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Lung cancer metabolomics analysis

ABSTRACT: This study explored models predictive of staging and chemotherapy response based on metabolomic analysis of fresh, patient-derived non-small cell lung cancer (NSCLC) core biopsies. Prospectively collected tissue samples before initial treatment were evaluated with high-resolution 2DLC-MS/MS and 13C-glucose enrichment, and the data were comprehensively analyzed with machine learning techniques. Patients were categorized as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD). Four major types of learning methods (partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), artificial neural networks, and random forests) were applied to differentiate between positive (DC and CR/PR) and poor (PD and SD/PD) responses, and between stage I/II/III and stage IV disease. Models were trained with forward feature selection based on variable importance and tested on validation subsets.

ORGANISM(S): Human Homo Sapiens

TISSUE(S): Tumor Cells

DISEASE(S): Cancer

SUBMITTER: Hermann Frieboes

PROVIDER: ST001527 | MetabolomicsWorkbench | Wed Sep 16 00:00:00 BST 2020

REPOSITORIES: MetabolomicsWorkbench

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Project description:Systemic chemotherapy, the standard first-line treatment option for patients with advanced oesophageal squamous cell carcinoma (OSCC), results in a median survival of approximately 1 year. Immune checkpoint inhibitors are a breakthrough oncology treatment option; however, most patients with advanced OSCC develop primary and acquired resistance to PD-1 monoclonal antibody, severely affecting their prognosis. Therefore, there is an urgent need to investigate the molecular mechanism underlying resistance to treatment. This study aimed to explore the mechanism of resistance to PD-1 monoclonal antibody. We collected plasma samples from patients with OSCC treated with immunotherapy, who achieved pathological response/partial response (CR/PR) or stable disease/progressive disease (SD/PD) after one treatment cycle. TM widely targeted metabolomics, widely targeted lipidomics, and DIA proteomics assays were performed. Differential metabolites were screened based on fold change (FC) ≥ 1.5 or ≤ 0.67 and a VIP ≥ 1; differential proteins were screened based on FC > 1.5 or < 0.67 and a p-value < 0.05. The identified metabolites were annotated and mapped using the KEGG pathway databases The differential proteins were annotated to the Gene Ontology and KEGG pathway databases. A correlation network diagram was drawn using differential expressed proteins and metabolites with (Pearson correlation coefficient) r > 0.80 and p-value <0.05. We finally screened 197 and 113 differential metabolites and proteins, respectively, in patients with CR/PR and SD/PD groups. The KEGG enrichment analysis revealed that all of these were enriched in cholesterol metabolism and in the NF-κB and phospholipase D signalling pathways. Our study is the first to demonstrate that PD-1 inhibitor resistance may be attributed to cholesterol metabolism or NF-κB and phospholipase D signalling pathway activation. This finding suggests that targeting these signalling pathways may be a promising novel therapeutic approach in OSCC which may improve prognosis in patients undergoing immunotherapy.

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Lung cancer metabolomics analysis

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