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 perform high-content screening of 17 cancer cell lines, generating more than 500 billion data points from similar to 850 million cells. We analyze these data 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%, and predict the potential mechanism of resistance, subsequently validating the role of MET and insulin-like growth factor 1 receptor (IGF1R) as drivers of cetuximab resistance in in vitro models of lung and head/neck cancer.

Automated summary

This study utilized deep neural networks to analyze the relationship between drug resistance in cancer cells and cell morphology, revealing that complex cell morphologies can encode states of signaling networks and unravel cellular mechanisms hidden to conventional approaches. By analyzing over 500 billion data points, a continuous 27-dimension space describing cell morphologies was identified, enabling accurate prediction of drug resistance and mechanisms with a validation in lung and head/neck cancer models.