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; Eph receptors; EPHA7; Immune checkpoint inhibitor; Pan-cancer
Zhen Zhang, Hao-Xiang Wu and Wu-Hao Lin contributed equally to this work.
Immune checkpoint inhibitors (ICIs), including monoclonal antibodies that target the programmed cell death protein (ligand) 1 [PD-(L)1] and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), have revolutionized treatments across multiple cancer types [[
As of today, PD-L1 expression, high microsatellite instability (MSI-H), tumor mutation burden (TMB), copy number alteration (CNA), neoantigen load (NAL), tumor immune microenvironment (TIME), gene expression profiles (GEPs), and some specific gene mutations were found associated with ICI response [[
As the largest family of receptor tyrosine kinases (RTKs), the erythropoietin-producing hepatocellular carcinoma (Eph) receptors are involved in a wide range of physiological activities, especially tumorigenesis, tumor immunity, and tumor angiogenesis [[
In this study, we performed a comprehensive analysis of the predictive function of mutations in Eph receptor-related genes. And we uncovered that mutated EPHA7 was predictive of better clinical outcomes in patients receiving ICI therapy and strongly associated with enhanced anti-tumor immunity across multiple cancer types.
Eph receptors comprise 14 members, and each of them has a related gene (Additional file 1: Table S1). Some of these genes are not included in commercial targeted sequencing panels such as MSK-IMPACT. To evaluate the predictive functions of all these 14 genes in ICI-treated patients, we systematically collect annotated clinical data and whole-exome sequencing (WES) data from seven published studies on cBioPortal (https://
Graph: 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. [[
Tumors with nonsynonymous somatic mutations in the coding region of EPHA7 were defined as EPHA7-mutant (EPHA7-MUT), while tumors without as EPHA7-wildtype (EPHA7-WT). To validate the predictive function of EPHA7 mutation, an independent pan-cancer cohort by Samstein et al. with only overall survival data and genomic data was retrieved from cBioPortal [[
Survival data were retrieved from TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) determined by Liu et al., which was used to investigate the prognostic impact of EPHA7 mutation [[
The primary clinical outcomes were objective response rate (ORR), durable clinical benefit (DCB), progression-free survival (PFS), and overall survival (OS). ORR was assessed using Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 (irRECIST for the Hugo et al. study) [[
TMB was defined as the total number of nonsynonymous somatic, coding, base substitution, and indel mutations per megabase (Mb) of genome examined [[
Data of CNA in the validation cohort and TCGA cohort was obtained from cBioPortal and presented as the fraction of copy number altered genome. The cutoff value for high and low CNA in this study was the median CNA within each cancer type [[
To investigate the association between anti-tumor immunity and EPHA7 mutation, we evaluated tumor-infiltrating leukocytes and immune-related genes in TCGA cohort. Twenty-two immune cells' infiltration status was analyzed using CIBERSORT web portal (https://cibersort.stanford.edu/) [[
To further characterize the TIME, we evaluated Hallmark pathways, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and Reactome pathways in EPHA7-MUT and EPHA7-WT patients. R package DESeq2 was used for differential gene expression (DGE) analysis [[
Statistical analyses were performed using R v. 4.0.2 (https://
The baseline patient characteristics of the discovery cohort were summarized in Table 1. Five cancer types were included: non-small cell lung cancer (NSCLC) (n = 129), melanoma (n = 185), clear cell renal cell carcinoma (n = 35), bladder cancer (n = 27), and head and neck cancer (n = 10). Fourteen Eph receptor-related genes, including EPHA1, EPHA2, EPHA3, EPHA4, EPHA5, EPHA6, EPHA7, EPHA8, EPHA10, EPHB1, EPHB2, EPHB3, EPHB4, and EPHB6, were investigated. Among these 14 genes, EPHA7-MUT was the only one that significantly gathered in patients with both ORR and DCB (Fig. 2a, both adjusted P < 0.05). This indicated that EPHA7-MUT may potentially predict the efficacy of ICI treatment.
Table 1 Patient characteristics in the discovery cohort
Characteristics No. (%) Male 234 (60.6) Female 152 (39.4) ≥ 60 157 (40.7) < 60 229 (59.3) Non-small cell lung cancer 129 (33.4) Melanoma 185 (47.9) Clear cell renal cell carcinoma 35 (9.1) Bladder cancer 27 (7.0) Head and neck cancer 10 (2.6) CTLA-4 (mono) 142 (36.8) PD-(L)1 (mono) 115 (29.8) CTLA-4 + PD-(L)1 (combo) 129 (33.4) CR/PR 118 (30.6) SD 94 (24.4) PD 163 (42.2) NEa 11 (2.8) DCB 163 (42.2) NDB 195 (50.5) NEb 28 (7.3) EPHA7-WT 348 (90.2) EPHA7-MUT 38 (9.8) 386
Abbreviations: CR complete response, CTLA-4 cytotoxic T cell lymphocyte-4, DCB durable clinical benefit, NDB no durable benefit, NE not evaluable, PD progressive disease, PD-(L)1 programmed cell death-1 or programmed death-ligand 1, PR partial response, SD stable disease
Graph: 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
Patients' characteristics stratified by EPHA7 status in the discovery cohort were shown in Additional file 2: Table S2. There were 38 EPHA7-MUT patients, including 33 melanomas (3 CR, 13 PR, 7 SD, and 9PD), 2 non-small cell lung cancers (2 PR), 2 clear cell renal cell carcinomas (1 SD and 1 PR), and 1 bladder cancer (1SD). Detailed analysis of ORR, DCB, PFS, and OS between EPHA7-MUT and EPHA7-WT was presented in Fig. 2b–e. The proportion of CR/PR in EPHA7-MUT patients was almost as twice as that in EPHA7-WT patients (52.6% vs 29.1%, P = 0.0051, FDR adjusted P = 0.0357). Proportion of DCB in EPHA7-MUT patients was 27.6% higher than that in EPHA7-WT patients (70.3% vs 42.7%, P = 0.0016, FDR adjusted P = 0.0200). Longer PFS was detected in EPHA7-MUT patients (median PFS 13.4 months vs 4.6 months, hazard ratio [HR] = 0.66 [95% CI, 0.42–1.05], log-rank test P = 0.0769, multivariable adjusted P = 0.3756). As for OS analysis, median OS was 28.1 months in EPHA7-MUT patients, which was 8.2 months longer than in EPHA7-WT patients (HR = 0.64 [95% CI, 0.40–1.03], log-rank test P = 0.0648, multivariable adjusted P = 0.0621). After adjusted for sex, age, cancer types, drug class, and TMB level, numerical OS benefit still existed. However, significant difference of PFS and OS was not observed, probably due to limited sample size.
To further investigate the survival benefit in ICI-treated patients with EPHA7 mutation, we performed the survival analysis in an independent validation cohort with a larger sample size (n = 1144). There were 83 EPHA7-MUT patients including 45 melanomas, 18 non-small cell lung cancers, 5 head and neck cancer cell carcinomas, 5 bladder cancers, 5 colorectal cancers, 4 esophagogastric cancers, and 1 glioma, which took up 7.3% of the population in the validation cohort. After adjusting confounding factors (sex, age, cancer type, drug class, and TMB level), EPHA7-MUT patients achieved significantly longer OS than EPHA7-WT patients in the validation cohort (median OS: not reach [NR] vs 17 months, HR = 0.62 [95% CI, 0.39–0.97], log-rank test P = 0.0001, multivariable adjusted P = 0.0367) (Fig. 3a). In the non-ICI-treated cohort, there were no significant differences between EPHA7-MUT and EPHA7-WT patients (median OS 2.33 years [MUT] vs 9.92 years [WT], HR = 1.14 [95% CI, 0.66–1.98], log-rank test P = 0.1615, multivariable adjusted P = 0.6310) (Fig. 3b). In TCGA cohort, however, significantly worse overall survival was observed in EPHA7-MUT patients (median OS 3.98 years [MUT] vs 4.83 years [WT], HR = 1.33 [95% CI, 1.06–1.67], log-rank test P = 0.0925, multivariable adjusted P = 0.0139) (Fig. 3b, c).
Graph: 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 EPHA7MUTTMBhigh, EPHA7MUTTMBlow, EPHA7WTTMBhigh, and EPHA7WTTMBlow groups in the validation cohort. f The Kaplan-Meier curves comparing OS among EPHA7MUTCNAhigh, EPHA7MUTCNAlow, EPHA7WTCNAhigh, and EPHA7WTCNAlow 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
In subgroup analysis, the survival advantage of EPHA7-MUT vs EPHA7-WT was prominent and consistent across sex, age, drug class, cancer type (except for colorectal cancer), TMB level, and CNA level (Fig. 3d, all P
EPHA7-MUT patients were further stratified into truncating EPHA7-MUT and non-truncating EPHA7-MUT subgroups in both discovery and validation cohorts. There are no significant differences between these two groups, which was presented in Additional file 4: Figure S2.
Mutational landscape of EPHA7 and its association with clinical characteristics were shown in Fig. 4a. The overall mutation frequency of EPHA7 was 2.7% (287/10,437) in TCGA pan-cancer cohort with melanoma (13.6%) ranking first followed by non-small cell lung cancer (5.6%) and endometrial carcinoma (5.6%) (Fig. 4b). The most frequent somatic mutation site of EPHA7 was p.R895, and generally, somatic mutations were evenly distributed without any annotated functional hotspot mutations from 3D Hotspots (https://
Graph: 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
EPHA7-MUT was associated with increased immunogenicity. TMB and NAL were higher in EPHA7-MUT tumors (both P < 0.0001), while CNA remained similar in both EPHA7-MUT and EPHA7-WT tumors (P = 0.2045) (Fig. 5a). Also, we used CIBERSORT to investigate infiltration of immune cells and results were recorded in Additional file 5: Table S3. As expected, enhanced anti-tumor immunity was observed in EPHA7-MUT tumors. Cytotoxic lymphocytes, including activated NK cells (P < 0.05) and cytotoxic T cells (P < 0.001), were more abundant in EPHA7-MUT tumors (Fig. 5b). Expression of cytotoxic activity-related genes (GZMA, PRF1), chemokine-related genes (CCL5, CXCL9), and checkpoint-related genes (PDCD1, LAG3, IDO1, CTLA-4, TIGHT) were also upregulated in EPHA7-MUT tumors (Fig. 5c, all P < 0.01). To further investigate the association between anti-tumor immunity and EPHA7-MUT across multiple cancer types, we thoroughly examined immune-related genes within each cancer type. A general upregulation of stimulatory immunomodulators was observed in EPHA7-MUT tumors except glioblastoma (GBM), which showed a general downregulation of both inhibitory and stimulatory immunomodulators (Fig. 5d).
Graph: 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
The results of enrichment analysis showed that several pathways varied significantly between EPHA7-MUT and EPHA7-WT tumors, including metabolism, intercellular interaction, immune function, and other biological functions (Fig. 6a). Significant results (P < 0.05 and FDR < 0.25) of enrichment analysis were summarized in Additional file 6: Table S4. Cholesterol efflux and metabolism, fatty acid degradation, glycolysis, cell-cell communication, cell-cell junction organization, integrin cell surface interactions, and angiogenesis were downregulated in EPHA7-MUT tumors (Fig. 6b, all P < 0.05). Oxidative phosphorylation, antigen processing and presentation, NK-mediated cytotoxicity, and interferon gamma response were upregulated in EPHA7-MUT tumors (Fig. 6b, all P < 0.05). According to the results of pathway enrichment analysis, the possible TIME of EPHA7-MUT and EPHA7-WT tumor was summarized in Fig. 6c.
Graph: 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
In our study, we systematically collected and consolidated both clinical and genomic data to evaluate the association between EPH gene status and clinical responses in ICI-treated cancer patients. Then, we carefully validated our findings in another independent cohort and thoroughly explored the corresponding TIME. We found EPHA7-MUT was significantly associated with better clinical outcomes in ICI-treated patients and enhanced anti-tumor immunity. Remarkably, this predicting value of EPHA7-MUT was independent of TMB and CNA. This is the first study performing a comprehensive analysis of the relationship between EPH gene mutation status and clinical outcomes in ICI-treated patients across multiple cancer types.
We found some meaningful changes in biological functions of EPHA7-MUT tumors, including intercellular communication, angiogenesis, and metabolism. First of all, Eph receptors and their ligands (ephrin) have been proven essential in the cell communication system [[
Ephrin receptors form a large family of receptor tyrosine kinase and regulate various biological functions. Both oncogenic and tumor suppressive roles have been reported for specific ephrin receptors [[
In the initial screening process, EPHA3 was ruled out since its FDR adjusted P value of DCB was 0.053. However, given this borderline P value, EPHA3 was worth following up. Hence, we have also done the survival analysis of EPHA3 in both discovery cohort and validation cohort. Results could be found in the supplementary material (Additional file 8: Figure S4). However, only numerical survival benefits were observed in both discovery and validation cohort for EPHA3-MUT patients. Therefore, EPHA3 might not be as effective as EPHA7 in predicting the efficacy of immunotherapy in current analysis.
In our primary analysis, it is individual EPH gene that was evaluated rather than cumulative effects of all 14 EPH genes. To investigate the combined effects of all 14 EPH genes, we performed further analysis combining all 14 genes (Additional file 9: Figure S5). EPH-MUT was defined as at least one EPH gene has mutation among 14 genes, while EPH-WT was defined as none of EPH genes has mutation. Although EPH-MUT patients presented with a higher ORR than EPH-WT patients (40.8% vs 24.8%), there were no significant differences between EPH-MUT and EPH-WT patients regarding DCB, PFS, and OS. Activation of different Eph receptors can have highly varied impacts on cellular processes, but exact function of each Eph receptor has not been fully understood. Therefore, it would be more reasonable to test the combined predictive value of EPH genes that have synergic effect in the future rather than all EPH genes.
This retrospective analysis also has several limitations. Firstly, only four out of fourteen EPH genes are included in the MSK-IMPACT panel. To analyze all EPH genes, we only included cohorts with WES data in the discovery cohort. Considering the limited sample size of the discovery cohort (n = 386), we should not completely exclude the predictive function of other EPH genes. Secondly, mutation rate of EPHA7 in melanoma was nearly 2.5 times higher than in other cancer types. The majority of EPHA-MUT samples were melanoma (33/38) in the discovery cohort, which is a major confounding factor causing bias. However, the survival advantages across multiple cancers in the validation cohort as well as the general upregulation of anti-tumor immunity in various cancers could compensate the bias to some degree. Still, the predictive value of EPHA7 mutation with regard to cancer types needs to be verified in future prospective trials. Additionally, the possible TIME and molecular mechanisms of EPHA7-MUT were demonstrated based on GSEA, which requires further molecular researches to validate. Finally, gene expression data has not been included in both the discovery and validation cohorts. Therefore, combination analysis of EPHA7 and other predictive biomarkers (e.g., expression of PD-L1) has not been performed. Clinical trials with expression data are needed to expand our findings and test the added value of tumor-infiltrating lymphocytes in the survival analysis of EPHA7-MUT.
Importantly, these limitations do not preclude the favorable clinical outcomes derived from immunotherapy in EPHA7-MUT patients. Unlike continuous variables such as TMB, CNA, or PD-L1 expression, EPHA7-MUT are easily detected by NGS and clearly classify patients into two groups that are associated with immunotherapy response. The scope of EPHA7-MUT falls in compensating the existing biomarkers to detect those patients who are most likely to benefit from immunotherapy. Our study not only paved the way for precise treatments tailored to molecular subtypes, but also indicated the association between Eph receptor-related TIME and immunotherapy response. Biological functions mediated by Eph receptors, especially tumor angiogenesis, intercellular contact, and tumor metabolism, should be better characterized in future studies. Although there are some researches or ongoing trials co-targeting these pathways and tumor immunity [[
Our study demonstrated the robust link between EPHA7-MUT and better clinical outcomes in ICI-treated cancer patients. Therefore, EPHA7-MUT has the potential to serve as a predictive biomarker for immune checkpoint blockades across multiple cancer types. Validation of the predictive value in future prospective trials and exploration of the molecular mechanism in further molecular researches are warranted for EPHA7-MUT.
We would like to thank Prof. Luc G. T. Morris from Memorial Sloan Kettering Cancer Center for generously sharing the clinical data of the non-ICI-treated cohort from Samstein et al., and the staff members of TCGA Research Network, the cBioPortal, the UCSC Xena data portal, and the CIBERSORT portal, as well as all the authors for making their valuable research data public.
Study concept and design: HL, ZZ, and HW. Acquisition, analysis, or interpretation of data: all authors. Drafting of the manuscript: all authors. Critical revision of the manuscript for important intellectual content: all authors. Study supervision: HL. All authors read and approved the final manuscript.
This study was funded by the National Natural Science Foundation of China (81930065, 81871985), Natural Science Foundation of Guangdong Province (2014A030312015, 2019A1515011109), and Science and Technology Program of Guangzhou (201803040019, 202002030208).
All of the data we used in this study were publicly available as described in the "Methods" section.
Ethical approval was waived since we used only publicly available data and materials in this study.
Not applicable
The authors declare that they have no competing interests.
Graph: Additional file 1: Table S1. EPH genes and corresponding clinical outcomes in the discovery cohort.
Graph: Additional file 2: Table S2. Patient characteristics in the discovery cohort stratified by EPHA7 status.
Graph: Additional file 3: Figure S1. Violin plot depicting the distribution of TMB and CNA in EPHA7-MUT and EPHA7-WT tumors.
Graph: Additional file 4: Figure S2. Truncating vs non-truncating EPHA7 mutation analysis in both discovery and validation cohort. Figure S2 A-D: discovery cohort. Figure S2 E: validation cohort.
Graph: Additional file 5: Table S3. Results of CIBERSORT analysis in TCGA cohort.
Graph: Additional file 6: Table S4. Significant pathways detected by gene set enrichment analysis. (EPHA7-MUT vs EPHA7-WT tumors).
Graph: Additional file 7: Figure S3. Survival analysis of cancer subgroups in the TCGA cohort.
Graph: Additional file 8: Figure S4. Survival analysis of EPHA3 in both discovery and validation cohort.
Graph: Additional file 9: Figure S5. Association between clinical outcomes and the combination of all 14 EPH genes in the discovery cohort.
• ACC
- Adrenocortical carcinoma
• BLCA
- Bladder urothelial carcinoma
• BRCA
- Invasive breast carcinoma
• CHOL
- Cholangiocarcinoma
• CI
- Confidence interval
• CNA
- Copy number alteration
• COAD
- Colorectal adenocarcinoma
• CR
- Complete response
• CRC
- Colorectal cancer
• CSCC
- Cervical squamous cell carcinoma
• CTLA-4
- Cytotoxic T lymphocyte antigen 4
• DCB
- Durable clinical benefit
• DLBC
- Diffuse large B cell lymphoma
• EAC
- Esophagogastric adenocarcinoma
• EC
- Endometrial carcinoma
• ECA
- Endocervical adenocarcinoma
• ESCC
- Esophageal squamous cell carcinoma
• FDA
- Food and Drug Administration
• FDR
- False discovery rate
• FPKM
- Fragments per kilobase of exon model per million mapped fragments
• GBM
- Glioblastoma
• HCC
- Hepatocellular carcinoma
• HNSC
- Head and neck squamous cell carcinoma
• HR
- Hazard ratio
• ICIs
- Immune checkpoint inhibitors
• KIRC
- Kidney renal clear cell carcinoma
• KIRP
- Kidney renal papillary cell carcinoma
• LAML
- Acute myeloid leukemia
• LGG
- Diffuse glioma
• Mb
- Megabase
• MESO
- Mesothelioma
• MSI-H
- High microsatellite instability
• NAL
- Neoantigen load
• NSCLC
- Non-small cell lung cancer
• ORR
- Objective response rate
• OS
- Overall survival
• OV
- Ovarian epithelial tumor
• PAAD
- Pancreatic adenocarcinoma
• PCC
- Pheochromocytoma
• PD
- Progression of disease
• PD-(L)1
- Programmed cell death (ligand) 1
• PFS
- Progression-free survival
• PGL
- Paraganglioma
• PRAD
- Prostate adenocarcinoma
• PR
- Partial response
• RECIST
- Response Evaluation Criteria in Solid Tumors
• SARC
• Sarcoma
• SD
- Stable disease
• SKCM
- Melanoma
• STAD
- Stomach adenocarcinoma
• TCGA
- The Cancer Genome Atlas
- EPHA7-MUT
- EPHA7-mutant
- EPHA7-WT
- EPHA7-wildtype
• TMB
- Tumor mutational burden
• UCES
- Uterine corpus endometrial carcinoma
• UVM
- Uveal melanoma
• WES
- Whole-exome sequencing
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By Zhen Zhang; Hao-Xiang Wu; Wu-Hao Lin; Zi-Xian Wang; Lu-Ping Yang; Zhao-Lei Zeng and Hui-Yan Luo
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