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Seven-signal proteomic signature for detection of operable pancreatic ductal adenocarcinoma and their discrimination from autoimmune pancreatitis.

ABSTRACT: There is urgent need for biomarkers that provide early detection of pancreatic ductal adenocarcinoma (PDAC) as well as discrimination of autoimmune pancreatitis, as current clinical approaches are not suitably accurate for precise diagnosis. We used mass spectrometry to analyze protein profiles of more than 300 plasma specimens obtained from PDAC, noncancerous pancreatic diseases including autoimmune pancreatitis patients and healthy subjects. We obtained 1063 proteomic signals from 160 plasma samples in the training cohort. A proteomic signature consisting of 7 mass spectrometry signals was used for construction of a proteomic model for detection of PDAC patients. Using the test cohort, we confirmed that this proteomic model had discrimination power equal to that observed with the training cohort. The overall sensitivity and specificity for detection of cancer patients were 82.6% and 90.9%, respectively. Notably, 62.5% of the stage I and II cases were detected by our proteomic model. We also found that 100% of autoimmune pancreatitis patients were correctly assigned as noncancerous individuals. In the present paper, we developed a proteomic model that was shown able to detect early-stage PDAC patients. In addition, our model appeared capable of discriminating patients with autoimmune pancreatitis from those with PDAC.

SUBMITTER: Yanagisawa K

PROVIDER: S-EPMC3361197 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Seven-signal proteomic signature for detection of operable pancreatic ductal adenocarcinoma and their discrimination from autoimmune pancreatitis.

Yanagisawa Kiyoshi K Tomida Shuta S Matsuo Keitaro K Arima Chinatsu C Kusumegi Miyoko M Yokoyama Yukihiro Y Ko Shigeru B H SB Mizuno Nobumasa N Kawahara Takeo T Kuroyanagi Yoko Y Takeuchi Toshiyuki T Goto Hidemi H Yamao Kenji K Nagino Masato M Tajima Kazuo K Takahashi Takashi T

International journal of proteomics 20120514

There is urgent need for biomarkers that provide early detection of pancreatic ductal adenocarcinoma (PDAC) as well as discrimination of autoimmune pancreatitis, as current clinical approaches are not suitably accurate for precise diagnosis. We used mass spectrometry to analyze protein profiles of more than 300 plasma specimens obtained from PDAC, noncancerous pancreatic diseases including autoimmune pancreatitis patients and healthy subjects. We obtained 1063 proteomic signals from 160 plasma s ...[more]

PMID: 22675630

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Project description:The differentiation of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) poses a relevant diagnostic challenge and can lead to misdiagnosis and consequently poor patient outcome. Recent studies have shown that radiomics-based models can achieve high sensitivity and specificity in predicting both entities. However, radiomic features can only capture low level representations of the input image. In contrast, convolutional neural networks (CNNs) can learn and extract more complex representations which have been used for image classification to great success. In our retrospective observational study, we performed a deep learning-based feature extraction using CT-scans of both entities and compared the predictive value against traditional radiomic features. In total, 86 patients, 44 with AIP and 42 with PDACs, were analyzed. Whole pancreas segmentation was automatically performed on CT-scans during the portal venous phase. The segmentation masks were manually checked and corrected if necessary. In total, 1411 radiomic features were extracted using PyRadiomics and 256 features (deep features) were extracted using an intermediate layer of a convolutional neural network (CNN). After feature selection and normalization, an extremely randomized trees algorithm was trained and tested using a two-fold shuffle-split cross-validation with a test sample of 20% (n = 18) to discriminate between AIP or PDAC. Feature maps were plotted and visual difference was noted. The machine learning (ML) model achieved a sensitivity, specificity, and ROC-AUC of 0.89 ± 0.11, 0.83 ± 0.06, and 0.90 ± 0.02 for the deep features and 0.72 ± 0.11, 0.78 ± 0.06, and 0.80 ± 0.01 for the radiomic features. Visualization of feature maps indicated different activation patterns for AIP and PDAC. We successfully trained a machine learning model using deep feature extraction from CT-images to differentiate between AIP and PDAC. In comparison to traditional radiomic features, deep features achieved a higher sensitivity, specificity, and ROC-AUC. Visualization of deep features could further improve the diagnostic accuracy of non-invasive differentiation of AIP 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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Seven-signal proteomic signature for detection of operable pancreatic ductal adenocarcinoma and their discrimination from autoimmune pancreatitis.

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Seven-signal proteomic signature for detection of operable pancreatic ductal adenocarcinoma and their discrimination from autoimmune pancreatitis.

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