicity from a specific intervention ( ER/PR, HER-2, KRAS mutations )
    • A factor can be:
      • Prognostic but not predictive
      • Predictive but not prognostic
      • Predictive and prognostic
      • Neither
    Modified from Clin cancer research 16 (6) 1745-55, 2010 & C. Tangen.
  • When is a Marker Clinically Useful?
    • It is either Prognostic or Predictive
    • The magnitude of effect is sufficiently large that clinical decisions based on the data result in outcomes that are acceptable
      • Greater chance for benefit
      • Smaller toxicity risk
    • The estimate of magnitude of effect is reliable
      • Assay is reproducible
      • Clinical trial/marker study design is appropriate
      • Results are validated in subsequent well-designed studies (Levels of Evidence I or II)
    Modified from D. Hayes
  • Tumor Marker Utility Grading System (TMUGS): Levels of Evidence
    • Level Definition
    • I Prospective, Marker Primary Objective, Well-powered OR Meta-analysis
    • II Prospective, Marker Secondary Objective
    • III Retrospective, Outcomes, Multivariate Analysis
    • IV Retrospective, Outcomes, Univariate
    • V Retrospective, Correlation with Other Marker, No Outcomes
    Hayes, et al; J Nat Cancer Institute 88:1456, 1996; Simon R.M., et al. J Natl Cancer Inst. 2009
    • Level Definition
    • I Prospective, Marker Primary Objective, Well-powered OR Meta-analysis
    • II Prospective, Marker Secondary Objective
    • III Retrospective, Outcomes, Multivariate Analysis
    • IV Retrospective, Outcomes, Univariate
    • V Retrospective, Correlation with Other Marker, No Outcomes
    Tumor Marker Utility Grading System (TMUGS): Levels of Evidence D. Hayes Hayes, et al; J Nat Cancer Institute 88:1456, 1996; Simon R.M., et al. J Natl Cancer Inst. 2009 Most Tumor Marker Studies (Studies of Convenience)
  • Challenges for Drug Development in the “Era of Targeted Therapies & Personalized Medicine”
    • Re-define “definitions” of diseases
      • Morphology or Molecular Profile
      • How to define a “relevant target”?
        • where is the marker to be measured, what expression rate is meaningful, tumor heterogeneity ?
    • Greater efficacy in selected population
      • Will result in smaller patient populations
    • Alternative study designs and Endpoints
      • Unselected, enriched, biomarker/target stratified, Adaptive
      • Endpoints and new outcome surrogates to be validated
  • How successful are we in selecting and targeting solid tumors? Target ER Her-2 EGFR C-Kit TS Disease Breast Breast Colon GIST Colon Drug Tamoxifen Herceptin Cetuximab Gleevec 5-FU Response (%) 50-60 15-26 (35) 10 60-70 15-20
  • Lung Cancer:EGFR Genotype Cannot Be Reliably Determined by Phenotype Jackman DM, et al. J Clin Oncol. 2008;26(May 20 suppl). Abstract 8035. In mutation positive 71%(I-PASS) response as first-line treatment and 42% (INTEREST) as second-line Phenotype of NSCLC Patient Prevalence of EGFR Mutation, Which Enhances Sensitivity to TKIs All 10-15% Elderly 10-15% PS2 10-15% Caucasian never-smokers ~ 35% Asian never-smokers ~ 65%
  • Gefitinib vs Carboplatin / Paclitaxel (CP) in Never- or Light Ex-Smokers Gefitinib HR = 0.19, 95% CI 0.13, 0.26, P < 0.0001 No. events M+ = 97 (73.5%) No. events M- = 88 (96.7%) Carboplatin / paclitaxel , HR = 0.78, 95% CI 0.57, 1.06, P = 0.1103 No. events M+ = 111 (86.0%) No. events M- = 70 (82.4%) 0 4 8 12 16 20 24 Time From Randomization (months) 0.0 0.2 0.4 0.6 0.8 1.0 Gefitinib EGFR M+ (n = 132) Gefitinib EGFR M- (n = 91) Carboplatin / paclitaxel EGFR M+ (n = 129) Carboplatin / paclitaxel EGFR M- (n = 85) Mok T, et al. N Engl J Med. 2009;361:947-957.
  • ETS Gene Fusions as Potential Predictive Biomarker Gene 1 (with androgen-sensitive promoter) Gene 2 (encoding ETS transcripton factor) ETS Gene Fusion (with androgen-sensitive promoter driving overexpression of ETS transcription factor )
    • ~50% of prostate cancers have ETS gene fusions
    • The predominant ETS fusion (80-90%) is TMPRSS2: ERG
  • Expression levels of ERG vs other commonly investigated biomarkers (e.g EGFR)
      • Overexpressed at a level that far exceeds other biomarkers in prostate cancer
      • Knockdown of ETS fusion products abrogates the malignant phenotype of ETS-positive cells
      • in preclinical models; overexpression reconstitutes these malignant phenotypes
  • AR signaling and DNA Damage/Repair
    • AR-mediated transcription is directly coupled with the induction of DNA damage
    • PARP1 function is critical to the pro-tumorigenic functions of the androgen receptor
    • A ddition of the PARP inhibitor ABT888 improves the response to hormone therapy in preclinical prostate cancer models
    Haffner MC, et al: Nat Genet 42:668-75, 2010, Lin C, et al: Cell 139:1069-83, 2009 Haffner M, et al: Clin Cancer Res, 2011
  • ERG-positive Xenografts are Preferentially Sensitive to PARP inhibitors ( Olaparib) ERG (the predominant ETS gene fusion product) physically interacts with PARP1 PARP1 is required for ERG-associated function (transcription/invasion) ERG-positive xenografts are preferentially sensitive to PARP inhibitors
  • LOI 9012:Objectives
    • Evaluate in pts with mCRPC:
    • 1. The role of ETS gene fusion as a predictive biomarker for response to hormone therapy alone or hormone therapy plus PARP targeted therapy.
    • 2. To evaluate whether combination hormone therapy + PARP targeted therapy is superior to hormone alone therapy based on fusion status.
  • LOI 9012: Randomized Phase II Trial of Gene Fusion-Targeted Therapy for Pts with mCRPC N=172 mCRPC 0-1 prior chemotherapy Assess ETS fusions status ETS fusion-positive (~50% of cases) Abiraterone + PARP1 inhibitor ETS fusion-negative patients (~50% of cases) Abiraterone Abiraterone Abiraterone + PARP1 inhibitor
  • Individualized Medicine in prostate Cancer Conclusions
    • Personalized therapy lags significantly behind other solid tumors
      • The bulk of the efforts are focused on “prognosis” in the setting of early stage disease
    • Timely & better drug development requires:
      • High quality/ impact translational science with a focus on:
        • disease biology
        • Defining relevant pathways & Validated predictive biomarkers
        • Adequate preclinical characterization of agents and combinations