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Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue - a GC-TOFMS based metabolomics study.
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2012 (English)In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 13, 334Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Changes in energy metabolism of the cells are common to many kinds of tumors and are considered a hallmark of cancer. Gas chromatography followed by time-of-flight mass spectrometry (GC-TOFMS) is a well-suited technique to investigate the small molecules in the central metabolic pathways. However, the metabolic changes between invasive carcinoma and normal breast tissues were not investigated in a large cohort of breast cancer samples so far.

RESULTS: A cohort of 271 breast cancer and 98 normal tissue samples was investigated using GC-TOFMS-based metabolomics. A total number of 468 metabolite peaks could be detected; out of these 368 (79%) were significantly changed between cancer and normal tissues (p<0.05 in training and validation set). Furthermore, 13 tumor and 7 normal tissue markers were identified that separated cancer from normal tissues with a sensitivity and a specificity of >80%. Two-metabolite classifiers, constructed as ratios of the tumor and normal tissues markers, separated cancer from normal tissues with high sensitivity and specificity. Specifically, the cytidine-5-monophosphate / pentadecanoic acid metabolic ratio was the most significant discriminator between cancer and normal tissues and allowed detection of cancer with a sensitivity of 94.8% and a specificity of 93.9%.

CONCLUSIONS: For the first time, a comprehensive metabolic map of breast cancer was constructed by GC-TOF analysis of a large cohort of breast cancer and normal tissues. Furthermore, our results demonstrate that spectrometry-based approaches have the potential to contribute to the analysis of biopsies or clinical tissue samples complementary to histopathology.

Place, publisher, year, edition, pages
2012. Vol. 13, 334
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:oru:diva-63667DOI: 10.1186/1471-2164-13-334PubMedID: 22823888OAI: oai:DiVA.org:oru-63667DiVA: diva2:1169230
Available from: 2017-12-22 Created: 2017-12-22 Last updated: 2017-12-22

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