Small gene networks delineate immune cell states and characterize immunotherapy response in melanoma

Autor: Donagh Egan, Martina Kreileder, Myriam Nabhan, Luis F. Iglesias-Martinez, Simon J. Dovedi, Viia Valge-Archer, Amit Grover, Robert W. Wilkinson, Timothy Slidel, Claus Bendtsen, Ian P. Barrett, Donal J. Brennan, Walter Kolch, Vadim Zhernovkov
Rok vydání: 2023
Předmět:
Zdroj: Cancer Immunology Research.
ISSN: 2326-6074
2326-6066
DOI: 10.1158/2326-6066.cir-22-0563
Popis: Single-cell technologies have elucidated mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are not amenable to a clinical diagnostic setting. In contrast, bulk RNA sequencing (RNA-seq) is now routine for research and clinical applications. Our workflow uses transcription factor (TF)-directed co-expression networks (regulons) inferred from single-cell RNA-seq data to deconvolute immune functional states from bulk RNA-seq data. Regulons preserve the phenotypic variation in CD45+ immune cells from metastatic melanoma samples (n=19, discovery dataset) treated with ICIs, despite reducing dimensionality by > 100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells were associated with therapy response; and were characterized by differentially active and cell-state specific regulons. Clustering of bulk RNA-seq melanoma samples from four independent studies (n=209, validation dataset) according to regulon-inferred scores identified four groups with significantly different response outcomes (p < 0.001). An intercellular link was established between exhausted T cells and monocyte lineage cells, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. The ligand–receptor expression analysis suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative co-stimulation. Together, our results demonstrate how regulon-based characterization of cell states provide robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.
Databáze: OpenAIRE
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