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.
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•Androgen receptor (AR) overexpression drives genome-wide chromatin relaxation•Bromodomain (BRD) inhibitors reduce local chromatin opening at AR target genes•AR overexpression upregulates BRD proteins ATAD2, BRD2, and BRD4•The BROMO-10 gene signature stratifies patients for therapy with BRD inhibitors
Urbanucci et al. report how upregulated androgen receptor (AR) and AR-targeted bromodomain proteins contribute to genome-wide chromatin relaxation and increased gene transcription in advanced prostate cancer. They show that ATAD2 is a strong tissue biomarker and propose a BROMO-10 gene signature to stratify patients for combination therapies with bromodomain inhibitors.
Introduction and objectives
ISL1 serves as a biomarker of metastasis and neuroendocrine neoplasia in multiple tumors. However, the expression and relation of ISL1 to other biomarkers in prostate ...cancer have not been fully elucidated. Here, we characterize the expression of ISL1 and its partners in PCa and document its association to disease progression and post castration resistance neuroendocrine differentiation.
Methods
The expression of ISL1 was interrogated in > 6000 primary samples from the Decipher GRID registry and 250 mCRPC samples to assess its prognostic value and relation to neuroendocrine differentiation.
Results
ISL1 was highly correlated to MEIS genes and other genes related to cell motility. ISL1 down-regulation in PCa was associated with cancer progression, aggressive primary tumors, and metastatic outcome. We found that ISL1 is highly correlated to MEIS genes across multiple primary PCa and mCRPC cohorts. The expression of ISL1 and MEIS genes were significantly and inversely related to metastasis-free survival and lethal disease, and were downregulated in CRPC and hormone naïve metastatic tumors but showed upregulation in neuroendocrine tumors. Co-immunoprecipitation showed MEIS2 and ISL1 interacting with each supporting their role in modulating transcriptional regulation and nominating this complex for potential targeted therapy.
Conclusions
ISL1 complex with MEIS2 serves a critical role in prostate tumor progression and its upregulation in mCRPC/NE provides a rationale for assessing the role of ISL1 and its associated protein in treatment resistance.
To date, few studies have systematically characterized microarray gene expression signal performance with degraded RNA from fixed (FFPE) in comparison with intact RNA from unfixed fresh-frozen (FF) ...specimens. RNA was extracted and isolated from paired tumor and normal samples from both FFPE and FF kidney, lung, and colon tissue specimens and microarray signal dynamics on both the raw probe and probeset level were evaluated. A contrast metric was developed to directly compare microarray signal derived from RNA extracted from matched FFPE and FF specimens. Gene-level summaries were then compared to determine the degree of overlap in expression profiles. RNA extracted from FFPE material was more degraded and fragmented than FF, resulting in a reduced dynamic range of expression signal. In addition, probe performance was not affected uniformly and declined sharply toward 5′ end of genes. The most significant differences in FFPE versus FF signal were consistent across three tissue types and enriched with ribosomal genes. Our results show that archived FFPE samples can be used to profile for expression signatures and assess differential expression similar to unfixed tissue sources. This study provides guidelines for application of these methods in the discovery, validation, and clinical application of microarray expression profiling with FFPE material.
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.
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).
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.
A 28-gene hypoxia signature has strong and independent prognostic value for prostate cancer patients.
•We identified genes regulated by hypoxia in prostate cancer cell lines.•In a cohort of primary tumours, the hypoxia regulated genes were reduced to a prognostic signature.•The mRNA abundance signature was prognostic of biochemical recurrence/metastatic recurrence in 11 independent cohorts.
Hypoxia, i.e. insufficient supply of oxygen, is an important micro-environmental factor in solid tumours, including prostate cancer. In this work, we identified genes whose abundance were affected by induced hypoxia (1% oxygen concentration) from prostate cancer cell lines. The hypoxia-regulated genes were then refined into a signature, i.e. a biomarker consisting of multiple genes, based on their ability to predict patient outcome after radical prostatectomy. The gene signature predicted the risk of developing biochemical and metastatic recurrence in >1000 patients with primary tumours receiving varying treatments. The prognostic value was independent from existing clinic-pathological factors.
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 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.
Two prostate cancer (PC) classification methods based on transcriptome profiles, a de novo method referred to as the "Prostate Cancer Classification System" (PCS) and a variation of the established ...PAM50 breast cancer algorithm, were recently proposed. Both studies concluded that most human PC can be assigned to one of three tumor subtypes, two categorized as luminal and one as basal, suggesting the two methods reflect consistency in underlying biology. Despite the similarity, differences and commonalities between the two classification methods have not yet been reported.
Here, we describe a comparison of the PCS and PAM50 classification systems. PCS and PAM50 signatures consisting of 37 (PCS37) and 50 genes, respectively, were used to categorize 9,947 PC patients into PCS and PAM50 classes. Enrichment of hallmark gene sets and luminal and basal marker gene expression were assessed in the same datasets. Finally, survival analysis was performed to compare PCS and PAM50 subtypes in terms of clinical outcomes.
PCS and PAM50 subtypes show clear differential expression of PCS37 and PAM50 genes. While only three genes are shared in common between the two systems, there is some consensus between three subtype pairs (PCS1 versus Luminal B, PCS2 versus Luminal A, and PCS3 versus Basal) with respect to gene expression, cellular processes, and clinical outcomes. PCS categories displayed better separation of cellular processes and luminal and basal marker gene expression compared to PAM50. Although both PCS1 and Luminal B tumors exhibited the worst clinical outcomes, outcomes between aggressive and less aggressive subtypes were better defined in the PCS system, based on larger hazard ratios observed.
The PCS and PAM50 classification systems are similar in terms of molecular profiles and clinical outcomes. However, the PCS system exhibits greater separation in multiple clinical outcomes and provides better separation of prostate luminal and basal characteristics.
Grade group 4 and 5 (GG-45) prostate cancer (PCa) patients are at the highest risk of lethal outcomes, yet lack genomic risk stratification for prognosis and treatment selection. Here, we assess ...whether transcriptomic interactions between tumor immune content score (ICS) and the Decipher genomic classifier can identify most lethal subsets of GG-45 PCa. We utilized whole transcriptome data from 8071 tumor tissue (6071 prostatectomy and 2000 treatment-naïve biopsy samples) to derive four immunogenomic subtypes using ICS and Decipher. When compared across all grade groups, GG-45 samples had the highest proportion of most aggressive subtype—ICSHigh/DecipherHigh. Subsequent analyses within the GG-45 patient samples (n = 1420) revealed that the ICSHigh/DecipherHigh subtype was associated with increased genomic radiosensitivity. Additionally, in a multivariable model (n = 335), ICSHigh/DecipherHigh subtype had a significantly higher risk of distant metastasis (hazard ratio HR = 5.41; 95% confidence interval CI, 2.76–10.6; p ≤ 0.0001) and PCa-specific mortality (HR = 10.6; 95% CI, 4.18–26.94; p ≤ 0.0001) as compared with ICSLow/DecipherLow. The novel immunogenomic subtypes establish a very strong synergistic interaction between ICS and Decipher in identifying GG-45 patients who experience the most lethal outcomes.
In this analysis, we identified a novel interaction between the total immune content of prostate tumors and genomic classifier to identify the most lethal subset of patients with grade groups 4 and 5. Our results will aid in the subtyping of aggressive prostate cancer patients who may benefit from combined immune-radiotherapy modalities.
In this analysis, we identified a novel interaction between the total immune content of prostate tumors and genomic classifier to identify the most lethal subset of grade group 4 and 5 patients. Our results will aid in the subtyping of aggressive prostate cancer patients who may benefit from combined immune-radiotherapy modalities.
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.
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.
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).
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.
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