Comparison of Whole Genome Sequencing with Clinical Targeted Panel Sequencing of Metastatic Prostate Cancer: Insights from Real- World Data

Comparison of Whole Genome Sequencing with Clinical Targeted Panel Sequencing of Metastatic Prostate Cancer: Insights from Real- World Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comparison of Whole Genome Sequencing with Clinical Targeted Panel Sequencing of Metastatic Prostate Cancer: Insights from Real- World Data Majd Al Assaad, Sangmoon Lee, Alissa Semaan, David C. Wilkes, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6993200/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We evaluated the clinical utility of Target-Enhanced Whole Genome Sequencing (TE-WGS) in comparison with targeted panel sequencing (TPS) for identifying clinically relevant genomic alterations in advanced prostate cancer. We applied TE-WGS to tumor/normal paired samples from patients with advanced prostate cancer previously tested with TPS during routine care. We compared the sensitivity of TE-WGS in detecting variants reported by TPS and assessed its added value in uncovering additional targetable alterations. A total of 45 samples from patients with advanced prostate cancer were analyzed using TE-WGS, which demonstrated 96.3% sensitivity in detecting clinically relevant variants reported by matched TPS. Furthermore, TE-WGS identified an additional 430 variants (85.0%) with clinical impact that were not reported by TPS. Notably, TE-WGS revealed rearrangements in DNA repair genes such as BRCA1/2, RAD51B, NBN, and CDK12. Overall, additional targetable alterations were detected by TE-WGS in 46.7% of samples, including 35.6% with no actionable findings by TPS, underscoring the added clinical value of WGS-based profiling. Our study highlights TE-WGS as a valuable complement to TPS, revealing clinically relevant targets that support its consideration in the management of advanced prostate cancer. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Genetics Health sciences/Oncology Metastatic Prostate Cancer Whole Genome Sequencing Targeted Sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Prostate cancer is the most frequently diagnosed solid tumor in men worldwide 1 . While patients with advanced prostate cancer initially respond to androgen deprivation therapy, this treatment eventually leads to castration-resistant prostate cancer (CRPC). Over the past decade, treatment options for CRPC have expanded significantly, including the introduction of potent androgen receptor signaling inhibitors, poly (ADP-ribose) polymerase inhibitors (PARPi), and radiopharmaceutical therapy 2 . A pivotal advancement in this field has been the advent of precision medicine, which leverages genetic profiling to predict responses to specific therapies 3 , 4 . Hence, genomic testing of CRPC tumors has increasingly made the transition from being primarily a research tool to becoming an integral part of routine clinical practice. This shift has been driven by evidence of targetable alterations, including those affecting DNA damage response pathways, particularly homologous recombination repair (HRR) and rarely mismatch repair (MMR), targetable gene fusions, and recent biomarkers that confer sensitivity to novel therapies 5 , 6 . Given the implications of cancer risk genes in treatment planning, the National Comprehensive Cancer Network (NCCN) Prostate Cancer Guidelines recommend genetic counseling, germline testing, and somatic tumor testing for patients with high-risk localized prostate cancer, metastatic CRPC or a strong family history 7 – 9 . Most cancer genomic tests, whether germline or somatic, focus on selected alterations within hundreds of cancer-associated genes 10 , 11 , which are designed to detect various types of genomic changes. Many comprehensive genomic profiling assays are currently used in clinical laboratories, and they may vary in terms of the number of genes analyzed, the specific regions of each gene covered, and analytical sensitivity. With the rapid evolution of molecular diagnostics and the growing number of clinical assays, professional guidelines and definitions of “standard of care” are continually shifting, making cross-platform comparisons of genomic results increasingly complex 12 , 13 . Whole genome sequencing (WGS) of paired tumor-normal samples enables comprehensive mutation detection across all genes, including both coding and non-coding regions. This approach accurately identifies a diverse spectrum of mutation types, including single nucleotide variants (SNVs), insertions/deletions (InDels), rearrangements, copy number variants (CNVs), and genome-wide mutational signatures. This approach helps reduce false negatives by addressing the limitations of targeted panel sequencing (TPS). It also improves detection of structural rearrangements, especially those involving clinically significant genes or non-coding regions often missed by TPS. To assess its clinical utility and potential scalability, we performed WGS with an innovative target-enhanced (TE-WGS) approach on paired tumor/normal samples from patients with advanced prostate cancer and compared these results with TPS performed as routine clinical care. Methods Patient Cohort and Pathology Selection Patients with metastatic (Stage IVA/ IVB) prostate cancer consented to participate in the institutional review board (IRB)-approved protocol at Weill Cornell Medicine (WCM) for Research in Precision Medicine (WCM IRB# 1305013903) and their archival tumor specimens were included in the study under the protocol for Comprehensive Cancer Characterization by Genomic and Transcriptomic Profiling (WCM IRB # 1007011157). Eligible patients included those with pathologically confirmed metastatic (Stage IVA/IVB) prostate cancer who had undergone clinical targeted panel sequencing and had available tumor tissue and matched germline DNA. Patients were excluded if archival tumor tissue was insufficient for DNA extraction, blood or saliva could not be obtained for germline sequencing, or if consent could not be obtained. No enrolled patients were excluded after consent if the samples meeting inclusion criteria were successfully processed and analyzed. Tumor-only sequencing results from the TPS clinical assays, Oncomine Comprehensive Panel v2 and v3 (OCP) 14 , TruSight Oncology 500 (TSO500) and/or Exome Cancer Test v1.0 (EXaCT-1) 15 , were available as part of standard-of-care at our institution. These three assays were performed as part of routine clinical care at the WCM clinical laboratory, which is approved by the New York State Clinical Laboratory Evaluation Program and operates in a Clinical Laboratory Improvement Amendments (CLIA)-certified, College of American Pathologists (CAP)-accredited setting. As this was an observational study using archival tumor samples, no randomization or group allocation was performed. Tumor material was obtained in the following order of priority: (1) remaining unused DNA from TPS, if available; (2) unstained slides from the same formalin-fixed paraffin-embedded (FFPE) tissue block used for clinical genomic testing, if DNA was unavailable; (3) unstained slides from another FFPE block from the same procedure and anatomic site; (4) archival frozen tissue DNA from the same procedure, matching FFPE tissue used in clinical genomics assays. Hematoxylin and eosin (H&E) stained slides from frozen and FFPE tissue were reviewed by the study pathologists to confirm tumor sufficiency and annotate tumor areas. The selected areas for DNA extraction aimed for at least 80% tumor purity and were collected by macrodissection 16 (FFPE slides) or needle coring (frozen cryomold) of tumor tissue 17 . For germline analysis, DNA was extracted from blood or saliva samples. A total 45 samples from 42 patients were included. All study participants were male, consistent with the diagnosis of prostate cancer. The age range of the study participants was 40 to 89 years; body weight was not recorded as it was not relevant to the genomic analyses performed. One sample was tested by two different targeted assays, resulting in a total of 46 TPS. All 45 samples, along with their matched germline DNA, underwent TE-WGS, 40 from FFPE tissue and 5 from frozen tissue. For the 46 TPS, 44 used FFPE tissue, and 2 used frozen tissue. In 22 out of 45 samples, both TE-WGS and TPS were performed on tumor DNA from the same extraction. In 16 cases (14 FFPE and 2 frozen), tumor DNA was extracted from the same surgical block but at different extraction instances. In 2 cases, tumor DNA came from tissue preserved by different methods (FFPE vs. frozen) during the same procedure. In the remaining five cases, TE-WGS and TPS were conducted using samples from two different tumor blocks obtained during the same procedure. DNA Library Preparation, Targeted-Enhanced Whole-Genome Sequencing, and Data Analysis Library preparation and nucleic acid preparation has been described in most recent publications 18 – 20 . It was done using the Watchmaker DNA Library Preparation Kit (Watchmaker Genomics, Boulder, CO). Briefly, DNA was enzymatically fragmented, adapters were ligated, and the resulting product underwent bead-based cleanup and library amplification. Quality control steps included assessing the size distribution of fragments (targeting ~ 300 bp) using the TapeStation 4200 System (RRID: SCR_018435 Agilent Technologies, Santa Clara, CA) and quantifying library yield with the Qubit DNA Assay Kit and Qubit 2.0 Fluorometer (RRID: SCR_020553 ThermoFisher Scientific, Waltham, MA, USA). Libraries were stored at − 20°C as per manufacturer guidelines until further processing. Sequencing and data analysis for this study were conducted using TE-WGS CancerVision system (Inocras Inc., San Diego, CA, USA). Prepared DNA libraries were sequenced on the Illumina NovaSeq 6000 system (RRID: SCR_016387 Illumina Inc.), with tumor samples achieving an average target coverage of 40x and matched blood samples 20x. Target-enhanced sequencing for tumor DNA utilized xGen Custom Hybridization Probes (IDT, Inc., Coralville, IA, USA), targeting 2.76Mb of genomic regions at an average target depth of 500x. Raw sequences were aligned to the GRCh38 reference genome using the BWA-MEM algorithm, with PCR duplicates removed via SAMBLASTER 21 , 22 . Germline variants were called using HaplotypeCaller and Strelka2, while somatic variants were identified with Strelka2 and Mutect2 23–25 . Structural variants were detected using Manta, and all variants underwent annotation with the Variant Effect Predictor (VEP) and manual curation within Inocras’s proprietary genome browser 26 . Tumor purity, ploidy, and allele-specific copy number were estimated from TE-WGS data using Sequenza, with additional adjustments to account for copy number stable tumors and minimize noise from FFPE specimens 27 . The analysis of homologous recombination deficiency (HRD) was done as detailed in Kim et al. 19 . Processed data, including FASTQ, VCF, and CRAM files, were encrypted and stored securely to ensure data integrity and confidentiality. Collaborators performing the TE-WGS and zdata analysis (Inocaras Inc. team) were blinded to patient clinical outcomes during the study to minimize bias. Comparison of Variants Called by TPS and TE-WGS We compared the sequencing results in two ways: (1) checking if the variants identified by TPS assays (TSO500, OCP, and EXaCT-1) were also detected by TE-WGS, and (2) determining if any variants identified by TE-WGS were missed by TPS. We first compiled and filtered the results from TPS for clinically relevant variants reported as Tier 1 (strong clinical significance) or Tier 2 (potential clinical significance) according to the AMP/ASCO/CAP tiered system 28 , then checked which of these variants were not reported by TE-WGS. For any variants not reported by WGS, we examined the type of variant, data source from TPS (DNA or RNA), and specific details (e.g., copy number, mutation location). To assess the variants missed by TPS, we filtered the variants based on the gene targets covered by TPS (TSO500 and OCP). No filtering was applied for EXaCT-1, (whole-exome sequencing). For genes with coverage limited to hotspot regions in TPS, we verified the variant location identified by TE-WGS using the COSMIC (Catalogue of Somatic Mutations in Cancer) database 29 and UCSC genome browser 30 . Results Cohort characteristics Forty-five samples from Forty-two patients with advanced stage prostate cancer were included in the study ( Fig. 1 A ) . Among the 45 samples, 40 were prostate adenocarcinoma (PRAD) (including 6 with neuroendocrine differentiation), four were prostate neuroendocrine carcinomas (PRNE), and one was squamous cell carcinoma, a rare variant of CRPC ( Fig. 1 B ) . The age range was 40–89 years old. Tumor tissue for sequencing was obtained from the following anatomic sites: 13 from lymph nodes, 10 from bone, 5 from liver, 5 from soft tissue (abdominal and thoracic), 1 from bladder, 1 from testis, 1 from brain, and 10 from prostate (synchronous metastatic sites were either not sampled or insufficient for TPS) ( Fig. 1 C ) . TPS Utility and TE-WGS Sensitivity To assess the clinical utility of TPS, we first quantified the number and type of clinically relevant variants identified. Across the 46 TPS assays (done on 45 samples, including one with 2 different assays), a total of 80 variants with either strong (Tier 1, n = 11) or potential (Tier 2, n = 69) clinical relevance were detected. Tier 1 variants (13.7% of all findings) included eight AR amplifications, one NCOA2 amplification, and pathogenic frameshift mutations in each of ATM (p.V3025Afs) and BRCA2 (p.W1692Mfs3*). The Tier 2 variants (86.3%) most frequently involved TP53 (n = 10), PTEN (n = 5), and MDM4 and AR (n = 3 each), in addition to 11 gene fusions. The distribution of variants by gene and tier classification is shown in Fig. 2 A. Overall, 78.3% (36/46) of TPS assays harbored at least one clinically relevant variant. Specifically, 21.7% (10/46) contained at least one Tier 1 variant, and 71.7% (33/46) contained at least one Tier 2 variant, with 6 assays harboring both (Fig. 2 B). To evaluate the sensitivity of TE-WGS, we assessed its ability to detect clinically relevant variants identified by TPS. TE-WGS successfully detected 77 out of 80 variants reported by TPS, yielding an overall sensitivity of 96.3%. This included validation of 100% (11/11) of Tier 1 variants and 95.7% (66/69) of Tier 2 variants, including all 11 genomic fusions identified by TPS (Figs. 2 A and 2 C). Two of the three variants not detected by TE-WGS were identified by the TSO500 TPS assay: a SETD2 p.R1407Gfs5* (c.4219delA) frameshift mutation with low variant allele frequency (VAF, 8.7%) and modest coverage (127×), and a NOTCH4 p.Q142Hfs7* (c.426_436del11) variant with a VAF of 6.6%. The third undetected variant was a missense mutation in AR (c.2632A > G, p.Thr878Ala) with high VAF, detected by the OCP panel. It is important to note that thirteen variants (a subset of the 80 variants reported by TPS)—comprising six CNVs and seven SNVs across 11 samples—were detected by both TPS and TE-WGS but were not classified as oncogenic by the TE-WGS/CancerVision© pipeline (Table 1 ). Among the six CNVs, five were amplifications that did not meet the TE-WGS reporting threshold (copy number > 5), and one was a monoallelic deletion, which is not considered oncogenic in this context. Of the seven SNVs reported by TPS, five were confirmed by TE-WGS as somatic variants but were classified as non-pathogenic frameshift mutations in oncogenes such as ETV1 , MGA , FOXA1 , and FOXP1 . These mutations are predicted to disrupt gene function rather than activate oncogenic signaling, and thus were not considered to represent driver alterations. The remaining two SNVs— FANCL p.T367Nfs and JAK1 c.6 + 1G > A—were identified by TE-WGS as germline variants and classified as non-pathogenic, a distinction made possible through the inclusion of matched germline analysis in the TE-WGS pipeline, which is not available in TPS. In addition, 13 of the 46 TPS assays were conducted using the TSO500 platform, which incorporates an RNA sequencing component. This allowed for the detection of seven AR splice variant transcripts and 2 transcriptomic fusions ( FOXP1::BRAF and SLC45A3::FL1 ). These RNA-based alterations are not detectable by TE-WGS, which does not include transcriptomic analysis. Table 1 Variants reported by both TPS and TE-WGS but classified as non-oncogenic by TE-WGS. Summary of 13 variants identified by both tumor profiling sequencing (TPS) and tumor-enriched whole genome sequencing (TE-WGS) across 11 samples, including 6 copy number variations (CNVs) and 7 single nucleotide variants (SNVs). These variants were not considered oncogenic by the TE-WGS/CancerVision© annotation pipeline. CNV: Copy number variation. SNV: Single nucleotide variant. N/A: Not applicable. Case ID Gene TPS Label TE-WGS Label Variant GI14 PIK3CA Tier 2 CNV Copy Number < 5 (Below Reporting Cutoff) N/A GI39 AKT1 Tier 2 CNV Copy Number < 5 (Below Reporting Cutoff) N/A GI46 NCOA2 Tier 1 CNV Copy Number < 5 (Below Reporting Cutoff) N/A GI46 AR Tier 1 CNV Copy Number < 5 (Below Reporting Cutoff) N/A GI79 MDM4 Tier 2 CNV Copy Number < 5 (Below Reporting Cutoff) N/A GI25 PTEN Tier 2 CNV Monoallelic Deletion N/A GI12 FANCL Tier 2 SNV Non-Pathogenic Germline Alteration p.T367Nfs c.1096_1099dupATTA GI71 JAK1 Tier 2 SNV Non-Pathogenic Germline Alteration p.? c.6 + 1G > A GI12 FOXP1 Tier 2 SNV Non-Pathogenic Somatic Alteration Variant p.1474Gfs'12 c.1420_i423de1ATTA GI13 FOXA1 Tier 2 SNV Non-Pathogenic Somatic Alteration Missense p.R219S c.655C > A GI79 MGA Tier 2 SNV Non-Pathogenic Somatic Alteration frameshift p.K2103Nfs*8 c.6309_6315del& GI80 ETV1 Tier 2 SNV Non-Pathogenic Somatic Alteration frameshift p.H452Qfs*3 c.1355dupA GI88 FOXA1 Tier 2 SNV Non-Pathogenic Somatic Alteration p.l660* c.1077delC frameshift TE-WGS Additional Findings To evaluate the utility of TE-WGS, we examined the total number and types of clinically relevant oncogenic alterations. Variants with clinical impact—either targetable or of potential clinical relevance—were detected in 100% of samples (45/45), with a total of 506 unique findings, ranging from 2 to 75 variants per sample (Supplementary Fig. 1) . These included 244 somatic alterations, comprising 157 small somatic variants (SNVs and InDels) and 87 CNVs, along with 236 genomic rearrangements and 25 gene fusions. To assess the additional clinical insights provided by TE-WGS, we next compared its findings with those of TPS. For each sample, TE-WGS-detected variants were mapped to the gene panels and mutation hotspots covered by the corresponding TPS assay. TE-WGS identified additional clinically relevant variants in 95.6% of cases (43/45), totaling 430 out of 506 findings (85.0%). The number of additional variants per sample ranged from 1 to 70, with an average of 10 clinically relevant variants per sample. We further categorized the additional clinically relevant variants identified by TE-WGS but not reported by TPS based on their variant type, genomic location, and whether they fell within the coverage scope of the TPS assays. The majority—54.8% (236/430)—were disruptive genomic rearrangements, many of which involved key cancer-associated genes. These included rearrangements in BRCA2 (2 cases), PTEN (3), TP53 (3), and STAG 1 (2), as well as additional alterations in genes such as CDKN1B , APC , MSH2 , and FANCF (Supplementary Fig. 1) . Furthermore, 3.5% (15/430) of the unreported variants were gene fusions involving oncogenic drivers, including ERG, BRAF, AFF1, AFF3, ETV5, ETV6, MECOM, NF1, RAF1 , and TMCC1 . To determine whether the missed variants in TPS were primarily due to limited gene coverage, we reviewed the reference gene lists for each matched TPS assay. We found that 29.8% (128/430) of the clinically relevant variants missed by TPS were located in genes or regions not covered by the respective TPS panels. These included genes with established or potential clinical relevance such as CDK12, FANCA, and FANCF (not covered by OCP), as well as BCOR2, GNAS, and SOX2 copy number alterations (not covered by TSO500), among others. In addition, 3.3% (14/430) of the missed variants were deleterious mutations in clinically actionable genes—such as AR, BRAF, CTNNB1, HRAS, MED12, MYD88, NFE2L2, PDGFRA, SPOP, ABL1 , and APC —where the TPS assay covered only specific hotspot regions and failed to include the mutated pathogenic sites (Supplementary Table 1) . Finally, we identified 8.6% (37/430) of missed variants that occurred in genes and genomic regions covered by the TPS assay used for each sample. Among these, 35.1% (13/37) were missed by OCP, 29.8% (11/37) by TSO500, and 35.1% (13/37) by EXaCT-1. Treatment Targets We then focused on the subset of variants identified by TE-WGS that were classified as clinically actionable treatment targets. Actionable alterations were observed in 46.7% of samples (21/45), with a total of 44 treatment-associated targets identified, ranging from 1 to 5 per sample ( Fig. 3 A ) . The remaining 477 variants, while not linked to currently approved therapies, were considered to have potential clinical relevance. The 44 actionable targets included structural rearrangements and SNV/InDel mutations in DNA damage repair genes such as BRCA1, BRCA2, CDK12, ATM, RAD51B, BRIP1, FANCA, NBN , and ATR . Additional actionable SNVs were observed in AR —specifically L702H in three samples and H875Y in one sample—as well as variants in other clinically relevant genes such as PTEN, FGFR2, CCNE 1, and a SVOPL::BRAF fusion. To evaluate the added clinical utility of TE-WGS, we compared its ability to detect treatment targets relative to TPS. TPS identified six actionable variants in 11.1% of samples (5/45), including targetable SNVs in PTEN ( n = 3), BRCA2 ( n = 1), and ATM ( n = 1). TE-WGS successfully detected all six of these TPS-reported alterations and identified additional treatment targets in 40.0% of cases (18/45). Notably, 35.6% of samples (16/45) harbored targetable alterations detected only by TE-WGS, with no actionable findings reported by TPS ( Fig. 3 B ) . Mutational Signatures Reveal HRD Phenotypes Detected by TE-WGS To further investigate the biological relevance of HRD-related alterations identified exclusively by TE-WGS, we examined their association with known HRD mutational signatures ( Table 2 ) . Among the 15 cases with HRR gene alterations not detected by TPS, ten (~ 67%) exhibited at least one HRD-associated signature. SBS3 and/or SBS8 occurred in 6 of 15 cases (40%), while ID6 was observed in 2 of 8 evaluable cases (25%). For instance, case GI25 harbored a BRCA2 deletion and exhibited high SBS3 (35.9%) and SBS8 (15.8%). In addition, cases GI39 (with ATM , CDK12 , and BRIP1 rearrangements) and GI50 (with NBN and CDK12 rearrangements, and FANCA mutations) demonstrated increased SBS3 (11.4% and 33.6%, respectively). These findings support the presence of functional HRD and highlight a connection between HRR gene rearrangements and mutational signature evidence. Interestingly, among the five cases with HRD-related alterations but no detectable mutational signatures, four involved ATM and/or RAD51B. These results suggest that TE-WGS can reveal cryptic HRD signatures that may be missed by TPS. Table 2 Genomic Alterations Related to Homologous Recombination Deficiency Exclusively Identified by Target-Enhanced Whole-Genome Sequencing and Their Associated Mutational Signatures in Metastatic Prostate Cancer Patients. GIID TE-WGS finding TMB (mut/Mb) SBS3 (%) SBS8 (%) ID6 (%) HRD-related Signature GI25 BRCA2 deletion 6.12 35.90 15.80 Not reported Present GI33 RAD51B rearrangement 1.89 0.00 0.00 0.00 Absent GI37 ATM rearrangement 1.27 0.00 0.00 0.00 Absent GI39 ATM , CDK12 , and BRIP1 rearrangements 6.49 11.40 12.80 0.00 Present GI42 ATM and RAD51B rearrangement 2.82 0.00 0.00 0.00 Absent GI49 BRCA2 rearrangement 6.21 39.90 0.00 18.90 Present GI50 NBN and CDK12 rearrangements, and FANCA mutations 20.61 33.60 0.00 0.00 Present GI6 NBN rearrangement 1.54 11.80 0.00 0.00 Present GI64 BRCA2 mutation 8.18 0.00 0.00 13.70 Present GI67 RAD51B rearrangement 1.22 25.10 10.20 Not reported Present GI72 FANCA mutation 0.85 0.00 12.00 Not reported Present GI76 CDK12 mutations and BRIP1 disruption 3.84 0.00 17.50 Not reported Present GI79 ATM mutation 2.24 0.00 14.40 Not reported Present GI82 CDK12 and BRCA1 frameshift mutations 79.14 0.00 0.00 Not reported Absent GI88 BRCA2, ATM , and BRCA1 mutations 5.1 0.00 0.00 Not reported Absent TE-WGS: Target-Enhanced Whole-Genome sequencing; TMB: Tumor mutation burden; HRD: homologous recombination deficiency Review of TE-WGS Findings in a Molecular Tumor Board Format Six patients from our cohort with metastatic CRPC and limited treatment options were presented at a research molecular tumor board that included experts in oncology, pathology, and genomics. TPS assays were performed as part of routine clinical care, whereas TE-WGS was conducted as part of a the current study. Across these cases, genomic alterations suggested potential therapeutic options, pending validation with clinically approved assays before treatment decisions. Findings included an NBN rearrangement and increased SBS3 signature (HRD signature), supporting eligibility for PARPi therapy 31 (Fig. 4 ); an FGFR2 splice donor variant, predicted to be activating, suggesting sensitivity to an FGFR-specific tyrosine kinase inhibitor; PTEN rearrangements in two patients, expanding potential options to include Pan-AKT inhibitors as supported by the ProCaid clinical trial 32 ; and a patient with a RAD51B rearrangement, pathogenic germline FANCC mutation (c.456 A > T), and a deleterious FANCF complex rearrangement, qualifying for PARPi therapy. Notably, a CDK12 rearrangement was identified in one patient 33 . CDK12 alterations are associated with a distinct molecular subtype of prostate cancer characterized by genomic instability and increased neoantigen burden, which has been linked to sensitivity to immune checkpoint blockade. While emerging evidence supports the use of immunotherapy in CDK12 -altered metastatic CRPC, further validation with clinically approved assays was recommended before determining eligibility for treatment 34 . Discussion Metastatic castration-resistant prostate cancer is an aggressive disease with limited treatment options. The NCCN guidelines have recommended molecular testing for prostate cancer since 2018 35 . However, existing molecular targeted panels differ in their gene coverage and cutoff criteria, making it difficult for clinicians to select the appropriate assay. In this study, we performed clinical-grade TE-WGS 20 on tumor/normal paired samples from patients with advanced prostate cancer. All patients had available clinical TPS results for comparison with TE-WGS. The added benefit of TE-WGS was evident across multiple dimensions. First, TE-WGS identified clinically relevant variants in 100% of samples. Of these, 85.0% (430/506) were not reported by TPS. To better understand the basis for discordance, we classified the additional TE-WGS findings into several categories: (1) 54.8% of the additional variants detected by TE-WGS were disruptive genomic rearrangements, many involving key tumor suppressors such as BRCA2, PTEN , TP53 , and STAG1 . The importance of detecting these variants is consistent with previous studies showing that structural variants can affect the targeting and management of certain cancers 36 . (2) Furthermore, 3.5% of the additional findings were oncogenic fusions (e.g., involving ERG, BRAF, ETV6, RAF1 ), which were missed by TPS despite their relevance to cancer pathogenesis and treatment sensitivity 37 , 38 . (3) variants (SNVs/InDels) in genes not covered by the TPS panel used, which accounted for 29.8% of cases; (4) variants in genes that were partially covered by TPS (e.g., limited to hotspot regions), comprising 3.3%; and (5) variants located within regions fully covered by TPS but nonetheless missed, making up 8.6%. This analysis illustrates that the added benefit of TE-WGS is not only due to its broader genomic scope, but also to its technical sensitivity across variant types. The clinical relevance of these findings was further reinforced by the detection of actionable alterations. TE-WGS identified treatment-associated targets in 46.7% of samples, with up to five actionable variants per case. These included alterations in DNA damage repair genes (e.g., BRCA1/2, ATM, CDK12, FANCA ), as well as targetable variants in AR, PTEN, FGFR2, CCNE1, and a SVOPL::BRAF fusion. While TPS detected six actionable variants in 11.1% of samples—all of which were also captured by TE-WGS—an additional 35.6% of samples harbored actionable findings that were only detected through TE-WGS. Notably, the rearrangements in HRR pathway genes such as BRCA1/2 , NBN, RAD51B , and FANCC correlated with HRD signatures like SBS3. As a result, the patients with variants in these genes were referred for consideration of targeted therapies. Detecting such rearrangements is crucial, as studies have shown they can cause HRD, making tumors sensitive to PARPi 36 . In terms of sensitivity, TE-WGS validated 96% of the findings reported by TPS and detectable by TE-WGS. For the discrepancies, low VAF was the most likely explanation. Tumor clonality may also play a role, as variations in sampling can affect which clonal mutations are detected, as reported by Opasic et al . 39 . Additionally, some reporting differences were related to the technique rather than detection as of these discrepancies involved transcriptomic variants identified by RNA sequencing, particularly in the AR gene. This highlights the need for adding RNA sequencing to WGS for prostate cancer to match the capabilities of TPS that include RNA analysis 40 . However, adding RNA sequencing is not universally scalable across clinical settings as RNA sequencing requires more and better-preserved tissue, which can be challenging, especially in limited core needle biopsy samples from metastases. We also found that some pathogenic mutations reported by TPS panels were identified as germline variants by TE-WGS (tumor/normal pair analysis), demonstrating a limitation of tumor-only sequencing strategies often used for TPS. TE-WGS showed no loss of heterozygosity for these genes. As sequencing technologies and analytical methods continue to evolve, our study supports TE-WGS as a robust, comprehensive assay capable of evaluating both somatic and germline molecular aberrations in advanced prostate cancer. This study demonstrated that TE-WGS showed high concordance with TPS and uncovered additional mutations and rearrangements of potential clinical significance. While not yet positioned to replace TPS, these results highlight the potential of TE-WGS to complement existing assays and expand actionable insights. Declarations Conflict of Interest: Sangmoon Lee and Erin Strong are employees of Inocras. Majd Al Assaad has received travel and accommodation support from Inocras. Data availability Data available upon request. Acknowledgments This work was supported by the Englander Institute for Precision Medicine. Whole-genome sequencing was performed at Inocras, Inc. Project support for this project was provided in part by the Center for Translational Pathology from the Department of Pathology and Laboratory Medicine at Weill Cornell Medicine (Ruben Diaz, Leticia Dizon, Bing He). The authors thank Alexis Barcomb, Samantha Henry, Amanda L. Vitale and Noah Greco, for project support. Author Contributions M.A.A. performed data analysis, generated figures, and wrote the manuscript. S.L. contributed to data analysis and critically reviewed the manuscript. A.S. coordinated the study and contributed to sample processing and data collection. D.W. assisted with sample processing. J.M., D.G., and C.F. coordinated patient consent and subject selection. G.C., G.L., O.H., E.N.K., and J.D.M. supported data collection and sample handling. C.H., O.E., S.T., A.M.M., D.M.N., J.T.N., and C.N.S. contributed to patient enrollment and reviewed the manuscript. E.C.S. supported study coordination and manuscript review. J.M.M. and J.S. supervised the study and provided critical revisions. All authors reviewed and approved the final manuscript. Competing Interests All authors declare no financial or non-financial competing interests. References Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2022. CA Cancer J Clin 72 , 7-33 (2022). https://doi.org/10.3322/caac.21708 Iannantuono, G. M. et al. Efficacy and safety of PARP inhibitors in metastatic castration-resistant prostate cancer: A systematic review and meta-analysis of clinical trials. Cancer Treat Rev 120 , 102623 (2023). https://doi.org/10.1016/j.ctrv.2023.102623 Abida, W. et al. Prospective Genomic Profiling of Prostate Cancer Across Disease States Reveals Germline and Somatic Alterations That May Affect Clinical Decision Making. JCO Precis Oncol 2017 (2017). https://doi.org/10.1200/po.17.00029 van Dessel, L. F. et al. 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Performance Characteristics of a Targeted Sequencing Platform for Simultaneous Detection of Single Nucleotide Variants, Insertions/Deletions, Copy Number Alterations, and Gene Fusions in Cancer Genome. Arch Pathol Lab Med 144 , 1535-1546 (2020). https://doi.org/10.5858/arpa.2019-0162-OA Rennert, H. et al. Development and validation of a whole-exome sequencing test for simultaneous detection of point mutations, indels and copy-number alterations for precision cancer care. NPJ Genom Med 1 , 16019- (2016). https://doi.org/10.1038/npjgenmed.2016.19 Wisner, L., Larsen, B. & Maguire, A. Enhancing Tumor Content through Tumor Macrodissection. J Vis Exp (2022). https://doi.org/10.3791/62961 Sailer, V. et al. Bone biopsy protocol for advanced prostate cancer in the era of precision medicine. Cancer 124 , 1008-1015 (2018). https://doi.org/10.1002/cncr.31173 Ferguson, S. et al. Analytical and Clinical Validation of a Target-Enhanced Whole Genome Sequencing-Based Comprehensive Genomic Profiling Test. Cancer Invest 42 , 390-399 (2024). https://doi.org/10.1080/07357907.2024.2352438 Kim, R. et al. Clinical application of whole-genome sequencing of solid tumors for precision oncology. Exp Mol Med 56 , 1856-1868 (2024). https://doi.org/10.1038/s12276-024-01288-x Lee, S. et al. Target-Enhanced Whole-Genome Sequencing (TE-WGS) Shows Clinical Validity Equivalent to Commercially Available Targeted Oncology Panel. Cancer Res Treat (2024). https://doi.org/10.4143/crt.2024.114 Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25 , 1754-1760 (2009). https://doi.org/10.1093/bioinformatics/btp324 Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30 , 2503-2505 (2014). https://doi.org/10.1093/bioinformatics/btu314 Ren, S., Bertels, K. & Al-Ars, Z. Efficient Acceleration of the Pair-HMMs Forward Algorithm for GATK HaplotypeCaller on Graphics Processing Units. Evol Bioinform Online 14 , 1176934318760543 (2018). https://doi.org/10.1177/1176934318760543 Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat Methods 15 , 591-594 (2018). https://doi.org/10.1038/s41592-018-0051-x Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31 , 213-219 (2013). https://doi.org/10.1038/nbt.2514 Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32 , 1220-1222 (2016). https://doi.org/10.1093/bioinformatics/btv710 Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann Oncol 26 , 64-70 (2015). https://doi.org/10.1093/annonc/mdu479 Li, M. M. et al. Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn 19 , 4-23 (2017). https://doi.org/10.1016/j.jmoldx.2016.10.002 Bamford, S. et al. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer 91 , 355-358 (2004). https://doi.org/10.1038/sj.bjc.6601894 Nassar, L. R. et al. The UCSC Genome Browser database: 2023 update. Nucleic Acids Res 51 , D1188-d1195 (2023). https://doi.org/10.1093/nar/gkac1072 Risdon, E. N., Chau, C. H., Price, D. K., Sartor, O. & Figg, W. D. PARP Inhibitors and Prostate Cancer: To Infinity and Beyond BRCA. Oncologist 26 , e115-e129 (2021). https://doi.org/10.1634/theoncologist.2020-0697 Crabb, S. J. et al. Pan-AKT Inhibitor Capivasertib With Docetaxel and Prednisolone in Metastatic Castration-Resistant Prostate Cancer: A Randomized, Placebo-Controlled Phase II Trial (ProCAID). J Clin Oncol 39 , 190-201 (2021). https://doi.org/10.1200/jco.20.01576 Schweizer, M. T. et al. CDK12-Mutated Prostate Cancer: Clinical Outcomes With Standard Therapies and Immune Checkpoint Blockade. JCO Precis Oncol 4 , 382-392 (2020). https://doi.org/10.1200/po.19.00383 Nguyen, C. B. et al. Evaluating Immune Checkpoint Blockade in Metastatic Castration-Resistant Prostate Cancers with Deleterious CDK12 Alterations in the Phase 2 IMPACT Trial. Clin Cancer Res 30 , 3200-3210 (2024). https://doi.org/10.1158/1078-0432.Ccr-24-0400 Schaeffer, E. M. et al. Prostate Cancer, Version 4.2023, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 21 , 1067-1096 (2023). https://doi.org/10.6004/jnccn.2023.0050 Hayes, M. Computational Analysis of Structural Variation in Cancer Genomes. Methods Mol Biol 1878 , 65-83 (2019). https://doi.org/10.1007/978-1-4939-8868-6_3 Chung, J. H. et al. Prospective Comprehensive Genomic Profiling of Primary and Metastatic Prostate Tumors. JCO Precis Oncol 3 (2019). https://doi.org/10.1200/po.18.00283 Kinnunen, M. et al. The Impact of ETV6-NTRK3 Oncogenic Gene Fusions on Molecular and Signaling Pathway Alterations. Cancers (Basel) 15 (2023). https://doi.org/10.3390/cancers15174246 Opasic, L., Zhou, D., Werner, B., Dingli, D. & Traulsen, A. How many samples are needed to infer truly clonal mutations from heterogenous tumours? BMC Cancer 19 , 403 (2019). https://doi.org/10.1186/s12885-019-5597-1 Basil, P. et al. Cistrome and transcriptome analysis identifies unique androgen receptor (AR) and AR-V7 splice variant chromatin binding and transcriptional activities. Sci Rep 12 , 5351 (2022). https://doi.org/10.1038/s41598-022-09371-x Additional Declarations No competing interests reported. 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Sample availability across EXaCT-1, Oncomine, TSO500, and WGS platforms with corresponding diagnosis, tissue site, and DNA source metadata (A). Distribution of diagnoses showing predominance of prostate adenocarcinoma (PRAD) (B). Distribution of tissue sites showing prostate as the most common site, followed by soft tissue and liver (C). DNA source comparison between TGS and WGS showing most samples derived from the same block but different DNA aliquots (D). FFPE: Formalin-fixed paraffin-embedded. PRAD: Prostate adenocarcinoma. PRNE: Prostate neuroendocrine carcinoma. PRSC: Prostate small cell carcinoma. TPS: Targeted panel sequencing. WGS: Whole genome sequencing.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6993200/v1/5f4c18e95c17f679e8849241.png"},{"id":85924133,"identity":"c240b64d-d826-4318-9e77-0e3302bb6241","added_by":"auto","created_at":"2025-07-03 08:24:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132046,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLandscape and validation of clinically relevant variants identified by targeted panel sequencing (TPS).\u003c/strong\u003e Oncoprint of genomic alterations detected by TPS showing the distribution of Tier 1 (clinically significant) and Tier 2 (potentially significant) variants across genes, with SNV/indels and amplifications as the most common alteration types (A). Stacked bar chart summarizing the frequency of cases with Tier 1 only, Tier 2 only, both, or none, with 21.7% of cases harboring at least one Tier 1 variant (B). Bar chart showing TE-WGS validation rates for variants initially detected by TPS, demonstrating 100% validation for Tier 1 and 95.7% for Tier 2 variants (C). SNV: Single nucleotide variant. Indel: Insertion/deletion. TE-WGS: Target-enhanced whole genome sequencing. TPS: targeted panel sequencing.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6993200/v1/9eab99754f57f41f01aad0e2.png"},{"id":85924136,"identity":"231f475b-fbad-421d-85b1-053cc7e2024d","added_by":"auto","created_at":"2025-07-03 08:24:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":138827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinically actionable targets revealed by TE-WGS beyond TPS detection.\u003c/strong\u003e Oncoprint showing alterations in DNA damage repair genes and other clinically relevant genes, with distinctions between alterations found by both TPS and TE-WGS versus those uniquely detected by TE-WGS (A). Bar chart summarizing the number of cases with actionable targets found only on WGS (35.6%), on both platforms (11.1%), or not found on either (53.3%) (B). SNV: Single nucleotide variant. Indel: Insertion/deletion. TMB: Tumor mutational burden. MSI: Microsatellite instability. TPS: Targeted panel sequencing. TE-WGS: Target-enhanced whole genome sequencing.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6993200/v1/8ac4ead44ee703d56cb71585.png"},{"id":85924159,"identity":"4e0ddf80-2c3a-4877-837b-2f59fe7a3e9e","added_by":"auto","created_at":"2025-07-03 08:24:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":274987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical case highlighting the added value of TE-WGS over clinical TPS in a patient with metastatic prostate cancer.\u003c/strong\u003e Clinical course and imaging showing progression from resected prostate cancer to bone metastases (A). Clinical TPS report showing no detectable variants (B). Circos plot of TE-WGS revealing complex genomic alterations, including rearrangements and copy number changes (C). Mutational signature analysis and detailed WGS findings identifying a homologous recombination deficiency (HRD) signature (SBS3), complex NBN rearrangement, and potential PARP inhibitor therapy (D). TPS: Targeted panel sequencing. TE-WGS: Target-enhanced whole genome sequencing. HRD: Homologous recombination deficiency. SBS: Single base substitution. PARP: Poly (ADP-ribose) polymerase. VAF: Variant allele frequency. CN: Copy number.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6993200/v1/08cc92348a68de55baaf60da.png"},{"id":91898729,"identity":"4477d270-e216-46f8-a6cb-ec30fd9b6dda","added_by":"auto","created_at":"2025-09-22 19:31:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1943743,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6993200/v1/af940780-e25f-4a07-9957-da0cdd643f9d.pdf"},{"id":85924131,"identity":"f6e6e2c9-8c37-4dc9-ab80-da28c24bfcd7","added_by":"auto","created_at":"2025-07-03 08:24:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":656033,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6993200/v1/28804be5065bcb522ecafea8.pdf"},{"id":85924123,"identity":"4a880aeb-bdaa-4f72-af20-3f2a0bf4d1f2","added_by":"auto","created_at":"2025-07-03 08:24:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14981,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6993200/v1/805fe67465de1d9b22dedc43.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of Whole Genome Sequencing with Clinical Targeted Panel Sequencing of Metastatic Prostate Cancer: Insights from Real- World Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer is the most frequently diagnosed solid tumor in men worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. While patients with advanced prostate cancer initially respond to androgen deprivation therapy, this treatment eventually leads to castration-resistant prostate cancer (CRPC). Over the past decade, treatment options for CRPC have expanded significantly, including the introduction of potent androgen receptor signaling inhibitors, poly (ADP-ribose) polymerase inhibitors (PARPi), and radiopharmaceutical therapy\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. A pivotal advancement in this field has been the advent of precision medicine, which leverages genetic profiling to predict responses to specific therapies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Hence, genomic testing of CRPC tumors has increasingly made the transition from being primarily a research tool to becoming an integral part of routine clinical practice. This shift has been driven by evidence of targetable alterations, including those affecting DNA damage response pathways, particularly homologous recombination repair (HRR) and rarely mismatch repair (MMR), targetable gene fusions, and recent biomarkers that confer sensitivity to novel therapies\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the implications of cancer risk genes in treatment planning, the National Comprehensive Cancer Network (NCCN) Prostate Cancer Guidelines recommend genetic counseling, germline testing, and somatic tumor testing for patients with high-risk localized prostate cancer, metastatic CRPC or a strong family history \u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Most cancer genomic tests, whether germline or somatic, focus on selected alterations within hundreds of cancer-associated genes\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, which are designed to detect various types of genomic changes. Many comprehensive genomic profiling assays are currently used in clinical laboratories, and they may vary in terms of the number of genes analyzed, the specific regions of each gene covered, and analytical sensitivity. With the rapid evolution of molecular diagnostics and the growing number of clinical assays, professional guidelines and definitions of \u0026ldquo;standard of care\u0026rdquo; are continually shifting, making cross-platform comparisons of genomic results increasingly complex\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhole genome sequencing (WGS) of paired tumor-normal samples enables comprehensive mutation detection across all genes, including both coding and non-coding regions. This approach accurately identifies a diverse spectrum of mutation types, including single nucleotide variants (SNVs), insertions/deletions (InDels), rearrangements, copy number variants (CNVs), and genome-wide mutational signatures. This approach helps reduce false negatives by addressing the limitations of targeted panel sequencing (TPS). It also improves detection of structural rearrangements, especially those involving clinically significant genes or non-coding regions often missed by TPS.\u003c/p\u003e \u003cp\u003e To assess its clinical utility and potential scalability, we performed WGS with an innovative target-enhanced (TE-WGS) approach on paired tumor/normal samples from patients with advanced prostate cancer and compared these results with TPS performed as routine clinical care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Cohort and Pathology Selection\u003c/h2\u003e \u003cp\u003e Patients with metastatic (Stage IVA/ IVB) prostate cancer consented to participate in the institutional review board (IRB)-approved protocol at Weill Cornell Medicine (WCM) for Research in Precision Medicine (WCM IRB# 1305013903) and their archival tumor specimens were included in the study under the protocol for Comprehensive Cancer Characterization by Genomic and Transcriptomic Profiling (WCM IRB # 1007011157). Eligible patients included those with pathologically confirmed metastatic (Stage IVA/IVB) prostate cancer who had undergone clinical targeted panel sequencing and had available tumor tissue and matched germline DNA. Patients were excluded if archival tumor tissue was insufficient for DNA extraction, blood or saliva could not be obtained for germline sequencing, or if consent could not be obtained. No enrolled patients were excluded after consent if the samples meeting inclusion criteria were successfully processed and analyzed. Tumor-only sequencing results from the TPS clinical assays, Oncomine Comprehensive Panel v2 and v3 (OCP) \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, TruSight Oncology 500 (TSO500) and/or Exome Cancer Test v1.0 (EXaCT-1)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, were available as part of standard-of-care at our institution. These three assays were performed as part of routine clinical care at the WCM clinical laboratory, which is approved by the New York State Clinical Laboratory Evaluation Program and operates in a Clinical Laboratory Improvement Amendments (CLIA)-certified, College of American Pathologists (CAP)-accredited setting. As this was an observational study using archival tumor samples, no randomization or group allocation was performed.\u003c/p\u003e \u003cp\u003eTumor material was obtained in the following order of priority: (1) remaining unused DNA from TPS, if available; (2) unstained slides from the same formalin-fixed paraffin-embedded (FFPE) tissue block used for clinical genomic testing, if DNA was unavailable; (3) unstained slides from another FFPE block from the same procedure and anatomic site; (4) archival frozen tissue DNA from the same procedure, matching FFPE tissue used in clinical genomics assays.\u003c/p\u003e \u003cp\u003eHematoxylin and eosin (H\u0026amp;E) stained slides from frozen and FFPE tissue were reviewed by the study pathologists to confirm tumor sufficiency and annotate tumor areas. The selected areas for DNA extraction aimed for at least 80% tumor purity and were collected by macrodissection\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e (FFPE slides) or needle coring (frozen cryomold) of tumor tissue\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. For germline analysis, DNA was extracted from blood or saliva samples.\u003c/p\u003e \u003cp\u003eA total 45 samples from 42 patients were included. All study participants were male, consistent with the diagnosis of prostate cancer. The age range of the study participants was 40 to 89 years; body weight was not recorded as it was not relevant to the genomic analyses performed. One sample was tested by two different targeted assays, resulting in a total of 46 TPS. All 45 samples, along with their matched germline DNA, underwent TE-WGS, 40 from FFPE tissue and 5 from frozen tissue. For the 46 TPS, 44 used FFPE tissue, and 2 used frozen tissue. In 22 out of 45 samples, both TE-WGS and TPS were performed on tumor DNA from the same extraction. In 16 cases (14 FFPE and 2 frozen), tumor DNA was extracted from the same surgical block but at different extraction instances. In 2 cases, tumor DNA came from tissue preserved by different methods (FFPE vs. frozen) during the same procedure. In the remaining five cases, TE-WGS and TPS were conducted using samples from two different tumor blocks obtained during the same procedure.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA Library Preparation, Targeted-Enhanced Whole-Genome Sequencing, and Data Analysis\u003c/h3\u003e\n\u003cp\u003eLibrary preparation and nucleic acid preparation has been described in most recent publications\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. It was done using the Watchmaker DNA Library Preparation Kit (Watchmaker Genomics, Boulder, CO). Briefly, DNA was enzymatically fragmented, adapters were ligated, and the resulting product underwent bead-based cleanup and library amplification. Quality control steps included assessing the size distribution of fragments (targeting\u0026thinsp;~\u0026thinsp;300 bp) using the TapeStation 4200 System (RRID: SCR_018435 Agilent Technologies, Santa Clara, CA) and quantifying library yield with the Qubit DNA Assay Kit and Qubit 2.0 Fluorometer (RRID: SCR_020553 ThermoFisher Scientific, Waltham, MA, USA). Libraries were stored at \u0026minus;\u0026thinsp;20\u0026deg;C as per manufacturer guidelines until further processing. Sequencing and data analysis for this study were conducted using TE-WGS CancerVision system (Inocras Inc., San Diego, CA, USA). Prepared DNA libraries were sequenced on the Illumina NovaSeq 6000 system (RRID: SCR_016387 Illumina Inc.), with tumor samples achieving an average target coverage of 40x and matched blood samples 20x. Target-enhanced sequencing for tumor DNA utilized xGen Custom Hybridization Probes (IDT, Inc., Coralville, IA, USA), targeting 2.76Mb of genomic regions at an average target depth of 500x. Raw sequences were aligned to the GRCh38 reference genome using the BWA-MEM algorithm, with PCR duplicates removed via SAMBLASTER\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Germline variants were called using HaplotypeCaller and Strelka2, while somatic variants were identified with Strelka2 and Mutect2\u003csup\u003e23\u0026ndash;25\u003c/sup\u003e. Structural variants were detected using Manta, and all variants underwent annotation with the Variant Effect Predictor (VEP) and manual curation within Inocras\u0026rsquo;s proprietary genome browser\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Tumor purity, ploidy, and allele-specific copy number were estimated from TE-WGS data using Sequenza, with additional adjustments to account for copy number stable tumors and minimize noise from FFPE specimens\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The analysis of homologous recombination deficiency (HRD) was done as detailed in Kim et al.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Processed data, including FASTQ, VCF, and CRAM files, were encrypted and stored securely to ensure data integrity and confidentiality. Collaborators performing the TE-WGS and zdata analysis (Inocaras Inc. team) were blinded to patient clinical outcomes during the study to minimize bias.\u003c/p\u003e\n\u003ch3\u003eComparison of Variants Called by TPS and TE-WGS\u003c/h3\u003e\n\u003cp\u003eWe compared the sequencing results in two ways: (1) checking if the variants identified by TPS assays (TSO500, OCP, and EXaCT-1) were also detected by TE-WGS, and (2) determining if any variants identified by TE-WGS were missed by TPS. We first compiled and filtered the results from TPS for clinically relevant variants reported as Tier 1 (strong clinical significance) or Tier 2 (potential clinical significance) according to the AMP/ASCO/CAP tiered system\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, then checked which of these variants were not reported by TE-WGS. For any variants not reported by WGS, we examined the type of variant, data source from TPS (DNA or RNA), and specific details (e.g., copy number, mutation location). To assess the variants missed by TPS, we filtered the variants based on the gene targets covered by TPS (TSO500 and OCP). No filtering was applied for EXaCT-1, (whole-exome sequencing). For genes with coverage limited to hotspot regions in TPS, we verified the variant location identified by TE-WGS using the COSMIC (Catalogue of Somatic Mutations in Cancer) database\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and UCSC genome browser\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eCohort characteristics\u003c/h2\u003e\n \u003cp\u003eForty-five samples from Forty-two patients with advanced stage prostate cancer were included in the study \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. Among the 45 samples, 40 were prostate adenocarcinoma (PRAD) (including 6 with neuroendocrine differentiation), four were prostate neuroendocrine carcinomas (PRNE), and one was squamous cell carcinoma, a rare variant of CRPC \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e. The age range was 40\u0026ndash;89 years old. Tumor tissue for sequencing was obtained from the following anatomic sites: 13 from lymph nodes, 10 from bone, 5 from liver, 5 from soft tissue (abdominal and thoracic), 1 from bladder, 1 from testis, 1 from brain, and 10 from prostate (synchronous metastatic sites were either not sampled or insufficient for TPS) \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eTPS Utility and TE-WGS Sensitivity\u003c/h2\u003e\n \u003cp\u003eTo assess the clinical utility of TPS, we first quantified the number and type of clinically relevant variants identified. Across the 46 TPS assays (done on 45 samples, including one with 2 different assays), a total of 80 variants with either strong (Tier 1, n\u0026thinsp;=\u0026thinsp;11) or potential (Tier 2, n\u0026thinsp;=\u0026thinsp;69) clinical relevance were detected. Tier 1 variants (13.7% of all findings) included eight \u003cem\u003eAR\u003c/em\u003e amplifications, one \u003cem\u003eNCOA2\u003c/em\u003e amplification, and pathogenic frameshift mutations in each of \u003cem\u003eATM\u003c/em\u003e (p.V3025Afs) and \u003cem\u003eBRCA2\u003c/em\u003e (p.W1692Mfs3*). The Tier 2 variants (86.3%) most frequently involved \u003cem\u003eTP53\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;10), \u003cem\u003ePTEN\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;5), and \u003cem\u003eMDM4\u003c/em\u003e and AR (n\u0026thinsp;=\u0026thinsp;3 each), in addition to 11 gene fusions. The distribution of variants by gene and tier classification is shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA. Overall, 78.3% (36/46) of TPS assays harbored at least one clinically relevant variant. Specifically, 21.7% (10/46) contained at least one Tier 1 variant, and 71.7% (33/46) contained at least one Tier 2 variant, with 6 assays harboring both (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eTo evaluate the sensitivity of TE-WGS, we assessed its ability to detect clinically relevant variants identified by TPS. TE-WGS successfully detected 77 out of 80 variants reported by TPS, yielding an overall sensitivity of 96.3%. This included validation of 100% (11/11) of Tier 1 variants and 95.7% (66/69) of Tier 2 variants, including all 11 genomic fusions identified by TPS (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Two of the three variants not detected by TE-WGS were identified by the TSO500 TPS assay: a \u003cem\u003eSETD2\u003c/em\u003e p.R1407Gfs5* (c.4219delA) frameshift mutation with low variant allele frequency (VAF, 8.7%) and modest coverage (127\u0026times;), and a \u003cem\u003eNOTCH4\u003c/em\u003e p.Q142Hfs7* (c.426_436del11) variant with a VAF of 6.6%. The third undetected variant was a missense mutation in \u003cem\u003eAR\u003c/em\u003e (c.2632A\u0026thinsp;\u0026gt;\u0026thinsp;G, p.Thr878Ala) with high VAF, detected by the OCP panel.\u003c/p\u003e\n \u003cp\u003eIt is important to note that thirteen variants (a subset of the 80 variants reported by TPS)\u0026mdash;comprising six CNVs and seven SNVs across 11 samples\u0026mdash;were detected by both TPS and TE-WGS but were not classified as oncogenic by the TE-WGS/CancerVision\u0026copy; pipeline (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the six CNVs, five were amplifications that did not meet the TE-WGS reporting threshold (copy number\u0026thinsp;\u0026gt;\u0026thinsp;5), and one was a monoallelic deletion, which is not considered oncogenic in this context. Of the seven SNVs reported by TPS, five were confirmed by TE-WGS as somatic variants but were classified as non-pathogenic frameshift mutations in oncogenes such as \u003cem\u003eETV1\u003c/em\u003e, \u003cem\u003eMGA\u003c/em\u003e, \u003cem\u003eFOXA1\u003c/em\u003e, and \u003cem\u003eFOXP1\u003c/em\u003e. These mutations are predicted to disrupt gene function rather than activate oncogenic signaling, and thus were not considered to represent driver alterations. The remaining two SNVs\u0026mdash;\u003cem\u003eFANCL\u003c/em\u003e p.T367Nfs and \u003cem\u003eJAK1\u003c/em\u003e c.6\u0026thinsp;+\u0026thinsp;1G\u0026thinsp;\u0026gt;\u0026thinsp;A\u0026mdash;were identified by TE-WGS as germline variants and classified as non-pathogenic, a distinction made possible through the inclusion of matched germline analysis in the TE-WGS pipeline, which is not available in TPS. In addition, 13 of the 46 TPS assays were conducted using the TSO500 platform, which incorporates an RNA sequencing component. This allowed for the detection of seven \u003cem\u003eAR\u003c/em\u003e splice variant transcripts and 2 transcriptomic fusions (\u003cem\u003eFOXP1::BRAF\u003c/em\u003e and \u003cem\u003eSLC45A3::FL1\u003c/em\u003e). These RNA-based alterations are not detectable by TE-WGS, which does not include transcriptomic analysis.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariants reported by both TPS and TE-WGS but classified as non-oncogenic by TE-WGS.\u003c/strong\u003e Summary of 13 variants identified by both tumor profiling sequencing (TPS) and tumor-enriched whole genome sequencing (TE-WGS) across 11 samples, including 6 copy number variations (CNVs) and 7 single nucleotide variants (SNVs). These variants were not considered oncogenic by the TE-WGS/CancerVision\u0026copy; annotation pipeline. CNV: Copy number variation. SNV: Single nucleotide variant. N/A: Not applicable.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCase ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTPS Label\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTE-WGS Label\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariant\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIK3CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 2 CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopy Number\u0026thinsp;\u0026lt;\u0026thinsp;5 (Below Reporting Cutoff)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 2 CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopy Number\u0026thinsp;\u0026lt;\u0026thinsp;5 (Below Reporting Cutoff)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNCOA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 1 CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopy Number\u0026thinsp;\u0026lt;\u0026thinsp;5 (Below Reporting Cutoff)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 1 CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopy Number\u0026thinsp;\u0026lt;\u0026thinsp;5 (Below Reporting Cutoff)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 2 CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopy Number\u0026thinsp;\u0026lt;\u0026thinsp;5 (Below Reporting Cutoff)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePTEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 2 CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonoallelic Deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFANCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 2 SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Pathogenic Germline Alteration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.T367Nfs c.1096_1099dupATTA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 2 SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Pathogenic Germline Alteration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.? c.6\u0026thinsp;+\u0026thinsp;1G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFOXP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 2 SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Pathogenic Somatic Alteration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariant p.1474Gfs\u0026apos;12 c.1420_i423de1ATTA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFOXA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 2 SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Pathogenic Somatic Alteration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissense p.R219S c.655C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 2 SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Pathogenic Somatic Alteration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eframeshift p.K2103Nfs*8 c.6309_6315del\u0026amp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 2 SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Pathogenic Somatic Alteration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eframeshift p.H452Qfs*3 c.1355dupA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFOXA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTier 2 SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Pathogenic Somatic Alteration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep.l660* c.1077delC frameshift\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eTE-WGS Additional Findings\u003c/h3\u003e\n\u003cp\u003eTo evaluate the utility of TE-WGS, we examined the total number and types of clinically relevant oncogenic alterations. Variants with clinical impact\u0026mdash;either targetable or of potential clinical relevance\u0026mdash;were detected in 100% of samples (45/45), with a total of 506 unique findings, ranging from 2 to 75 variants per sample \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;1)\u003c/strong\u003e. These included 244 somatic alterations, comprising 157 small somatic variants (SNVs and InDels) and 87 CNVs, along with 236 genomic rearrangements and 25 gene fusions.\u003c/p\u003e\n\u003cp\u003eTo assess the additional clinical insights provided by TE-WGS, we next compared its findings with those of TPS. For each sample, TE-WGS-detected variants were mapped to the gene panels and mutation hotspots covered by the corresponding TPS assay. TE-WGS identified additional clinically relevant variants in 95.6% of cases (43/45), totaling 430 out of 506 findings (85.0%). The number of additional variants per sample ranged from 1 to 70, with an average of 10 clinically relevant variants per sample.\u003c/p\u003e\n\u003cp\u003eWe further categorized the additional clinically relevant variants identified by TE-WGS but not reported by TPS based on their variant type, genomic location, and whether they fell within the coverage scope of the TPS assays. The majority\u0026mdash;54.8% (236/430)\u0026mdash;were disruptive genomic rearrangements, many of which involved key cancer-associated genes. These included rearrangements in \u003cem\u003eBRCA2\u003c/em\u003e (2 cases), \u003cem\u003ePTEN\u003c/em\u003e (3), \u003cem\u003eTP53\u003c/em\u003e (3), and \u003cem\u003eSTAG\u003c/em\u003e1 (2), as well as additional alterations in genes such as \u003cem\u003eCDKN1B\u003c/em\u003e, \u003cem\u003eAPC\u003c/em\u003e, \u003cem\u003eMSH2\u003c/em\u003e, and \u003cem\u003eFANCF\u003c/em\u003e \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;1)\u003c/strong\u003e. Furthermore, 3.5% (15/430) of the unreported variants were gene fusions involving oncogenic drivers, including \u003cem\u003eERG, BRAF, AFF1, AFF3, ETV5, ETV6, MECOM, NF1, RAF1\u003c/em\u003e, and \u003cem\u003eTMCC1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eTo determine whether the missed variants in TPS were primarily due to limited gene coverage, we reviewed the reference gene lists for each matched TPS assay. We found that 29.8% (128/430) of the clinically relevant variants missed by TPS were located in genes or regions not covered by the respective TPS panels. These included genes with established or potential clinical relevance such as CDK12, FANCA, and FANCF (not covered by OCP), as well as BCOR2, GNAS, and SOX2 copy number alterations (not covered by TSO500), among others.\u003c/p\u003e\n\u003cp\u003eIn addition, 3.3% (14/430) of the missed variants were deleterious mutations in clinically actionable genes\u0026mdash;such as \u003cem\u003eAR, BRAF, CTNNB1, HRAS, MED12, MYD88, NFE2L2, PDGFRA, SPOP, ABL1\u003c/em\u003e, and \u003cem\u003eAPC\u003c/em\u003e\u0026mdash;where the TPS assay covered only specific hotspot regions and failed to include the mutated pathogenic sites \u003cstrong\u003e(Supplementary Table\u0026nbsp;1)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, we identified 8.6% (37/430) of missed variants that occurred in genes and genomic regions covered by the TPS assay used for each sample. Among these, 35.1% (13/37) were missed by OCP, 29.8% (11/37) by TSO500, and 35.1% (13/37) by EXaCT-1.\u003c/p\u003e\n\u003ch3\u003eTreatment Targets\u003c/h3\u003e\n\u003cp\u003eWe then focused on the subset of variants identified by TE-WGS that were classified as clinically actionable treatment targets. Actionable alterations were observed in 46.7% of samples (21/45), with a total of 44 treatment-associated targets identified, ranging from 1 to 5 per sample \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. The remaining 477 variants, while not linked to currently approved therapies, were considered to have potential clinical relevance.\u003c/p\u003e\n\u003cp\u003eThe 44 actionable targets included structural rearrangements and SNV/InDel mutations in DNA damage repair genes such as \u003cem\u003eBRCA1, BRCA2, CDK12, ATM, RAD51B, BRIP1, FANCA, NBN\u003c/em\u003e, and \u003cem\u003eATR\u003c/em\u003e. Additional actionable SNVs were observed in \u003cem\u003eAR\u003c/em\u003e\u0026mdash;specifically \u003cem\u003eL702H\u003c/em\u003e in three samples and \u003cem\u003eH875Y\u003c/em\u003e in one sample\u0026mdash;as well as variants in other clinically relevant genes such as \u003cem\u003ePTEN, FGFR2, CCNE\u003c/em\u003e1, and a \u003cem\u003eSVOPL::BRAF\u003c/em\u003e fusion.\u003c/p\u003e\n\u003cp\u003eTo evaluate the added clinical utility of TE-WGS, we compared its ability to detect treatment targets relative to TPS. TPS identified six actionable variants in 11.1% of samples (5/45), including targetable SNVs in \u003cem\u003ePTEN\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3), \u003cem\u003eBRCA2\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1), and \u003cem\u003eATM\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1). TE-WGS successfully detected all six of these TPS-reported alterations and identified additional treatment targets in 40.0% of cases (18/45). Notably, 35.6% of samples (16/45) harbored targetable alterations detected only by TE-WGS, with no actionable findings reported by TPS \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eMutational Signatures Reveal HRD Phenotypes Detected by TE-WGS\u003c/h2\u003e\n \u003cp\u003eTo further investigate the biological relevance of HRD-related alterations identified exclusively by TE-WGS, we examined their association with known HRD mutational signatures \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. Among the 15 cases with HRR gene alterations not detected by TPS, ten (~\u0026thinsp;67%) exhibited at least one HRD-associated signature. SBS3 and/or SBS8 occurred in 6 of 15 cases (40%), while ID6 was observed in 2 of 8 evaluable cases (25%). For instance, case GI25 harbored a \u003cem\u003eBRCA2\u003c/em\u003e deletion and exhibited high SBS3 (35.9%) and SBS8 (15.8%). In addition, cases GI39 (with \u003cem\u003eATM\u003c/em\u003e, \u003cem\u003eCDK12\u003c/em\u003e, and \u003cem\u003eBRIP1\u003c/em\u003e rearrangements) and GI50 (with \u003cem\u003eNBN\u003c/em\u003e and \u003cem\u003eCDK12\u003c/em\u003e rearrangements, and \u003cem\u003eFANCA\u003c/em\u003e mutations) demonstrated increased SBS3 (11.4% and 33.6%, respectively). These findings support the presence of functional HRD and highlight a connection between HRR gene rearrangements and mutational signature evidence. Interestingly, among the five cases with HRD-related alterations but no detectable mutational signatures, four involved \u003cem\u003eATM\u003c/em\u003e and/or \u003cem\u003eRAD51B.\u003c/em\u003e These results suggest that TE-WGS can reveal cryptic HRD signatures that may be missed by TPS.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenomic Alterations Related to Homologous Recombination Deficiency Exclusively Identified by Target-Enhanced Whole-Genome Sequencing and Their Associated Mutational Signatures in Metastatic Prostate Cancer Patients.\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGIID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTE-WGS finding\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTMB (mut/Mb)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSBS3 (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSBS8 (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eID6 (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHRD-related Signature\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA2\u003c/em\u003e deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRAD51B\u003c/em\u003e rearrangement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eATM\u003c/em\u003e rearrangement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eATM\u003c/em\u003e, \u003cem\u003eCDK12\u003c/em\u003e, and \u003cem\u003eBRIP1\u003c/em\u003e rearrangements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eATM\u003c/em\u003e and \u003cem\u003eRAD51B\u003c/em\u003e rearrangement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA2\u003c/em\u003e rearrangement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNBN\u003c/em\u003e and \u003cem\u003eCDK12\u003c/em\u003e rearrangements, and \u003cem\u003eFANCA\u003c/em\u003e mutations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNBN\u003c/em\u003e rearrangement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA2\u003c/em\u003e mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRAD51B\u003c/em\u003e rearrangement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFANCA\u003c/em\u003e mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCDK12\u003c/em\u003e mutations and \u003cem\u003eBRIP1\u003c/em\u003e disruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eATM\u003c/em\u003e mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCDK12 and BRCA1\u003c/em\u003e frameshift mutations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGI88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA2, ATM\u003c/em\u003e, and \u003cem\u003eBRCA1\u003c/em\u003e mutations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTE-WGS: Target-Enhanced Whole-Genome sequencing; TMB: Tumor mutation burden; HRD: homologous recombination deficiency\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eReview of TE-WGS Findings in a Molecular Tumor Board Format\u003c/h2\u003e\n \u003cp\u003eSix patients from our cohort with metastatic CRPC and limited treatment options were presented at a research molecular tumor board that included experts in oncology, pathology, and genomics. TPS assays were performed as part of routine clinical care, whereas TE-WGS was conducted as part of a the current study. Across these cases, genomic alterations suggested potential therapeutic options, pending validation with clinically approved assays before treatment decisions. Findings included an \u003cem\u003eNBN\u003c/em\u003e rearrangement and increased SBS3 signature (HRD signature), supporting eligibility for PARPi therapy\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e); an \u003cem\u003eFGFR2\u003c/em\u003e splice donor variant, predicted to be activating, suggesting sensitivity to an FGFR-specific tyrosine kinase inhibitor; \u003cem\u003ePTEN\u003c/em\u003e rearrangements in two patients, expanding potential options to include Pan-AKT inhibitors as supported by the ProCaid clinical trial\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e; and a patient with a \u003cem\u003eRAD51B\u003c/em\u003e rearrangement, pathogenic germline \u003cem\u003eFANCC\u003c/em\u003e mutation (c.456 A\u0026thinsp;\u0026gt;\u0026thinsp;T), and a deleterious \u003cem\u003eFANCF\u003c/em\u003e complex rearrangement, qualifying for PARPi therapy. Notably, a \u003cem\u003eCDK12\u003c/em\u003e rearrangement was identified in one patient\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCDK12\u003c/em\u003e alterations are associated with a distinct molecular subtype of prostate cancer characterized by genomic instability and increased neoantigen burden, which has been linked to sensitivity to immune checkpoint blockade. While emerging evidence supports the use of immunotherapy in \u003cem\u003eCDK12\u003c/em\u003e-altered metastatic CRPC, further validation with clinically approved assays was recommended before determining eligibility for treatment\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMetastatic castration-resistant prostate cancer is an aggressive disease with limited treatment options. The NCCN guidelines have recommended molecular testing for prostate cancer since 2018\u003csup\u003e35\u003c/sup\u003e. However, existing molecular targeted panels differ in their gene coverage and cutoff criteria, making it difficult for clinicians to select the appropriate assay. In this study, we performed clinical-grade TE-WGS\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e on tumor/normal paired samples from patients with advanced prostate cancer. All patients had available clinical TPS results for comparison with TE-WGS.\u003c/p\u003e \u003cp\u003eThe added benefit of TE-WGS was evident across multiple dimensions. First, TE-WGS identified clinically relevant variants in 100% of samples. Of these, 85.0% (430/506) were not reported by TPS. To better understand the basis for discordance, we classified the additional TE-WGS findings into several categories: (1) 54.8% of the additional variants detected by TE-WGS were disruptive genomic rearrangements, many involving key tumor suppressors such as \u003cem\u003eBRCA2, PTEN\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, and \u003cem\u003eSTAG1\u003c/em\u003e. The importance of detecting these variants is consistent with previous studies showing that structural variants can affect the targeting and management of certain cancers\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. (2) Furthermore, 3.5% of the additional findings were oncogenic fusions (e.g., involving \u003cem\u003eERG, BRAF, ETV6, RAF1\u003c/em\u003e), which were missed by TPS despite their relevance to cancer pathogenesis and treatment sensitivity\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. (3) variants (SNVs/InDels) in genes not covered by the TPS panel used, which accounted for 29.8% of cases; (4) variants in genes that were partially covered by TPS (e.g., limited to hotspot regions), comprising 3.3%; and (5) variants located within regions fully covered by TPS but nonetheless missed, making up 8.6%. This analysis illustrates that the added benefit of TE-WGS is not only due to its broader genomic scope, but also to its technical sensitivity across variant types.\u003c/p\u003e \u003cp\u003eThe clinical relevance of these findings was further reinforced by the detection of actionable alterations. TE-WGS identified treatment-associated targets in 46.7% of samples, with up to five actionable variants per case. These included alterations in DNA damage repair genes (e.g., \u003cem\u003eBRCA1/2, ATM, CDK12, FANCA\u003c/em\u003e), as well as targetable variants \u003cem\u003ein AR, PTEN, FGFR2, CCNE1, and a SVOPL::BRAF\u003c/em\u003e fusion. While TPS detected six actionable variants in 11.1% of samples\u0026mdash;all of which were also captured by TE-WGS\u0026mdash;an additional 35.6% of samples harbored actionable findings that were only detected through TE-WGS.\u003c/p\u003e \u003cp\u003eNotably, the rearrangements in HRR pathway genes such as \u003cem\u003eBRCA1/2\u003c/em\u003e, \u003cem\u003eNBN, RAD51B\u003c/em\u003e, and \u003cem\u003eFANCC\u003c/em\u003e correlated with HRD signatures like SBS3. As a result, the patients with variants in these genes were referred for consideration of targeted therapies. Detecting such rearrangements is crucial, as studies have shown they can cause HRD, making tumors sensitive to PARPi\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn terms of sensitivity, TE-WGS validated 96% of the findings reported by TPS and detectable by TE-WGS. For the discrepancies, low VAF was the most likely explanation. Tumor clonality may also play a role, as variations in sampling can affect which clonal mutations are detected, as reported by Opasic \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Additionally, some reporting differences were related to the technique rather than detection as of these discrepancies involved transcriptomic variants identified by RNA sequencing, particularly in the AR gene. This highlights the need for adding RNA sequencing to WGS for prostate cancer to match the capabilities of TPS that include RNA analysis\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. However, adding RNA sequencing is not universally scalable across clinical settings as RNA sequencing requires more and better-preserved tissue, which can be challenging, especially in limited core needle biopsy samples from metastases. We also found that some pathogenic mutations reported by TPS panels were identified as germline variants by TE-WGS (tumor/normal pair analysis), demonstrating a limitation of tumor-only sequencing strategies often used for TPS. TE-WGS showed no loss of heterozygosity for these genes.\u003c/p\u003e \u003cp\u003eAs sequencing technologies and analytical methods continue to evolve, our study supports TE-WGS as a robust, comprehensive assay capable of evaluating both somatic and germline molecular aberrations in advanced prostate cancer. This study demonstrated that TE-WGS showed high concordance with TPS and uncovered additional mutations and rearrangements of potential clinical significance. While not yet positioned to replace TPS, these results highlight the potential of TE-WGS to complement existing assays and expand actionable insights.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e Sangmoon Lee and Erin Strong are employees of Inocras. Majd Al Assaad has received travel and accommodation support from Inocras.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Englander Institute for Precision Medicine. Whole-genome sequencing was performed at Inocras, Inc. Project support for this project was provided in part by the Center for Translational Pathology from the Department of Pathology and Laboratory Medicine at Weill Cornell Medicine (Ruben Diaz, Leticia Dizon, Bing He). The authors thank Alexis Barcomb, Samantha Henry, Amanda L. Vitale and Noah Greco, for project support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.A.A. performed data analysis, generated figures, and wrote the manuscript. S.L. contributed to data analysis and critically reviewed the manuscript. A.S. coordinated the study and contributed to sample processing and data collection. D.W. assisted with sample processing. J.M., D.G., and C.F. coordinated patient consent and subject selection. G.C., G.L., O.H., E.N.K., and J.D.M. supported data collection and sample handling. C.H., O.E., S.T., A.M.M., D.M.N., J.T.N., and C.N.S. contributed to patient enrollment and reviewed the manuscript. E.C.S. supported study coordination and manuscript review. J.M.M. and J.S. supervised the study and provided critical revisions. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSiegel, R. L., Miller, K. D., Fuchs, H. E. \u0026amp; Jemal, A. Cancer statistics, 2022. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 7-33 (2022). https://doi.org/10.3322/caac.21708\u003c/li\u003e\n \u003cli\u003eIannantuono, G. 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H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Prospective Comprehensive Genomic Profiling of Primary and Metastatic Prostate Tumors. \u003cem\u003eJCO Precis Oncol\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e (2019). https://doi.org/10.1200/po.18.00283\u003c/li\u003e\n \u003cli\u003eKinnunen, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The Impact of ETV6-NTRK3 Oncogenic Gene Fusions on Molecular and Signaling Pathway Alterations. \u003cem\u003eCancers (Basel)\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e (2023). https://doi.org/10.3390/cancers15174246\u003c/li\u003e\n \u003cli\u003eOpasic, L., Zhou, D., Werner, B., Dingli, D. \u0026amp; Traulsen, A. How many samples are needed to infer truly clonal mutations from heterogenous tumours? \u003cem\u003eBMC Cancer\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 403 (2019). https://doi.org/10.1186/s12885-019-5597-1\u003c/li\u003e\n \u003cli\u003eBasil, P.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Cistrome and transcriptome analysis identifies unique androgen receptor (AR) and AR-V7 splice variant chromatin binding and transcriptional activities. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 5351 (2022). https://doi.org/10.1038/s41598-022-09371-x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metastatic Prostate Cancer, Whole Genome Sequencing, Targeted Sequencing ","lastPublishedDoi":"10.21203/rs.3.rs-6993200/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6993200/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe evaluated the clinical utility of Target-Enhanced Whole Genome Sequencing (TE-WGS) in comparison with targeted panel sequencing (TPS) for identifying clinically relevant genomic alterations in advanced prostate cancer.\u003c/p\u003e \u003cp\u003e We applied TE-WGS to tumor/normal paired samples from patients with advanced prostate cancer previously tested with TPS during routine care. We compared the sensitivity of TE-WGS in detecting variants reported by TPS and assessed its added value in uncovering additional targetable alterations.\u003c/p\u003e \u003cp\u003eA total of 45 samples from patients with advanced prostate cancer were analyzed using TE-WGS, which demonstrated 96.3% sensitivity in detecting clinically relevant variants reported by matched TPS. Furthermore, TE-WGS identified an additional 430 variants (85.0%) with clinical impact that were not reported by TPS. Notably, TE-WGS revealed rearrangements in DNA repair genes such as BRCA1/2, RAD51B, NBN, and CDK12. Overall, additional targetable alterations were detected by TE-WGS in 46.7% of samples, including 35.6% with no actionable findings by TPS, underscoring the added clinical value of WGS-based profiling.\u003c/p\u003e \u003cp\u003eOur study highlights TE-WGS as a valuable complement to TPS, revealing clinically relevant targets that support its consideration in the management of advanced prostate cancer.\u003c/p\u003e","manuscriptTitle":"Comparison of Whole Genome Sequencing with Clinical Targeted Panel Sequencing of Metastatic Prostate Cancer: Insights from Real- World Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 08:23:35","doi":"10.21203/rs.3.rs-6993200/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76e862df-a9e3-4a2f-8990-c091cac12619","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50768572,"name":"Health sciences/Biomarkers"},{"id":50768573,"name":"Biological sciences/Cancer"},{"id":50768574,"name":"Biological sciences/Genetics"},{"id":50768575,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-09-22T19:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-03 08:23:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6993200","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6993200","identity":"rs-6993200","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}