Tsai, PC., Lee, TH., Kuo, KC. et al. Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients. Nat Commun 14, 2102 (2023). https://doi.org/10.1038/s41467-023-37179-4
Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients
|Author:||Tsai, Pei-Chen1,2; Lee, Tsung-Hua2; Kuo, Kun-Chi2;|
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
2Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan ROC
3Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan ROC
4Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology, Boston, MA, USA
5Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
6Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
7Cancer and Translational Medicine Research Unit, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
8Department of Medicine, Dana Farber Cancer Institute, Boston, MA, USA
9Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
10Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
11Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
12Broad Institute of MIT and Harvard, Cambridge, MA, USA
|Online Access:||PDF Full Text (PDF, 3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230906120693
|Publish Date:|| 2023-09-06
Histopathologic assessment is indispensable for diagnosing colorectal cancer (CRC). However, manual evaluation of the diseased tissues under the microscope cannot reliably inform patient prognosis or genomic variations crucial for treatment selections. To address these challenges, we develop the Multi-omics Multi-cohort Assessment (MOMA) platform, an explainable machine learning approach, to systematically identify and interpret the relationship between patients’ histologic patterns, multi-omics, and clinical profiles in three large patient cohorts (n = 1888). MOMA successfully predicts the overall survival, disease-free survival (log-rank test P-value<0.05), and copy number alterations of CRC patients. In addition, our approaches identify interpretable pathology patterns predictive of gene expression profiles, microsatellite instability status, and clinically actionable genetic alterations. We show that MOMA models are generalizable to multiple patient populations with different demographic compositions and pathology images collected from distinctive digitization methods. Our machine learning approaches provide clinically actionable predictions that could inform treatments for colorectal cancer patients.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
K-H.Y. is partly supported by the National Institute of General Medical Sciences grant R35GM142879, Google Research Scholar Award, and the Blavatnik Center for Computational Biomedicine Award. We thank the AWS Cloud Credits for Research, Microsoft Azure for Research Award, the NVIDIA GPU Grant Program, and the Extreme Science and Engineering Discovery Environment (XSEDE) at the Pittsburgh Supercomputing Center (allocation TG-BCS180016) for their computational support. The Nurses’ Health Study and the Health Professionals Follow-up Study were supported in part by U.S. National Institutes of Health (NIH) grants (P01 CA87969, UM1 CA186107, P01 CA55075, UM1 CA167552, U01 CA167552, R35 CA197735, R01 CA151993, R01 CA248857); by Cancer Research UK Grand Challenge Award (UK C10674/A27140 to the OPTIMISTICC Team). Funds for this work were provided to P.-C.T., T.-H.L., K.-C.K., F.-Y.S., J-H.C. by the National Science and Technology Council (NSTC), Taiwan (MOST 110-2634-F-006-021 and NSTC 111-2634-F-006-011) and National Center for High-performance Computing (NCHC), Taiwan.
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