Posters
Presenting Author Academic/Professional Position
Medical Student
Academic Level (Author 1)
Medical Student
Discipline/Specialty (Author 1)
Immunology and Microbiology
Academic Level (Author 2)
Medical Student
Academic Level (Author 3)
Medical Student
Academic Level (Author 4)
Faculty
Discipline/Specialty (Author 4)
Immunology and Microbiology
Academic Level (Author 5)
Staff
Discipline/Specialty (Author 5)
Immunology and Microbiology
Presentation Type
Oral Presentation
Discipline Track
Biomedical Science
Abstract Type
Research/Clinical
Abstract
Background: Mucin 13 (MUC13), a transmembrane glycoprotein, plays a crucial role in the progression of epithelial cancers, particularly liver and pancreatic cancers. It is frequently overexpressed in malignant cells, disrupting cellular adhesion and facilitating invasive behavior. The extracellular domain and phosphorylation potential in its cytoplasmic region connect MUC13 to oncogenic signaling pathways, making it a valuable target for immunotherapy. However, challenges such as the immunosuppressive tumor microenvironment remain significant obstacles, and addressing these issues in the lab is challenging without computational intervention. To address this, we have employed an in-silico approach to design CD8+ T-cell multi-epitopes targeting MUC13 HLA class I variants. This strategy accelerates experimental planning for vaccine development in laboratories. Our project focuses on creating an HLA-peptide vaccine aimed at MUC13-overexpressing cancer cells, thereby eliciting an immune response from CD8+ T cells. The initial phase involves designing and validating MUC13 peptides using bioinformatics tools to identify major histocompatibility complex (MHC) proteins with high affinity for common HLA alleles.
Methodology & Results: The MUC13 sequence was obtained from Uniprot and processed through the NetMHCpan database to identify class 1 HLA sequences with strong binding affinities and a length of 10 peptides. The 8,106 epitopes identified were organized in an Excel list based on prevalence and reduced to 63 sequences. Further screening based on Antigen Prediction Scores of 0.2894 and above narrowed this list to 38 epitopes. Toxicity analysis confirmed that none of these 38 epitopes were toxic. Subsequently, IFN-γ epitope prediction was conducted using an IFN prediction database, identifying 7 positive sequences. The final test involved global population coverage, where all 7 sequences demonstrated 100% coverage. The final sequences, ranked from highest to lowest prevalence, were RSSSSNFLNY, LQRPNPQSPF, RPNPQSPFCV, KAIRSSSSNF, TPSFPTATSP, YYYNSSTCKK, and GYYYNSSTCK.
Conclusion: When considering adverse forms of cancer, such as pancreatic and liver cancer, it is important to identify prominent biomarkers. Previous research has shown that MUC13 (Mucin 13), a protein critical for cell-cell communication and other functions, is significantly overexpressed in these types of cancers. To leverage this, we utilized databases to extract CD8+ T cell multi epitope HLA sequences that are most optimal for targeting MUC13 in cancer cells. By analyzing 8,106 strong-binding epitopes found on NetMHCpan and selecting the most viable candidates based on IFN-γ prediction and toxicity analysis, a total of 7 sequences were identified as fitting the ideal archetype while also demonstrating 100% global population coverage. The data-driven aspect of this research is critical in identifying the most promising candidates for the proposed protein vaccine. This work aspires to advance cancer immunotherapy by offering a targeted approach to treat predominant cancers, such as liver and pancreatic cancer, ultimately contributing to more effective treatments for cancer patients.
Recommended Citation
Gomez, Abigail; Mills, John; Mata, Aidan; Dhasmana, Anupam; Dhasmana, Swati; Khan, Sheema; Yallapu, Murali; and Chauhan, Subhash, "In silico prediction of CD8+ T-cell multi-epitopes and HLA distribution analysis for MUC13: An onco-vaccine design strategy" (2026). Research Symposium. 189.
https://scholarworks.utrgv.edu/somrs/2025/posters/189
Included in
In silico prediction of CD8+ T-cell multi-epitopes and HLA distribution analysis for MUC13: An onco-vaccine design strategy
Background: Mucin 13 (MUC13), a transmembrane glycoprotein, plays a crucial role in the progression of epithelial cancers, particularly liver and pancreatic cancers. It is frequently overexpressed in malignant cells, disrupting cellular adhesion and facilitating invasive behavior. The extracellular domain and phosphorylation potential in its cytoplasmic region connect MUC13 to oncogenic signaling pathways, making it a valuable target for immunotherapy. However, challenges such as the immunosuppressive tumor microenvironment remain significant obstacles, and addressing these issues in the lab is challenging without computational intervention. To address this, we have employed an in-silico approach to design CD8+ T-cell multi-epitopes targeting MUC13 HLA class I variants. This strategy accelerates experimental planning for vaccine development in laboratories. Our project focuses on creating an HLA-peptide vaccine aimed at MUC13-overexpressing cancer cells, thereby eliciting an immune response from CD8+ T cells. The initial phase involves designing and validating MUC13 peptides using bioinformatics tools to identify major histocompatibility complex (MHC) proteins with high affinity for common HLA alleles.
Methodology & Results: The MUC13 sequence was obtained from Uniprot and processed through the NetMHCpan database to identify class 1 HLA sequences with strong binding affinities and a length of 10 peptides. The 8,106 epitopes identified were organized in an Excel list based on prevalence and reduced to 63 sequences. Further screening based on Antigen Prediction Scores of 0.2894 and above narrowed this list to 38 epitopes. Toxicity analysis confirmed that none of these 38 epitopes were toxic. Subsequently, IFN-γ epitope prediction was conducted using an IFN prediction database, identifying 7 positive sequences. The final test involved global population coverage, where all 7 sequences demonstrated 100% coverage. The final sequences, ranked from highest to lowest prevalence, were RSSSSNFLNY, LQRPNPQSPF, RPNPQSPFCV, KAIRSSSSNF, TPSFPTATSP, YYYNSSTCKK, and GYYYNSSTCK.
Conclusion: When considering adverse forms of cancer, such as pancreatic and liver cancer, it is important to identify prominent biomarkers. Previous research has shown that MUC13 (Mucin 13), a protein critical for cell-cell communication and other functions, is significantly overexpressed in these types of cancers. To leverage this, we utilized databases to extract CD8+ T cell multi epitope HLA sequences that are most optimal for targeting MUC13 in cancer cells. By analyzing 8,106 strong-binding epitopes found on NetMHCpan and selecting the most viable candidates based on IFN-γ prediction and toxicity analysis, a total of 7 sequences were identified as fitting the ideal archetype while also demonstrating 100% global population coverage. The data-driven aspect of this research is critical in identifying the most promising candidates for the proposed protein vaccine. This work aspires to advance cancer immunotherapy by offering a targeted approach to treat predominant cancers, such as liver and pancreatic cancer, ultimately contributing to more effective treatments for cancer patients.
