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High accuracy differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma by immunoglobulin G glycosylation.

ABSTRACT: Background:Misdiagnosis of autoimmune pancreatitis (AIP) as pancreatic cancer (PDAC) or vice versa can cause dismal patents' outcomes. Changes in IgG glycosylation are associated with cancers and autoimmune diseases. This study investigated the IgG glycosylation profiles as diagnostic and prognostic biomarkers in PDAC and AIP. Methods:Serum IgG-glycosylation profiles from 86 AIP patients, 115 PDAC patients, and 57 controls were analyzed using liquid chromatography-electrospray ionization mass spectrometry. Classification and regression tree (CART) analysis was applied to build a decision tree for discriminating PDAC from AIP. The result was validated in an independent cohort. Results:Compared with AIP patients and controls, PDAC patients had significantly higher agalactosylation, lower fucosylation, and sialylation of IgG1, a higher agalactosylation ratio of IgG1 and a higher agalactosylation ratio of IgG2. AIP patients had significantly higher fucosylation of IgG1 and a higher sialylation ratio of IgG subclasses 1, 2 and 4. Using the CART analysis of agalactosylation and sialylation ratios in the IgG to discriminate AIP from PDAC, the diagnostic accuracy of the glycan markers was 93.8% with 94.6% sensitivity and 92.9% specificity. There were no statistically significant difference of IgG-glycosylation profiles between diffuse type and focal type AIP. Conclusions:AIP and PDAC patients have distinct IgG-glycosylation profilings. IgG-glycosylation could different PDAC from AIP with high accuracy.

SUBMITTER: Shih HC

PROVIDER: S-EPMC6317216 | biostudies-other | 2019

REPOSITORIES: biostudies-other

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High accuracy differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma by immunoglobulin G glycosylation.

Shih Hsi-Chang HC Chang Ming-Chu MC Chen Chein-Hung CH Tsai I-Lin IL Wang San-Yuan SY Kuo Ya-Po YP Chen Chung-Hsuan CH Chang Yu-Ting YT

Clinical proteomics 20190103

<h4>Background</h4>Misdiagnosis of autoimmune pancreatitis (AIP) as pancreatic cancer (PDAC) or vice versa can cause dismal patents' outcomes. Changes in IgG glycosylation are associated with cancers and autoimmune diseases. This study investigated the IgG glycosylation profiles as diagnostic and prognostic biomarkers in PDAC and AIP.<h4>Methods</h4>Serum IgG-glycosylation profiles from 86 AIP patients, 115 PDAC patients, and 57 controls were analyzed using liquid chromatography-electrospray ion ...[more]

PMID: 30622446

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Project description:Background18F-fluorodeoxyglucose positron emission tomography-computerized tomography (18F-FDG PET-CT) has demonstrated high sensitivity in the diagnosis of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC), while also exhibiting the ability to distinguish AIP from PDAC lesions. The objective of this investigation was to assess the efficacy of multiparametric 18F-FDG PET with serological examination for distinguishing focal AIP (f-AIP) from PDAC.MethodsA total of 127 patients (43 with f-AIP and 84 with PDAC) who received 18F-FDG PET-CT before treatment were retrospectively included in the cohort study conducted at two centers, Beijing Friendship Hospital and Chinese PLA General Hospital, from January 2015 to December 2021. The baseline characteristics and clinical data were collected. The metabolism parameters of 18F-FDG PET, including maximum standardized uptake value (SUVmax), tumor-to-normal liver SUV ratio (SUVR), mean SUV (SUVmean), total lesion glycolysis (TLG), and metabolic tumor volume (MTV) were evaluated. The area under the receiver operating characteristic (ROC) curve was used to evaluate the differential diagnostic efficacy. The diagnostic efficacy improvement was assessed through the integrated discriminatory improvement (IDI), net reclassification improvement (NRI), and DeLong test.ResultsSerum immunoglobulin G4 (IgG4) >280 mg/dL, carbohydrate antigen 19-9 (CA19-9) <85 U/mL, and metabolic parameters differed significantly between patients with f-AID and PDAC. The ROC curve analysis of MTV showed the highest differentiating diagnostic value [sensitivity =0.814, 95% confidence interval (CI): 0.661-0.911; specificity =0.893, 95% CI: 0.802-0.947; area under the curve (AUC) =0.890, 95% CI: 0.820-0.957]. The combined diagnostics model of serum IgG4 >280 mg/dL, CA19-9 <85 U/mL, and MTV resulted in the highest AUC of 0.991 (95% CI: 0.978-1.000; sensitivity =0.953, 95% CI: 0.829-0.992; specificity =0.964, 95% CI: 0.892-0.991).ConclusionsThe multiparameter diagnostic model based on 18F-FDG PET and serological examination has excellent clinical value in the differential diagnosis of f-AID and PDAC.

Project description:BackgroundThe fecal microbiota in pancreatic ductal adenocarcinoma (PDAC) and in autoimmune pancreatitis (AIP) patients remains largely unknown. We aimed to characterize the fecal microbiota in patients with PDAC and AIP, and explore the possibility of fecal microbial biomarkers for distinguishing PDAC and AIP.Methods32 patients with PDAC, 32 patients with AIP and 32 age- and sex-matched healthy controls (HC) were recruited and the fecal microbiotas were analyzed through high-throughput metagenomic sequencing. Alterations of fecal short-chain fatty acids were measured using gas chromatographic method.ResultsPrincipal coordinate analysis (PCoA) revealed that microbial compositions differed significantly between PDAC and HC samples; whereas, AIP and HC individuals tended to cluster together. Significant reduction of phylum Firmicutes (especially butyrate-producing bacteria, including Eubacterium rectale, Faecalibacterium prausnitzii and Roseburia intestinalis) and significant increase of phylum Proteobacteria (especially Gammaproteobacteria) were observed only among PDAC samples. At species level, when compared with HC samples, we revealed 24 and 12 differently enriched bacteria in PDAC and AIP, respectively. Functional analysis showed a depletion of short-chain fatty acids synthesis associated KO modules (e.g. Wood-Ljungdahl pathway) and an increase of KO modules associated with bacterial virulence (e.g. type II general secretion pathway). Consistent with the downregulation of butyrate-producing bacteria, gas chromatographic analysis showed fecal butyrate content was significantly decreased in PDAC group. Eubacterium rectale, Eubacterium ventrisum and Odoribacter splanchnicus were among the most important biomarkers in distinguishing PDAC from HC and from AIP individuals. Receiver Operating Characteristic analysis showed areas under the curve of 90.74% (95% confidence interval [CI] 86.47-100%), 88.89% (95% CI 73.49-100%), and 76.54% (95% CI 52.5-100%) for PDAC/HC, PDAC/AIP and AIP/HC, respectively.ConclusionsIn conclusion, alterations in fecal microbiota and butyrate of patients with PDAC suggest an underlying role of gut microbiota for the pathogenesis of PDAC. Fecal microbial and butyrate as potential biomarkers may facilitate to distinguish patients with PDAC from patients with AIP and HCs which worth further validation.

Project description:ObjectivesThe purpose of this study was to develop and validate an CT-based radiomics nomogram for the preoperative differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma.Methods96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study (32 and 64 cases respectively). All cases have been confirmed by imaging, clinical follow-up and/or pathology. The imaging data were considered as: 70% training cohort and 30% test cohort. Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images. Independent-sample T tests and LASSO regression were used for feature selection. The training cohort was classified using a variety of machine learning-based classifiers, and 5-fold cross-validation has been performed. The classification performance was evaluated using the test cohort. Multivariate logistic regression analysis was then used to develop a radiomics nomogram model, containing the CT findings and Rad-Score. Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model. Different patients have been selected to test and evaluate the model prediction process. Finally, receiver operating characteristic curves and decision curves were plotted, and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability.ResultsA total of 158 radiomics features were extracted from each image. 7 features were selected to construct the radiomics model, then a variety of classifiers were used for classification and multinomial logistic regression (MLR) was selected to be the optimal classifier. Combining CT findings with radiomics model, a prediction model based on CT findings and radiomics was finally obtained. The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts, respectively. The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually.ConclusionsThe CT image-based radiomics nomogram model can accurately distinguish between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma patients and provide additional clinical benefits.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is characterized by an extensive stroma being also present in chronic pancreatitis (CP). Using immunohistochemistry, the stroma of CP and PDAC was comprehensively analyzed and correlated with epithelial/carcinoma-related alterations and clinicopathological patient characteristics. While there were no significant differences between CP and PDAC regarding the distribution of CD3+ T cells and ?-SMA+ fibroblasts, proportions of CD4+ and CD8+ T cells were significantly lower and numbers of CD25+(CD4+) and FoxP3+(CD4+) regulatory T cells were greater in PDAC compared with CP. Macrophages were more prevalent in CP, but localized more closely to carcinoma cells in PDAC, as were ??-T cells. Duct-related FoxP3 and L1CAM expression increased from CP to PDAC, while vimentin expression was similarly abundant in both diseases. Moreover, stromal and epithelial compartments of well-differentiated tumors and CPs shared considerable similarities, while moderately and poorly differentiated tumors significantly differed from CP tissues. Analysis of 27 parameters within each pancreatic disease revealed a significant correlation of i) CD4+ and FoxP3+CD4+ T cells with FoxP3 expression in PDAC cells, ii) ?-SMA+ fibroblasts with L1CAM expression and proliferation in PDAC cells, iii) CD3 and CD8 expression with ??-TCR expression in both pancreatic diseases and iv) CD68+ and CD163+ macrophages with vimentin expression in PDAC cells. High expression of FoxP3, vimentin and L1CAM in PDAC cells as well as a tumor-related localization of macrophages each tended to correlate with higher tumor grade. Multivariate survival analysis revealed a younger age at time of surgery as a positive prognostic marker for PDAC patients with the most frequently operated disease stage T3N1M0. Overall this study identified several interrelationships between stroma and epithelial/carcinoma cells in PDACs but also in CP, which in light of previous experimental data strongly support the view that the inflammatory stroma contributes to malignancy-associated alterations already in precursor cells during CP.

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High accuracy differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma by immunoglobulin G glycosylation.

Publications

High accuracy differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma by immunoglobulin G glycosylation.

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