Computational grant for cancer vaccine discovery

Identyfikator grantu: PT00975

Kierownik projektu: Sachin Kote


  • Javier Alfaro
  • 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

Streszczenie projektu

The ICCVS continues to utilize resources generously awarded by CI-TASK. In this reporting period we have been quite successful, and a list of publications crediting CI-TASK resources has been included at the end of the document. The projects continue to be developed into the next reporting period.

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.


  1. Ken Weke, Sachin Kote, Jakub Faktor, Sofian Al Shboul, Naomi Uwugiaren, Paul M. Brennan, David Goodlett, Ted R. Hupp, Irena Dapic, DIA-MS proteome analysis of formalin-fixed paraffin-embedded glioblastoma tissues, Analytica Chimica Acta 1204, (2022) 1-12
  2. Ashita Singh, Monikaben Padariya, Jakub Faktor, Sachin Kote, Sara Mikac, Alicja Dziadosz, Tak W. Lam, Jack Brydon, Martin A. Wear, Kathryn L. Ball, Ted Hupp, Alicja Sznarkowska, Borek Vojtesek, Umesh Kalathiya, Identifcation of novel interferon responsive protein partners of human leukocyte antigen A (HLA‑A) using cross‑linking mass spectrometry (CLMS) approach, Scientific Reports 12, (2022) 1-16
  3. Jakub Faktor, Sachin Kote, Ted Hupp, Natalia Marek-Trzonkowska, Novel Formalin-Fixed Paraffin-Embedded Tissue Processing Method for Proteome Analysis Suggests Prolactin Induced Protein (PIP) as a Spatial Biomarker of Hormone Induced Cytoskeleton Remodeling, Communications Biology NA, (2022) NA
  4. Sachin Kote et al, Arabidopsis quantitative protein profile changes caused by Phospholipid:Diacylglcerol Acyltransferase1 overexpression, SREP-23-03509, Scientific Reports -, (2023) -
  5. Sachin Kote et al, Novel Formalin-Fixed Paraffin-Embedded Tissue Processing Method for Proteome Analysis Suggests Prolactin Induced Protein (PIP) as a Spatial Biomarker of Hormone Induced Cytoskeleton Remodeling, COMMSBIO-23-0245A-Z, COMMSBIO journal -, (2023) -
  6. Sachin Kote et al, Metaproteomic analysis from cervical biopsies and cytologies identifies proteinaceous biomarkers representing both human and microbial species, TAL-D-24-01208, TALANTA journal -, (2023) -

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