Deconvolving Clinically Relevant Cellular Immune Cross-talk from Bulk Gene Expression Using CODEFACS and LIRICS Stratifies Patients with Melanoma to Anti–PD-1 Therapy

Abstract

The tumor microenvironment (TME) is a complex mixture of cell types whose interactions affect tumor growth and clinical outcome. To discover such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a tool deconvolving cell type–specific gene expression in each sample from bulk expression, and LIRICS (Ligand–Receptor Interactions between Cell Subsets), a statistical framework prioritizing clinically relevant ligand–receptor interactions between cell types from the deconvolved data. We first demonstrate the superiority of CODEFACS versus the state-of-the-art deconvolution method CIBERSORTx. Second, analyzing The Cancer Genome Atlas, we uncover cell type–specific ligand–receptor interactions uniquely associated with mismatch-repair deficiency across different cancer types, providing additional insights into their enhanced sensitivity to anti–programmed cell death protein 1 (PD-1) therapy compared with other tumors with high neoantigen burden. Finally, we identify a subset of cell type–specific ligand–receptor interactions in the melanoma TME that stratify survival of patients receiving anti–PD-1 therapy better than some recently published bulk transcriptomics-based methods. SIGNIFICANCE: This work presents two new computational methods that can deconvolve a large collection of bulk tumor gene expression profiles into their respective cell type–specific gene expression profiles and identify cell type–specific ligand–receptor interactions predictive of response to immunecheckpoint blockade therapy.