Abstract
MHC class I proteins present intracellular peptides on the cell’s surface, enabling the immune system to recognize tumor-specific neoantigens of early neoplastic cells and eliminate them before the tumor develops further. However, variability in peptide-MHC-I affinity results in variable presentation of oncogenic peptides, leading to variable likelihood of immune evasion across both individuals and mutations. Since the major determinant of peptide-MHC-I affinity in patients is individual MHC-I genotype, we developed a residue-centric presentation score taking both mutated residues and MHC-I genotype into account and hypothesized that high scores (which correspond to poor presentation) would correlate to high mutation frequencies within tumors. We applied our scoring system to 9176 tumor samples from TCGA across 1018 recurrent mutations and found that, indeed, presentation scores predicted mutation probability. These findings open the door to more personalized treatment plans based on simple genotyping. Here, we outline the computational tools and statistical methods used to arrive at this conclusion.
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Acknowledgments
This work has been supported by NIH K99/R00CA191152 grant to J. F-B and funded in part through the NIH/NCI Cancer Center Support Grant P30 CA006927. David Rossell was partially funded by the grant RyC-2015-18544, Plan Estatal PGC2018-101643-B-I00, and Ayudas Fundacion BBVA a equipos de investigacion cientifica 2017.
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Beauchemin, L., Slifker, M., Rossell, D., Font-Burgada, J. (2020). Characterizing MHC-I Genotype Predictive Power for Oncogenic Mutation Probability in Cancer Patients. In: Tomar, N. (eds) Immunoinformatics. Methods in Molecular Biology, vol 2131. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0389-5_8
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DOI: https://doi.org/10.1007/978-1-0716-0389-5_8
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