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Small Gene Networks Delineate Immune Cell States and Characterize Immunotherapy Response in Melanoma
Donagh Egan1, Martina Kreileder1, Myriam Nabhan1
1Precision Oncology Ireland, Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Republic of Ireland.
Insights
This study introduces a novel workflow using transcription factor networks to analyze bulk RNA sequencing data, identifying immune cell states that predict response to immune checkpoint inhibitors in melanoma patients. This method offers a clinically applicable approach to deconvolve complex immune profiles.
Area of Science:
- Immunology
- Computational Biology
- Oncology
Background:
- Single-cell technologies reveal immune checkpoint inhibitor (ICI) response mechanisms but are not clinically feasible.
- Bulk RNA sequencing (RNA-seq) is a routine clinical and research tool.
- Developing methods to extract granular immune insights from bulk RNA-seq is crucial for clinical applications.
Purpose of the Study:
- To develop a workflow that deconvolutes immune functional states from bulk RNA-seq data using transcription factor (TF)-directed coexpression networks (regulons) inferred from single-cell RNA-seq.
- To identify immune cell states associated with ICI therapy response in metastatic melanoma.
- To validate the clinical applicability of the regulon-based approach for predicting ICI responders.
Main Methods:
- Inferred transcription factor (TF)-directed coexpression networks (regulons) from single-cell RNA-seq data of metastatic melanoma samples.
- Applied regulons to deconvolute immune functional states from bulk RNA-seq data.
- Validated regulon-inferred cell state scores by clustering bulk RNA-seq data from independent melanoma studies.
Main Results:
- Regulons preserved phenotypic variation in immune cells, enabling >100-fold dimensionality reduction.
- Four immune cell states (exhausted T cells, monocyte lineage cells, memory T cells, B cells) were associated with therapy response.
- Clustering of validation datasets identified four groups with significantly different ICI response outcomes (P < 0.001).
- An intercellular link was found between exhausted T cells and monocyte lineage cells, with monocyte lineage cells potentially driving T cell exhaustion.
Conclusions:
- Regulon-based characterization of cell states provides robust, functionally informative markers for bulk RNA-seq data.
- This approach can deconvolve bulk RNA-seq to identify immune checkpoint inhibitor (ICI) responders.
- The findings suggest potential therapeutic strategies targeting monocyte lineage cells to enhance ICI efficacy.
Abstract:
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 coexpression 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 costimulation. 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.
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