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PURPOSE Guidelines recommend somatic and germline testing for men with advanced prostate cancer (PCa). Barriers to widespread implementation result in underutilization of germline testing. Somatic testing alone risks missing pathogenic germline variants (PGVs). We sought to determine whether the addition of germline testing to tumor-only sequencing improves detection of PGVs in men with advanced PCa. Secondarily, we sought to define the added value of combining somatic and germline testing to optimize detection of clinically actionable alterations. PATIENTS AND METHODS We analyzed results of independent germline testing and tumor-only sequencing from 100 men with advanced PCa from a prospective clinical trial (ClinicalTrials.gov identifier: NCT03328091 ). The primary outcome was the proportion of PGVs not reported with tumor-only sequencing. The secondary outcome was the association of locus-specific loss of heterozygosity for PGVs in homologous recombination genes with clinical-genomic features. RESULTS In the 100 men who underwent germline testing and tumor-only sequencing, 24 PGVs were identified, 17 of which were clinically actionable, in 23 patients. Tumor-only sequencing failed to report four (17%) of the PGVs. One additional PGV (4.2%) had variant allele frequency on tumor-sequencing below the threshold for follow-up germline testing. When integrating tumor-only sequencing with germling testing results, 33% of patients harbored clinically actionable alterations. Rates of locus-specific loss of heterozygosity were higher for BRCA2 PGVs in castration-resistant PCa than PGVs in other homologous recombination genes in hormone-sensitive PCa ( P = .029). CONCLUSION Tumor-only sequencing failed to report more than 20% of PGVs in men with advanced PCa. These findings strongly support guideline recommendations for universal germline and somatic testing in this population. Combining tumor and germline sequencing doubled the chance of detecting a clinically actionable alteration.

BackgroundAlthough clinical features of multi-parametric magnetic resonance imaging (mpMRI) have been associated with biochemical recurrence in localized prostate cancer, such features are subject to inter-observer variability.ObjectiveTo evaluate whether the volume of the dominant intraprostatic lesion (DIL), as provided by a deep learning segmentation algorithm, could provide prognostic information for patients treated with definitive radiation therapy (RT).Design, Setting, and ParticipantsRetrospective study of 438 patients with localized prostate cancer who underwent an endorectal coil, high B-value, 3-Tesla mpMRI and were treated with RT between 2010 and 2017.InterventionRT.Outcome Measurements and Statistical AnalysisBiochemical recurrence and metastasis risk, assessed with a cause-specific Cox regression and time-dependent receiver operating characteristic analysis.Results and LimitationsThe artificial intelligence (AI) model identified DILs with an area under the receiver operating characteristic curve (AUROC) of 0.827 at the patient level. For the 233 patients with available PI-RADS scores, with a median follow-up of 5.6 years, AI-defined DIL volume was significantly associated with biochemical failure (adjusted hazard ratio 1.54, 95% confidence interval 1.09-2.17, p=0.014) after adjustment for PI-RADS score. Among all 438 patients with a median follow-up of 6.9 years, the AUROC for predicting 7-year biochemical failure for AI volume (0.790) was similar to that for an expanded National Comprehensive Cancer Network (NCCN+) category (p=0.17). The AUROC for predicting 7-year metastasis for AI volume trended towards being higher compared to NCCN+ categories (0.854 vs 0.769, p=0.06).ConclusionsA deep learning algorithm could identify the DIL with good performance. AI-defined DIL volume may be able to provide prognostic information independent of the NCCN+ risk group or other radiologic factors for patients with localized prostate cancer treated with RT.

Feature-region combination AUCs and benchmarking Sheet 1: Log2 fold-change, p-value, and q-value between ARPC and NEPC lines for central mean coverage in all queried (338) TFs (two tailed Mann-Whitney U test, Benjamini-Hochberg adjusted). Sheet 2: 100-fold cross-validation AUCs for all region and feature combinations subset by the ‘AR10’ overlapping features (see Methods). Sheet 3: 100-fold cross-validation AUCs for all region and feature combinations subset by the ‘Phenotype 47-defining’ overlapping features. Sheet 4: 100-fold cross-validation AUCs for all region and feature combinations (global). Sheet 5: Predictions scores, tumor fraction, depth of coverage, and subtype for benchmarking admixtures. Sheet 6: AUCs for unsupervised prediction of admixture subtypes grouped by tumor fraction and depth. Sheet 7: NEPC:ARPC ratio, tumor fraction, and ARPC and NEPC fraction predictions for mixed phenotype admixtures using Keraon (see Methods).

PDX sequencing metrics Phenotype and sequencing metrics for all PDX LuCaP lines.

Supplementary Figures S1-S18 and Supplementary Table legends.

Differential regulation metrics and values for select features Sheet 1: Log2 fold-change, p-value, and q-value between ARPC and NEPC lines for NPS in the 47 phenotype defining gene bodies (two tailed Mann-Whitney U test, Benjamini-Hochberg adjusted). Sheet 2: Differentially expressed list of 514 transcription factor (TF). All statistical comparison and fold change estimation was done against ARPCs. Sheet 3: Log2 fold-change, p-value, and q-value between ARPC and NEPC lines for central mean coverage in 108 TFs overlapping RNA-Seq up/down regulated TFs (two tailed Mann-Whitney U test, Benjamini-Hochberg adjusted). Sheet 4: Paralogous transcription factors (TFs) for each of the 38 TFs with differential RNA expression between ARPC and NEPC and differential TFBS accessibility. Paralogous TFs that are also differentially expressed by RNAseq analysis of PDX tumors are shown in red text. Paralogs were obtained from ensembl biomart human genes GRCh38.p13 (http://uswest.ensembl.org/biomart/martview/64d8bd7fe9851a2501aece9b74b03631) Sheet 5: Mean values in ARPC, NEPC, and HD lines for central and window means for ARPC and NEPC specific open chromatin regions.

PTM peak data and phenotype 47 fragment variability Sheet 1: PDX sample representation in 3 histone PTM CUT&RUN nucleosome profiling assays (H3K4me1, H2K27ac and H3K27me3). Sheet 2: Log2 fold-change, p-value, and q-value between ARPC and NEPC lines for coefficient of variation in the 47 phenotype defining gene bodies (two tailed Mann-Whitney U test, Benjamini-Hochberg adjusted). Sheet 3: Log2 fold-change, p-value, and q-value between ARPC and NEPC lines for coefficient of variation in the 47 phenotype defining gene promoters (two tailed Mann-Whitney U test, Benjamini-Hochberg adjusted).

Unsupervised model predictions and patient/validation cohort sequencing metrics Sheet 1: DFCI Cohort I: Tumor phenotype by histology and estimates of tumor fraction, subtyping score, and inferred subtype calls using ctdPheno. Sheet 2: DFCI cohort I: Tumor phenotype by histology and estimates of tumor fraction, subtyping score, and inferred phenotypic subtype calls using different ATAC-seq site restricted analysis using ctdPheno. Sheet 3: DFCI cohort I: Tumor phenotype by histology and estimates of tumor fraction, subtyping score, and inferred phenotypic subtype calls using different ATAC-seq site restricted analysis using Keraon. Sheet 4: DFCI Cohort II: Tumor phenotype by histology, summary of clinical correlatives and estimates of tumor fraction, subtyping score, and inferred subtype calls using ctdPheno. Sheet 5: Histology, tumor fraction, subtype score, NEPC fraction, and subtype calls for WGS and ULP patient samples (UW cohort). Sheet 6: Complete sequencing metrics for DFCI cohort I. Sheet 7: Complete sequencing metrics for DFCI cohort II (ULP). Sheet 8: Complete sequencing metrics for DFCI cohort II (deep WGS) Sheet 9: Complete sequencing metrics and ichorCNA estimated tumor fractions for the UW cohort (ULP). Sheet 10: Complete sequencing metrics for the UW cohort (deep WGS) Sheet 11: Complete sequencing metrics for the healthy donor cohort. Sheet 12: Clinical data summary for UW cohort.

African-American (AA) men are more likely to be diagnosed with and die from prostate cancer than European American (EA) men. Despite the central role of the androgen receptor (AR) transcription factor in prostate cancer, little is known about the contribution of epigenetics to observed racial disparities. We performed AR chromatin immunoprecipitation sequencing on primary prostate tumors from AA and EA men, finding that sites with greater AR binding intensity in AA relative to EA prostate cancer are enriched for lipid metabolism and immune response genes. Integration with transcriptomic and metabolomic data demonstrated coinciding upregulation of lipid metabolism gene expression and increased lipid levels in AA prostate cancer. In a metastatic prostate cancer cohort, upregulated lipid metabolism associated with poor prognosis. These findings offer the first insights into ancestry-specific differences in the prostate cancer AR cistrome. The data suggest a model whereby increased androgen signaling may contribute to higher levels of lipid metabolism, immune response, and cytokine signaling in AA prostate tumors. Given the association of upregulated lipogenesis with prostate cancer progression, our study provides a plausible biological explanation for the higher incidence and aggressiveness of prostate cancer observed in AA men. Significance: With immunotherapies and inhibitors of metabolic enzymes in clinical development, the altered lipid metabolism and immune response in African-American men provides potential therapeutic opportunities to attenuate racial disparities in prostate cancer.

Purpose:Neuroendocrine prostate cancer (NEPC) is a resistance phenotype that emerges in men with metastatic castration-resistant prostate adenocarcinoma (CR-PRAD) and has important clinical implications, but is challenging to detect in practice. Herein, we report a novel tissue–informed epigenetic approach to noninvasively detect NEPC.Experimental Design:We first performed methylated immunoprecipitation and high-throughput sequencing (MeDIP-seq) on a training set of tumors, identified differentially methylated regions between NEPC and CR-PRAD, and built a model to predict the presence of NEPC (termed NEPC Risk Score). We then performed MeDIP-seq on cell-free DNA (cfDNA) from two independent cohorts of men with NEPC or CR-PRAD and assessed the accuracy of the model to predict the presence NEPC.Results:The test cohort comprised cfDNA samples from 48 men, 9 with NEPC and 39 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 4.3 × 10–7) and discriminated between NEPC and CR-PRAD with high accuracy (AUROC 0.96). The optimal NEPC Risk Score cutoff demonstrated 100% sensitivity and 90% specificity for detecting NEPC. The independent, multi-institutional validation cohort included cfDNA from 53 men, including 12 with NEPC and 41 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 7.5×10–12) and perfectly discriminated NEPC from CR-PRAD (AUROC 1.0). Applying the predefined NEPC Risk Score cutoff to the validation cohort resulted in 100% sensitivity and 95% specificity for detecting NEPC.Conclusions:Tissue-informed cfDNA methylation analysis is a promising approach for noninvasive detection of NEPC in men with advanced prostate cancer.

Supplementary Table from Detecting Neuroendocrine Prostate Cancer Through Tissue-Informed Cell-Free DNA Methylation Analysis

Supplementary Data from The Prostate Cancer Androgen Receptor Cistrome in African American Men Associates with Upregulation of Lipid Metabolism and Immune Response

African-American (AA) men are more likely to be diagnosed with and die from prostate cancer than European American (EA) men. Despite the central role of the androgen receptor (AR) transcription factor in prostate cancer, little is known about the contribution of epigenetics to observed racial disparities. We performed AR chromatin immunoprecipitation sequencing on primary prostate tumors from AA and EA men, finding that sites with greater AR binding intensity in AA relative to EA prostate cancer are enriched for lipid metabolism and immune response genes. Integration with transcriptomic and metabolomic data demonstrated coinciding upregulation of lipid metabolism gene expression and increased lipid levels in AA prostate cancer. In a metastatic prostate cancer cohort, upregulated lipid metabolism associated with poor prognosis. These findings offer the first insights into ancestry-specific differences in the prostate cancer AR cistrome. The data suggest a model whereby increased androgen signaling may contribute to higher levels of lipid metabolism, immune response, and cytokine signaling in AA prostate tumors. Given the association of upregulated lipogenesis with prostate cancer progression, our study provides a plausible biological explanation for the higher incidence and aggressiveness of prostate cancer observed in AA men.Significance:With immunotherapies and inhibitors of metabolic enzymes in clinical development, the altered lipid metabolism and immune response in African-American men provides potential therapeutic opportunities to attenuate racial disparities in prostate cancer.

Supplementary Figure from Detecting Neuroendocrine Prostate Cancer Through Tissue-Informed Cell-Free DNA Methylation Analysis

Supplementary Figure from The Prostate Cancer Androgen Receptor Cistrome in African American Men Associates with Upregulation of Lipid Metabolism and Immune Response

Advanced prostate cancers comprise distinct phenotypes, but tumor classification remains clinically challenging. Here, we harnessed circulating tumor DNA (ctDNA) to study tumor phenotypes by ascertaining nucleosome positioning patterns associated with transcription regulation. We sequenced plasma ctDNA whole genomes from patient-derived xenografts representing a spectrum of androgen receptor active (ARPC) and neuroendocrine (NEPC) prostate cancers. Nucleosome patterns associated with transcriptional activity were reflected in ctDNA at regions of genes, promoters, histone modifications, transcription factor binding, and accessible chromatin. We identified the activity of key phenotype-defining transcriptional regulators from ctDNA, including AR, ASCL1, HOXB13, HNF4G, and GATA2. To distinguish NEPC and ARPC in patient plasma samples, we developed prediction models that achieved accuracies of 97% for dominant phenotypes and 87% for mixed clinical phenotypes. Although phenotype classification is typically assessed by IHC or transcriptome profiling from tumor biopsies, we demonstrate that ctDNA provides comparable results with diagnostic advantages for precision oncology. Significance: This study provides insights into the dynamics of nucleosome positioning and gene regulation associated with cancer phenotypes that can be ascertained from ctDNA. New methods for classification in phenotype mixtures extend the utility of ctDNA beyond assessments of somatic DNA alterations with important implications for molecular classification and precision oncology. This article is highlighted in the In This Issue feature, p. 517

Purpose: Neuroendocrine prostate cancer (NEPC) is a resistance phenotype that emerges in men with metastatic castration-resistant prostate adenocarcinoma (CR-PRAD) and has important clinical implications, but is challenging to detect in practice. Herein, we report a novel tissue–informed epigenetic approach to noninvasively detect NEPC. Experimental Design: We first performed methylated immunoprecipitation and high-throughput sequencing (MeDIP-seq) on a training set of tumors, identified differentially methylated regions between NEPC and CR-PRAD, and built a model to predict the presence of NEPC (termed NEPC Risk Score). We then performed MeDIP-seq on cell-free DNA (cfDNA) from two independent cohorts of men with NEPC or CR-PRAD and assessed the accuracy of the model to predict the presence NEPC. Results: The test cohort comprised cfDNA samples from 48 men, 9 with NEPC and 39 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 4.3 × 10–7) and discriminated between NEPC and CR-PRAD with high accuracy (AUROC 0.96). The optimal NEPC Risk Score cutoff demonstrated 100% sensitivity and 90% specificity for detecting NEPC. The independent, multi-institutional validation cohort included cfDNA from 53 men, including 12 with NEPC and 41 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 7.5×10–12) and perfectly discriminated NEPC from CR-PRAD (AUROC 1.0). Applying the predefined NEPC Risk Score cutoff to the validation cohort resulted in 100% sensitivity and 95% specificity for detecting NEPC. Conclusions: Tissue-informed cfDNA methylation analysis is a promising approach for noninvasive detection of NEPC in men with advanced prostate cancer.