Cancer heterogeneity explored through immune cell deconvolution of the tumor microenvironment

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⁺ T cell ratio in skin cutaneous melanoma. Although immune cell diversity varied across cancers, it was not broadly predictive of prognosis, highlighting the cancer type-specific nature of immune remodeling. Our results demonstrated cancer heterogeneity through the analysis of immune cell composition and diversity using deep learning models and prognostic analysis. The significant findings revealed varying patterns across different cancer types, indicating that no unifying standard can be established across all cancers.