Computational grant for cancer vaccine discovery
Kierownik projektu: Javier Alfaro
- Sachin Kote
- Marcos Yebenes Mayordomo
- Georges Bedran
- Artur Piróg
- Jakub Faktor
- Kenneth Weke
University of Gdańsk
International Centre for Cancer Vaccine Science
Data otwarcia: 2022-06-01
The clinical relevance of immune cells in the control of human cancers is now well established. However, the identification of tumour-specific antigens that allow the immune system to differentiate cancer cells from normal cells remains a challenge. To be immunogenic, somatic mutations must give rise to peptides that are processed and bind to any of the major histocompatibility complex (MHC) class I or class II allelic products in the patient. Breakthroughs in genomics and proteomics have made it possible to discover recurring and patient-specific neoantigens arising as a consequence of tumor-specific mutations. However, the fraction of somatic mutations yielding an epitope in any patient is low, as is the fraction of the population expected to present a recurring mutation. Hence, the prioritization of which neoantigens to characterize is essential for the success of cancer vaccine science and relies on the development of bioinformatics pipelines. The International Centre for Cancer Vaccine Science (ICCVS), funded by the Polish Foundation for Science (FNP), is an innovative new partnership between the University of Gdańsk and the University of Edinburgh addressing this major challenge in cancer medicine. The centre in Gdansk is seeking dedicated computational resources for the development and application of bioinformatics tools and methods.
Project aim 1: Cancer neo-epitope discovery platform development.
The first stage for the project involves neo-epitope discovery using matched tumour and normal patient samples and cancer cell lines. The team is expected to develop and apply a computational pipeline to identify mutated genes, mutant mRNA, RNA editing events, intron-translation, and chromosomal fusions from next generation DNA and RNA sequencing data. Having identified these aberrations, the team will characterize immunopeptidomes by mass spectrometry and standard immunoaffinity purification.
Project aim 2: Predicting neo-antigen presentation from genomics and transcriptomics.
The centre will generate a large dataset of immunopeptidomes derived by mass-spectrometry alongside matching genomic and transcriptomic datasets. Further, a large dataset of publicly available immuno-peptidomic data will be collected. The team will use machine learning strategies to develop a predictor of neo-antigen presentation based on these and publicly available data. The goal will be to predict from genomics and transcriptomic datasets, which cancer-specific peptides will later be detected by mass-spectrometry as cell-surface antigens. The resulting model will be used to accelerate discovery and reduce costs for neo-antigen discovery.