Predicting Clinical and Biochemical Endpoints after Radical Prostatectomy Andrew J. Stephenson, MD FRCSC FACS Director, Ur...
Postoperative Predictions: Why? <ul><li>Patient counseling </li></ul><ul><li>Surveillance schedule </li></ul><ul><li>Secon...
Postoperative vs. Pretreatment Models <ul><li>Generally perform with greater accuracy </li></ul><ul><li>Limitations of cli...
Postoperative Nomogram Kattan et al.  J Clin Oncol  1999; Graefen et al.  J Clin Oncol  2002;  Bianco et al.  J Urol  2003...
Postoperative Nomogram v. 2.0 <ul><li>2 surgeons, 1881 pts </li></ul><ul><li>Externally-validated on two cohorts (> 3000 p...
Deciding Upon Secondary Therapy <ul><li>Nomogram predicting outcome of adjuvant RT </li></ul>Stephenson et al.  In prepara...
Building a Better Nomogram? <ul><li>New biomarkers: </li></ul><ul><ul><li>IL-6sR, TGF- β 1, VCAM1, uPA improved accuracy o...
Building a Better Nomogram: Clinical Endpoints <ul><li>Current treatment decision-making and prediction tools largely base...
Postoperative Nomogram for PCSM <ul><li>Modeling cohort : 11,521 patients treated at Cleveland Clinic, MSKCC, Baylor Colle...
Postoperative Nomogram for PCSM <ul><li>Prostate cancer-specific mortality at 15 years </li></ul><ul><ul><li>Modeling: 7% ...
Prostate Cancer-Specific Survival: Gleason Score Analysis Eggener et al.  J Urol  2011 Gleason 6 Glsn 3+4 Glsn 4+3 Glsn 8-...
Prostate Cancer-Specific Survival: Pathological Stage Eggener et al.  J Urol  2011 Organ- Confined EPE SVI LNI Age < 60 Ag...
Nomogram Predicting 15-year PCSM <ul><li>Externally-validated concordance index: 0.92 </li></ul><ul><li>Nomogram predictio...
Building a Better Postoperative PCSM Nomogram <ul><li>Unlikely that new biomarkers will improve predictive accuracy to jus...
Building a Better Postoperative PCSM Nomogram <ul><li>Unlikely that new biomarkers will improve predictive accuracy to jus...
Summary <ul><li>Robust prognostic information contained within radical prostatectomy specimen </li></ul><ul><li>PCSM can b...
 
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NY Prostate Cancer Conference - A. Stephenson - Session 4: Predicting clinical and biochemical endpoints after surgery

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NY Prostate Cancer Conference - A. Stephenson - Session 4: Predicting clinical and biochemical endpoints after surgery

  1. 1. Predicting Clinical and Biochemical Endpoints after Radical Prostatectomy Andrew J. Stephenson, MD FRCSC FACS Director, Urologic Oncology Glickman Urological and Kidney Institute Cleveland Clinic
  2. 2. Postoperative Predictions: Why? <ul><li>Patient counseling </li></ul><ul><li>Surveillance schedule </li></ul><ul><li>Secondary therapy </li></ul><ul><ul><li>Adjuvant or salvage </li></ul></ul><ul><ul><li>Local or systemic </li></ul></ul><ul><li>Clinical trial design </li></ul><ul><li>Build a better preop model… </li></ul>
  3. 3. Postoperative vs. Pretreatment Models <ul><li>Generally perform with greater accuracy </li></ul><ul><li>Limitations of clinical grading and staging </li></ul><ul><ul><li>Biopsy Gleason 2-6  2-3% pathological Gleason 8-10 </li></ul></ul><ul><ul><li> 5-7% pathological Gleason 4+3 </li></ul></ul><ul><ul><li>Clinical stage T1  20-25% non-organ-confined </li></ul></ul><ul><ul><li> 5% SVI or LN metastases </li></ul></ul><ul><ul><li>Clinical stage T3  20-25% organ-confined </li></ul></ul><ul><li>Ability to consider more parameters (e.g. surgical margins) that may influence prognosis </li></ul>
  4. 4. Postoperative Nomogram Kattan et al. J Clin Oncol 1999; Graefen et al. J Clin Oncol 2002; Bianco et al. J Urol 2003 Single surgeon, 996 pts, open RP, 1983-1997, BCR Endpoint: PSA 0.4 ng/mL and rising Bootstrapped c-index: 0.88 Model predictions remarkably robust when applied to diverse external validation populations C-index 0.80-0.83
  5. 5. Postoperative Nomogram v. 2.0 <ul><li>2 surgeons, 1881 pts </li></ul><ul><li>Externally-validated on two cohorts (> 3000 pts) </li></ul><ul><li>10-year endpoint </li></ul><ul><li>Predictions adjusted for year of surgery and adjuvant radiotherapy </li></ul><ul><li>C-index: 0.79-0.81 </li></ul>Stephenson et al. J Clin Oncol 2005
  6. 6. Deciding Upon Secondary Therapy <ul><li>Nomogram predicting outcome of adjuvant RT </li></ul>Stephenson et al. In preparation N = 326 pts, median FU 70 mos, externally-validated on SWOG 8794, c-index 0.67
  7. 7. Building a Better Nomogram? <ul><li>New biomarkers: </li></ul><ul><ul><li>IL-6sR, TGF- β 1, VCAM1, uPA improved accuracy of model when added to standard postoperative parameters (bootstrapped c-index 0.86 vs. 0.82) </li></ul></ul><ul><li>New clinical parameters: </li></ul><ul><ul><li>SM’s: focal v. extensive, solitary v. multiple, apex v. other </li></ul></ul><ul><ul><ul><li>C-index: 0.851 v. 0.850 v. 850 </li></ul></ul></ul><ul><ul><li>Surgeon experience: </li></ul></ul><ul><ul><ul><li>7724 pts, 72 urologists, C-index 0.812 v. 0.811 +/- surgeon experience </li></ul></ul></ul>Svatek et al. Prostate 2009; Stephenson et al. J Urol 2009; Vickers et al. Cancer 2009
  8. 8. Building a Better Nomogram: Clinical Endpoints <ul><li>Current treatment decision-making and prediction tools largely based on PSA recurrence endpoint </li></ul><ul><ul><li>Not a surrogate for PCSM or overall survival </li></ul></ul><ul><ul><li>At 15 years, risk of PCSM (32%) is similar to death from other causes (34%) for men with rising PSA </li></ul></ul><ul><li>Need to understand natural history of screen-detected cancers treated by radical prostatectomy </li></ul><ul><li>Need for nomograms that predict probability of PCSM for treatment decision-making and clinical trial design </li></ul>
  9. 9. Postoperative Nomogram for PCSM <ul><li>Modeling cohort : 11,521 patients treated at Cleveland Clinic, MSKCC, Baylor College of Medicine, University of Michigan between 1987-2005 </li></ul><ul><li>Validation cohort : 12,893 patients treated at Johns Hopkins University between 1987-2005 </li></ul><ul><li>All pathological specimens reviewed by genitourinary pathologists at each institution </li></ul><ul><li>Endpoint: PCSM </li></ul><ul><ul><li>Death attributed to prostate cancer by review of death certificate and documented evidence of castrate-resistant metastatic disease </li></ul></ul><ul><li>Median follow-up 56 and 92 months in the modeling and validation cohorts, respectively </li></ul><ul><ul><li>22% and 16% of eligible patients lost to follow-up at 10 and 15 years </li></ul></ul><ul><ul><li>3163 and 638 surviving patients with > 10 and 15 year follow-up </li></ul></ul><ul><li>Deaths: </li></ul><ul><ul><li>Prostate cancer: 338 </li></ul></ul><ul><ul><li>Other causes: 1204 </li></ul></ul>Eggener et al. J Urol 2011
  10. 10. Postoperative Nomogram for PCSM <ul><li>Prostate cancer-specific mortality at 15 years </li></ul><ul><ul><li>Modeling: 7% (95% CI: 6-9) </li></ul></ul><ul><ul><li>Validation: 4% (95% CI: 3-5) </li></ul></ul><ul><li>All-cause mortality at 15 years </li></ul><ul><ul><li>Modeling: 33% (95% CI: 30-36) </li></ul></ul><ul><ul><li>Validation: 16% (95% CI: 14-17) </li></ul></ul><ul><li>Predictors of PCSM in multivariable analysis </li></ul><ul><ul><li>Primary and secondary Gleason grade 4/5, seminal vesicle invasion, year of surgery ( P < 0.001 for all) </li></ul></ul><ul><ul><li>Not significant: PSA, age, extraprostatic extension, lymph node metastasis, positive surgical margins * ( P > 0.05 for all) </li></ul></ul>Eggener et al. J Urol 2011; Stephenson et al. In preparation * SM’s not associated with PCSM, even after adjusting for postop radiotherapy
  11. 11. Prostate Cancer-Specific Survival: Gleason Score Analysis Eggener et al. J Urol 2011 Gleason 6 Glsn 3+4 Glsn 4+3 Glsn 8-10 Age < 60 Age 60-69 Age > 69
  12. 12. Prostate Cancer-Specific Survival: Pathological Stage Eggener et al. J Urol 2011 Organ- Confined EPE SVI LNI Age < 60 Age 60-69 Age > 69
  13. 13. Nomogram Predicting 15-year PCSM <ul><li>Externally-validated concordance index: 0.92 </li></ul><ul><li>Nomogram predictions adjusted for treatment year </li></ul><ul><li>Model assumes the patient is treated in 2005 </li></ul><ul><li>Effect of treatment year has stabilized since mid-1990’s </li></ul><ul><li>Stephenson et al. J Clin Oncol 2009 </li></ul>Eggener et al. J Urol 2011
  14. 14. Building a Better Postoperative PCSM Nomogram <ul><li>Unlikely that new biomarkers will improve predictive accuracy to justify inclusion into standard practise </li></ul><ul><li>New biomarkers: Systems pathology to predict clinical failure </li></ul><ul><ul><li>Stephenson et al. J Clin Oncol 2005: c-index 0.85 </li></ul></ul><ul><ul><li>Cox model based on std parameters: c-index 0.84 </li></ul></ul><ul><ul><li>Systems pathology model 1: c-index 0.81 </li></ul></ul><ul><ul><li>Systems pathology model 2: c-index 0.85 </li></ul></ul><ul><li>To build a better preop nomogram…. </li></ul><ul><li>Try to predict pathological Gleason score 8-10 and SVI/LNI (treatment intensification) or Gleason score 6 and organ-confined (active surveillance) </li></ul>Donovan et al. J Clin Oncol 2008; Eggener et al. Cancer 2009
  15. 15. Building a Better Postoperative PCSM Nomogram <ul><li>Unlikely that new biomarkers will improve predictive accuracy to justify inclusion into standard practise </li></ul><ul><li>New biomarkers: Systems pathology to predict clinical failure </li></ul><ul><ul><li>Stephenson et al. J Clin Oncol 2005: c-index 0.85 </li></ul></ul><ul><ul><li>Cox model based on std parameters: c-index 0.84 </li></ul></ul><ul><ul><li>Systems pathology model 1: c-index 0.81 </li></ul></ul><ul><ul><li>Systems pathology model 2: c-index 0.85 </li></ul></ul><ul><li>To build a better preop nomogram…. </li></ul><ul><li>Try to predict pathological Gleason score 8-10 and SVI/LNI (treatment intensification) or Gleason score 6 and organ-confined (active surveillance) </li></ul>Donovan et al. J Clin Oncol 2008; Eggener et al. Cancer 2009
  16. 16. Summary <ul><li>Robust prognostic information contained within radical prostatectomy specimen </li></ul><ul><li>PCSM can be predicted with near-perfect accuracy once the pathological features of prostate cancer are known </li></ul><ul><li>Issues going forward…… </li></ul><ul><li>“ Tweaking” of the Gleason grading system (2005 ISUP) </li></ul><ul><li>Epstein et al. Am J Surg Pathol 2005 </li></ul><ul><li>Difficulty adjusting for the use of secondary therapy when making predictions </li></ul><ul><ul><li>E.g. All patients who die from prostate cancer receive ADT </li></ul></ul>

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