Deconvolution of bulk tumors into distinct immune cell states predicts colorectal cancer recurrence

• DOI: 10.1016/j.isci.2022.105392
• Publication Date: 2022-10-17T18:25:09Z
• Authors: Donghyo Kim; Jinho Kim; Juhun Lee; Seong Kyu Han; Kwanghwan Lee; JungHo Kong; Yeon Jeong Kim; Woo Yong Lee; Seong Hyeon Yun; Hee Cheol Kim; Hye Kyung Hong; Yong Beom Cho; Donghyun Park; Sanguk Kim
• Keywords: Health informatics; 0301 basic medicine; 03 medical and health sciences; Oncology; Biocomputational method; Bioinformatics; Science; Immunology; Q; Health sciences; Article; 3. Good health

Predicting colorectal cancer recurrence after tumor resection is crucial because it promotes the administration of proper subsequent treatment or management to improve the clinical outcomes of patients. Several clinical or molecular factors, including tumor stage, metastasis, and microsatellite instability status, have been used to assess the risk of recurrence, although their predictive ability is limited. Here, we predicted colorectal cancer recurrence based on cellular deconvolution of bulk tumors into two distinct immune cell states: cancer-associated (tumor-infiltrating immune cell-like) and noncancer-associated (peripheral blood mononuclear cell-like). Prediction model performed significantly better when immune cells were deconvoluted into two states rather than a single state, suggesting that the difference in cancer recurrence was better explained by distinct states of immune cells. It indicates the importance of distinguishing immune cell states using cellular deconvolution to improve the prediction of colorectal cancer recurrence.