Dataset Information

Cyst Fluid Telomerase Activity Predicts the Histologic Grade of Cystic Neoplasms of the Pancreas.

ABSTRACT: Pancreatic cysts frequently pose clinical dilemmas. On one hand, cysts with high-grade dysplasia offer opportunities for cure, on the other hand, those with low-grade dysplasia are easily over treated. Cyst fluid markers have the potential to improve the evaluation of these cysts. Because telomerase activity is commonly activated in malignant cells, we evaluated the diagnostic performance of cyst fluid telomerase activity measurements for predicting histologic grade.Telomerase activity was measured using telomerase repeat amplification with digital-droplet PCR in surgically aspirated cyst fluid samples from 219 patients who underwent pancreatic resection for a cystic lesion (184 discovery, 35 validation) and 36 patients who underwent endoscopic ultrasound fine-needle aspiration. Methodologic and clinical factors associated with telomerase activity were examined.Telomerase activity was reduced in samples that had undergone prior thawing. Among 119 samples not previously thawed, surgical cyst fluids from cystic neoplasms with high-grade dysplasia ± associated invasive cancer had higher telomerase activity [median (interquartile range), 1,158 (295.9-13,033)] copies/?L of cyst fluid than those without [19.74 (2.58-233.6) copies/?L; P < 0.001)]. Elevated cyst fluid telomerase activity had a diagnostic accuracy for invasive cancer/high-grade dysplasia of 88.1% (discovery), 88.6% (validation), and 88.2% (merged). Among cysts classified preoperatively as having "worrisome features," cyst fluid telomerase activity had high diagnostic performance (sensitivity 73.7%, specificity 90.6%, accuracy, 86.1%). In multivariate analysis, telomerase activity independently predicted the presence of invasive cancer/high-grade dysplasia.Cyst fluid telomerase activity can be a useful predictor of the neoplastic grade of pancreatic cysts. Clin Cancer Res; 22(20); 5141-51. ©2016 AACRSee related commentary by Allen et al., p. 4966.

SUBMITTER: Hata T

PROVIDER: S-EPMC5065739 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cyst Fluid Telomerase Activity Predicts the Histologic Grade of Cystic Neoplasms of the Pancreas.

Hata Tatsuo T Dal Molin Marco M Suenaga Masaya M Yu Jun J Pittman Meredith M Weiss Matthew M Canto Marcia I MI Wolfgang Christopher C Lennon Anne Marie AM Hruban Ralph H RH Goggins Michael M

Clinical cancer research : an official journal of the American Association for Cancer Research 20160526 20

<h4>Purpose</h4>Pancreatic cysts frequently pose clinical dilemmas. On one hand, cysts with high-grade dysplasia offer opportunities for cure, on the other hand, those with low-grade dysplasia are easily over treated. Cyst fluid markers have the potential to improve the evaluation of these cysts. Because telomerase activity is commonly activated in malignant cells, we evaluated the diagnostic performance of cyst fluid telomerase activity measurements for predicting histologic grade.<h4>Experimen ...[more]

PMID: 27230749

Similar Datasets

Project description:OBJECTIVE:Preliminary work by our group suggested that proteins within the pancreatic cyst fluid (CF) may discriminate degree of IPMN dysplasia. We sought to externally validate these markers and determine whether their inclusion in a preoperative clinical nomogram could increase diagnostic accuracy. SUMMARY BACKGROUND DATA:IPMN is the most common radiographically identifiable precursor to pancreatic cancer; however, the timing and frequency of its malignant progression are unknown, and there are currently no reliable preoperative tests that can determine the grade of dysplasia in IPMN. METHODS:Clinical and radiographic data, as well as CF samples, were obtained from 149 patients who underwent resection for IPMN at 1 of 3 institutions. High-risk disease was defined as the presence of high-grade dysplasia or invasive carcinoma. Multianalyte bead array analysis (Luminex) of CF was performed for 4 protein markers that were previously associated with high-risk disease. Logistic regression models were fit on training data, with and without adjustment for a previously developed clinical nomogram and validated with an external testing set. The models incorporating clinical risk score were presented graphically as nomograms. RESULTS:Within the group of 149 resected patients, 89 (60%) had low-risk disease, and 60 (40%) had high-risk disease. All 4 CF markers (MMP9, CA72-4, sFASL, and IL-4) were overexpressed in patients with high-risk IPMN (P < 0.05). Two predictive models based on preselected combinations of CF markers had concordance indices of 0.76 (Model-1) and 0.80 (Model-2). Integration of each CF marker model into a previously described clinical nomogram leads to increased discrimination compared with either the CF models or nomogram alone (c-indices of 0.84 and 0.83, respectively). CONCLUSIONS:This multi-institutional study validated 2 CF protein marker models for preoperative identification of high-risk IPMN. When combined with a clinical nomogram, the ability to predict high-grade dysplasia was even stronger.

Project description:ObjectiveOur aim was to propose a preoperative computer-aided diagnosis scheme to differentiate pancreatic serous cystic neoplasms from other pancreatic cystic neoplasms, providing supportive opinions for clinicians and avoiding overtreatment.Materials and methodsIn this retrospective study, 260 patients with pancreatic cystic neoplasm were included. Each patient underwent a multidetector row computed tomography scan and pancreatic resection. In all, 200 patients constituted a cross-validation cohort, and 60 patients formed an independent validation cohort. Demographic information, clinical information, and multidetector row computed tomography images were obtained from Picture Archiving and Communication Systems. The peripheral margin of each neoplasm was manually outlined by experienced radiologists. A radiomics system containing 24 guideline-based features and 385 radiomics high-throughput features was designed. After the feature extraction, least absolute shrinkage selection operator regression was used to select the most important features. A support vector machine classifier with 5-fold cross-validation was applied to build the diagnostic model. The independent validation cohort was used to validate the performance.ResultsOnly 31 of 102 serous cystic neoplasm cases in this study were recognized correctly by clinicians before the surgery. Twenty-two features were selected from the radiomics system after 100 bootstrapping repetitions of the least absolute shrinkage selection operator regression. The diagnostic scheme performed accurately and robustly, showing the area under the receiver operating characteristic curve = 0.767, sensitivity = 0.686, and specificity = 0.709. In the independent validation cohort, we acquired similar results with receiver operating characteristic curve = 0.837, sensitivity = 0.667, and specificity = 0.818.ConclusionThe proposed radiomics-based computer-aided diagnosis scheme could increase preoperative diagnostic accuracy and assist clinicians in making accurate management decisions.

Project description:Background Incidental detection of pancreatic cystic neoplasm (PCN) has increased. Since a small percentage of PCNs possess malignant potential, management is challenging. The recently revised American College of Radiology (ACR) recommendations define PCN measurement and growth for different categories based on baseline cyst size. However, no data are available regarding PCN growth rate under the ACR-defined size categories. Purpose To assess growth of incidentally detected PCNs on long-term imaging follow-up using revised ACR recommendations and to evaluate the association between baseline imaging features and growth. Materials and Methods This retrospective study included PCNs with baseline imaging performed between January 2002 and May 2017, with two or more cross-sectional imaging studies performed at least 12 months apart. PCN assessment was based on ACR 2017 recommendations. Cyst features, including location, septations, and mural nodules and multiplicity, were noted. Time to cyst progression (growth by ACR criteria) was examined by using baseline PCN size, among other factors. Results A total of 646 cysts in 390 patients were followed up for a median of 50 months (range, 12-186 months). A total of 184 (28.5%) cysts increased in size, 52 (8.1%) decreased in size, and 410 (63.4%) remained stable. For groups in which baseline PCN size was smaller than 5 mm, 5-14 mm, 15-25 mm, and larger than 25 mm, growth was noted in seven (13.2%), 106 (28.9%), 49 (32.2%), and 22 (29.7%) cysts, respectively. ACR baseline size categories (subhazard ratio: 2.8 [5-14-mm PCN group], 3.4 [15-25-mm PCN group], and 2.7 [>25 mm group], as compared with the <5 mm PCN group; P < .05 for each) demonstrated association with growth. Presence of mural nodules, septations, or lesion multiplicity failed to demonstrate association with growth. Among PCNs smaller than 5 mm at baseline, 100% of PCNs at 3-year follow-up and 94.2% of PCNs at 5-year follow-up were likely to remain stable. Conclusion American College of Radiology baseline size category of 15-25-mm pancreatic cystic neoplasms (PCNs) demonstrated the highest (3.1 times) likelihood of growth, as compared with the category of PCNs smaller than 5 mm. PCNs smaller than 5 mm at baseline did not demonstrate growth at 3-year imaging follow-up. © RSNA, 2019 Online supplemental material is available for this article.

Project description:BackgroundThe application of advanced imaging technologies for identifying pancreatic cysts has become widespread. However, accurately differentiating between low-grade dysplasia (LGD), high-grade dysplasia (HGD), and invasive intraductal papillary mucinous neoplasms (IPMNs) remains a diagnostic challenge with current biomarkers, necessitating the development of novel biomarkers that can distinguish IPMN malignancy.MethodsCyst fluid samples were collected from nine IPMN patients (3 LGD, 3 HGD, and 3 invasive IPMN) during their pancreatectomies. An integrated proteomics approach that combines filter-aided sample preparation, stage tip-based high-pH fractionation, and high-resolution MS was applied to acquire in-depth proteomic data of pancreatic cyst fluid and discover marker candidates for IPMN malignancy. Biological processes of differentially expressed proteins that are related to pancreatic cysts and aggressive malignancy were analyzed using bioinformatics tools such as gene ontology analysis and Ingenuity pathway analysis. In order to confirm the validity of the marker candidates, 19 cyst fluid samples were analyzed by western blot.ResultsA dataset of 2992 proteins was constructed from pancreatic cyst fluid samples. A subsequent analysis found 2963 identified proteins in individual samples, 2837 of which were quantifiable. Differentially expressed proteins between histological grades of IPMN were associated with pancreatic diseases and malignancy according to ingenuity pathway analysis. Eighteen biomarker candidates that were differentially expressed across IPMN histological grades were discovered-7 DEPs that were upregulated and 11 that were downregulated in more malignant grades. HOOK1 and PTPN6 were validated by western blot in an independent cohort, the results of which were consistent with our proteomic data.ConclusionsThis study demonstrates that novel biomarker candidates for IPMN malignancy can be discovered through proteomic analysis of pancreatic cyst fluid.

Dataset Information

Cyst Fluid Telomerase Activity Predicts the Histologic Grade of Cystic Neoplasms of the Pancreas.

Publications

Cyst Fluid Telomerase Activity Predicts the Histologic Grade of Cystic Neoplasms of the Pancreas.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets