Dataset Information

Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data.

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.

SUBMITTER: Zhao M

PROVIDER: S-EPMC10257677 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data.

Zhao Melissa M Lau Mai Chan MC Haruki Koichiro K Väyrynen Juha P JP Gurjao Carino C Väyrynen Sara A SA Dias Costa Andressa A Borowsky Jennifer J Fujiyoshi Kenji K Arima Kota K Hamada Tsuyoshi T Lennerz Jochen K JK Fuchs Charles S CS Nishihara Reiko R Chan Andrew T AT Ng Kimmie K Zhang Xuehong X Meyerhardt Jeffrey A JA Song Mingyang M Wang Molin M Giannakis Marios M Nowak Jonathan A JA Yu Kun-Hsing KH Ugai Tomotaka T Ogino Shuji S

NPJ precision oncology 20230610 1

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.-w ...[more]

PMID: 37301916

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Project description:Using whole genome sequence data might improve genomic prediction accuracy, when compared with high-density SNP arrays, and could lead to identification of casual mutations affecting complex traits. For some traits, the most accurate genomic predictions are achieved with non-linear Bayesian methods. However, as the number of variants and the size of the reference population increase, the computational time required to implement these Bayesian methods (typically with Monte Carlo Markov Chain sampling) becomes unfeasibly long.Here, we applied a new method, HyB_BR (for Hybrid BayesR), which implements a mixture model of normal distributions and hybridizes an Expectation-Maximization (EM) algorithm followed by Markov Chain Monte Carlo (MCMC) sampling, to genomic prediction in a large dairy cattle population with imputed whole genome sequence data. The imputed whole genome sequence data included 994,019 variant genotypes of 16,214 Holstein and Jersey bulls and cows. Traits included fat yield, milk volume, protein kg, fat% and protein% in milk, as well as fertility and heat tolerance. HyB_BR achieved genomic prediction accuracies as high as the full MCMC implementation of BayesR, both for predicting a validation set of Holstein and Jersey bulls (multi-breed prediction) and a validation set of Australian Red bulls (across-breed prediction). HyB_BR had a ten fold reduction in compute time, compared with the MCMC implementation of BayesR (48 hours versus 594 hours). We also demonstrate that in many cases HyB_BR identified sequence variants with a high posterior probability of affecting the milk production or fertility traits that were similar to those identified in BayesR. For heat tolerance, both HyB_BR and BayesR found variants in or close to promising candidate genes associated with this trait and not detected by previous studies.The results demonstrate that HyB_BR is a feasible method for simultaneous genomic prediction and QTL mapping with whole genome sequence in large reference populations.

Dataset Information

Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data.

Publications

Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data.

Similar Datasets

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