The RAS family of small GTPases represents the most commonly activated oncogenes in human cancers. To better understand the prevalence of somatic RAS mutations and the compendium of genes that are coaltered in RAS-mutant tumors, we analyzed targeted next-generation sequencing data of 607,863 mutations from 66,372 tumors in 51 cancer types in the AACR Project GENIE Registry. Bayesian hierarchical models were implemented to estimate the cancer-specific prevalence of RAS and non-RAS somatic mutations, to evaluate co-occurrence and mutual exclusivity, and to model the effects of tumor mutation burden and mutational signatures on comutation patterns. These analyses revealed differential RAS prevalence and comutations with non-RAS genes in a cancer lineage-dependent and context-dependent manner, with differences across age, sex, and ethnic groups. Allele-specific RAS co-mutational patterns included an enrichment in NTRK3 and chromatin-regulating gene mutations in KRAS G12C-mutant non–small cell lung cancer. Integrated multiomic analyses of 10,217 tumors from The Cancer Genome Atlas (TCGA) revealed distinct genotype-driven gene expression programs pointing to differential recruitment of cancer hallmarks as well as phenotypic differences and immune surveillance states in the tumor microenvironment of RAS-mutant tumors. The distinct genomic tracks discovered in RAS-mutant tumors reflected differential clinical outcomes in TCGA cohort and in an independent cohort of patients with KRAS G12C-mutant non–small cell lung cancer that received immunotherapy-containing regimens. The RAS genetic architecture points to cancer lineage–specific therapeutic vulnerabilities that can be leveraged for rationally combining RAS-mutant allele-directed therapies with targeted therapies and immunotherapy. Significance: The complex genomic landscape of RAS-mutant tumors is reflective of selection processes in a cancer lineage–specific and context-dependent manner, highlighting differential therapeutic vulnerabilities that can be clinically translated.

Background: Lung cancer is a leading cause of cancer-related mortality worldwide, often diagnosed in advanced stages, making early detection critical. This study aimed to evaluate the performance of various machine learning models in predicting lung cancer risk based on epidemiological questionnaires, comparing them with traditional logistic regression models. Methods: A retrospective case–control study was conducted using data from 5421 lung cancer cases and 10,831 matched controls. The dataset included a wide range of demographic, clinical, and behavioral risk factors from epidemiological questionnaires. We developed and compared multiple machine learning algorithms, including LightGBM and stacking ensemble models, alongside logistic regression for predicting lung cancer risk. Model performance was evaluated using accuracy, area under the curve (AUC), and recall. Results: The stacking model outperformed traditional logistic regression, achieving an AUC of 0.887 (0.870–0.903) compared to 0.858 (0.839–0.878) for logistic regression. LightGBM also performed well, with an AUC of 0.884 (0.867–0.901). The stacking model achieved an accuracy of 81.2%, with a recall of 0.755, higher than the logistic regression model’s accuracy of 79.4%. Compared to classical lung cancer prediction models (LLP and PLCO), the logistic regression and ML models improved AUC by 12% to 27%. Conclusions: Integrating machine learning models into lung cancer screening programs can significantly enhance early detection efforts. Machine learning approaches, such as LightGBM and stacking, offer improved accuracy and predictive power over traditional models. However, efforts to enhance model interpretability through explainable AI techniques are necessary for broader clinical adoption.

The association of dietary factors with urinary bladder cancer prognosis has scarcely been investigated, and results of studies conducted to date are inconsistent. We investigated whether empirically derived dietary patterns are associated with risks of recurrence and progression in non‐muscle‐invasive bladder cancer (NMIBC) patients. Data from 595 newly diagnosed NMIBC patients from an ongoing prospective cohort study were used to derive dietary patterns using exploratory factor analysis. Factor scores were calculated and then categorized in sex‐specific tertiles. Multivariable‐adjusted proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals for the associations between tertiles of adherence to the dietary patterns and risks of recurrence and progression. We identified four dietary patterns: “fruits and vegetables,” “Western,” “low‐fat,” and “Tex‐Mex.” Patients in the highest tertile of adherence to the Western pattern experienced a 1.48 times higher risk of recurrence (95% CI 1.06–2.06) compared to patients in the lowest tertile. No statistically significant associations of a Western diet with risk of progression or of the other dietary patterns with risk of recurrence and progression were found. Overall, we found that adherence to a Western diet was associated with a higher risk of recurrence but further studies are needed to confirm our findings.

Background: We assessed the associations of visceral adiposity indexes such as Chinese Visceral Adiposity Index (CVAI), Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), waist circumference (WC), and waist-hip ratio (WHR) with overall and specific cancer incidence in a Chinese population. Methods: 332,297 individuals from the Taiwan MJ cohort (1996–2007) were included. We utilized multivariable Cox proportional hazards models to examine associations of baseline visceral adiposity indexes and cancer incidences. Sex-specific CVAI, VAI, and LAP were calculated, incorporating WC and triglycerides levels. CVAI and VAI also included body mass index and high-density lipoprotein, with CVAI further incorporating age. Results: Higher CVAI was consistently associated with higher overall cancer incidence, with HRs of 1.45 (95% CI: 1.2–1.76) and 2.03 (95% CI: 1.52–2.72) for males and females, respectively, comparing the fifth quintile to the first. The HRs for WC were 1.27 (95% CI: 1.08–1.49) and 1.19 (95% CI: 1.01–1.40) for males and females, WHR was significantly associated with cancer risk in males (HR:1.28; 95% CI: 1.13–1.45), and LAP was significantly associated with cancer risk in females (HR: 1.25; 95% CI: 1.04–1.5). VAI was not associated with overall cancer incidence. Discussion: CVAI is a superior clinical biomarker for predicting cancer incidence in the Chinese population compared to traditional visceral obesity indices.