Dataset Information

TIM3 expression on tumor cells predicts response to anti-PD-1 therapy for renal cancer.

ABSTRACT: This study aimed to identify novel prognostic biomarker for advanced renal cell carcinoma (RCC) patients treated with anti-PD-1 therapy, using quantitative multi-immunofluorescence (IF) analysis of tumor immunity. Twenty-five consecutive patients who had metastatic or unresectable RCC treated with anti-PD-1 therapy were studied. The patients were divided into a responder group (n = 12) and a non-responder group (n = 13). Quantitative multi-IF staining was performed on biopsy or surgical kidney samples using a panel of antibodies. Sections were scanned using a Mantra microscope, and the images were analyzed with inForm™ software. Responders had significantly higher rate of TIM3-positive tumor (100% versus 53.9%, p < 0.01) than non-responders. Multi-IF analysis showed that TIM3 expression on tumor cells was most strongly related to response to anti-PD-1 therapy, while some of the known immune-related prognostic factors in RCC (CD45RO, FOXP3, VEGF, PD-L1, PD-L2, CD163) had no significant association. Patients with TIM3-positive tumor showed significantly longer overall survival (not reached median time versus 6.0 months, p < 0.01) and progression-free survival (18.9 versus 1.1 months, p < 0.01) than those with TIM3-negative tumor. Immunohistochemistry study using samples obtained after anti-PD-1 therapy showed infiltration of CD163 macrophages and release of HMGB1, a ligand of TIM3, in necrotic tumor area. In conclusion, our study found clinical correlation between TIM3 expression on tumor cells and response to anti-PD-1 therapy. Further studies are warranted to verify whether TIM3 expression on tumor cells before systemic therapy predicts the efficacy of anti-PD-1 therapy for RCC in the clinical setting.

SUBMITTER: Kato R

PROVIDER: S-EPMC7586238 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:A large proportion of anti-tumor immunity research is focused on major histocompatibility complex class I (MHC-I) molecules and CD8+ T cells. Despite mounting evidence has shown that CD4+ T cells play a major role in anti-tumor immunity, the role of the MHC-II molecules in tumor immunotherapy has not been thoroughly researched and reported. In this study, we defined a MHC-II signature for the first time by calculating the enrichment score of MHC-II protein binding pathway with a single sample gene set enrichment analysis (ssGSEA) algorithm. To evaluate and validate the predictive value of the MHC class II (MHC-II) signature, we collected the transcriptome, mutation data and matched clinical data of bladder cancer patients from IMvigor210, The Cancer Genome Atlas (TCGA) databases and Gene Expression Omnibus (GEO) databases. Comprehensive analyses of immunome, transcriptome, metabolome, genome and drugome were performed in order to determine the association of MHC-II signature and tumor immunotherapy. We identified that MHC-II signature is an independent and favorable predictor of immune response and the prognosis of bladder cancer treated with immune checkpoint inhibitors (ICIs), one that may be superior to tumor mutation burden. MHC-II signature was significantly associated with increased immune cell infiltration and levels of immune-related gene expression signatures. Additionally, transcriptomic analysis showed immune activation in the high-MHC-II signature subgroup, whereas it showed fatty acid metabolism and glucuronidation in the low-MHC-II signature subgroup. Moreover, exploration of corresponding genomic profiles highlighted the significance of tumor protein p53 (TP53) and fibroblast growth factor receptor 3 (FGFR3) alterations. Our results also allowed for the identification of candidate compounds for combined immunotherapy treatment that may be beneficial for patients with bladder cancer and a high MHC-II signature. In conclusion, this study provides a new perspective on MHC-II signature, as an independent and favorable predictor of immune response and prognosis of bladder cancer treated with ICIs.

Dataset Information

TIM3 expression on tumor cells predicts response to anti-PD-1 therapy for renal cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets