{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/21509"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/21509","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Development of a Neoepitope Prioritization Platform to Identify Immunogenic Neoantigens from Oncogenic FGFR3-TACC3 Fusion in Glioblastoma","abstract":"This study investigates the FGFR3-TACC3 fusion as a tumor-specific neoantigen source and establishes an end-to-end computational and experimental framework for the identification, prioritization, and validation of immunogenic neoantigens derived from chimeric RNAs. The FGFR3-TACC3 fusion, generated through an intrachromosomal rearrangement on chromosome 4, produces a unique junctional sequence that constitutively activates oncogenic signaling while remaining absent in normal tissues, thereby representing a highly specific therapeutic target. To evaluate its neoantigenic potential, 27 peptides (9-11 mers) spanning the fusion junction were assessed for HLA class I binding using NetMHCpan 4.1, MixMHCpred 3.0, and BigMHC 1.0, alongside an in vitro flow cytometry-based binding assay. Eleven peptides, including 10 fusion-derived neoantigens, were validated as strong binders to HLA-A*01:01. NetMHCpan 4.1 demonstrated superior predictive performance, exhibiting a significant inverse correlation with experimental binding and high classification accuracy (AUC = 0.9669). An experimentally derived threshold (EL Rank < 32.7) achieved maximal sensitivity while maintaining high specificity. This was further refined to a more stringent cutoff (EL Rank < 25.7) using an expanded multi-fusion dataset comprising FGFR3-TACC3, CD74-NRG1, and ESR1-PRKN across multiple HLA alleles, improving predictive robustness and specificity. Functional validation using ELISpot assays demonstrated that 9 of 10 fusion derived in vitro validated binders induced significant IFN-γ secretion from CD8 T cells, confirming their immunogenicity. Cross-reactivity assessment using the CrossDome pipeline identified two neoantigens with potential T cell off-target effects, enabling safety-informed prioritization of candidate peptides. Integration of single-cell transcriptomic and TCR repertoire analyses revealed that peptide stimulation induces coordinated activation of immune programs, including T cell activation, cytokine signaling, proliferation, and metabolic pathways. The best peptide pool elicited increased abundance weighted clonotype diversity and convergence without evidence of oligoclonal dominance. Clonal analysis further identified antigen-responsive CD8 T cell populations with conserved CDR3 features, supporting structural stability and antigen specificity. Collectively, this work establishes a scalable and reproducible workflow integrating fusion detection, fusion reconstruction, neoepitope prediction, experimental validation, and safety filtering. This platform enables both fusion-centric and peptide-centric strategies for multi-epitope vaccine design and TCR-based therapies, while introducing data-driven refinement of NetMHCpan 4.1 thresholds to enhance translational precision in neoantigen selection.","abstract_html":"This study investigates the FGFR3-TACC3 fusion as a tumor-specific neoantigen source and establishes an end-to-end computational and experimental framework for the identification, prioritization, and validation of immunogenic neoantigens derived from chimeric RNAs. The FGFR3-TACC3 fusion, generated through an intrachromosomal rearrangement on chromosome 4, produces a unique junctional sequence that constitutively activates oncogenic signaling while remaining absent in normal tissues, thereby representing a highly specific therapeutic target. To evaluate its neoantigenic potential, 27 peptides (9-11 mers) spanning the fusion junction were assessed for HLA class I binding using NetMHCpan 4.1, MixMHCpred 3.0, and BigMHC 1.0, alongside an in vitro flow cytometry-based binding assay. Eleven peptides, including 10 fusion-derived neoantigens, were validated as strong binders to HLA-A*01:01. NetMHCpan 4.1 demonstrated superior predictive performance, exhibiting a significant inverse correlation with experimental binding and high classification accuracy (AUC = 0.9669). An experimentally derived threshold (EL Rank < 32.7) achieved maximal sensitivity while maintaining high specificity. This was further refined to a more stringent cutoff (EL Rank < 25.7) using an expanded multi-fusion dataset comprising FGFR3-TACC3, CD74-NRG1, and ESR1-PRKN across multiple HLA alleles, improving predictive robustness and specificity. Functional validation using ELISpot assays demonstrated that 9 of 10 fusion derived in vitro validated binders induced significant IFN-γ secretion from CD8 T cells, confirming their immunogenicity. Cross-reactivity assessment using the CrossDome pipeline identified two neoantigens with potential T cell off-target effects, enabling safety-informed prioritization of candidate peptides. Integration of single-cell transcriptomic and TCR repertoire analyses revealed that peptide stimulation induces coordinated activation of immune programs, including T cell activation, cytokine signaling, proliferation, and metabolic pathways. The best peptide pool elicited increased abundance weighted clonotype diversity and convergence without evidence of oligoclonal dominance. Clonal analysis further identified antigen-responsive CD8 T cell populations with conserved CDR3 features, supporting structural stability and antigen specificity. Collectively, this work establishes a scalable and reproducible workflow integrating fusion detection, fusion reconstruction, neoepitope prediction, experimental validation, and safety filtering. This platform enables both fusion-centric and peptide-centric strategies for multi-epitope vaccine design and TCR-based therapies, while introducing data-driven refinement of NetMHCpan 4.1 thresholds to enhance translational precision in neoantigen selection.","abstract_has_math":false,"creators":["Thevasagayampillai, Shiyanth 1994-"],"institution":"University of Houston","degree_name":"Doctor of Philosophy","degree_level":null,"degree_discipline":"Biochemistry","degree_department":null,"school":null,"contributors":[],"advisors":["Gunaratne, Preethi"],"committee_chairs":[],"committee_members":["Chin, Yo-Lin","Antunes, Dinler A","Sumazin, Pavel"],"year":2026,"date_issued":"2026-05","date_published":"2026-05","updated_at":"2026-07-24T02:32:32Z","subjects":["TCR","Cancer","Vaccine","FGFR3-TACC3","Fusion","Neoantigen"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/21509","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Gunaratne, Preethi"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Chin, Yo-Lin","Antunes, Dinler A","Sumazin, Pavel"]},{"key":"dc:creator","label":"Author","values":["Thevasagayampillai, Shiyanth 1994-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-07-13T21:27:16Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-05"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biochemistry"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["TCR","Cancer","Vaccine","FGFR3-TACC3","Fusion","Neoantigen"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10657/21509"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This study investigates the FGFR3-TACC3 fusion as a tumor-specific neoantigen source and establishes an end-to-end computational and experimental framework for the identification, prioritization, and validation of immunogenic neoantigens derived from chimeric RNAs. The FGFR3-TACC3 fusion, generated through an intrachromosomal rearrangement on chromosome 4, produces a unique junctional sequence that constitutively activates oncogenic signaling while remaining absent in normal tissues, thereby representing a highly specific therapeutic target. To evaluate its neoantigenic potential, 27 peptides (9-11 mers) spanning the fusion junction were assessed for HLA class I binding using NetMHCpan 4.1, MixMHCpred 3.0, and BigMHC 1.0, alongside an in vitro flow cytometry-based binding assay. Eleven peptides, including 10 fusion-derived neoantigens, were validated as strong binders to HLA-A*01:01. NetMHCpan 4.1 demonstrated superior predictive performance, exhibiting a significant inverse correlation with experimental binding and high classification accuracy (AUC = 0.9669). An experimentally derived threshold (EL Rank < 32.7) achieved maximal sensitivity while maintaining high specificity. This was further refined to a more stringent cutoff (EL Rank < 25.7) using an expanded multi-fusion dataset comprising FGFR3-TACC3, CD74-NRG1, and ESR1-PRKN across multiple HLA alleles, improving predictive robustness and specificity. Functional validation using ELISpot assays demonstrated that 9 of 10 fusion derived in vitro validated binders induced significant IFN-γ secretion from CD8 T cells, confirming their immunogenicity. Cross-reactivity assessment using the CrossDome pipeline identified two neoantigens with potential T cell off-target effects, enabling safety-informed prioritization of candidate peptides. Integration of single-cell transcriptomic and TCR repertoire analyses revealed that peptide stimulation induces coordinated activation of immune programs, including T cell activation, cytokine signaling, proliferation, and metabolic pathways. The best peptide pool elicited increased abundance weighted clonotype diversity and convergence without evidence of oligoclonal dominance. Clonal analysis further identified antigen-responsive CD8 T cell populations with conserved CDR3 features, supporting structural stability and antigen specificity. Collectively, this work establishes a scalable and reproducible workflow integrating fusion detection, fusion reconstruction, neoepitope prediction, experimental validation, and safety filtering. This platform enables both fusion-centric and peptide-centric strategies for multi-epitope vaccine design and TCR-based therapies, while introducing data-driven refinement of NetMHCpan 4.1 thresholds to enhance translational precision in neoantigen selection."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Development of a Neoepitope Prioritization Platform to Identify Immunogenic Neoantigens from Oncogenic FGFR3-TACC3 Fusion in Glioblastoma"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gunaratne, Preethi"],"dc:contributor.committeemember":["Chin, Yo-Lin","Antunes, Dinler A","Sumazin, Pavel"],"dc:creator":["Thevasagayampillai, Shiyanth 1994-"],"dc:date.accessioned":["2026-07-13T21:27:16Z"],"dc:date.issued":["2026-05"],"dc:description.abstract":["This study investigates the FGFR3-TACC3 fusion as a tumor-specific neoantigen source and establishes an end-to-end computational and experimental framework for the identification, prioritization, and validation of immunogenic neoantigens derived from chimeric RNAs. The FGFR3-TACC3 fusion, generated through an intrachromosomal rearrangement on chromosome 4, produces a unique junctional sequence that constitutively activates oncogenic signaling while remaining absent in normal tissues, thereby representing a highly specific therapeutic target. To evaluate its neoantigenic potential, 27 peptides (9-11 mers) spanning the fusion junction were assessed for HLA class I binding using NetMHCpan 4.1, MixMHCpred 3.0, and BigMHC 1.0, alongside an in vitro flow cytometry-based binding assay. Eleven peptides, including 10 fusion-derived neoantigens, were validated as strong binders to HLA-A*01:01. NetMHCpan 4.1 demonstrated superior predictive performance, exhibiting a significant inverse correlation with experimental binding and high classification accuracy (AUC = 0.9669). An experimentally derived threshold (EL Rank < 32.7) achieved maximal sensitivity while maintaining high specificity. This was further refined to a more stringent cutoff (EL Rank < 25.7) using an expanded multi-fusion dataset comprising FGFR3-TACC3, CD74-NRG1, and ESR1-PRKN across multiple HLA alleles, improving predictive robustness and specificity. Functional validation using ELISpot assays demonstrated that 9 of 10 fusion derived in vitro validated binders induced significant IFN-γ secretion from CD8 T cells, confirming their immunogenicity. Cross-reactivity assessment using the CrossDome pipeline identified two neoantigens with potential T cell off-target effects, enabling safety-informed prioritization of candidate peptides. Integration of single-cell transcriptomic and TCR repertoire analyses revealed that peptide stimulation induces coordinated activation of immune programs, including T cell activation, cytokine signaling, proliferation, and metabolic pathways. The best peptide pool elicited increased abundance weighted clonotype diversity and convergence without evidence of oligoclonal dominance. Clonal analysis further identified antigen-responsive CD8 T cell populations with conserved CDR3 features, supporting structural stability and antigen specificity. Collectively, this work establishes a scalable and reproducible workflow integrating fusion detection, fusion reconstruction, neoepitope prediction, experimental validation, and safety filtering. This platform enables both fusion-centric and peptide-centric strategies for multi-epitope vaccine design and TCR-based therapies, while introducing data-driven refinement of NetMHCpan 4.1 thresholds to enhance translational precision in neoantigen selection."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10657/21509"],"dc:language.iso":["English"],"dc:subject":["TCR","Cancer","Vaccine","FGFR3-TACC3","Fusion","Neoantigen"],"dc:title":["Development of a Neoepitope Prioritization Platform to Identify Immunogenic Neoantigens from Oncogenic FGFR3-TACC3 Fusion in Glioblastoma"],"dc:type":["Thesis"],"thesis:degree_discipline":["Biochemistry"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:32:32Z"}