Tolerable glycometabolic stress boosts cancer cell resilience through altered N-glycosylation and Notch signaling activation.

Chronic metabolic stress paradoxically elicits pro-tumorigenic signals that facilitate cancer stem cell (CSC) development. Therefore, elucidating the metabolic sensing and signaling mechanisms governing cancer cell stemness can provide insights into ameliorating cancer relapse and therapeutic resistance. Here, we provide convincing evidence that chronic metabolic stress triggered by hyaluronan production augments CSC-like traits and chemoresistance by partially impairing nucleotide sugar metabolism, dolichol lipid-linked oligosaccharide (LLO) biosynthesis and N-glycan assembly. Notably, preconditioning with either low-dose tunicamycin or 2-deoxy-D-glucose, which partially interferes with LLO biosynthesis, reproduced the promoting effects of hyaluronan production on CSCs. Multi-omics revealed characteristic changes in N-glycan profiles and Notch signaling activation in cancer cells exposed to mild glycometabolic stress. Restoration of N-glycan assembly with glucosamine and mannose supplementation and Notch signaling blockade attenuated CSC-like properties and further enhanced the therapeutic efficacy of cisplatin. Therefore, our findings uncover a novel mechanism by which tolerable glycometabolic stress boosts cancer cell resilience through altered N-glycosylation and Notch signaling activation.

Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinoma.

PURPOSE: To identify stage I lung adenocarcinoma patients with a poor prognosis who will benefit from adjuvant therapy. PATIENTS AND METHODS: Whole gene expression profiles were obtained at 19 time points over a 48-hour time course from human primary lung epithelial cells that were stimulated with epidermal growth factor (EGF) in the presence or absence of a clinically used EGF receptor tyrosine kinase (RTK)-specific inhibitor, gefitinib. The data were subjected to a mathematical simulation using the State Space Model (SSM). "Gefitinib-sensitive" genes, the expressional dynamics of which were altered by addition of gefitinib, were identified. A risk scoring model was constructed to classify high- or low-risk patients based on expression signatures of 139 gefitinib-sensitive genes in lung cancer using a training data set of 253 lung adenocarcinomas of North American cohort. The predictive ability of the risk scoring model was examined in independent cohorts of surgical specimens of lung cancer. RESULTS: The risk scoring model enabled the identification of high-risk stage IA and IB cases in another North American cohort for overall survival (OS) with a hazard ratio (HR) of 7.16 (P = 0.029) and 3.26 (P = 0.0072), respectively. It also enabled the identification of high-risk stage I cases without bronchioalveolar carcinoma (BAC) histology in a Japanese cohort for OS and recurrence-free survival (RFS) with HRs of 8.79 (P = 0.001) and 3.72 (P = 0.0049), respectively. CONCLUSION: The set of 139 gefitinib-sensitive genes includes many genes known to be involved in biological aspects of cancer phenotypes, but not known to be involved in EGF signaling. The present result strongly re-emphasizes that EGF signaling status in cancer cells underlies an aggressive phenotype of cancer cells, which is useful for the selection of early-stage lung adenocarcinoma patients with a poor prognosis. TRIAL REGISTRATION: The Gene Expression Omnibus (GEO) GSE31210.