Metabolomic characterization of human rectal adenocarcinoma with intact tissue magnetic resonance spectroscopy

Abstract

Purpose: This study was designed to test whether metabolic characterization of intact, unaltered human rectal adenocarcinoma specimens is possible using the high-resolution magic angle spinning proton (1H) magnetic resonance spectroscopy technique.

Methods: The study included 23 specimens from five patients referred for ultrasonographic staging of suspected rectal cancer. Multiple biopsies of macroscopically malignant rectal tumors and benign rectal mucosa were obtained from each patient for a total of 14 malignant and 9 benign samples. Unaltered tissue samples were spectroscopically analyzed. Metabolic profiles were established from the spectroscopy data and correlated with histopathologic findings.

Results: Metabolomic profiles represented by principle components of metabolites measured from spectra differentiated between malignant and benign samples and correlated with the volume percent of cancer (P = 0.0065 and P = 0.02, respectively) and benign epithelium (P = 0.0051 and P = 0.0255, respectively), and with volume percent of stroma, and inflammation.

Conclusions: Magnetic resonance spectroscopy of rectal biopsies has the ability to metabolically characterize samples and differentiate between pathological features of interest. Future studies should determine its utility in in vivo applications for non-invasive pathologic evaluations of suspicious rectal lesions.

Figures

Figure 1

Illustration of principal component analysis (PCA). This process starts with the top S-P matrix, where S and P represent samples and spectral peaks, respectively, and p#,# is the value of the standardized peak concentration. Standardized concentration refers to the difference between the particular intensity of the concerned metabolite for a particular sample and the mean intensity normalized by the standard deviation, with both mean and standard deviation values calculated for the metabolite from the entire sample set. From this S-P matrix, the PCA process generates a principal component (PC) coefficient matrix (bottom) to be used to form PCs of linear combinations of the standardized peak concentrations. All PCs are orthogonal to each other, with PC1 representing the greatest and PC23, the least changes in the standardized peak concentrations.

Figure 2

A) HRMAS 1HMRS spectrum of Patient 3, sample ii (benign mucosa); entire metabolite spectrum (top) and enlargement of the 3.2–3.7 ppm area, which significantly contributed to the variation in PC 2; B) Sample 3-i contained 0 percent malignant cells, 70 percent benign epithelium, 20 percent stromal tissue, and 10 percent inflammation; C) Spectrum from Patient 3, sample iv (tumor tissue), entire spectrum (top) and enlargement of the 3.2–3.7 ppm area; D). Sample 3 iv contained 20 percent malignant cells, 0 percent benign epithelium, 60 percent stromal tissue, and 20 percent inflammation.