Figure 1.

Schematic overview of how data from common tumours can be used in combination with machine learning to predict immune checkpoint inhibitor responses in rare tumours. A big-data warehouse is constructed by pooling data from public repositories, clinical trials and biobanks. Data consist of clinicopathological, multi-omics and imaging data from common and rare tumours. By applying appropriate statistical inference on this big-data warehouse, clinicopathological, omics and imaging features can be selected that are strongly associated with immunological parameters potentially relevant to the cancer-immune setpoint. These selected features have the highest likelihood of contributing to the accuracy of a predictive model for response to immunotherapy. By using only these selected features as input parameters, the relatively small-scale cohorts of patients treated with immunotherapy can be used to train an accurate and non-overfitted predictive model, which will ultimately improve patient selection for this treatment.