ArticleiScience2022
Deconvolution of bulk tumors into distinct immune cell states predicts colorectal cancer recurrence.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Tumor-infiltrating immune cell signature score reveals prognostic biomarkers and therapeutic targets for colorectal cancer.Frontiers in immunology · 2025Article
- Plasma Cell-Free Tumor Methylome as a Biomarker in Solid Tumors: Biology and Applications.Current oncology (Toronto, Ont.) · 2024Review
- Information about immune cell proportions and tumor stage improves the prediction of recurrence in patients with colorectal cancer.Patterns (New York, N.Y.) · 2023Article
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Authors and funding
14 authors.
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No grant is acknowledged in the PubMed record.
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.
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