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. 2012 May 14;4(5):42.
doi: 10.1186/gm341.

Serum metabolomic profile as a means to distinguish stage of colorectal cancer

Farshad Farshidfar  1 , Aalim M Weljie, Karen Kopciuk, W Don Buie, Anthony Maclean, Elijah Dixon, Francis R Sutherland, Andrea Molckovsky, Hans J Vogel, Oliver F Bathe
Affiliations

Serum metabolomic profile as a means to distinguish stage of colorectal cancer

Farshad Farshidfar et al. Genome Med. .

Abstract

Background: Presently, colorectal cancer (CRC) is staged preoperatively by radiographic tests, and postoperatively by pathological evaluation of available surgical specimens. However, present staging methods do not accurately identify occult metastases. This has a direct effect on clinical management. Early identification of metastases isolated to the liver may enable surgical resection, whereas more disseminated disease may be best treated with palliative chemotherapy.

Methods: Sera from 103 patients with colorectal adenocarcinoma treated at the same tertiary cancer center were analyzed by proton nuclear magnetic resonance (1H NMR) spectroscopy and gas chromatography-mass spectroscopy (GC-MS). Metabolic profiling was done using both supervised pattern recognition and orthogonal partial least squares-discriminant analysis (O-PLS-DA) of the most significant metabolites, which enables comparison of the whole sample spectrum between groups. The metabolomic profiles generated from each platform were compared between the following groups: locoregional CRC (N = 42); liver-only metastases (N = 45); and extrahepatic metastases (N = 25).

Results: The serum metabolomic profile associated with locoregional CRC was distinct from that associated with liver-only metastases, based on 1H NMR spectroscopy (P = 5.10 × 10-7) and GC-MS (P = 1.79 × 10-7). Similarly, the serum metabolomic profile differed significantly between patients with liver-only metastases and with extrahepatic metastases. The change in metabolomic profile was most markedly demonstrated on GC-MS (P = 4.75 × 10-5).

Conclusions: In CRC, the serum metabolomic profile changes markedly with metastasis, and site of disease also appears to affect the pattern of circulating metabolites. This novel observation may have clinical utility in enhancing staging accuracy and selecting patients for surgical or medical management. Additional studies are required to determine the sensitivity of this approach to detect subtle or occult metastatic disease.

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Figures

Figure 1
Figure 1
Comparison of metabolomic profiles from patients with locoregional CRC and liver-only disease. (a) O-PLS-DA scatter plot depicting metabolomic profiles analyzed by 1H NMR spectroscopy. (b) O-PLS-DA scatter plot depicting metabolomic profiles analyzed by GC-MS. (c) Coefficient plot demonstrating relative abundance of specific metabolites detected by 1H NMR spectroscopy. Metabolites on the left are more abundant in sera from patients with liver metastases, and metabolites on the right are most abundant in locoregional disease. (d) Coefficient plot demonstrating relative abundance of specific metabolites detected by GC-MS. Only identified metabolites are included. t[1], score for the predictive component in O-PLS-DA; to[1], score for the Y orthogonal component in O-PLS-DA.
Figure 2
Figure 2
Comparison of metabolomic profiles from patients with liver-only metastases and with extrahepatic metastases. (a) O-PLS-DA scatter plot depicting metabolomic profiles analyzed by 1H NMR spectroscopy. (b) O-PLS-DA scatter plot depicting metabolomic profiles analyzed by GC-MS. (c) Coefficient plot demonstrating relative abundance of specific metabolites detected by 1H NMR spectroscopy. Metabolites on the left are more abundant in extrahepatic metastases, and metabolites on the right are most abundant in liver metastases. (d) Coefficient plot demonstrating relative abundance of specific metabolites detected by GC-MS. Only identified metabolites are included. t[1], score for the predictive component in O-PLS-DA; to[1], score for the Y orthogonal component in O-PLS-DA.
Figure 3
Figure 3
ROC curves depicting the predictive performance of generated classifiers in each comparison. (a) ROC curve illustrating performance of the NMR model in distinguishing liver-only metastases from locoregional CRC. (b) ROC curve illustrating performance of the GC-MS model in distinguishing liver-only metastases from locoregional CRC. (c) ROC curve for the NMR model distinguishing extrahepatic metastases from liver-only metastases. (d) ROC curve for the GC-MS model distinguishing extrahepatic metastases from liver-only metastases. AUC, area under the ROC curve; FPF, false positive fraction; TPF, true positive fraction.
Figure 4
Figure 4
Pathway analysis derived by comparison of the relative abundance of metabolites from sera derived from patients with locoregional CRC and liver-only metastases, as determined by GC-MS. More centrally located molecules in the illustrated networks have a greater probability of participating in the biological processes involved in metastasis, but also represent hubs of diverse known biological functions. (a) The first network highlights the contribution of mediators of proliferation, apoptosis and energy consumption, as well as a prominent role of inflammatory mediators. As indicated, some of the molecules putatively involved are known for their contribution to the pathogenesis of metastasis in colorectal cancer. (b) The second network demonstrates that inflammatory processes are highly involved in the metastatic process.
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