Association of EYS mutations with immune checkpoint inhibitor outcome and response in melanoma and non-small cell lung cancer

Association of EYS mutations with immune checkpoint inhibitor outcome and response in melanoma and non-small cell lung cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of EYS mutations with immune checkpoint inhibitor outcome and response in melanoma and non-small cell lung cancer Xueying Wang, Zhiyuan Wang, Yixin Xu, Wenjing Zhang, Zhenpeng Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7648150/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Immunotherapy with immune checkpoint inhibitor (ICI) improved outcomes in advanced/metastatic melanoma and non-small cell lung cancer (NSCLC), but only a subset of patients benefited significantly. Existing biomarkers like tumor mutational burden (TMB) and microsatellite instability (MSI) had limitations, prompting the search for novel biomarkers. EYS , which is primarily involved in retinal photoreceptor function and visual system development, had not been explored for its potential role in predicting ICI therapy responses. In this study, data on pretreatment mutations, ICI treatment information, and clinicopathological data of 631 melanoma samples and 109 NSCLC samples were integrated. Meanwhile, immune infiltration and signaling pathway enrichment associated with EYS mutation were evaluated based on transcriptome gene expression profiles. In the melanoma cohort, EYS mutations were associated with significantly improved ICI outcome (HR: 0.62, 95%CI: 0.45–0.86, P = 0.004) and response rate (43.4% vs. 28.6%, P = 0.004). The findings were corroborated in NSCLC samples, where patients with EYS mutations exhibited a significantly better prognosis under ICI therapy (HR: 0.26, 95% CI: 0.08–0.83, P = 0.023) and a higher response rate (75.0% vs. 30.0%, P = 0.004). Further analysis showed that EYS mutations were associated with elevated tumor mutational burden, enhanced immune cell infiltration, and activation of immune-related signaling pathways. Our results showed that EYS mutation was associated with better ICI response, which provided a theoretical basis for clinical tumor immunotherapy strategy development and provided a possible molecular marker for assessing treatment response. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Health sciences/Oncology EYS mutation Immune checkpoint inhibitor Molecular markers Melanoma NSCLC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction In recent years, the field of cancer immunotherapy witnessed revolutionary breakthroughs. Immune checkpoint inhibitor (ICI), which target key molecules such as PD-1/PD-L1/CTLA-4, effectively relieved the suppression of T cell functions in the tumor microenvironment, significantly improved the survival outcomes for patients with highly immunogenic cancers, including melanoma and non-small cell lung cancer (NSCLC). Some patients achieved long-term progression-free survival or complete remission [1]. However, in clinical practice, only 20%-40% of patients exhibited durable responses [2]. Moreover, existing predictive biomarkers such as PD-L1 expression, tumor mutation burden (TMB), and microsatellite instability (MSI) had significant limitations [3, 4]: PD-L1 expression is not linearly correlated with therapeutic efficacy [5, 6], the predictive performance of TMB is affected by detection methods and threshold values [7, 8], and MSI is applicable to less than 5% of solid tumor patients [9]。 Previous studies have revealed that the heterogeneous efficacy of ICI stems from the dynamic interplay between the tumor genome, host immune status, and microenvironmental signaling pathways [10, 11]. Specific gene mutations, such as POLE/POLD1 mutations that increase neoantigen burden (NB) [12] and STK11/LKB1 or KEAP1 loss-of-function mutations that inhibit immune signaling [13], can reshape the immune microenvironment and thereby modulate therapeutic responses. Current research has largely focused on high-frequency driver genes, while the immune regulatory mechanisms of low-frequency mutations or functionally unknown genes remain to be explored [14]. There is an urgent need to identify new molecular markers through multi-omics integration to improve predictive accuracy [15]. The EYS (Eyes Shut Homolog) gene, one of the largest retinal-specific expressed genes identified to date, is located on human chromosome 6q12. Its genomic span exceeds 2 Mb and it encodes a functional protein composed of 3,145 amino acids. This protein contains five laminin G domains (involved in cell adhesion and signaling) and 27 epidermal growth factor (EGF)-like repeats (mediating protein-protein interactions) and is primarily expressed in photoreceptor cells of the retina, where it plays a key role in visual system development by regulating extracellular matrix assembly and light signal transduction [16-22]. Clinical studies have shown that EYS mutations are strongly associated with autosomal recessive inherited retinal diseases, with approximately 5%-16% of familial cases worldwide harboring functionally validated mutations, predominantly truncation and missense mutations, which significantly impair protein function and lead to photoreceptor cell dysfunction [21, 23-28]. In recent years, tumor genomics data have suggested that EYS may be involved in tumorigenesis. In brain metastasis (BM) from colorectal cancer, by comparing the mutation profiles of the TCGA CRC cohort with the BM cohort, EYS was identified as one of the highly mutated genes [29], the study did not provide direct evidence supporting the involvement of EYS in DNA damage repair (DDR) pathways, such as homologous recombination or mismatch repair. The functional role of EYS in BM still requires experimental validation. TCGA data also show amplification of the 6q12 region in squamous cell lung cancer [30], with EYS located within this region. However, whether EYS amplification drives oncogenic phenotypes through genomic imbalance mediated by extrachromosomal DNA (ecDNA) remains to be further verified through functional experiments, such as CRISPR screening or single-cell multi-omics analysis. In summary, the pleiotropic effects of EYS in tumors are still preliminary findings, and its mechanisms as an oncogene and clinical significance urgently need to be clarified through functional experiments and multi-omics analysis across cancer types. To date, the predictive role of EYS mutations in cancer ICI treatment has not been reported. Given the wide application of ICI therapy in melanoma and NSCLC [31], this study retrospectively integrated pretreatment multi-omics data and clinical ICI treatment information from melanoma and NSCLC patients to elucidate the clinical significance of EYS mutations in cancer immunotherapy. Methods This study was reported in accordance with Shandong Second Medical University guidelines. All methods were performed in accordance with the relevant guidelines and regulations. Collection of melanoma and NSCLC samples This study integrated pretreatment somatic mutation data, clinicopathological features, and immune checkpoint inhibitor (ICI) treatment information from 631 melanoma [ 32 – 37 ] and 109 NSCLC patients [ 5 , 14 ] from multiple centers (Supplementary Table S1 and S2). All patients received anti-CTLA-4, anti-PD-1/PD-L1 monotherapy or combination therapy, with treatment response status including complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Since the original mutation data were derived from different sequencing platforms, this study employed Oncotator [ 38 ] for unified mutation annotation to ensure cross-platform data comparability and consistency. Somatic mutation data, mRNA transcriptome expression profiles, and clinical prognostic information for melanoma and NSCLC were obtained based on The Cancer Genome Atlas (TCGA). Utilizing TCGA transcriptome expression data, this study investigated the relationship between EYS mutations and immune characteristics in the tumor microenvironment. Assessment of tumor-infiltrating immune cells To compare immune infiltration differences between EYS -mutant and wild-type patients, this study employed two methods to evaluate the abundance of tumor-infiltrating immune cells. First, the CIBERSORT algorithm (using the LM22 matrix based on gene expression profiles of 547 signature genes) was utilized to estimate the infiltration proportions of 22 immune cell types [ 39 ]. This was supplemented with the xCell algorithm to assess enrichment scores of 64 immune and stromal cell types for cross-validation [ 40 ]. Extraction of mutational signature This study employed the method proposed by Kim et al. [ 41 ] to extract mutational signature from mutation data of melanoma and NSCLC samples. The core of this approach is Bayesian non-negative matrix factorization (NMF), which decomposes the mutational signature matrix A (containing 96 base substitution types) into two non-negative matrices W and H (A ≈ W × H), where W represents the extracted mutational signatures and H denotes the mutational activity of each signature. All extracted mutational signatures were compared with the 30 annotated mutational signatures in the COSMIC database by calculating cosine similarity. Collection of immune signatures Current evidence indicates that multiple immune signatures are closely associated with tumor immunogenicity regulation and immunotherapy efficacy. This study systematically screened immune signature gene sets with well-established prognostic predictive value, integrating 14 hallmark immune signatures (Supplementary Table S3) through literature-based curation, including T cell inflammation, interferon response pathway, and others. GSVA and GSEA Single sample gene set enrichment analysis (ssGSEA) enables precise characterization of immune infiltration features in the tumor microenvironment by quantifying the enrichment level of predefined gene sets (e.g., immune signature gene sets) within individual samples. For investigating EYS mutation-associated signaling pathways, genome-wide differential expression analysis was first performed using the R DESeq2 package [ 42 ], followed by gene set enrichment analysis (GSEA) to identify significantly enriched pathways. Background gene sets were retrieved from the Molecular Signature Database (MSigDB) [ 43 , 44]‌. TMB and NB Tumor mutation burden (TMB) refers to the total number of non-synonymous somatic mutations per megabase (Mb) in the tumor genome. In this study, TMB was calculated based on integrated samples and TCGA cohorts, with normalization via log2 transformation of the total non-synonymous mutations per megabase. Neoantigen data for 341 melanoma and 662 NSCLC samples in the TCGA cohort were obtained from The Cancer Immune Atlas (TCIA), and neoantigen burden (NB) was computed using an algorithm based on somatic mutation profiles [ 45 ]. Statistical Analysis Statistical analyses and data visualization were performed using R software (version 4.4.3). Survival curves were generated using the Kaplan-Meier method, and the log-rank test was applied to evaluate the significance of survival differences. Logistic regression and Cox regression models incorporated multiple clinical confounders (e.g., age, sex, stage, and therapy type) and were implemented with the forestmodel package. The Wilcoxon rank sum test and Fisher’s exact test were used to assess differences in continuous variables and binary categorical variables, respectively, between EYS -mutant and wild-type patients. Unless otherwise specified, two-sided P -values less than 0.05 were considered statistically significant. Results EYS mutational status in melanoma The workflow of this study is illustrated in Fig. 1 . Among the 631 melanoma samples included, 193 cases (30.6%) achieved complete response (CR) or partial response (PR) on ICI treatment, while 430 cases (68.1%) exhibited stable disease (SD) or progressive disease (PD) as their ICI response status. Response data were unavailable for the remaining 8 samples (1.3%). Whole-genome mutation analysis revealed that C > T base substitutions were the predominant somatic mutational signature in this cohort (Supplementary Fig. S1 ), and detailed mutational patterns of melanoma driver genes associated with EYS mutations are shown in Supplementary Fig. S1 . Among the 631 melanoma samples, 99 (15.7%) harbored EYS mutations. The amino acid level alterations caused by EYS mutations are depicted in Supplementary Fig. S2 . EYS mutations are associated with improved ICI treatment prognosis and response rates in melanoma patients Among the 631 melanoma samples analyzed, 99 patients (16%) harbored EYS mutations. Kaplan-Meier survival analysis demonstrated that patients with EYS mutations exhibited significantly prolonged survival following ICI therapy compared to EYS wild-type patients (median survival time: 48.8 vs. 26.5 months; Log-rank test P = 0.008; Fig. 2 A). Multivariable Cox regression model adjusted for age, sex, clinical stage, and therapy type further confirmed the association between EYS mutations and improved survival (HR: 0.62, 95% CI: 0.45–0.86, P = 0.004; Fig. 2 B). The prognostic predictive power of EYS mutations in a single ICI cohort and survival analyses stratified by therapy types are shown in Supplementary Fig. S3 and S4. Furthermore, EYS -mutant patients showed a higher objective response rate compared to wild-type patients (43.4% vs. 28.6%; Fisher’s exact test P = 0.004; Fig. 2 C). After adjusting for confounders using a multivariable logistic regression model, the association between EYS mutations and ICI response rates remained statistically significant (OR: 0.62, 95% CI: 0.39–0.97, P = 0.039; Fig. 2 D). Association of EYS mutations with mutation burden in melanoma patients Existing studies have shown that TMB is an important biomarker for predicting immunotherapy efficacy in melanoma. This study focused on the association between EYS mutations and TMB. The results revealed that melanoma patients harboring EYS mutations exhibited significantly elevated TMB levels (Wilcoxon rank-sum test, P < 0.001; Fig. 3 A). To further explore the genomic mutational signatures, we characterized the melanoma mutation spectrum based on the NMF approach and identified four biologically significant mutational signatures (Supplementary Table S4): Signature 1 (age-relevant), Signature 4 (smoking-relevant), Signature 7 (ultra-violet light exposure-induced), and Signature 11 (alkylating agent-induced). To refine the association between EYS mutations and TMB, a multivariable logistic regression model was applied, adjusting for clinical variables (age, sex, stage), the four identified mutational signatures, and mutations in DNA damage repair genes. The results confirmed that EYS -mutant patients still demonstrated significantly higher TMB (OR: 4.06, 95% CI: 2.18–8.02, P < 0.001; Fig. 3 B). Additionally, EYS -mutant patients showed markedly increased NB levels (Wilcoxon rank-sum test, P < 0.001; Fig. 3 C). These findings were validated in melanoma patients from TCGA dataset, where EYS -mutant patients also exhibited significantly elevated TMB and NB levels (Wilcoxon rank-sum test, P < 0.001; Fig. 3 D and 3 E). Validation of the relationship between EYS mutations and ICI treatment response and mutation burden in NSCLC Among the 109 collected NSCLC samples, 12 patients (11%) harbored EYS mutations. Kaplan-Meier survival analysis revealed that NSCLC patients with EYS mutations had significantly better prognosis compared to EYS wild-type patients (median survival time: NA vs. 6.5 months, log-rank test, P = 0.010; Fig. 4 A). Consistent results were obtained from a multivariable Cox regression model adjusted for confounding factors (HR: 0.26, 95% CI: 0.08–0.83, P = 0.023; Fig. 4 B). The impact of mutations on ICI prognosis across different therapy types is shown in Supplementary Fig. S5. Further analysis demonstrated a significantly increased objective response rate in EYS -mutant patients (75.0% vs. 30.0%, Fisher’s exact test, P = 0.004; Fig. 4 C). After adjusting for confounders in multivariate logistic analysis, this association remained significant (OR: 0.12, 95% CI: 0.02–0.51, P = 0.007; Fig. 4 D). This study further analyzed the relationship between EYS mutations and NSCLC mutation burden. Results indicated that TMB was significantly higher in EYS -mutant patients compared to wild-type patients (Wilcoxon rank-sum test, P = 0.001; Fig. 5 A). By analyzing the somatic mutation spectrum of NSCLC, three mutational signatures were identified (Supplementary Table S5), including Signature 1 (age-relevant), Signature 4 (smoking-relevant), Signature 7 (ultra-violet light exposure-induced). Multivariable adjusted logistic regression model showed a trend toward higher TMB levels in EYS -mutant patients, though statistical significance was not reached ( P = 0.205; Fig. 5 B). Subsequent analyses revealed a significant association between EYS mutations and elevated NB (Wilcoxon test, P = 0.001; Fig. 5 C). In the TCGA NSCLC validation dataset, EYS -mutant patients also exhibited significantly increased TMB and NB levels (Wilcoxon rank-sum test, P = 0.001 and P < 0.001; Fig. 5 D and 5 E). EYS mutation-related immune infiltration, immune signaling and molecular pathways enrichment To further elucidate the potential immunological mechanisms underlying EYS mutations in melanoma patients, this study conducted multi-faceted immunological and pathway enrichment analyses. Results from the CIBERSORT algorithm revealed significantly increased infiltration of resting natural killer (NK) cells and decreased infiltration of resting myeloid dendritic cells in EYS -mutant patients (both P < 0.05; Fig. 6 A). In addition, the xCell algorithm showed that the abundance of endothelial cells and naive CD4 + T cells was significantly decreased, and the infiltration level of natural killer cells was increased (all P < 0.05; Fig. 6 B). Subsequent analysis of immune signature enrichment heatmaps and differential enrichment scores between EYS -mutant and wild-type subgroups indicated that stromal cell signaling enrichment scores were significantly lower in EYS -mutant patients ( P < 0.05), while type II interferon (IFN-γ) response signaling exhibited a negative correlation with EYS mutations ( P 0, FDR < 0.05; Fig. 6 D). Conversely, the epithelial-mesenchymal transition pathway, which facilitates tumor immune evasion, showed prominent enrichment in wild-type counterparts (NES = -1.85, FDR < 0.05; Fig. 6 E). Detailed GSEA results of EYS -mutant patients analyzed with GO and KEGG gene sets are presented in Supplementary Fig. S6. Finally, in this study, immune cell infiltration and molecular pathway enrichment analysis were performed in NSCLC patients. The CIBERSORT algorithm showed significantly increased infiltration of cytotoxic T cells and resting mast cells, alongside reduced infiltration of activated mast cells in EYS -mutant NSCLC patients (all P < 0.05; Supplementary Fig. S7A). The xCell algorithm indicated elevated infiltration of type 2 helper T cells and decreased infiltration of common myeloid progenitors and M2 macrophages (all P < 0.05; Supplementary Fig. S7B). Enrichment of the interferon α response pathway was also observed in EYS -mutant NSCLC patients (Supplementary Fig. S7C). Discussion Given that EYS is a key regulator of extracellular matrix remodeling and photoreceptor maintenance, its functional aberrations may impinge upon antitumor immune responses by affecting immune recognition processes within the tumor microenvironment. To date, no studies have elucidated the correlation between EYS gene mutations and the efficacy of ICI therapy in tumors. Against this backdrop, our study integrates multi-omics data and ICI therapeutic data from melanoma and NSCLC patients, revealing that individuals harboring EYS mutations exhibit significantly prolonged survival and higher objective response rates in both tumor types. These findings provide the basis for stratifying immunotherapy regimens according to EYS gene status. Our study demonstrated that, in the context of ICI therapy, EYS mutations were significantly associated with enhanced immunotherapy survival benefits in both melanoma and NSCLC patients. To further investigate the specificity of this association, we analyzed patients with the two types of cancer who underwent conventional chemotherapy in TCGA cohort. The results showed no statistically significant differences in survival curves between EYS -mutant and wild-type patients (all P > 0.05; Supplementary Fig. S8). TMB has shown clinical value as a predictive marker for immunotherapy efficacy in a variety of malignancies, and high TMB is significantly associated with better ICI treatment response rates [ 8 , 46 – 48 ]. However, TMB assessment relies on whole-exome sequencing technology, and its threshold definition has significant cancer heterogeneity, which limits its clinical application universality [ 49 ]. Recent studies suggest that specific single gene mutations (such as POLE/POLD1 [ 12 ], TP53 [ 50 ], FAT1 [ 51 ], MUC16 [ 52 ] and PBRM1 [ 53 ])may be associated with increased TMB levels and improved ICI efficacy, which provides new ideas for the development of alternative predictive markers. In this study, patients with tumors harboring EYS mutations not only showed significantly elevated TMB levels but were also associated with a significantly enhanced survival benefit from ICI treatment, a finding that provides new potential targets for the development of single-gene mutation-based immunotherapy prediction models. Based on the immune infiltration analysis in this study, the aberrant enrichment of resting natural killer (NK) cells in melanoma patients with EYS mutations may be associated with dysregulation of their functional control. Combining this with prior research, we posit that EYS mutations may disrupt the IL-12/IL-18 signaling pathway (e.g., by affecting receptor expression or signal transduction), promoting the differentiation of NK cells toward a high IFN-γ-secreting effecter phenotype. However, this differentiation might be stalled in a "resting activation" state due to microenvironmental pressure, leading to impaired antitumor functionality [ 54 ]. Concurrently, the reduced infiltration of resting myeloid dendritic cells (mDCs) may mitigate immune suppression, as the immune tolerance maintained by their secretion of IL-10/TGF-β and induction of Treg differentiation is weakened, thereby unleashing CD8 + T cell activity. Further analysis suggests that EYS mutations may downregulate CXCL12 secretion by tumor-associated endothelial cells (thereby inhibiting MDSC recruitment) and PD-L1 expression (blocking PD1/PD-L1–mediated T cell suppression), thereby releasing dual inhibitory effects on CD8 + T cell infiltration and function. This mechanism aligns with previous reports showing that blocking the CXCL12 pathway or using PD1/PD-L1 inhibitors can improve T cell function in liver cancer [ 55 ], indicating that EYS mutations may serve as a potential biomarker for predicting immune therapy response. Although the reduced initial CD4 + T cell population may limit Th1/Th17 differentiation and consequently diminish IFN-γ/IL-17 secretion [ 56 ], a concurrent reduction in Treg proportion could overall alleviate immune suppression [ 57 ]. Notably, the overall increase in NK cell infiltration (especially activated NK cells) clearly underscores the immunopromoting effects of EYS mutations. Activated NK cells kill tumor cells directly by releasing perforin and granzymes and activate other immune cells via IFN-γ secretion [ 58 ]. For instance, in patients with pulmonary adenocarcinoma harboring EGFR mutations who received combination therapy with NK cells and afatinib, the response rate increased from 16.7% to 75%, and the median progression-free survival was extended to 9 months [ 59 ]. In summary, EYS mutations may reshape the microenvironment by weakening immune suppressive networks and enhancing NK cell activity, thereby conferring an overall immunopromoting bias, although further investigation is needed to elucidate the functional heterogeneity of resting NK subtypes and the compensatory mechanisms of initial CD4 + T cell differentiation. Our study elucidates unique immunomodulatory features in EYS -mutated NSCLC patients. Through analysis using CIBERSORT and xCell algorithms, we found that EYS mutations significantly enhanced the tumor infiltration of cytotoxic T lymphocytes (CTLs), likely related to their direct induction of tumor cell apoptosis via the release of perforin and granzyme B through immunological synapses. Notably, the IFN-γ secreted by CTLs in the EYS -mutated microenvironment may augment MHC-I expression on antigen-presenting cells, thereby enhancing immune recognition, while TNF-α-induced tumor cell senescence, in concert with IL-2-maintained CTL clonal expansion, collectively establishes a durable antitumor response [ 60 ]. In terms of mast cell dynamics, an increased proportion of resting mast cells and a concomitant reduction in activated mast cells were observed in the EYS -mutated microenvironment. This phenotypic shift may resemble the favorable anti-metastatic pattern observed in colorectal cancer [ 61 ], suggesting that EYS may regulate mast cell activation thresholds in a tissue-specific manner. The increased infiltration of Th2 cells implies that EYS may modulate the IL-4/IL-10 signaling balance, thereby exerting immunopromoting effects in humoral and anti-parasitic immunity. Importantly, EYS mutations may inhibit the differentiation of common myeloid progenitors (CMPs) into myeloid-derived suppressor cells (MDSCs), thereby alleviating MDSC-mediated suppression of T cell and NK cell functions [ 62 , 63 ]. Meanwhile, the substantial reduction in M2-type macrophages could weaken their oncogenic effects mediated through the CCL18/CCL22-activated JAK2/STAT3 and FAK pathways [ 64 , 65 ]. Together, these dual regulatory mechanisms collectively reshape the tumor immune microenvironment, tipping the balance toward an antitumor immune response advantage in EYS -mutated patients. These findings systematically reveal the multidimensional regulatory role of EYS in tumor immune editing and provide novel targets for developing combination immunotherapy strategies for EYS -mutated patients. This study showed that the enriched signal of stromal cell characteristics was significantly weakened in the melanoma EYS mutation subgroup, and the high expression of type Ⅱ interferon (IFN-γ) was significantly negatively correlated with EYS mutation status. Previous studies have shown that IFN-γ induces anti-tumor effects by activating the JAK-STAT pathway. However, prolonged stimulation may trigger negative feedback inhibition through STAT1-dependent SOCS1 expression, thereby reducing signal sensitivity [ 66 , 67 ]. The present study speculated that EYS mutations may enhance SOCS1-mediated negative feedback through epigenetic remodeling or interaction with signaling pathways, which in turn reduces the IFN-γ signaling threshold. In support of this mechanism, it has been experimentally confirmed that JAK1/STAT1 loss-of-function mutations, such as JAK1 Glu890 or STAT1 Asp257, significantly impair IFN-γ signaling in colorectal cancer cells [ 68 ]. This study has certain limitations. First, the gene mutation data and immunotherapy data of melanoma and NSCLC patients come from the integrated analysis of multi-center retrospective cohorts. Despite the use of standardized quality control procedures, the differences in sequencing depth and clinical data collection standards between different datasets may still introduce selection bias and affect the generalization of the results. Second, the association of EYS mutations with ICI response has been tested only in melanoma and NSCLC, so whether the findings apply to other solid tumors (e.g., breast cancer, colorectal cancer) needs to be confirmed in cross-cancer cohort studies. Finally, the specific molecular mechanisms by which EYS mutations affect sensitivity to immunotherapy are not yet clear, and functional experiments are needed to clarify their regulatory effects on antigen presentation, T-cell infiltration, or metabolic reprogramming. In conclusion, this study by integrating the multi-omics data and clinicopathological information of melanoma and NSCLC, we found that EYS mutations is significantly associated with enhanced response to ICI treatment, which provides clues and basis for the development of clinical trials and treatment strategies, and provides potential molecular markers for the evaluation of ICI efficacy. Declarations Data availability All data generated or analysed during this study are included in this published article (and its Supplementary Information files). Author contributions ZL, QW, and DS designed this study; XW, ZW, YX, WZ, and ZL collected and integrated the related data; XW, ZW and YX conducted main data analysis; XW, QW, and DS composed and corrected the manuscript. Funding This research was funded by the National Natural Science Foundation of China (No. 81872719) and the Natural Science Foundation of Shandong Province (No. ZR2022MH127). Competing interests The authors declare no competing interests. Ethical Statement This study synthesized and analyzed publicly accessible datasets from multiple centers. All data were obtained from open-access repositories. The original datasets had been approved by the respective ethics committees in their initial studies, and written informed consent had been provided by all participants. Consequently, no additional ethical approval was required for the present analysis References Sharma, P. & Allison, J. P. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell 161, 205-214 (2015). Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568-571 (2014). Hou Q. & Xu H. Rational discovery of response biomarkers: candidate prognostic factors and biomarkers for checkpoint inhibitor-based immunotherapy. Adv. Exp. Med. Biol. 1248, 143-166 (2020). Gibney, G. T., Weiner, L. M. & Atkins, M. 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Influenza a virus antagonizes type I and type II interferon responses via SOCS1-dependent ubiquitination and degradation of JAK1. Virol. J. 17, 74 (2020). Coelho, M. A. et al. Base editing screens map mutations affecting interferon-γ signaling in cancer. Cancer Cell 41, 288-303 (2023). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx SupplementaryFigures.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":217026,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the study. Analysis of the association between \u003cem\u003eEYS\u003c/em\u003e mutations and ICI efficacy in melanoma and NSCLC patients\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7648150/v1/e7cc2639bc151d1e6cbe59d8.png"},{"id":93340296,"identity":"8f44fae9-91e0-48fb-ac87-75a4ce091743","added_by":"auto","created_at":"2025-10-12 14:31:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":276184,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of \u003cem\u003eEYS\u003c/em\u003e mutations with ICI treatment prognosis and response rates in melanoma patients. (\u003cstrong\u003eA\u003c/strong\u003e) Kaplan-Meier survival curves for \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients. (\u003cstrong\u003eB\u003c/strong\u003e) Multivariable Cox regression model evaluating the association between \u003cem\u003eEYS\u003c/em\u003emutations and ICI prognosis, adjusted for age, sex, clinical stage, and therapy type. (\u003cstrong\u003eC\u003c/strong\u003e) Differences in ICI treatment response rates between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients. (\u003cstrong\u003eD\u003c/strong\u003e) Multivariable logistic regression model analyzing the relationship between \u003cem\u003eEYS\u003c/em\u003e mutations and ICI response rates.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7648150/v1/e49f223a03b85c32ddeb23d6.png"},{"id":93343872,"identity":"ad4a67e6-93bb-4c83-b50c-f257e64a3a75","added_by":"auto","created_at":"2025-10-12 14:55:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":196971,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of \u003cem\u003eEYS\u003c/em\u003emutations with mutation burden in melanoma patients. (\u003cstrong\u003eA\u003c/strong\u003e) Differences in TMB levels between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients. (\u003cstrong\u003eB\u003c/strong\u003e) Multivariable logistic regression model assessing the association between \u003cem\u003eEYS\u003c/em\u003emutations and TMB, adjusted for multiple confounders. (\u003cstrong\u003eC\u003c/strong\u003e) Differences in NB levels between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients. (\u003cstrong\u003eD\u003c/strong\u003e) TMB and (\u003cstrong\u003eE\u003c/strong\u003e) NB differences across \u003cem\u003eEYS\u003c/em\u003e mutation subgroups in TCGA melanoma patients.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7648150/v1/afd652760fe625e30445352c.png"},{"id":93341648,"identity":"704e2b19-1049-4ed3-8eea-3e4e1e4dd8c9","added_by":"auto","created_at":"2025-10-12 14:39:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":272857,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between \u003cem\u003eEYS\u003c/em\u003e mutations and ICI treatment prognosis and response rates in NSCLC patients. (\u003cstrong\u003eA\u003c/strong\u003e) Kaplan-Meier survival curves for \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients. (\u003cstrong\u003eB\u003c/strong\u003e) Multivariable Cox regression model analyzing the association between \u003cem\u003eEYS\u003c/em\u003emutations and ICI prognosis, adjusted for age, sex, histology, smoking status, and other variables. (\u003cstrong\u003eC\u003c/strong\u003e) Differences in ICI treatment response rates between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients. (\u003cstrong\u003eD\u003c/strong\u003e) Multivariable logistic regression model evaluating the association between \u003cem\u003eEYS\u003c/em\u003emutations and ICI response rates.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7648150/v1/c21ef29f1de846a0d7d06ea0.png"},{"id":93340304,"identity":"6b8c3277-4521-4ddd-b2d5-a81953468763","added_by":"auto","created_at":"2025-10-12 14:31:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":190765,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between \u003cem\u003eEYS\u003c/em\u003e mutations and mutation burden in NSCLC patients. (\u003cstrong\u003eA\u003c/strong\u003e) Differences in TMB levels between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients. (\u003cstrong\u003eB\u003c/strong\u003e) Multivariable logistic regression model analyzing the association between \u003cem\u003eEYS\u003c/em\u003e mutations and TMB adjusted for multiple confounders. (\u003cstrong\u003eC\u003c/strong\u003e) Differences in NB levels between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients. (\u003cstrong\u003eD\u003c/strong\u003e) TMB and (\u003cstrong\u003eE\u003c/strong\u003e) NB differences across \u003cem\u003eEYS\u003c/em\u003esubgroups in the TCGA NSCLC dataset.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7648150/v1/4552c38b11737d01b2cb5833.png"},{"id":93341655,"identity":"47b86963-3b94-4a55-b5f7-1c89d602df12","added_by":"auto","created_at":"2025-10-12 14:39:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":400935,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration, immune signaling and molecular pathways associated with \u003cem\u003eEYS\u003c/em\u003e mutations in melanoma.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Differences in infiltration levels of 22 immune cell types between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients based on the CIBERSORT algorithm. Immune cells with significant differences are highlighted in blue. (\u003cstrong\u003eB\u003c/strong\u003e) Differential enrichment scores of 64 immune/stromal cell types between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients analyzed via the xCell algorithm. (\u003cstrong\u003eC\u003c/strong\u003e) Heatmap of enrichment scores for 14 immune signatures based on \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type subgroups. Significantly differentially enriched immune signatures are highlighted in green. Signaling pathways enriched in the Hallmark gene sets of (\u003cstrong\u003eD\u003c/strong\u003e) \u003cem\u003eEYS\u003c/em\u003e-mutant and (\u003cstrong\u003eE\u003c/strong\u003e) wild-type patients. * \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7648150/v1/f20ff2dd2ecb11dee76c7fdd.png"},{"id":104906540,"identity":"5949760a-8698-48b1-98af-785edba60280","added_by":"auto","created_at":"2026-03-18 14:13:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2492871,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7648150/v1/ea6e5ce4-36d6-4ca9-a1c9-bcd8857ea905.pdf"},{"id":93341651,"identity":"0c3afaa5-1a93-4ced-aec7-82be73997e2e","added_by":"auto","created_at":"2025-10-12 14:39:02","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":107703,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7648150/v1/d07859640a3c2cd2d3284203.xlsx"},{"id":93340301,"identity":"c7000941-c2db-4b55-a4bb-da3c5e61bf67","added_by":"auto","created_at":"2025-10-12 14:31:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1234725,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7648150/v1/60e02e82f18e200fbb99247e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of EYS mutations with immune checkpoint inhibitor outcome and response in melanoma and non-small cell lung cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the field of cancer immunotherapy witnessed revolutionary breakthroughs. Immune checkpoint inhibitor (ICI), which target key molecules such as PD-1/PD-L1/CTLA-4, effectively relieved the suppression of T cell functions in the tumor microenvironment, significantly improved the survival outcomes for patients with highly immunogenic cancers, including melanoma and non-small cell lung cancer (NSCLC). Some patients achieved long-term progression-free survival or complete remission\u0026nbsp;[1]. However, in clinical practice, only 20%-40% of patients exhibited durable responses [2]. Moreover, existing predictive biomarkers such as PD-L1 expression, tumor mutation burden (TMB), and microsatellite instability (MSI) had significant limitations [3, 4]: PD-L1 expression is not linearly correlated with therapeutic efficacy [5, 6], the predictive performance of TMB is affected by detection methods and threshold values [7, 8], and MSI is applicable to less than 5% of solid tumor patients [9]。\u003c/p\u003e\n\u003cp\u003ePrevious studies have revealed that the heterogeneous efficacy of ICI stems from the dynamic interplay between the tumor genome, host immune status, and microenvironmental signaling pathways [10, 11]. Specific gene mutations, such as \u003cem\u003ePOLE/POLD1\u003c/em\u003e mutations that increase neoantigen burden (NB) [12] and \u003cem\u003eSTK11/LKB1\u003c/em\u003e or \u003cem\u003eKEAP1\u003c/em\u003e loss-of-function mutations that inhibit immune signaling [13], can reshape the immune microenvironment and thereby modulate therapeutic responses. Current research has largely focused on high-frequency driver genes, while the immune regulatory mechanisms of low-frequency mutations or functionally unknown genes remain to be explored [14]. There is an urgent need to identify new molecular markers through multi-omics integration to improve predictive accuracy [15].\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eEYS\u003c/em\u003e (Eyes Shut Homolog) gene, one of the largest retinal-specific expressed genes identified to date, is located on human chromosome 6q12. Its genomic span exceeds 2 Mb and it encodes a functional protein composed of 3,145 amino acids. This protein contains five laminin G domains (involved in cell adhesion and signaling) and 27 epidermal growth factor (EGF)-like repeats (mediating protein-protein interactions) and is primarily expressed in photoreceptor cells of the retina, where it plays a key role in visual system development by regulating extracellular matrix assembly and light signal transduction [16-22]. Clinical studies have shown that \u003cem\u003eEYS\u003c/em\u003e mutations are strongly associated with autosomal recessive inherited retinal diseases, with approximately 5%-16% of familial cases worldwide harboring functionally validated mutations, predominantly truncation and missense mutations, which significantly impair protein function and lead to photoreceptor cell dysfunction [21, 23-28]. In recent years, tumor genomics data have suggested that \u003cem\u003eEYS\u003c/em\u003e may be involved in tumorigenesis. In brain metastasis (BM) from colorectal cancer, by comparing the mutation profiles of the TCGA CRC cohort with the BM cohort, \u003cem\u003eEYS\u003c/em\u003e was identified as one of the highly mutated genes [29], the study did not provide direct evidence supporting the involvement of \u003cem\u003eEYS\u003c/em\u003e in DNA damage repair (DDR) pathways, such as homologous recombination or mismatch repair. The functional role of \u003cem\u003eEYS\u003c/em\u003e in BM still requires experimental validation. TCGA data also show amplification of the 6q12 region in squamous cell lung cancer [30], with \u003cem\u003eEYS\u003c/em\u003e located within this region. However, whether \u003cem\u003eEYS\u003c/em\u003e amplification drives oncogenic phenotypes through genomic imbalance mediated by extrachromosomal DNA (ecDNA) remains to be further verified through functional experiments, such as CRISPR screening or single-cell multi-omics analysis. In summary, the pleiotropic effects of \u003cem\u003eEYS\u003c/em\u003e in tumors are still preliminary findings, and its mechanisms as an oncogene and clinical significance urgently need to be clarified through functional experiments and multi-omics analysis across cancer types.\u003c/p\u003e\n\u003cp\u003eTo date, the predictive role of \u003cem\u003eEYS\u003c/em\u003e mutations in cancer ICI treatment has not been reported. Given the wide application of ICI therapy in melanoma and NSCLC [31], this study retrospectively integrated pretreatment multi-omics data and clinical ICI treatment information from melanoma and NSCLC patients to elucidate the clinical significance of \u003cem\u003eEYS\u003c/em\u003e mutations in cancer immunotherapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was reported in accordance with Shandong Second Medical University guidelines. \u0026nbsp;All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\n\u003ch3\u003eCollection of melanoma and NSCLC samples\u003c/h3\u003e\n\u003cp\u003eThis study integrated pretreatment somatic mutation data, clinicopathological features, and immune checkpoint inhibitor (ICI) treatment information from 631 melanoma [\u003cspan additionalcitationids=\"CR33 CR34 CR35 CR36\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and 109 NSCLC patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] from multiple centers (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). All patients received anti-CTLA-4, anti-PD-1/PD-L1 monotherapy or combination therapy, with treatment response status including complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Since the original mutation data were derived from different sequencing platforms, this study employed Oncotator [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] for unified mutation annotation to ensure cross-platform data comparability and consistency.\u003c/p\u003e\u003cp\u003eSomatic mutation data, mRNA transcriptome expression profiles, and clinical prognostic information for melanoma and NSCLC were obtained based on The Cancer Genome Atlas (TCGA). Utilizing TCGA transcriptome expression data, this study investigated the relationship between \u003cem\u003eEYS\u003c/em\u003e mutations and immune characteristics in the tumor microenvironment.\u003c/p\u003e\n\u003ch3\u003eAssessment of tumor-infiltrating immune cells\u003c/h3\u003e\n\u003cp\u003eTo compare immune infiltration differences between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients, this study employed two methods to evaluate the abundance of tumor-infiltrating immune cells. First, the CIBERSORT algorithm (using the LM22 matrix based on gene expression profiles of 547 signature genes) was utilized to estimate the infiltration proportions of 22 immune cell types [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This was supplemented with the xCell algorithm to assess enrichment scores of 64 immune and stromal cell types for cross-validation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eExtraction of mutational signature\u003c/h2\u003e\u003cp\u003eThis study employed the method proposed by Kim et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] to extract mutational signature from mutation data of melanoma and NSCLC samples. The core of this approach is Bayesian non-negative matrix factorization (NMF), which decomposes the mutational signature matrix A (containing 96 base substitution types) into two non-negative matrices W and H (A\u0026thinsp;\u0026asymp;\u0026thinsp;W \u0026times; H), where W represents the extracted mutational signatures and H denotes the mutational activity of each signature. All extracted mutational signatures were compared with the 30 annotated mutational signatures in the COSMIC database by calculating cosine similarity.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCollection of immune signatures\u003c/h3\u003e\n\u003cp\u003eCurrent evidence indicates that multiple immune signatures are closely associated with tumor immunogenicity regulation and immunotherapy efficacy. This study systematically screened immune signature gene sets with well-established prognostic predictive value, integrating 14 hallmark immune signatures (Supplementary Table S3) through literature-based curation, including T cell inflammation, interferon response pathway, and others.\u003c/p\u003e\n\u003ch3\u003eGSVA and GSEA\u003c/h3\u003e\n\u003cp\u003eSingle sample gene set enrichment analysis (ssGSEA) enables precise characterization of immune infiltration features in the tumor microenvironment by quantifying the enrichment level of predefined gene sets (e.g., immune signature gene sets) within individual samples. For investigating \u003cem\u003eEYS\u003c/em\u003e mutation-associated signaling pathways, genome-wide differential expression analysis was first performed using the R DESeq2 package [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], followed by gene set enrichment analysis (GSEA) to identify significantly enriched pathways. Background gene sets were retrieved from the Molecular Signature Database (MSigDB) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, 44]\u0026zwnj;.\u003c/p\u003e\n\u003ch3\u003eTMB and NB\u003c/h3\u003e\n\u003cp\u003eTumor mutation burden (TMB) refers to the total number of non-synonymous somatic mutations per megabase (Mb) in the tumor genome. In this study, TMB was calculated based on integrated samples and TCGA cohorts, with normalization via log2 transformation of the total non-synonymous mutations per megabase. Neoantigen data for 341 melanoma and 662 NSCLC samples in the TCGA cohort were obtained from The Cancer Immune Atlas (TCIA), and neoantigen burden (NB) was computed using an algorithm based on somatic mutation profiles [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses and data visualization were performed using R software (version 4.4.3). Survival curves were generated using the Kaplan-Meier method, and the log-rank test was applied to evaluate the significance of survival differences. Logistic regression and Cox regression models incorporated multiple clinical confounders (e.g., age, sex, stage, and therapy type) and were implemented with the forestmodel package. The Wilcoxon rank sum test and Fisher\u0026rsquo;s exact test were used to assess differences in continuous variables and binary categorical variables, respectively, between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients. Unless otherwise specified, two-sided \u003cem\u003eP\u003c/em\u003e-values less than 0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eEYS\u003c/b\u003e \u003cb\u003emutational status in melanoma\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe workflow of this study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the 631 melanoma samples included, 193 cases (30.6%) achieved complete response (CR) or partial response (PR) on ICI treatment, while 430 cases (68.1%) exhibited stable disease (SD) or progressive disease (PD) as their ICI response status. Response data were unavailable for the remaining 8 samples (1.3%). Whole-genome mutation analysis revealed that C\u0026thinsp;\u0026gt;\u0026thinsp;T base substitutions were the predominant somatic mutational signature in this cohort (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and detailed mutational patterns of melanoma driver genes associated with \u003cem\u003eEYS\u003c/em\u003e mutations are shown in Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Among the 631 melanoma samples, 99 (15.7%) harbored \u003cem\u003eEYS\u003c/em\u003e mutations. The amino acid level alterations caused by \u003cem\u003eEYS\u003c/em\u003e mutations are depicted in Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEYS\u003c/b\u003e \u003cb\u003emutations are associated with improved ICI treatment prognosis and response rates in melanoma patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the 631 melanoma samples analyzed, 99 patients (16%) harbored \u003cem\u003eEYS\u003c/em\u003e mutations. Kaplan-Meier survival analysis demonstrated that patients with \u003cem\u003eEYS\u003c/em\u003e mutations exhibited significantly prolonged survival following ICI therapy compared to \u003cem\u003eEYS\u003c/em\u003e wild-type patients (median survival time: 48.8 vs. 26.5 months; Log-rank test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Multivariable Cox regression model adjusted for age, sex, clinical stage, and therapy type further confirmed the association between \u003cem\u003eEYS\u003c/em\u003e mutations and improved survival (HR: 0.62, 95% CI: 0.45\u0026ndash;0.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The prognostic predictive power of \u003cem\u003eEYS\u003c/em\u003e mutations in a single ICI cohort and survival analyses stratified by therapy types are shown in Supplementary Fig. S3 and S4. Furthermore, \u003cem\u003eEYS\u003c/em\u003e-mutant patients showed a higher objective response rate compared to wild-type patients (43.4% vs. 28.6%; Fisher\u0026rsquo;s exact test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). After adjusting for confounders using a multivariable logistic regression model, the association between \u003cem\u003eEYS\u003c/em\u003e mutations and ICI response rates remained statistically significant (OR: 0.62, 95% CI: 0.39\u0026ndash;0.97, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation of\u003c/b\u003e \u003cb\u003eEYS\u003c/b\u003e \u003cb\u003emutations with mutation burden in melanoma patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eExisting studies have shown that TMB is an important biomarker for predicting immunotherapy efficacy in melanoma. This study focused on the association between \u003cem\u003eEYS\u003c/em\u003e mutations and TMB. The results revealed that melanoma patients harboring \u003cem\u003eEYS\u003c/em\u003e mutations exhibited significantly elevated TMB levels (Wilcoxon rank-sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). To further explore the genomic mutational signatures, we characterized the melanoma mutation spectrum based on the NMF approach and identified four biologically significant mutational signatures (Supplementary Table S4): Signature 1 (age-relevant), Signature 4 (smoking-relevant), Signature 7 (ultra-violet light exposure-induced), and Signature 11 (alkylating agent-induced). To refine the association between \u003cem\u003eEYS\u003c/em\u003e mutations and TMB, a multivariable logistic regression model was applied, adjusting for clinical variables (age, sex, stage), the four identified mutational signatures, and mutations in DNA damage repair genes. The results confirmed that \u003cem\u003eEYS\u003c/em\u003e-mutant patients still demonstrated significantly higher TMB (OR: 4.06, 95% CI: 2.18\u0026ndash;8.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Additionally, \u003cem\u003eEYS\u003c/em\u003e-mutant patients showed markedly increased NB levels (Wilcoxon rank-sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). These findings were validated in melanoma patients from TCGA dataset, where \u003cem\u003eEYS\u003c/em\u003e-mutant patients also exhibited significantly elevated TMB and NB levels (Wilcoxon rank-sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eValidation of the relationship between\u003c/b\u003e \u003cb\u003eEYS\u003c/b\u003e \u003cb\u003emutations and ICI treatment response and mutation burden in NSCLC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the 109 collected NSCLC samples, 12 patients (11%) harbored \u003cem\u003eEYS\u003c/em\u003e mutations. Kaplan-Meier survival analysis revealed that NSCLC patients with \u003cem\u003eEYS\u003c/em\u003e mutations had significantly better prognosis compared to \u003cem\u003eEYS\u003c/em\u003e wild-type patients (median survival time: NA vs. 6.5 months, log-rank test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Consistent results were obtained from a multivariable Cox regression model adjusted for confounding factors (HR: 0.26, 95% CI: 0.08\u0026ndash;0.83, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The impact of mutations on ICI prognosis across different therapy types is shown in Supplementary Fig. S5. Further analysis demonstrated a significantly increased objective response rate in \u003cem\u003eEYS\u003c/em\u003e-mutant patients (75.0% vs. 30.0%, Fisher\u0026rsquo;s exact test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). After adjusting for confounders in multivariate logistic analysis, this association remained significant (OR: 0.12, 95% CI: 0.02\u0026ndash;0.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eThis study further analyzed the relationship between \u003cem\u003eEYS\u003c/em\u003e mutations and NSCLC mutation burden. Results indicated that TMB was significantly higher in \u003cem\u003eEYS\u003c/em\u003e-mutant patients compared to wild-type patients (Wilcoxon rank-sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). By analyzing the somatic mutation spectrum of NSCLC, three mutational signatures were identified (Supplementary Table S5), including Signature 1 (age-relevant), Signature 4 (smoking-relevant), Signature 7 (ultra-violet light exposure-induced). Multivariable adjusted logistic regression model showed a trend toward higher TMB levels in \u003cem\u003eEYS\u003c/em\u003e-mutant patients, though statistical significance was not reached (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.205; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Subsequent analyses revealed a significant association between \u003cem\u003eEYS\u003c/em\u003e mutations and elevated NB (Wilcoxon test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In the TCGA NSCLC validation dataset, \u003cem\u003eEYS\u003c/em\u003e-mutant patients also exhibited significantly increased TMB and NB levels (Wilcoxon rank-sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEYS\u003c/b\u003e \u003cb\u003emutation-related immune infiltration, immune signaling and molecular pathways enrichment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further elucidate the potential immunological mechanisms underlying \u003cem\u003eEYS\u003c/em\u003e mutations in melanoma patients, this study conducted multi-faceted immunological and pathway enrichment analyses. Results from the CIBERSORT algorithm revealed significantly increased infiltration of resting natural killer (NK) cells and decreased infiltration of resting myeloid dendritic cells in \u003cem\u003eEYS\u003c/em\u003e-mutant patients (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In addition, the xCell algorithm showed that the abundance of endothelial cells and naive CD4\u0026thinsp;+\u0026thinsp;T cells was significantly decreased, and the infiltration level of natural killer cells was increased (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Subsequent analysis of immune signature enrichment heatmaps and differential enrichment scores between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type subgroups indicated that stromal cell signaling enrichment scores were significantly lower in \u003cem\u003eEYS\u003c/em\u003e-mutant patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while type II interferon (IFN-γ) response signaling exhibited a negative correlation with \u003cem\u003eEYS\u003c/em\u003e mutations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Furthermore, GSEA using Hallmark gene sets demonstrated significant enrichment of the allograft rejection pathway in \u003cem\u003eEYS\u003c/em\u003e-mutant patients (NES\u0026thinsp;\u0026gt;\u0026thinsp;0, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Conversely, the epithelial-mesenchymal transition pathway, which facilitates tumor immune evasion, showed prominent enrichment in wild-type counterparts (NES = -1.85, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Detailed GSEA results of \u003cem\u003eEYS\u003c/em\u003e-mutant patients analyzed with GO and KEGG gene sets are presented in Supplementary Fig. S6.\u003c/p\u003e\u003cp\u003eFinally, in this study, immune cell infiltration and molecular pathway enrichment analysis were performed in NSCLC patients. The CIBERSORT algorithm showed significantly increased infiltration of cytotoxic T cells and resting mast cells, alongside reduced infiltration of activated mast cells in \u003cem\u003eEYS\u003c/em\u003e-mutant NSCLC patients (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplementary Fig. S7A). The xCell algorithm indicated elevated infiltration of type 2 helper T cells and decreased infiltration of common myeloid progenitors and M2 macrophages (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplementary Fig. S7B). Enrichment of the interferon α response pathway was also observed in \u003cem\u003eEYS\u003c/em\u003e-mutant NSCLC patients (Supplementary Fig. S7C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGiven that \u003cem\u003eEYS\u003c/em\u003e is a key regulator of extracellular matrix remodeling and photoreceptor maintenance, its functional aberrations may impinge upon antitumor immune responses by affecting immune recognition processes within the tumor microenvironment. To date, no studies have elucidated the correlation between \u003cem\u003eEYS\u003c/em\u003e gene mutations and the efficacy of ICI therapy in tumors. Against this backdrop, our study integrates multi-omics data and ICI therapeutic data from melanoma and NSCLC patients, revealing that individuals harboring \u003cem\u003eEYS\u003c/em\u003e mutations exhibit significantly prolonged survival and higher objective response rates in both tumor types. These findings provide the basis for stratifying immunotherapy regimens according to \u003cem\u003eEYS\u003c/em\u003e gene status.\u003c/p\u003e\u003cp\u003eOur study demonstrated that, in the context of ICI therapy, \u003cem\u003eEYS\u003c/em\u003e mutations were significantly associated with enhanced immunotherapy survival benefits in both melanoma and NSCLC patients. To further investigate the specificity of this association, we analyzed patients with the two types of cancer who underwent conventional chemotherapy in TCGA cohort. The results showed no statistically significant differences in survival curves between \u003cem\u003eEYS\u003c/em\u003e-mutant and wild-type patients (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Supplementary Fig. S8).\u003c/p\u003e\u003cp\u003eTMB has shown clinical value as a predictive marker for immunotherapy efficacy in a variety of malignancies, and high TMB is significantly associated with better ICI treatment response rates [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, TMB assessment relies on whole-exome sequencing technology, and its threshold definition has significant cancer heterogeneity, which limits its clinical application universality [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Recent studies suggest that specific single gene mutations (such as \u003cem\u003ePOLE/POLD1\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], \u003cem\u003eTP53\u003c/em\u003e [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], \u003cem\u003eFAT1\u003c/em\u003e [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], \u003cem\u003eMUC16\u003c/em\u003e [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and \u003cem\u003ePBRM1\u003c/em\u003e [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e])may be associated with increased TMB levels and improved ICI efficacy, which provides new ideas for the development of alternative predictive markers. In this study, patients with tumors harboring \u003cem\u003eEYS\u003c/em\u003e mutations not only showed significantly elevated TMB levels but were also associated with a significantly enhanced survival benefit from ICI treatment, a finding that provides new potential targets for the development of single-gene mutation-based immunotherapy prediction models.\u003c/p\u003e\u003cp\u003eBased on the immune infiltration analysis in this study, the aberrant enrichment of resting natural killer (NK) cells in melanoma patients with \u003cem\u003eEYS\u003c/em\u003e mutations may be associated with dysregulation of their functional control. Combining this with prior research, we posit that \u003cem\u003eEYS\u003c/em\u003e mutations may disrupt the IL-12/IL-18 signaling pathway (e.g., by affecting receptor expression or signal transduction), promoting the differentiation of NK cells toward a high IFN-γ-secreting effecter phenotype. However, this differentiation might be stalled in a \"resting activation\" state due to microenvironmental pressure, leading to impaired antitumor functionality [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Concurrently, the reduced infiltration of resting myeloid dendritic cells (mDCs) may mitigate immune suppression, as the immune tolerance maintained by their secretion of IL-10/TGF-β and induction of Treg differentiation is weakened, thereby unleashing CD8\u0026thinsp;+\u0026thinsp;T cell activity. Further analysis suggests that \u003cem\u003eEYS\u003c/em\u003e mutations may downregulate CXCL12 secretion by tumor-associated endothelial cells (thereby inhibiting MDSC recruitment) and PD-L1 expression (blocking PD1/PD-L1\u0026ndash;mediated T cell suppression), thereby releasing dual inhibitory effects on CD8\u0026thinsp;+\u0026thinsp;T cell infiltration and function. This mechanism aligns with previous reports showing that blocking the CXCL12 pathway or using PD1/PD-L1 inhibitors can improve T cell function in liver cancer [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], indicating that \u003cem\u003eEYS\u003c/em\u003e mutations may serve as a potential biomarker for predicting immune therapy response. Although the reduced initial CD4\u0026thinsp;+\u0026thinsp;T cell population may limit Th1/Th17 differentiation and consequently diminish IFN-γ/IL-17 secretion [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], a concurrent reduction in Treg proportion could overall alleviate immune suppression [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Notably, the overall increase in NK cell infiltration (especially activated NK cells) clearly underscores the immunopromoting effects of \u003cem\u003eEYS\u003c/em\u003e mutations. Activated NK cells kill tumor cells directly by releasing perforin and granzymes and activate other immune cells via IFN-γ secretion [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. For instance, in patients with pulmonary adenocarcinoma harboring EGFR mutations who received combination therapy with NK cells and afatinib, the response rate increased from 16.7% to 75%, and the median progression-free survival was extended to 9 months [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In summary, \u003cem\u003eEYS\u003c/em\u003e mutations may reshape the microenvironment by weakening immune suppressive networks and enhancing NK cell activity, thereby conferring an overall immunopromoting bias, although further investigation is needed to elucidate the functional heterogeneity of resting NK subtypes and the compensatory mechanisms of initial CD4\u0026thinsp;+\u0026thinsp;T cell differentiation.\u003c/p\u003e\u003cp\u003eOur study elucidates unique immunomodulatory features in \u003cem\u003eEYS\u003c/em\u003e-mutated NSCLC patients. Through analysis using CIBERSORT and xCell algorithms, we found that \u003cem\u003eEYS\u003c/em\u003e mutations significantly enhanced the tumor infiltration of cytotoxic T lymphocytes (CTLs), likely related to their direct induction of tumor cell apoptosis via the release of perforin and granzyme B through immunological synapses. Notably, the IFN-γ secreted by CTLs in the \u003cem\u003eEYS\u003c/em\u003e-mutated microenvironment may augment MHC-I expression on antigen-presenting cells, thereby enhancing immune recognition, while TNF-α-induced tumor cell senescence, in concert with IL-2-maintained CTL clonal expansion, collectively establishes a durable antitumor response [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In terms of mast cell dynamics, an increased proportion of resting mast cells and a concomitant reduction in activated mast cells were observed in the \u003cem\u003eEYS\u003c/em\u003e-mutated microenvironment. This phenotypic shift may resemble the favorable anti-metastatic pattern observed in colorectal cancer [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], suggesting that \u003cem\u003eEYS\u003c/em\u003e may regulate mast cell activation thresholds in a tissue-specific manner. The increased infiltration of Th2 cells implies that \u003cem\u003eEYS\u003c/em\u003e may modulate the IL-4/IL-10 signaling balance, thereby exerting immunopromoting effects in humoral and anti-parasitic immunity. Importantly, \u003cem\u003eEYS\u003c/em\u003e mutations may inhibit the differentiation of common myeloid progenitors (CMPs) into myeloid-derived suppressor cells (MDSCs), thereby alleviating MDSC-mediated suppression of T cell and NK cell functions [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Meanwhile, the substantial reduction in M2-type macrophages could weaken their oncogenic effects mediated through the CCL18/CCL22-activated JAK2/STAT3 and FAK pathways [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Together, these dual regulatory mechanisms collectively reshape the tumor immune microenvironment, tipping the balance toward an antitumor immune response advantage in \u003cem\u003eEYS\u003c/em\u003e-mutated patients. These findings systematically reveal the multidimensional regulatory role of \u003cem\u003eEYS\u003c/em\u003e in tumor immune editing and provide novel targets for developing combination immunotherapy strategies for \u003cem\u003eEYS\u003c/em\u003e-mutated patients.\u003c/p\u003e\u003cp\u003eThis study showed that the enriched signal of stromal cell characteristics was significantly weakened in the melanoma \u003cem\u003eEYS\u003c/em\u003e mutation subgroup, and the high expression of type Ⅱ interferon (IFN-γ) was significantly negatively correlated with \u003cem\u003eEYS\u003c/em\u003e mutation status. Previous studies have shown that IFN-γ induces anti-tumor effects by activating the JAK-STAT pathway. However, prolonged stimulation may trigger negative feedback inhibition through STAT1-dependent SOCS1 expression, thereby reducing signal sensitivity [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The present study speculated that \u003cem\u003eEYS\u003c/em\u003e mutations may enhance SOCS1-mediated negative feedback through epigenetic remodeling or interaction with signaling pathways, which in turn reduces the IFN-γ signaling threshold. In support of this mechanism, it has been experimentally confirmed that JAK1/STAT1 loss-of-function mutations, such as JAK1 Glu890 or STAT1 Asp257, significantly impair IFN-γ signaling in colorectal cancer cells [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study has certain limitations. First, the gene mutation data and immunotherapy data of melanoma and NSCLC patients come from the integrated analysis of multi-center retrospective cohorts. Despite the use of standardized quality control procedures, the differences in sequencing depth and clinical data collection standards between different datasets may still introduce selection bias and affect the generalization of the results. Second, the association of \u003cem\u003eEYS\u003c/em\u003e mutations with ICI response has been tested only in melanoma and NSCLC, so whether the findings apply to other solid tumors (e.g., breast cancer, colorectal cancer) needs to be confirmed in cross-cancer cohort studies. Finally, the specific molecular mechanisms by which \u003cem\u003eEYS\u003c/em\u003e mutations affect sensitivity to immunotherapy are not yet clear, and functional experiments are needed to clarify their regulatory effects on antigen presentation, T-cell infiltration, or metabolic reprogramming.\u003c/p\u003e\u003cp\u003eIn conclusion, this study by integrating the multi-omics data and clinicopathological information of melanoma and NSCLC, we found that \u003cem\u003eEYS\u003c/em\u003e mutations is significantly associated with enhanced response to ICI treatment, which provides clues and basis for the development of clinical trials and treatment strategies, and provides potential molecular markers for the evaluation of ICI efficacy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article (and its Supplementary Information files).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZL, QW, and DS designed this study; XW, ZW, YX, WZ, and ZL collected and integrated the related data; XW, ZW and YX conducted main data analysis; XW, QW, and DS composed and corrected the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China (No. 81872719) and the Natural Science Foundation of Shandong Province (No. ZR2022MH127).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study synthesized and analyzed publicly accessible datasets from multiple centers. All data were obtained from open-access repositories. The original datasets had been approved by the respective ethics committees in their initial studies, and written informed consent had been provided by all participants. Consequently, no additional ethical approval was required for the present analysis\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSharma, P. \u0026amp; Allison, J. P. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e161,\u003c/strong\u003e 205-214 (2015).\u003c/li\u003e\n\u003cli\u003eTumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e515,\u003c/strong\u003e 568-571 (2014).\u003c/li\u003e\n\u003cli\u003eHou Q. \u0026amp; Xu H. 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Base editing screens map mutations affecting interferon-\u0026gamma; signaling in cancer. \u003cem\u003eCancer Cell\u003c/em\u003e\u003cstrong\u003e41,\u003c/strong\u003e 288-303 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"EYS mutation, Immune checkpoint inhibitor, Molecular markers, Melanoma, NSCLC","lastPublishedDoi":"10.21203/rs.3.rs-7648150/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7648150/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImmunotherapy with immune checkpoint inhibitor (ICI) improved outcomes in advanced/metastatic melanoma and non-small cell lung cancer (NSCLC), but only a subset of patients benefited significantly. Existing biomarkers like tumor mutational burden (TMB) and microsatellite instability (MSI) had limitations, prompting the search for novel biomarkers. \u003cem\u003eEYS\u003c/em\u003e, which is primarily involved in retinal photoreceptor function and visual system development, had not been explored for its potential role in predicting ICI therapy responses. In this study, data on pretreatment mutations, ICI treatment information, and clinicopathological data of 631 melanoma samples and 109 NSCLC samples were integrated. Meanwhile, immune infiltration and signaling pathway enrichment associated with \u003cem\u003eEYS\u003c/em\u003e mutation were evaluated based on transcriptome gene expression profiles. In the melanoma cohort, \u003cem\u003eEYS\u003c/em\u003e mutations were associated with significantly improved ICI outcome (HR: 0.62, 95%CI: 0.45\u0026ndash;0.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and response rate (43.4% vs. 28.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). The findings were corroborated in NSCLC samples, where patients with \u003cem\u003eEYS\u003c/em\u003e mutations exhibited a significantly better prognosis under ICI therapy (HR: 0.26, 95% CI: 0.08\u0026ndash;0.83, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) and a higher response rate (75.0% vs. 30.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Further analysis showed that \u003cem\u003eEYS\u003c/em\u003e mutations were associated with elevated tumor mutational burden, enhanced immune cell infiltration, and activation of immune-related signaling pathways. Our results showed that \u003cem\u003eEYS\u003c/em\u003e mutation was associated with better ICI response, which provided a theoretical basis for clinical tumor immunotherapy strategy development and provided a possible molecular marker for assessing treatment response.\u003c/p\u003e","manuscriptTitle":"Association of EYS mutations with immune checkpoint inhibitor outcome and response in melanoma and non-small cell lung cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-12 14:30:57","doi":"10.21203/rs.3.rs-7648150/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f1a9ac9-d4cd-4539-ae10-6f82baf455af","owner":[],"postedDate":"October 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56050802,"name":"Health sciences/Biomarkers"},{"id":56050803,"name":"Biological sciences/Cancer"},{"id":56050804,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56050805,"name":"Biological sciences/Immunology"},{"id":56050806,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-18T14:10:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-12 14:30:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7648150","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7648150","identity":"rs-7648150","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}