Our study demonstrates that large scale metabolic profiling using GC-TOF mass spectrometry and database annotation yields numerous significant differences between colon carcinoma and normal colon mucosa. We have utilized both unsupervised and supervised approaches to investigate these metabolic differences. The metabolite signatures are capable of predicting the status (normal tissue or colon carcinoma) of a previously unknown test tumor at sensitivity and specificity around 95%. Importantly, we could show that the classification results are robust against different choices of the classificator and the training set (figure 6). Regarding the extent of changes detected, it is important to note that – from a tumorbiological point of view – the comparison of normal tissue and carcinoma tissue means that two completely different entities are compared. Therefore we would expect that comparison of those two tissue types leads to a large set of completely different biomarkers. Similar results have been reported in gene expression analysis. For example in the study by Hlubek et al.  39% of transcripts were differentially expressed between colon tumor center and colon normal tissue. Since metabolites are regarded as an amplified output of a biological system, the expected metabolite changes could be even more prominent compared to genomics, as shown in our analysis.
Many of these metabolic events can be ascribed to known metabolic dysregulation in cancer thus validating the method itself. Metabolites involved in the citric acid cycle were generally found at lower amounts in cancer tissues compared to normal colon samples, in accordance with results published earlier . Purines were detected at increased levels in malignant tissues as indicator for higher capacity for DNA synthetic capacity. Similarly, we found almost all amino acids to be up regulated in carcinoma tissues, which may be interpreted as reflecting cellular needs for higher turnover of structural proteins. This finding is in agreement with earlier publications for select amino acids, notably glutamate and aspartate . Similarly, the high GABA contents had been described in colon cancer tissues in a previous study . Certain amino acids are synthesized by mammalian metabolic routes, often using TCA intermediates as precursor such as alpha-ketoglutarate for glutamate and its derived amino acids, and oxaloacetate for aspartate-derived amino acids. With higher needs in amino acids but lower use of the TCA cycle, an alternative route is needed to deliver carbon backbones for such TCA-derived intermediates. Such higher import may be accomplished by up regulation of amino acid transporter, facilitating higher cellular needs for energy metabolism as well as delivering carbon backbones for biosynthesis of cellular molecules. This interpretation is supported by our finding of increased levels in urea cycle intermediates in colon carcinoma tissues, indicating higher turnover of amino acids. Interestingly, beta-alanine was found as the most upregulated (f.c. = 4.9) metabolite in carcinoma tissues with very high statistical significance (p = 5.8e-13). In humans, beta-alanine is a unidirectional catabolic product from aspartate in a decarboxylation reaction (EC:126.96.36.199) or by catabolic routes from pyrimidine metabolism (EC:188.8.131.52). However, the eventual fate of beta-alanine in humans is yet unclear, since no enzymes are known that would transfer its backbone into acetyl-CoA or towards pantothenate metabolism, as it occurs in other species. We therefore suggest that beta-alanine might be important for metabolic alterations in colon cancer. In addition, we found that not all amino acids were up regulated in the same manner. In fact, the glutamate/glutamine ratio was greatly altered in comparison to normal colon tissue, indicating a lesser role of aminotransferase reactions utilizing glutamine or less need for transport of nitrogen across cells.
A limitation of this study was found in the need of normalizing the raw data to the total sum of known metabolites. The normalization strategy was developed in analogy to gene expression studies and was chosen because frozen tissue sections (as detailed in the methods section) should not be weighed on fine balances in order to preserve the cold chain and to prevent reactivation of metabolism prior to extraction. We found that the raw data for carcinoma tissues were significantly higher (p = 0.03) than those for normal tissue, relating to roughly a 33% increase in overall metabolic levels. However, this might be due to either a higher number of tumor cells per area of tissue or for generally enhanced metabolism. More detailed studies would be needed to address this question. In an additional validation using the raw (unnormalized) data as input the major metabolic differences between both tissue types could be detected, as well, suggesting that the major metabolite differences are not dependent on the normalization strategy.
The total time between surgery and freezing tissues was kept as minimal as possible due to the fact that the frozen section pathology laboratory was directly adjacent to the operating room. Nevertheless, clinical and pathological workflows do not allow for exact measures and timing of tissue dissection parameters for samples collected during routine surgical interventions. In addition, depending on the surgical technique there is a variable amount of intraoperative tissue ischemia due to surgical ligation of blood vessels. This fact may account for the inability to quantify glycolytic intermediates which have such a high turnover in non-frozen tissues that these are found to be depleted if metabolism is not immediately quenched after disruption of blood flow. To minimize unrelated technical noise related to surgical procedures we have chosen to compare tumor tissue and normal tissue collected during the same surgery.
We have used a metabolomic approach by GC-TOF mass spectrometry in order to gain a broad overview over primary metabolism at limited costs but at high sensitivity and selectivity. In total more than 100 compounds could be identified by chemical structure from as little as 5 mg fresh tissue, which compares favorably to reports using one dimensional 1H-NMR data acquisition. Specifically, we here demonstrate for the first time the efficacy of an automated annotation using a customized database approach. On the one hand, the BinBase database unambiguously identifies chemically or biochemically known compounds that are utilized for pathway mapping. On the other hand, the database also facilitates adding novel and potentially unique metabolic signals that yet are to be structurally identified but that nevertheless were often found to be differentially regulated at high significance levels. These compounds are stored in the database by unique identifiers combining mass spectra and retention index information that enable re-using these database entries for later studies aimed at validating initial biomarkers or at structural identification of these metabolic signals.
In the study presented here, we have focused on using information from identified compounds by developing and applying a new biochemical mapping method, PROFILE. This method maximizes the interpretability of results by facilitating physiological and biochemical understanding of metabolic alterations in carcinoma. Specifically, PROFILE leads to simplified output of results than mapping on single pathway maps from KEGG which would focus on a small number of select metabolites rather than taking into account the relative distances of metabolites across the metabolic network.
As a conclusion, our results show that metabolic signatures as well as individual metabolites can be detected from fresh-frozen tumor tissue of colon cancer and that these alterations can be linked to relevant biochemical pathways. Based on our results, we suggest that metabolomics is a promising approach complementary to transcriptomics and proteomics for analyses of changes in the malignant phenotype. As metabolites constitute the amplified output of a biological system, their quantitative and qualitative analysis will be relevant for tumor biology in different types of investigations. Databases such as the one presented here will enable comparisons of findings across studies and laboratories. Metabolomics can be used for biochemical classification of different tumor types and for comparison of malignant tumors with their corresponding normal tissue. Recently, it has been suggested that therapeutic approaches directed against metabolic abnormalities may be useful in the treatment of malignant tumors [27, 28]. In this context, the metabolic profiling approach described here may be useful to monitor the complex changes in tumor metabolism that may occur under these treatments. Furthermore, analysis of metabolic alterations may be used as a new method for molecular pathology to develop classifiers for therapy response prediction, which may ultimately lead to the identification of new prognostic markers. With the combination of advanced instrumentation, standardized database algorithms and the development of tools for interpretation of data, our study provides a methodological basis for these further investigations.