AUTHOR=Yan Xin , Zhang Xiao , Wu Hua-Hui , Wu Shao-Jie , Tang Xiao-Yu , Liu Tong-Zu , Li Sheng 

TITLE=Novel T-cell signature based on cell pair algorithm predicts survival and immunotherapy response for patients with bladder urothelial carcinoma

JOURNAL=Frontiers in Immunology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.994594

DOI=10.3389/fimmu.2022.994594

ISSN=1664-3224

ABSTRACT=Background: T cell-T cell interactions play important roles in the regulation of T cell’s cytotoxic function, further impacting the anti-tumor efficacy of immunotherapy. There is a lack of comprehensive study of T cell types in bladder urothelial carcinoma (BLCA) and T cell related signature predicting prognosis and monitoring immunotherapy efficacy.
Methods: More than 3,400 BLCA patients were collected and used in the present study. ssGSEA algorithm was applied to calculate the infiltration level of 19 T cell types. Cell pair algorithm was applied to construct a T cell related prognostic index (TCRPI). Survival analysis was performed to measure the survival difference across TCRPI-risk groups. Spearman correlation analysis was used to the relevance assessment. Wilcox test was used to measure the expression level difference.
Results: 19 T cell types were collected, 171 T cell pairs (TCPs) were established, 26 of which were picked out by least absolute shrinkage and selection operator (LASSO) analysis. Based on these TCPs, the TCRPI was constructed and validated to play crucial roles in survival stratification and dynamic monitoring of immunotherapy effect. We also explored out several candidate drugs targeting TCRPI. A composite TCRPI and clinical prognostic index (CTCPI) was then constructed, which achieved more accuracy estimation of BLCA’ survival, being a better choice for prognosis prediction in BLCA.
Conclusions: All in all, we constructed and validated TCRPI based on cell pair algorithm in this study, which might put forward some new insights to increase the survival estimation and clinical response to immune therapy for individual BLCA patient and contribute on the personalized precision immunotherapy strategy of BLCA.