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

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

doi:10.1016/j.isci.2022.105392

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

iScience. 2022 Oct 17;25(11):105392. doi: 10.1016/j.isci.2022.105392. eCollection 2022 Nov 18.

Authors

Donghyo Kim¹, Jinho Kim², Juhun Lee¹, Seong Kyu Han¹, Kwanghwan Lee¹, JungHo Kong¹, Yeon Jeong Kim³, Woo Yong Lee⁴, Seong Hyeon Yun⁴, Hee Cheol Kim⁴, Hye Kyung Hong⁵, Yong Beom Cho^{4

6}, Donghyun Park⁷, Sanguk Kim^{1

8}

Affiliations

¹ Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea.
² Precision Medicine Center, Future Innovation Research Division, Seoul National University Bundang Hospital, Seongnam13620, Korea.
³ Samsung Genome Institute, Samsung Medical Center, Seoul06351, Korea.
⁴ Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul06351, Korea.
⁵ Institute for Future Medicine, Samsung Medical Center, Seoul06351, Korea.
⁶ Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul06351, Korea.
⁷ GENINUS Inc., Seoul05836, Korea.
⁸ Institute of Convergence Science, Yonsei University, Seoul120-749, Korea.

Abstract

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.

Keywords: Biocomputational method; Bioinformatics; Cancer systems biology; Health informatics; Health sciences; Immunology; Oncology; Systems biology.