Dynamic expression of SNAI2 in prostate cancer predicts tumor progression and drug sensitivity.

Prostate cancer is a highly heterogeneous disease, understanding the crosstalk between complex genomic and epigenomic alterations will aid in developing targeted therapeutics. We demonstrate that, even though snail family transcriptional repressor 2 (SNAI2) is frequently amplified in prostate cancer, it is epigenetically silenced in this disease, with dynamic changes in SNAI2 levels showing distinct clinical relevance. Integrative clinical data from 18 prostate cancer cohorts and experimental evidence showed that gene fusion between transmembrane serine protease 2 (TMPRSS2) and ETS transcription factor ERG (ERG) (TMPRSS2-ERG fusion) is involved in the silencing of SNAI2. We created a silencer score to evaluate epigenetic repression of SNAI2, which can be reversed by treatment with DNA methyltransferase inhibitors and histone deacetylase inhibitors. Silencing of SNAI2 facilitated tumor cell proliferation and luminal differentiation. Furthermore, SNAI2 has a major influence on the tumor microenvironment by reactivating tumor stroma and creating an immunosuppressive microenvironment in prostate cancer. Importantly, SNAI2 expression levels in part determine sensitivity to the cancer drugs dasatinib and panobinostat. For the first time, we defined the distinct clinical relevance of SNAI2 expression at different disease stages. We elucidated how epigenetic silencing of SNAI2 controls the dynamic changes of SNAI2 expression that are essential for tumor initiation and progression and discovered that restoring SNAI2 expression by treatment with panobinostat enhances dasatinib sensitivity, indicating a new therapeutic strategy for prostate cancer.

Prognostic and therapeutic significance of COP9 signalosome subunit CSN5 in prostate cancer.

Chromosome 8q gain is associated with poor clinical outcomes in prostate cancer, but the underlying biological mechanisms remain to be clarified. CSN5, a putative androgen receptor (AR) partner that is located on chromosome 8q, is the key subunit of the COP9 signalosome, which deactivates ubiquitin ligases. Deregulation of CSN5 could affect diverse cellular functions that contribute to tumor development, but there has been no comprehensive study of its function in prostate cancer. The clinical significance of CSN5 amplification/overexpression was evaluated in 16 prostate cancer clinical cohorts. Its oncogenic activity was assessed by genetic and pharmacologic perturbations of CSN5 activity in prostate cancer cell lines. The molecular mechanisms of CSN5 function were assessed, as was the efficacy of the CSN5 inhibitor CSN5i-3 in vitro and in vivo. Finally, the transcription cofactor activity of CSN5 in prostate cancer cells was determined. The prognostic significance of CSN5 amplification and overexpression in prostate cancer was independent of MYC amplification. Inhibition of CSN5 inhibited its oncogenic function by targeting AR signaling, DNA repair, multiple oncogenic pathways, and spliceosome regulation. Furthermore, inhibition of CSN5 repressed metabolic pathways, including oxidative phosphorylation and glycolysis in AR-negative prostate cancer cells. Targeting CSN5 with CSN5i-3 showed potent antitumor activity in vitro and in vivo. Importantly, CSN5i-3 synergizes with PARP inhibitors to inhibit prostate cancer cell growth. CSN5 functions as a transcription cofactor to cooperate with multiple transcription factors in prostate cancer. Inhibiting CSN5 strongly attenuates prostate cancer progression and could enhance PARP inhibition efficacy in the treatment of prostate cancer.

Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays.

Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1-48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA.

To understand cancer, researchers need to know which molecules tumor cells use. These so-called 'biomarkers' tag cancer cells as being different from healthy cells, and can be used to predict how aggressive a tumor may be, or how well it might respond to treatment. A popular technique for assessing biomarkers across multiple tumors is to use tissue microarrays. This involves taking samples from different tumors and embedding them in a block of wax, which is then cut into micro-thin slices and stained with reagents that can detect specific biomarkers, such as proteins. Each block contains hundreds of samples, which all experience the same conditions. So, any patterns detected in the staining are likely to represent real variations in the biomarkers present. Many cancer studies, however, often compare samples from multiple tissue microarrays, which may increase the risk of technical artifacts: for example, staining may look stronger in one batch of tissue samples than another, even though the amount of biomarker present in these different arrays is roughly the same. These 'batch effects' could potentially bias the results of the experiment and lead to the identification of misleading patterns. To evaluate how batch effects impact tissue microarray studies, Stopsack et al. examined 14 wax blocks which contained tumor samples from 1,448 men with prostate cancer. This revealed that for some biomarkers, but not others, there were noticeable differences between tissue microarrays that were clearly the result of batch effects. Stopsack et al. then tested six different ways of fixing these discrepancies using statistical methods. All six approaches were successful, even if the arrays included tumors with different characteristics, such as tumors that had been diagnosed more or less recently. This work highlights the importance of considering batch effects when using tissue microarrays to study cancer. Stopsack et al. have used their statistical approaches to develop freely available software which can reduce the biases that sometimes arise from these technical artifacts. This could help researchers avoid misleading patterns in their data and make it easier to detect real variations in the biomarkers present between tumor samples.

A prospective study of inflammatory biomarkers and growth factors and risk of glioma in the UK Biobank.

PURPOSE: The role of growth factors and inflammation in the onset of glioma is poorly understood, and conflicting reports of associations of circulating IGF-1 and inflammatory biomarkers with glioma risk exist in the literature. We examined associations between C-reactive protein (CRP), white blood cell count (WBC), neutrophil-to-lymphocyte ratio (NLR), and insulin-like growth factor-1 (IGF-1) and glioma risk in the UK Biobank cohort. METHODS: Hazard ratios (HR) and 95% confidence intervals (CI) for glioma according to circulating biomarkers concentrations were calculated using Cox proportional hazards regression, adjusted for age, sex, race, and education. Analyses were conducted separately for glioma overall and by glioma subtype. RESULTS: We identified 417 incident glioma cases among 428,537 participants with 3,255,815 person-years of follow up. Weak, non-significant associations were observed with increasing levels of these biomarkers for risk of glioma overall or by glioma subtype. Among women only, IGF-1 in the highest quartile was positively associated with glioma risk compared to the lowest quartile (HR=1.64, 95%CI: 1.03-2.60, p-trend=0.08), as was NLR (HR=1.54, 95%CI: 1.00-2.39, p-trend=0.05). CONCLUSION: In this prospective cohort, we found no significant associations between the inflammatory biomarkers CRP and WBC and the development of glioma. NLR and IGF-1 were associated with risk in women, but not men. When considered with previous studies, further investigation of NLR and IGF-1 as markers of glioma risk appears warranted, particularly in women.

Accessible Pipeline for Translational Research Using TCGA: Examples of Relating Gene Mechanism to Disease-Specific Outcomes.

Bioinformatic scientists are often asked to do widespread analyses of publicly available datasets in order to identify genetic alterations in cancer for genes of interest; therefore, we sought to create a set of tools to conduct common statistical analyses of The Cancer Genome Atlas (TCGA) data. These tools have been developed in response to requests from our collaborators to ask questions, validate findings, and better understand the function of their gene of interest. We describe here what data we have used, how to obtain it, and what figures we have found useful.

Molecular Determinants of Thyroid Nodules with Indeterminate Cytology and RAS Mutations.

Background:RAS gene family mutations are the most prevalent in thyroid nodules with indeterminate cytology and are present in a wide spectrum of histological diagnoses. We evaluated differentially expressed genes and signaling pathways across the histological/clinical spectrum of RAS-mutant nodules to determine key molecular determinants associated with a high risk of malignancy. Methods: Sixty-one thyroid nodules with RAS mutations were identified. Based on the histological diagnosis and biological behavior, the nodules were grouped into five categories indicating their degree of malignancy: non-neoplastic appearance, benign neoplasm, indeterminate malignant potential, low-risk cancer, or high-risk cancer. Gene expression profiles of these nodules were determined using the NanoString PanCancer Pathways and IO 360 Panels, and Angiopoietin-2 level was determined by immunohistochemical staining. Results: The analysis of differentially expressed genes using the five categories as supervising parameters unearthed a significant correlation between the degree of malignancy and genes involved in cell cycle and apoptosis (BAX, CCNE2, CDKN2A, CDKN2B, CHEK1, E2F1, GSK3B, NFKB1, and PRKAR2A), PI3K pathway (CCNE2, CSF3, GSKB3, NFKB1, PPP2R2C, and SGK2), and stromal factors (ANGPT2 and DLL4). The expression of Angiopoietin-2 by immunohistochemistry also showed the same trend of increasing expression from non-neoplastic appearance to high-risk cancer (p < 0.0001). Conclusions: The gene expression analysis of RAS-mutant thyroid nodules suggests increasing upregulation of key oncogenic pathways depending on their degree of malignancy and supports the concept of a stepwise progression. The utility of ANGPT2 expression as a potential diagnostic biomarker warrants further evaluation.

Multiplex Immunofluorescence in Formalin-Fixed Paraffin-Embedded Tumor Tissue to Identify Single-Cell-Level PI3K Pathway Activation.

PURPOSE: Identifying cancers with high PI3K pathway activity is critical for treatment selection and eligibility into clinical trials of PI3K inhibitors. Assessments of tumor signaling pathway activity need to consider intratumoral heterogeneity and multiple regulatory nodes. EXPERIMENTAL DESIGN: We established a novel, mechanistically informed approach to assessing tumor signaling pathways by quantifying single-cell-level multiplex immunofluorescence using custom algorithms. In a proof-of-concept study, we stained archival formalin-fixed, paraffin-embedded (FFPE) tissue from patients with primary prostate cancer in two prospective cohort studies, the Health Professionals Follow-up Study and the Physicians' Health Study. PTEN, stathmin, and phospho-S6 were quantified on 14 tissue microarrays as indicators of PI3K activation to derive cell-level PI3K scores. RESULTS: In 1,001 men, 988,254 tumor cells were assessed (median, 743 per tumor; interquartile range, 290-1,377). PI3K scores were higher in tumors with PTEN loss scored by a pathologist, higher Gleason grade, and a new, validated bulk PI3K transcriptional signature. Unsupervised machine-learning approaches resulted in similar clustering. Within-tumor heterogeneity in cell-level PI3K scores was high. During long-term follow-up (median, 15.3 years), rates of progression to metastases and death from prostate cancer were twice as high in the highest quartile of PI3K activation compared with the lowest quartile (hazard ratio, 2.04; 95% confidence interval, 1.13-3.68). CONCLUSIONS: Our novel pathway-focused approach to quantifying single-cell-level immunofluorescence in FFPE tissue identifies prostate tumors with PI3K pathway activation that are more aggressive and may respond to pathway inhibitors.

Prostate-specific antigen dynamics predict individual responses to intermittent androgen deprivation.

Intermittent androgen deprivation therapy (IADT) is an attractive treatment for biochemically recurrent prostate cancer (PCa), whereby cycling treatment on and off can reduce cumulative dose and limit toxicities. We simulate prostate-specific antigen (PSA) dynamics, with enrichment of PCa stem-like cell (PCaSC) during treatment as a plausible mechanism of resistance evolution. Simulated PCaSC proliferation patterns correlate with longitudinal serum PSA measurements in 70 PCa patients. Learning dynamics from each treatment cycle in a leave-one-out study, model simulations predict patient-specific evolution of resistance with an overall accuracy of 89% (sensitivity = 73%, specificity = 91%). Previous studies have shown a benefit of concurrent therapies with ADT in both low- and high-volume metastatic hormone-sensitive PCa. Model simulations based on response dynamics from the first IADT cycle identify patients who would benefit from concurrent docetaxel, demonstrating the feasibility and potential value of adaptive clinical trials guided by patient-specific mathematical models of intratumoral evolutionary dynamics.

Baldness and Risk of Prostate Cancer in the Health Professionals Follow-up Study.

BACKGROUND: The association between male pattern baldness and prostate cancer has been inconsistent. We prospectively investigated the association between baldness at age 45 and prostate cancer risk in the Health Professionals Follow-up Study (HPFS), focusing on clinical and molecular markers. METHODS: Baldness was self-reported on the 1992 questionnaire using the modified Norwood-Hamilton scale prior to diagnosis. We estimated HRs between baldness and prostate cancer risk among 36,760 men, with follow-up through 2014. We also investigated whether baldness was associated with prostate cancer defined by tumor protein expression of androgen receptor and the presence of the TMPRSS2:ERG fusion. RESULTS: During 22 years, 5,157 prostate cancer cases were identified. Fifty-six percent of the men had either frontal or vertex baldness. No significant associations were found between baldness and prostate cancer risk. Among men younger than 60 years, there was a statistically significant association between frontal and severe vertex baldness and overall prostate cancer (HR: 1.74; 95% confidence interval: 1.23-2.48). Baldness was not significantly associated with expression of molecular subtypes defined by AR and TMPRSS2:ERG IHC of prostate tumors. CONCLUSIONS: This study showed no association between baldness at age 45 and prostate cancer risk, overall or for clinical or molecular markers. The association between baldness and overall prostate cancer among younger men is intriguing, but caution is warranted when interpreting this finding. IMPACT: The null findings from this large cohort study, together with previous literature's inconclusive findings across baldness patterns, suggest that baldness is not a consistent biomarker for prostate cancer risk or progression.

A prospective study of coffee and tea consumption and the risk of glioma in the UK Biobank.

BACKGROUND: Coffee and tea have been hypothesised to reduce the risk of some cancers; however, their impact on glioma is less well studied. METHODS: We examined associations between self-reported intake of tea and coffee in relation to glioma risk in the UK Biobank. We identified 487 incident glioma cases among 379,259 participants. Hazard ratios (HR) and 95% confidence intervals (CI) for glioma according to caffeinated beverage consumption were calculated using Cox proportional hazards regression with adjustment for age, gender, race and education; daily cups of tea or coffee were included in models considering the other beverage. RESULTS: Consuming 4 or more cups of tea was associated with reduced risk of glioma when compared to no tea consumption (HR = 0.69; 95% CI, 0.51-0.94). A significant inverse association was observed for glioblastoma (HR = 0.93 per 1 cup/d increment; 95% CI, 0.89-0.98) and among males for all gliomas combined (HR = 0.95 per 1 cup/d increment; 95% CI, 0.90-1.00). A suggestive inverse association was also observed with greater consumption of coffee (HR = 0.71; 95% CI, 0.49-1.05 for >4 versus 0 cups/d). Results were not materially changed with further adjustment for smoking, alcohol and body mass index. Associations were similar in 2-year and 3-year lagged analyses. CONCLUSIONS: In this prospective study, we found a significant inverse association between tea consumption and the risk of developing glioma, and a suggestive inverse association for the consumption of coffee. Further investigation on the possible preventive role of caffeine in glioma is warranted.

Effects of Tobacco Smoking on the Tumor Immune Microenvironment in Head and Neck Squamous Cell Carcinoma.

PURPOSE: Patients with head and neck squamous cell carcinoma (HNSCC) who actively smoke during treatment have worse survival compared with never-smokers and former-smokers. We hypothesize the poor prognosis in tobacco smokers with HNSCC is, at least in part, due to ongoing suppression of immune response. We characterized the tumor immune microenvironment (TIM) of HNSCC in a retrospective cohort of 177 current, former, and never smokers. EXPERIMENTAL DESIGN: Tumor specimens were subjected to analysis of CD3, CD8, FOXP3, PD-1, PD-L1, and pancytokeratin by multiplex immunofluorescence, whole-exome sequencing, and RNA sequencing. Immune markers were measured in tumor core, tumor margin, and stroma. RESULTS: Our data indicate that current smokers have significantly lower numbers of CD8+ cytotoxic T cells and PD-L1+ cells in the TIM compared with never- and former-smokers. While tumor mutation burden and mutant allele tumor heterogeneity score do not associate with smoking status, gene-set enrichment analyses reveal significant suppression of IFNα and IFNγ response pathways in current smokers. Gene expression of canonical IFN response chemokines, CXCL9, CXCL10, and CXCL11, are lower in current smokers than in former smokers, suggesting a mechanism for the decreased immune cell migration to tumor sites. CONCLUSIONS: These results suggest active tobacco use in HNSCC has an immunosuppressive effect through inhibition of tumor infiltration of cytotoxic T cells, likely as a result of suppression of IFN response pathways. Our study highlights the importance of understanding the interaction between smoking and TIM in light of emerging immune modulators for cancer management.

Commercial Gene Expression Tests for Prostate Cancer Prognosis Provide Paradoxical Estimates of Race-Specific Risk.

BACKGROUND: Commercial gene expression signatures of prostate cancer prognosis were developed and validated in cohorts of predominantly European American men (EAM). Limited research exists on the value of such signatures in African American men (AAM), who have poor prostate cancer outcomes. We explored differences in gene expression between EAM and AAM for three commercially available panels recommended by the National Comprehensive Cancer Network for prostate cancer prognosis. METHODS: A total of 232 EAM and 95 AAM patients provided radical prostatectomy specimens. Gene expression was quantified using NanoString for 60 genes spanning the Oncotype DX Prostate, Prolaris, and Decipher panels. A continuous expression-based risk score was approximated for each. Differential expression, intrapanel coexpression, and risk by race were assessed. RESULTS: Clinical and pathologic features were similar between AAM and EAM. Differential expression by race was observed for 48% of genes measured, although the magnitudes of expression differences were small. Coexpression patterns were more strongly preserved by race group for Oncotype DX and Decipher than Prolaris. Poorer prognosis was estimated in EAM versus AAM for Oncotype DX (P < 0.001), whereas negligible prognostic differences were predicted between AAM and EAM using Prolaris or Decipher (P > 0.05). CONCLUSIONS: Because of observed racial differences across three commercial gene expression panels for prostate cancer prognosis, caution is warranted when applying these panels in clinical decision-making in AAM. IMPACT: Differences in gene expression by race for three commercial panels for prostate cancer prognosis indicate that further study of their effectiveness in AAM with long-term follow-up is warranted.

Statin Use Is Associated with Lower Risk of PTEN-Null and Lethal Prostate Cancer.

PURPOSE: Statins are associated with lower risk of aggressive prostate cancer, but lethal prostate cancer is understudied and contributing mechanisms are unclear. We prospectively examined statins and lethal prostate cancer risk in the Health Professionals Follow-up Study (HPFS), tested associations with molecular subtypes, and integrated gene expression profiling to identify putative mechanisms. EXPERIMENTAL DESIGN: Our study included 44,126 men cancer-free in 1990, followed for prostate cancer incidence through 2014, with statin use recorded on biennial questionnaires. We used multivariable Cox regression to examine associations between statins and prostate cancer risk overall, by measures of clinically significant disease, and by ERG and PTEN status. In an exploratory analysis, age-adjusted gene set enrichment analysis identified statin-associated pathways enriched in tumor and adjacent normal prostate tissue. RESULTS: During 24 years of follow-up, 6,305 prostate cancers were diagnosed and 801 (13%) were lethal (metastatic at diagnosis or metastatic/fatal during follow-up). Relative to never/past use, current statin use was inversely associated with risk of lethal prostate cancer [HR, 0.76; 95% confidence interval (CI), 0.60-0.96] but not overall disease. We found a strong inverse association for risk of PTEN-null cancers (HR, 0.40; 95% CI, 0.19-0.87) but not PTEN-intact cancers (HR, 1.18; 95% CI, 0.95-1.48; P heterogeneity = 0.01). Associations did not differ by ERG. Inflammation and immune pathways were enriched in normal prostate tissue of statin ever (n = 10) versus never users (n = 103). CONCLUSIONS: Molecular tumor classification identified PTEN and inflammation/immune activation as potential mechanisms linking statins with lower lethal prostate cancer risk. These findings support a potential causal association and could inform selection of relevant biomarkers for statin clinical trials.

Association of statin use with risk of Gleason score-specific prostate cancer: A hospital-based cohort study.

BACKGROUND: Conflicting evidence suggests that statins act chemopreventively against prostate cancer (PCa). Whether the association of statin use with PCa risk is Gleason score-dependent, time-, dose-respondent is not well studied. METHODS: We conducted a cohort study at a tertiary hospital in the Southeastern US using longitudinal data of electronic medical records (EMR) from 1994 to 2016. Only cancer-free men aged >18 years at baseline with follow-up time of ≥12 months were included. Time-dependent Cox proportional hazards regression was used to estimate adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs). RESULTS: Among 13 065 men, 2976 were diagnosed with PCa over median follow-up of 6.6 years. Statin use was associated with lower risk of both Gleason low- (score <7: aHR, 0.85; 95% CI, 0.74-0.96) and high-grade PCa (score ≥7: aHR, 0.54; 95% CI, 0.42-0.69). The protective association was observed only when statins had been used for a relatively longer duration (≥11 months) or higher dose (≥121 defined daily doses), and were more pronounced for PCa of higher Gleason score (<7: aHR, 0.85, 95% CI, 0.74-0.96; 7 [3 + 4]: aHR, 0.62, 95% CI, 0.43-0.90; 7 [4 + 3]: aHR, 0.49, 95% CI, 0.29-0.82; 8: aHR, 0.60, 95% CI, 0.37-0.96; 9-10: aHR, 0.24, 95% CI, 0.11-0.54). Lipophilic statins (aHR, 0.83; 95% CI, 0.72-0.95) might be more protective than hydrophilic statins (aHR, 0.91, 95% CI, 0.63-1.33) against PCa. CONCLUSION: Statin use might be associated with reduced PCa risk only when used for a relatively longer duration, and the risk reduction was higher for PCa of higher Gleason score.

Methylmercury exposure, genetic variation in metabolic enzymes, and the risk of glioma.

Methylmercury (MeHg) is an environmental neurotoxin with human exposure mainly from dietary intake of contaminated fish. Exposure to MeHg has been implicated in neurological damage, but research on its role in cancers, specifically glioma, is limited. In a glioma case-control study, we examined associations between toenail mercury (Hg) and glioma risk. We also examined genetic polymorphisms in 13 genes related to MeHg metabolism for association with glioma risk; genetic associations were also studied in the UK Biobank cohort. Median toenail Hg in cases and controls, respectively, was 0.066 µg/g and 0.069 µg/g (interquartile range (IQR): 0.032-0.161 and 0.031-0.150 µg/g). Toenail Hg was not found to be significantly associated with glioma risk (Odds Ratio: 1.02; 95% Confidence Interval: 0.91, 1.14; p = 0.70 in analysis for ordinal trend with increasing quartile of toenail MeHg). No genetic variant was statistically significant in both of the studies; one variant, rs11859163 (MMP2) had a combined p-value of 0.02 though it was no longer significant after adjustment for multiple testing (Bonferroni corrected p = 1). This study does not support the hypothesis that exposure to MeHg plays a role in the development of glioma at levels of exposure found in this study population.

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

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

epiTAD: a web application for visualizing chromosome conformation capture data in the context of genetic epidemiology.

SUMMARY: Complementary advances in genomic technology and public data resources have created opportunities for researchers to conduct multifaceted examination of the genome on a large scale. To meet the need for integrative genome wide exploration, we present epiTAD. This web-based tool enables researchers to compare genomic 3D organization and annotations across multiple databases in an interactive manner to facilitate in silico discovery. AVAILABILITY AND IMPLEMENTATION: epiTAD can be accessed at https://apps.gerkelab.com/epiTAD/ where we have additionally made publicly available the source code and a Docker containerized version of the application.

Aneuploidy drives lethal progression in prostate cancer.

Aneuploidy, defined as chromosome gains and losses, is a hallmark of cancer. However, compared with other tumor types, extensive aneuploidy is relatively rare in prostate cancer. Thus, whether numerical chromosome aberrations dictate disease progression in prostate cancer patients is not known. Here, we report the development of a method based on whole-transcriptome profiling that allowed us to identify chromosome-arm gains and losses in 333 primary prostate tumors. In two independent cohorts (n = 404) followed prospectively for metastases and prostate cancer-specific death for a median of 15 years, increasing extent of tumor aneuploidy as predicted from the tumor transcriptome was strongly associated with higher risk of lethal disease. The 23% of patients whose tumors had five or more predicted chromosome-arm alterations had 5.3 times higher odds of lethal cancer (95% confidence interval, 2.2 to 13.1) than those with the same Gleason score and no predicted aneuploidy. Aneuploidy was associated with lethality even among men with high-risk Gleason score 8-to-10 tumors. These results point to a key role of aneuploidy in driving aggressive disease in primary prostate cancer.