PT  - JOURNAL ARTICLE
AU  - Tinyi Chu
AU  - Charles G. Danko
TI  - Bayesian Inference of Cell Composition and Gene Expression Reveals Tumor-Microenvironment Interactions
AID  - 10.1101/2020.01.07.897900
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.01.07.897900
4099  - http://biorxiv.org/content/early/2020/01/08/2020.01.07.897900.short
4100  - http://biorxiv.org/content/early/2020/01/08/2020.01.07.897900.full
AB  - Understanding the dynamic interactions between malignant cells and the tumor stroma is a major goal of cancer research. Here we developed a Bayesian model that jointly infers both cellular composition and gene expression in each cell type, including heterogeneous malignant cells, from bulk RNA-seq using scRNA-seq as prior information. We conducted an integrative analysis of 85 single-cell and 1,412 bulk RNA-seq datasets in primary human glioblastoma, head and neck squamous cell carcinoma, and melanoma. We identified cell types correlated with clinical outcomes and explored regional heterogeneity in tumor state and stromal composition. We redefined common molecular subtypes using gene expression in malignant cells, after excluding confounding non-malignant cell types. Finally, we identified genes whose expression in malignant cells correlated with infiltration of macrophages, T-cells, fibroblasts, and endothelial cells across multiple tumor types. Our work provides a new lens that we used to measure cellular composition and expression in a statistically powered cohort of three primary human malignancies.