Karnes, R. Jeffrey

5000 Background: It is clinically challenging to integrate genomic classifier results that report a continuous numerical risk of recurrence into treatment decisions for prostate cancer (PCa). We aimed to develop a novel clinical-genomic risk system that can readily be incorporated into treatment guidelines for localized PCa. Methods: Four multi-center cohorts (n = 6928 men; 5937 prospective samples and 991 retrospective samples with long-term follow-up) were utilized to identify and validate our clinical-genomic risk system in radical prostatectomy (RP) samples and subsequently in pre-treatment biopsy samples. All patients’ FFPE tissue underwent microarray analysis, and the expression values for 22 prespecified biomarkers that constitute Decipher were extracted. Cumulative incidence curves were constructed to estimate metastasis risk. C-indices were calculated to compare NCCN and CAPRA score to our clinical-genomic system. Results: With a median follow-up of 8 years for men in our RP cohort, the 10-year distant metastasis rates for NCCN low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.8%, 9.4%, 40.1%, and 41.4%, respectively. Our 3-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.7%, 30.7%, and 57.7%, for low, intermediate, and high-risk, which were validated in our pre-treatment biopsy cohort with 10-year rate of distant metastasis of 0%, 30.3%, and 63.2%, respectively. C-indices for the clinical-genomic system (0.84, 95%CI 0.62-0.92) were significantly improved over NCCN (0.71, 95%CI 0.59-0.84) and CAPRA (0.71, 95%CI 0.60-0.81) score. A total of 33.4% of men would be reclassified by the clinical-genomic system, and specifically 17.1%, 41.3%, and 19.4% of men in NCCN low, intermediate and high risk groups would be reclassified by our new system. Conclusions: The use of a readily available genomic classifier in combination with clinicopathologic variables can generate a simple to use 3-tier clinical-genomic risk system that is highly prognostic for distant metastasis, is more accurate than clinical risk, and can be easily incorporated into NCCN guidelines to inform treatment decisions.

AbstractBackground: We studied the utility of the tumor suppressor Tristetraprolin (TTP, ZFP36) as a clinically relevant biomarker of aggressive disease in prostate cancer patients after radical prostatectomy (RP).Methods: TTP RNA expression was measured in an RP cohort of patients treated at Moffitt Cancer Center (MCC) and obtained from six publically available RP datasets with biochemical recurrence (BCR; total n = 1,394) and/or metastatic outcome data (total n = 1,222). TTP protein expression was measured by immunohistochemistry in a tissue microarray of 153 MCC RP samples. The time to BCR or metastasis based on TTP RNA or protein levels was calculated using the Kaplan–Meier analysis. Univariable and multivariable Cox proportional hazard models were performed on multiple cohorts to evaluate if TTP is a clinically relevant biomarker and to assess if TTP improves upon the Cancer of the Prostate Risk Assessment postsurgical (CAPRA-S) score for predicting clinical outcomes.Results: In all of the RP patient cohorts, prostate cancer with low TTP RNA or protein levels had decreased time to BCR or metastasis versus TTP-high tumors. Further, the decreased time to BCR in TTP-low prostate cancer was more pronounced in low-grade tumors. Finally, pooled survival analysis suggests that TTP RNA expression provides independent information beyond CAPRA-S to predict BCR.Conclusions: TTP is a promising prostate cancer biomarker for predicting which RP patients will have poor outcomes, especially for low-grade prostate cancer patients.Impact: This study suggests that TTP RNA expression can be used to enhance the accuracy of CAPRA-S to predict outcomes in patients treated with RP. Cancer Epidemiol Biomarkers Prev; 27(11); 1376–83. ©2018 AACR.

Purpose Molecular characterization of prostate cancer, including The Cancer Genome Atlas, has revealed distinct subtypes with underlying genomic alterations. One of these core subtypes, SPOP (speckle-type POZ protein) mutant prostate cancer, has previously only been identifiable via DNA sequencing, which has made the impact on prognosis and routinely used risk stratification parameters unclear. Methods We have developed a novel gene expression signature, classifier (Subclass Predictor Based on Transcriptional Data), and decision tree to predict the SPOP mutant subclass from RNA gene expression data and classify common prostate cancer molecular subtypes. We then validated and further interrogated the association of prostate cancer molecular subtypes with pathologic and clinical outcomes in retrospective and prospective cohorts of 8,158 patients. Results The subclass predictor based on transcriptional data model showed high sensitivity and specificity in multiple cohorts across both RNA sequencing and microarray gene expression platforms. We predicted approximately 8% to 9% of cases to be SPOP mutant from both retrospective and prospective cohorts. We found that the SPOP mutant subclass was associated with lower frequency of positive margins, extraprostatic extension, and seminal vesicle invasion at prostatectomy; however, SPOP mutant cancers were associated with higher pretreatment serum prostate-specific antigen (PSA). The association between SPOP mutant status and higher PSA level was validated in three independent cohorts. Despite high pretreatment PSA, the SPOP mutant subtype was associated with a favorable prognosis with improved metastasis-free survival, particularly in patients with high-risk preoperative PSA levels. Conclusion Using a novel gene expression model and a decision tree algorithm to define prostate cancer molecular subclasses, we found that the SPOP mutant subclass is associated with higher preoperative PSA, less adverse pathologic features, and favorable prognosis. These findings suggest a paradigm in which the interpretation of common risk stratification parameters, particularly PSA, may be influenced by the underlying molecular subtype of prostate cancer.

Abstract Purpose: Currently, no genomic signature exists to distinguish men most likely to progress on adjuvant androgen deprivation therapy (ADT) after radical prostatectomy for high-risk prostate cancer. Here we develop and validate a gene expression signature to predict response to postoperative ADT. Experimental Design: A training set consisting of 284 radical prostatectomy patients was established after 1:1 propensity score matching metastasis between adjuvant-ADT (a-ADT)-treated and no ADT–treated groups. An ADT Response Signature (ADT-RS) was identified from neuroendocrine and AR signaling–related genes. Two independent cohorts were used to form three separate data sets for validation (set I, n = 232; set II, n = 435; set III, n = 612). The primary endpoint of the analysis was postoperative metastasis. Results: Increases in ADT-RS score were associated with a reduction in risk of metastasis only in a-ADT patients. On multivariable analysis, ADT-RS by ADT treatment interaction term remained associated with metastasis in both validation sets (set I: HR = 0.18, Pinteraction = 0.009; set II: HR = 0.25, Pinteraction = 0.019). In a matched validation set III, patients with Low ADT-RS scores had similar 10-year metastasis rates in the a-ADT and no-ADT groups (30.1% vs. 31.0%, P = 0.989). Among High ADT-RS patients, 10-year metastasis rates were significantly lower for a-ADT versus no-ADT patients (9.4% vs. 29.2%, P = 0.021). The marginal ADT-RS by ADT interaction remained significant in the matched dataset (Pinteraction = 0.035). Conclusions: Patients with High ADT-RS benefited from a-ADT. In combination with prognostic risk factors, use of ADT-RS may thus allow for identification of ADT-responsive tumors that may benefit most from early androgen blockade after radical prostatectomy. We discovered a gene signature that when present in primary prostate tumors may be useful to predict patients who may respond to early ADT after surgery. Clin Cancer Res; 24(16); 3908–16. ©2018 AACR.

125 Background: Men with lymph node involvement (LNI) at prostatectomy (RP) are at high risk of dying from prostate cancer. However, survival following RP is highly variable, with some men apparently cured. Nomograms developed for men with LNI have been based on series where all or the vast majority of men received adjuvant treatment. Because administration of adjuvant treatment is not universal, even for LNI, we evaluated whether the Decipher genomic classifier (GC) can improve upon clinical models to predict metastasis within 5 years (MET5) and prostate cancer specific mortality within 10 years (PCSM10) in a cohort of LNI patients, the majority of whom did not receive adjuvant treatment. Methods: 141 patients from 4 institutions (Johns Hopkins, Mayo Clinic, Leuven, MD Anderson) had LNI at RP, had adequate tissue and clinical data for analysis of MET5, and 86 were analyzed for PCSM10. 43% of men received adjuvant therapy. RP tumor tissue was analyzed by Affymetrix Human Exon 1.0 ST GeneChip; the GC was calculated based on 22 genes in the previously trained and validated algorithm. Logistic regression evaluated whether the GC, dichotomized as high risk (GC score > 0.6) vs low-intermediate risk (≤0.6), improved prediction of MET5 and PCSM10 beyond that achieved with established clinical prognostic factors. Results: 62 men (43%) developed MET5, and 35 (41%) developed PCSM10. For both MET5 and PCSM10 CAPRA-S, number of positive lymph nodes, and age were significant; adjuvant therapy was not significant. GC was a significant independent strong prognostic factor when added to the clinical model for prediction of MET5, odds ratio = 4.04 (95% CI: 1.48, 11.02), p = 0.006, and prediction of PCSM10, odds ratio = 6.71 (95% CI: 2.01, 22.38), p = 0.002. Addition of GC to the clinical model improved the AUC from 0.77 to 0.79 for MET5, and from 0.65 to 0.74 for PCSM10. Conclusions: The Decipher GC significantly improves upon clinical variables to predict metastasis and prostate cancer specific mortality in men at high risk due to LNI. This is the first study to show a genomic classifier predicts outcomes in men with LNI; validation is needed to determine if Decipher can improve treatment decisions in men with LNI.

Abstract The trend toward precision-based therapeutic approaches dictated by molecular alterations offers substantial promise for men with metastatic castration-resistant prostate cancer (mCRPC). However, current approaches for molecular characterization are primarily tissue based, necessitating serial biopsies to understand changes over time and are limited by the challenges inherent to extracting genomic material from predominantly bone metastases. Therefore, a circulating tumor cell (CTC)-based assay was developed to determine gene expression across a panel of clinically relevant and potentially actionable prostate cancer–related genes. CTCs were isolated from the whole blood of mCRPC patients (n = 41) and multiplex qPCR was performed to evaluate expression of prostate cancer–related target genes (n = 78). A large fraction of patients (27/41, 66%) had detectable CTCs. Increased androgen receptor (AR) expression (70% of samples) and evidence of Wnt signaling (67% of samples) were observed. The TMPRSS2:ERG fusion was expressed in 41% of samples, and the aggressive prostate cancer–associated long noncoding RNA SChLAP1 was upregulated in 70%. WNT5a [HR 3.62, 95% confidence interval (CI), 1.63–8.05, P = 0.002], AURKA (HR 5.56, 95% CI, 1.79–17.20, P = 0.003), and BMP7 (HR 3.86, 95% CI, 1.60–9.32, P = 0.003) were independently predictive of overall survival (FDR < 10%) after adjusting for a panel of previously established prognostic variables in mCRPC (Halabi nomogram). A model including Halabi, WNT5a, and AURKA expression, termed the miCTC score, outperformed the Halabi nomogram alone (AUC = 0.89 vs. AUC = 0.70). Understanding the molecular landscape of CTCs has utility in predicting clinical outcomes in patients with aggressive prostate cancer and provides an additional tool in the arsenal of precision-based therapeutic approaches in oncology. Implications: Analysis of CTC gene expression reveals a clinically prognostic “liquid biopsy” signature in patients with metastatic castrate-resistance prostate cancer. Mol Cancer Res; 16(4); 643–54. ©2018 AACR.