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A gene expression signature of TREM2hi macrophages and γδ T cells predicts immunotherapy response
Donghai Xiong1, Yian Wang1, Ming You2
1Center for Disease Prevention Research and Department of Pharmacology and Toxicology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
Abstract:
Identifying factors underlying resistance to immune checkpoint therapy (ICT) is still challenging. Most cancer patients do not respond to ICT and the availability of the predictive biomarkers is limited. Here, we re-analyze a publicly available single-cell RNA sequencing (scRNA-seq) dataset of melanoma samples of patients subjected to ICT and identify a subset of macrophages overexpressing TREM2 and a subset of gammadelta T cells that are both overrepresented in the non-responding tumors. In addition, the percentage of a B cell subset is significantly lower in the non-responders. The presence of these immune cell subtypes is corroborated in other publicly available scRNA-seq datasets. The analyses of bulk RNA-seq datasets of the melanoma samples identify and validate a signature - ImmuneCells.Sig - enriched with the genes characteristic of the above immune cell subsets to predict response to immunotherapy. ImmuneCells.Sig could represent a valuable tool for clinical decision making in patients receiving immunotherapy.
Insights
Predicting response to immune checkpoint therapy (ICT) is difficult. This study identifies specific immune cell subsets in melanoma tumors that can predict patient response to ICT, offering a potential new biomarker.
Area of Science:
- Immunology
- Oncology
- Genomics
Background:
- Identifying factors for resistance to immune checkpoint therapy (ICT) remains a significant challenge in oncology.
- Most cancer patients do not respond to ICT, and predictive biomarkers are scarce.
Purpose of the Study:
- To re-analyze single-cell RNA sequencing (scRNA-seq) data from melanoma patients undergoing ICT to identify immune cell subsets associated with treatment response.
- To validate these findings in independent datasets and develop a gene signature for predicting immunotherapy response.
Main Methods:
- Re-analysis of publicly available scRNA-seq datasets from melanoma patients treated with ICT.
- Identification and quantification of specific immune cell populations (macrophages, gammadelta T cells, B cells).
- Analysis of bulk RNA-seq datasets to develop and validate a predictive gene signature (ImmuneCells.Sig).
Main Results:
- A subset of TREM2-overexpressing macrophages and gammadelta T cells were overrepresented in non-responding tumors.
- A specific B cell subset was found at significantly lower percentages in non-responders.
- The developed ImmuneCells.Sig gene signature accurately predicted immunotherapy response in validation datasets.
Conclusions:
- Specific immune cell subtypes, including TREM2+ macrophages and gammadelta T cells, are associated with non-response to ICT in melanoma.
- The ImmuneCells.Sig gene signature, derived from these immune cell markers, shows promise as a predictive biomarker for immunotherapy response.
- This signature could aid clinical decision-making for patients receiving immunotherapy.
