Nature CommunicationsCancer BiologyPeer-reviewed journal article
Adaptation to cystine limitation stress confers a targetable lipid metabolism vulnerability in pancreatic ductal adenocarcinoma
Yunzhan Li, Zekun LiiD, Qin LiiD, Dongxiao SuniD, Bo Ni, Mingjun TaniD, Ashley Elizabeth ShayiD, M. Wang, Chenyang Meng, Guangcong SheniD, Boyang Fu, Yu ShaniD, Shiqi ZhangiD, Rifah Rownak TansheeiD, Tianxing ZhouiD, Yongjie XieiD, Kun-Ming CheniD, Bin QiaoiD, Yunkun DangiD, Scot R. KimballiD, Guanshi ZhangiD, Girish Halemirle RajacharyaiD, Pankaj K. SinghiD, Xiuchao WangiD, Jihui HaoiD, Shengyu YangiD
- Citations
- 7
- FWCI
- 2.90
- Field percentile
- top 8%
Abstract
Cystine/cysteine is critical for antioxidant response and sulfur metabolism in cancer cells and is one of the most depleted amino acids in the microenvironment of pancreatic ductal adenocarcinoma (PDAC). The effects of cystine limitation stress (CLS) on PDAC progression are poorly understood. Here we report that adaptation to CLS (CLSA) promotes PDAC cell proliferation and tumor growth through translational upregulation of the oxidative pentose phosphate pathway (OxPPP). OxPPP activates the de novo synthesis of nucleotides and fatty acids to support tumor growth. On the other hand, CLSA-mediated lipidomic reprogramming depends on triacylglycerides synthesis and lipid droplet formation to mitigate lipotoxicity. Through drug screening, we identify lomitapide as an inhibitor of CLSA PDAC tumor growth and a potent sensitizer of chemotherapy. Lomitapide inhibits triacylglycerides synthesis to interfere with CLSA and chemotherapy-induced lipidomic reprogramming. Taken together, we demonstrate that CLSA promotes PDAC tumor growth through metabolic reprogramming and lomitapide could be used to target the dysregulated lipid metabolism in PDAC.
- organism: human
- assay: bulk RNA-seq
- assay: metabolomics
- research focus: cancer & tumor microenvironment
- research focus: drug response / drug discovery
- research focus: metabolism & bioenergetics
- approach: experimental assay
- approach: statistical model
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