Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data
Zhao, Melissa; Lau, Mai Chan; Haruki, Koichiro; Väyrynen, Juha P.; Gurjao, Carino; Väyrynen, Sara A.; Costa, Andressa Dias; Borowsky, Jennifer; Fujiyoshi, Kenji; Arima, Kota; Hamada, Tsuyoshi; Lennerz, Jochen K.; Fuchs, Charles S.; Nishihara, Reiko; Chan, Andrew T.; Ng, Kimmie; Zhang, Xuehong; Meyerhardt, Jeffrey A.; Song, Mingyang; Wang, Molin; Giannakis, Marios; Nowak, Jonathan A.; Yu, Kun-Hsing; Ugai, Tomotaka; Ogino, Shuji (2023-06-10)
Zhao, M., Lau, M.C., Haruki, K. et al. Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data. npj Precis. Onc. 7, 57 (2023). https://doi.org/10.1038/s41698-023-00406-8
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https://urn.fi/URN:NBN:fi-fe2023081897876
Tiivistelmä
Abstract
Routine tumor-node-metastasis (TNM) staging of colorectal cancer is imperfect in predicting survival due to tumor pathobiological heterogeneity and imprecise assessment of tumor spread. We leveraged Bayesian additive regression trees (BART), a statistical learning technique, to comprehensively analyze patient-specific tumor characteristics for the improvement of prognostic prediction. Of 75 clinicopathologic, immune, microbial, and genomic variables in 815 stage II–III patients within two U.S.-wide prospective cohort studies, the BART risk model identified seven stable survival predictors. Risk stratifications (low risk, intermediate risk, and high risk) based on model-predicted survival were statistically significant (hazard ratios 0.19–0.45, vs. higher risk; P < 0.0001) and could be externally validated using The Cancer Genome Atlas (TCGA) data (P = 0.0004). BART demonstrated model flexibility, interpretability, and comparable or superior performance to other machine-learning models. Integrated bioinformatic analyses using BART with tumor-specific factors can robustly stratify colorectal cancer patients into prognostic groups and be readily applied to clinical oncology practice.
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