biostudies-literatureHachey SJNCATS NIH HHSNCI NIH HHSDepartment of Defense921https://www.ebi.ac.uk/biostudies/studies/S-EPMC12474151biostudies-literatureUnknown13(9)<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.VaccinesImmuniT Platform for Improved Neoantigen Prediction in Lung Cancer.PMC1247415175N91022C00004U54 CA217378W81XWH2110393TL1 TR001415Forsythe AGKeshava HBHachey SJHughes CCWfalseImmuniT Platform for Improved Neoantigen Prediction in Lung Cancer.<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.2025-01-01T00:00:00Z2025 Aug2026-05-02T03:16:22.635Z2026-05-02T03:11:12.913ZS-EPMC124741514101212410.3390/vaccines13090921