Development of a predictive model for stromal content in prostate cancer samples to improve signature performance.

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 R2 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.

Characterization of transcriptomic signature of primary prostate cancer analogous to prostatic small cell neuroendocrine carcinoma.

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.

African American Specific Gene Panel Predictive of Poor Prostate Cancer Outcome.

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.

Multiparametric Magnetic Resonance Imaging Features Identify Aggressive Prostate Cancer at the Phenotypic and Transcriptomic Level.

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-ß 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.

Evaluation of a 24-gene signature for prognosis of metastatic events and prostate cancer-specific mortality.

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.

SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations.

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.

Prediction of Lymph Node Metastasis in Patients with Bladder Cancer Using Whole Transcriptome Gene Expression Signatures.

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.

Computational biomarker pipeline from discovery to clinical implementation: plasma proteomic biomarkers for cardiac transplantation.

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.

Targeting p90 ribosomal S6 kinase eliminates tumor-initiating cells by inactivating Y-box binding protein-1 in triple-negative breast cancers.

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.

A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers.

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.

Proteomic signatures in plasma during early acute renal allograft rejection.

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.

Estrogen receptor expression is required for low-dose resveratrol-mediated repression of aryl hydrocarbon receptor activity.

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.

Discovery and preclinical evaluation of anti-miR-17 oligonucleotide RGLS4326 for the treatment of polycystic kidney disease.

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.

Distinct transcriptional repertoire of the androgen receptor in ETS fusion-negative prostate cancer.

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.

Biologic Significance of Magnetic Resonance Imaging Invisibility in Localized Prostate Cancer.

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.

Divergent Biological Response to Neoadjuvant Chemotherapy in Muscle-invasive Bladder Cancer.

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.

High-fat diet fuels prostate cancer progression by rewiring the metabolome and amplifying the MYC program.

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.

Validation of a 10-gene molecular signature for predicting biochemical recurrence and clinical metastasis in localized prostate cancer.

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.

Transcriptome Wide Analysis of Magnetic Resonance Imaging-targeted Biopsy and Matching Surgical Specimens from High-risk Prostate Cancer Patients Treated with Radical Prostatectomy: The Target Must Be Hit.

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.

Validation of a Genomic Risk Classifier to Predict Prostate Cancer-specific Mortality in Men with Adverse Pathologic Features.

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.