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.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) represents 90% of pancreatic neoplasms and the fourth leading cause of cancer death in the United States. Recently, several research groups have focused on conducting metabolomics based clinical investigations to identify metabolite markers of PDAC. Since a central biomarker repository for PA is lacking, however, it remains challenging to delineate the total number of metabolites reported, and importantly, which bio-signatures overlap between the disparate studies. Approach: We performed an extensive literature search to delineate PDAC case-control studies reporting dysregulated metabolite biomarkers for PDAC utilizing three main sources: PubMed, EDRN and GDOC. We refined our search to 36 publications that reported blood based metabolomics/lipidomic biomarkers. Data from these studies was compiled based on up/down-regulation of specific metabolites in PDAC, study sample size, and other clinical determinants. The final list consisted of 12 metabolites (including amino acids, fatty acids and small organic acids), that were reported by two or more research groups as being dysregulated in PDAC. Of note, these 36 disparate studies were carried out using different methodologies, analyzed on diverse analytical platforms, and utilized different statistical and bioinformatics methodologies. Resultant common features, therefore, were deemed significant as candidate biomarkers for cross-validation. Next, we used stable isotope dilution multiple-reaction monitoring mass spectrometry (SID-MRM-MS) for targeted quantitation of these 12 metabolite markers in plasma samples obtained from patients that were diagnosed with PDAC and compared the same profiles to matched normal controls. Results: We delineated of a sub-set of the original 12 metabolites that showed concordance with those reported in the literature. Statistical analyses to determine the sensitivity and specificity of the resultant panel, and any influence provided by age, gender, and type 2 diabetes, on the predictive performance of the biomarker panel are ongoing. These data will be presented at the AACR meeting. Conclusion: Developing a specific and sensitive panel of blood-derived biomarkers offers a practical approach towards screening and allows increasing overall survival rates for PDAC with early diagnosis. Such disease-specific bio-signatures allow identification of molecular targets for therapeutic development; improve early treatment strategies and thereby clinical outcomes. Creating a compendium of existing biomarker data and performing cross-validation studies represent the first steps for developing clinical assays for the diagnosis and prognosis determination of PDAC. The integration of data generated from multiple analytic platforms and diverse subject cohorts is likely to help identify robust bio-signatures that would then be ready for large scale validation studies. Supported by American Cancer Society Citation Format: Hung-Jen Wu, Khyati Mehta, Smrithi S. Menon, Keith Unger, Massimo S. Fiandaca, Yassi Fallah, Mark Mapstone, Howard J. Federoff, Amrita K. Cheema. Prognostic biomarkers of PDAC - a cross-validation study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2517. doi:10.1158/1538-7445.AM2017-2517