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Cancer Vaccines

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New tools for MHC research from machine learning and predictive algorithms to the tumour immunopeptidome.

Immunology
At a time when immunology seeks to progress ever more rapidly from characterization of a microbial or tumour antigen to the immune correlates that may define protective T-cell immunity, there is a need for robust tools to enable accurate predictions ...

Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes.

Cancer immunology research
Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity usi...

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Cancer immunology research
Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on estimation of MHC binding aff...

pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens.

Cancer immunology research
Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational a...

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence.

International journal of molecular sciences
The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has resulted in long-term survival in advanced cancer patient subgroups. However, the majority of patients...