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Prostatic small cell neuroendocrine carcinoma (SC/NE) is well studied in metastatic castration-resistant prostate cancer; however, it is not well characterized in the primary setting. Herein, we used gene expression profiling of SC/NE prostate cancer (PCa) to develop a 212 gene signature to identify treatment-naïve primary prostatic tumors that are molecularly analogous to SC/NE (SC/NE-like PCa). The 212 gene signature was tested in several cohorts confirming similar molecular profile between prostatic SC/NE and small cell lung carcinoma. The signature was then translated into a genomic score (SCGScore) using modularized logistic regression modeling and validated in four independent cohorts achieving an average AUC >0.95. The signature was evaluated in more than 25,000 primary adenocarcinomas to characterize the biology, prognosis and potential therapeutic response of predicted SC/NE-like tumors. Assessing SCGScore in a prospective cohort of 17,967 RP and 6,697 biopsy treatment-naïve primary tumors from the Decipher Genomic Resource Information Database registry, approximately 1% of the patients were found to have a SC/NE-like transcriptional profile, whereas 0.5 and 3% of GG1 and GG5 patients respectively showed to be SC/NE-like. More than 80% of these patients are genomically high-risk based on Decipher score. Interrogating in vitro drug sensitivity analyses, SC/NE-like prostatic tumors showed higher response to PARP and HDAC inhibitors., (© 2019 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Prostate cancer is heterogeneous in both cellular composition and patient outcome, and development of biomarker signatures to distinguish indolent from aggressive tumours is a high priority. Stroma plays an important role during prostate cancer progression and undergoes histological and transcriptional changes associated with disease. However, identification and validation of stromal markers is limited by a lack of datasets with defined stromal/tumour ratio. We have developed a prostate-selective signature to estimate the stromal content in cancer samples of mixed cellular composition. We identified stromal-specific markers from transcriptomic datasets of developmental prostate mesenchyme and prostate cancer stroma. These were experimentally validated in cell lines, datasets of known stromal content, and by immunohistochemistry in tissue samples to verify stromal-specific expression. Linear models based upon six transcripts were able to infer the stromal content and estimate stromal composition in mixed tissues. The best model had a coefficient of determination R 2 of 0.67. Application of our stromal content estimation model in various prostate cancer datasets led to improved performance of stromal predictive signatures for disease progression and metastasis. The stromal content of prostate tumours varies considerably; consequently, deconvolution of stromal proportion may yield better results than tumour cell deconvolution. We suggest that adjusting expression data for cell composition will improve stromal signature performance and lead to better prognosis and stratification of men with prostate cancer. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland., (© 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.)

Systemic metabolic alterations associated with increased consumption of saturated fat and obesity are linked with increased risk of prostate cancer progression and mortality, but the molecular underpinnings of this association are poorly understood. Here, we demonstrate in a murine prostate cancer model, that high-fat diet (HFD) enhances the MYC transcriptional program through metabolic alterations that favour histone H4K20 hypomethylation at the promoter regions of MYC regulated genes, leading to increased cellular proliferation and tumour burden. Saturated fat intake (SFI) is also associated with an enhanced MYC transcriptional signature in prostate cancer patients. The SFI-induced MYC signature independently predicts prostate cancer progression and death. Finally, switching from a high-fat to a low-fat diet, attenuates the MYC transcriptional program in mice. Our findings suggest that in primary prostate cancer, dietary SFI contributes to tumour progression by mimicking MYC over expression, setting the stage for therapeutic approaches involving changes to the diet.

Autosomal dominant polycystic kidney disease (ADPKD), caused by mutations in either PKD1 or PKD2 genes, is one of the most common human monogenetic disorders and the leading genetic cause of end-stage renal disease. Unfortunately, treatment options for ADPKD are limited. Here we report the discovery and characterization of RGLS4326, a first-in-class, short oligonucleotide inhibitor of microRNA-17 (miR-17), as a potential treatment for ADPKD. RGLS4326 is discovered by screening a chemically diverse and rationally designed library of anti-miR-17 oligonucleotides for optimal pharmaceutical properties. RGLS4326 preferentially distributes to kidney and collecting duct-derived cysts, displaces miR-17 from translationally active polysomes, and de-represses multiple miR-17 mRNA targets including Pkd1 and Pkd2. Importantly, RGLS4326 demonstrates a favorable preclinical safety profile and attenuates cyst growth in human in vitro ADPKD models and multiple PKD mouse models after subcutaneous administration. The preclinical characteristics of RGLS4326 support its clinical development as a disease-modifying treatment for ADPKD.

Purpose: After cisplatin-based neoadjuvant chemotherapy (NAC), 60% of patients with muscle-invasive bladder cancer (MIBC) still have residual invasive disease at radical cystectomy. The NAC-induced biological alterations in these cisplatin-resistant tumors remain largely unstudied., Experimental Design: Radical cystectomy samples were available for gene expression analysis from 133 patients with residual invasive disease after cisplatin-based NAC, of whom 116 had matched pre-NAC samples. Unsupervised consensus clustering (CC) was performed and the consensus clusters were investigated for their biological and clinical characteristics. Hematoxylin & Eosin and IHC on tissue microarrays were used to confirm tissue sampling and gene expression analysis., Results: Established molecular subtyping models proved to be inconsistent in their classification of the post-NAC samples. Unsupervised CC revealed four distinct consensus clusters. The CC1-Basal and CC2-Luminal subtypes expressed genes consistent with a basal and a luminal phenotype, respectively, and were similar to the corresponding established pretreatment molecular subtypes. The CC3-Immune subtype had the highest immune activity, including T-cell infiltration and checkpoint molecule expression, but lacked both basal and luminal markers. The CC4-Scar-like subtype expressed genes associated with wound healing/scarring, although the proportion of tumor cell content in this subtype did not differ from the other subtypes. Patients with CC4-Scar-like tumors had the most favorable prognosis., Conclusions: This study expands our knowledge on MIBC not responding to cisplatin by suggesting molecular subtypes to understand the biology of these tumors. Although these molecular subtypes imply consequences for adjuvant treatments, this ultimately needs to be tested in clinical trials., (©2018 American Association for Cancer Research.)

Purpose: Most prostate cancer in African American men lacks the ETS (E26 transforming specific) family fusion event (ETS-). We aimed to establish clinically relevant biomarkers in African American men by studying ETS dependent gene expression patterns to identified race specific genes predictive of outcomes., Materials and Methods: Two multicenter cohorts of a total of 1,427 men were used for the discovery and validation (635 and 792 men, respectively) of race specific predictive biomarkers. We used false discovery rate adjusted q values to identify race and ETS dependent genes which were differentially expressed in African American men who experienced biochemical recurrence within 5 years. Principal component modeling along with survival analysis was done to assess the accuracy of the gene panel in predicting recurrence., Results: We identified 3,047 genes which were differentially expressed based on ETS status. Of these genes 362 were differentially expressed in a race specific manner (false discovery rate 0.025 or less). A total of 81 genes were race specific and over expressed in African American men who experienced biochemical recurrence. The final gene panel included APOD, BCL6, EMP1, MYADM, SRGN and TIMP3. These genes were associated with 5-year biochemical recurrence (HR 1.97, 95% CI 1.27-3.06, p = 0.002) and they improved the predictive accuracy of clinicopathological variables only in African American men (60-month time dependent AUC 0.72)., Conclusions: In an effort to elucidate biological features associated with prostate cancer aggressiveness in African American men we identified ETS dependent biomarkers predicting early onset biochemical recurrence only in African American men. Thus, these ETS dependent biomarkers representing ideal candidates for biomarkers of aggressive disease in this patient population.

Purpose: Multiparametric magnetic resonance imaging (mpMRI) is used widely for prostate cancer (PCa) evaluation. Approximately 35% of aggressive tumors, however, are not visible on mpMRI. We sought to identify the molecular alterations associated with mpMRI-invisible tumors and determine whether mpMRI visibility is associated with PCa prognosis., Methods: Discovery and validation cohorts included patients who underwent mpMRI before radical prostatectomy and were found to harbor both mpMRI-visible (Prostate Imaging and Reporting Data System 3 to 5) and -invisible (Prostate Imaging and Reporting Data System 1 or 2) foci on surgical pathology. Next-generation sequencing was performed to determine differential gene expression between mpMRI-visible and -invisible foci. A genetic signature for tumor mpMRI visibility was derived in the discovery cohort and assessed in an independent validation cohort. Its association with long-term oncologic outcomes was evaluated in a separate testing cohort., Results: The discovery cohort included 10 patients with 26 distinct PCa foci on surgical pathology, of which 12 (46%) were visible and 14 (54%) were invisible on preoperative mpMRI. Next-generation sequencing detected prioritized genetic mutations in 14 (54%) tumor foci (n = 8 mpMRI visible, n = 6 mpMRI invisible). A nine-gene signature (composed largely of cell organization/structure genes) associated with mpMRI visibility was derived (area under the curve = 0.89), and the signature predicted MRI visibility with 75% sensitivity and 100% specificity (area under the curve = 0.88) in the validation cohort. In the testing cohort (n = 375, median follow-up 8 years) there was no significant difference in biochemical recurrence, distant metastasis, or cancer-specific mortality in patients with predicted mpMRI-visible versus -invisible tumors (all P > .05)., Conclusion: Compared with mpMRI-invisible disease, mpMRI-visible tumors are associated with underexpression of cellular organization genes. mpMRI visibility does not seem to be predictive of long-term cancer outcomes, highlighting the need for biopsy strategies that detect mpMRI-invisible tumors., Competing Interests: The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center. Jeremy B. KaplanEmployment: Tempus LabsMandeep TakharEmployment: GenomeDxDaniel H. HovelsonEmployment: Strata Oncology Consulting or Advisory Role: Terumo Travel, Accommodations, Expenses: Thermo Fisher ScientificSamuel D. KaffenbergerConsulting or Advisory Role: MDxHealth, Clovis Oncology Travel, Accommodations, Expenses: Bristol-Myers SquibbEdward M. SchaefferConsulting or Advisory Role: OPKO Diagnostics, AbbVieR. Jeffrey KarnesResearch Funding: GenomeDx Patents, Royalties, Other Intellectual Property: GenomeDx Travel, Accommodations, Expenses: GenomeDxTamara L. LotanConsulting or Advisory Role: Janssen Oncology Research Funding: Ventana Medical SystemsTodd M. MorganConsulting or Advisory Role: Myriad Genetics, Terumo BCT Research Funding: Myriad Genetics (Inst), MDxHealth (Inst), GenomeDx (Inst)Arvin K. GeorgeConsulting or Advisory Role: Trod Medical Research Funding: Nanospectra Biosciences (Inst) Travel, Accommodations, Expenses: Profound Medical, Trod MedicalMatthew S. DavenportPatents, Royalties, Other Intellectual Property: Royalties from Wolters Kluwer, royalties from uptodate.comScott A. TomlinsEmployment: Strata Oncology Leadership: Strata Oncology Stock and Other Ownership Interests: Strata Oncology Consulting or Advisory Role: AbbVie, Janssen, Astellas Medivation, Strata Oncology, Sanofi, Almac Diagnostics Research Funding: Astellas Medivation (Inst), GenomeDx (Inst) Patents, Royalties, Other Intellectual Property: I am a coauthor on a patent issued to the University of Michigan on ETS gene fusions in prostate cancer. The diagnostic field of use has been licensed to Hologic/Gen-Probe, who has sublicensed some rights to Ventana Medical Systems/Roche. Travel, Accommodations, Expenses: Strata OncologyHyung L. KimResearch Funding: GenomeDx Patents, Royalties, Other Intellectual Property: I am an inventor of a prognostic biomarker signature and immunotherapies.Daniel E. SprattConsulting or Advisory Role: Blue Earth Diagnostics, Janssen OncologyAaron M. UdagerResearch Funding: Ventana Medical SystemsGanesh S. PalapattuStock and Other Ownership Interests: NantKwest Consulting or Advisory Role: Janssen Scientific Affairs Research Funding: Minomic Patents, Royalties, Other Intellectual Property: Implantable nanotechnology for long-term testosterone delivery functionalized fiducial marker for drug delivery No other potential conflicts of interest were reported., (© 2019 by American Society of Clinical Oncology.)

Background: Prostate cancer (PCa) tumors harboring translocations of ETS family genes with the androgen responsive TMPRSS2 gene (ETS+ tumors) provide a robust biomarker for detecting PCa in approximately 70% of patients. ETS+ PCa express high levels of the androgen receptor (AR), yet PCa tumors lacking ETS fusions (ETS-) also express AR and demonstrate androgen-regulated growth. In this study, we evaluate the differences in the AR-regulated transcriptomes between ETS+ and ETS- PCa tumors., Methods: 10,608 patient tumors from three independent PCa datasets classified as ETS+ (samples overexpressing ERG or other ETS family members) or ETS- (all other PCa) were analyzed for differential gene expression using false-discovery-rate adjusted methods and gene-set enrichment analysis (GSEA)., Results: Based on the expression of AR-dependent genes and an unsupervised Principal Component Analysis (PCA) model, AR-regulated gene expression alone was able to separate PCa samples into groups based on ETS status in all PCa databases. ETS status distinguished several differentially expressed genes in both TCGA (6.9%) and GRID (6.6%) databases, with 413 genes overlapping in both databases. Importantly, GSEA showed enrichment of distinct androgen-responsive genes in both ETS- and ETS+ tumors, and AR ChIP-seq data identified 131 direct AR-target genes that are regulated in an ETS-specific fashion. Notably, dysregulation of ETS-dependent AR-target genes within the metabolic and non-canonical WNT pathways was associated with clinical outcomes., Conclusions: ETS status influences the transcriptional repertoire of the AR, and ETS- PCa tumors appear to rely on distinctly different AR-dependent transcriptional programs to drive and sustain tumorigenesis.

Purpose: Multiparametric magnetic resonance imaging is a diagnostic tool for prostate cancer with limited data on prognostic use. We sought to determine whether multiparametric magnetic resonance could predict aggressive prostate cancer features., Materials and Methods: We retrospectively analyzed the records of 206 patients who underwent radical prostatectomy between 2013 and 2017. All patients had available RNA expression data on the final pathology specimen obtained from a location corresponding to a lesion location on multiparametric magnetic resonance imaging. The association between the PIRADS™ (Prostate Imaging Reporting and Data System) score and adverse pathology features were analyzed. We also performed differential transcriptomic analysis between the PIRADS groups. Factors associated with adverse pathology were analyzed using a multivariable logistic regression model., Results: Lesion size (p = 0.03), PIRADS score (p = 0.02) and extraprostatic extension (p = 0.01) associated significantly with the Decipher® score. Multivariable analysis showed that the PIRADS score (referent PIRADS 3, OR 8.1, 95% CI 1.2-57.5, p = 0.04), the Gleason Grade Group (referent 3, OR 5.6, 95% CI 1.5-21.1, p = 0.01) and prostate specific antigen (OR 1.103, 95% CI 1.011-1.203) were risk factors for adverse pathology findings. The difference between PIRADS 4 and 5 did not reach significance (OR 1.9, 95% CI 0.8-4.5, p = 0.12). However, the PI3K-AKT-mTOR, WNT-β and E2F signaling pathways were more active in PIRADS 5 than in PIRADS 4 cases., Conclusions: The PIRADS score is associated with adverse pathology results, increased metastatic risk and differential genomic pathway activation., (Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

Background: 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., (©2018 American Association for Cancer Research.)

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, P interaction = 0.009; set II: HR = 0.25, P interaction = 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 ( P interaction = 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 ., (©2018 American Association for Cancer Research.)

Background: The most suspicious lesions on multiparametric magnetic resonance imaging (MRI) may be representative of final pathology., Objective: We connect imaging with high-precision spatial annotation of biopsies and genomic cancer signatures to compare the genomic signals of the index lesion and biopsy cores of adjacent and far away locations., Design, Setting, and Participants: Eleven patients diagnosed with high-risk prostate cancer on MRI/transrectal ultrasound-fusion biopsy (Bx) and treated with radical prostatectomy (RP). Five tissue specimens were collected from each patient., Outcome Measurements and Statistical Analysis: Whole transcriptome RNA-expression was profiled for each sample. Genomic signatures were used to compare signals in MRI invisible versus visible foci using Pearson's correlation and to assess intratumoral heterogeneity using hierarchical clustering., Results and Limitations: Ten RP and 27 Bx-samples passed quality control. Gene expression between RP and index Bx, but not adjacent benign samples, was highly correlated. Genomic Gleason grade classifier features measured across the different samples showed concordant expression across Bx and RP tumor samples, while an inverse expression pattern was observed between tumor and benign samples indicating the lack of a strong field-effect. The distribution of low and high Prostate Imaging Reporting and Data System (PI-RADS) samples was 10 and 11, respectively. Genomics of all low PI-RADS samples resembled benign tissue and most high PI-RADS samples resembled cancer tissue. A strong association was observed between PI-RADS version 2 and Decipher as well as the genomic Gleason grade classifier score. Clustering analysis showed that most samples cluster tightly by patient. One patient showed unique tumor biology in index versus secondary lesion suggesting the presence of intrapatient heterogeneity and the utility in profiling multiple foci identified by MRI., Conclusions: MRI-targeted Bx-genomics show excellent correlation with RP-genomics and confirm the information captured by PI-RADS. Sampling of the target lesion must be precise as correlation between index and benign lesions was not seen., Patient Summary: In this report, we tested if targeted prostate sampling using magnetic resonance imaging-fusion biopsy allows to genetically describe index tumors of prostate cancer. We found that imaging genomics correlated well with final prostatectomy provided that the target is hit precisely., (Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

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.

Background: Hypoxia is associated with a poor prognosis in prostate cancer. This work aimed to derive and validate a hypoxia-related mRNA signature for localized prostate cancer., Method: Hypoxia genes were identified in vitro via RNA-sequencing and combined with in vivo gene co-expression analysis to generate a signature. The signature was independently validated in eleven prostate cancer cohorts and a bladder cancer phase III randomized trial of radiotherapy alone or with carbogen and nicotinamide (CON)., Results: A 28-gene signature was derived. Patients with high signature scores had poorer biochemical recurrence free survivals in six of eight independent cohorts of prostatectomy-treated patients (Log rank test P < .05), with borderline significances achieved in the other two (P < .1). The signature also predicted biochemical recurrence in patients receiving post-prostatectomy radiotherapy (n = 130, P = .007) or definitive radiotherapy alone (n = 248, P = .035). Lastly, the signature predicted metastasis events in a pooled cohort (n = 631, P = .002). Prognostic significance remained after adjusting for clinic-pathological factors and commercially available prognostic signatures. The signature predicted benefit from hypoxia-modifying therapy in bladder cancer patients (intervention-by-signature interaction test P = .0026), where carbogen and nicotinamide was associated with improved survival only in hypoxic tumours., Conclusion: A 28-gene hypoxia signature has strong and independent prognostic value for prostate cancer patients., (Copyright © 2018. Published by Elsevier B.V.)

Purpose: To validate a previously characterized 10-gene signature in prostate cancer with implication to distinguish aggressive and indolent disease within low and intermediate patients' risk groups., Methods: A case-control study design used to select 545 patients from the Mayo clinic tumor registry who underwent radical prostatectomy. A training set from this cohort (n = 359) was used to build a 10-gene model, based on high-dimensional discriminant analysis (HDDA10) to predict several endpoints of clinical patients' outcome. An independent set (n = 219) from the same institution was used as validation set., Results: HDDA10 showed significant performance for predicting metastasis (Mets) (AUC 0.68, p = 6.4E - 6) and biochemical recurrence (BCR) (AUC = 0.65, p = 0.003) in the validation set outperforming Gleason grade grouping (GG) for BCR (AUC 0.57, p = 0.03) and with comparable performance for Mets endpoint (GG AUC 0.66, p = 8.1E - 5). HDDA10 prognostic significance was superior to any clinical-pathological parameter within GG2 + 3 (GS7) patients achieving an AUC of 0.74 (p = 0.0037) for BCR compared to Gleason pattern 4 (AUC 0.64) (p = 0.015) and AUC for Mets of 0.68 versus AUC of 0.65 for Gleason pattern 4 (p = 0.01). HDDA10 remained significant for both BCR and Mets in multivariate analysis, suggesting that it can be used to increase accuracy in stratifying patients eligible for active surveillance., Conclusion: HDDA10 is of added value to GG and other clinical-pathological parameters in predicting BCR and Mets endpoint, especially in the low to intermediate patients' risk groups. HDDA10 prognostic value should be further validated prospectively in stratifying patients specifically in low to intermediate GS (GG1-2), such as active surveillance programs.

Background: Clinical grading systems using clinical features alongside nomograms lack precision in guiding treatment decisions in prostate cancer (PCa). There is a critical need for identification of biomarkers that can more accurately stratify patients with primary PCa., Objective: To identify a robust prognostic signature to better distinguish indolent from aggressive prostate cancer (PCa)., Design, Setting, and Participants: To develop the signature, whole-genome and whole-transcriptome sequencing was conducted on five PCa patient-derived xenograft (PDX) models collected from independent foci of a single primary tumor and exhibiting variable metastatic phenotypes. Multiple independent clinical cohorts including an intermediate-risk cohort were used to validate the biomarkers., Outcome Measurements and Statistical Analysis: The outcome measurement defining aggressive PCa was metastasis following radical prostatectomy. A generalized linear model with lasso regularization was used to build a 93-gene stroma-derived metastasis signature (SDMS). The SDMS association with metastasis was assessed using a Wilcoxon rank-sum test. Performance was evaluated using the area under the curve (AUC) for the receiver operating characteristic, and Kaplan-Meier curves. Univariable and multivariable regression models were used to compare the SDMS alongside clinicopathological variables and reported signatures. AUC was assessed to determine if SDMS is additive or synergistic to previously reported signatures., Results and Limitations: A close association between stromal gene expression and metastatic phenotype was observed. Accordingly, the SDMS was modeled and validated in multiple independent clinical cohorts. Patients with higher SDMS scores were found to have worse prognosis. Furthermore, SDMS was an independent prognostic factor, can stratify risk in intermediate-risk PCa, and can improve the performance of other previously reported signatures., Conclusions: Profiling of stromal gene expression led to development of an SDMS that was validated as independently prognostic for the metastatic potential of prostate tumors., Patient Summary: Our stroma-derived metastasis signature can predict the metastatic potential of early stage disease and will strengthen decisions regarding selection of active surveillance versus surgery and/or radiation therapy for prostate cancer patients. Furthermore, profiling of stroma cells should be more consistent than profiling of diverse cellular populations of heterogeneous tumors., (Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Background: Risk of prostate cancer-specific mortality (PCSM) is highly variable for men with adverse pathologic features at radical prostatectomy (RP); a majority will die of other causes. Accurately stratifying PCSM risk can improve therapy decisions., Objective: Validate the 22 gene Decipher genomic classifier (GC) to predict PCSM in men with adverse pathologic features after RP., Design, Setting, and Participants: Men with adverse pathologic features: pT3, pN1, positive margins, or Gleason score >7 who underwent RP in 1987-2010 at Johns Hopkins, Cleveland Clinic, Mayo Clinic, and Durham Veteran's Affairs Hospital. We also analyzed subgroups at high risk (prostate-specific antigen >20 ng/ml, RP Gleason score 8-10, or stage >pT3b), or very high risk of PCSM (biochemical recurrence in<2 yr [BCR2], or men who developed metastasis after RP [MET])., Outcome Measurements and Statistical Analysis: Logistic regression evaluated the association of GC with PCSM within 10 yr of RP (PCSM10), adjusted for the Cancer of the Prostate Risk Assessment Postsurgical Score (CAPRA-S). GC performance was evaluated with area under the receiver operating characteristic curve (AUC) and decision curves., Results and Limitations: Five hundred and sixty-one men (112 with PCSM10), median follow-up 13.0 yr (patients without PCSM10). For high GC score (> 0.6) versus low-intermediate (≤ 0.6), the odds ratio for PCSM10 adjusted for CAPRA-S was 3.91 (95% confidence interval: 2.43-6.29), with AUC=0.77, an increase of 0.04 compared with CAPRA-S. Subgroup odds ratios were 3.96, 3.06, and 1.95 for high risk, BCR2, or MET, respectively (all p<0.05), with AUCs 0.64-0.72. GC stratified cumulative PCSM10 incidence from 2.8% to 30%. Combined use of case-control and cohort data is a potential limitation., Conclusions: In a large cohort with the longest follow-up to date, Decipher GC demonstrated clinically important prediction of PCSM at 10 yr, independent of CAPRA-S, in men with adverse pathologic features, BCR2, or MET after RP., Patient Summary: Decipher genomic classifier may improve treatment decision-making for men with adverse or high risk pathology after radical prostatectomy., (Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO’s conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center. Deli Liu No relationship to disclose Mandeep Takhar Employment: GenomeDx Mohammed Alshalalfa Employment: GenomeDx Travel, Accommodations, Expenses: GenomeDx Nicholas Erho Employment: GenomeDx Jonathan Shoag No relationship to disclose Robert B. Jenkins Patents, Royalties, Other Intellectual Property: Abbott Molecular, GenomeDx R. Jeffrey Karnes Research Funding: GenomeDx Patents, Royalties, Other Intellectual Property: GenomeDx Travel, Accommodations, Expenses: GenomeDx Ashley E. Ross Ashley E. Ross Stock and Other Ownership Interests: GenomeDx Honoraria: Healthtronics Consulting or Advisory Role: Healthtronics Research Funding: Metamark Genetics Edward M. Schaeffer Consulting or Advisory Role: OPKO Diagnostics, AbbVie Mark A. Rubin Research Funding: Eli Lilly, Janssen Pharmaceuticals Bruce Trock Consulting or Advisory Role: GenomeDx Consulting or Advisory Role: Myriad Genetics Research Funding: Myriad Genetics, MDxHealth Eric A. Klein Consulting or Advisory Role: GenomeDx, Genomic Health Speakers’ Bureau: Genomic Health Robert B. Den Consulting or Advisory Role: GenomeDx Speakers’ Bureau: Bayer Research Funding: Medivation, Astellas Pharma, GenomeDx Travel, Accommodations, Expenses: GenomeDx Scott A. Tomlins Leadership: Strata Oncology Stock and Other Ownership Interests: Strata Oncology Consulting or Advisory Role: AbbVie, Janssen Pharmaceuticals, Astellas Pharma, Medivation, Strata Oncology, Sanofi, Almac Diagnostics Research Funding: Astellas Pharma (Inst), Medivation (Inst), GenomeDx (Inst) Patents, Royalties, Other Intellectual Property: Coauthor on a patent issued to the University of Michigan on ETS gene fusions in prostate cancer Travel, Accommodations, Expenses: Strata Oncology Daniel E. Spratt No relationship to disclose Elai Davicioni Employment: GenomeDx Leadership: GenomeDx Stock and Other Ownership Interests: GenomeDx Patents, Royalties, Other Intellectual Property: Cancer diagnostics using biomarkers 20140066323 Travel, Accommodations, Expenses: GenomeDx Andrea Sboner No relationship to disclose Christopher E. Barbieri Patents, Royalties, Other Intellectual Property: Coinventor on a patent application filed regarding SPOP mutations in prostate cancer by Weill Cornell Medicine

Androgen receptor (AR) signaling is a key driver of prostate cancer, and androgen-deprivation therapy (ADT) is a standard treatment for patients with advanced and metastatic disease. However, patients receiving ADT eventually develop incurable castration-resistant prostate cancer (CRPC). Here, we report that the chromatin modifier LSD1, an important regulator of AR transcriptional activity, undergoes epigenetic reprogramming in CRPC. LSD1 reprogramming in this setting activated a subset of cell-cycle genes, including CENPE, a centromere binding protein and mitotic kinesin. CENPE was regulated by the co-binding of LSD1 and AR to its promoter, which was associated with loss of RB1 in CRPC. Notably, genetic deletion or pharmacological inhibition of CENPE significantly decreases tumor growth. Our findings show how LSD1-mediated epigenetic reprogramming drives CRPC, and they offer a mechanistic rationale for its therapeutic targeting in this disease. Cancer Res; 77(20); 5479-90. ©2017 AACR ., (©2017 American Association for Cancer Research.)

Prostate cancer is the most common male cancer and androgen receptor (AR) is the major driver of the disease. Here we show that Enoyl-CoA delta isomerase 2 (ECI2) is a novel AR-target that promotes prostate cancer cell survival. Increased ECI2 expression predicts mortality in prostate cancer patients (p = 0.0086). ECI2 encodes for an enzyme involved in lipid metabolism, and we use multiple metabolite profiling platforms and RNA-seq to show that inhibition of ECI2 expression leads to decreased glucose utilization, accumulation of fatty acids and down-regulation of cell cycle related genes. In normal cells, decrease in fatty acid degradation is compensated by increased consumption of glucose, and here we demonstrate that prostate cancer cells are not able to respond to decreased fatty acid degradation. Instead, prostate cancer cells activate incomplete autophagy, which is followed by activation of the cell death response. Finally, we identified a clinically approved compound, perhexiline, which inhibits fatty acid degradation, and replicates the major findings for ECI2 knockdown. This work shows that prostate cancer cells require lipid degradation for survival and identifies a small molecule inhibitor with therapeutic potential.

Global changes in chromatin accessibility may drive cancer progression by reprogramming transcription factor (TF) binding. In addition, histone acetylation readers such as bromodomain-containing protein 4 (BRD4) have been shown to associate with these TFs and contribute to aggressive cancers including prostate cancer (PC). Here, we show that chromatin accessibility defines castration-resistant prostate cancer (CRPC). We show that the deregulation of androgen receptor (AR) expression is a driver of chromatin relaxation and that AR/androgen-regulated bromodomain-containing proteins (BRDs) mediate this effect. We also report that BRDs are overexpressed in CRPCs and that ATAD2 and BRD2 have prognostic value. Finally, we developed gene stratification signature (BROMO-10) for bromodomain response and PC prognostication, to inform current and future trials with drugs targeting these processes. Our findings provide a compelling rational for combination therapy targeting bromodomains in selected patients in which BRD-mediated TF binding is enhanced or modified as cancer progresses., (Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: To determine the prognostic potential of a 24-gene signature, Sig24, for identifying patients with prostate cancer who are at risk of developing metastases or of prostate cancer-specific mortality (PCSM) after radical prostatectomy (RP)., Patients and Methods: Sig24 scores were calculated from previously collected gene expression microarray data from the Cleveland Clinic and Mayo Clinic (I and II). The performance of Sig24 was determined using time-dependent c-index analysis, Cox proportional hazards regression and Kaplan-Meier survival analysis., Results: Higher Sig24 scores were significantly associated with higher pathological Gleason scores in all three cohorts. Analysis of the Mayo Clinic II cohort, which included time-to-event information, indicated that patients with high Sig24 scores also had a higher risk of developing metastasis (hazard ratio [HR] 3.78, 95% confidence interval [CI]: 1.96-7.29; P < 0.001) or of PCSM (HR 6.54, 95% CI: 2.16-19.83; P < 0.001)., Conclusions: The findings of the present study show the applicability of Sig24 for the prognosis of metastasis or PCSM after RP. Future studies investigating the combination of Sig24 with available prognostic tests may provide new approaches to improve risk stratification for patients with prostate cancer., (© 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.)

The quantitation of proteins using shotgun proteomics has gained popularity in the last decades, simplifying sample handling procedures, removing extensive protein separation steps and achieving a relatively high throughput readout. The process starts with the digestion of the protein mixture into peptides, which are then separated by liquid chromatography and sequenced by tandem mass spectrometry (MS/MS). At the end of the workflow, recovering the identity of the proteins originally present in the sample is often a difficult and ambiguous process, because more than one protein identifier may match a set of peptides identified from the MS/MS spectra. To address this identification problem, many MS/MS data processing software tools combine all plausible protein identifiers matching a common set of peptides into a protein group. However, this solution introduces new challenges in studies with multiple experimental runs, which can be characterized by three main factors: i) protein groups' identifiers are local, i.e., they vary run to run, ii) the composition of each group may change across runs, and iii) the supporting evidence of proteins within each group may also change across runs. Since in general there is no conclusive evidence about the absence of proteins in the groups, protein groups need to be linked across different runs in subsequent statistical analyses. We propose an algorithm, called Protein Group Code Algorithm (PGCA), to link groups from multiple experimental runs by forming global protein groups from connected local groups. The algorithm is computationally inexpensive and enables the connection and analysis of lists of protein groups across runs needed in biomarkers studies. We illustrate the identification problem and the stability of the PGCA mapping using 65 iTRAQ experimental runs. Further, we use two biomarker studies to show how PGCA enables the discovery of relevant candidate protein group markers with similar but non-identical compositions in different runs.

Treatment-induced neuroendocrine transdifferentiation (NEtD) complicates therapies for metastatic prostate cancer (PCa). Based on evidence that PCa cells can transdifferentiate to other neuroectodermally-derived cell lineages in vitro, we proposed that NEtD requires first an intermediary reprogramming to metastable cancer stem-like cells (CSCs) of a neural class and we demonstrate that several different AR+/PSA+ PCa cell lines were efficiently reprogrammed to, maintained and propagated as CSCs by growth in androgen-free neural/neural crest (N/NC) stem medium. Such reprogrammed cells lost features of prostate differentiation; gained features of N/NC stem cells and tumor-initiating potential; were resistant to androgen signaling inhibition; and acquired an invasive phenotype in vitro and in vivo. When placed back into serum-containing mediums, reprogrammed cells could be re-differentiated to N-/NC-derived cell lineages or return back to an AR+ prostate-like state. Once returned, the AR+ cells were resistant to androgen signaling inhibition. Acute androgen deprivation or anti-androgen treatment in serum-containing medium led to the transient appearance of a sub-population of cells with similar characteristics. Finally, a 132 gene signature derived from reprogrammed PCa cell lines distinguished tumors from PCa patients with adverse outcomes. This model may explain neural manifestations of PCa associated with lethal disease. The metastable nature of the reprogrammed stem-like PCa cells suggests that cycles of PCa cell reprogramming followed by re-differentiation may support disease progression and therapeutic resistance. The ability of a gene signature from reprogrammed PCa cells to identify tumors from patients with metastasis or PCa-specific mortality implies that developmental reprogramming is linked to aggressive tumor behaviors.

Background: Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms., Results: The stability of modules increased as sample size increased and stable modules were more likely to be replicated in larger sets of samples. Random modules derived from permutated gene expression data were consistently unstable, as assessed by SABRE, and provide a useful baseline value for our proposed stability criterion. Gene module sets identified by different algorithms varied with respect to their stability, as assessed by SABRE. Finally, stable modules were more readily annotated in various curated gene set databases., Conclusions: The SABRE procedure and proposed stability criterion may provide guidance when designing systems biology studies in complex human disease and tissues.

Purpose: Clinical staging in patients with muscle invasive bladder cancer misses up to 25% of lymph node metastasis. These patients are at high risk for disease recurrence and improved clinical staging is critical to guide management., Materials and Methods: Whole transcriptome expression profiles were generated in 199 patients who underwent radical cystectomy and extended pelvic lymph node dissection. The cohort was divided randomly into a discovery set of 133 patients and a validation set of 66. In the discovery set features were identified and modeled in a KNN51 (K-nearest neighbor classifier 51) to predict pathological lymph node metastases. Two previously described bladder cancer gene signatures, including RF15 (15-gene cancer recurrence signature) and LN20 (20-gene lymph node signature), were also modeled in the discovery set for comparison. The AUC and the OR were used to compare the performance of these signatures., Results: In the validation set KNN51 achieved an AUC of 0.82 (range 0.71-0.93) to predict lymph node positive cases. It significantly outperformed RF15 and LN20, which had an AUC of 0.62 (range 0.47-0.76) and 0.46 (range 0.32-0.60), respectively. Only KNN51 showed significant odds of predicting LN metastasis with an OR of 2.65 (range 1.68-4.67) for every 10% increase in score (p <0.001). RF15 and LN20 had a nonsignificant OR of 1.21 (range 0.97-1.54) and 1.39 (range 0.52-3.77), respectively., Conclusions: The new KNN51 signature was superior to previously described gene signatures for predicting lymph node metastasis. If validated prospectively in transurethral resection of bladder tumor samples, KNN51 could be used to guide patients at high risk to early multimodal therapy., (Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

Prostate cancer is a biologically heterogeneous disease with variable molecular alterations underlying cancer initiation and progression. Despite recent advances in understanding prostate cancer heterogeneity, better methods for classification of prostate cancer are still needed to improve prognostic accuracy and therapeutic outcomes. In this study, we computationally assembled a large virtual cohort (n = 1,321) of human prostate cancer transcriptome profiles from 38 distinct cohorts and, using pathway activation signatures of known relevance to prostate cancer, developed a novel classification system consisting of three distinct subtypes (named PCS1-3). We validated this subtyping scheme in 10 independent patient cohorts and 19 laboratory models of prostate cancer, including cell lines and genetically engineered mouse models. Analysis of subtype-specific gene expression patterns in independent datasets derived from luminal and basal cell models provides evidence that PCS1 and PCS2 tumors reflect luminal subtypes, while PCS3 represents a basal subtype. We show that PCS1 tumors progress more rapidly to metastatic disease in comparison with PCS2 or PCS3, including PSC1 tumors of low Gleason grade. To apply this finding clinically, we developed a 37-gene panel that accurately assigns individual tumors to one of the three PCS subtypes. This panel was also applied to circulating tumor cells (CTC) and provided evidence that PCS1 CTCs may reflect enzalutamide resistance. In summary, PCS subtyping may improve accuracy in predicting the likelihood of clinical progression and permit treatment stratification at early and late disease stages. Cancer Res; 76(17); 4948-58. ©2016 AACR., Competing Interests: of Potential Conflicts of Interest: N. Erho is a bioinformatics group lead at GenomeDx Biosciences Inc. M. Alshalalfa is a bioinformatician at GenomeDx Biosciences Inc. H. Al-deen Ashab is a data scientist at Genomedx Biosciences Inc. E. Davicioni has ownership interest (including patents) in GenomeDx Biosciences Inc. R.J. Karnes reports receiving other commercial research support from GenomeDx Biosciences Inc. E.A. Klein has received speakers bureau honoraria from GenomeDx Biosciences Inc. A.E. Ross has ownership interest (including patents) in GenomeDx Biosciences Inc. Mandeep Takhar is a bioinformatician at GenomeDX. No potential conflicts of interest were disclosed by the other authors., (©2016 American Association for Cancer Research.)

Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective was to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.Global gene expression profiles were generated from 17 mpMRI-directed diagnostic prostate biopsies using an Affimetrix platform. Spatially distinct imaging areas ('habitats') were identified on MRI/3D-Ultrasound fusion. Radiomic features were extracted from biopsy regions and normal appearing tissues. We correlated 49 radiomic features with three clinically available gene signatures associated with adverse outcome. The signatures contain genes that are over-expressed in aggressive prostate cancers and genes that are under-expressed in aggressive prostate cancers. There were significant correlations between these genes and quantitative imaging features, indicating the presence of prostate cancer prognostic signal in the radiomic features. Strong associations were also found between the radiomic features and significantly expressed genes. Gene ontology analysis identified specific radiomic features associated with immune/inflammatory response, metabolism, cell and biological adhesion. To our knowledge, this is the first study to correlate radiogenomic parameters with prostate cancer in men with MRI-guided biopsy., Competing Interests: M.T., L.L.C.L, M.A., C.B., H.A.A., N.E., and E.D. are currently and were employees of GenomeDx Biosciences during the execution of the study.

Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinical-pathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated., Competing Interests: Conflicts of Interest: M.T., N.E., and E.D. are currently and were employees of GenomeDx Biosciences during the execution of the study. The other authors have no conflicts of interest to declare.

Loss of tumor suppressor proteins, such as the retinoblastoma protein (Rb), results in tumor progression and metastasis. Metastasis is facilitated by low oxygen availability within the tumor that is detected by hypoxia inducible factors (HIFs). The HIF1 complex, HIF1α and dimerization partner the aryl hydrocarbon receptor nuclear translocator (ARNT), is the master regulator of the hypoxic response. Previously, we demonstrated that Rb represses the transcriptional response to hypoxia by virtue of its association with HIF1. In this report, we further characterized the role Rb plays in mediating hypoxia-regulated genetic programs by stably ablating Rb expression with retrovirally-introduced short hairpin RNA in LNCaP and 22Rv1 human prostate cancer cells. DNA microarray analysis revealed that loss of Rb in conjunction with hypoxia leads to aberrant expression of hypoxia-regulated genetic programs that increase cell invasion and promote neuroendocrine differentiation. For the first time, we have established a direct link between hypoxic tumor environments, Rb inactivation and progression to late stage metastatic neuroendocrine prostate cancer. Understanding the molecular pathways responsible for progression of benign prostate tumors to metastasized and lethal forms will aid in the development of more effective prostate cancer therapies., Competing Interests: The authors have no conflicts of interest to report.

Emerging evidence demonstrates that the DNA repair kinase DNA-PKcs exerts divergent roles in transcriptional regulation of unsolved consequence. Here, in vitro and in vivo interrogation demonstrate that DNA-PKcs functions as a selective modulator of transcriptional networks that induce cell migration, invasion, and metastasis. Accordingly, suppression of DNA-PKcs inhibits tumor metastases. Clinical assessment revealed that DNA-PKcs is significantly elevated in advanced disease and independently predicts for metastases, recurrence, and reduced overall survival. Further investigation demonstrated that DNA-PKcs in advanced tumors is highly activated, independent of DNA damage indicators. Combined, these findings reveal unexpected DNA-PKcs functions, identify DNA-PKcs as a potent driver of tumor progression and metastases, and nominate DNA-PKcs as a therapeutic target for advanced malignancies., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Localized hypoxia in solid tumors activates transcriptional programs that promote the metastatic transformation of cells. Like hypoxia-inducible hyper-vascularization, loss of the retinoblastoma protein (Rb) is a trait common to advanced stages of tumor progression in many metastatic cancers. However, no link between the role of Rb and hypoxia-driven metastatic processes has been established. We demonstrated that Rb is a key mediator of the hypoxic response mediated by HIF1α/β, the master regulator of the hypoxia response, and its essential co-activator, the thyroid hormone receptor/retinoblastoma-interacting protein (TRIP230). Furthermore, loss of Rb unmasks the full co-activation potential of TRIP230. Using small inhibitory RNA approaches in vivo, we established that Rb attenuates the normal physiological response to hypoxia by HIF1α. Notably, loss of Rb results in hypoxia-dependent biochemical changes that promote acquisition of an invasive phenotype in MCF7 breast cancer cells. In addition, Rb is present in HIF1α-ARNT/HIF1β transcriptional complexes associated with TRIP230 as determined by co-immuno-precipitation, GST-pull-down and ChIP assays. These results demonstrate that Rb is a negative modulator of hypoxia-regulated transcription by virtue of its direct effects on the HIF1 complex. This work represents the first link between the functional ablation of Rb in tumor cells and HIF1α-dependent transcriptional activation and invasion.

Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.

Background: Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble?, Results: The first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity., Conclusion: Proteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway.

Purpose: This proteomics study was designed to determine the utility of iTRAQ MALDI-TOF/TOF technology to compare plasma samples from carefully phenotyped mild, atopic asthma subjects undergoing allergen inhalation challenge., Experimental Design: Eight adult subjects with mild, allergic asthma (four early responders (ERs) and four dual responders (DRs)) participated in the allergen inhalation challenge. Blood samples were collected prior to and 2 h after the inhalation challenge. Sixteen plasma samples (two per subject), technical replicates, and pooled controls were analyzed using iTRAQ. Technical validation was performed using LC-MRM/MS. Moderated robust regression was used to determine differentially expressed proteins., Results: Although this study did not show significant differences between pre- and post-challenge samples, discriminant analysis indicated that certain proteins responded differentially to allergen challenge with respect to responder type. At pre-challenge, fibronectin was significantly elevated in DRs compared to ERs and remained significant in the multiple reaction monitoring validation., Conclusions and Clinical Relevance: This proof of principle demonstration has shown that iTRAQ can uncover differences in the human plasma proteome between two endotypes of asthma and merits further application of iTRAQ to larger cohorts of asthma and other respiratory diseases., (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Y-box binding protein-1 (YB-1) is the first reported oncogenic transcription factor to induce the tumor-initiating cell (TIC) surface marker CD44 in triple-negative breast cancer (TNBC) cells. In order for CD44 to be induced, YB-1 must be phosphorylated at S102 by p90 ribosomal S6 kinase (RSK). We therefore questioned whether RSK might be a tractable molecular target to eliminate TICs. In support of this idea, injection of MDA-MB-231 cells expressing Flag-YB-1 into mice increased tumor growth as well as enhanced CD44 expression. Despite enrichment for TICs, these cells were sensitive to RSK inhibition when treated ex vivo with BI-D1870. Targeting RSK2 with small interfering RNA (siRNA) or small molecule RSK kinase inhibitors (SL0101 and BI-D1870) blocked TNBC monolayer cell growth by ∼100%. In a diverse panel of breast tumor cell line models RSK2 siRNA predominantly targeted models of TNBC. RSK2 inhibition decreased CD44 promoter activity, CD44 mRNA, protein expression, and mammosphere formation. CD44(+) cells had higher P-RSK(S221/227) , P-YB-1(S102) , and mitotic activity relative to CD44(-) cells. Importantly, RSK2 inhibition specifically suppressed the growth of TICs and triggered cell death. Moreover, silencing RSK2 delayed tumor initiation in mice. In patients, RSK2 mRNA was associated with poor disease-free survival in a cohort of 244 women with breast cancer that had not received adjuvant treatment, and its expression was highest in the basal-like breast cancer subtype. Taking this further, we report that P-RSK(S221/227) is present in primary TNBCs and correlates with P-YB-1(S102) as well as CD44. In conclusion, RSK2 inhibition provides a novel therapeutic avenue for TNBC and holds the promise of eliminating TICs., (Copyright © 2012 AlphaMed Press.)

The activated AHR/ARNT complex (AHRC) regulates the expression of target genes upon exposure to environmental contaminants such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Importantly, evidence has shown that TCDD represses estrogen receptor (ER) target gene activation through the AHRC. Our data indicates that AHR and ARNT act independently from each other at non-dioxin response element sites. Therefore, we sought to determine the specific functions of AHR and ARNT in estrogen-dependent signaling in human MCF7 breast cancer and human ECC-1 endometrial carcinoma cells. Knockdown of AHR with siRNA abrogates dioxin-inducible repression of estrogen-dependent gene transcription. Intriguingly, knockdown of ARNT does not effect TCDD-mediated repression of estrogen-regulated transcription, suggesting that AHR represses ER function independently of ARNT. This theory is supported by the ability of the selective AHR modulator 3',4'-dimethoxy-α-naphthoflavone (DiMNF) to repress estrogen-inducible transcription. Furthermore, basal and estrogen-activated transcription of the genes encoding cathepsin-D and pS2 are down-regulated in MCF7 cells but up-regulated in ECC-1 cells in response to loss of ARNT. These responses are mirrored at the protein level with cathepsin-D. Furthermore, knock-down of ARNT led to opposite but corresponding changes in estrogen-stimulated proliferation in both MCF7 and ECC-1 cells. We have obtained experimental evidence demonstrating a dioxin-dependent repressor function for AHR and a dioxin-independent co-activator/co-repressor function for ARNT in estrogen signalling. These results provide us with further insight into the mechanisms of transcription factor crosstalk and putative therapeutic targets in estrogen-positive cancers.

The putative cardioprotective and chemopreventive properties of the red wine phenolic resveratrol (RES) have made it the subject of a growing body of clinical and basic research. We have begun investigations focusing on the effects of RES on the activity of the aryl hydrocarbon receptor (AHR) complex. Our evidence suggests that RES is a potent repressor of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-inducible gene transcription in estrogen receptor (ER)-positive human breast, lung, and colon cancer cell lines. RES activates the transcription of the ER target genes to the same degree as estradiol (E(2)) in human MCF-7 breast cancer cells. Unlike E(2), which can only diminish TCDD-inducible CYP1A1 gene transcription by approximately 50%, RES can completely abrogate this response. Furthermore, 50% repression of TCDD-inducible transcription can be achieved with 100 nM RES, approximately 2.5 orders of magnitude lower than concentrations required for maximal inhibition, suggesting that multiple mechanisms are responsible for this effect. RES (100 nM) does not prevent ligand binding of a TCDD analog, nor does it prevent AHR from binding to its response element in the 5'-regulatory region of the CYP1A1 gene. Small inhibitory RNAs directed to ERα have demonstrated that RES-mediated repression of CYP1A1 depends on ERα. Whereas CYP1A1 protein levels in MCF-7 cells are refractory to the low-dose transcriptional effects of RES, a concomitant decrease in CYP1A1 protein levels is observed in Caco-2 cells. These results highlight a low-dose RES effect that could occur at nutritionally relevant exposures and are distinct from the high-dose effects often characterized.

Acute graft rejection is an important clinical problem in renal transplantation and an adverse predictor for long term graft survival. Plasma biomarkers may offer an important option for post-transplant monitoring and permit timely and effective therapeutic intervention to minimize graft damage. This case-control discovery study (n = 32) used isobaric tagging for relative and absolute protein quantification (iTRAQ) technology to quantitate plasma protein relative concentrations in precise cohorts of patients with and without biopsy-confirmed acute rejection (BCAR). Plasma samples were depleted of the 14 most abundant plasma proteins to enhance detection sensitivity. A total of 18 plasma proteins that encompassed processes related to inflammation, complement activation, blood coagulation, and wound repair exhibited significantly different relative concentrations between patient cohorts with and without BCAR (p value <0.05). Twelve proteins with a fold-change >or=1.15 were selected for diagnostic purposes: seven were increased (titin, lipopolysaccharide-binding protein, peptidase inhibitor 16, complement factor D, mannose-binding lectin, protein Z-dependent protease and beta(2)-microglobulin) and five were decreased (kininogen-1, afamin, serine protease inhibitor, phosphatidylcholine-sterol acyltransferase, and sex hormone-binding globulin) in patients with BCAR. The first three principal components of these proteins showed clear separation of cohorts with and without BCAR. Performance improved with the inclusion of sequential proteins, reaching a primary asymptote after the first three (titin, kininogen-1, and lipopolysaccharide-binding protein). Longitudinal monitoring over the first 3 months post-transplant based on ratios of these three proteins showed clear discrimination between the two patient cohorts at time of rejection. The score then declined to baseline following treatment and resolution of the rejection episode and remained comparable between cases and controls throughout the period of quiescent follow-up. Results were validated using ELISA where possible, and initial cross-validation estimated a sensitivity of 80% and specificity of 90% for classification of BCAR based on a four-protein ELISA classifier. This study provides evidence that protein concentrations in plasma may provide a relevant measure for the occurrence of BCAR and offers a potential tool for immunologic monitoring.