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Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data |
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| Author: | Zhao, Melissa1; Lau, Mai Chan1; Haruki, Koichiro1; |
| Organizations: |
1Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA 2Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA 3Cancer and Translational Medicine Research Unit, Medical Research Center Oulu, Oulu University Hospital, and University of Oulu, Oulu, Finland
4Broad Institute of MIT and Harvard, Cambridge, MA, USA
5Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA 6Genentech/Roche, South San Francisco, CA, USA 7Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA 8Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA 9Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA 10Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA 11Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA 12Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA 13Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA 14Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA 15Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA 16Cancer Immunology and Cancer Epidemiology Programs, Dana-Farber Harvard Cancer Center, Boston, MA, USA |
| Format: | article |
| Version: | published version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 4.3 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2023081897876 |
| Language: | English |
| Published: |
Springer Nature,
2023
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| Publish Date: | 2023-08-18 |
| Description: |
AbstractRoutine 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. see all
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| Series: |
npj precision oncology |
| ISSN: | 2397-768X |
| ISSN-E: | 2397-768X |
| ISSN-L: | 2397-768X |
| Volume: | 7 |
| Issue: | 1 |
| Article number: | 57 |
| DOI: | 10.1038/s41698-023-00406-8 |
| OADOI: | https://oadoi.org/10.1038/s41698-023-00406-8 |
| Type of Publication: |
A1 Journal article – refereed |
| Field of Science: |
3122 Cancers |
| Subjects: | |
| Funding: |
This work was supported by U.S. National Institutes of Health (NIH) grants (P01 CA87969; UM1 CA186107; P01 CA55075; UM1 CA167552; U01 CA167552; R01 CA137178 to A.T.C.; K24 DK098311 to A.T.C.; R35 CA197735 to S.O.; R01 CA151993 to S.O.; R01 CA248857 to S.O.; K07 CA188126 to X.Z.; R21 CA252962 to X.Z.; R37 CA225655 to J.K.L.; and R35 GM142879 to K.-H.Y.); by Cancer Research UK Grand Challenge Award (UK C10674/A27140 to K.N., M.G., and S.O.); by Nodal Award (2016–02) from the Dana-Farber Harvard Cancer Center (to S.O.); by the Stand Up to Cancer Colorectal Cancer Dream Team Translational Research Grant (SU2C-AACR-DT22–17 to C.S.F. and M.G.), administered by the American Association for Cancer Research, a scientific partner of SU2C; and by grants from the Project P Fund, the Crush Colon Cancer Fund, The Friends of the Dana-Farber Cancer Institute, Bennett Family Fund, and the Entertainment Industry Foundation through National Colorectal Cancer Research Alliance and SU2C. J.B. was supported by a grant from the Australia Awards-Endeavour Scholarships and Fellowships Program. K.H. was supported by fellowship grants from the Uehara Memorial Foundation and the Mitsukoshi Health and Welfare Foundation. K.F. was supported by a fellowship grant from the Uehara Memorial Foundation. K.A. was supported by a grant from Overseas Research Fellowship (JP2018–60083) from the Japan Society for the Promotion of Science. T.U. was supported by grants from Prevent Cancer Foundation and Harvey V. Fineberg Fellowship in Cancer Prevention. S.A.V. was supported by the Finnish Cultural Foundation and Orion Research Foundation. M.G. is supported by an ASCO Conquer Cancer Foundation Career Development Award and a High Pointe Investigatorship in Gastrointestinal Oncology. A.T.C. is a Stuart and Suzanne Steele MGH Research Scholar. J.A.M. research is supported by the Douglas Gray Woodruff Chair Fund, the Guo Shu Shi Fund, Anonymous Family Fund for Innovations in Colorectal Cancer, P fund and the George Stone Family Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
| Copyright information: |
© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
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https://creativecommons.org/licenses/by/4.0/ |