---
title: "Prostate cancer whole-genome profiling in African American men identifies novel drivers"
id: "plos-one-12-personalized-genomic-landscape-of-prostate-cancer-in-african-american-men"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-12-personalized-genomic-landscape-of-prostate-cancer-in-african-american-men"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360389"
published_at: "2026-10-14T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Prostate cancer whole-genome profiling in African American men identifies novel drivers
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-12-personalized-genomic-landscape-of-prostate-cancer-in-african-american-men
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360389)
- **Published At:** 2026-10-14T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Investigators performed whole-genome sequencing (WGS) on 10 matched pairs of primary tumor and adjacent non-malignant epithelial cell cultures derived from African American (AA) prostatectomy specimens to enable precise germline subtraction. - The cohort included tumors staged T2–T3 with Gleason patterns 3+4 or 4+3 and PSA values reported between 3.4 and 13 ng/mL; median BMI and cohort clinical distributions were provided in the manuscript. - Mutation density was heterogeneously distributed across chromosomes, with clustering on chromosomes 1, 2, 3, 11, 17, and X; when normalized for length, chromosome 17 had the highest **mutation density**. - Tumor mutational burden (TMB) varied widely across samples (reported range 0.73–46.37 mutations/Mb), and prominent copy-number variation (CNV) events concentrated on chromosomes 1 and 2 in several samples. - The sequencing revealed pronounced genomic instability, with recurrent signatures of **chromothripsis** and **kataegis**; affected genes included E2F3, NOTCH4, HBS1L, TTN, ATRX, and KDM6A. - Recurrently mutated pathways implicated **chromatin remodeling** (KDM6A, ATRX, ARID1A, CHD1) and **DNA damage response** (ATM, BRCA1/2, RAD51); the authors reported specific frequency values for select genes in the cohort. - The paired primary culture model used conditional reprogramming and included steps to remove feeder-layer mouse reads (alignment filtering and BLAST validation) prior to variant calling with GATK MuTect2. - Data annotation used ANNOVAR and VEP with ClinVar and COSMIC cross-references; actionable alterations in epigenetic and DNA repair pathways were emphasized as potential therapeutic targets. - The authors framed analyses as exploratory and hypothesis-generating given the small cohort (N = 10), and statistical approaches (Wilcoxon signed-rank, Spearman) and multiple-comparison correction were described. - The study provides a renewable AA-specific WGS resource from patient-matched primary cultures but the manuscript notes limited sample size and exploratory interpretation without claiming confirmatory clinical efficacy.
## Clinical Analysis & Structured Key Points
Personalized genomic landscape of prostate cancer in African American men reveals novel drivers and therapeutic opportunities | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract African American (AA) men experience disproportionately high prostate cancer incidence and mortality. Despite this disparity, current genomic repositories are predominantly derived from European populations, and studies using archived tissues are often limited by stromal contamination. We sought to characterize the high-resolution somatic mutational landscape of AA prostate cancer using a high-fidelity, patient-matched model system to identify population-specific drivers and therapeutic targets. We performed whole-genome sequencing (WGS) on 10 matched pairs of primary cell cultures derived from prostate tumors and adjacent non-malignant tissues of AA patients. Our analyses revealed a complex, heterogeneous landscape characterized by significant genomic instability, including frequent chromothripsis and kataegis signatures. Mutation hotspots were concentrated on chromosomes 1, 2, 3, and X, with the highest mutation density on chromosome 17. Functionally impactful mutations were identified in critical oncogenic pathways, including chromatin remodeling (CHD1, ARID1A, ATRX, KDM6A) and DNA damage response (ATM, BRCA1/2, RAD51). Additionally, we identified potentially novel candidate mutations unique to this AA cohort, distinct from those in European-centric datasets. This study provides a foundational, patient-specific WGS dataset derived from African American prostate cancer cell models. The identification of high-frequency actionable alterations in epigenetic and DNA repair pathways highlights the utility of this renewable resource for developing tailored therapeutic interventions and addressing racial disparities in prostate cancer outcomes. Citation: Murthy S, Fago K, Knight J, Kowalczyk K, Hankins R, Bandi G, et al. (2026) Personalized genomic landscape of prostate cancer in African American men reveals novel drivers and therapeutic opportunities. PLoS One 21(10): e0360389. https://doi.org/10.1371/journal.pone.0360389 Editor: Nejat Mahdieh, Shaheed Rajaei Cardiovascular Medical and Research Center: Rajaie Cardiovascular Medical and Research Center, IRAN, ISLAMIC REPUBLIC OF Received: April 10, 2026; Accepted: September 26, 2026; Published: October 14, 2026 Copyright: © 2026 Murthy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the manuscript and its Supporting Information files. Funding: NIH SBIR Contract Number HHSN261201800016C to Shuttle Pharmaceuticals, Inc., Sub-contract: HHSN261201800016C to Georgetown University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: No authors have competing interests. Introduction Prostate cancer remains the most frequently diagnosed solid tumor in men, characterized by profound racial and ethnic disparities in outcomes [ 1 ]. In the United States, African American (AA) men face a significantly higher incidence of the disease and a mortality rate more than double that of Caucasian American men [ 2 – 7 ]. This disparity persists even in nonmetastatic settings, suggesting that while socioeconomic and environmental factors are contributory, distinct biological drivers likely underpin the aggressive tumor phenotypes observed in this population [ 3 – 5 , 8 ]. Consequently, identifying these molecular determinants is critical for the equitable application of precision oncology [ 9 , 10 ]. Despite the urgent need for ancestry-informed genomic data, current understanding is heavily skewed. Large-scale repositories, such as The Cancer Genome Atlas (TCGA), are predominantly derived from patients of European ancestry (~90%) [ 11 , 12 ]. Although limited data have pointed to recurrent mutations in genes such as SPOP, FOXA1, TP53 , and TMPRSS2 in AA men, broader pan-ethnic analyses often fail to capture population-specific structural variants or DNA repair defects due to the overwhelming representation of European samples [ 13 ]. Furthermore, technical limitations have historically hampered genomic discovery in this demographic. Studies using archived formalin-fixed paraffin-embedded (FFPE) tissues often encounter DNA degradation and stromal contamination [ 14 , 15 ], which can confound the detection of true somatic variants. To overcome these barriers, we developed a high-fidelity model system comprising matched pairs of primary epithelial cell cultures derived from fresh radical prostatectomy specimens of AA men. By expanding these cells via conditional reprogramming [ 16 , 17 ], we created an isogenic framework—matching tumor-derived cells with adjacent nonmalignant cells—that serves as a robust internal control for germline subtraction. In this study, we present the whole-genome sequencing (WGS) analysis of these paired primary cultures. We report a complex mutational landscape characterized by high mutation density on chromosome 17, widespread genomic instability (evidenced by chromothripsis and kataegis), and actionable alterations in chromatin remodeling and DNA damage response pathways. This dataset serves as a foundational resource for identifying novel therapeutic targets and addressing longstanding health disparities in prostate cancer. Materials and methods Patient cohort, tissue, and data collection The purpose of this study is to establish cell lines for use in prostate cancer health disparities studies. Patients undergoing elective prostatectomies were enrolled in this study under an Institutional Review Board–approved protocol (IRB Study Number: 2017−0614), underwent preoperative physical examinations, and biopsies to confirm a cancer diagnosis. This was a prospective, IRB-approved clinical trial to obtain annotated clinical samples from African American patients. IRB-approved Informed Consent for Clinical Research was obtained at the time of enrollment. All data were fully anonymized before accessing them. The fresh specimens were obtained from prostatectomies performed utilizing the DaVinci robotic system. Clinical specimens were immediately transferred from the operating room to pathology. A board-certified pathologist identified and dissected tumor regions based on anatomical mapping and pathological grading (adenocarcinoma) versus non-tumor regions. Non-tumor samples were collected from areas of the prostate not visibly affected by malignancy within the same patient to serve as matched controls. Specimens were stored in media on ice and transported for immediate processing. Establishment of paired primary cell cultures Paired tumor and non-tumor tissues underwent mechanical dissociation and were plated in collagen-coated dishes using keratinocyte serum-free medium supplemented with 5% fetal bovine serum (FBS). Primary cells were cultured for up to four passages. To expand the primary epithelium, we utilized Conditional Reprogramming (CR) technology as previously described [ 16 , 17 ]. Briefly, irradiated 3T3 J-2 mouse embryonic fibroblasts (30 Gy) were used as a feeder layer. J-2 cells were maintained in complete DMEM with 10% heat-inactivated FBS. Primary human epithelial cells were cocultured in a medium comprising complete DMEM and Ham’s F-12 nutrient mix (3:1) supplemented with hydrocortisone, epidermal growth factor, bovine insulin, cholera toxin, amphotericin B, gentamicin, and a ROCK inhibitor (Y-27632). The detailed protocol for establishing cell cultures is available at the following link; https://www.protocols.io/file-manager/9148D4BE307811EDA5460A58A9FEAC02 . Whole-genome sequencing and alignment Genomic DNA was isolated from 5 x 10 6 cells using the DNA Purification Kit (Promega). Whole-genome sequencing (WGS) was performed at the Yale Center for Genome Analysis (West Haven, CT) (RRID:SCR_016637). Following quality control (QC), libraries were sequenced with 151-bp paired-end reads on an Illumina platform. QC parameters were monitored using Illumina’s Sequence Analysis Viewer. Post-run assessment required samples to possess a percentage of bases ≥Q30 and generate at least 90% of the expected number of reads. Samples were demultiplexed using bcl2fastq2 v2.19.0 (Illumina Casava Pipeline) [ 18 ]. Passing samples met thresholds of ≥110 Gb total output, ~ 30X mean coverage, and ≥ 95% coverage at 10X. To filter mouse sequences originating from the feeder layer, paired-end reads were aligned separately to mouse (mm39) and human (hs38DH) reference genomes using BWA-MEM (RRID:SCR_022192) [ 19 ]. Reads with higher alignment scores for the mouse reference were strictly removed from the dataset. We independently mapped raw paired-end reads to the mouse (mm39) and human (hs38DH) reference genomes using BWA-MEM. Reads were filtered based on the BWA-MEM-generated AS (Alignment Score) tag: AS human - AS mouse > 10. Any read pair where AS mouse ≥ AS human or where the alignment score difference was ≤ 10 was removed using a custom Python filter script prior to GATK variant calling. This strategy was verified post-calling by performing NCBI BLAST validation on candidate somatic variants to ensure zero residual mouse read contamination. Variant calling and annotation Filtered reads were processed using GATK 4 Best Practices3 (RRID:SCR_001876). Germline variants were identified by aligning to hs38DH, marking duplicates (Picard), and performing joint calling via HaplotypeCaller and GenotypeGVCFs [ 18 – 20 ]. Somatic variants were called using GATK MuTect2, utilizing the matched non-tumor sample as the germline control. Variants were annotated using ANNOVAR (RRID:SCR_012821) [ 21 ] and the Ensembl Variant Effect Predictor (VEP) (RRID:SCR_005194) [ 22 ], incorporating databases such as ClinVar (RRID:SCR_006169), COSMIC (RRID:SCR_002260), and GO. Somatic mutations were further validated using BLAST (NCBI) (RRID:SCR_011824) and the UCSC Genome Browser (LiftOver) (RRID:SCR_005780) ( https://genome.ucsc.edu ) to ensure no residual mouse contamination. Genomic instability and structural variant analysis Tumor mutational burden (TMB) was calculated as the number of nonsynonymous somatic mutations per megabase (Mb) of the coding genome [ 23 , 24 ]. Copy number variation (CNV) [ 25 ] was analyzed using CNVkit with tumor-normal matching; log2 ratios were segmented to identify significant amplifications and deletions and intersected with the COSMIC Cancer Gene Census. Chromothripsis was defined by the presence of mutation clusters within single chromosomes, complex rearrangements, or oscillating CNV states. Kataegis was identified by localized hypermutation clusters (C > T or C > G substitutions in cytosine-rich motifs) near rearrangement breakpoints. Statistical analysis and visualization All statistical analyses were executed using R (v4.3.x)/ GraphPad Prism (v10). Normality was evaluated using the Shapiro-Wilk test. Continuous clinical variables were summarized using mean ± standard deviation (SD) and median with interquartile range (IQR), as appropriate. Categorical clinicopathologic variables were summarized as frequencies and percentages. Because tumor-derived and non-tumor samples were obtained from the same patients, paired comparisons were performed using the Wilcoxon signed-rank test . Associations between genomic and clinicopathologic variables were evaluated using Spearman rank correlation , which was selected because of the small cohort size and the ordinal or non-normally distributed nature of several variables. Genotype–phenotype analyses evaluated relationships between measures of genomic instability and clinical features, including preoperative PSA, Grade Group, and pathologic T stage. Where exact quantitative TMB and CNV burden values were available, these metrics were analyzed similarly. Multiple-comparison correction was performed using the Benjamini–Hochberg false-discovery-rate method where appropriate. All statistical tests were two-sided. Exact P values are reported, and P T or C > G substitutions in cytosine-rich motifs, 3) Analyzing strand bias in mutation patterns. https://doi.org/10.1371/journal.pone.0360389.g003 Recurrently mutated pathways and therapeutic targets To identify actionable drivers, we analyzed mutation frequencies across established cancer genes ( Fig 4 ). The landscape was dominated by alterations in chromatin remodeling and epigenetic regulation, with KDM6A (31.6%), ATRX (21.1%), ARID1A (7.9%), and CHD1 (5.3%) emerging as top hits. Download: PNG larger image TIFF original image Fig 4. The distribution of the top 10 mutated genes. The pie chart represents the distribution of the top 10 mutated genes, illustrating their relative contribution to the total mutation burden. https://doi.org/10.1371/journal.pone.0360389.g004 Crucially, we observed sig
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