Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data.

ABSTRACT:

Objectives

Despite extensive effort, the search for clinically-relevant metabolite biomarkers for early detection, disease monitoring, and outcome prediction in lung cancer remains unfulfilled. Although biofluid evaluation has been explored, the complexity inherent in metabolite data and the dynamic discrepancy between metabolites in biofluids vs. tumor tissue have prevented conclusive results. This proof-of-concept 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.

Materials and methods

Samples (n = 36) were evaluated with high-resolution 2DLC-MS/MS and ¹³C-glucose enrichment, and the data were comprehensively analyzed with machine learning

SUBMITTER: Miller HA 

PROVIDER: S-EPMC8138715 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature