Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response
Menée à partir de données du projet "The Cancer Genome Atlas", cette étude met en évidence l'intérêt d'un algorithme d'apprentissage automatique, développé à partir de données issues de l'analyse de plus de 700 modèles murins de tumeurs syngéniques, pour prédire l'immunité tumorale et la réponse aux immunothérapies
Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in The Cancer Genome Atlas, which agreed well with existing clinical reports. Last, feature analysis implicated factors associated with ICB response. In summary, our computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies. Machine learning integrating mouse transcriptome and experimental variables predicts tumor immunity and immunotherapy response.
Science Advances , article en libre accès, 2021