biostudies-literatureTsai PCGoogleU.S. Department of Health &amp; Human Services | NIH | National Institute of General Medical SciencesU.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)Harvard Medical School2102https://www.ebi.ac.uk/biostudies/studies/S-EPMC10102208biostudies-literatureUnknown14(1)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 genNature communicationsHistopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients.PMC10102208R35GM142879Google Research Scholar AwardBlavatnik Center for Computational Biomedicine AwardWu KNowak JTsai PCLee TMChan ATGiannakis MLee THLau MCUgai TVayrynen JPKuo KCKahaki SMOgino SZhao MSong MMarostica ETakashima YSu FYYu KHMeyerhardt JAChiang JHfalseHistopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients.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 gen2023-01-01T00:00:00Z2023 Apr2025-04-04T12:44:34Z2025-04-04T12:44:34ZS-EPMC101022083705539310.1038/s41467-023-37179-4