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An interpretable ML model to characterize patient-specific HLA-I antigen presentation.
Shaoheng Liang1,2, Xianli Jiang1, Yulun Chiu3,4
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, USA.
Biorxiv : the Preprint Server for Biology
|March 30, 2023
Summary
We developed epiNB, a new computational method for identifying personalized cancer immunotherapy targets. This approach accurately models tumor-specific antigen presentation, improving cancer treatment strategies.
Area of Science:
- Computational biology
- Immunology
- Oncology
Background:
- Personalized immunotherapy offers transformative potential for cancer prevention and treatment.
- Selecting patient-specific, tumor-associated HLA-bound peptides for immunotherapy is hindered by a lack of accurate antigen presentation models.
Conclusions:
- epiNB provides a powerful and accessible tool for personalized cancer immunotherapy target selection.
- The method offers valuable insights into the mechanisms of tumor-specific antigen presentation.
- epiNB's ease of use and computational efficiency facilitate its application in translational cancer research and clinical settings.
