Interpretable Deep Learning for Cancer-Associated T-Cell Receptors

Bo Li Lab

T cells are critical in mediating adaptive immunity by selectively killing the target cells. The recognition of target cells is through the binding of the T cell receptor (TCR) and the antigens presented on the surface of the target cells. TCRs are genetically diversified through a biological process called V(D)J recombination, which is able to produce 10^15-10^16 different types of T cells in humans. Such diversity allows efficient recognition of a wide spectrum of antigens, including virus, bacteria, cancer, etc. We developed a novel deep learning algorithm, DeepCAT, that is able to distinguish cancer vs non-cancer TCRs, with ~80% AUC. It can also de novo predict neoantigen-specific TCRs that were never seen in the training data. This indicates that either cancer or non-cancer TCRs share common biochemical signatures, a fact that was previously unknown. We want to learn these signatures through approach of interpretable deep learning, by looking through the 'black box' of the layers of neural network, and find which parameters are reflective of the cancer/non-cancer distinction. The findings from this research will provide useful insights of the co-evolution of tumor and T cells in the microenvironment and inspire novel diagnostic or therapeutic approaches.

For more information about this project email Dr. Bo Li.