{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Singhal K"],"funding":["NCATS NIH HHS","NHGRI NIH HHS","NCI NIH HHS","NIGMS NIH HHS"],"pubmed_abstract":["Personalized neoantigen vaccines represent a promising immunotherapy approach that harnesses tumor-specific antigens to stimulate anti-tumor immune responses. However, the design of these vaccines requires sophisticated computational workflows to predict and prioritize neoantigen candidates from patient sequencing data, coupled with rigorous review to ensure candidate quality. While numerous computational tools exist for neoantigen prediction, to our knowledge, there are no established protocols detailing the complete process from raw sequencing data through systematic candidate selection. Here, we present ImmunoNX (Immunogenomics Neoantigen eXplorer), an end-to-end protocol for neoantigen prediction and vaccine design that has supported over 185 patients across 11 clinical trials. The wor"],"journal":["ArXiv"],"pagination":["arXiv:2512.08226v1"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12709488"],"repository":["biostudies-literature"],"pubmed_title":["ImmunoNX: a robust bioinformatics workflow to support personalized neoantigen vaccine trials."],"pmcid":["PMC12709488"],"funding_grant_id":["U01 CA248235","UL1 TR002345","U01 CA231844","U01 CA209936","K22 CA282364","T32 GM139774","T32 CA009621","U24 CA237719","P30 CA091842","P50 CA196510","R00 HG007940","P50 CA272213","R01 CA240983"],"pubmed_authors":["Garza JE","McMichael JF","Gillanders WE","Hoos WA","Foltz JA","Maruska J","Karchin R","Hundal J","Schmidt E","Singhal K","Chang GS","Goedegebuure SP","Davies SR","Xia H","Miller CA","Fehniger TA","Griffith OL","Khanfar M","Inabinett WB","Mooney TB","Yao J","Li J","Hoang MH","Risch I","Richters MM","Coffman A","Hendrickson L","Kiwala S","O'Laughlin S","Johanns TM","Ward JP","McLellan MD","Myers N","Du F","Dunn GP","Griffith M","Vickery T","Cotto KC","Pachynski RK"],"additional_accession":[]},"is_claimable":false,"name":"ImmunoNX: a robust bioinformatics workflow to support personalized neoantigen vaccine trials.","description":"Personalized neoantigen vaccines represent a promising immunotherapy approach that harnesses tumor-specific antigens to stimulate anti-tumor immune responses. However, the design of these vaccines requires sophisticated computational workflows to predict and prioritize neoantigen candidates from patient sequencing data, coupled with rigorous review to ensure candidate quality. While numerous computational tools exist for neoantigen prediction, to our knowledge, there are no established protocols detailing the complete process from raw sequencing data through systematic candidate selection. Here, we present ImmunoNX (Immunogenomics Neoantigen eXplorer), an end-to-end protocol for neoantigen prediction and vaccine design that has supported over 185 patients across 11 clinical trials. The wor","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-06-30T03:24:29.95Z","creation":"2026-06-30T03:19:40.457Z"},"accession":"S-EPMC12709488","cross_references":{"pubmed":["41415611"]}}