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 < 0.05). In the discovery cohort (n = 386), significant differences were detected between EPHA7-MUT and EPHA7-wildtype (EPHA7-WT) patients regarding objective response rate (ORR, 52.6% vs 29.1%, FDR adjusted P = 0.0357) and durable clinical benefit (DCB, 70.3% vs 42.7%, FDR adjusted P = 0.0200). In the validation cohort (n = 1144), significant overall survival advantage was observed in EPHA7-MUT patients (HR = 0.62 [95% confidence interval, 0.39 to 0.97], multivariable adjusted P = 0.0367), which was independent of tumor mutational burden (TMB) and copy number alteration (CNA). Notably, EPHA7-MUT patients without ICI therapy had significantly worse overall survival in TCGA cohort (HR = 1.33 [95% confidence interval, 1.06 to 1.67], multivariable adjusted P = 0.0139). Further gene set enrichment analysis revealed enhanced anti-tumor immunity in EPHA7-MUT tumor.

Conclusions: EPHA7-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.

Keywords: Biomarker; EPHA7; Eph receptors; Immune checkpoint inhibitor; Pan-cancer.

Fig. 1

Flowchart of the study design. a Consolidation of the discovery cohort from seven published studies. Samples from the first four studies (Rizvi et al. [22], Snyder et al. [23], Van Allen et al. [24], Miao et al. [25]) have been curated and filtered by Miao et al. *Hellmann et al. cohort did not include OS data and Hugo et al. cohort did not include PFS data. b Consolidation of the validation cohort and the non-ICI-treated cohort from Samstein et al. c Consolidation of TCGA pan-cancer dataset. OS, overall survival; TMB, tumor mutation burden; CNA, copy number alteration; NAL, neoantigen analysis; GDC, Genomic Data Commons; MSKCC, Memorial Sloan Kettering Cancer Center

Fig. 2

Association between EPH7A mutation and clinical outcomes in the discovery cohort. a Associations between EPH gene mutation and clinical responses (ORR and DCB). Both dashed lines indicated B-H adjusted P = 0.05 regarding DCB and ORR, respectively (two-tailed Fisher’s exact test). b Histogram depicting proportions of ORR in EPHA7-MUT and EPHA7-WT patients (two-tailed Fisher’s exact test). c Histogram depicting proportions of DCB in EPHA7-MUT and EPHA7-WT patients (two-tailed Fisher’s exact test). d The Kaplan-Meier survival analysis comparing PFS between EPHA7-MUT and EPHA7-WT patients in the discovery cohort (n = 349). There were 349 patients with available PFS data for PFS analysis. Missing PFS data consisted of 37 patients from Hugo et al. cohort. e The Kaplan-Meier survival analysis comparing OS between EPHA7-MUT and EPHA7-WT patients in the discovery cohort. There were 311 patients with available OS data for OS analysis. Missing OS data consisted of 75 patients from Hellman et al. cohort. HR and adjusted P in d and e were calculated by the Cox proportional hazards regression analysis. Available confounding factors were adjusted: age, sex, cancer type, drug class, and TMB level. ORR, objective response rate; SD, stable disease; PD, progressive disease; CR, complete response; PR, partial response; DCB, durable clinical benefit; NCB, no clinical benefits; PFS, progression-free survival; OS, overall survival; B-H: Benjamini-Hochberg procedure

Fig. 3

Validation of the predictive value of EPHA7-MUT. a The Kaplan-Meier curves comparing OS between EPHA7-MUT and EPHA7-WT patients in the validation cohort. b The Kaplan-Meier curves comparing OS between EPHA7-MUT and EPHA7-WT patients in the non-ICI-treated cohort. c The Kaplan-Meier curves comparing OS between EPHA7-MUT and EPHA7-WT patients in TCGA cohort. d Forest plot depicting subgroup analysis in the validation cohort. Drug class “Combination” indicated combination therapy of CTLA-4 and PD-(L)1 antibodies. EPHA7-MUT cases were insufficient for hazard ratio calculation in ESCA and glioma subgroups. There were only 694 patients with available CNA data for survival analysis. NSCLC, non-small cell lung cancer; SKCM, melanoma; HNSC, head and neck cancer; CRC, colorectal cancer; BLCA, bladder cancer; ESCA, esophagogastric cancer. e The Kaplan-Meier curves comparing OS among EPHA7^MUTTMB^high, EPHA7^MUTTMB^low, EPHA7^WTTMB^high, and EPHA7^WTTMB^low groups in the validation cohort. f The Kaplan-Meier curves comparing OS among EPHA7^MUTCNA^high, EPHA7^MUTCNA^low, EPHA7^WTCNA^high, and EPHA7^WTCNA^low groups in the validation cohort. HR and adjusted P were calculated by the Cox proportional hazards regression analysis. Available confounding factors were adjusted: validation cohort (age, sex, cancer type, drug class, TMB level), non-ICI-treated cohort (sex, cancer type, TMB level), and TCGA cohort (age, sex, race, cancer type, histology grade, tumor stage). NR indicated the median OS has not been reached

Fig. 4

Mutational landscape of EPHA7 in TCGA cohort. a Association of EPHA7 status and clinical characteristics in TCGA cohort. The cancer type, sex, age, CNA, TMB, PFS, and OS were annotated. Samples were sorted by EPHA7 status, while EPHA7-MUT and EPHA7-WT samples were separated by a gap. b The proportion of EPHA7-MUT tumors identified in each cancer type with at least one mutation case. Numbers above the barplot indicated the alteration frequency, and numbers close to cancer names indicated the number of EPHA7-MUT patients and the total number of patients. “Truncating mutations” included nonsense, splice site mutations, and frameshift insertion and deletion; “Non-truncating mutations” included missense mutations and inframe insertion and deletion

Fig. 5

EPHA7-MUT was associated with enhanced anti-tumor immunity in TCGA cohort. a Violin plot depicting the distribution of TMB, CNA, and NAL in EPHA7-MUT and EPHA7-WT tumors. b Boxplot depicting the infiltration of 22 immune cells in EPHA7-MUT and EPHA7-WT tumors. CIBERSORT was used to calculate the infiltration degree of these immune cells. Gene expression profiles were uploaded to CIBERSORT web portal, and the algorithm was configured with 1000 permutations. CIBERSORT results were recorded in Additional file 5: Table S3. Samples with deconvolution P value ≥ 0.05 were excluded (n = 2967) (Mann-Whitney U test; ns, not significant; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001). c Boxplot depicting the expression level of immune-related genes in EPHA7-MUT and EPHA7-WT groups (Mann-Whitney U test; ns, not significant; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001). d Heatmap depicting the log2-transformed fold change in the expression level of immune-related genes across multiple cancer types (EPHA7-MUT vs EPHA7-WT). Blue indicated downregulation and red indicated upregulation

Fig. 6

Pathway enrichment analysis in TCGA dataset and possible tumor immune microenvironment in EPHA7-MUT and EPHA7-WT tumors. a Differences in pathway activities scored by GSEA between EPHA7-MUT and EPHA7-WT tumors in TCGA dataset. Significant results (P < 0.05 and FDR < 0.25) of enrichment analysis were summarized in Additional file 6: Table S4. Pathways which might potentially impact the tumor immune microenvironment were presented in a. These pathways were divided into four groups: immune function (blue), intercellular signaling (brown), metabolism (green), and other biological functions (gray). b GSEA plot depicting representative pathways identified by GSEA between EPHA-MUT and EPHA7-WT tumors, including metabolism, cell communication, immune response, and angiogenesis. c Comparison of possible tumor immune microenvironment between EPHA7-MUT and EPHA7-WT tumors. APCs, antigen presenting cells; NK cell, nature killer cell; ECM, extracellular matrix