Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment | Health & Environmental Research Online (HERO)

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Citation
Tags

HERO ID

4929283

Reference Type

Journal Article

Title

Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment

Author(s)

Hao, D; Sarfaraz, MO; Farshidfar, F; Bebb, DG; Lee, CY; Card, CM; David, M; Weljie, AM

Year

2016

Is Peer Reviewed?

Yes

Journal

Metabolomics
ISSN: 1573-3882
EISSN: 1573-3890

Volume

Page Numbers

1-9

Language

English

PMID

27073350

DOI

10.1007/s11306-016-0961-5

Web of Science Id

WOS:000372156000019

Abstract

Lung cancer causes more deaths in men and women than any other cancer related disease. Currently, few effective strategies exist to predict how patients will respond to treatment. We evaluated the serum metabolomic profiles of 25 lung cancer patients undergoing chemotherapy ± radiation to evaluate the feasibility of metabolites as temporal biomarkers of clinical outcomes. Serial serum specimens collected prospectively from lung cancer patients were analyzed using both nuclear magnetic resonance (1H-NMR) spectroscopy and gas chromatography mass spectrometry (GC-MS). Multivariate statistical analysis consisted of unsupervised principal component analysis or orthogonal partial least squares discriminant analysis with significance assessed using a cross-validated ANOVA. The metabolite profiles were reflective of the temporal distinction between patient samples before during and after receiving therapy (1H-NMR, p < 0.001: and GC-MS p < 0.01). Disease progression and survival were strongly correlative with the GC-MS metabolite data whereas stage and cancer type were associated with 1H-NMR data. Metabolites such as hydroxylamine, tridecan-1-ol, octadecan-1-ol, were indicative of survival (GC-MS p < 0.05) and metabolites such as tagatose, hydroxylamine, glucopyranose, and threonine that were reflective of progression (GC-MS p < 0.05). Metabolite profiles have the potential to act as prognostic markers of clinical outcomes for lung cancer patients. Serial 1H-NMR measurements appear to detect metabolites diagnostic of tumor pathology, while GC-MS provided data better related to prognostic clinical outcomes, possibility due to physiochemical bias related to specific biochemical pathways. These results warrant further study in a larger cohort and with various treatment options.