Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response
Zeng, Zexian (2022), Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response, Dryad, Dataset, https://doi.org/10.5061/dryad.b8gtht7g1
Most cancer patients 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 novel 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 TCGA, which agreed well with existing clinical reports. Finally, feature analysis implicated factors associated with ICB response. In summary, our novel 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.
Breast Cancer Research Foundation, Award: BCRF-20-100
National Institutes of Health, Award: R01CA234018
National Institutes of Health, Award: U24CA224316
National Institutes of Health, Award: T15LM007092