Biotech Research & Innovation Centre, University of Copenhagen, Copenhagen, 2200, Denmark.
Biotech Research & Innovation Centre, University of Copenhagen, Copenhagen, 2200, Denmark.
Cell Rep. 2021 Jan 19;34(3):108657. doi: 10.1016/j.celrep.2020.108657.
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 ∼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.
众所周知，癌细胞的耐药性发展会导致细胞形态发生变化。在这里，我们描述了使用深度神经网络来分析这种关系，证明了复杂的细胞形态可以编码信号网络的状态，并揭示了传统方法所隐藏的细胞机制。我们对 17 种癌细胞系进行了高通量筛选，从大约 8500 万个细胞中生成了超过 5000 亿个数据点。我们使用深度学习模型分析这些数据，结果确定了一个连续的 27 维空间，可描述所有观察到的细胞形态。仅从其形态上，我们就可以预测细胞对 ErbB 家族药物是否有耐药性，准确率为 74%，并预测耐药的潜在机制，随后在体外肺癌和头颈部癌症模型中验证了 MET 和胰岛素样生长因子 1 受体 (IGF1R) 作为西妥昔单抗耐药的驱动因素。
