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 OginoKun Hsing Yu

研究成果: Article同行評審

19 引文 斯高帕斯(Scopus)


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.

期刊Nature communications
出版狀態Published - 2023 12月

All Science Journal Classification (ASJC) codes

  • 一般化學
  • 一般生物化學,遺傳學和分子生物學
  • 一般物理與天文學


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