Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning
Timothy J. O’Donnell, Chakravarthi Kanduri, Giulio Isacchini, Julien Limenitakis, Rebecca A. Brachman, Raymond Alvarez, Ingrid Hobæk Haff, Geir Kjetil Sandve, Victor Greiff
Abstract
The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.