ENPEP/PD-L2/SSTR2 As Potential Biomarker For Immune Checkpoint Inhibitor

Research Background: Immune checkpoint inhibitors(ICIs),which activated immunity by targeting to immune checkpoint proteins,was an emerging therapy for cancer patients.Clinical researches proved ICIs,such as anti-programmed cell death(ligand)-1(anti-PD(L)-1)drugs or anti-cytotoxic T lymphocyte-associated antigen 4(anti-CTLA4)drugs,were effective for various advanced cancers.Patients received ICIs treatment experienced longer overall survival(OS)than patients received radiotherapy or chemotherapy.However,not all patients obtained benefit from ICIs.Clinically,distinguishing ICIs responders were mainly based on biomarkers.Previous studies had identified several biomarkers for ICIs,such as,PD-L1 immunohistochemistry(IHC),tumor mutational burden(TMB),microsatellite instability(MSI)and predictive model based on multiple genes.But they shown certain problems in clinical practice.For example,PD-L1 IHC results were not fully consistent with ICIs response.There were patients with positive PD-L1 stain not responding ICIs,and patients who were not detecting PD-L1 expression responding well to ICIs.TMB was a controversial biomarkers,which lacking both standard calculation formula,and recognized Cut-off values.Evaluating of MSI was mainly affected by tumor heterogeneity,and different results may be obtained when evaluating different parts from same tumor.Using multiple genes models was expensive in clinical practice.Therefore,novel biomarkers are needed for clinical practice of ICIs.The efficacy of immune checkpoint inhibitor was mainly influenced by the tumor microenvironment(TME).Tumor cells and their lived microenvironment interacted complexly,resulting in a specific immune landscape.TME was populated by a variety of immune cells,such as T cells,natural killer cells(NK cells),dendritic cells(DC)and so on.These cells would monitor and kill cancer cells in normal immune environment.But,when they were interacted with tumor cell in TME,their status changed,and immune function may be inhibited or hi-jacked.The infiltration and activation of immune cell was vital to ICIs efficacy.For example,T cell was directly killer of tumor cells in TME.There were many studies proving that patients with better T cell infiltration may more likely obtained favorable benefit of ICIs.Meanwhile,T cell exhaustion was associated with ICIs non-response.On the other hand,Neutrophils and tumor-associated macrophages would block their infiltration and activation of other immune cells,causing immune escape of tumor cells,which might also reflect on ICIs efficacy.Development of novel ICIs biomarkers based on TME,enhancing the understanding of tumor immunity,could lay a solid foundation for the clinical practice of biomarkers.RNA sequencing(RNAseq)provided the opportunity to understand the TME of many patients.RNAseq could provide accurate and clear transcriptome information of tumors,including RNA expression levels and gene mutation information.There were multiple ICI cohorts publishing their patients’ clinical information and sequencing data.The Cancer Genome Atlas(TCGA)project also provided sufficient samples to study the relationship between genes effect in TME.Here,by collecting public data,the authors intended to explore the relationship between certain gene effect in TME,and also the association with ICIs efficacy.The possible mechanisms were also developed by bioinformatics tools.Through this study,new potential biomarkers of ICIs would be identified,which would improve the understanding on tumor immunity and provide a basis for the following studies.Method: 1.Collection of Public data.1.1 Collection of transcriptome information,mutation information and clinical information of TCGA patients.1.2 Collection of transcriptome information,mutation information and clinical information of ICIs treated patients.1.3 Collection immunohistochemical staining data from the The Human Protein Atlas(HPA)database.1.4 Division of TCGA patients based on certain gene expression median.1.5 Division of ICIs treated patients based on certain gene expression median.2.Gene Set Enrichment Analysis(GSEA)2.1 Collection of immune cell signatures and gamma interferon signatures from published studies.2.2 Collection of immune function signatures from GSEA database,Bio Carta database and Reactome databases.2.3 Using GSEA to obtain enrichment situation of immune signatures between High group and Low group in TCGA pooled cohort.2.3 Using GSEA to obtain enrichment situation of immune signatures between High group and Low group in TCGA pooled cohort.2.4 Using single sample GSEA method to calculate immune cell or immune function score of TCGA patients or ICIs treated patients.3.Statistical analysis 3.1 The chi-square test was used to compare the response rate between high and low groups of ICIs cohorts.3.2 The univariate Cox model was used to explore the effect of certain gene expression on the ICIs prognosis.3.3 The log-rank test and the Kaplan-Meier method(KM method)were used to test the difference in overall survival between the high and low groups of ICI cohorts.3.4 The log-rank test and the Kaplan-Meier method were used to test the difference in overall survival between the high and low groups of TCGA cohorts.3.5 Using Pearson correlation analysis to test the correlation between certain genes and immune cell scores.3.6 Using Pearson correlation analysis to test the correlation between certain genes and immune cell-related genes.3.7 The Wilcoxon test was used to compare the differences in immune cell scores between high and low groups of certain genes of the ICIs cohorts.Results: 1.ENPEP is a potential predictive biomarker for ICIs.1.1 The expression of ENPEP is associated with the prognosis of ICIs in pan-cancer cohort.1.2 The expression of ENPEP is associated with the prognosis of ICIs cohorts.1.3 The mutation of ENPEP is associated with the prognosis of ICIs in pan-cancer cohort.1.4 The expression of ENPEP is not a prognostic factor for TCGA patients.1.5 ENPEP is associated with M2 macrophage in TME.2.PDL2 is a potential predictive biomarker for ICIs.2.1 PD-L2 is associated to the anti-tumor immunity activation for TCGA patients.2.2 The expression of PDL2 is associated with the prognosis of ICIs cohorts.2.3 The expression of PDL2 is not a prognostic factor in TCGA pooled cohort.2.4 PD-L2 is associated to the anti-tumor immunity activation for ICIs treated patients.3.SSTR2 is a potential predictive biomarker for ICIs.3.1 SSTR2 is detectable in various cancer.3.2 SSTR2 is associated to the anti-tumor immunity activation for TCGA patients.3.3 The expression of SSTR2 is not a prognostic factor in TCGA pooled cohort.3.4 The expression of SSTR2 is associated with the prognosis of ICIs cohorts.Conclusion: 1.ENPEP is a potential predictive biomarker for ICIs.2.PDL2 is a potential predictive biomarker for ICIs.3.SSTR2 is a potential predictive biomarker for ICIs.