MRS-based Metabolomics in Cancer Research

Abstract

Metabolomics is a relatively new technique that is gaining importance very rapidly. MRS-based metabolomics, in particular, is becoming a useful tool in the study of body fluids, tissue biopsies and whole organisms. Advances in analytical techniques and data analysis methods have opened a new opportunity for such technology to contribute in the field of diagnostics. In the MRS approach to the diagnosis of disease, it is important that the analysis utilizes all the essential information in the spectra, is robust, and is non-subjective. Although some of the data analytic methods widely used in chemical and biological sciences are sketched, a more extensive discussion is given of a 5-stage Statistical Classification Strategy. This proposes powerful feature selection methods, based on, for example, genetic algorithms and novel projection techniques. The applications of MRS-based metabolomics in breast cancer, prostate cancer, colorectal cancer, pancreatic cancer, hepatobiliary cancers, gastric cancer, and brain cancer have been reviewed. While the majority of these applications relate to body fluids and tissue biopsies, some in vivo applications have also been included. It should be emphasized that the number of subjects studied must be sufficiently large to ensure a robust diagnostic classification. Before MRS-based metabolomics can become a widely used clinical tool, however, certain challenges need to be overcome. These include manufacturing user-friendly commercial instruments with all the essential features, and educating physicians and medical technologists in the acquisition, analysis, and interpretation of metabolomics data.

Keywords: cancer; classification; diagnosis; magnetic resonance spectroscopy; metabolomics.

Figures

Figure 1

Representation of the risks of reaching conclusions with a sparse data set. Increasing the number of subjects generally lowers the accuracy, but this is much closer to the true accuracy. The lower accuracy solution will also be more robust: challenging the resultant classifier with new specimens will yield accuracy similar to that found by a reliable classifier.

Figure 2

1H MR spectra (360 MHz, 37°C) of prostate tissue specimens. A, cancer (Gleason grade: 3 + 3). Chos, cholines; Crs. creatines; Lac. lactic acid; Tau, taurine; HOD, partially deuterated water. Although certain substances are assigned on figure, this does not imply that these are the only substances contributing to a particular resonance. B. BPH (Benign Prostatic Hyperplasia).

Figure 3

Mean and ± one standard deviation about the mean of (A) the 412 1D 1H MR spectra (400 MHz, 300 K) for the normal samples and (B) of the 111 1D 1H MR spectra (400 MHz, 300 K) for the colorectal cancer samples.

Figure 4

1H MRS spectra (360 MHz) of bile from (a) control, (b) chronic pancreatitis and (c) pancreatic cancer patients showing the relative levels of D-Glucuronate. It can be seen that the levels of D-Glucuronate are highly elevated in the pancreatic cancer patient.