An Atlas of Accessible Chromatin in Advanced Prostate Cancer Reveals the Epigenetic Evolution during Tumor Progression.

Metastatic castration-resistant prostate cancer (mCRPC) is a lethal disease that resists therapy targeting androgen signaling, the primary driver of prostate cancer. mCRPC resists androgen receptor (AR) inhibitors by amplifying AR signaling or by evolving into therapy-resistant subtypes that do not depend on AR. Elucidation of the epigenetic underpinnings of these subtypes could provide important insights into the drivers of therapy resistance.

The Genomic and Epigenomic Landscape of Double-Negative Metastatic Prostate Cancer.

Unlabelled: Systemic targeted therapy in prostate cancer is primarily focused on ablating androgen signaling. Androgen deprivation therapy and second-generation androgen receptor (AR)-targeted therapy selectively favor the development of treatment-resistant subtypes of metastatic castration-resistant prostate cancer (mCRPC), defined by AR and neuroendocrine (NE) markers. Molecular drivers of double-negative (AR-/NE-) mCRPC are poorly defined.

Transcriptional profiling of matched patient biopsies clarifies molecular determinants of enzalutamide-induced lineage plasticity.

The androgen receptor (AR) signaling inhibitor enzalutamide (enza) is one of the principal treatments for metastatic castration-resistant prostate cancer (CRPC). Several emergent enza clinical resistance mechanisms have been described, including lineage plasticity in which the tumors manifest reduced dependency on the AR. To improve our understanding of enza resistance, herein we analyze the transcriptomes of matched biopsies from men with metastatic CRPC obtained prior to treatment and at progression (n = 21).

Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer.

Purpose: The purpose of this study was to measure genomic changes that emerge with enzalutamide treatment using analyses of whole-genome sequencing and RNA sequencing.

Experimental Design: One hundred and one tumors from men with metastatic castration-resistant prostate cancer (mCRPC) who had not been treated with enzalutamide ( = 64) or who had enzalutamide-resistant mCRPC ( = 37) underwent whole genome sequencing. Ninety-nine of these tumors also underwent RNA sequencing.

Down-regulation of ADRB2 expression is associated with small cell neuroendocrine prostate cancer and adverse clinical outcomes in castration-resistant prostate cancer.

Objectives: The net oncogenic effect of β2-adrenergic receptor ADRB2, whose downstream elements induce neuroendocrine differentiation and whose expression is regulated by EZH2, is unclear. ADRB2 expression and associated clinical outcomes in metastatic castration-resistant prostate cancer (mCRPC) are unknown.

Methods And Materials: This was a retrospective analysis of a multi-center, prospectively enrolled cohort of mCRPC patients.

Transcriptional profiling identifies an androgen receptor activity-low, stemness program associated with enzalutamide resistance.

The androgen receptor (AR) antagonist enzalutamide is one of the principal treatments for men with castration-resistant prostate cancer (CRPC). However, not all patients respond, and resistance mechanisms are largely unknown. We hypothesized that genomic and transcriptional features from metastatic CRPC biopsies prior to treatment would be predictive of de novo treatment resistance.

Clinical and genomic characterization of Low PSA Secretors: a unique subset of metastatic castration resistant prostate cancer.

Background: Metastatic disease burden out of proportion to serum PSA has been used as a marker of aggressive phenotype prostate cancer but is not well defined as a distinct subgroup. We sought to prospectively characterize the molecular features and clinical outcomes of Low PSA Secretors.

Methods: Eligible metastatic castration resistant prostate cancer (mCRPC) patients without prior small cell histology underwent metastatic tumor biopsy with molecular characterization.

Hydra: A mixture modeling framework for subtyping pediatric cancer cohorts using multimodal gene expression signatures.

Precision oncology has primarily relied on coding mutations as biomarkers of response to therapies. While transcriptome analysis can provide valuable information, incorporation into workflows has been difficult. For example, the relative rather than absolute gene expression level needs to be considered, requiring differential expression analysis across samples.

Using Transcriptional Signatures to Find Cancer Drivers with LURE.

Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event.

Clinical and Genomic Characterization of Treatment-Emergent Small-Cell Neuroendocrine Prostate Cancer: A Multi-institutional Prospective Study.

Purpose The prevalence and features of treatment-emergent small-cell neuroendocrine prostate cancer (t-SCNC) are not well characterized in the era of modern androgen receptor (AR)-targeting therapy. We sought to characterize the clinical and genomic features of t-SCNC in a multi-institutional prospective study. Methods Patients with progressive, metastatic castration-resistant prostate cancer (mCRPC) underwent metastatic tumor biopsy and were followed for survival.

TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal.

Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex "omics" data in an interactive and easily interpretable way.