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Prediction of clinical response to checkpoint blockade immunotherapy is improved with ensembling

Predicting clinical response to checkpoint blockade immunotherapy is a major challenge in oncology. In the case of melanoma, we show how prediction is improved with the use of averaging, a simple ensembling method in machine learning. We report +3.7 percent improvement of the best response predictor (from AUC=0.81 to AUC=0.84), on a clinical dataset of 70 patients.