Two layers of information are critical for understanding tumor composition: (1) the proportion of each cell type and (2) the levels of gene expression in each cell type. These studies motivate the direct measurement of cell types within tissues. Nonmalignant cells can differ markedly among patients and tumor types 6, and certain nonmalignant cell populations are used as clinical biomarkers 7 and therapeutic targets 8. Numerous studies over the past two decades have revealed interactions between cells in the TME that promote diverse functions, including angiogenesis 3, metastasis 4 and immunosuppression 5. A quintessential example is that between malignant cells and diverse nonmalignant cell types within the tumor microenvironment (TME) 1, 2. Our work introduces a new lens to accurately infer cellular composition and expression in large cohorts of bulk RNA-seq data.Ĭell–cell interactions are highly complex and can strongly impact cell behavior in biological contexts, often with medical ramifications. Finally, we identify genes whose expression in malignant cells correlates with macrophage infiltration, T cells, fibroblasts and endothelial cells across multiple tumor types. We refine current cancer subtypes using gene expression annotation after exclusion of confounding nonmalignant cells. We conduct integrative analyses in primary glioblastoma, head and neck squamous cell carcinoma and skin cutaneous melanoma to correlate cell type composition with clinical outcomes across tumor types, and explore spatial heterogeneity in malignant and nonmalignant cell states. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq as prior information. Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology.
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