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.

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.

Autosomal dominant polycystic kidney disease (ADPKD) is a leading genetic cause of end-stage renal disease with limited treatment options. Here the authors discover and characterize a microRNA inhibitor as a potential treatment for ADPKD.

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.

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.

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.

What's new?

While genomic/transcriptomic data analysis has revolutionized cancer biology, this analysis is frequently only available late in the cancer history, often after years of therapy. Here the authors built a single sample genomic classifier to predict primary prostate cancer tumors with early small cell neuroendocrine differentiation. They show in three independent cohorts that small cell neuroendocrine tumors of the prostate are similar to small cell tumors of the lung and predict the specific prostate tumors to be responsive to inhibitors of poly ADP ribose polymerase and histone deacetylases, underscoring the use of these drugs in this subtype of prostate cancer.

Radiogenomic characterization of multifocal prostate cancer

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Background: Up to 20% of patients with negative multiparametric magnetic resonance imaging (MRI) harbor Gleason score ≥7 prostate cancer (PCa). We sought to elucidate the molecular basis of and determine the prognostic significance of PCa visibility on MRI. Methods: We identified a retrospective cohort of patients who underwent MRI prior to prostatectomy with both MRI visible (PIRADS 3 – 5) and invisible PCa. MRI for each patient was re-reviewed and co-registered with whole-mount histopathology. DNA and RNA were co-isolated from all tumor foci pre-identified on FFPE specimens. High depth, targeted DNA and RNA next generation sequencing was performed to characterize the molecular profile of each tumor focus using the Oncomine Comprehensive Panel (DNA) and a custom targeted RNAseq panel assessing PCa relevant alterations. A multigene RNAseq model was developed and validated in two independent cohorts to predict MRI visible PCa and to determine the prognostic significance of MRI visibility. Results: A total of 26 primary tumor foci from 10 patients were analyzed. Of the 14 (54%) invisible lesions on MRI, 5 (36%) were Gleason 3+4 = 7 and the remainder were Gleason 6. We detected high-confidence prioritized PCa relevant mutations in 54% (14/26) of tumor foci, 43% (6/14) of which were in MRI invisible lesions. Notable point mutations were in APC, AR, ARID1B, ATM, ATRX, BRCA2, FAT1, MAP3K1, NF1, SPEN, SPOP, and TP53. A 9-gene RNA signature, the majority of which were under-expressed cellular organization and structure genes, was developed to predict MRI visibility with an AUC of 0.89. Validation of this signature in an independent data set (n = 16) yielded an AUC of 0.88 with a specificity of 100% for predicting MRI visible tumors. Using this signature in a cohort of 375 patients with clinical follow up, we found that predicted MRI visibility status was not an independent predictor of biochemical recurrence, metastasis-free survival, or PCa specific mortality (all p > 0.05). Conclusions: We observed under-expression of cellular organization and structural genes in MRI visible tumors compared to MRI invisible cancer foci. Using our validated signature to predict MRI visibility status, we found that MRI visibility is not a significant predictor of oncological outcomes.

The long noncoding RNA landscape of neuroendocrine prostate cancer and its clinical implications

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.

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.

Abstract B069: Drug response variability between luminal and basal prostate cancer tumors

Introduction: Prostate cancer (PCa) is a genomically heterogeneous disease that has been subtyped into molecularly distinct subtypes. Over the past several years, multiple drugs have demonstrated improvements in overall survival in men with advanced prostate cancer, prompting further investigations of these drugs. However, characterizing drug response in the localized disease setting and in the context of PCa molecular subtypes needs further investigation. Here in a large prospective cohort of men with adverse pathology we explore the heterogeneity of patient drug response between basal, luminal, and neuroendocrine prostate cancer subtypes.

Methods: Whole-transcriptome RNA expression profiles of 9,640 radical prostatectomy (RP) samples from prospective use of the Decipher test were obtained from the GRID registry database. Patients were subtyped into basal, luminal A, luminal B, and neuroendocrine subtypes using the PAM50 and small cell gene expression signatures. Drug response scores (DRS) predicting patient sensitivity for 89 oncology drugs were determined using in vitro drug sensitivity and microarray data from the NCI-60 panel. Pearson’s chi squared test was used to determine significant differences in drug sensitivity among PCa subtypes.

Results: Applying the subtype signatures to the cohort, we classified 43% of samples as basal, 26% as luminal A, 30% as luminal B, and 2% as neuroendocrine. DRS was highly variable across the subtypes. Basal tumors showed a distinct drug response profile where basal tumors were more sensitive to kinase inhibitors (e.g., cabozantanib, dasatnib, erlotinib), mTOR inhibitors (e.g., everolimus, temsirolumus), DNA repair inhibitors (e.g., olaparib, mitoxantrone), and alkylating (e.g., cisplatin, carboplatin) chemotherapy. Luminal A and B were more sensitive to steroid inhibition (e.g., abiraterone, tamoxifen) and anti-microtuble (e.g., docetaxel, paclitaxel, vinorelbine) chemotherapy, whereas neuroendocrine had highest DRS for antiproliferative agents (e.g., mitomycin, cytabarine, carmustine, topotecan), Significant differences in average DRS scores in subtypes were observed for all 89 drugs (p<0.001).

Conclusion: Prostate cancer subtypes in the localized disease setting have distinct drug response profiles, suggesting that subtyping and DRS scores may be useful for selecting candidates for systemic therapy trials.

Citation Format: Robert Den, Jonathan Lehrer, Mandeep Takhar, Mohammed Alshalalfa, Nicholas Erho, Elai Davicioni, Felix Feng. Drug response variability between luminal and basal prostate cancer tumors [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B069.

Impact of the SPOP Mutant Subtype on the Interpretation of Clinical Parameters in Prostate Cancer

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.

Development and Validation of a Prostate Cancer Genomic Signature that Predicts Early ADT Treatment Response Following Radical Prostatectomy

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.

Development and Validation of a 28-gene Hypoxia-related Prognostic Signature for Localized Prostate Cancer

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.

Lipid degradation promotes prostate cancer cell survival

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.

Androgen Receptor Deregulation Drives Bromodomain-Mediated Chromatin Alterations in Prostate Cancer

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.

Genomic variations associated with prostate cancer in large cohort of African American men

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Background: Racial disparities in prostate cancer (PCa) incidence and mortality are well known. PCa is known to be more aggressive in African American men (AAM) in terms of higher incidence and mortality rates. Here we validate a tumor gene expression pan-cancer race model in men with PCa and further characterize genomic differences that may contribute to disparate clinical outcomes Methods: We obtained de-identified genome-wide expression profiles from clinical use of the Decipher RP test in 9,953 men from the GRID registry database. A subset of men (n = 313) had known race status. A pan-cancer race model, developed to predict patient AAM race from analysis of gene expression patterns in 4,162 tumors from retrospective cohorts with known race status was applied to the prospective cohort for race prediction. Gene expression data was used to define genomic differences. Results: The race model has an AUC of 0.98 discriminating EAM from AAM in independent PCa cohort. The model was then applied to the 9,640 GRID patients with unknown race status and classified 6,831 as EAM, 1,058 as AAM with 1,751 as having indeterminate race. Characterizing the molecular subtypes, we found known and predicted AAM to be enriched with SPINK1+ tumors (21% and 24%, respectively) compared to predicted EAM (8%). In contrast, while ERG+ was found 22% and 19% in known and predicted AAM, respectively compared to 46% in predicted EAM. Based on PAM50 prostate cancer classifier, 61% of AAM were classified as basal-like tumors, whereas 41% were basal-like in EAM. Similarly, 28% of AAM had low AR-A while only 11% of EAM had low AR-A. AAM tumors had higher levels of immune infiltration signatures as well as higher scores for inflammatory and interferon gamma responses, and Interleukin 6 (IL6) signaling activity scores. AAM had lower DNA repair and glycolysis pathway activity compared to EAM Conclusions: Known and predicted AAM, were enriched with SPINK1+ tumors, higher immune infiltration and activation but lower ERG+, DNA repair and AR activity tumors. Using such large GRID data with known race, we will further understand the underlying causes associated with prostate cancer racial disparities which could lead to personalized diagnosis and treatment.

Apparent plasticity in the biological response to neoadjuvant chemotherapy in muscle-invasive bladder cancer

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Background: After cisplatin-based neoadjuvant chemotherapy (NAC) almost two thirds of patients have residual muscle-invasive bladder cancer (MIBC) present at radical cystectomy (RC). The alterations induced by NAC in these cisplatin-resistant tumors remain largely unstudied. Here, we aim to investigate the characteristics of cisplatin-resistant tumors. Methods: RC 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. In addition, the tumor bed (scar tissue) of 21 post-NAC RC specimens with no residual tumor was profiled. Unsupervised consensus clustering (CC) was performed and the CC were investigated for their biological and clinical characteristics. H&E and immunohistochemistry (KRT5/6, GATA3, KI67 and CD8) were used to confirm tissue sampling and gene expression analysis. Results: Unsupervised consensus clustering yielded four distinct consensus clusters (CC). Consistent basal-(CC1) and luminal-like (CC2) phenotype similar to pre-NAC subtyping was observed in 42% of cases. One third of cases became immune-infiltrated (CC3) in the post-NAC setting but lacked basal and luminal markers. These tumors expressed a strong T-cell signature, chemokine signaling and checkpoint molecules. Conversely, CC4 was associated with healing/scarring. This ‘scar-like’ character of CC4 was consistent with the scar samples. Despite being pathological non-responders, the relative risk of death for CC4 was 2.8 and 3 times less than CC2-Luminal (p = 0.038) and CC3-Infiltrated (p = 0.018), respectively. Luminal-like pre-NAC samples were more likely to adopt a scar-like character (CC4) in the post-NAC setting, while the basal-like tumors were more likely to develop luminal features (CC2). Conclusions: This study expands our knowledge of cisplatin-resistant MIBC by suggesting molecular subtypes to understand the biology of these tumors. Clinical trials are necessary to test the impact of these molecular subtypes with respect to selection of adjuvant and salvage treatments. Post-NAC immune infiltration could have implications for subsequent immunotherapy.

Hypoxia related mRNA biomarker to predict biochemical failure and metastasis for prostate cancer

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Background: Hypoxia is an important regulatory factor in tumorigenesis and is associated with a poor prognosis. Patients with high risk locally advanced disease account for 13-21% of prostate cancer cases and the ten year cancer specific survival rate for these patients is 62%. Patients with hypoxic tumours could benefit from hypoxia modifying therapeutics in addition to radiotherapy. Clinical companion biomarkers are needed to stratify patients who would benefit from hypoxia modifying therapy in addition to radiotherapy. Methods: RNA-seq analysis was performed on prostate cell lines (PNT2-C2, PC-3, LNCaP and DU145) exposed to 1% hypoxia for 24 hrs. A prostate cancer hypoxia gene signature was derived in silico using publicly available prostate gene expression data sets and the RNA-seq data. The biomarker was then independently validated in multiple cohorts of prostate cancer patients with localized diseases receiving prostatectomy alone, prostatectomy plus adjuvant radiotherapy, prostatectomy plus salvage radiotherapy, or definitive radiotherapy alone. Results: In vitro the hypoxia inducible expression of the hypoxia gene signature was tested at 1% and 0.1% oxygen of which 21 of the 28 genes were regulated by hypoxia. Patients stratified as high hypoxia were associated with significantly poorer 5-year biochemical recurrence free survival in patients undergoing prostatectomy alone, prostatectomy plus adjuvant radiotherapy and definitive radiotherapy alone. In multivariable analysis, the biomarker retained significance after correcting for confounding factors including Gleason group, PSA, a molecular classifier, etc. In another cohort of prostatectomy and salvage radiotherapy treated patients, the mRNA signature predicts metastasis free survival in both univariable and multivariable analyses. Conclusions: We derived a de novo mRNA signature based on hypoxia-regulated genes. The biomarker consistently predicts biochemical failure and metastasis for prostate cancer patients with localized disease.

Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies

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.

LSD1-Mediated Epigenetic Reprogramming Drives CENPE Expression and Prostate Cancer Progression

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.

Therapy-induced developmental reprogramming of prostate cancer cells and acquired therapy resistance

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.

Abstract 4908: Neuropilin-1 is up-regulated in the adaptive response of prostate tumors to androgen targeted therapies and is prognostic of metastatic progression and patient mortality

Aims: Androgen-targeted therapies (ATTs) are the mainstay treatment for metastatic prostate cancer (PCa). However, ATTs promote adaptation of tumour cells and lead to castration resistant disease (CRPC). We have recently identified the cell surface receptor, Neuropilin-1 (NRP1) as increased during EMT and in CRPC. However, the role of NRP1 in the prostate epithelium is poorly understood. This study aims to determine whether the inhibition of NRP1 will be a feasible therapeutic strategy for blocking PCa metastasis and therapy resistance.

Methods: qPCR and western blotting were used to assess NRP1 expression in PCa cell lines. NRP1 expression in CRPC was assessed using a murine LNCaP xenograft model of castration. NRP1 was knocked down with shRNA sequences from the pLKO.1 lentiviral construct. For metastasis assays, PC3 cells were microinjected into the zebrafish yolk sac and metastatic dissemination imaged 5 days later. NRP1 expression in radical prostatectomy (RP) samples from Mayo Clinic (545 patients) and Johns Hopkins Medical Institutions (JHMI; 188 patients) cohorts was quantified by Affymetrix exon arrays and multivariable analysis performed. Wound scratch migration and invasion assays were performed with the WoundMaker™ tool and IncuCyte™ FLR imaging systems.

Results: NRP1 levels were elevated in humanCRPC xenografts, metastatic and castrate resistant clinical PCa samples (p &lt;0.0001), and PCa cell lines. NRP1 suppression significantly reduced metastasis of human xenografts in zebrafish and the migratory and invasive behaviour of metastatic PCa cells (p=0.0002). Multivariable analysis identified NRP1 as a significant independent prognostic indicator of metastasis and prostate cancer specific mortality in two large clinical cohorts (Mayo Clinic and JHMI; p=0.008/0.048 and 0.013/0,034 respectively). We show that NRP1 knockdown promotes E-Cadherin expression and loss of vimentin in mesenchymal PCa cells.

Conclusion: These results will provide the preclinical data necessary to rationalise the use of anti-NRP1 directed adjuvant therapies for clinical use in PCa patients receiving ATTs, and will pave the way for larger scale preclinical and clinical trials in the PCa setting.

Citation Format: Marianna Volpert, Brian Tse, Ellca Ratther, Nataly Stylianou, Mannan Nouri, Melanie Lehman, Stephen McPherson, Mani Roshan-Moniri, Mandeep Takhar, Nicholas Erho, Mohamed Alshalafa, Elai Davicioni, Robert Jenkins, Ashley Ross, Jeffrey Karnes, Robert Den, Ladan Fazli, Martin Gleave, Elizabeth Williams, Paul Rennie, Ralph Buttyan, Pamela Russell, Colleen Nelson, Brett Hollier. Neuropilin-1 is up-regulated in the adaptive response of prostate tumors to androgen targeted therapies and is prognostic of metastatic progression and patient mortality [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4908. doi:10.1158/1538-7445.AM2017-4908

PGCA: An algorithm to link protein groups created from MS/MS data

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.

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.

Stromal Gene Expression is Predictive for Metastatic Primary Prostate Cancer

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.

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.

Potential Impact on Clinical Decision Making via a Genome-Wide Expression Profiling: A Case Report

Management of men with prostate cancer is fraught with uncertainty as physicians and patients balance efficacy with potential toxicity and diminished quality of life. Utilization of genomics as a prognostic biomarker has improved the informed decision-making process by enabling more rationale treatment choices. Recently investigations have begun to determine whether genomic information from tumor transcriptome data can be used to impact clinical decision-making beyond prognosis. Here we discuss the potential of genomics to alter management of a patient who presented with high-risk prostate adenocarcinoma. We suggest that this information help selecting patients for advanced imaging, chemotherapies, or clinical trial.

Prostate cancer radiomics and the promise of radiogenomics

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.

Abstract 1502: Three intrinsic subtypes of prostate cancer with distinct pathway activation profiles differ in prognosis and treatment response

In the era of precision medicine, genomic classifications of prostate cancer (PC) have not broadly impacted patient care. Here we present an integrative transcriptome analysis of 14 disease-related pathways in over 4,600 clinical specimens and 25 PC preclinical models. This approach provides a novel classification scheme, inclusive of 3 PC subtypes (PCS1-3), which predict disease progression and drug resistance. The PCS1 and PCS2 categories possess characteristics of luminal cells, while PCS3 exhibits basal features. Castration-resistant and metastatic cancers are over-represented in PCS1 and PCS3 tumors. All 3 subtypes are represented in commonly used PC cell lines; however, none of the mouse models we analyzed resembles PCS3. PCS classification using RNA expression data appears to be stable across primary and metastatic human tumors, cell lines, xenografts and mouse models. This new subtyping method provides novel opportunities for patient stratification that reflect specific features of tumor biology and may influence therapeutic decisions.

Citation Format: Sungyong You, Beatrice Knudsen, Nicholas Erho, Mohammed Alshalalfa, Mandeep Takhar, Hussam Al-deen Ashab, Elai Davicioni, Robert Jeffrey Karnes, Eric A. Klein, Robert B. Den, Ashley E. Ross, Edward M. Schaeffer, Isla P. Garraway, Jayoung Kim, Michael R. Freeman. Three intrinsic subtypes of prostate cancer with distinct pathway activation profiles differ in prognosis and treatment response. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1502.

Abstract 2922: Loss of retinoblastoma protein dysregulates HIF1-mediated genetic programs, and promotes tumor cell invasiveness and neuroendocrine differentiation in prostate cancer cells

Loss of tumour suppressor proteins, such as the retinoblastoma protein (Rb), results in tumour progression and metastasis. Metastasis is facilitated by low oxygen availability within the tumour that is detected by hypoxia inducible factors (HIFs). The HIF1 complex, HIF1α and its 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 of Rb in HIF1-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 Rb regulates specific chromosomal gene clusters and loss of Rb in conjunction with hypoxia leads to dysregulation of HIF1-regulated genetic programs that promote cell invasion and neuroendocrine differentiation. Gene ontology analysis of the hypoxia-inducible genes sensitive to loss of Rb revealed that a significant portion of these genes are involved in neuroendocrine differentiation (NED), specifically ENO2, KISS1R and HTR5A. ENO2 is the bonafide marker of neuroendocrine differentiation and it's presence is a signature of late stage castrate resistant prostate cancer. Furthermore, we have functional evidence KISS1R is linked to intracellular calcium mobilization in 22RV1 cells. We have demonstrated that increased expression of HIF-regulated genes in response to loss of Rb activates Akt and ERK signaling pathways and promotes neuroendocrine differentiation and invasion. Inhibition of these signaling pathways significantly decreased actin polymerization in LNCaP cells. For the first time, we have established a direct link between hypoxic tumour environments, Rb inactivation and progression to late stage metastatic neuroendocrine prostate cancer. Understanding the molecular pathways responsible for progression of benign prostate tumours to metastasized and lethal forms will aid in the development of more effective prostate cancer therapies.

Citation Format: Mark Labrecque, Mandeep Takhar, Rebecca J. Nason, Stephanie Santacruz, Kevin Tam, Shabnam Massah, Anne Haegert, Robert Bell, Manuel Altamirano-Dimas, Colin Collins, Frank Lee, Gratien Prefontaine, Michael Cox, Timothy Beischlag. Loss of retinoblastoma protein dysregulates HIF1-mediated genetic programs, and promotes tumor cell invasiveness and neuroendocrine differentiation in prostate cancer cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2922.

Integrated Classification of Prostate Cancer Reveals a Novel Luminal Subtype with Poor Outcome

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.

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.

The frequency of druggable targets in localized prostate cancer: Initial analysis from the Decipher GRID

98

Background: Prostate cancers patient management has been enhanced with commercially available genomic prognostic tests such as the Decipher prostate cancer classifier that are useful for making treatment decision-making. In addition to being the most validated predictor of metastasis in prostate cancer, Decipher is also a genome-wide assay that measures the expression of many druggable targets. Methods: Decipher GRID (Genomic Resource Information Database), was queried to assess the expression patterns of 14 genes from 5 biological pathways (Table) in 1,850 patients from previously published Decipher validation studies. The frequency of high (or low) expression of each gene was ascertained using a standard and more conservative thresholds based on the median absolute deviation (MAD) metric. For the standard threshold, genes whose high expression is of clinical relevance, patients with gene expression above the median + 1.48*MAD were annotated as high expression and for genes whose low expression is of clinical relevance, patients with gene expression below the median - 1.48*MAD were annotated as low expression. For the conservative threshold, median +/- 2*1.48*MAD was used. Results: See Table. Conclusions: Since every patient receiving the Decipher test also has a genome-wide expression profile, the Decipher GRID will allow researchers to evaluate on a systematic population-level the expression of genes that may be targeted with existing therapies. Such information may be useful for selection of optimal systemic therapy and inclusion into clinical trials of novel targeted agents. This rich genomic resource is being made available on a research use only basis to prostate cancer researchers and to clinicians seeking to uncover individualized genomic insights for patients to advance precision medicine. [Table: see text]

The relationship of B7H3 expression to androgen and prostate cancer outcomes in a large natural history cohort of men undergoing prostatectomy

256

Background: B7-H3 (CD276), part of the B7 superfamily, has been shown to play an immunomodulatory role, however its regulation, receptor and mechanism of action remain unclear. Protein levels of B7-H3 have been previously shown to relate to prostate cancer outcomes and currently, humanized monoclonal antibodies are being developed for clinical use (MGA271, Macrogenics). Here we use genomic expression data to examine the relationship of B7-H3 to prostate cancer outcomes and molecular subtypes. Methods: Prostatectomy tissue from 905 patients were profiled using the Affymetrix HuEx 1.0 ST microarray. Kruskal-Wallis tests were used to identify significant associations of B7-H3 expression with clinico-pathologic variables, and survival analysis were used to evaluate the prognostic value of B7-H3. Pearson’s correlation analyses were also performed to assess the relationship of B7-H3 expression with molecular subtypes and individual transcripts. Androgen receptor (AR) occupancy of promoter regions was derived in silico from chromosomal immune-precipitation (ChIP) data. Results: B7-H3 expression was positively associated with Gleason score (p < 0.01) and tumor stage (p < 0.01). High B7-H3 expression also correlated with the development of metastasis and prostate cancer specific mortality (HR of 3.4 and 2.4 respectively, p < 0.05 for both), but this was not significant on multi-variable analysis. B7-H3 was positively associated with ERG+ disease (n = 670, r = 0.85, p < 0.05) and AR expression (n = 670, r = 0.46, p < 0.001). B7-H3 was found to be one of the most correlated genes with AR (95th percentile) and ChIP analysis revealed AR binding upstream of B7-H3, suggesting potential androgen dependent regulation. Conclusions: B7-H3 expression correlates with high Gleason grade and advanced prostate cancer stage with higher quartiles of expression portending poor oncologic outcomes in two independent prostatectomy cohorts. B7-H3 expression appears to relate to the androgen receptor.

Abstract 1860: DNA-PK-mediated transcriptional regulation drives tumor progression and metastasis

Emerging evidence demonstrates that the DNA repair kinase DNA-PK exerts divergent roles in transcriptional regulation of unsolved consequence. Here, in vitro and in vivo interrogation demonstrate that DNA-PK functions as a selective modulator of transcriptional networks that induce cell migration, invasion, and metastasis. Accordingly, suppression of DNA-PK inhibits tumor metastases. Clinical assessment revealed that DNA-PK is significantly elevated in advanced disease, and independently predicts for metastases, recurrence, and reduced overall survival. Further investigation demonstrated that DNA-PK in advanced tumors is highly activated, independently of DNA damage indicators. Combined, these findings put forth new paradigms for DNA-PK function, identify DNA-PK as a potent driver of tumor progression and metastases, and nominate DNA-PK as a therapeutic target for advanced malignancies.

Citation Format: Jonathan F. Goodwin, Vishal Kothari, Justin M. Drake, Shuang Zhao, Emanuela Dylgjeri, Jeffry L. Dean, Matthew J. Schiewer, Christopher McNair, Michael S. Magee, Robert B. Den, Ziqi Zhu, Nicholas A. Graham, Ajay A. Vashisht, James A. Wohlschlegel, Thomas G. Graeber, R Jeffrey Karnes, Mandeep Takhar, Elai Davicioni, Scott A. Tomlins, Nima Sharifi, Owen N. Witte, Felix Y. Feng, Karen E. Knudsen. DNA-PK-mediated transcriptional regulation drives tumor progression and metastasis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1860. doi:10.1158/1538-7445.AM2015-1860

DNA-PKcs-Mediated Transcriptional Regulation Drives Prostate Cancer Progression and Metastasis

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.

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.

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.