Zaid Haddad

Discovery and Validation of a Prostate Cancer Genomic Classifier that Predicts Early Metastasis Following Radical Prostatectomy

Purpose Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis. Methods A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set. Results Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67–0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression. Conclusion A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.

Validation of a Genomic Classifier that Predicts Metastasis Following Radical Prostatectomy in an At Risk Patient Population

PURPOSE Patients with locally advanced prostate cancer after radical prostatectomy are candidates for secondary therapy. However, this higher risk population is heterogeneous. Many cases do not metastasize even when conservatively managed. Given the limited specificity of pathological features to predict metastasis, newer risk prediction models are needed. We report a validation study of a genomic classifier that predicts metastasis after radical prostatectomy in a high risk population. MATERIALS AND METHODS A case-cohort design was used to sample 1,010 patients after radical prostatectomy at high risk for recurrence who were treated from 2000 to 2006. Patients had preoperative prostate specific antigen greater than 20 ng/ml, Gleason 8 or greater, pT3b or a Mayo Clinic nomogram score of 10 or greater. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded from analysis. A 20% random sampling created a subcohort that included all patients with metastasis. We generated 22-marker genomic classifier scores for 219 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance. RESULTS The genomic classifier AUC was 0.79 for predicting 5-year metastasis after radical prostatectomy. Decision curves showed that the genomic classifier net benefit exceeded that of clinical only models. The genomic classifier was the predominant predictor of metastasis on multivariable analysis. The cumulative incidence of metastasis 5 years after radical prostatectomy was 2.4%, 6.0% and 22.5% in patients with low (60%), intermediate (21%) and high (19%) genomic classifier scores, respectively (p<0.001). CONCLUSIONS Results indicate that genomic information from the primary tumor can identify patients with adverse pathological features who are most at risk for metastasis and potentially lethal prostate cancer.

A Genomic Classifier Improves Prediction of Metastatic Disease Within 5 Years After Surgery in Node-negative High-risk Prostate Cancer Patients Managed by Radical Prostatectomy Without Adjuvant Therapy

BACKGROUND Surgery is a standard first-line therapy for men with intermediate- or high-risk prostate cancer. Clinical factors such as tumor grade, stage, and prostate-specific antigen (PSA) are currently used to identify those who are at risk of recurrence and who may benefit from adjuvant therapy, but novel biomarkers that improve risk stratification and that distinguish local from systemic recurrence are needed. OBJECTIVE To determine whether adding the Decipher genomic classifier, a validated metastasis risk-prediction model, to standard risk-stratification tools (CAPRA-S and Stephenson nomogram) improves accuracy in predicting metastatic disease within 5 yr after surgery (rapid metastasis [RM]) in an independent cohort of men with adverse pathologic features after radical prostatectomy (RP). DESIGN, SETTING, AND PARTICIPANTS The study population consisted of 169 patients selected from 2641 men who underwent RP at the Cleveland Clinic between 1987 and 2008 who met the following criteria: (1) preoperative PSA>20 ng/ml, stage pT3 or margin positive, or Gleason score≥8; (2) pathologic node negative; (3) undetectable post-RP PSA; (4) no neoadjuvant or adjuvant therapy; and (5) minimum of 5-yr follow-up for controls. The final study cohort consisted of 15 RM patients and 154 patients as non-RM controls. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The performance of Decipher was evaluated individually and in combination with clinical risk factors using concordance index (c-index), decision curve analysis, and logistic regression for prediction of RM. RESULTS AND LIMITATIONS RM patients developed metastasis at a median of 2.3 yr (interquartile range: 1.7-3.3). In multivariable analysis, Decipher was a significant predictor of RM (odds ratio: 1.48; p=0.018) after adjusting for clinical risk factors. Decipher had the highest c-index, 0.77, compared with the Stephenson model (c-index: 0.75) and CAPRA-S (c-index: 0.72) as well as with a panel of previously reported prostate cancer biomarkers unrelated to Decipher. Integration of Decipher into the Stephenson nomogram increased the c-index from 0.75 (95% confidence interval [CI], 0.65-0.85) to 0.79 (95% CI, 0.68-0.89). CONCLUSIONS Decipher was independently validated as a genomic metastasis signature for predicting metastatic disease within 5 yr after surgery in a cohort of high-risk men treated with RP and managed conservatively without any adjuvant therapy. Integration of Decipher into clinical nomograms increased prediction of RM. Decipher may allow identification of men most at risk for metastatic progression who should be considered for multimodal therapy or inclusion in clinical trials. PATIENT SUMMARY Use of Decipher in addition to standard clinical information more accurately identified men who developed metastatic disease within 5 yr after surgery. The results suggest that Decipher allows improved identification of the men who should consider secondary therapy from among the majority that may be managed conservatively after surgery.

Characterization of 1577 primary prostate cancers reveals novel biological and clinicopathologic insights into molecular subtypes

BACKGROUND Prostate cancer (PCa) molecular subtypes have been defined by essentially mutually exclusive events, including ETS gene fusions (most commonly involving ERG) and SPINK1 overexpression. Clinical assessment may aid in disease stratification, complementing available prognostic tests. OBJECTIVE To determine the analytical validity and clinicopatholgic associations of microarray-based molecular subtyping. DESIGN, SETTING, AND PARTICIPANTS We analyzed Affymetrix GeneChip expression profiles for 1577 patients from eight radical prostatectomy cohorts, including 1351 cases assessed using the Decipher prognostic assay (GenomeDx Biosciences, San Diego, CA, USA) performed in a laboratory with Clinical Laboratory Improvements Amendment certification. A microarray-based (m-) random forest ERG classification model was trained and validated. Outlier expression analysis was used to predict other mutually exclusive non-ERG ETS gene rearrangements (ETS(+)) or SPINK1 overexpression (SPINK1(+)). OUTCOME MEASUREMENTS Associations with clinical features and outcomes by multivariate logistic regression analysis and receiver operating curves. RESULTS AND LIMITATIONS The m-ERG classifier showed 95% accuracy in an independent validation subset (155 samples). Across cohorts, 45% of PCas were classified as m-ERG(+), 9% as m-ETS(+), 8% as m-SPINK1(+), and 38% as triple negative (m-ERG(-)/m-ETS(-)/m-SPINK1(-)). Gene expression profiling supports three underlying molecularly defined groups: m-ERG(+), m-ETS(+), and m-SPINK1(+)/triple negative. On multivariate analysis, m-ERG(+) tumors were associated with lower preoperative serum prostate-specific antigen and Gleason scores, but greater extraprostatic extension (p<0.001). m-ETS(+) tumors were associated with seminal vesicle invasion (p=0.01), while m-SPINK1(+)/triple negative tumors had higher Gleason scores and were more frequent in Black/African American patients (p<0.001). Clinical outcomes were not significantly different among subtypes. CONCLUSIONS A clinically available prognostic test (Decipher) can also assess PCa molecular subtypes, obviating the need for additional testing. Clinicopathologic differences were found among subtypes based on global expression patterns. PATIENT SUMMARY Molecular subtyping of prostate cancer can be achieved using extra data generated from a clinical-grade, genome-wide expression-profiling prognostic assay (Decipher). Transcriptomic and clinical analysis support three distinct molecular subtypes: (1) m-ERG(+), (2) m-ETS(+), and (3) m-SPINK1(+)/triple negative (m-ERG(-)/m-ETS(-)/m-SPINK1(-)). Incorporation of subtyping into a clinically available assay may facilitate additional applications beyond routine prognosis.

Decipher Genomic Classifier Measured on Prostate Biopsy Predicts Metastasis Risk

OBJECTIVES To evaluate the ability of the Decipher genomic classifier in predicting metastasis from analysis of prostate needle biopsy diagnostic tumor tissue specimens. MATERIALS AND METHODS Fifty-seven patients with available biopsy specimens were identified from a cohort of 169 men treated with radical prostatectomy in a previously reported Decipher validation study at Cleveland Clinic. A Cox multivariable proportional hazards model and survival C-index were used to evaluate the performance of Decipher. RESULTS With a median follow up of 8 years, 8 patients metastasized and 3 died of prostate cancer. The Decipher plus National Comprehensive Cancer Network (NCCN) model had an improved C-index of 0.88 (95% confidence interval [CI] 0.77-0.96) compared to NCCN alone (C-index 0.75, 95% CI 0.64-0.87). On multivariable analysis, Decipher was the only significant predictor of metastasis when adjusting for age, preoperative prostate-specific antigen and biopsy Gleason score (Decipher hazard ratio per 10% increase 1.72, 95% CI 1.07-2.81, P = .02). CONCLUSION Biopsy Decipher predicted the risk of metastasis at 10 years post radical prostatectomy. While further validation is required on larger cohorts, preoperative knowledge of Decipher risk derived from biopsy could indicate the need for multimodality therapy and help set patient expectations of therapeutic burden.

Novel Biomarker Signature That May Predict Aggressive Disease in African American Men With Prostate Cancer

PURPOSE We studied the ethnicity-specific expression of prostate cancer (PC) -associated biomarkers to evaluate whether genetic/biologic factors affect ethnic disparities in PC pathogenesis and disease progression. PATIENTS AND METHODS A total of 154 African American (AA) and 243 European American (EA) patients from four medical centers were matched according to the Cancer of the Prostate Risk Assessment postsurgical score within each institution. The distribution of mRNA expression levels of 20 validated biomarkers reported to be associated with PC initiation and progression was compared with ethnicity using false discovery rate, adjusted Wilcoxon-Mann-Whitney, and logistic regression models. A conditional logistic regression model was used to evaluate the interaction between ethnicity and biomarkers for predicting clinicopathologic outcomes. RESULTS Of the 20 biomarkers examined, six showed statistically significant differential expression in AA compared with EA men in one or more statistical models. These include ERG (P < .001), AMACR (P < .001), SPINK1 (P = .001), NKX3-1 (P = .03), GOLM1 (P = .03), and androgen receptor (P = .04). Dysregulation of AMACR (P = .036), ERG (P = .036), FOXP1 (P = .041), and GSTP1 (P = .049) as well as loss-of-function mutations for tumor suppressors NKX3-1 (P = .025) and RB1 (P = .037) predicted risk of pathologic T3 disease in an ethnicity-dependent manner. Dysregulation of GOLM1 (P = .037), SRD5A2 (P = .023), and MKi67 (P = .023) predicted clinical outcomes, including 3-year biochemical recurrence and metastasis at 5 years. A greater proportion of AA men than EA men had triple-negative (ERG-negative/ETS-negative/SPINK1-negative) disease (51% v 35%; P = .002). CONCLUSION We have identified a subset of PC biomarkers that predict the risk of clinicopathologic outcomes in an ethnicity-dependent manner. These biomarkers may explain in part the biologic contribution to ethnic disparity in PC outcomes between EA and AA men.

Individual Patient-Level Meta-Analysis of the Performance of the Decipher Genomic Classifier in High-Risk Men After Prostatectomy to Predict Development of Metastatic Disease

Purpose To perform the first meta-analysis of the performance of the genomic classifier test, Decipher, in men with prostate cancer postprostatectomy. Methods MEDLINE, EMBASE, and the Decipher genomic resource information database were searched for published reports between 2011 and 2016 of men treated by prostatectomy that assessed the benefit of the Decipher test. Multivariable Cox proportional hazards models fit to individual patient data were performed; meta-analyses were conducted by pooling the study-specific hazard ratios (HRs) using random-effects modeling. Extent of heterogeneity between studies was determined with the I2 test. Results Five studies (975 total patients, and 855 patients with individual patient-level data) were eligible for analysis, with a median follow-up of 8 years. Of the total cohort, 60.9%, 22.6%, and 16.5% of patients were classified by Decipher as low, intermediate, and high risk, respectively. The 10-year cumulative incidence metastases rates were 5.5%, 15.0%, and 26.7% ( P < .001), respectively, for the three risk classifications. Pooling the study-specific Decipher HRs across the five studies resulted in an HR of 1.52 (95% CI, 1.39 to 1.67; I2 = 0%) per 0.1 unit. In multivariable analysis of individual patient data, adjusting for clinicopathologic variables, Decipher remained a statistically significant predictor of metastasis (HR, 1.30; 95% CI, 1.14 to 1.47; P < .001) per 0.1 unit. The C-index for 10-year distant metastasis of the clinical model alone was 0.76; this increased to 0.81 with inclusion of Decipher. Conclusion The genomic classifier test, Decipher, can independently improve prognostication of patients postprostatectomy, as well as within nearly all clinicopathologic, demographic, and treatment subgroups. Future study of how to best incorporate genomic testing in clinical decision-making and subsequent treatment recommendations is warranted.

Discovery and Validation of Novel Expression Signature for Postcystectomy Recurrence in High-Risk Bladder Cancer

Background Nearly half of muscle-invasive bladder cancer patients succumb to their disease following cystectomy. Selecting candidates for adjuvant therapy is currently based on clinical parameters with limited predictive power. This study aimed to develop and validate genomic-based signatures that can better identify patients at risk for recurrence than clinical models alone. Methods Transcriptome-wide expression profiles were generated using 1.4 million feature-arrays on archival tumors from 225 patients who underwent radical cystectomy and had muscle-invasive and/or node-positive bladder cancer. Genomic (GC) and clinical (CC) classifiers for predicting recurrence were developed on a discovery set (n = 133). Performances of GC, CC, an independent clinical nomogram (IBCNC), and genomic-clinicopathologic classifiers (G-CC, G-IBCNC) were assessed in the discovery and independent validation (n = 66) sets. GC was further validated on four external datasets (n = 341). Discrimination and prognostic abilities of classifiers were compared using area under receiver-operating characteristic curves (AUCs). All statistical tests were two-sided. Results A 15-feature GC was developed on the discovery set with area under curve (AUC) of 0.77 in the validation set. This was higher than individual clinical variables, IBCNC (AUC = 0.73), and comparable to CC (AUC = 0.78). Performance was improved upon combining GC with clinical nomograms (G-IBCNC, AUC = 0.82; G-CC, AUC = 0.86). G-CC high-risk patients had elevated recurrence probabilities (P < .001), with GC being the best predictor by multivariable analysis (P = .005). Genomic-clinicopathologic classifiers outperformed clinical nomograms by decision curve and reclassification analyses. GC performed the best in validation compared with seven prior signatures. GC markers remained prognostic across four independent datasets. Conclusions The validated genomic-based classifiers outperform clinical models for predicting postcystectomy bladder cancer recurrence. This may be used to better identify patients who need more aggressive management.

Cyclin D1 Loss Distinguishes Prostatic Small Cell Carcinoma from Most Prostatic Adenocarcinomas

Purpose: Small-cell neuroendocrine differentiation in prostatic carcinoma is an increasingly common resistance mechanism to potent androgen deprivation therapy (ADT), but can be difficult to identify morphologically. We investigated whether cyclin D1 and p16 expression can inform on Rb functional status and distinguish small-cell carcinoma from adenocarcinoma. Experimental Design: We used gene expression data and immunohistochemistry to examine cyclin D1 and p16 levels in patient-derived xenografts (PDX), and prostatic small-cell carcinoma and adenocarcinoma specimens. Results: Using PDX, we show proof-of-concept that a high ratio of p16 to cyclin D1 gene expression reflects underlying Rb functional loss and distinguishes morphologically identified small-cell carcinoma from prostatic adenocarcinoma in patient specimens (n = 13 and 9, respectively). At the protein level, cyclin D1, but not p16, was useful to distinguish small-cell carcinoma from adenocarcinoma. Overall, 88% (36/41) of small-cell carcinomas showed cyclin D1 loss by immunostaining compared with 2% (2/94) of Gleason score 7–10 primary adenocarcinomas at radical prostatectomy, 9% (4/44) of Gleason score 9–10 primary adenocarcinomas at needle biopsy, and 7% (8/115) of individual metastases from 39 patients at autopsy. Though rare adenocarcinomas showed cyclin D1 loss, many of these were associated with clinical features of small-cell carcinoma, and in a cohort of men treated with adjuvant ADT who developed metastasis, lower cyclin D1 gene expression was associated with more rapid onset of metastasis and death. Conclusions: Cyclin D1 loss identifies prostate tumors with small-cell differentiation and may identify a small subset of adenocarcinomas with poor prognosis. Clin Cancer Res; 21(24); 5619–29. ©2015 AACR.

Validation of a Genomic Classifier for Predicting Post-Prostatectomy Recurrence in a Community Based Health Care Setting

PURPOSE We determined the value of Decipher®, a genomic classifier, to predict prostate cancer outcomes among patients after prostatectomy in a community health care setting. MATERIALS AND METHODS We examined the experience of 224 men treated with radical prostatectomy from 1997 to 2009 at Kaiser Permanente Northwest, a large prepaid health plan in Portland, Oregon. Study subjects had aggressive prostate cancer with at least 1 of several criteria such as preoperative prostate specific antigen 20 ng/ml or greater, pathological Gleason score 8 or greater, stage pT3 disease or positive surgical margins at prostatectomy. The primary end point was clinical recurrence or metastasis after surgery evaluated using a time dependent c-index. Secondary end points were biochemical recurrence and salvage treatment failure. We compared the performance of Decipher alone to the widely used CAPRA-S (Cancer of the Prostate Risk Assessment Post-Surgical) score, and assessed the independent contributions of Decipher, CAPRA-S and their combination for the prediction of recurrence and treatment failure. RESULTS Of the 224 patients treated 12 experienced clinical recurrence, 68 had biochemical recurrence and 34 experienced salvage treatment failure. At 10 years after prostatectomy the recurrence rate was 2.6% among patients with low Decipher scores but 13.6% among those with high Decipher scores (p=0.02). When CAPRA-S and Decipher scores were considered together, the discrimination accuracy of the ROC curve was increased by 0.11 compared to the CAPRA-S score alone (combined c-index 0.84 at 10 years after radical prostatectomy) for clinical recurrence. CONCLUSIONS Decipher improves our ability to predict clinical recurrence in prostate cancer and adds precision to conventional pathological prognostic measures.