Personalized cancer vaccine design through 3D modelling boosted geometric learning

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Personalized cancer vaccines have recently achieved clinical breakthroughs in eliminating advanced tumors. Tailored to a patient’s individual cancer mutations, cancer vaccines train the immune system to attack tumors. However, the translation of cancer vaccine to a routine therapy is hindered by one key challenge: Which of a patient’s many tumor mutations serve best as vaccine candidates? To be good candidates, mutated tumor peptides must bind MHC proteins (forming peptide-MHC complex), which ship the peptides to the cell surface to be visible to T-cells.

This project combines the power of physics-based 3D modelling and data-driven geometric deep learnings (GDLs). GDLs are recent advances of deep learning that are specially designed for 3D data. We train GDL networks on 3D models of peptide-MHC to predict the binding of the two. Rotation invariant GDLs allow efficient usage of enriched structural information in 3D models, which is not exploited by hitherto used sequence-based methods. Our approach naturally deals with the challenge caused by the high length variance of peptides binding MHC class II, which dominates T-cell responses upon cancer vaccinations. Efficient training of GDLs on millions of 3D atomic-level models and associated experimental binding data requires efficient parallel data handling, and massive HPC and GPU use.

The resulting software will be integrated into our DeepRank platform, distributed on GitHub, and disseminated through user-community workshops. A user-friendly web server with a user-support forum will be developed for the biomedical community. The GDL software will be designed generally applicable to structural biology, chemistry, human genetics, and beyond.

Participating organisations

Netherlands eScience Center
Radboud University Medical Center

Team

Contact person

GC

Giulia Crocioni

Netherlands eScience Center
Mail Giulia
DB
Dani Bodor
eScience Research Engineer
Netherlands eScience Center
GC
Giulia Crocioni
eScience Research Engineer
Netherlands eScience Center
LX
Li Xue
Principal investigator
Radboud University Medical Center
PL
Pablo Lopez-Tarifa
eScience Coordinator
Netherlands eScience Center

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