Jul 2026· Journal of Medical Genetics· pp. jmg-2026-111746· 0 citations· 39 references
Medicine
TL;DR
An integrated continuum genetic risk model, GenProb-PCa, improves PCa risk stratification beyond binary approaches by capturing heterogeneity in inherited risk and may enable more precise risk-based screening strategies.
Abstract
Background
Current clinical approaches to inherited prostate cancer (PCa) risk rely on binary classification of pathogenic variant (PV) carrier status without accounting for gene-specific heterogeneity or polygenic risk. We developed an integrated genetic risk model reflecting a continuum of inherited susceptibility.
Methods
In the UK Biobank (UKB; n=218 484), we evaluated associations of PVs in 11 clinically recommended genes and a polygenic risk score (PRS) with incident PCa using Cox models. A continuum model (GenProb-PCa) incorporating gene-specific PVs and PRS was developed and compared with binary PV-based models. Model performance was assessed using discrimination, calibration and continuous net reclassification index (cNRI). External validation was performed in a health system cohort, the Genomic Health Initiative (GHI; n=6590).
Results
Five genes (ATM, BRCA2, CHEK2, HOXB13, MSH2) and the PRS were independently associated with PCa risk (all p<0.001) in UKB. Compared with binary models, GenProb-PCa demonstrated superior discrimination (C-index 0.69 vs 0.52; p<0.001), with significant improvement in reclassification (cNRI 0.58; p<0.001). Findings were validated in GHI using the UKB-derived coefficients (C-index 0.64 vs 0.54, p<0.001; cNRI 0.31, p<0.001). Compared with binary PV-based models, 20% of non-PV carriers were reclassified to higher risk groups and 60% of carriers to lower risk groups. The model identified individuals at markedly elevated lifetime risk, with cumulative incidence exceeding 20% by age 75 among the top 8% of the distribution.
Conclusions
An integrated continuum genetic risk model, GenProb-PCa, improves PCa risk stratification beyond binary approaches by capturing heterogeneity in inherited risk. This framework may enable more precise risk-based screening strategies.
Germline PVs in NCCN-recommended DDR genes and the KLK3 I179T variant identify a subset of men at elevated risk of PCSM, including those with apparently localized disease.
Lucy Lu, Julian Xu, Zhu-Qing Shi et al.· Prostate Cancer and Prostati...· 1 citation
BACKGROUND
We previously developed a tier-based germline classification using the National Comprehensive Cancer Network (NCCN)-recommended DNA damage repair (DDR) genes and KLK3 I179T to predict prostate cancer (PCa)-specific mortality (PCSM). To provide an easier-to-use single inherited risk score while preserving gene-specific effects, we developed GenProb-PCSM.
METHODS
We analyzed 14,644 men with incident PCa from the UK Biobank. The cohort was randomly divided into training (60%) and independent testing (40%) datasets. GenProb-PCSM was developed in the training cohort by integrating pathogenic variants in ten NCCN-recommended DDR genes and the KLK3 I179T variant using gene-specific weights derived from Fine-Gray competing-risk models. Performance was evaluated in the independent testing cohort by discrimination, calibration, and risk stratification. Secondary analyzes evaluated metastatic progression and the composite endpoint of metastatic progression and/or PCSM.
RESULTS
Among 14,644 men with incident PCa, 1,581 died from PCa. GenProb-PCSM remained significantly associated with PCSM in the independent testing cohort (HR per SD, 1.18; 95% CI, 1.12-1.24; p < 0.001). Using predefined risk thresholds derived from the training cohort, patients in the intermediate- and high-risk groups had significantly increased risks of PCSM compared with the low-risk group (HR 1.49, 95% CI 1.22-1.84; and HR 4.04, 95% CI 2.58-6.33, respectively). GenProb-PCSM also predicted independent metastatic progression and the composite endpoint of metastatic progression and/or PCSM.
CONCLUSIONS
GenProb-PCSM transforms complex germline findings into a single inherited risk score for PCSM and metastatic progression. Its simplicity and preservation of gene-specific effects may facilitate clinical implementation of germline prognostic assessment in PCa.
Jun Wei, Zhu-Qing Shi, Lucy Lu et al.· The Prostate· 0 citations
IMPORTANCE
Patients diagnosed with breast cancer (BCa) are at increased risk of multiple common diseases; however, the spectrum of these diseases and the contribution of inherited genetic susceptibility remain incompletely characterized.
METHODS
We evaluated 15 common diseases and tested their associations with BCa exposure and disease-specific polygenic risk scores (PRS) in the UK Biobank (UKB; N = 254,736). Analyses were performed using cause-specific Cox proportional hazards models within a full-cohort framework, with time-updated BCa status, delayed entry at study recruitment, and age as the underlying time scale.
RESULTS
After recruitment, incident BCa was diagnosed in 11,386 women (4.47%), including 2,742 (24.08%) with metastatic BCa. Patients with BCa had an increased risk of nine diseases spanning cardiovascular, metabolic, and neuropsychiatric domains (P<0.003, Bonferroni-corrected). Elevated risks were generally observed among patients with both early staged and advanced BCa. Inherited susceptibility further stratified disease risk, with the highest risks observed among patients with BCa with elevated disease-specific PRS. For example, compared with women without BCa, the hazard ratio (HR; 95% CI) for osteoporosis was 2.33 (2.15-2.52) among women with any BCa, 2.38 (2.18-2.59) among those with non-metastatic BCa, and 2.12 (1.78-2.54) among those with metastatic BCa; the HR was 4.48 (3.99-5.02) among patients with BCa in the highest quartile of osteoporosis-specific PRS (all P<0.001). In contrast, BCa was not significantly associated with risk of coronary artery disease.
CONCLUSION
BCa and inherited genetic susceptibility jointly contribute to increased risk of multiple common diseases, supporting the integration of genetic risk stratification into survivorship care.
Annabelle Ashworth, Zhu-Qing Shi, Huy Tran et al.· JNCI Cancer Spectrum· 0 citations
Background It remains unclear whether cardiovascular-kidney-metabolic (CKM) syndrome and genetic susceptibility are associated with cancer incidence. This study aims to evaluate the associations between CKM health status, genetic susceptibility, and cancer incidence, and to develop a machine learning-based prediction model for cancer risk in patients with advanced CKM syndrome. Methods This study included 399,034 UK Biobank participants (389,289 with genetic data). CKM health was categorized into stages 0–4. Genetic susceptibility was assessed via a polygenic risk score (PRS). Associations were evaluated using Cox proportional hazard models. For participants with advanced CKM, candidate predictors were screened via the Boruta algorithm, LASSO regression, and multivariable logistic regression. Eight machine learning models were constructed and validated, and their predictive discrimination, calibration performance, and clinical practical value were further assessed. Additionally, Shapley Additive Explanations (SHAP) were adopted to interpret the internal mechanism of the optimal model. The dose–response relationship was assessed using restricted cubic splines (RCS), and mediation analysis was further performed to explore the underlying mechanism of the observed associations. Results During a median follow-up of 13.7 years, 48,247 incident cancer cases were identified. Compared to CKM stage 0, multivariable-adjusted hazard ratios (HRs) were 1.01 (95% CI: 0.91-1.12) for stage 1, 1.21 (1.10–1.33) for stage 2, and 2.17 (1.95–2.42) for advanced CKM (stage 3–4). Participants with high PRS and advanced CKM faced the highest cancer risk (HR 3.24, 95% CI 2.68–3.93). A total of 14 predictors were retained after screening. The GBM model achieved the best predictive performance (test AUC = 0.801). A web-based calculator was developed to realize individualized cancer risk prediction. RCS analyses indicated that diastolic blood pressure (Pnonlinearity<0.05), neutrophil count(Pnonlinearity<0.05), alkaline phosphatase (Pnonlinearity=0.001), and TyG-BMI (Pnonlinearity<0.05) were significantly and nonlinearly associated with cancer risk. Total bilirubin the strongest positive mediating effect, explaining 44.45% of the total association (P < 0.001). Conclusions Advanced CKM syndrome independently increases incident cancer risk, and this elevated risk is significantly amplified by high genetic susceptibility. The validated GBM model provides an interpretable tool for individualized risk estimation, which serves as a valuable complementary tool for clinical risk stratification and cancer patients with, cancer.
Xue He, J. An, Chun-Jia Yan et al.· Frontiers in Endocrinology· 0 citations
Background: Several breast cancer (BC) risk prediction models have been developed to provide personal risk assessments. Though individually validated, their performance has not been systematically evaluated across a wide range of populations or ages. Methods: We harmonized individual-level baseline questionnaire data and incident BC diagnoses from 21 cohorts from North America, Europe, and Australia participating in the Breast Cancer Risk Prediction Project. Five-year absolute risk of invasive BC was estimated for five established risk prediction models using classical risk factors only. Discrimination was evaluated by area under the curve (AUC). Calibration was assessed using average and risk-decile specific expected to observed (E/O) ratios. Performance metrics were meta-analyzed across cohorts and models. Metaregression tested associations between cohort characteristics and performance metrics. Results: This analysis included 1,595,977 women aged 20-75 years, enrolled in studies between 1976-2015, with 19,062 (1.2%) invasive BC cases ascertained within 5 years from exposure assessment. Age-adjusted AUCs were similar across models and cohorts (pooled AUCs by model: 0.57-0.58), while E/O ratios varied substantially (pooled E/O ratios by model: 0.83-1.25). Overestimation was common among predicted high-risk individuals (>3%). No appreciable differences in model performance by cohort age, birth year, race, and variable missingness emerged. Calibration improved after assigning race-specific incidence rates. Conclusion: Existing BC risk prediction models provided similar risk discrimination across multiple cohorts, although there was overestimation of risk for high-risk individuals. Performance variation across cohorts was not driven by specific characteristics, which supports development of a unified risk model for diverse populations that leverages appropriate incidence rates.
K. Brantley, T. Ahearn, E. Norton et al.· medRxiv· 0 citations
Background/Objectives: GWASs have identified more than 250 prostate cancer (PCa) predisposition loci, predominantly in European and partly Asian cohorts. The Kazakh population is markedly under-represented in international genetic studies, limiting existing risk models. This study aimed to analyze the distribution of 102 PCa-associated single-nucleotide polymorphism (SNP) genotypes and alleles and to identify reliable population-specific associations with PCa risk in Kazakh men. Methods: This retrospective case–control study included 941 Kazakh men (476 with histologically confirmed PCa and 465 cancer-free controls). Genomic DNA extracted from peripheral blood was genotyped with TaqMan® OpenArray® technology on a QuantStudio 12K Flex system. Associations were assessed by Pearson’s χ2 test and logistic regression, with genotypic and allelic odds ratios (OR) and 95% confidence intervals (CI). Two-step multiple-testing correction (Bonferroni and Benjamini–Hochberg false-discovery rate, FDR) was applied. Predictive models were built using classification and regression trees (CART) and stepwise logistic regression. Results: Of 102 SNPs, 39 showed nominally significant genotypic differences; 12 remained significant after Bonferroni correction and 2 after FDR (14 in total). Several of the corrected loci were significant at both the genotypic and allelic level. Allelic ORs ranged from 0.37 (protective rs10187424 T allele) to 4.81 (rs1545985). A parsimonious seven-SNP autosomal logistic-regression model achieved an apparent AuROC of 0.84 (10-fold cross-validated 0.82); adding age as a covariate raised discrimination to 0.87. Ten of the fourteen significant loci remained significant after age adjustment, and six of these formed a core signal robust to both age imbalance and genotyping-quality concerns. Conclusions: This first large-scale SNP-association study in Kazakh men shows allele-frequency profiles resembling East Asian rather than European populations, confirming the need for population-specific genetic risk-assessment tools. The seven-SNP model showed high discriminatory power in the training set and requires external validation before clinical application.
K. Kazbekov, Yerbol Zhapparov, N. Shanazarov et al.· Genes· 0 citations
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