Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space

It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode states of signaling networks and unravel cellular mechanisms hidden to conventional approaches. We performed high content screening of 17 cancer cell lines, generating over 500 billion data points from ~850 million cells. These data were analyzed using a deep learning model resulting in the identification of a continuous 27-dimension space describing all of the observed cell morphologies. From its morphology alone, we could thus predict whether a cell was resistant to ErbB-family drugs, with an accuracy of 74%. In addition, we could also predict the potential mechanism of resistance, subsequently validating the role of MET and IGF1R as drivers of cetuximab resistance in in vitro models of lung and head/neck cancer.