Characterizing and Forecasting Neoantigens Resulting from MUC Mutations in COAD

Characterizing and Forecasting Neoantigens Resulting from MUC Mutations in COAD | 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 Research Article Characterizing and Forecasting Neoantigens Resulting from MUC Mutations in COAD Min Chen, xin zhang, Zihe Ming, Xiaorong Feng, Han-Xiang An, Ling yu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3839089/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2024 Read the published version in Journal of Translational Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background: The treatment for colon adenocarcinoma (COAD) faces challenges in terms of immunotherapy effectiveness due to multiple factors. Because of the high tumor specificity and immunogenicity, neoantigen has been considered a pivotal target for cancer immunotherapy. Therefore, this study aims to identify and predict the potential tumor antigens of MUC somatic mutations (MUCmut) in COAD. Methods: Three databases of TCGA, TIMER2.0, and cBioPortal were used for a detailed evaluation of the association between MUCmut and multi-factors like tumor mutation burden (TMB), microsatellite instability (MSI), prognosis, and the tumor microenvironment within the context of total 2242 COAD patients. Next, TSNAdb and the differential agretopicity index (DAI) were utilized to predict high-confidence neopeptides for MUCmut based on 531 COAD patients' genomic information. DAI was calculated by subtraction of its predicted HLA binding affinity of the MUCmut peptide from the corresponding wild-type peptide. Results: The top six mutation frequencies (14 to 2.9%) were from MUC16, MUC17, MUC5B, MUC2, MUC4 and MUC6. COAD patients with MUC16 and MUC4 mutations had longer DFS and PFS. However, patients with MUC13 and MUC20 mutations had shorter OS. Patients with the mutation of MUC16, MUC5B, MUC2, MUC4, and MUC6 exhibited higher TMB and MSI. Moreover, these mutations from the MUC family were associated with the infiltration of diverse lymphocyte cells and the expression of immune checkpoint genes. Through TSNAdb 1.0/NetMHCpan v2.8, 452 single nucleotide variants (SNVs) of MUCmut peptides were identified. Moreover, through TSNAdb2.0/NetMHCpan v4.0, 57 SNVs, 1 Q-frame shift (TS), and 157 short insertions/deletions (INDELs) of MUCmut were identified. Finally, 10 high-confidence neopeptides of MUCmut were predicted by DAI. Conclusions: In conclusion, this study showed the immunogenic role of neoantigens against MUCmut in COAD. Through combining the tools of TSNAdb and DAI, a group of novel MUCmut neoantigens were identified as potential targets for immunotherapy. MUC tumor neoantigens colon adenocarcinoma Bioinformatics immunotherapy. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Supplementary Files Suppl1.pdf Figure S1. Relations between the abundance of tumor-infiltrating lymphocytes (TILs) and expression, copy number, methylation, or mutation of MUC16. The immune-related signatures types from Charoentong's study, the relative abundance of TILs in COAD was inferred by using gene set variation analysis (GSVA) based on gene expression profile. Suppl2.pdf Figure S2. Relations between the abundance of TILs and expression, copy number, methylation, or mutation of MUC5B. The immune-related signatures types from Charoentong's study, the relative abundance of TILs in COAD was inferred by using GSVA based on gene expression profile. Suppl3.pdf Figure S3. Relations between the abundance of TILs and expression, copy number, methylation, or mutation of MUC17. The immune-related signatures types from Charoentong's study, the relative abundance of TILs in COAD was inferred by using GSVA based on gene expression profile. Suppl4.pdf Figure S4. Relations between the abundance of TILs and expression, copy number, methylation, or mutation of MUC6. The immune-related signatures types from Charoentong's study, the relative abundance of TILs in COAD was inferred by using GSVA based on gene expression profile. Suppl5.pdf Figure S5. The top 20 genes and HLA alleles with the number of predicted neoantigens are displayed in COAD by TSNAdb1.0/NetMHCpan v2.8. Supplementaltable1MutationRatesofMUCs.xlsx Table S1. Mutation Rates of MUC family within the TCGA Dataset. Supplementaltable2TSNADv1.0NetMHCpanv2.8.xlsx Table S2. MUCmut neoantigens predicted by TSNAD v1.0 /NetMHCpan v2.8 Supplementaltable3TSNADv2.0NetMHCpanv4.0.xlsx Table S3. MUCmut neoantigens by TSNAD v2.0 /NetMHCpan v4.0 Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2024 Read the published version in Journal of Translational Medicine → Version 1 posted Reviewers agreed at journal 15 Jan, 2024 Reviewers invited by journal 08 Jan, 2024 Editor assigned by journal 06 Jan, 2024 First submitted to journal 04 Jan, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3839089","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266087153,"identity":"f15a43a3-3a56-48d1-9fc8-189d89ad8407","order_by":0,"name":"Min Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYFACHjApB8SMBxgYEojWYmDMwMDMQJqWxAaitei2nz34uODPn/R+ifwDBz5UpDHwt3fj12d2Ji/ZeGabQe7MGckMB2ecyWGQOHN2A34tN3jMpHkbDHI33EhmOMzbVsFgIJFLUIv5b54/BukGpGgxY+ZhM0iAaskhQsuZHGNp3jZjw5k9jw2AfknjIeyX42cMP/P8kZPnZ098+OBDRbIcf3svfi0YgIc05aNgFIyCUTAKsAIAwudGFyQumGMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-5811-0649","institution":"Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Chen","suffix":""},{"id":266087154,"identity":"8d52dbaf-4724-4aa0-92c9-a9598d89edb7","order_by":1,"name":"xin zhang","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"prefix":"","firstName":"xin","middleName":"","lastName":"zhang","suffix":""},{"id":266087155,"identity":"4380f98f-90bd-4e8b-8990-3953088dd477","order_by":2,"name":"Zihe Ming","email":"","orcid":"","institution":"Xiang'an Hospital of Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Zihe","middleName":"","lastName":"Ming","suffix":""},{"id":266087156,"identity":"27da2385-9a31-4bbe-a5d1-8eede76d5725","order_by":3,"name":"Xiaorong Feng","email":"","orcid":"","institution":"Shantou University","correspondingAuthor":false,"prefix":"","firstName":"Xiaorong","middleName":"","lastName":"Feng","suffix":""},{"id":266087157,"identity":"e54cc00b-f3c5-47c2-abe0-937b2d930f22","order_by":4,"name":"Han-Xiang An","email":"","orcid":"","institution":"Shanxi Bethune Hospital","correspondingAuthor":false,"prefix":"","firstName":"Han-Xiang","middleName":"","lastName":"An","suffix":""},{"id":266087158,"identity":"516b3cb1-2fd7-473f-b351-2281d73b5fec","order_by":5,"name":"Ling yu","email":"","orcid":"","institution":"Shanxi Bethune Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"yu","suffix":""},{"id":266087159,"identity":"0dbb0328-fcc3-456b-8f33-028e5a2b71f2","order_by":6,"name":"Han Zhenguo","email":"","orcid":"","institution":"Shanxi Bethune Hospital","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Zhenguo","suffix":""}],"badges":[],"createdAt":"2024-01-06 07:00:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3839089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3839089/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12967-024-05103-z","type":"published","date":"2024-03-27T15:01:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49486015,"identity":"3acb96c8-455d-40fd-9847-add2cdaf592e","added_by":"auto","created_at":"2024-01-11 16:17:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":464992,"visible":true,"origin":"","legend":"\u003cp\u003eInterrogating the Mutational Landscape of MUC Genes in the Context \u0026nbsp;of COAD Across Two Distinct Datasets. A. Illuminating the Mutational \u0026nbsp;Frequencies of Nineteen MUC Genes on cbioportal website; B. Providing a Tabular \u0026nbsp;Representation of the Mutation Rates for these Genes; C. The mutation frequencies \u0026nbsp;of the 19 MUCs genes within the TCGA datasets.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/1f26e452cc2aaeb32e75a62e.png"},{"id":49484947,"identity":"5e280082-db13-4317-bc36-625e76b44fb6","added_by":"auto","created_at":"2024-01-11 16:09:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":344700,"visible":true,"origin":"","legend":"\u003cp\u003eRevealing the Prognostic Significance of MUC Genes in two databases. \u0026nbsp;A. Exploring DFS, PFS, and DSS among COAD Patients, stratified between those \u0026nbsp;with mutations in the 19 MUC genes and those without, as documented on the \u0026nbsp;CbioPortal platform; B. The Kaplan-Meier curve presents a portrayal of the four \u0026nbsp;preeminent genes in COAD, characterized by their remarkable significance, as \u0026nbsp;elucidated on the CbioPortal website; C. The OS rate of the 19 MUCs genes \u0026nbsp;mutation group and the non-mutation group in COAD cancer patients on the TCGA \u0026nbsp;website; D. The KM curve captures the top 4 genes with significant P value in the \u0026nbsp;context of COAD within the TCGA website. Disease-Free Survival (DFS); \u0026nbsp;Progression-Free Survival (PFS); Disease-Specific Survival (DSS); Overall Survival \u0026nbsp;(OS).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/05d9acd61be10e3de603fce6.png"},{"id":49484950,"identity":"f4ffa6ab-fe16-4f13-9ce8-c0cc3220837b","added_by":"auto","created_at":"2024-01-11 16:09:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":828855,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation of MUCs with highly mutated genes and genetic \u0026nbsp;instability. A. The top ten genes with the highest mutation frequencies in COAD on the cbioportal website; B. The correlation of mutated MUC genes and the top ten \u0026nbsp;highest mutation genes were analyzed by the cytoscope; C. The relationship between \u0026nbsp;the mutation status of MUC genes and MSI ((MUC16, MUC5B, MUC2, MUC4, and \u0026nbsp;MUC6); D. The correlation between the mutation status of five MUC genes (MUC16, \u0026nbsp;MUC5B, MUC2, MUC4, and MUC6) (MUC2, MUC4, MUC5B, MUC6, MUC16) \u0026nbsp;and the expression of TMB. Microsatellite Instability (MSI); Tumor Mutational \u0026nbsp;Burden (TMB).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/de4a0f5fed4393864f67c9c5.png"},{"id":49486814,"identity":"30e66b97-dbce-4c41-9d5a-7a8ce4aec271","added_by":"auto","created_at":"2024-01-11 16:25:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":144103,"visible":true,"origin":"","legend":"\u003cp\u003eFive MUC genes comprehensive analysis of tumor-infiltrating immune \u0026nbsp;cells (B cell, CD8+ T cell, CD4+ T cell, Macrophage, Neutrophil, Dendritic cell) \u0026nbsp;in COAD using the Timer database. A. MUC16; B. MUC4; C. MUC5B; MUC17; \u0026nbsp;E. MUC2. The mutation module compares the levels of immune infiltrates with or \u0026nbsp;without the presence of a given mutation. Box plots are generated for each immune \u0026nbsp;subset, to compare the distributions of immune infiltration levels under different gene \u0026nbsp;mutation statuses, with statistical significance estimated using a two-sided Wilcoxon \u0026nbsp;rank-sum test.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/b043b4152d399663397034ad.png"},{"id":49486013,"identity":"ae8d2d51-b1c8-4887-b7bb-fc97e91dac00","added_by":"auto","created_at":"2024-01-11 16:17:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94461,"visible":true,"origin":"","legend":"\u003cp\u003eSCNA module provides the comparison of tumor infiltration levels \u0026nbsp;among tumors with different somatic copy number alterations for a given gene. SCNAs are defined by GISTIC 2.0, including deep deletion (-2), arm-level deletion \u0026nbsp;(-1), diploid/normal (0), arm-level gain (1), and high amplification (2). Box plots are \u0026nbsp;presented to show the distributions of each immune subset at each copy number \u0026nbsp;status in COAD, i.e., A. MUC16; B. MUC4; C. MUC5B; D. MUC17; E. MUC2; F. \u0026nbsp;MUC6. The infiltration level for each SCNA category is compared with the normal \u0026nbsp;using a two-sided Wilcoxon rank-sum test.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/c2dd3155cea5335ce9f14919.png"},{"id":49484952,"identity":"56d1d87a-686a-4dd3-b4df-5d68d00e627d","added_by":"auto","created_at":"2024-01-11 16:09:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":258330,"visible":true,"origin":"","legend":"\u003cp\u003eRelations between three kinds of immunomodulators and expression, \u0026nbsp;copy number, methylation, or mutation of MUC family. A. immunoinhibitors; B. \u0026nbsp;immunostimulators; C. MHC molecules; D. chemokine; E. receptor. These \u0026nbsp;immunomodulators were collected from Charoentong's study. F. MUC16 expression \u0026nbsp;was high associated with immune and molecular subtypes, respectively in COAD, \u0026nbsp;Kruskal-Wallis Test: P value=1.96e-06; n=C1 332, C2 85, C3 9, C4 12, C6 3; G. \u0026nbsp;MUC 5B expression was high associated with immune and molecular subtypes, \u0026nbsp;respectively in COAD, Kruskal-Wallis Test: P value=9.5e-08; n=CIN 226, GS 49, \u0026nbsp;HM-SNV 6, HM-INDEL 60.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/fa7030ba8a8d75196bec6893.png"},{"id":49484957,"identity":"da023719-a081-418a-9c19-fde62c03fa26","added_by":"auto","created_at":"2024-01-11 16:09:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":163419,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted neoantigens of mutated MUC family for COAD. The top 12 \u0026nbsp;HLA alleles (A) in MUCmut (B) with the number of predicted neoantigens are displayed in COAD, with the detailed neoantigen information listed (C) The binding \u0026nbsp;level ‘Strong’ indicates strong binding with IC50 \u0026lt; 150nM based on Mutate binding \u0026nbsp;level or DAI \u0026gt; 150nM. (D) The mutational signatures of MUCmut in three different \u0026nbsp;cancer types.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/f45a5909ea4b7abdc87c954a.png"},{"id":53869567,"identity":"b5a2f20a-2722-45d2-860a-9e3b1f56ac15","added_by":"auto","created_at":"2024-04-01 15:10:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1050837,"visible":true,"origin":"","legend":"","description":"","filename":"CharacterizingandForecastingNeoantigens.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1_covered_86fcd43b-15fb-4be9-9c0d-ebb22736a2e0.pdf"},{"id":49484948,"identity":"de63915d-f32d-43e2-8f14-095ad4d10863","added_by":"auto","created_at":"2024-01-11 16:09:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1311864,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. Relations between the abundance of tumor-infiltrating lymphocytes (TILs) \u0026nbsp;and expression, copy number, methylation, or mutation of MUC16. The \u0026nbsp;immune-related signatures types from Charoentong's study, the relative abundance of \u0026nbsp;TILs in COAD was inferred by using gene set variation analysis (GSVA) based on \u0026nbsp;gene expression profile.\u003c/p\u003e","description":"","filename":"Suppl1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/544bf66da7df7818d4ba0b8a.pdf"},{"id":49484954,"identity":"4a47448c-8249-410c-96fd-06fce2108cac","added_by":"auto","created_at":"2024-01-11 16:09:37","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1402036,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2. Relations between the abundance of TILs and expression, copy number, \u0026nbsp;methylation, or mutation of MUC5B. The immune-related signatures types from \u0026nbsp;Charoentong's study, the relative abundance of TILs in COAD was inferred by using \u0026nbsp;GSVA based on gene expression profile.\u003c/p\u003e","description":"","filename":"Suppl2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/174fe76d60f360de39d5bb9d.pdf"},{"id":49484960,"identity":"4bd18af8-3eb5-4b71-89e0-b2c4b124cb2c","added_by":"auto","created_at":"2024-01-11 16:09:37","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":694013,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3. Relations between the abundance of TILs and expression, copy number, \u0026nbsp;methylation, or mutation of MUC17. The immune-related signatures types from \u0026nbsp;Charoentong's study, the relative abundance of TILs in COAD was inferred by using \u0026nbsp;GSVA based on gene expression profile.\u003c/p\u003e","description":"","filename":"Suppl3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/2e188e2a44f0697ac254179b.pdf"},{"id":49484961,"identity":"8f371c04-970a-499d-aa98-867fa4c30fe2","added_by":"auto","created_at":"2024-01-11 16:09:37","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":784350,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S4. Relations between the abundance of TILs and expression, copy number, \u0026nbsp;methylation, or mutation of MUC6. The immune-related signatures types from \u0026nbsp;Charoentong's study, the relative abundance of TILs in COAD was inferred by using \u0026nbsp;GSVA based on gene expression profile.\u003c/p\u003e","description":"","filename":"Suppl4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/0400524ede8131a2330840d8.pdf"},{"id":49484955,"identity":"36eec28a-c562-40c6-b5a5-9902a81bc214","added_by":"auto","created_at":"2024-01-11 16:09:37","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1201722,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S5. The top 20 genes and HLA alleles with the number of predicted \u0026nbsp;neoantigens are displayed in COAD by TSNAdb1.0/NetMHCpan v2.8.\u003c/p\u003e","description":"","filename":"Suppl5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/83bcf280ca473a47816c4431.pdf"},{"id":49486016,"identity":"debaf4c8-6b47-439f-a336-a305c599ff91","added_by":"auto","created_at":"2024-01-11 16:17:37","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":12036,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1. Mutation Rates of MUC family within the TCGA Dataset.\u003c/p\u003e","description":"","filename":"Supplementaltable1MutationRatesofMUCs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/c221cb015c546eae4113adda.xlsx"},{"id":49484959,"identity":"358c274f-8669-4fef-8cb3-3968a18924f8","added_by":"auto","created_at":"2024-01-11 16:09:37","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":65502,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2. MUCmut neoantigens predicted by TSNAD v1.0 /NetMHCpan v2.8\u003c/p\u003e","description":"","filename":"Supplementaltable2TSNADv1.0NetMHCpanv2.8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/c5b50cfbfd9f823c9dc1c262.xlsx"},{"id":49484956,"identity":"0a9b7a4e-3357-4589-aaea-0ee1aa640c3e","added_by":"auto","created_at":"2024-01-11 16:09:37","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":32207,"visible":true,"origin":"","legend":"\u003cp\u003eTable S3. MUCmut neoantigens by TSNAD v2.0 /NetMHCpan v4.0\u003c/p\u003e","description":"","filename":"Supplementaltable3TSNADv2.0NetMHCpanv4.0.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3839089/v1/d46b99623d58ea3624029129.xlsx"}],"financialInterests":"","formattedTitle":"Characterizing and Forecasting Neoantigens Resulting from MUC Mutations in COAD","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MUC, tumor neoantigens, colon adenocarcinoma, Bioinformatics, immunotherapy. ","lastPublishedDoi":"10.21203/rs.3.rs-3839089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3839089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The treatment for colon adenocarcinoma (COAD) faces challenges in terms of immunotherapy effectiveness due to multiple factors. Because of the high tumor specificity and immunogenicity, neoantigen has been considered a pivotal target for cancer immunotherapy. Therefore, this study aims to identify and predict the potential tumor antigens of MUC somatic mutations (MUCmut) in COAD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Three databases of TCGA, TIMER2.0, and cBioPortal were used for a detailed evaluation of the association between MUCmut and multi-factors like tumor mutation burden (TMB), microsatellite instability (MSI), prognosis, and the tumor microenvironment within the context of total 2242 COAD patients. Next, TSNAdb and the differential agretopicity index (DAI) were utilized to predict high-confidence neopeptides for MUCmut based on 531 COAD patients' genomic information. DAI was calculated by subtraction of its predicted HLA binding affinity of the MUCmut peptide from the corresponding wild-type peptide.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The top six mutation frequencies (14 to 2.9%) were from MUC16, MUC17, MUC5B, MUC2, MUC4 and MUC6. COAD patients with MUC16 and MUC4 mutations had longer DFS and PFS. However, patients with MUC13 and MUC20 mutations had shorter OS. Patients with the mutation of MUC16, MUC5B, MUC2, MUC4, and MUC6 exhibited higher TMB and MSI. Moreover, these mutations from the MUC family were associated with the infiltration of diverse lymphocyte cells and the expression of immune checkpoint genes. Through TSNAdb 1.0/NetMHCpan v2.8, 452 single nucleotide variants (SNVs) of MUCmut peptides were identified. Moreover, through TSNAdb2.0/NetMHCpan v4.0, 57 SNVs, 1 Q-frame shift (TS), and 157 short insertions/deletions (INDELs) of MUCmut were identified. Finally, 10 high-confidence neopeptides of MUCmut were predicted by DAI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e In conclusion, this study showed the immunogenic role of neoantigens against MUCmut in COAD. Through combining the tools of TSNAdb and DAI, a group of novel MUCmut neoantigens were identified as potential targets for immunotherapy.\u003c/p\u003e","manuscriptTitle":"Characterizing and Forecasting Neoantigens Resulting from MUC Mutations in COAD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-11 16:09:32","doi":"10.21203/rs.3.rs-3839089/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-15T07:53:14+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-08T17:17:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-06T11:15:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2024-01-05T01:41:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24e841d5-9931-4990-aa86-aa48f1e0ac7e","owner":[],"postedDate":"January 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-01T15:04:10+00:00","versionOfRecord":{"articleIdentity":"rs-3839089","link":"https://doi.org/10.1186/s12967-024-05103-z","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2024-03-27 15:01:00","publishedOnDateReadable":"March 27th, 2024"},"versionCreatedAt":"2024-01-11 16:09:32","video":"","vorDoi":"10.1186/s12967-024-05103-z","vorDoiUrl":"https://doi.org/10.1186/s12967-024-05103-z","workflowStages":[]},"version":"v1","identity":"rs-3839089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3839089","identity":"rs-3839089","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}