NLRP13 mutation as a predictive indicator for immune checkpoint inhibitor efficacy in melanoma and NSCLC

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A key point in immunotherapies is to select tumor patients who will respond to immune checkpoint inhibitor (ICI). Effective indicators could accurately evaluate the ICI treatment efficacy. NLRP proteins have been revealed to implicate in inflammatory processes via the inflammasomes. NLRP family pyrin domain containing 13 ( NLRP13 ) is frequently mutated in cancer genomes. However, the association between NLRP family pyrin domain containing 13 ( NLRP13 ) mutation and ICI efficacy is never reported. Methods This study collected ICI treatment data and somatic mutational information from totaling 631 melanoma patients and integrated as the discovery cohort to explore the relationship of NLRP13 mutations with ICI efficacy. Besides, 109 non-small cell lung cancer (NSCLC) patients were consolidated as the validation cohort. Based on the genomic data from The Cancer Genome Atlas (TCGA), we investigated the potential biological mechanisms behind NLRP13 mutations. Findings: In melanoma, NLRP13 mutated ( NLRP13 -MUT) patients obtained a significant ICI survival advantage as compared with NLRP13 wildtype ( NLRP13 -WT) patients (HR: 0.65, 95% CI: 0.48–0.89, P = 0.007). A higher ICI response rate was also observed in such mutated subgroup (39.3% vs. 28.9%, P = 0.029). In NSCLC, the preferable ICI survival and response rate were corroborated in NLRP13 -MUT patients. Further analyses demonstrated that a better tumor microenvironment and elevated immunogenicity were enriched in NLRP13 -MUT patients. Interpretation: Our findings indicate that NLRP13 mutations could serve as a possible indicator for ICI treatment efficacy. NLRP13 mutations immunotherapies melanoma NSCLC efficacy indicator Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Traditional treatment methods, such as radiotherapies and chemotherapies, were often unsatisfactory for advanced/metastatic cancer patients [ 1 ]. Recently, the advent of immune checkpoint inhibitor (ICI) therapies greatly changed this clinical situation and treatment problem [ 2 ]. ICI agents could effectively kill tumor cells via reactivating the immune system, therefore improving survival outcomes for several cancer types [ 2 ]. Despite the remarkable clinical treatment efficacy, only a subset of cancer patients could obtain ICI benefits or durable responses [ 3 , 4 ]. Therefore, predictive biomarkers were necessary for choosing patients who would respond deeply to ICI treatments and assessing clinical efficacy. NLRP13 , as a member of the NLRP inflammasome family, activated caspase-1 by assembling a multiprotein complex, thereby regulating the release of inflammatory factors such as IL-1β and playing a central role in innate immunity [ 5 – 7 ]. Its high expression in immune-privileged sites such as the placenta and testis suggested its potential involvement in local inflammatory regulation [ 5 , 6 , 8 ]. Although direct research was limited, the tumor-related mechanisms of other family members (such as NLRP3 and NLRP12 ) provided important clues for understanding NLRP13 . NLRP3 promoted inflammation and tumor progression through ROS signaling in colorectal cancer [ 7 , 9 – 12 ] and activated the caspase-1/IL-1β axis in lung cancer, leading to decreased lung function and accelerated malignancy [ 7 , 10 ]. In contrast, NLRP12 inhibited the occurrence of colorectal cancer by suppressing the ERK/AKT pathway [ 7 , 9 , 10 ]. NLRP13 exhibited tissue-specific dual roles in tumors. In colorectal cancer, its expression was significantly elevated, and the dynamic correlation between promoter methylation levels and tumor staging [ 5 ] suggested that it might influence cancer progression through caspase-1-dependent inflammatory pathways, such as high-confidence interactions with MEFV and NLRC4 [ 5 , 8 ]. Conversely, in breast cancer, head and neck cancer, and prostate cancer, its expression was significantly reduced in tumor tissues [ 5 , 10 ]. The epigenetic drug decitabine (DAC) could upregulate NLRP13 expression in vitro and modulate inflammatory responses [ 5 , 13 ]. This contradiction might arise from its epigenetic regulation (such as promoter methylation negatively correlated with prognosis [ 5 , 8 , 10 ]) and differential activation of inflammatory signaling pathways (such as ROS/NF-κB pathway-induced chronic inflammation [ 7 , 9 , 12 ]). NLRP13 might influence cancer progression and treatment response by remodeling the tumor immune microenvironment. At the level of inflammatory factors, it might, like NLRP3 , affect cancer progression through the caspase-1/IL-1β axis [ 7 ]. In head and neck squamous cell carcinoma, NLRP3 inflammasome activation could mediate IL-1β release, promoting inflammation-induced carcinogenesis [ 10 , 14 , 15 ]. In terms of immune evasion, the inflammasome pathway might be exploited by tumor cells. This immune regulatory role endowed it with potential clinical value. The expression level of NLRP13 was associated with poor prognosis in breast and prostate cancer, and its promoter methylation status might serve as a potential diagnostic biomarker [ 5 , 15 ]. Moreover, inflammasome pathway inhibitors (such as NLRP3 -targeted agents) could enhance the immunogenicity of chemotherapy [ 16 , 17 ]. For example, the combination of paclitaxel and an IRE1α inhibitor could activate pyroptosis to reverse "cold" tumors [ 18 ]. The correlation of NLRP3 mutations in melanoma with TMB, immune cell infiltration, and ICI efficacy [ 10 , 12 ] suggested that NLRP13 might become a predictive biomarker for ICI efficacy through similar pathways, such as influencing tumor immunogenicity or T cell function [ 14 ]. However, its direct association with ICI response in melanoma and NSCLC still needed to be verified. This study systematically reviewed and integrated the multi-omics data of these two types of tumor patients before treatment and their corresponding ICI treatment response information, aiming to elucidate the clinical value of NLRP13 mutations in tumor immunotherapy. 2 Materials and Methods 2.1 Sources and processing of melanoma and NSCLC samples We integrated 631 melanoma [ 19 – 24 ] and 109 NSCLC samples [ 25 , 26 ] from published studies, covering somatic mutation data, ICI treatment information, and clinical-pathological data ( Supplementary Tables 1 and 2 ). All samples received anti-PD-1/PD-L1, anti-CTLA-4, or combination therapy. As somatic mutation data came from diverse sequencing platforms, we used Oncotator [ 27 ] for standardized annotation. This study used somatic mutation data, mRNA expression profiles, and clinical prognosis information of melanoma and NSCLC patients from TCGA. Based on TCGA transcriptome data, it analyzed the link between NLRP13 mutations and immune-related characteristics in the tumor immune microenvironment. 2.2 Mutation signature identification In this study, based on the methodological framework established by Kim et al. [ 28 ], we extracted mutation signatures from the mutational spectra of melanoma and NSCLC samples. The method employed a Bayesian non-negative matrix factorization (NMF) algorithm to decompose the mutation signature matrix A, which includes 96 types of base substitution patterns, into the product of two non-negative matrices (A ≈ W×H). Here, the W matrix represented the extracted mutation signatures, while the H matrix characterized the mutational activity of each signature. By calculating the cosine similarity, the extracted mutation signatures were systematically compared with the 30 known mutation signatures documented in the COSMIC database. 2.3 TMB and NB Tumor Mutational Burden (TMB) was the number of non-synonymous somatic mutations per megabase (Mb) in the tumor genome. In this study, TMB values were calculated based on integrated samples and the TCGA cohort, with the number of non-synonymous mutations per Mb standardized using log2 transformation. For the 341 melanoma and 662 NSCLC samples in the TCGA cohort, neoantigen data came from The Cancer Immune Atlas (TCIA). Neoantigen Burden (NB) was calculated using an algorithm based on the somatic mutation profiles [ 29 ]. 2.4 GSVA and GSEA Single-sample Gene Set Enrichment Analysis (ssGSEA) quantified the enrichment of predefined gene sets, such as immune-related ones, in individual samples. This enabled precise characterization of immune infiltration in the tumor microenvironment. To explore NLRP13 mutation-related pathways, we first used the R package DESeq2 [ 30 ] for genome-wide differential expression analysis. Then, Gene Set Enrichment Analysis (GSEA) was conducted to identify significantly enriched pathways. The background gene sets for this analysis were from the Molecular Signatures Database (MSigDB) [ 31 , 32 ]. 2.5 Collection of immune-related signatures Existing studies had demonstrated that multiple immune signals were closely associated with the regulation of tumor immunogenicity and the response to immunotherapy. In this study, through systematic review of the literature, we identified 14 hallmark immune signals with clear prognostic predictive value ( Supplementary Table 3 ). These signals encompassed a spectrum of core immune features, such as the T-cell-inflamed signature, type I/II interferon response pathways, cytolytic activity, tertiary lymphoid structures (TLS), immune cell subsets (B/P, T/NK, M/D Metagene), and immune signaling molecules. 2.6 Analysis of tumor immune infiltration status To investigate the differences in immune cell infiltration levels between the NLRP13 -mutant and wild-type groups, this study employed both the CIBERSORT method and the algorithm proposed by Angelova et al. for analysis. CIBERSORT calculated the tumor infiltration levels of 22 immune cell types based on the LM22 signature matrix containing 547 representative genes [ 33 ]. In contrast, the method proposed by Angelova et al. [ 34 ] evaluated the infiltration abundance of 31 immune cells using their 812 characteristic genes. The specific signature genes corresponding to each immune cell subtype are detailed in Supplementary Table 4 . 2.7 Statistical Analysis In this study, statistical analysis and data visualization were performed using R software (version 4.5.0). For survival analysis, Kaplan-Meier curves were constructed, and the significance of survival differences between groups was assessed using the Log-rank test. To adjust for potential confounding effects, confounders such as age, sex, disease stage, and treatment regimen were included in the Logistic regression and Cox proportional hazards regression models, with results presented using the forestmodel package. The distribution of continuous and categorical variables between the NLRP13 mutant and wild-type groups was compared using the Wilcoxon rank-sum test and Fisher's exact test, respectively. Unless otherwise specified, two-sided tests were used, and a significance level of P < 0.05 was employed to determine statistical significance. 3 Results 3.1 NLRP13 mutational status in melanoma The analysis process of this study is shown in Fig. 1 . In the 631 melanoma samples included in the study, 193 cases (30.6%) achieved a complete response (CR) or partial response (PR) to ICI therapy, 430 cases (68.1%) showed stable disease (SD) or progressive disease (PD) under ICI treatment, and the remaining 8 samples (1.3%) had unknown response data to ICI treatment. The whole-genome mutation landscape revealed that C > T base substitution was the predominant somatic mutation pattern in this cohort ( Supplementary Fig. 1 ). The mutation patterns of melanoma-related driver genes linked to NLRP13 mutations are detailed in Supplementary Fig. 1 . Among the 631 melanoma samples, 122 cases (19.3%) had NLRP13 mutations. The amino acid sequence changes mediated by NLRP13 mutations are shown in Supplementary Fig. 2 . 3.2 NLRP13 mutations enhance the prognosis and remission rate of ICI therapy for melanoma patients This study included 631 melanoma samples, of which 122 (19.3%) had NLRP13 mutations. Survival analysis showed that NLRP13 -mutated patients had a significantly longer median survival after ICI treatment than wild-type patients (36.9 months vs. 25.6 months, Log-rank test P = 0.002, Fig. 2 A). After multivariate Cox regression adjusted for age, sex, clinical stage, and treatment type, NLRP13 mutations remained significantly associated with survival benefits (HR: 0.65, 95% CI: 0.48–0.89, P = 0.007, Fig. 2 B). The predictive value of NLRP13 mutations in an independent ICI cohort and prognostic analyses of different treatment regimens are shown in Supplementary Figs. 3 and 4 . Also, the objective response rate (ORR) was significantly higher in NLRP13 -mutated patients than in wild-type patients (39.3% vs. 28.9%, Fisher's exact test P = 0.029, Fig. 2 C). After multivariate logistic regression adjusted for confounders, NLRP13 mutations were still statistically significantly associated with ICI response (OR: 0.61, 95% CI: 0.40–0.95, P = 0.031, Fig. 2 D). 3.3 The association between NLRP13 mutations and the tumor mutational burden in melanoma Previous studies have confirmed that TMB was a key biomarker for predicting the efficacy of ICI in melanoma. In this study, the association between NLRP13 mutations and TMB was investigated. It was found that patients with NLRP13 mutations had significantly higher TMB levels compared to wild-type patients (Wilcoxon rank-sum test P < 0.001; Fig. 3 A). To further elucidate the genomic mutational signatures, NMF was applied to the mutational spectra of melanoma, identifying four biologically relevant mutational signatures: Signature 1, 4, 7, and 11. Detailed mutational activity for each signature in each sample is provided in Supplementary Table 5 . To more accurately assess the relationship between NLRP13 mutations and TMB, a multivariate logistic regression model was constructed to adjust for clinical variables such as age, sex, disease stage, as well as the aforementioned mutational signatures and mutations in DNA repair genes. The results showed that NLRP13 mutation was still significantly associated with elevated TMB (OR: 5.37, 95% CI: 2.93–10.45, P < 0.001; Fig. 3 B). Additionally, patients with NLRP13 mutations also exhibited significantly increased NB (Wilcoxon rank-sum test P < 0.001; Fig. 3 C). Validation using the TCGA melanoma cohort further confirmed that patients with NLRP13 mutations had significantly elevated levels of both TMB and NB (Wilcoxon rank-sum test, both P < 0.001; Fig. 3 D and 3 E). 3.4 The impact of NLRP13 mutations on ICI treatment outcomes and TMB was validated in the NSCLC cohort Among the 109 NSCLC samples included, 12 cases (11%) were identified with NLRP13 mutations. Kaplan-Meier survival analysis revealed that patients with NLRP13 mutations had significantly better prognosis compared with those with the wild-type (median survival time: NA vs. 6.3 months; Log-rank test P = 0.013; Fig. 4 A). After adjusting for potential confounding factors using a multivariate Cox regression model, this association remained significant (HR: 0.30, 95% CI: 0.11–0.85, P = 0.021; Fig. 4 B). The correlation between NLRP13 mutations and ICI outcomes across different treatment modalities is detailed in Supplementary Fig. 5 . Moreover, the ORR of patients with NLRP13 mutations was significantly higher than that of wild-type patients (90.9% vs. 28.6%; Fisher's exact test P < 0.001; Fig. 4 C). This association remained significant after adjustment for confounding factors using a multivariate logistic regression model (OR: 0.03, 95% CI: 0.00-0.21, P = 0.003; Fig. 4 D). Further investigation into the relationship between NLRP13 mutations and TMB demonstrated that patients with NLRP13 mutations had significantly higher TMB levels compared with wild-type patients (Wilcoxon rank-sum test P = 0.003; Fig. 5 A). Signature analysis of somatic mutations in NSCLC identified three major mutational signatures, namely signatures 1, 4, and 7, with their mutational activities presented in Supplementary Table 6 . Multivariate logistic regression analysis showed that patients with NLRP13 mutations had higher TMB levels (OR: 5.68, 95% CI: 1.26–14.71, P = 0.029; Fig. 5 B). Additionally, a significant association was found between NLRP13 mutations and elevated NB (Wilcoxon test P < 0.001; Fig. 5 C). In the independent validation cohort from TCGA NSCLC dataset, patients with NLRP13 mutations also exhibited significantly increased TMB and NB levels (Wilcoxon rank-sum test, both P < 0.001; Fig. 5 D and 5 E). 3.5 Analysis of immune infiltration characteristics and pathway enrichment associated with NLRP13 mutations To explore the immunological mechanisms underlying NLRP13 mutations in melanoma patients, we conducted multi-level immunological and pathway enrichment analyses. Using the CIBERSORT algorithm, we found that in the NLRP13 -mutant group, the infiltration of CD8 + T cells, M0 macrophages, and M1 macrophages was significantly increased, while M2 macrophages infiltration was significantly reduced (all P < 0.05; Fig. 6 A). Similarly, using the method by Angelova et al., we detected higher infiltration levels of activated CD4 + T cells, activated CD8 + T cells, and effector memory CD4 + T cells, but lower regulatory T cell infiltration in NLRP13 -mutant patients (all P < 0.05; Fig. 6 B). Enrichment analysis of immune signals between NLRP13 -mutant and wild-type subgroups revealed that patients harboring NLRP13 mutations exhibited a significant enhancement in type II interferon response signal enrichment scores ( P < 0.05; Fig. 6 C). GSEA further indicated that in NLRP13 -mutant melanoma patients, the Interferon γ response (FDR < 0.001; Fig. 6 D), Interferon α response (FDR < 0.001; Fig. 6 E), and G2M checkpoint (FDR < 0.001; Fig. 6 F) were all significantly enriched ( Supplementary Fig. 6 ). Ultimately, this study conducted an analysis of immune infiltration and pathway enrichment in patients with NSCLC. Utilizing the CIBERSORT algorithm, we determined that in the NLRP13 -mutant subgroup, the infiltration proportions of CD8 + T cells, memory CD4 + T cells, and M1 macrophages were significantly elevated, while the infiltration level of regulatory T cells was reduced (all P < 0.05; Supplementary Fig. 7A ). Moreover, further analysis using the algorithm proposed by Angelova et al. revealed a significant enrichment of activated CD4 + T cells, activated CD8 + T cells, and effector memory CD4 + T cells in NLRP13 -mutant patients (all P < 0.05; Supplementary Fig. 7B ). 4 Discussion Although the classic function of NLRP13 as a pro-inflammatory inflammasome molecule suggested that its inactivation may impair anti-tumor immunity, the correlation between NLRP13 mutations and the efficacy of immune checkpoint inhibitors (ICI) had not yet been systematically elucidated. In this study, we integrated genomic, transcriptomic data, and clinical response data to immunotherapy from cohorts of melanoma and non-small cell lung cancer patients and found that patients harboring NLRP13 mutations exhibited significantly prolonged overall survival (OS) and higher objective response rates (ORR). These findings provided a molecular biological basis for constructing a precision stratification model for immunotherapy based on NLRP13 mutation status and suggested the complex immunomodulatory role of NLRP13 in the tumor microenvironment. This study found that for patients with the two types of tumors receiving ICI therapy, the mutation status of NLRP13 was significantly associated with survival benefits. To investigate whether this association was therapy-specific, we further analyzed patients with the two types of tumors receiving conventional chemotherapy in the TCGA cohort. The results showed that in patients with the two types of tumors treated with conventional chemotherapy, there was no statistically significant difference in the survival curves between NLRP13 -mutant and wild-type patients (all P > 0.05; Supplementary Fig. 8 ). TMB had been proven to be an effective predictive marker for immunotherapy efficacy in various malignant tumors [ 35 – 38 ]. However, TMB detection relied on whole-exome sequencing (WES), and its threshold varied across cancer types, which limited its universal clinical application [ 39 ]. Recent studies had indicated that certain specific gene mutations (such as FAT1 [ 40 ], TP53 [ 41 ], MUC16 [ 42 ], POLE/POLD1 [ 43 ], and PBRM1 [ 44 ]) might be linked to both increased TMB levels and improved ICI effectiveness, opening up a new avenue for identifying alternative predictive markers. In this study, it was found that patients with NLRP13 mutations not only exhibited a significantly higher TMB, but were also closely associated with enhanced survival benefits from ICI therapy, thus offering a new target for developing immune-therapy prediction models based on single-gene mutations. This study had certain limitations. First, the genetic mutation and immunotherapy data for melanoma and NSCLC were consolidated from multicenter retrospective cohorts. Although a standardized quality control process was used, differences in sequencing depth and clinical data collection standards across datasets might have caused bias and restricted result extrapolation. Second, the link between NLRP13 mutations and ICI effectiveness was only confirmed in melanoma and NSCLC, and its applicability to other solid tumors like colorectal and breast cancer needed cross-cancer-type studies to verify. Finally, the molecular mechanism of how NLRP13 mutations regulated immunotherapy sensitivity remained unclear and requires future functional experiments to explore. This study integrated the multi-omics data and clinicopathological information of melanoma and NSCLC patients and revealed a significant correlation between NLRP13 mutations and enhanced response to ICI therapy. This finding provided important references for the advancement of subsequent clinical trials and the optimization of treatment protocols, and potentially served as a molecular marker for assessing the efficacy of ICI treatment. Declarations Author Contributions Qinghua Wang and Zhenpeng Li conceived and instructed this work. Material preparation, data collection and analysis were performed by Xueying Wang, Wenjing Zhang and Yixin Xu. All authors contributed to the study conception and design. The first draft of the manuscript was written by Xueying Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding This study was supported by the National Natural Science Foundation of China (No. 81872719), and the Natural Science Foundation of Shandong Province (No. ZR2022MH127). Data availability Data will be made available on request. Competing interests The authors declare no competing interests. Ethical approval and accordance The protocol was approved by the Ethics Committee of Shandong Second Medical University in accordance with the relevant guidelines and regulations. Clinical trial number Not applicable . Consent to publish Not applicable. Consent to participate Not applicable. References Tsimberidou AM, Fountzilas E, Nikanjam M, et al. Review of precision cancer medicine: Evolution of the treatment paradigm. Cancer Treat Rev. 2020;86:102019. Yang Y. 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Evaluation of POLE and POLD1 Mutations as Biomarkers for Immunotherapy Outcomes Across Multiple Cancer Types. JAMA Oncol. 2019;5(10):1504-1506. Braun DA, Ishii Y, Walsh AM, et al. Clinical Validation of PBRM1 Alterations as a Marker of Immune Checkpoint Inhibitor Response in Renal Cell Carcinoma. JAMA Oncol. 2019;5(11):1631-1633. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx SupplementaryFigures.docx 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7155327","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492316862,"identity":"68a263f1-f785-43e0-a6a7-29fedbf771db","order_by":0,"name":"Xueying Wang","email":"","orcid":"","institution":"Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xueying","middleName":"","lastName":"Wang","suffix":""},{"id":492316863,"identity":"126584e2-3d5b-4ba4-8dbf-60d75c8a1b5b","order_by":1,"name":"Wenjing Zhang","email":"","orcid":"","institution":"Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Zhang","suffix":""},{"id":492316864,"identity":"9ee122e2-2db7-40c6-a653-ec1b3c405385","order_by":2,"name":"Yixin Xu","email":"","orcid":"","institution":"Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yixin","middleName":"","lastName":"Xu","suffix":""},{"id":492316866,"identity":"78c628cb-90d6-4135-8347-0282112c64e4","order_by":3,"name":"Zhenpeng Li","email":"","orcid":"","institution":"Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenpeng","middleName":"","lastName":"Li","suffix":""},{"id":492316868,"identity":"1ce56596-8158-4744-bb09-9bd13042ec66","order_by":4,"name":"Qinghua Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYNACAzBifMDABuFLEKuF2YAELRBdbBJEaTE4fvbwizcFd+y2SyQ/q/xRVhdtcID54G0eBrs8nFrO5KVZzjF4lrxzRprZbZ5zh3M3HGBLtuZhSC7GpcXsQI6ZMY/B4WSDGwlmtxnbDgC18JhJ8zAcSGzApeX8G5iW9G+FP9vqgFr4v+HXciPH+DFQi53BjRwzBt42ZpAtbHi12N94Y8Y4x+BwgsGZN8XSIL/MPMxmDPRdMk4tkv05xh/e/Dlsb3A8feNHYIjl9h1vfnjjTYUdTi0MoOjgYWBAUsAMIgxwqwcp+QDUYo9XySgYBaNgFIxsAADh1l3JZS0BQwAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong Second Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qinghua","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-18 08:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7155327/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7155327/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88005385,"identity":"bca741f0-c7ea-4706-832b-ebe8ae4bd581","added_by":"auto","created_at":"2025-07-31 10:40:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":237767,"visible":true,"origin":"","legend":"\u003cp\u003eThe design process of this study. The association between \u003cem\u003eNLRP13\u003c/em\u003e mutations and the efficacy of ICI in melanoma and NSCLC patients.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7155327/v1/bf32a24fe4345e89ac05907d.png"},{"id":88005386,"identity":"04134891-1a5b-4aa5-b585-8b03921bc84b","added_by":"auto","created_at":"2025-07-31 10:40:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":291594,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of \u003cem\u003eNLRP13\u003c/em\u003e mutations on melanoma patient outcomes and response to ICI therapy. (\u003cstrong\u003eA\u003c/strong\u003e) Kaplan-Meier survival curves for \u003cem\u003eNLRP13\u003c/em\u003e-mutated and wild-type patients. (\u003cstrong\u003eB\u003c/strong\u003e) Multivariate Cox regression analysis of \u003cem\u003eNLRP13\u003c/em\u003e mutations and ICI prognosis, adjusted for age, sex, disease stage, and treatment regimen. (\u003cstrong\u003eC\u003c/strong\u003e) Differences in objective response rate to ICI therapy between \u003cem\u003eNLRP13\u003c/em\u003e-mutated and wild-type patients. (\u003cstrong\u003eD\u003c/strong\u003e) Multivariate logistic regression analysis of the relationship between \u003cem\u003eNLRP13\u003c/em\u003e mutation status and ICI response.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7155327/v1/3eab110015c85f54f6081b81.png"},{"id":88005763,"identity":"6c1fccd2-5605-4246-8002-17cfd9ca0947","added_by":"auto","created_at":"2025-07-31 10:48:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226288,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between \u003cem\u003eNLRP13 \u003c/em\u003emutation status and tumor mutation burden in melanoma. (\u003cstrong\u003eA\u003c/strong\u003e) Differences in tumor mutation burden levels between \u003cem\u003eNLRP13\u003c/em\u003e-mutated and wild-type patients. (\u003cstrong\u003eB\u003c/strong\u003e) Multivariate logistic regression analysis of the correlation between \u003cem\u003eNLRP13\u003c/em\u003e mutations and TMB after adjusting for confounding factors. (\u003cstrong\u003eC\u003c/strong\u003e) Differences in the distribution of neoantigen burden between \u003cem\u003eNLRP13\u003c/em\u003e-mutated and wild-type patients. (\u003cstrong\u003eD\u003c/strong\u003e) Distribution differences of TMB and (\u003cstrong\u003eE\u003c/strong\u003e) NB across \u003cem\u003eNLRP13\u003c/em\u003e mutation status groups in the TCGA melanoma cohort.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7155327/v1/3f1938f6b0721d4a25b168ed.png"},{"id":88003555,"identity":"5267a305-c0d0-4d14-b3ac-b71b7b5b9142","added_by":"auto","created_at":"2025-07-31 10:32:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":282017,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of \u003cem\u003eNLRP13\u003c/em\u003e mutation status on ICI treatment response and prognosis in patients with NSCLC. (\u003cstrong\u003eA\u003c/strong\u003e) Kaplan-Meier survival curve analysis comparing \u003cem\u003eNLRP13\u003c/em\u003e-mutated and wild-type patients. (\u003cstrong\u003eB\u003c/strong\u003e) Multivariate Cox regression analysis of the correlation between \u003cem\u003eNLRP13\u003c/em\u003e mutation and ICI prognosis after adjusting for confounding variables. (\u003cstrong\u003eC\u003c/strong\u003e) Differences in response rates to ICI treatment between \u003cem\u003eNLRP13\u003c/em\u003e-mutated and wild-type patients. (\u003cstrong\u003eD\u003c/strong\u003e) Multivariate logistic regression analysis of the correlation between \u003cem\u003eNLRP13\u003c/em\u003emutation and ICI treatment response.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7155327/v1/9e981aea36f1975837784b47.png"},{"id":88003574,"identity":"a809afd5-2e6a-423e-98bb-30ffd778aab3","added_by":"auto","created_at":"2025-07-31 10:32:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":225194,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the association between \u003cem\u003eNLRP13 \u003c/em\u003emutation status and TMB in NSCLC patients. (\u003cstrong\u003eA\u003c/strong\u003e) Differences in TMB distribution between \u003cem\u003eNLRP13\u003c/em\u003e mutant and wild-type patients. (\u003cstrong\u003eB\u003c/strong\u003e) Multivariate logistic regression analysis of the association between \u003cem\u003eNLRP13\u003c/em\u003emutation and TMB, adjusted for confounding factors. (\u003cstrong\u003eC\u003c/strong\u003e) Comparison of neoantigen burden between \u003cem\u003eNLRP13\u003c/em\u003e mutant and wild-type patients. In the TCGA cohort, differences in expression of (\u003cstrong\u003eD\u003c/strong\u003e) TMB and (\u003cstrong\u003eE\u003c/strong\u003e) NB among different \u003cem\u003eNLRP13\u003c/em\u003e subgroups.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7155327/v1/d449d045fcdf0e985ee0b738.png"},{"id":88003576,"identity":"a1c964e1-b15d-4035-a528-92f40e350549","added_by":"auto","created_at":"2025-07-31 10:32:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":348866,"visible":true,"origin":"","legend":"\u003cp\u003eImmune microenvironment features related to \u003cem\u003eNLRP13\u003c/em\u003e mutation in melanoma. (\u003cstrong\u003eA\u003c/strong\u003e) Differences in the abundance of 22 immune cell infiltrations between \u003cem\u003eNLRP13\u003c/em\u003e-mutant and wild-type subgroups were evaluated using the CIBERSORT algorithm, with cell types showing significant differences highlighted in green. (\u003cstrong\u003eB\u003c/strong\u003e) Based on \u003cem\u003eNLRP13\u003c/em\u003emutation status, the abundance of 31 immune cell infiltrations was analyzed using the algorithm by Angelova et al., revealing significant differences. (\u003cstrong\u003eC\u003c/strong\u003e) A heatmap of the enrichment scores of 14 immune signals was generated based on \u003cem\u003eNLRP13\u003c/em\u003emutation status, with signals showing significant enrichment differences between groups highlighted in red. (\u003cstrong\u003eD-F\u003c/strong\u003e) Significantly enriched signaling pathways observed in \u003cem\u003eNLRP13\u003c/em\u003e-mutant melanoma 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-7155327/v1/c7b1f3c16fa6cb4087f71b46.png"},{"id":93051183,"identity":"393bbe4f-051f-47d4-a3b7-1dbd4d1e2c91","added_by":"auto","created_at":"2025-10-08 14:16:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2625511,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7155327/v1/3c78fb68-edb7-4152-9c42-469f25ee7a55.pdf"},{"id":88003554,"identity":"5e30a85d-c806-4001-8267-bf7ed58eba55","added_by":"auto","created_at":"2025-07-31 10:32:51","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":131064,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7155327/v1/ae65209b283746876986eab7.xlsx"},{"id":88005388,"identity":"e53e6dac-dd5f-403f-a7d9-d283c738519e","added_by":"auto","created_at":"2025-07-31 10:40:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1889898,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7155327/v1/2ec4f2e522ae65f80cccbb3b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"NLRP13 mutation as a predictive indicator for immune checkpoint inhibitor efficacy in melanoma and NSCLC","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTraditional treatment methods, such as radiotherapies and chemotherapies, were often unsatisfactory for advanced/metastatic cancer patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recently, the advent of immune checkpoint inhibitor (ICI) therapies greatly changed this clinical situation and treatment problem [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. ICI agents could effectively kill tumor cells via reactivating the immune system, therefore improving survival outcomes for several cancer types [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite the remarkable clinical treatment efficacy, only a subset of cancer patients could obtain ICI benefits or durable responses [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, predictive biomarkers were necessary for choosing patients who would respond deeply to ICI treatments and assessing clinical efficacy.\u003c/p\u003e\u003cp\u003e\u003cem\u003eNLRP13\u003c/em\u003e, as a member of the NLRP inflammasome family, activated caspase-1 by assembling a multiprotein complex, thereby regulating the release of inflammatory factors such as IL-1β and playing a central role in innate immunity [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Its high expression in immune-privileged sites such as the placenta and testis suggested its potential involvement in local inflammatory regulation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although direct research was limited, the tumor-related mechanisms of other family members (such as \u003cem\u003eNLRP3\u003c/em\u003e and \u003cem\u003eNLRP12\u003c/em\u003e) provided important clues for understanding \u003cem\u003eNLRP13\u003c/em\u003e. \u003cem\u003eNLRP3\u003c/em\u003e promoted inflammation and tumor progression through ROS signaling in colorectal cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and activated the caspase-1/IL-1β axis in lung cancer, leading to decreased lung function and accelerated malignancy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In contrast, \u003cem\u003eNLRP12\u003c/em\u003e inhibited the occurrence of colorectal cancer by suppressing the ERK/AKT pathway [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. \u003cem\u003eNLRP13\u003c/em\u003e exhibited tissue-specific dual roles in tumors. In colorectal cancer, its expression was significantly elevated, and the dynamic correlation between promoter methylation levels and tumor staging [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] suggested that it might influence cancer progression through caspase-1-dependent inflammatory pathways, such as high-confidence interactions with MEFV and NLRC4 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Conversely, in breast cancer, head and neck cancer, and prostate cancer, its expression was significantly reduced in tumor tissues [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The epigenetic drug decitabine (DAC) could upregulate \u003cem\u003eNLRP13\u003c/em\u003e expression in vitro and modulate inflammatory responses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This contradiction might arise from its epigenetic regulation (such as promoter methylation negatively correlated with prognosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]) and differential activation of inflammatory signaling pathways (such as ROS/NF-κB pathway-induced chronic inflammation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]).\u003c/p\u003e\u003cp\u003e\u003cem\u003eNLRP13\u003c/em\u003e might influence cancer progression and treatment response by remodeling the tumor immune microenvironment. At the level of inflammatory factors, it might, like \u003cem\u003eNLRP3\u003c/em\u003e, affect cancer progression through the caspase-1/IL-1β axis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In head and neck squamous cell carcinoma, \u003cem\u003eNLRP3\u003c/em\u003e inflammasome activation could mediate IL-1β release, promoting inflammation-induced carcinogenesis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In terms of immune evasion, the inflammasome pathway might be exploited by tumor cells. This immune regulatory role endowed it with potential clinical value. The expression level of \u003cem\u003eNLRP13\u003c/em\u003e was associated with poor prognosis in breast and prostate cancer, and its promoter methylation status might serve as a potential diagnostic biomarker [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, inflammasome pathway inhibitors (such as \u003cem\u003eNLRP3\u003c/em\u003e-targeted agents) could enhance the immunogenicity of chemotherapy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For example, the combination of paclitaxel and an IRE1α inhibitor could activate pyroptosis to reverse \"cold\" tumors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The correlation of \u003cem\u003eNLRP3\u003c/em\u003e mutations in melanoma with TMB, immune cell infiltration, and ICI efficacy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] suggested that \u003cem\u003eNLRP13\u003c/em\u003e might become a predictive biomarker for ICI efficacy through similar pathways, such as influencing tumor immunogenicity or T cell function [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, its direct association with ICI response in melanoma and NSCLC still needed to be verified. This study systematically reviewed and integrated the multi-omics data of these two types of tumor patients before treatment and their corresponding ICI treatment response information, aiming to elucidate the clinical value of \u003cem\u003eNLRP13\u003c/em\u003e mutations in tumor immunotherapy.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Sources and processing of melanoma and NSCLC samples\u003c/h2\u003e\u003cp\u003eWe integrated 631 melanoma [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and 109 NSCLC samples [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] from published studies, covering somatic mutation data, ICI treatment information, and clinical-pathological data (\u003cb\u003eSupplementary Tables\u0026nbsp;1 and 2\u003c/b\u003e). All samples received anti-PD-1/PD-L1, anti-CTLA-4, or combination therapy. As somatic mutation data came from diverse sequencing platforms, we used Oncotator [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] for standardized annotation.\u003c/p\u003e\u003cp\u003eThis study used somatic mutation data, mRNA expression profiles, and clinical prognosis information of melanoma and NSCLC patients from TCGA. Based on TCGA transcriptome data, it analyzed the link between \u003cem\u003eNLRP13\u003c/em\u003e mutations and immune-related characteristics in the tumor immune microenvironment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Mutation signature identification\u003c/h2\u003e\u003cp\u003eIn this study, based on the methodological framework established by Kim et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], we extracted mutation signatures from the mutational spectra of melanoma and NSCLC samples. The method employed a Bayesian non-negative matrix factorization (NMF) algorithm to decompose the mutation signature matrix A, which includes 96 types of base substitution patterns, into the product of two non-negative matrices (A\u0026thinsp;\u0026asymp;\u0026thinsp;W\u0026times;H). Here, the W matrix represented the extracted mutation signatures, while the H matrix characterized the mutational activity of each signature. By calculating the cosine similarity, the extracted mutation signatures were systematically compared with the 30 known mutation signatures documented in the COSMIC database.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 TMB and NB\u003c/h2\u003e\u003cp\u003eTumor Mutational Burden (TMB) was the number of non-synonymous somatic mutations per megabase (Mb) in the tumor genome. In this study, TMB values were calculated based on integrated samples and the TCGA cohort, with the number of non-synonymous mutations per Mb standardized using log2 transformation. For the 341 melanoma and 662 NSCLC samples in the TCGA cohort, neoantigen data came from The Cancer Immune Atlas (TCIA). Neoantigen Burden (NB) was calculated using an algorithm based on the somatic mutation profiles [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 GSVA and GSEA\u003c/h2\u003e\u003cp\u003eSingle-sample Gene Set Enrichment Analysis (ssGSEA) quantified the enrichment of predefined gene sets, such as immune-related ones, in individual samples. This enabled precise characterization of immune infiltration in the tumor microenvironment. To explore \u003cem\u003eNLRP13\u003c/em\u003e mutation-related pathways, we first used the R package DESeq2 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] for genome-wide differential expression analysis. Then, Gene Set Enrichment Analysis (GSEA) was conducted to identify significantly enriched pathways. The background gene sets for this analysis were from the Molecular Signatures Database (MSigDB) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Collection of immune-related signatures\u003c/h2\u003e\u003cp\u003eExisting studies had demonstrated that multiple immune signals were closely associated with the regulation of tumor immunogenicity and the response to immunotherapy. In this study, through systematic review of the literature, we identified 14 hallmark immune signals with clear prognostic predictive value (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). These signals encompassed a spectrum of core immune features, such as the T-cell-inflamed signature, type I/II interferon response pathways, cytolytic activity, tertiary lymphoid structures (TLS), immune cell subsets (B/P, T/NK, M/D Metagene), and immune signaling molecules.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Analysis of tumor immune infiltration status\u003c/h2\u003e\u003cp\u003eTo investigate the differences in immune cell infiltration levels between the \u003cem\u003eNLRP13\u003c/em\u003e-mutant and wild-type groups, this study employed both the CIBERSORT method and the algorithm proposed by Angelova et al. for analysis. CIBERSORT calculated the tumor infiltration levels of 22 immune cell types based on the LM22 signature matrix containing 547 representative genes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In contrast, the method proposed by Angelova et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] evaluated the infiltration abundance of 31 immune cells using their 812 characteristic genes. The specific signature genes corresponding to each immune cell subtype are detailed in \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e\u003cp\u003eIn this study, statistical analysis and data visualization were performed using R software (version 4.5.0). For survival analysis, Kaplan-Meier curves were constructed, and the significance of survival differences between groups was assessed using the Log-rank test. To adjust for potential confounding effects, confounders such as age, sex, disease stage, and treatment regimen were included in the Logistic regression and Cox proportional hazards regression models, with results presented using the forestmodel package. The distribution of continuous and categorical variables between the \u003cem\u003eNLRP13\u003c/em\u003e mutant and wild-type groups was compared using the Wilcoxon rank-sum test and Fisher's exact test, respectively. Unless otherwise specified, two-sided tests were used, and a significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was employed to determine statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 \u003cem\u003eNLRP13\u003c/em\u003e mutational status in melanoma\u003c/h2\u003e\u003cp\u003eThe analysis process of this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the 631 melanoma samples included in the study, 193 cases (30.6%) achieved a complete response (CR) or partial response (PR) to ICI therapy, 430 cases (68.1%) showed stable disease (SD) or progressive disease (PD) under ICI treatment, and the remaining 8 samples (1.3%) had unknown response data to ICI treatment. The whole-genome mutation landscape revealed that C\u0026thinsp;\u0026gt;\u0026thinsp;T base substitution was the predominant somatic mutation pattern in this cohort (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). The mutation patterns of melanoma-related driver genes linked to \u003cem\u003eNLRP13\u003c/em\u003e mutations are detailed in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e. Among the 631 melanoma samples, 122 cases (19.3%) had \u003cem\u003eNLRP13\u003c/em\u003e mutations. The amino acid sequence changes mediated by \u003cem\u003eNLRP13\u003c/em\u003e mutations are shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 \u003cem\u003eNLRP13\u003c/em\u003e mutations enhance the prognosis and remission rate of ICI therapy for melanoma patients\u003c/h2\u003e\u003cp\u003eThis study included 631 melanoma samples, of which 122 (19.3%) had \u003cem\u003eNLRP13\u003c/em\u003e mutations. Survival analysis showed that \u003cem\u003eNLRP13\u003c/em\u003e-mutated patients had a significantly longer median survival after ICI treatment than wild-type patients (36.9 months vs. 25.6 months, Log-rank test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). After multivariate Cox regression adjusted for age, sex, clinical stage, and treatment type, \u003cem\u003eNLRP13\u003c/em\u003e mutations remained significantly associated with survival benefits (HR: 0.65, 95% CI: 0.48\u0026ndash;0.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The predictive value of \u003cem\u003eNLRP13\u003c/em\u003e mutations in an independent ICI cohort and prognostic analyses of different treatment regimens are shown in \u003cb\u003eSupplementary Figs.\u0026nbsp;3 and 4\u003c/b\u003e. Also, the objective response rate (ORR) was significantly higher in \u003cem\u003eNLRP13\u003c/em\u003e-mutated patients than in wild-type patients (39.3% vs. 28.9%, Fisher's exact test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). After multivariate logistic regression adjusted for confounders, \u003cem\u003eNLRP13\u003c/em\u003e mutations were still statistically significantly associated with ICI response (OR: 0.61, 95% CI: 0.40\u0026ndash;0.95, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 The association between \u003cem\u003eNLRP13\u003c/em\u003e mutations and the tumor mutational burden in melanoma\u003c/h2\u003e\u003cp\u003ePrevious studies have confirmed that TMB was a key biomarker for predicting the efficacy of ICI in melanoma. In this study, the association between \u003cem\u003eNLRP13\u003c/em\u003e mutations and TMB was investigated. It was found that patients with \u003cem\u003eNLRP13\u003c/em\u003e mutations had significantly higher TMB levels compared to wild-type patients (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 elucidate the genomic mutational signatures, NMF was applied to the mutational spectra of melanoma, identifying four biologically relevant mutational signatures: Signature 1, 4, 7, and 11. Detailed mutational activity for each signature in each sample is provided in \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e. To more accurately assess the relationship between \u003cem\u003eNLRP13\u003c/em\u003e mutations and TMB, a multivariate logistic regression model was constructed to adjust for clinical variables such as age, sex, disease stage, as well as the aforementioned mutational signatures and mutations in DNA repair genes. The results showed that \u003cem\u003eNLRP13\u003c/em\u003e mutation was still significantly associated with elevated TMB (OR: 5.37, 95% CI: 2.93\u0026ndash;10.45, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Additionally, patients with \u003cem\u003eNLRP13\u003c/em\u003e mutations also exhibited significantly increased NB (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). Validation using the TCGA melanoma cohort further confirmed that patients with \u003cem\u003eNLRP13\u003c/em\u003e mutations had significantly elevated levels of both TMB and NB (Wilcoxon rank-sum test, both \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\u003e3.4 The impact of\u003c/b\u003e \u003cb\u003eNLRP13\u003c/b\u003e \u003cb\u003emutations on ICI treatment outcomes and TMB was validated in the NSCLC cohort\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the 109 NSCLC samples included, 12 cases (11%) were identified with \u003cem\u003eNLRP13\u003c/em\u003e mutations. Kaplan-Meier survival analysis revealed that patients with \u003cem\u003eNLRP13\u003c/em\u003e mutations had significantly better prognosis compared with those with the wild-type (median survival time: NA vs. 6.3 months; Log-rank test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). After adjusting for potential confounding factors using a multivariate Cox regression model, this association remained significant (HR: 0.30, 95% CI: 0.11\u0026ndash;0.85, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The correlation between \u003cem\u003eNLRP13\u003c/em\u003e mutations and ICI outcomes across different treatment modalities is detailed in \u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e. Moreover, the ORR of patients with \u003cem\u003eNLRP13\u003c/em\u003e mutations was significantly higher than that of wild-type patients (90.9% vs. 28.6%; Fisher's exact test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). This association remained significant after adjustment for confounding factors using a multivariate logistic regression model (OR: 0.03, 95% CI: 0.00-0.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eFurther investigation into the relationship between \u003cem\u003eNLRP13\u003c/em\u003e mutations and TMB demonstrated that patients with \u003cem\u003eNLRP13\u003c/em\u003e mutations had significantly higher TMB levels compared with wild-type patients (Wilcoxon rank-sum test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Signature analysis of somatic mutations in NSCLC identified three major mutational signatures, namely signatures 1, 4, and 7, with their mutational activities presented in \u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e. Multivariate logistic regression analysis showed that patients with \u003cem\u003eNLRP13\u003c/em\u003e mutations had higher TMB levels (OR: 5.68, 95% CI: 1.26\u0026ndash;14.71, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Additionally, a significant association was found between \u003cem\u003eNLRP13\u003c/em\u003e mutations and elevated NB (Wilcoxon test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In the independent validation cohort from TCGA NSCLC dataset, patients with \u003cem\u003eNLRP13\u003c/em\u003e mutations also exhibited significantly increased TMB and NB levels (Wilcoxon rank-sum test, both \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\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Analysis of immune infiltration characteristics and pathway enrichment associated with \u003cem\u003eNLRP13\u003c/em\u003e mutations\u003c/h2\u003e\u003cp\u003eTo explore the immunological mechanisms underlying \u003cem\u003eNLRP13\u003c/em\u003e mutations in melanoma patients, we conducted multi-level immunological and pathway enrichment analyses. Using the CIBERSORT algorithm, we found that in the \u003cem\u003eNLRP13\u003c/em\u003e-mutant group, the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells, M0 macrophages, and M1 macrophages was significantly increased, while M2 macrophages infiltration was significantly reduced (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Similarly, using the method by Angelova et al., we detected higher infiltration levels of activated CD4\u0026thinsp;+\u0026thinsp;T cells, activated CD8\u0026thinsp;+\u0026thinsp;T cells, and effector memory CD4\u0026thinsp;+\u0026thinsp;T cells, but lower regulatory T cell infiltration in \u003cem\u003eNLRP13\u003c/em\u003e-mutant patients (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Enrichment analysis of immune signals between \u003cem\u003eNLRP13\u003c/em\u003e-mutant and wild-type subgroups revealed that patients harboring \u003cem\u003eNLRP13\u003c/em\u003e mutations exhibited a significant enhancement in type II interferon response signal enrichment scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). GSEA further indicated that in \u003cem\u003eNLRP13\u003c/em\u003e-mutant melanoma patients, the Interferon γ response (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), Interferon α response (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE), and G2M checkpoint (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF) were all significantly enriched (\u003cb\u003eSupplementary Fig.\u0026nbsp;6\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eUltimately, this study conducted an analysis of immune infiltration and pathway enrichment in patients with NSCLC. Utilizing the CIBERSORT algorithm, we determined that in the \u003cem\u003eNLRP13\u003c/em\u003e-mutant subgroup, the infiltration proportions of CD8\u0026thinsp;+\u0026thinsp;T cells, memory CD4\u0026thinsp;+\u0026thinsp;T cells, and M1 macrophages were significantly elevated, while the infiltration level of regulatory T cells was reduced (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003cb\u003eSupplementary Fig.\u0026nbsp;7A\u003c/b\u003e). Moreover, further analysis using the algorithm proposed by Angelova et al. revealed a significant enrichment of activated CD4\u0026thinsp;+\u0026thinsp;T cells, activated CD8\u0026thinsp;+\u0026thinsp;T cells, and effector memory CD4\u0026thinsp;+\u0026thinsp;T cells in \u003cem\u003eNLRP13\u003c/em\u003e-mutant patients (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003cb\u003eSupplementary Fig.\u0026nbsp;7B\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eAlthough the classic function of \u003cem\u003eNLRP13\u003c/em\u003e as a pro-inflammatory inflammasome molecule suggested that its inactivation may impair anti-tumor immunity, the correlation between \u003cem\u003eNLRP13\u003c/em\u003e mutations and the efficacy of immune checkpoint inhibitors (ICI) had not yet been systematically elucidated. In this study, we integrated genomic, transcriptomic data, and clinical response data to immunotherapy from cohorts of melanoma and non-small cell lung cancer patients and found that patients harboring \u003cem\u003eNLRP13\u003c/em\u003e mutations exhibited significantly prolonged overall survival (OS) and higher objective response rates (ORR). These findings provided a molecular biological basis for constructing a precision stratification model for immunotherapy based on \u003cem\u003eNLRP13\u003c/em\u003e mutation status and suggested the complex immunomodulatory role of \u003cem\u003eNLRP13\u003c/em\u003e in the tumor microenvironment.\u003c/p\u003e\u003cp\u003eThis study found that for patients with the two types of tumors receiving ICI therapy, the mutation status of \u003cem\u003eNLRP13\u003c/em\u003e was significantly associated with survival benefits. To investigate whether this association was therapy-specific, we further analyzed patients with the two types of tumors receiving conventional chemotherapy in the TCGA cohort. The results showed that in patients with the two types of tumors treated with conventional chemotherapy, there was no statistically significant difference in the survival curves between \u003cem\u003eNLRP13\u003c/em\u003e-mutant and wild-type patients (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; \u003cb\u003eSupplementary Fig.\u0026nbsp;8\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTMB had been proven to be an effective predictive marker for immunotherapy efficacy in various malignant tumors [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, TMB detection relied on whole-exome sequencing (WES), and its threshold varied across cancer types, which limited its universal clinical application [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Recent studies had indicated that certain specific gene mutations (such as \u003cem\u003eFAT1\u003c/em\u003e [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], \u003cem\u003eTP53\u003c/em\u003e [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], \u003cem\u003eMUC16\u003c/em\u003e [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], \u003cem\u003ePOLE/POLD1\u003c/em\u003e [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and \u003cem\u003ePBRM1\u003c/em\u003e [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]) might be linked to both increased TMB levels and improved ICI effectiveness, opening up a new avenue for identifying alternative predictive markers. In this study, it was found that patients with \u003cem\u003eNLRP13\u003c/em\u003e mutations not only exhibited a significantly higher TMB, but were also closely associated with enhanced survival benefits from ICI therapy, thus offering a new target for developing immune-therapy prediction models based on single-gene mutations.\u003c/p\u003e\u003cp\u003eThis study had certain limitations. First, the genetic mutation and immunotherapy data for melanoma and NSCLC were consolidated from multicenter retrospective cohorts. Although a standardized quality control process was used, differences in sequencing depth and clinical data collection standards across datasets might have caused bias and restricted result extrapolation. Second, the link between \u003cem\u003eNLRP13\u003c/em\u003e mutations and ICI effectiveness was only confirmed in melanoma and NSCLC, and its applicability to other solid tumors like colorectal and breast cancer needed cross-cancer-type studies to verify. Finally, the molecular mechanism of how \u003cem\u003eNLRP13\u003c/em\u003e mutations regulated immunotherapy sensitivity remained unclear and requires future functional experiments to explore.\u003c/p\u003e\u003cp\u003eThis study integrated the multi-omics data and clinicopathological information of melanoma and NSCLC patients and revealed a significant correlation between \u003cem\u003eNLRP13\u003c/em\u003e mutations and enhanced response to ICI therapy. This finding provided important references for the advancement of subsequent clinical trials and the optimization of treatment protocols, and potentially served as a molecular marker for assessing the efficacy of ICI treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQinghua Wang and Zhenpeng Li conceived and instructed this work. Material preparation, data collection and analysis were performed by Xueying Wang, Wenjing Zhang and Yixin Xu. All authors contributed to the study conception and design. The first draft of the manuscript was written by Xueying Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported 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\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and accordance\u0026nbsp;\u003c/strong\u003eThe protocol was approved by the Ethics Committee of Shandong Second Medical University in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003eNot applicable\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTsimberidou AM, Fountzilas E, Nikanjam M, et al. 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JAMA Oncol. 2019;5(11):1631-1633.\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":"NLRP13 mutations, immunotherapies, melanoma, NSCLC, efficacy indicator","lastPublishedDoi":"10.21203/rs.3.rs-7155327/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7155327/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eRobustly predictive indicators could assist in maximizing immune checkpoint inhibitor (ICI) treatment responses. A key point in immunotherapies is to select tumor patients who will respond to immune checkpoint inhibitor (ICI). Effective indicators could accurately evaluate the ICI treatment efficacy. NLRP proteins have been revealed to implicate in inflammatory processes via the inflammasomes. NLRP family pyrin domain containing 13 (\u003cem\u003eNLRP13\u003c/em\u003e) is frequently mutated in cancer genomes. However, the association between NLRP family pyrin domain containing 13 (\u003cem\u003eNLRP13\u003c/em\u003e) mutation and ICI efficacy is never reported.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study collected ICI treatment data and somatic mutational information from totaling 631 melanoma patients and integrated as the discovery cohort to explore the relationship of \u003cem\u003eNLRP13\u003c/em\u003e mutations with ICI efficacy. Besides, 109 non-small cell lung cancer (NSCLC) patients were consolidated as the validation cohort. Based on the genomic data from The Cancer Genome Atlas (TCGA), we investigated the potential biological mechanisms behind \u003cem\u003eNLRP13\u003c/em\u003e mutations.\u003c/p\u003e\u003ch2\u003eFindings:\u003c/h2\u003e\u003cp\u003eIn melanoma, \u003cem\u003eNLRP13\u003c/em\u003e mutated (\u003cem\u003eNLRP13\u003c/em\u003e-MUT) patients obtained a significant ICI survival advantage as compared with \u003cem\u003eNLRP13\u003c/em\u003e wildtype (\u003cem\u003eNLRP13\u003c/em\u003e-WT) patients (HR: 0.65, 95% CI: 0.48\u0026ndash;0.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). A higher ICI response rate was also observed in such mutated subgroup (39.3% vs. 28.9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). In NSCLC, the preferable ICI survival and response rate were corroborated in \u003cem\u003eNLRP13\u003c/em\u003e-MUT patients. Further analyses demonstrated that a better tumor microenvironment and elevated immunogenicity were enriched in \u003cem\u003eNLRP13\u003c/em\u003e-MUT patients.\u003c/p\u003e\u003ch2\u003eInterpretation:\u003c/h2\u003e\u003cp\u003eOur findings indicate that \u003cem\u003eNLRP13\u003c/em\u003e mutations could serve as a possible indicator for ICI treatment efficacy.\u003c/p\u003e","manuscriptTitle":"NLRP13 mutation as a predictive indicator for immune checkpoint inhibitor efficacy in melanoma and NSCLC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 10:32:47","doi":"10.21203/rs.3.rs-7155327/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":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T14:08:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 10:32:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7155327","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7155327","identity":"rs-7155327","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}