Dataset Information

EPHA7 mutation as a predictive biomarker for immune checkpoint inhibitors in multiple cancers.

ABSTRACT:

Background

A critical and challenging process in immunotherapy is to identify cancer patients who could benefit from immune checkpoint inhibitors (ICIs). Exploration of predictive biomarkers could help to maximize the clinical benefits. Eph receptors have been shown to play essential roles in tumor immunity. However, the association between EPH gene mutation and ICI response is lacking.

Methods

Clinical data and whole-exome sequencing (WES) data from published studies were collected and consolidated as a discovery cohort to analyze the association between EPH gene mutation and efficacy of ICI therapy. Another independent cohort from Memorial Sloan Kettering Cancer Center (MSKCC) was adopted to validate our findings. The Cancer Genome Atlas (TCGA) cohort was used to perform anti-tumor immunity and pathway enrichment analysis.

Results

Among fourteen EPH genes, EPHA7-mutant (EPHA7-MUT) was enriched in patients responding to ICI therapy (FDR adjusted P?ConclusionsEPHA7-MUT successfully predicted better clinical outcomes in ICI-treated patients across multiple cancer types, indicating that EPHA7-MUT could serve as a potential predictive biomarker for immune checkpoint inhibitors.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC7852135 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

EPHA7 mutation as a predictive biomarker for immune checkpoint inhibitors in multiple cancers.

Zhang Zhen Z Wu Hao-Xiang HX Lin Wu-Hao WH Wang Zi-Xian ZX Yang Lu-Ping LP Zeng Zhao-Lei ZL Luo Hui-Yan HY

BMC medicine 20210202 1

<h4>Background</h4>A critical and challenging process in immunotherapy is to identify cancer patients who could benefit from immune checkpoint inhibitors (ICIs). Exploration of predictive biomarkers could help to maximize the clinical benefits. Eph receptors have been shown to play essential roles in tumor immunity. However, the association between EPH gene mutation and ICI response is lacking.<h4>Methods</h4>Clinical data and whole-exome sequencing (WES) data from published studies were collect ...[more]

PMID: 33526018

Similar Datasets

Project description:BACKGROUND:Immune checkpoint inhibitors (ICIs) have achieved impressive success in different cancer types, yet responses vary and predictive biomarkers are urgently needed. Growing evidence points to a link between DNA methylation and anti-tumor immunity, while clinical data on the association of genomic alterations in DNA methylation-related genes and ICI response are lacking. METHODS:Clinical cohorts with annotated response and survival data and matched mutational data from published studies were collected and consolidated. The predictive function of specific mutated genes was first tested in the discovery cohort and later validated in the validation cohort. The association between specific mutated genes and tumor immunogenicity and anti-tumor immunity was further investigated in the Cancer Genome Altas (TCGA) dataset. RESULTS:Among twenty-one key genes involving in the regulation of DNA methylation, TET1-mutant (TET1-MUT) was enriched in patients responding to ICI treatment in the discovery cohort (P?<?0.001). TET1 was recurrently mutated across multiple cancers and more frequently seen in skin, lung, gastrointestinal, and urogenital cancers. In the discovery cohort (n?=?519), significant differences were observed between TET1-MUT and TET1-wildtype (TET1-WT) patients regarding objective response rate (ORR, 60.9% versus 22.8%, P?<?0.001), durable clinical benefit (DCB, 71.4% versus 31.6%, P?<?0.001), and progression-free survival (PFS, hazard ratio?=?0.46 [95% confidence interval, 0.25 to 0.82], P?=?0.008). In the validation cohort (n?=?1395), significant overall survival (OS) benefit was detected in the TET1-MUT patients compared to TET1-WT patients (hazard ratio?=?0.47 [95% confidence interval, 0.25 to 0.88], P?=?0.019), which was, importantly, independent of tumor mutational burden and high microsatellite instability; as well as not attributed to the prognostic impact of TET1-MUT (P?>?0.05 in both two non-ICI-treated cohorts). In TCGA dataset, TET1-MUT was strongly associated with higher tumor mutational burden and neoantigen load, and inflamed pattern of tumor-infiltrating T lymphocytes, immune signatures and immune-related gene expressions. CONCLUSIONS:TET1-MUT was strongly associated with higher ORR, better DCB, longer PFS, and improved OS in patients receiving ICI treatment, suggesting that TET1-MUT is a novel predictive biomarker for immune checkpoint blockade across multiple cancer types.

Project description:Background: Despite an increasing understanding about tumor mutation burden (TMB) in cancer immunity and cancer immunotherapy, the comprehensive cognition between TMB and efficiency of immune checkpoint inhibitors (ICIs) is still lacking. A systematic review and meta-analysis was conducted to evaluate the predictive value of TMB on efficacy of ICIs. Methods: Systematic literature search was conducted on PubMed, EMBASE, Web of Science and Cochrane Library up to June 16, 2019. Pooled odds ratio (OR) of objective response rate (ORR), hazard ratio (HR) of progression-free survival (PFS) and overall survival (OS) were estimated by inverse variance weighted fixed-effects model (I 2 ≤ 50%) or DerSimonian-Laird random-effects model (I 2 > 50%). In addition, heterogeneity analysis, sensitivity analysis, publication bias and subgroup analysis were conducted. Moreover, fractional polynomial regression was conducted to investigate the dose-response relationship between TMB cutoffs and efficacy of ICIs. Furthermore, we assessed ORR by TMB and programmed cell death ligand 1 (PD-L1) expression after layering each other in studies which the two could be both acquired. Results: Three thousand six hundred fifty-seven records were retrieved through database searching, and 29 studies with 4,431 patients were finally included in the meta-analysis. TMB high group had significantly improved ORR (pooled OR 3.31, 95% CI 2.61, 4.19, P < 0.001), PFS (pooled HR 0.59, 95% CI 0.49, 0.71, P < 0.001) and OS (pooled HR 0.68, 95% CI 0.53, 0.89, P = 0.004). Sensitivity analyses illustrated the results were stable, and publication bias was identified in ORR. Subgroup analyses showed the predictive value of TMB was significant in non-small-cell lung cancer (except for the OS) and melanoma. In addition, heterogeneity was substantial in targeted next generation sequencing group but tiny in whole exome sequencing group. Furthermore, TMB and PD-L1 expression were capable to predict improved ORR of ICIs after stratification of each other, with tiny heterogeneity. Conclusions: High tumor mutation burden predicted improved efficacy of immune checkpoint inhibitors in cancers, and targeted next generation sequencing for estimating tumor mutation burden in clinic should be standardized to eliminate heterogeneity in the future. Moreover, tumor mutation burden and programmed cell death ligand 1 expression were independent factors on predicting efficacy of immune checkpoint inhibitors.

Project description:Treatment with immune checkpoint inhibitors (ICPIs) extends survival in a proportion of patients across multiple cancers. Tumor mutational burden (TMB)-the number of somatic mutations per DNA megabase (Mb)-has emerged as a proxy for neoantigen burden that is an independent biomarker associated with ICPI outcomes. Based on findings from recent studies, TMB can be reliably estimated using validated algorithms from next-generation sequencing assays that interrogate a sufficiently large subset of the exome as an alternative to whole-exome sequencing. Biological processes contributing to elevated TMB can result from exposure to cigarette smoke and ultraviolet radiation, from deleterious mutations in mismatch repair leading to microsatellite instability, or from mutations in the DNA repair machinery. A variety of clinical studies have shown that patients with higher TMB experience longer survival and greater response rates following treatment with ICPIs compared with those who have lower TMB levels; this includes a prospective randomized clinical trial that found a TMB threshold of ?10 mutations per Mb to be predictive of longer progression-free survival in patients with non-small cell lung cancer. Multiple trials are underway to validate the predictive values of TMB across cancer types and in patients treated with other immunotherapies. Here we review the rationale, algorithm development methodology, and existing clinical data supporting the use of TMB as a predictive biomarker for treatment with ICPIs. We discuss emerging roles for TMB and its potential future value for stratifying patients according to their likelihood of ICPI treatment response. IMPLICATIONS FOR PRACTICE: Tumor mutational burden (TMB) is a newly established independent predictor of immune checkpoint inhibitor (ICPI) treatment outcome across multiple tumor types. Certain next-generation sequencing-based techniques allow TMB to be reliably estimated from a subset of the exome without the use of whole-exome sequencing, thus facilitating the adoption of TMB assessment in community oncology settings. Analyses of multiple clinical trials across several cancer types have demonstrated that TMB stratifies patients who are receiving ICPIs by response rate and survival. TMB, alongside other genomic biomarkers, may provide complementary information in selecting patients for ICPI-based therapies.

Project description:BackgroundSeveral immune checkpoint inhibitors (ICIs) are approved for the treatment of advanced urothelial carcinoma (UC). There are limited biomarkers for ICI-treated patients with UC. We investigated the association between body composition and clinical outcomes in ICI-treated UC patients.Materials and methodsWe conducted a retrospective analysis of 70 ICI-treated patients with advanced UC at Winship Cancer Institute from 2015 to 2020. Baseline computed tomography images within 2 months of ICI initiation were collected at mid-L3 and muscle and fat compartments (subcutaneous, intermuscular, and visceral) were segmented using SliceOMatic v5.0 (TomoVision, Magog, Canada). A prognostic body composition risk score (high: 0-1, intermediate: 2-3, or low-risk: 4) was created based on the β coefficient from the multivariate Cox model (MVA) following best-subset variable selection. Our body composition risk score was skeletal muscle index (SMI) + 2 × attenuated skeletal muscle (SM) mean + visceral fat index (VFI). Concordance statistics (C-statistics) were used to quantify the discriminatory magnitude of the predictive model.ResultsMost patients (70%) were men and the majority received ICIs in the second- (46%) or third-line (21%) setting. High-risk patients had significantly shorter overall survival (OS; hazard ratio [HR], 6.72; p < .001), progression-free survival (HR, 5.82; p < .001), and lower chance of clinical benefit (odds ratio [OR], 0.02; p = .003) compared with the low-risk group in MVA. The C-statistics for our body composition risk group and myosteatosis analyses were higher than body mass index for all clinical outcomes.ConclusionBody composition variables such as SMI, SM mean, and VFI may be prognostic and predictive of clinical outcomes in ICI-treated patients with UC. Larger, prospective studies are warranted to validate this hypothesis-generating data.Implications for practiceThis study developed a prognostic body composition risk scoring system using radiographic biomarkers for patients with bladder cancer treated with immunotherapy. The study found that the high-risk patients had significantly worse clinical outcomes. Notably, the study's model was better at predicting outcomes than body mass index. Importantly, these results suggest that radiographic measures of body composition should be considered for inclusion in updated prognostic models for patients with urothelial carcinoma treated with immunotherapy. These findings are useful for practicing oncologists in the academic or community setting, particularly given that baseline imaging is routine for patients starting on treatment with immunotherapy.

Dataset Information

EPHA7 mutation as a predictive biomarker for immune checkpoint inhibitors in multiple cancers.

Background

Methods

Results

Publications

EPHA7 mutation as a predictive biomarker for immune checkpoint inhibitors in multiple cancers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure