Decoupling the correlation between cytotoxic and exhausted T lymphocyte states enhances melanoma immunotherapy response prediction

Binbin Wang, Kun Wang, Di Wu, Sahil Sahni, Peng Jiang, Eytan Ruppin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Cytotoxic T lymphocyte (CTL) and terminal exhausted T lymphocyte (ETL) activities crucially influence immune checkpoint inhibitor (ICI) response. Despite this, the efficacy of ETL and CTL transcriptomic signatures for response prediction remains limited. Investigating this across the TCGA and publicly available single-cell cohorts, we find a strong positive correlation between ETL and CTL expression signatures in most cancers. We hence posited that their limited predictability arises due to their mutually canceling effects on ICI response. Thus, we developed DETACH, a computational method to identify a gene set whose expression pinpoints to a subset of melanoma patients where the CTL and ETL correlation is low. DETACH enhances CTL's prediction accuracy, outperforming existing signatures. DETACH signature genes activity also demonstrates a positive correlation with lymphocyte infiltration and the prevalence of reactive T cells in the tumor microenvironment (TME), advancing our understanding of the CTL cell state within the TME.

Original languageEnglish
Article number109926
JournaliScience
Volume27
Issue number6
DOIs
StatePublished - 21 Jun 2024
Externally publishedYes

Funding

FundersFunder number
National Institutes of Health
National Cancer Institute
MedAware Ltd.

    Keywords

    • Biocomputational method
    • Biological sciences
    • Cancer
    • Computational bioinformatics
    • Immunology

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