Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients

Pei-Chen Tsai, Tsung-Hua Lee, Kun-Chi Kuo, Fang-Yi Su, Tsung-Lu Michael Lee, Eliana Marostica, Tomotaka Ugai, Melissa Zhao, Mai Chan Lau, Juha P. Väyrynen, Marios Giannakis, Yasutoshi Takashima, Seyed Mousavi Kahaki, Kana Wu, Mingyang Song, Jeffrey A. Meyerhardt, Andrew T. Chan, Jung-Hsien Chiang (), Jonathan Nowak, Shuji Ogino and Kun-Hsing Yu ()
Additional contact information
Pei-Chen Tsai: Harvard Medical School
Tsung-Hua Lee: National Cheng Kung University
Kun-Chi Kuo: National Cheng Kung University
Fang-Yi Su: National Cheng Kung University
Tsung-Lu Michael Lee: Southern Taiwan University of Science and Technology
Eliana Marostica: Harvard Medical School
Tomotaka Ugai: Harvard T.H. Chan School of Public Health
Melissa Zhao: Brigham and Women’s Hospital
Mai Chan Lau: Brigham and Women’s Hospital
Juha P. Väyrynen: Oulu University Hospital and University of Oulu
Marios Giannakis: Dana Farber Cancer Institute
Yasutoshi Takashima: Brigham and Women’s Hospital
Seyed Mousavi Kahaki: Brigham and Women’s Hospital
Kana Wu: Harvard T.H. Chan School of Public Health
Mingyang Song: Harvard T.H. Chan School of Public Health
Jeffrey A. Meyerhardt: Dana Farber Cancer Institute
Andrew T. Chan: Massachusetts General Hospital
Jung-Hsien Chiang: National Cheng Kung University
Jonathan Nowak: Brigham and Women’s Hospital
Shuji Ogino: Harvard T.H. Chan School of Public Health
Kun-Hsing Yu: Harvard Medical School

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract 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

Date: 2023
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DOI: 10.1038/s41467-023-37179-4

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