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BMC Cancer. 2018 May 5;18(1):532. doi: 10.1186/s12885-018-4437-z.

Metabolomic profiles in breast cancer:a pilot case-control study in the breast cancer family registry.

Author information

1
Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA. marcelle.dougan@sjsu.edu.
2
San Jose State University, San Jose, CA, USA. marcelle.dougan@sjsu.edu.
3
Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA.
4
Cancer Prevention Institute of California, Fremont, California, USA.
5
Cedars-Sinai Medical Center, Los Angeles, CA, USA.
6
National Cancer Institute, Bethesda, MD, USA.
7
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, University of Toronto, Toronto, ON, Canada.
8
Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, California, USA.

Abstract

BACKGROUND:

Metabolomics is emerging as an important tool for detecting differences between diseased and non-diseased individuals. However, prospective studies are limited.

METHODS:

We examined the detectability, reliability, and distribution of metabolites measured in pre-diagnostic plasma samples in a pilot study of women enrolled in the Northern California site of the Breast Cancer Family Registry. The study included 45 cases diagnosed with breast cancer at least one year after the blood draw, and 45 controls. Controls were matched on age (within 5 years), family status, BRCA status, and menopausal status. Duplicate samples were included for reliability assessment. We used a liquid chromatography/gas chromatography mass spectrometer platform to measure metabolites. We calculated intraclass correlations (ICCs) among duplicate samples, and coefficients of variation (CVs) across metabolites.

RESULTS:

Of the 661 named metabolites detected, 338 (51%) were found in all samples, and 490 (74%) in more than 80% of samples. The median ICC between duplicates was 0.96 (25th - 75th percentile: 0.82-0.99). We observed a greater than 20% case-control difference in 24 metabolites (p < 0.05), although these associations were not significant after adjusting for multiple comparisons.

CONCLUSIONS:

These data show that assays are reproducible for many metabolites, there is a minimal laboratory variation for the same sample, and a large between-person variation. Despite small sample size, differences between cases and controls in some metabolites suggest that a well-powered large-scale study is likely to detect biological meaningful differences to provide a better understanding of breast cancer etiology.

PMID:
29728083
PMCID:
PMC5935968
DOI:
10.1186/s12885-018-4437-z
[Indexed for MEDLINE]
Free PMC Article

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