Metabolomic biomarkers of pancreatic cancer: a meta-analysis study

Abstract

Pancreatic cancer (PC) is an aggressive disease with high mortality rates, however, there is no blood test for early detection and diagnosis of this disease. Several research groups have reported on metabolomics based clinical investigations to identify biomarkers of PC, however there is a lack of a centralized metabolite biomarker repository that can be used for meta-analysis and biomarker validation. Furthermore, since the incidence of PC is associated with metabolic syndrome and Type 2 diabetes mellitus (T2DM), there is a need to uncouple these common metabolic dysregulations that may otherwise diminish the clinical utility of metabolomic biosignatures. Here, we attempted to externally replicate proposed metabolite biomarkers of PC reported by several other groups in an independent group of PC subjects. Our study design included a T2DM cohort that was used as a non-cancer control and a separate cohort diagnosed with colorectal cancer (CRC), as a cancer disease control to eliminate possible generic biomarkers of cancer. We used targeted mass spectrometry for quantitation of literature-curated metabolite markers and identified a biomarker panel that discriminates between normal controls (NC) and PC patients with high accuracy. Further evaluation of our model with CRC, however, showed a drop in specificity for the PC biomarker panel. Taken together, our study underscores the need for a more robust study design for cancer biomarker studies so as to maximize the translational value and clinical implementation.

Keywords: biomarkers; metabolomics; pancreatic cancer.

Figure 1. Boxplots for the ten metabolite panel

Group separation based on normalized abundance of the ten dysregulated metabolites in PC as compared to NC. Solid line represents median value.

Figure 2. Receiver-operating characteristic (ROC) curve for PC (n = 59) vs NC (n = 48) using the ten metabolite panel yields AUC = 0.992

Figure 3. Receiver-operating characteristic (ROC) curve for CRC (n = 66) vs NC (n = 48) using the ten metabolite panel yields AUC = 0.986

Figure 4. Receiver-operating characteristic (ROC) curve for T2DM (n = 19) vs NC (n = 48) using the ten metabolite panel yields AUC = 0.957

Figure 5. Receiver-operating characteristic (ROC) curve for CRC (n = 66) vs PC (n = 59) using the ten metabolite panel yields AUC = 0.653

Figure 6. Receiver-operating characteristic (ROC) curve T2DM (n = 19) vs PC (n = 59) using the ten metabolite panel yields AUC = 0.997

Figure 7. Boxplot depiction of the plasma 10 metabolite index (P10MI)

Solid black horizontal line within the boxplots represents mean value.

Figure 8. Receiver-operating characteristic (ROC) curve and for PC (n = 59) vs NC (n = 48) metabolite threonine yields AUC = 0.843

Figure 9. Schema for curating metabolites mined from literature search and meta- analyses