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Repurposing sertraline sensitizes non–small cell lung cancer cells to erlotinib by inducing autophagy
Xingwu Jiang, Weiqiang Lu, Xiaoyang Shen, Quan Wang, Jing Lv, Mingyao Liu, Feixiong Cheng, Zhongming Zhao, Xiufeng Pang
Xingwu Jiang, Weiqiang Lu, Xiaoyang Shen, Quan Wang, Jing Lv, Mingyao Liu, Feixiong Cheng, Zhongming Zhao, Xiufeng Pang
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Research Article Oncology Therapeutics

Repurposing sertraline sensitizes non–small cell lung cancer cells to erlotinib by inducing autophagy

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Abstract

Lung cancer patients treated with tyrosine kinase inhibitors (TKIs) often develop resistance. More effective and safe therapeutic agents are urgently needed to overcome TKI resistance. Here, we propose a medical genetics–based approach to identify indications for over 1,000 US Food and Drug Administration–approved (FDA-approved) drugs with high accuracy. We identified a potentially novel indication for an approved antidepressant drug, sertraline, for the treatment of non–small cell lung cancer (NSCLC). We found that sertraline inhibits the viability of NSCLC cells and shows a synergy with erlotinib. Specifically, the cotreatment of sertraline and erlotinib effectively promotes autophagic flux in cells, as indicated by LC3-II accumulation and autolysosome formation. Mechanistic studies further reveal that dual treatment of sertraline and erlotinib reciprocally regulates the AMPK/mTOR pathway in NSCLC cells. The blockade of AMPK activation decreases the anticancer efficacy of either sertraline alone or the combination. Efficacy of this combination regimen is decreased by pharmacological inhibition of autophagy or genetic knockdown of ATG5 or Beclin 1. Importantly, our results suggest that sertraline and erlotinib combination suppress tumor growth and prolong mouse survival in an orthotopic NSCLC mouse model (P = 0.0005). In summary, our medical genetics–based approach facilitates discovery of new anticancer indications for FDA-approved drugs for the treatment of NSCLC.

Authors

Xingwu Jiang, Weiqiang Lu, Xiaoyang Shen, Quan Wang, Jing Lv, Mingyao Liu, Feixiong Cheng, Zhongming Zhao, Xiufeng Pang

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