Metabolic profiling detects field effects in non-dysplastic tissue from esophageal cancer patients

The variable rate of missed cancer in endoscopic biopsies and lack of other biomarkers reduce the effectiveness of surveillance programmes in esophageal cancer. Based on the “field cancerization” hypothesis, that tumors arise within a transformed field with an altered biochemical phenotype; we sought to test if metabolic profiling could differentiate between histologically-normal tissue from individuals with and without esophageal cancer. Thirty five patients with esophageal adenocarcinoma and 52 age-matched controls participated in the study. Using 1 H magic angle spinning – nuclear magnetic resonance spectroscopy of intact tissue, we generated metabolic profiles of tumor tissue, proximal histologically normal mucosa from cancer patients (PHINOM) and proximal histologically normal mucosa from a control group. Using multivariate regression and receiver-operator characteristic analysis we identified a panel of metabolites discriminating malignant and histologically-normal tissues from cancer patients from that of controls. While 26% and 12% of the spectral profile regions were uniquely discriminating tumor or control tissue respectively, 5% of the profile exhibited a significant progressive change in signal intensity from controls to PHINOM to tumor. Regions identified were assigned to phosphocholine, glutamate, myo-inositol, adenosine-containing compounds, uridine-containing compounds and inosine. In particular, the phosphocholine to glutamate ratio in histologically-normal tissue signified the presence of esophageal cancer (n=123, AUC 0.84, p<0.001). In conclusion, our findings support the hypothesis of presence of metabonomic field effects in esophageal cancer, even in non-Barrett’s segments. This indicates that metabolic profiling of tissue can potentially


Introduction
Esophageal cancer accounts for 6% of all cancer deaths worldwide (1). Early detection of esophageal cancer is the most effective strategy to improve its outcome (2).
Conventional white-light endoscopy remains the gold standard surveillance procedure for pre-malignant conditions of the upper gastro-intestinal tract, although its costeffectiveness is debated (3;4). The surveillance endoscopy, as mandated by the American College of Gastroenterology guidelines, yields about 20 biopsies, most of which are normal on histological assessment. Even with stepwise endoscopic biopsy in patients with confirmed high grade dysplasia; variable rates as low as 7% and as high as 41-66% of resected specimens, had missed cancer lesions (5,6).
The concept of 'field cancerization' was introduced by Slaughter in 1953 to describe the existence of generalized carcinogen-induced early genetic changes in the epithelium from which multiple independent lesions occur, leading to the development of multifocal tumors (7). This idea came from the belief that the lateral spread of tumors was due to progressive transformation of cells adjacent to a tumor, rather than the spread and destruction of the adjacent epithelium by pre-existing cancer cells (8). Since this time, many studies have been conducted to test this hypothesis in various cancers (9-15) by investigating genetic and epigenetic mutations, transcriptional effects and the proteome of tumors (16).
Metabonomics is defined as "The quantitative measurement of the time-related multiparametric metabolic response of living systems to patho-physiological stimuli or genetic 5 controls. 16 patients from the cancer group had chemotherapy during the course of the study, typically 3-6 courses of Cisplatin+5-fluorouracil or Epirubicin+Capecitabin.

Sample collection
Samples ranged 30-50 mg in weight, and were taken from cancer patients either (i) endoscopically during staging laparoscopy under a general anesthetic; (ii) during endoscopic ultrasound staging under sedation or (iii) surgical specimens immediately after retrieval in the course of esophagectomy. Samples were collected both from tumor tissue as well as normal mucosa 20 cms from incisors at endoscopy or at least 5 cms away from visible tumor in surgical specimens. Control samples were taken from noncancer patients during endoscopy for indications other than suspicion of malignancy (initial and follow up endoscopy for heart burn, hiatus hernia and history of gastritis/duodenal ulcer). All but 21 of these patients received local anesthetic during their endoscopy. In patients who received neoadjuvant chemotherapy, samples were collected 3-5 weeks after the last chemotherapy dose as this is the recommended time for post chemotherapy surgical intervention. Samples for NMR analysis were snap frozen in liquid nitrogen within twelve minutes of extraction from the body and stored at -80 0 C.
Another set of matched samples from all sampled areas were fixed in paraffin for pathological examination. All samples were collected after an overnight fasting. Samples were annotated with full clinical details including demographic data, co-morbidities and drug intake. Patient and sample characteristics are summarized in Table 1. 6 Tissue samples were thawed at room temperature; washed with 200 micro liters of 95% D 2 O for a maximum of 10 seconds to prevent leakage of solutes. Wet samples were loaded in a zirconium rotor (Bruker, Germany), weighed, and then spun in a 600MHz MAS NMR Spectrometer (Bruker, Germany) at 5 KHz with probe temperature preserved at 283 Kelvin.

NMR Data Acquisition
NMR spectra were acquired on a Bruker DRX600 spectrometer (Bruker Biospin, Rheinstetten, Germany) operating at 600.13-MHz 1 H NMR frequency and 283 K.
Shimming was done manually for each sample. 1 H NMR spectra of the samples were acquired using a 1D Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence (RD-90°-{ τ -180°-τ } n -acquire). The CPMG sequence generates spectra edited by T 2 relaxation times, i.e., with reduced signals from high molecular weight species or systems in intermediate chemical exchange. In our experiments n = 300 and τ =400 μs, for a total T2 relaxation time of 240 ms. For all spectra, 256 free induction decays (FIDs) were collected into 32K complex data points; using a spectral width of 12 019 Hz (20 ppm), with a 2-s relaxation delay between pulses. A water pre-saturation pulse was applied throughout the relaxation delay.

NMR Spectral Data Processing
Data were zero-filled by a factor of 2, and the FIDs were multiplied by an exponential weighting function equivalent to a line broadening of 1 Hz prior to Fourier transformation using XWINNMR software (Bruker Biospin, Rheinstetten, Germany).

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The improved resolution gained by zero filling often benefits the upstream (visual) and downstream (integration) analysis surrounding pattern recognition. The acquired NMR spectra were calibrated to the lactate-CH 3 resonance at 1.33 ppm and manually corrected for phase and (linear) baseline distortions. All subsequent data processing and analysis, unless specifically stated otherwise, was conducted using in-house software written and Spectra were interpolated from 32K to 42K data points using a cubic spline function to regularize the abscissa and improve calibration accuracy (final resolution 0.29 Hz/pt) prior to pattern recognition analysis.

Pattern Recognition and statistical analysis
For pattern recognition analysis, spectra were divided and signal integral computed in δ 0.01 intervals across the chemical shift range δ 0.24-9.96. The region δ 4.7-5.2 was excluded to remove variation in the presaturation of the water resonance, also regions δ<0.8 and δ>8.95, were excluded to avoid the effect of baseline noise. To remove the effect of signals from the local anesthetic the following regions were further excluded, which is defined as the predicted explained variance in Y from 7-fold cross validation (29). The significance of Q 2 Y was assessed by comparison to the null distribution estimated from PLS-DA models regressed to random permutations (n=100) of the Y matrix.
The t-test was used for selection of variables showing a progressive metabolic change between tissue subtypes. Type I error was controlled for by taking only the intersect between variables showing pair-wise group differences, and the expected false discovery estimated using the binomial distribution. Resonances corresponding to the selected intensity variables (spectral regions) were re-integrated to define the relative intensity of the entire resonance or the major resolved portion of the resonance in the normalised data (in house software written in MATLAB by H. Keun; see Table 2 for integrated spectral regions). Hierarchical clustering analysis was conducted on the resonance intensity data after conversion to z-scores (scaled to unit variance) and using average linkage (within the R environment 'gplots' package; http://www.r-project.org).

Pathological examination
Research.
on In order to avoid confounding variation from chemotherapy, gender and the route of sampling, a training set was established that contained data from samples obtained only from male individuals that were not receiving neoadjuvant chemotherapy, and that were collected by endoscopy during routine diagnostic testing or staging laparoscopy (n=45).
From visual inspection of the profiles from the training set, several metabolites appeared to exhibit a progressive change in intensity from tumor tissue to PHINOM to tissue from controls, consistent with the hypothesis that tumors arise within a biochemically altered tissue field ( Figure 1C). Using partial least squares discriminant analysis (PLS-DA) we were able to generate a robust and significant model that could discriminate profiles Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Copyright © 2010 American Association for Cancer Research 11 Q 2 Y statistic, which is defined as the predicted explained variance in Y from 7-fold cross validation (29). The significance of Q 2 Y was assessed by comparison to the null distribution estimated from PLS-DA models regressed to random permutations (n=100) of the Y matrix. The variances in the regression coefficients were estimated by cross validation (7-fold) and spectral regions that were significantly correlated to the presence of cancer in each model were identified (by t-test, p<0.1).
While 26% and 12% of the spectral profile regions were uniquely discriminating tumor or control tissue respectively, 5% of the profile exhibited a significant and progressive change in signal intensity from controls to PHINOM to tumor, significantly higher than expected by chance (p~10 -26 assuming a binomial distribution, expected value 0.005%, Figure 3A). Regions identified as showing progressive change were interpreted as indicating the presence of putative biomarkers for a field effect, and resonances indicated by these regions were integrated for further analysis. Several of these resonances could be assigned to specific metabolites, namely: phosphocholine, glutamate, myo-inositol, adenosine-containing compounds, uracil and inosine by reference to literature data ( Table   2). Hierarchical clustering of these metabolites indicated that they fell into two main groups, those increasing or decreasing with the presence of cancer, and clustering of the  have been used to map fields in the esophagus (34), and that PC levels are affected by p53 status, our observation that PC levels were elevated in PHINOM is consistent with the hypothesis that tumors arise within a widespread genetically predisposed field.
Furthermore, it has recently been shown that loss of p53 function could directly contribute to other well established aspects of the tumor metabolic phenotype, such as the Warburg effect (increased glucose uptake and metabolism to lactate), via regulation of mitochondrial metabolism and glycolysis (35). While we did not see a progressive change in lactate or glucose levels, lactate levels were higher in tissue from cancer patients compared to controls (Figure 2A)   18  Table 2. Discriminatory spectral regions. *tentatively assigned. XP indicates the mono-, di-and triphosphates collectively.