• Is serum or plasma more appropriate for intersubject comparisons in metabolomic studies? An assessment in patients with small-cell lung cancer.

      Wedge, D C; Allwood, J W; Dunn, W; Vaughan, A A; Simpson, Kathryn L; Brown, M; Priest, Lynsey; Blackhall, Fiona H; Whetton, Anthony D; Dive, Caroline; et al. (2011-09-01)
      In clinical analyses, the most appropriate biofluid should be analyzed for optimal assay performance. For biological fluids, the most readily accessible is blood, and metabolomic analyses can be performed either on plasma or serum. To determine the optimal agent for analysis, metabolic profiles of matched human serum and plasma were assessed by gas chromatography/time-of-flight mass spectrometry and ultrahigh-performance liquid chromatography mass spectrometry (in positive and negative electrospray ionization modes). Comparison of the two metabolomes, in terms of reproducibility, discriminative ability and coverage, indicated that they offered similar analytical opportunities. An analysis of the variation between 29 small-cell lung cancer (SCLC) patients revealed that the differences between individuals are markedly similar for the two biofluids. However, significant differences between the levels of some specific metabolites were identified, as were differences in the intersubject variability of some metabolite levels. Glycerophosphocholines, erythritol, creatinine, hexadecanoic acid, and glutamine in plasma, but not in serum, were shown to correlate with life expectancy for SCLC patients, indicating the utility of metabolomic analyses in clinical prognosis and the particular utility of plasma in relation to the clinical management of SCLC.
    • Ketones and lactate increase cancer cell "stemness," driving recurrence, metastasis and poor clinical outcome in breast cancer: achieving personalized medicine via Metabolo-Genomics.

      Martinez-Outschoorn, U E; Prisco, M; Ertel, A; Tsirigos, A; Lin, Z; Pavlides, S; Wang, C; Flomenberg, N; Knudsen, E S; Howell, Anthony; et al. (2011-04-15)
      Previously, we showed that high-energy metabolites (lactate and ketones) "fuel" tumor growth and experimental metastasis in an in vivo xenograft model, most likely by driving oxidative mitochondrial metabolism in breast cancer cells. To mechanistically understand how these metabolites affect tumor cell behavior, here we used genome-wide transcriptional profiling. Briefly, human breast cancer cells (MCF7) were cultured with lactate or ketones, and then subjected to transcriptional analysis (exon-array). Interestingly, our results show that treatment with these high-energy metabolites increases the transcriptional expression of gene profiles normally associated with "stemness," including genes upregulated in embryonic stem (ES) cells. Similarly, we observe that lactate and ketones promote the growth of bonafide ES cells, providing functional validation. The lactate- and ketone-induced "gene signatures" were able to predict poor clinical outcome (including recurrence and metastasis) in a cohort of human breast cancer patients. Taken together, our results are consistent with the idea that lactate and ketone utilization in cancer cells promotes the "cancer stem cell" phenotype, resulting in significant decreases in patient survival. One possible mechanism by which these high-energy metabolites might induce stemness is by increasing the pool of Acetyl-CoA, leading to increased histone acetylation, and elevated gene expression. Thus, our results mechanistically imply that clinical outcome in breast cancer could simply be determined by epigenetics and energy metabolism, rather than by the accumulation of specific "classical" gene mutations. We also suggest that high-risk cancer patients (identified by the lactate/ketone gene signatures) could be treated with new therapeutics that target oxidative mitochondrial metabolism, such as the anti-oxidant and "mitochondrial poison" metformin. Finally, we propose that this new approach to personalized cancer medicine be termed "Metabolo-Genomics," which incorporates features of both 1) cell metabolism and 2) gene transcriptional profiling. Importantly, this powerful new approach directly links cancer cell metabolism with clinical outcome, and new therapeutic strategies for inhibiting the TCA cycle and mitochondrial oxidative phosphorylation in cancer cells.