TCR Coexpression Signature Predicts Immunotherapy Resistance in NSCLC

Yuntao Wang; Yi Liu; Xiaohua Li; Weiming Li; Zhihong Xue; Xiaoqian He; Weijie Xiong; Lang He; Yifeng Bai

doi:10.3389/fphar.2022.875149

TCR Coexpression Signature Predicts Immunotherapy Resistance in NSCLC

Front Pharmacol. 2022 May 4:13:875149. doi: 10.3389/fphar.2022.875149. eCollection 2022.

Authors

Yuntao Wang¹, Yi Liu², Xiaohua Li³, Weiming Li³, Zhihong Xue¹, Xiaoqian He¹, Weijie Xiong¹, Lang He¹, Yifeng Bai⁴

Affiliations

¹ Department of Oncology, The Fifth People's Hospital Affiliated to Chengdu University of Traditional Chinese Medicine the Second Clinical Medical College, Chengdu, China.
² Wenjiang District People's Hospital of Chengdu City, Chengdu, China.
³ Department of Respiratory and Critical Care Medicine, Sixth People's Hospital of Chengdu, Chengdu, China.
⁴ Department of Oncology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.

Abstract

Background: Lung cancer has the highest morbidity and mortality rate among types of malignant tumors, and as such, research into prolonging the survival time of patients is vital. The emergence of immune checkpoint inhibitors (ICIs) has greatly improved the survival of patients with non-small cell lung cancer (NSCLC), however, the lack of effective biomarkers to predict the prognosis of immunotherapy has made it difficult to maximize the benefits. T cell receptor (TCR) is one of the most important components for recognizing tumor cells, and with this study we aim to clarify the relationship between TCR coexpression and the prognosis of NSCLC patients receiving immunotherapy. Methods: Univariate COX regression, logistics regression, and KM survival analysis were used to evaluate the relationship between TCR coexpression and the prognosis of immunotherapy. Additionally, CIBERSORT, Gene Set Enrichment Analysis (GSEA), and single-sample GSEA (ssGSEA) algorithms were used to evaluate the tumor immune microenvironment (TIME) of NSCLC patients. Results: Univariate Cox regression analysis showed that the TCR coexpression signature can be used as a clinical prognostic indicator for NSCLC patients receiving immunotherapy (p = 0.0205). In addition, those in the NSCLC group with a high TCR coexpression signature had significantly improved progression-free survival (PFS) (p = 0.014). In the ICI treatment cohort (GSE35640). In addition, there was a high infiltration of CD8+T cells, activated memory CD4+T cells, and M1 macrophages in the TIME of those with a high TCR coexpression signature. The results of pathway enrichment analysis showed that patients with a high TCR coexpression signature had significantly activated signal pathways such as lymphocyte proliferation and activation, chemokine binding, and inflammatory cytokine production. Also, we found that patients with a high TCR coexpression signature had an elevated T cell inflammation gene expression profile (GEP). Conclusion: We show that the TCR coexpression signature may be useful as a new biomarker for the prognosis of NSCLC patients undergoing immunotherapy, with high signatures indicating better treatment response. Additionally, we found that patients with a high TCR coexpression signature had tumor immune microenvironments with beneficial anti-tumor characteristics.

Keywords: ICIs; NSCLC; TCR—T cell receptor; biomarker; tumor immune microenvironment.