{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hachey SJ"],"funding":["NCATS NIH HHS","NCI NIH HHS","Department of Defense"],"pagination":["921"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12474151"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(9)"],"pubmed_abstract":["<h4>Background</h4>Lung cancer remains the leading cause of cancer-related mortality, with many patients responding poorly to immunotherapy due to limited tumor recognition. Neoantigen-based strategies offer a promising solution, but current discovery methods often miss key targets, particularly those with low or heterogeneous expression. To address this, we developed ImmuniT, a three-phase platform for enhanced neoantigen discovery and validation.<h4>Methods</h4>Under an IRB-approved protocol, patients with lung cancer consented to tumor collection for ex vivo processing to modulate antigen expression. Autologous T cells from matched blood were co-cultured with treated cancer cells to expand tumor-reactive populations. The nextneopi pipeline integrated mutational, transcriptomic, and HLA data to predict candidate neoantigens, which were validated using MHCepitope tetramer staining.<h4>Results</h4>In five patient samples, ImmuniT identified a broader spectrum of neoantigens and induced stronger T cell activation in vitro compared to conventional approaches. Notably, in one case, two neoantigens missed by standard methods were confirmed to elicit tumor-specific T cell responses in both the tumor-infiltrating and peripheral compartments.<h4>Conclusions</h4>These findings highlight ImmuniT's potential to expand the repertoire of actionable tumor antigens and improve personalized immunotherapy strategies, particularly for patients with limited response to existing treatments."],"journal":["Vaccines"],"pubmed_title":["ImmuniT Platform for Improved Neoantigen Prediction in Lung Cancer."],"pmcid":["PMC12474151"],"funding_grant_id":["75N91022C00004","U54 CA217378","W81XWH2110393","TL1 TR001415"],"pubmed_authors":["Forsythe AG","Keshava HB","Hachey SJ","Hughes CCW"],"additional_accession":[]},"is_claimable":false,"name":"ImmuniT Platform for Improved Neoantigen Prediction in Lung Cancer.","description":"<h4>Background</h4>Lung cancer remains the leading cause of cancer-related mortality, with many patients responding poorly to immunotherapy due to limited tumor recognition. Neoantigen-based strategies offer a promising solution, but current discovery methods often miss key targets, particularly those with low or heterogeneous expression. To address this, we developed ImmuniT, a three-phase platform for enhanced neoantigen discovery and validation.<h4>Methods</h4>Under an IRB-approved protocol, patients with lung cancer consented to tumor collection for ex vivo processing to modulate antigen expression. Autologous T cells from matched blood were co-cultured with treated cancer cells to expand tumor-reactive populations. The nextneopi pipeline integrated mutational, transcriptomic, and HLA data to predict candidate neoantigens, which were validated using MHCepitope tetramer staining.<h4>Results</h4>In five patient samples, ImmuniT identified a broader spectrum of neoantigens and induced stronger T cell activation in vitro compared to conventional approaches. Notably, in one case, two neoantigens missed by standard methods were confirmed to elicit tumor-specific T cell responses in both the tumor-infiltrating and peripheral compartments.<h4>Conclusions</h4>These findings highlight ImmuniT's potential to expand the repertoire of actionable tumor antigens and improve personalized immunotherapy strategies, particularly for patients with limited response to existing treatments.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-05-02T03:16:22.635Z","creation":"2026-05-02T03:11:12.913Z"},"accession":"S-EPMC12474151","cross_references":{"pubmed":["41012124"],"doi":["10.3390/vaccines13090921"]}}