Angela Y. Jia

Introduction: Prostate cancer (PCa) is the most commonly diagnosed cancer in men worldwide. A rising proportion of men present with de novo metastatic disease, a state associated with poor survival outcomes and limited curative treatment options. Within this group, a subset of patients exhibit oligometastatic disease, defined by a limited number of metastases, which may represent a distinct clinical entity with potential for long-term disease control. Emerging evidence suggests that metastasis-directed radiotherapy (MDRT), including stereotactic body radiation therapy (SBRT), may improve progression-free survival in this population. However, current evidence is largely limited to phase II trials in the metachronous setting and retrospective studies in the synchronous setting. Furthermore, predictive biomarkers of treatment response in these patients remain undefined. We aim to evaluate, within a large randomised multicentre phase II trial, the efficacy and biological impact of MDRT in men with de novo oligometastatic PCa. Methods and analysis: Patients with de novo oligometastatic hormone-sensitive prostate cancer (omHSPC), defined as up to 10 sites of metastatic disease by prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/CT or up to five sites of metastatic disease by conventional imaging, are randomised (1:1) to standard of care (SOC) or SOC plus MDRT. SOC includes 12 months of androgen deprivation therapy plus an androgen receptor pathway inhibitor, with definitive treatment to the prostate by radiotherapy (RT). A predefined subset is allowed to undergo radical prostatectomy if clinically appropriate. MDRT involves SBRT to all metastatic sites identified on conventional imaging or PSMA PET/CT. Target enrolment is 200 patients, stratified by diagnostic imaging modality, number of bone metastases, plan to MDRT all sites of metastases and local therapy approach. Prostate RT may be delivered using moderate (20 fractions) or ultra-hypofractionation (five fractions), with optional simultaneous-integrated-boost to dominant intraprostatic lesions. MDRT regimens aim for a biologically effective dose ≥100 Gy (α/β=1.5) using 1–5 fractions. Gross tumour volumes are delineated based on MRI, CT and PSMA PET/CT. The primary endpoint is failure-free survival, defined using cause-specific PCa death. The study is powered at 80% to detect a HR of 0.58 using a one-sided alpha of 0.05. Secondary endpoints include radiographic progression-free survival, overall survival (OS), time to next intervention, time to castration-resistant PCa, PCa-specific mortality and patient-reported outcomes. Biospecimen collection and imaging data will support future translational analyses. Data from this trial is preplanned to be pooled with the STAMPEDE 2 ( NCT06320067 ) trial, a phase III randomised trial to assess OS benefit of MDRT in men with de novo omHSPC. Ethics and dissemination: This clinical trial is approved by the Case Comprehensive Cancer Center Institutional Review Board (STUDY20231440). The results will be disseminated via peer-reviewed scientific journals and conference presentations. Trial registration number: NCT06150417 ( https://clinicaltrials.gov/study/NCT06150417 )

There is an unmet need for a tool that could predict early favorable prostate-specific antigen (PSA) response in metastatic hormone sensitive prostate cancer (mHSPC) patients receiving androgen receptor pathway inhibitor (ARPI). Here, we train and validate a multivariable logistic regression model to predict early favorable PSA response (≤0.2 ng/mL by 6 months) in these patients. Patients randomly allocated to the ARPI arms of the LATITUDE (abiraterone), TITAN (apalutamide), and ARASENS (darolutamide) trials, are split 60:40 into training (n = 1030) and internal validation (n = 688) cohorts. The locked model is validated in an independent external validation cohort - the enzalutamide arm of the ENZAMET trial (n = 540). The area under curve and Brier score for the locked model in the external validation cohort are 0.82 (95% confidence interval [CI] = 0.78–0.85) and 0.16, respectively. Stratification by predicted probability tertiles show PSA response rates of 92% (95% CI = 88–96), 74% (95% CI = 68–81), and 39% (95% CI = 32–47), respectively. Pending prospective validation, our model predicts early favorable PSA response supporting its potential role in guiding treatment decisions.

Purpose: Genomic classifiers are endorsed by guidelines and commonly used to inform prognosis in prostate cancer. We aimed to understand the distribution of genomic risk within the validated staging collaboration for cancer of the prostate (STAR-CAP) and propose a system integrating genomic and clinicopathologic risk. We hypothesized that genomic heterogeneity would have implications on risk estimates and may inform treatment decisions. Materials and methods: Genomic risk was assessed using the Decipher genomic classifier in two separate multi-institutional, prospectively collected population-based registries: (1) Decipher Genomics Resource for Intelligent Discovery (GRID) [ n = 50,891] and (2) Michigan Urological Surgery Improvement Collaborative (MUSIC-Decipher) [ n = 1602]. The primary endpoint was estimated prostate cancer-specific mortality (PCSM), and secondary endpoint was distant metastasis (DM). Marginal risk estimates provided by STAR-CAP were combined with hazard ratios of Decipher to calculate integrated risk estimates. Results: Median age and PSA was 68 years and 6.2 ng/mL in GRID, and 66 years and 10.5 ng/mL in MUSIC. The GRID population had 50.2%, 18.5%, and 31.4% with low-, intermediate-, and high-Decipher risk, compared to 48.0%, 16.2%, and 35.8% in MUSIC. Decipher-based genomic risk varied across STAR-CAP stages in both registries. Estimates of 10-year PCSM (0.1% to 48.8%) and DM (0.3%-72.9%) varied widely after integration of clinical-genomic risk. Use of an integrated Decipher-STAR-CAP system led to significant stage reclassification, including 23.4% upstaging and 45.6% downstaging at least one stage. Conclusions: These findings suggest integration of genomic and clinicopathologic risk may lead to more nuanced risk assessment in prostate cancer and may help individualize treatment consideration.

Purpose: This study aims to identify specific genotypes within the UK Biobank (UKB) cohort contributing to a genetic predisposition for bladder cancer (UBC). It highlighted the impact of environmental exposures and the broader role of certain genes in UBC development, offering a comprehensive understanding of the genetic basis for UBC susceptibility. Experimental Design: Leveraging the rich data from the UKB- a longitudinal study involving participants across the UK-the primary outcome was the presence of UBC, determined using ICD-10 and ICD-9 codes. The study employed rigorous Genome-Wide Association Study (GWAS) protocols, Phenome-Wide Association (PheWAS) frameworks, and gene-level pleiotropy analyses. Quality control measures were applied, such as single-nucleotide polymorphisms (SNP) missingness and minor allele frequency thresholds. Polygenic Risk Score (PRS) evaluations were also conducted based on the Mavaddat score using UKB's high-density genome-wide SNP dataset. Results: Our GWAS identified significant associations between UBC risk and genetic variants, notably in the PSCA and TERT genes. The UGT1A polymorphism was found to be protective against UBC, particularly in heavy smokers. The PheWAS framework linked UBC-predisposition polymorphisms to other conditions, such as prostate cancer. Conclusions: Our GWAS identified significant associations between UBC risk and genetic variants across loci, including PSCA, TERT, TACC3 and TMEM129. The protective effect of the UGT1A variant against UBC, especially concerning tobacco exposure, suggests the potential for genetic-based preventive strategies in UBC management. Patient summary In our study of a large group from the United Kingdom (UK), we explored genetic factors that might increase the likelihood of developing UBC. We discovered that certain genetic changes offer protection against UBC, particularly in individuals exposed to tobacco smoke. Understanding these genetic factors could improve strategies for preventing and treating UBC.

PURPOSE: The 22-gene Decipher genomic classifier (GC) (22-gene GC) is the only gene expression test with National Comprehensive Cancer Network (NCCN) level 1 evidence for localized prostate cancer (PCa) treatment decision making. It is unclear whether other commercial signatures—Genomic Prostate Score (GPS) or Prolaris (cell cycle progression [CCP])—correlate sufficiently to explain differences in evidence strength. This study assesses correlation between these three classifiers on a per-patient basis by performing a cross-comparison of these signatures in a large cohort of patients diagnosed with PCa. MATERIALS AND METHODS: Primary PCa biopsy samples underwent whole-transcriptome gene expression microarray analysis. 22-gene GC scores were calculated using the commercially locked model. To reduce bias, GPS and CCP signatures were retrained for prediction of metastasis to harmonize end points. Pearson correlations and linear regressions (univariable/multivariable) adjusting for age, grade group, prostate-specific antigen (PSA), and T stage were calculated between signatures. RESULTS: Among 50,881 patients (median age 68 years, median PSA 6.2 ng/mL), 60% were NCCN intermediate-risk. The GPS-derived and CCP-derived models had poor goodness-of-fit to 22-gene GC ( R ² = 0.36 and 0.32, respectively). Multivariable analysis adjusting for clinical factors showed similar results. Given the many variables that may contribute to or correlate with the 22-gene GC, a multivariable R ² analysis was performed to assess how much of the variation could be attributed to known factors. A variance analysis revealed approximately 60% of 22-gene GC variation remained unexplained. GPS-derived and CCP-derived models accounted for 24.7% and 22.7% of variance, respectively, with additional contributions from Gleason score. CONCLUSION: Correlation between 22-gene GC and either GPS-derived or CCP-derived signatures is minimal to moderate. These tests are not interchangeable, and their use should be guided by the specific evidence supporting each signature.

Background: An immune checkpoint inhibitor (ICI) backbone is a standard of care for frontline metastatic clear cell renal cell carcinoma, involving either ICI doublet or tyrosine kinase inhibitor (TKI) with ICI. These phase 3 trials used a sunitinib control arm. The optimal regimen is uncertain. Objective: To compare long-term responders of these trials using extended follow-up data stratified by International Metastatic RCC Database Consortium risk group. Methods: Phase 3 trial data (CheckMate 214, KEYNOTE-426, CheckMate 9ER, and CLEAR) with a minimum follow-up of four years was used. Pseudo individual patient data (IPD) was obtained from Kaplan-Meier curves using the graph digitizer software IPDfromKM R package to extract coordinates of points on the curves and to apply a numerical algorithm to reconstruct progression-free survival (PFS) and overall survival (OS) results. Durable response (DR) was defined as PFS ≥ 24 months, extreme durable response (EDR) as PFS ≥ 36 months, and long-term OS as OS ≥ 48 months. Results: For the favorable risk group, lenvatinib-pembrolizumab had the highest DR and EDR, while ipilimumab-nivolumab had the lowest DR, and cabozantinib-nivolumab had the lowest EDR; there was no difference in long-term OS among the regimens ( p = 0.11). For the intermediate/poor risk group, lenvatinib-pembrolizumab also had the highest DR and EDR compared to other regimens with similar DR and EDR; there was also no difference in long-term OS among the regimens ( p = 0.22). Conclusion: TKI-ICI was overall associated with higher DR and EDR regardless of risk status compared to ICI doublet. Yet, OS at 48 months were similar when stratified by favorable versus intermediate/poor risk.