Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy
Menée à partir d'échantillons tumoraux de patients et à partir de données portant sur des patients atteints d'un carcinome basocellulaire, d'un carcinome épidermoïde ou d'un mélanome, cette étude met en évidence l'intérêt d'un algorithme d'apprentissage automatique, utilisant des données de séquençage du récepteur des lymphocytes T, pour prédire et expliquer la réponse clinique à une immunothérapie
T cell receptor (TCR) sequencing has been used to characterize the immune response to cancer. However, most analyses have been restricted to quantitative measures such as clonality that do not leverage the complementarity-determining region 3 (CDR3) sequence. We use DeepTCR, a framework of deep learning algorithms, to reveal sequence concepts that are predictive of response to immunotherapy. We demonstrate that DeepTCR can predict response and use the model to infer the antigenic specificities of the predictive signature and their unique dynamics during therapy. The predictive signature of nonresponse is associated with high frequencies of TCRs predicted to recognize tumor-specific antigens, and these tumor-specific TCRs undergo a higher degree of dynamic changes on therapy in nonresponders versus responders. These results are consistent with a biological model where the hallmark of nonresponders is an accumulation of tumor-specific T cells that undergo turnover on therapy, possibly because of the dysfunctional state of these T cells in nonresponders. Deep learning of T cell receptor sequencing data is able to predict and explain clinical response to immunotherapy in melanoma.
Science Advances 2022