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

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Title: Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data.
Authors: Zhao, Melissa, Lau, Mai Chan, Haruki, Koichiro, Väyrynen, Juha P., Gurjao, Carino, Väyrynen, Sara A., Dias Costa, Andressa, 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
Source: NPJ Precision Oncology; 6/10/2023, Vol. 7 Issue 1, p1-13, 13p
Subject Terms: COLORECTAL cancer, CANCER-related mortality, PREDICTION models, STATISTICAL learning, CLINICAL pathology
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. [ABSTRACT FROM AUTHOR]
Copyright of NPJ Precision Oncology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 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 &lt; 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
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  Data: &lt;i&gt;Copyright of NPJ Precision Oncology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder&#39;s express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.1038/s41698-023-00406-8
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