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bioRxiv · 10.1101/2021.01.20.427515

Deconvolving clinically relevant cellular immune crosstalk from bulk gene expression using CODEFACS and LIRICS

Wang, K.·Patkar, S.·Lee, J. S.·Gertz, E. M.·Robinson, W.·Schischlik, F.·Crawford, D.·Schaffer, A. A.·Ruppin, E.
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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 TCGA, we uncover cell-type-specific interactions of mismatch-repair-deficient tumors that are associated with their higher anti-PD1 response rates, including specific T-cell co-stimulating interactions that enhance immunotherapy response independently of the tumors mutation burden levels. Finally, we identify a subset of ligand-receptor interactions in the melanoma TME that predict patient response to anti-PD1 therapy better than recently published transcriptomics-based methods.

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Wang, K., Patkar, S., Lee, J. S., Gertz, E. M., Robinson, W., Schischlik, F., Crawford, D., Schaffer, A. A., Ruppin, E.. 2021-01-21. Deconvolving clinically relevant cellular immune crosstalk from bulk gene expression using CODEFACS and LIRICS. https://doi.org/10.1101/2021.01.20.427515

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