Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer

摘要: Abstract Immune therapies have shown promise in a number of cancers, and clinical trials using the anti-PD-L1/PD-1 checkpoint inhibitor lung cancer been successful for patients. However, some patients either do not respond to treatment or recurrence after an initial response. It is clear which might fall into these categories what mechanisms are responsible failure. To explore different underlying biological resistance, we created spatially explicit mathematical model with modular framework. This construction enables potential be turned on off order adjust specific tumor tissue interactions match patient9s disease. In parallel, developed software suite identify significant computed tomography (CT) imaging features correlated outcome data from anti-PDL-1 trial tool that extracts both patient CT images “virtual CT” cellular density profile model. The combination our two toolkits provides framework feeds through iterative pipeline predictive associated outcome, whilst at same time proposing hypotheses about resistance mechanisms.