Dataset Information

Metabolomics based predictive classifier for early detection of pancreatic ductal adenocarcinoma.

ABSTRACT: The availability of robust classification algorithms for the identification of high risk individuals with resectable disease is critical to improving early detection strategies and ultimately increasing survival rates in PC. We leveraged high quality biospecimens with extensive clinical annotations from patients that received treatment at the Medstar-Georgetown University hospital. We used a high resolution mass spectrometry based global tissue profiling approach in conjunction with multivariate analysis for developing a classification algorithm that would predict early stage PC with high accuracy. The candidate biomarkers were annotated using tandem mass spectrometry. We delineated a six metabolite panel that could discriminate early stage PDAC from benign pancreatic disease with >95% accuracy of classification (Specificity = 0.85, Sensitivity = 0.9). Subsequently, we used multiple reaction monitoring mass spectrometry for evaluation of this panel in plasma samples obtained from the same patients. The pattern of expression of these metabolites in plasma was found to be discordant as compared to that in tissue. Taken together, our results show the value of using a metabolomics approach for developing highly predictive panels for classification of early stage PDAC. Future investigations will likely lead to the development of validated biomarker panels with potential for clinical translation in conjunction with CA-19-9 and/or other biomarkers.

SUBMITTER: Unger K

PROVIDER: S-EPMC5955422 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Metabolomics based predictive classifier for early detection of pancreatic ductal adenocarcinoma.

Unger Keith K Mehta Khyati Y KY Kaur Prabhjit P Wang Yiwen Y Menon Smrithi S SS Jain Shreyans K SK Moonjelly Rose A RA Suman Shubhankar S Datta Kamal K Singh Rajbir R Fogel Paul P Cheema Amrita K AK

Oncotarget 20180501 33

The availability of robust classification algorithms for the identification of high risk individuals with resectable disease is critical to improving early detection strategies and ultimately increasing survival rates in PC. We leveraged high quality biospecimens with extensive clinical annotations from patients that received treatment at the Medstar-Georgetown University hospital. We used a high resolution mass spectrometry based global tissue profiling approach in conjunction with multivariate ...[more]

PMID: 29796173

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Project description:Background & aimsPancreatic ductal adenocarcinoma (PDAC) incidence is rising worldwide, and most patients present with an unresectable disease at initial diagnosis. Measurement of carbohydrate antigen 19-9 (CA19-9) levels lacks adequate sensitivity and specificity for early detection; hence, there is an unmet need to develop alternate molecular diagnostic biomarkers for PDAC. Emerging evidence suggests that tumor-derived exosomal cargo, particularly micro RNAs (miRNAs), offer an attractive platform for the development of cancer-specific biomarkers. Herein, genomewide profiling in blood specimens was performed to develop an exosome-based transcriptomic signature for noninvasive and early detection of PDAC.MethodsSmall RNA sequencing was undertaken in a cohort of 44 patients with an early-stage PDAC and 57 nondisease controls. Using machine-learning algorithms, a panel of cell-free (cf) and exosomal (exo) miRNAs were prioritized that discriminated patients with PDAC from control subjects. Subsequently, the performance of the biomarkers was trained and validated in independent cohorts (n = 191) using quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays.ResultsThe sequencing analysis initially identified a panel of 30 overexpressed miRNAs in PDAC. Subsequently using qRT-PCR assays, the panel was reduced to 13 markers (5 cf- and 8 exo-miRNAs), which successfully identified patients with all stages of PDAC (area under the curve [AUC] = 0.98 training cohort; AUC = 0.93 validation cohort); but more importantly, was equally robust for the identification of early-stage PDAC (stages I and II; AUC = 0.93). Furthermore, this transcriptomic signature successfully identified CA19-9 negative cases (<37 U/mL; AUC = 0.96), when analyzed in combination with CA19-9 levels, significantly improved the overall diagnostic accuracy (AUC = 0.99 vs AUC = 0.86 for CA19-9 alone).ConclusionsIn this study, an exosome-based liquid biopsy signature for the noninvasive and robust detection of patients with PDAC was developed.

Dataset Information

Metabolomics based predictive classifier for early detection of pancreatic ductal adenocarcinoma.

Publications

Metabolomics based predictive classifier for early detection of pancreatic ductal adenocarcinoma.

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