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

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 (COnfi dent DEconvolution For All Cell Subsets), a tool deconvolving cell type???specifi c 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 fi rst demonstrate the superiority of CODEFACS versus the state-of-the-art deconvolution method CIBERSORTx. Second, analyzing The Cancer Genome Atlas , we uncover cell type???specifi c ligand???receptor interactions uniquely associated with mismatch-repair defi ciency 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???specifi c 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 immune checkpoint blockade therapy.

Automated summary

This study presents two new computational methods that deconvolve tumor gene expression profiles and predict response to immune checkpoint blockade therapy.