Independent Validation and Assay Standardization of Improved Metabolic Biomarker Signature to Differentiate Pancreatic Ductal Adenocarcinoma From Chronic Pancreatitis.

BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma cancer (PDAC) is a highly lethal malignancy requiring efficient detection when the primary tumor is still resectable. We previously developed the MxPancreasScore comprising 9 analytes and serum carbohydrate antigen 19-9 (CA19-9), achieving an accuracy of 90.6%. The necessity for 5 different analytical platforms and multiple analytical runs, however, hindered clinical applicability. We therefore aimed to develop a simpler single-analytical run, single-platform diagnostic signature. METHODS: We evaluated 941 patients (PDAC, 356; chronic pancreatitis [CP], 304; nonpancreatic disease, 281) in 3 multicenter independent tests, and identification (ID) and validation cohort 1 (VD1) and 2 (VD2) were evaluated. Targeted quantitative plasma metabolite analysis was performed on a liquid chromatography-tandem mass spectrometry platform. A machine learning-aided algorithm identified an improved (i-Metabolic) and minimalistic metabolic (m-Metabolic) signatures, and compared them for performance. RESULTS: The i-Metabolic Signature, (12 analytes plus CA19-9) distinguished PDAC from CP with area under the curve (95% confidence interval) of 97.2% (97.1%-97.3%), 93.5% (93.4%-93.7%), and 92.2% (92.1%-92.3%) in the ID, VD1, and VD2 cohorts, respectively. In the VD2 cohort, the m-Metabolic signature (4 analytes plus CA19-9) discriminated PDAC from CP with a sensitivity of 77.3% and specificity of 89.6%, with an overall accuracy of 82.4%. For the subset of 45 patients with PDAC with resectable stages IA-IIB tumors, the sensitivity, specificity, and accuracy were 73.2%, 89.6%, and 82.7%, respectively; for those with detectable CA19-9 >2 U/mL, 81.6%, 88.7%, and 84.5%, respectively; and for those with CA19-9 <37 U/mL, 39.7%, 94.1%, and 76.3%, respectively. CONCLUSIONS: The single-platform, single-run, m-Metabolic signature of just 4 metabolites used in combination with serum CA19-9 levels is an innovative accurate diagnostic tool for PDAC at the time of clinical presentation, warranting further large-scale evaluation.

Plasma Metabolome Profiling Identifies Metabolic Subtypes of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Developing biomarkers for early detection and chemotherapeutic response prediction is crucial to improve the dismal prognosis of PDAC patients. However, molecular cancer signatures based on transcriptome analysis do not reflect intratumoral heterogeneity. To explore a more accurate stratification of PDAC phenotypes in an easily accessible matrix, plasma metabolome analysis using MxP® Global Profiling and MxP® Lipidomics was performed in 361 PDAC patients. We identified three metabolic PDAC subtypes associated with distinct complex lipid patterns. Subtype 1 was associated with reduced ceramide levels and a strong enrichment of triacylglycerols. Subtype 2 demonstrated increased abundance of ceramides, sphingomyelin and other complex sphingolipids, whereas subtype 3 showed decreased levels of sphingolipid metabolites in plasma. Pathway enrichment analysis revealed that sphingolipid-related pathways differ most among subtypes. Weighted correlation network analysis (WGCNA) implied PDAC subtypes differed in their metabolic programs. Interestingly, a reduced expression among related pathway genes in tumor tissue was associated with the lowest survival rate. However, our metabolic PDAC subtypes did not show any correlation to the described molecular PDAC subtypes. Our findings pave the way for further studies investigating sphingolipids metabolisms in PDAC.

Identification and validation of a multivariable prediction model based on blood plasma and serum metabolomics for the distinction of chronic pancreatitis subjects from non-pancreas disease control subjects.

OBJECTIVE: Chronic pancreatitis (CP) is a fibroinflammatory syndrome leading to organ dysfunction, chronic pain, an increased risk for pancreatic cancer and considerable morbidity. Due to a lack of specific biomarkers, diagnosis is based on symptoms and specific but insensitive imaging features, preventing an early diagnosis and appropriate management. DESIGN: We conducted a type 3 study for multivariable prediction for individual prognosis according to the TRIPOD guidelines. A signature to distinguish CP from controls (n=160) was identified using gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry on ethylenediaminetetraacetic acid (EDTA)-plasma and validated in independent cohorts. RESULTS: A Naive Bayes algorithm identified eight metabolites of six ontology classes. After algorithm training and computation of optimal cut-offs, classification according to the metabolic signature detected CP with an area under the curve (AUC) of 0.85 ((95% CI 0.79 to 0.91). External validation in two independent cohorts (total n=502) resulted in similar accuracy for detection of CP compared with non-pancreatic controls in EDTA-plasma (AUC 0.85 (95% CI 0.81 to 0.89)) and serum (AUC 0.87 (95% CI 0.81 to 0.95)). CONCLUSIONS: This is the first study that identifies and independently validates a metabolomic signature in plasma and serum for the diagnosis of CP in large, prospective cohorts. The results could provide the basis for the development of the first routine laboratory test for CP.

New Onset of DiabetEs in aSsociation with pancreatic ductal adenocarcinoma (NODES Trial): protocol of a prospective, multicentre observational trial.

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis with an overall 5-year survival of approximately 8%. The success in reducing the mortality rate of PDAC is related to the discovery of new therapeutic agents, and to a significant extent to the development of early detection and prevention programmes. Patients with new-onset diabetes mellitus (DM) represent a high-risk group for PDAC as they have an eightfold higher risk of PDAC than the general population. The proposed screening programme may allow the detection of PDAC in the early, operable stage. Diagnosing more patients in the curable stage might decrease the morbidity and mortality rates of PDAC and additionally reduce the burden of the healthcare. METHODS AND ANALYSIS: This is a prospective, multicentre observational cohort study. Patients ≥60 years old diagnosed with new-onset (≤6 months) diabetes will be included. Exclusion criteria are (1) Continuous alcohol abuse; (2) Chronic pancreatitis; (3) Previous pancreas operation/pancreatectomy; (4) Pregnancy; (5) Present malignant disease and (6) Type 1 DM. Follow-up visits are scheduled every 6 months for up to 36 months. Data collection is based on questionnaires. Clinical symptoms, body weight and fasting blood will be collected at each, carbohydrate antigen 19-9 and blood to biobank at every second visit. The blood samples will be processed to plasma and analysed with mass spectrometry (MS)-based metabolomics. The metabolomic data will be used for biomarker validation for early detection of PDAC in the high-risk group patients with new-onset diabetes. Patients with worrisome features will undergo MRI or endoscopic ultrasound investigation, and surgical referral depending on the radiological findings. One of the secondary end points is the incidence of PDAC in patients with newly diagnosed DM. ETHICS AND DISSEMINATION: The study has been approved by the Scientific and Research Ethics Committee of the Hungarian Medical Research Council (41085-6/2019). We plan to disseminate the results to several members of the healthcare system includining medical doctors, dietitians, nurses, patients and so on. We plan to publish the results in a peer-reviewed high-quality journal for professionals. In addition, we also plan to publish it for lay readers in order to maximalise the dissemination and benefits of this trial. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov NCT04164602.

An Integrated Analysis of Metabolites, Peptides, and Inflammation Biomarkers for Assessment of Preanalytical Variability of Human Plasma.

Discrepancies in blood sample collection and processing could have a significant impact on levels of metabolites, peptides, and protein biomarkers of inflammation in the blood; thus, sample quality control is critical for successful biomarker identification and validation. In this study, we analyzed the effects of several preanalytical processing conditions, including different storage times and temperatures for blood or plasma samples and different centrifugation forces on the levels of metabolites, peptides, and inflammation biomarkers in human plasma samples using ethylenediaminetetraacetic acid (EDTA) as an anticoagulant. Temperature was found to be the major factor for metabolite variation, and both time and temperature were identified as major factors for peptide variation. For inflammation biomarkers, temperature played different roles depending on the sample type (blood or plasma). Low temperature affected inflammation biomarkers in blood, while room temperature impacted inflammation biomarkers in plasma.

Metabolic biomarker signature to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis.

OBJECTIVE: Current non-invasive diagnostic tests can distinguish between pancreatic cancer (pancreatic ductal adenocarcinoma (PDAC)) and chronic pancreatitis (CP) in only about two thirds of patients. We have searched for blood-derived metabolite biomarkers for this diagnostic purpose. DESIGN: For a case-control study in three tertiary referral centres, 914 subjects were prospectively recruited with PDAC (n=271), CP (n=282), liver cirrhosis (n=100) or healthy as well as non-pancreatic disease controls (n=261) in three consecutive studies. Metabolomic profiles of plasma and serum samples were generated from 477 metabolites identified by gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry. RESULTS: A biomarker signature (nine metabolites and additionally CA19-9) was identified for the differential diagnosis between PDAC and CP. The biomarker signature distinguished PDAC from CP in the training set with an area under the curve (AUC) of 0.96 (95% CI 0.93-0.98). The biomarker signature cut-off of 0.384 at 85% fixed specificity showed a sensitivity of 94.9% (95% CI 87.0%-97.0%). In the test set, an AUC of 0.94 (95% CI 0.91-0.97) and, using the same cut-off, a sensitivity of 89.9% (95% CI 81.0%-95.5%) and a specificity of 91.3% (95% CI 82.8%-96.4%) were achieved, successfully validating the biomarker signature. CONCLUSIONS: In patients with CP with an increased risk for pancreatic cancer (cumulative incidence 1.95%), the performance of this biomarker signature results in a negative predictive value of 99.9% (95% CI 99.7%-99.9%) (training set) and 99.8% (95% CI 99.6%-99.9%) (test set). In one third of our patients, the clinical use of this biomarker signature would have improved diagnosis and treatment stratification in comparison to CA19-9.

Integration of tissue metabolomics, transcriptomics and immunohistochemistry reveals ERG- and gleason score-specific metabolomic alterations in prostate cancer.

Integrated analysis of metabolomics, transcriptomics and immunohistochemistry can contribute to a deeper understanding of biological processes altered in cancer and possibly enable improved diagnostic or prognostic tests. In this study, a set of 254 metabolites was determined by gas-chromatography/liquid chromatography-mass spectrometry in matched malignant and non-malignant prostatectomy samples of 106 prostate cancer (PCa) patients. Transcription analysis of matched samples was performed on a set of 15 PCa patients using Affymetrix U133 Plus 2.0 arrays. Expression of several proteins was immunohistochemically determined in 41 matched patient samples and the association with clinico-pathological parameters was analyzed by an integrated data analysis. These results further outline the highly deregulated metabolism of fatty acids, sphingolipids and polyamines in PCa. For the first time, the impact of the ERG translocation on the metabolome was demonstrated, highlighting an altered fatty acid oxidation in TMPRSS2-ERG translocation positive PCa specimens. Furthermore, alterations in cholesterol metabolism were found preferentially in high grade tumors, enabling the cells to create energy storage. With this integrated analysis we could not only confirm several findings from previous metabolomic studies, but also contradict others and finally expand our concepts of deregulated biological pathways in PCa.

Tissue metabolite profiling identifies differentiating and prognostic biomarkers for prostate carcinoma.

Metabolomic research offers a deeper insight into biochemical changes in cancer metabolism and is a promising tool for identifying novel biomarkers. We aimed to evaluate the diagnostic and prognostic potential of metabolites in prostate cancer (PCa) tissue after radical prostatectomy. In matched malignant and nonmalignant prostatectomy samples from 95 PCa patients, aminoadipic acid, cerebronic acid, gluconic acid, glycerophosphoethanolamine, 2-hydroxybehenic acid, isopentenyl pyrophosphate, maltotriose, 7-methylguanine and tricosanoic acid were determined within a global metabolite profiling study using gas chromatography/liquid chromatography-mass spectrometry. The data were related to clinicopathological variables like prostate volume, tumor stage, Gleason score, preoperative prostate-specific antigen and disease recurrence in the follow-up. All nine metabolites showed higher concentrations in malignant than in nonmalignant samples except for gluconic acid and maltotriose, which had lower levels in tumors. Receiver -operating characteristics analysis demonstrated a significant discrimination for all metabolites between malignant and nonmalignant tissue with a maximal area under the curve of 0.86 for tricosanoic acid, whereas no correlation was observed between the metabolite levels and the Gleason score or tumor stage except for gluconic acid. Univariate Cox regression and Kaplan-Meier analyses showed that levels of aminoadipic acid, gluconic acid and maltotriose were associated with the biochemical tumor recurrence (prostate-specific antigen > 0.2 ng/mL). In multivariate Cox regression analyses, aminoadipic acid together with tumor stage and Gleason score remained in a model as independent marker for prediction of biochemical recurrence. This study proved that metabolites in PCa tissue can be used, in combination with traditional clinicopathological factors, as promising diagnostic and prognostic tools.

Sarcosine in prostate cancer tissue is not a differential metabolite for prostate cancer aggressiveness and biochemical progression.

PURPOSE: Sarcosine in prostate cancer tissue samples was recently reported to be increased during prostate cancer progression to metastasis and suggested to be a key metabolite of cancer cell invasion and aggressiveness. We reevaluated sarcosine in prostate cancer tissue samples as a potential indicator of tumor aggressiveness, and as a predictor of recurrence-free survival. MATERIALS AND METHODS: Sarcosine in matched samples of malignant and nonmalignant tissue from 92 patients with prostate cancer after radical prostatectomy was measured in the framework of a global metabolite profiling study of prostate cancer by gas chromatography/mass spectrometry. We related results to age, prostate volume, tumor stage, Gleason score, preoperative prostate specific antigen and biochemical recurrence, defined as a persistent prostate specific antigen increase of greater than 0.2 ng/ml. Nonparametric statistical tests, ROC curves and Kaplan-Meier analyses were done. RESULTS: Median sarcosine content in tissue was about 7% higher in matched malignant vs nonmalignant samples, which was significantly. Sarcosine values were not associated with tumor stage (pT2 vs pT3), tumor grade (Gleason score less than 7 vs 7 or greater) or biochemical recurrence. The lack of metastatic tissue samples was a study limitation. CONCLUSIONS: Sarcosine in prostate cancer tissue samples cannot be considered a suitable predictor of tumor aggressiveness or biochemical recurrence.