Regulatory T cells (Tregs) are enriched in the tumor microenvironment and play key roles in immune evasion of cancer cells. Cell surface markers specific for tumor-infiltrating Tregs (TI-Tregs) can be effectively targeted to enhance antitumor immunity and used for stratification of immunotherapy outcomes. Here, we present a systems biology approach to identify functional cell surface markers for TI-Tregs. We selected differentially expressed genes for surface proteins of TI-Tregs and compared these with other CD4+ T cells using bulk RNA-sequencing data from murine lung cancer models. Thereafter, we filtered for human orthologues with conserved expression in TI-Tregs using single-cell transcriptome data from patients with non-small cell lung cancer (NSCLC). To evaluate the functional importance of expression-based markers of TI-Tregs, we utilized network-based measure of context-associated centrality in a Treg-specific coregulatory network. We identified TNFRSF9 (also known as 4-1BB or CD137), a previously reported target for enhancing antitumor immunity, among the final candidates for TI-Treg markers with high functional importance score. We found that the low TNFRSF9 expression level in Tregs was associated with enhanced overall survival rate and response to anti-PD-1 immunotherapy in patients with NSCLC, proposing that TNFRSF9 promotes immune suppressive activity of Tregs in tumor. Collectively, these results demonstrated that integrative transcriptome and network analysis can facilitate the discovery of functional markers of tumor-specific immune cells to develop novel therapeutic targets and biomarkers for boosting cancer immunotherapy.
|Number of pages||9|
|Journal||Computational and Structural Biotechnology Journal|
|Publication status||Published - 2021 Jan|
Bibliographical noteFunding Information:
This research was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (2018R1A5A2025079, 2018M3C9A5064709, 2019M3A9B6065192 to I.L., 2019M3A9B6065221, 2018R1A2A1A05076997, 2017R1A5A1014560 to S.-J.H.) and Brain Korea 21 (BK21) FOUR program.
© 2021 The Author(s)
All Science Journal Classification (ASJC) codes
- Structural Biology
- Computer Science Applications