Open AccessAbstract 3179: Deconvolving bulk gene expression to chart the immune activity and crosstalk in the tumor microenvironment
Author(s) -
Kun Wang,
Sushant Patkar,
JooSang Lee,
E. Michael Gertz,
Welles Robinson,
Fiorella Schischlik,
Alejandro A. Schäffer,
Eytan Ruppin
Publication year - 2021
Publication title -
cancer research
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 1.055
H-Index - 84
eISSN - 1538-7445
pISSN - 0008-5472
DOI - 10.1158/1538-7445.am2021-3179
Subject(s) - tumor microenvironment , cell type , computational biology , immune checkpoint , transcriptome , gene expression , biology , cell , immune system , gene , crosstalk , deconvolution , cancer research , computer science , immunotherapy , immunology , genetics , algorithm , physics , optics
The tumor microenvironment is a complex mixture of cell-types that interact with each other to affect tumor growth and clinical outcomes. To accelerate the discovery of such interactions, we develop CODEFACS (Confident Deconvolution For All Cell Subsets), a deconvolution tool that infers cell-type-specific gene expression in each sample from bulk expression measurements. We additionally developed LIRICS (Ligand Receptor Interactions between Cell Subsets), a pipeline that analyzes the deconvolved gene expression to further identify clinically relevant ligand-receptor interactions between cell-types. Using 15 benchmark datasets, we first demonstrate that CODEFACS substantially improves our ability to reconstruct cell-type-specific transcriptomes from individual bulk samples compared to the state-of-the-art method, CIBERSORTx. We next analyze the TCGA using CODEFACS and LIRICS to uncover tumor-immunological cell-type-specific interactions that are differentially activate in mismatch repair deficient tumors. This analysis shows that T-cell co-stimulating interactions are associated with higher survival rates in these tumors independent of their mutation burden levels. Finally, using machine learning, we identify key cell-type-specific ligand-receptor interactions that predict patient response to immune checkpoint blockade therapy in melanoma. Taken together, we present and validate a computational framework for analyzing large bulk gene expression datasets at a cell-type specific resolution that has many potential future applications, complementing single cell transcriptomics in a cost-effective manner. Citation Format: Kun Wang, Sushant Patkar, JooSang Lee, E. Michael Gertz, Welles Robinson, Fiorella Schischlik, Alejandro Schäffer, Eytan Ruppin. Deconvolving bulk gene expression to chart the immune activity and crosstalk in the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3179.