Deep Learning Moves Cancer Vaccines Toward Reality

What is it about?

The immune system cannot by itself easily distinguish between a healthy and cancerous cell. The way personalized cancer vaccines work is that they externally synthesize a peptide that when passed into the patient helps the immune system identify cancerous cells. This is done by forming a bond between the injected peptide and cancerous cells in the body. Since cancerous cells differ from person to person, such an approach requires analysis to choose the right peptides that can trigger an appropriate immune response. One of the major steps in the synthesis of personalized cancer vaccines is to computationally predict whether a given peptide will bind with the patient’s Major Histocompatibility Complex (MHC) allele. This new deep learning model, which the authors call MHCAttnNet, uses Bi-LSTMs to predict the MHC-peptide binding more accurately than existing methods. MHCAttnNet also uses the attention mechanism, a technique from natural language processing, to highlight the important subsequences from the amino-acid sequences of peptides and MHC alleles that were used by the MHCAttnNet model to make the binding prediction.

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Photo by CDC on Unsplash

Photo by CDC on Unsplash

Why is it important?

This work will help researchers develop personalized cancer vaccines by improving the understanding of the MHC-peptide binding mechanism. The higher accuracy of this model will improve the performance of the computational verification step of personalized vaccine synthesis. This, in turn, would improve the likelihood of a personalized cancer vaccine that works on a given patient.