ArticleQuantitative biology (Beijing, China)2026
Cancer heterogeneity explored through immune cell deconvolution of the tumor microenvironment.
Article in Quantitative biology (Beijing, China), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Cancer is a highly heterogeneous disease, characterized by significant variability across multiple dimensions. This diversity has been extensively studied from various perspectives. In this study, we aim to examine cancer heterogeneity through the lens of immune cell composition within the tumor microenvironment. Transcriptome profiles of 11,274 subjects from 33 cancer types were deconvoluted to infer the composition of 22 immune cell types. A deep learning-based model was adapted to model a binary outcome for cancer type, tumor versus normal. The Shannon index was used to represent immune cell diversity. Cox proportional hazards regression was used to evaluate the prognostic values of immune cells. Tumor and normal tissues showed significantly different immune cell compositions in 183 of 352 comparisons, with macrophage M0 levels consistently elevated in most tumors except lung adenocarcinoma. ResNet models using immune cell data achieved strong performance in distinguishing tumor versus normal samples (average F1 score = 0.64 when combined with clinical features). Four immune features were significantly associated with disease-specific survival across three cancer types: macrophage M0 and macrophage M2 in bladder urothelial carcinoma, macrophage M0 in kidney renal clear cell carcinoma, and the Treg/CD8
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