Metabolomics approaches in pancreatic adenocarcinoma: tumor metabolism profiling predicts clinical outcome of patients

Abstract

Background: Pancreatic adenocarcinomas (PAs) have very poor prognoses even when surgery is possible. Currently, there are no tissular biomarkers to predict long-term survival in patients with PA. The aims of this study were to (1) describe the metabolome of pancreatic parenchyma (PP) and PA, (2) determine the impact of neoadjuvant chemotherapy on PP and PA, and (3) find tissue metabolic biomarkers associated with long-term survivors, using metabolomics analysis.

Methods: ¹H high-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy using intact tissues was applied to analyze metabolites in PP tissue samples (n = 17) and intact tumor samples (n = 106), obtained from 106 patients undergoing surgical resection for PA.

Results: An orthogonal partial least square-discriminant analysis (OPLS-DA) showed a clear distinction between PP and PA. Higher concentrations of myo-inositol and glycerol were shown in PP, whereas higher levels of glucose, ascorbate, ethanolamine, lactate, and taurine were revealed in PA. Among those metabolites, one of them was particularly obvious in the distinction between long-term and short-term survivors. A high ethanolamine level was associated with worse survival. The impact of neoadjuvant chemotherapy was higher on PA than on PP.

Conclusions: This study shows that HRMAS NMR spectroscopy using intact tissue provides important and solid information in the characterization of PA. Metabolomics profiling can also predict long-term survival: the assessment of ethanolamine concentration can be clinically relevant as a single metabolic biomarker. This information can be obtained in 20 min, during surgery, to distinguish long-term from short-term survival.

Keywords: Biomarker; HRMAS; Long-term survival; Metabolomics; NMR; Pancreatic adenocarcinoma.

Fig. 1

HRMAS NMR spectra of pancreatic healthy tissue (PP). a PP without neoadjuvant chemotherapy (n = 9), b PP with neoadjuvant chemotherapy (n = 8). The spectra metabolic contents are directly comparable because the intensity of each spectrum was normalized with respect to the weight of the analyzed sample. For display purposes, the amplitudes of the choline peak at 3.23 ppm, the glycine peak at 3.56 ppm, and the lactate peak at 1.33 ppm were graphically shortened. Metabolite assignments are given in Table 1

Fig. 2

HRMAS NMR spectra of pancreatic adenocarcinoma (PA). a PA without neoadjuvant chemotherapy (n = 44), b PA with neoadjuvant chemotherapy (n = 62). The spectra metabolic contents are directly comparable because the intensity of each spectrum was normalized with respect to the weight of the analyzed sample. For display purposes, the amplitude of the lactate peak at 1.33 ppm was graphically shortened. Metabolite assignments are given in Table 1

Fig. 3

OPLS-DA comparing pancreatic adenocarcinoma (PA) with pancreatic healthy tissue (PP). A two-class model including 53 samples without neoadjuvant chemotherapy: 9 samples of PP and 44 of PA. A clear distinction between the different classes of tissues is shown in this model (R²Y = 0.79; Q² = 0.62)

Fig. 4

Impact of neoadjuvant chemotherapy on healthy tissue (PP). PP with neoadjuvant chemotherapy-related samples (n = 8) were compared to PP samples with no neoadjuvant chemotherapy (n = 9). Metabolic network analysis according to ADEMA results. The red, green, and blue arrows, respectively, indicate the metabolites that are predicted to increase, decrease, or remain stable in the population who received neoadjuvant chemotherapy

Fig. 5

Impact of neoadjuvant chemotherapy on pancreatic adenocarcinoma (PA). PA with neoadjuvant chemotherapy-related samples (n = 62) were compared to PA without neoadjuvant chemotherapy (n = 44). Metabolic network analysis according to ADEMA results. The red, green, and blue arrows, respectively, indicate the metabolites that are predicted to increase, decrease, or remain stable in PA with neoadjuvant chemotherapy-related samples

Fig. 6

HRMAS NMR spectra of long-term and short-term survivors. a PA with long-term survival (n = 8), b PA with short-term survival (n = 9). The spectra metabolic contents are directly comparable because the intensity of each spectrum was normalized with respect to the weight of the analyzed sample. For display purposes, the amplitudes of the choline peak at 3.23 ppm, the fatty acids peak at 1.30 ppm, and the lactate peak at 1.33 ppm were graphically shortened. Metabolite assignments are given in Table 1

Fig. 7

Metabolic network analysis enables pancreatic adenocarcinoma (PA) prognostication. Long-term survival-related samples (n = 8) were compared to short-term survival samples (n = 9) according to ADEMA results. No neoadjuvant chemotherapy was used. The red, green, and blue arrows, respectively, indicate the metabolites that are predicted to increase, decrease, or remain stable in long-term survivors

Fig. 8

Ethanolamine concentration as a single metabolic biomarker predicting the overall survival in patients with PA. a ROC and b Kaplan-Meier curves obtained from the analysis of ethanolamine concentrations for the diagnosis of long-term survival in patients with PA. The AUC was 0.861 ± 0.101, the threshold value was 0.740 nmol/mg, and sensitivity and specificity were, respectively, equal to 77.80% and 75.00%. The Kaplan-Meier curve shows differences between long-term and short-term survival patients. The p value was 0.005 (for the log-rank test)