Erho, Nicholas

4565 Background: The efficient delivery of adjuvant therapy after radical prostatectomy (RP) in patients with prostate cancer is limited by the lack of biomarkers, beyond clinicopathologic factors, that are able to assess the risk of clinically significant disease progression. Previously, routine FFPE patient specimens from the Mayo Clinic Radical Prostatectomy Registry with long term follow-up were selected to develop a genomic classifier (GC) to predict clinical progression. Here, we present the validation of a GC in a cohort of patients at high risk of disease progression. Methods: A case-cohort study of high-risk RP patients from the Mayo Clinic (N=219) was used to validate the genomic classifier (GC) for predicting clinical progression (defined by positive bone or CT scan post-RP). Its performance was compared to a multivariable clinical classifier (CC) and a genomic-clinical classifier (GCC) which combines GC with established clinicopathologic variables. Concordance index, Cox modeling and decision curve analysis were used to compare the different models. Results: GC and GCC were predictive of clinical progression in the high-risk cohort with c-indices of 0.79 and 0.82, respectively, compared to the clinical classifier (0.70). Multivariable survival analysis showed that the majority of prognostic information of GCC came from the GC with a minor contribution from Gleason score. Decision curve analysis showed that GCC had a higher overall net benefit compared to CC over a wide range of ‘decision-to-treat’ thresholds for the risk of progression. Conclusions: In this high-risk cohort, GC and GCC classifiers showed improved performance over CC in prediction of clinical progression. GC is an independent prognostic factor in this cohort and captures the majority of prognostic information. GC and GCC’s prognostic performance and their usefulness in guiding decision-making in the adjuvant setting after RP need further testing in studies of additional prostate cancer risk groups.

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.

Abstract Background Treatment-induced neuroendocrine prostate cancer (tNEPC) is an aggressive variant of late-stage metastatic castrate-resistant prostate cancer that commonly arises through neuroendocrine transdifferentiation (NEtD). Treatment options are limited, ineffective, and, for most patients, result in death in less than a year. We previously developed a first-in-field patient-derived xenograft (PDX) model of NEtD. Longitudinal deep transcriptome profiling of this model enabled monitoring of dynamic transcriptional changes during NEtD and in the context of androgen deprivation. Long non-coding RNA (lncRNA) are implicated in cancer where they can control gene regulation. Until now, the expression of lncRNAs during NEtD and their clinical associations were unexplored. Results We implemented a next-generation sequence analysis pipeline that can detect transcripts at low expression levels and built a genome-wide catalogue (n = 37,749) of lncRNAs. We applied this pipeline to 927 clinical samples and our high-fidelity NEtD model LTL331 and identified 821 lncRNAs in NEPC. Among these are 122 lncRNAs that robustly distinguish NEPC from prostate adenocarcinoma (AD) patient tumours. The highest expressed lncRNAs within this signature are H19, LINC00617, and SSTR5-AS1. Another 742 are associated with the NEtD process and fall into four distinct patterns of expression (NEtD lncRNA Class I, II, III, and IV) in our PDX model and clinical samples. Each class has significant (z-scores >2) and unique enrichment for transcription factor binding site (TFBS) motifs in their sequences. Enriched TFBS include (1) TP53 and BRN1 in Class I, (2) ELF5, SPIC, and HOXD1 in Class II, (3) SPDEF in Class III, (4) HSF1 and FOXA1 in Class IV, and (5) TWIST1 when merging Class III with IV. Common TFBS in all NEtD lncRNA were also identified and include E2F, REST, PAX5, PAX9, and STAF. Interrogation of the top deregulated candidates (n = 100) in radical prostatectomy adenocarcinoma samples with long-term follow-up (median 18 years) revealed significant clinicopathological associations. Specifically, we identified 25 that are associated with rapid metastasis following androgen deprivation therapy (ADT). Two of these lncRNAs (SSTR5-AS1 and LINC00514) stratified patients undergoing ADT based on patient outcome. Discussion To date, a comprehensive characterization of the dynamic landscape of lncRNAs during the NEtD process has not been performed. A temporal analysis of the PDX-based NEtD model has for the first time provided this dynamic landscape. TFBS analysis identified NEPC-related TF motifs present within the NEtD lncRNA sequences, suggesting functional roles for these lncRNAs in NEPC pathogenesis. Furthermore, select NEtD lncRNAs appear to be associated with metastasis and patients receiving ADT. Treatment-related metastasis is a clinical consequence of NEPC tumours. Top candidate lncRNAs FENDRR, H19, LINC00514, LINC00617, and SSTR5-AS1 identified in this study are implicated in the development of NEPC. We present here for the first time a genome-wide catalogue of NEtD lncRNAs that characterize the transdifferentiation process and a robust NEPC lncRNA patient expression signature. To accomplish this, we carried out the largest integrative study that applied a PDX NEtD model to clinical samples. These NEtD and NEPC lncRNAs are strong candidates for clinical biomarkers and therapeutic targets and warrant further investigation.

2 Background: Significant genomic diversity exists in the androgen receptor ( AR) and its activity (AR-A) in mCRPC. In localized prostate cancer, the biologic, prognostic, and therapeutic clinical implications of AR-A heterogeneity have yet to be interrogated Methods: Genome-wide expression profiles of FFPE RP or biopsy tumor samples were evaluated from a prospective registry cohort (n = 5,239, NCT02609269) and six retrospective institutional cohorts (n = 1,170). AR-A was calculated based on expression of 9 targets of AR.. Results: Utilizing 6,409 localized prostate adenocarcinomas with full transcriptomic data, we found there was marked inter-individual transcriptomic diversity in AR and AR-A expression, and weak correlation between them (r = 0.08 to 0.36 based on cohort), in contrast to mCRPC that has a strong correlation between AR and AR-A expression (r = 0.76). Additionally, serum PSA had no correlation to intratumoral AR-A (r = 0.06). Unsupervised hierarchical clustering identified a distinct subclass of low AR-A prostate tumors, which had increased markers of immunogenicity (decreased T-regs and MDSCs, and increased CD3 effector T-cells), increased neuroendocrine marker expression ( NCAM1, ENO2, and SCG2), and decreased DNA repair pathway expression (all p < 0.001). Clinically, low AR-A tumors had more rapid development of metastatic disease in three independent cohorts, were more prone to develop resistance to hormonal therapy and develop CRPC, and were found at an increased frequency in African-American men. Interrogating in vitro drug sensitivity analyses utilizing the NCI-60 panel, low AR-A tumors appear more sensitive to platinum chemotherapy and PARP inhibition, and less sensitive to hormone therapy and taxanes. Conclusions: The diversity in AR-signaling in localized prostate cancer represents important biological heterogeneity that is both prognostic and predictive of treatment response. These findings are provocative in that low AR-A tumors may be more susceptible to immunotherapy, PARP inhibition, platinum chemotherapy, and/or radiotherapy. Patients with low AR-A tumors warrant dedicated biomarker enhanced clinical trials.