[HTML][HTML] Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue–a GC-TOFMS based metabolomics study

J Budczies, C Denkert, BM Müller, SF Brockmöller… - BMC genomics, 2012 - Springer
J Budczies, C Denkert, BM Müller, SF Brockmöller, F Klauschen, B Györffy, M Dietel…
BMC genomics, 2012Springer
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 …
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.
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