s41416-019-0464-z.pdf (2.08 MB)
Metabolic reprogramming and Notch activity distinguish between non-small cell lung cancer subtypes.
journal contributionposted on 2020-01-08, 16:57 authored by Katherine Sellers, Thaddeus D Allen, Michael Bousamra, JinLian Tan, Andrés Méndez-Lucas, Wei Lin, Nourdine Bah, Yelena Chernyavskaya, James I MacRae, Richard M Higashi, Andrew N Lane, Teresa W-M Fan, Mariia O Yuneva
BACKGROUND: Previous studies suggested that the metabolism is differently reprogrammed in the major subtypes of non-small cell lung cancer (NSCLC), squamous cell carcinomas (SCC) and adenocarcinomas (AdC). However, a comprehensive analysis of this differential metabolic reprogramming is lacking. METHODS: Publicly available gene expression data from human lung cancer samples and cell lines were analysed. Stable isotope resolved metabolomics were performed on SCC and ADC tumours in human patients and in freshly resected tumour slices. RESULTS: Analysis of multiple transcriptomics data from human samples identified a SCC-distinguishing enzyme gene signature. SCC tumours from patients infused with [U-13C]-glucose and SCC tissue slices incubated with stable isotope tracers demonstrated differential glucose and glutamine catabolism compared to AdCs or non-cancerous lung, confirming increased activity through pathways defined by the SCC metabolic gene signature. Furthermore, the upregulation of Notch target genes was a distinguishing feature of SCCs, which correlated with the metabolic signature. Notch and MYC-driven murine lung tumours recapitulated the SCC-distinguishing metabolic reprogramming. However, the differences between SCCs and AdCs disappear in established cell lines in 2D culture. CONCLUSIONS: Our data emphasise the importance of studying lung cancer metabolism in vivo. They also highlight potential targets for therapeutic intervention in SCC patients including differentially expressed enzymes that catalyse reactions in glycolysis, glutamine catabolism, serine, nucleotide and glutathione biosynthesis.
Crick (Grant ID: 10223, Grant title: Yuneva FC001223)