Dataset Information

A combination of molecular markers and clinical features improve the classification of pancreatic cysts.

ABSTRACT:

Background & aims

The management of pancreatic cysts poses challenges to both patients and their physicians. We investigated whether a combination of molecular markers and clinical information could improve the classification of pancreatic cysts and management of patients.

Methods

We performed a multi-center, retrospective study of 130 patients with resected pancreatic cystic neoplasms (12 serous cystadenomas, 10 solid pseudopapillary neoplasms, 12 mucinous cystic neoplasms, and 96 intraductal papillary mucinous neoplasms). Cyst fluid was analyzed to identify subtle mutations in genes known to be mutated in pancreatic cysts (BRAF, CDKN2A, CTNNB1, GNAS, KRAS, NRAS, PIK3CA, RNF43, SMAD4, TP53, and VHL); to identify loss of heterozygozity at CDKN2A, RNF43, SMAD4, TP53, and VHL tumor suppressor loci; and to identify aneuploidy. The analyses were performed using specialized technologies for implementing and interpreting massively parallel sequencing data acquisition. An algorithm was used to select markers that could classify cyst type and grade. The accuracy of the molecular markers was compared with that of clinical markers and a combination of molecular and clinical markers.

Results

We identified molecular markers and clinical features that classified cyst type with 90%-100% sensitivity and 92%-98% specificity. The molecular marker panel correctly identified 67 of the 74 patients who did not require surgery and could, therefore, reduce the number of unnecessary operations by 91%.

Conclusions

We identified a panel of molecular markers and clinical features that show promise for the accurate classification of cystic neoplasms of the pancreas and identification of cysts that require surgery.

SUBMITTER: Springer S

PROVIDER: S-EPMC4782782 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Publications

A combination of molecular markers and clinical features improve the classification of pancreatic cysts.

Springer Simeon S Wang Yuxuan Y Dal Molin Marco M Masica David L DL Jiao Yuchen Y Kinde Isaac I Blackford Amanda A Raman Siva P SP Wolfgang Christopher L CL Tomita Tyler T Niknafs Noushin N Douville Christopher C Ptak Janine J Dobbyn Lisa L Allen Peter J PJ Klimstra David S DS Schattner Mark A MA Schmidt C Max CM Yip-Schneider Michele M Cummings Oscar W OW Brand Randall E RE Zeh Herbert J HJ Singhi Aatur D AD Scarpa Aldo A Salvia Roberto R Malleo Giuseppe G Zamboni Giuseppe G Falconi Massimo M Jang Jin-Young JY Kim Sun-Whe SW Kwon Wooil W Hong Seung-Mo SM Song Ki-Byung KB Kim Song Cheol SC Swan Niall N Murphy Jean J Geoghegan Justin J Brugge William W Fernandez-Del Castillo Carlos C Mino-Kenudson Mari M Schulick Richard R Edil Barish H BH Adsay Volkan V Paulino Jorge J van Hooft Jeanin J Yachida Shinichi S Nara Satoshi S Hiraoka Nobuyoshi N Yamao Kenji K Hijioka Susuma S van der Merwe Schalk S Goggins Michael M Canto Marcia Irene MI Ahuja Nita N Hirose Kenzo K Makary Martin M Weiss Matthew J MJ Cameron John J Pittman Meredith M Eshleman James R JR Diaz Luis A LA Papadopoulos Nickolas N Kinzler Kenneth W KW Karchin Rachel R Hruban Ralph H RH Vogelstein Bert B Lennon Anne Marie AM

Gastroenterology 20150804 6

<h4>Background & aims</h4>The management of pancreatic cysts poses challenges to both patients and their physicians. We investigated whether a combination of molecular markers and clinical information could improve the classification of pancreatic cysts and management of patients.<h4>Methods</h4>We performed a multi-center, retrospective study of 130 patients with resected pancreatic cystic neoplasms (12 serous cystadenomas, 10 solid pseudopapillary neoplasms, 12 mucinous cystic neoplasms, and 9 ...[more]

PMID: 26253305

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Project description:ObjectiveOur objective was to develop an approach for selecting combinatorial markers of pathology from diverse clinical data types. We demonstrate this approach on the problem of pancreatic cyst classification.Materials and methodsWe analyzed 1026 patients with surgically resected pancreatic cysts, comprising 584 intraductal papillary mucinous neoplasms, 332 serous cystadenomas, 78 mucinous cystic neoplasms, and 42 solid-pseudopapillary neoplasms. To derive optimal markers for cyst classification from the preoperative clinical and radiological data, we developed a statistical approach for combining any number of categorical, dichotomous, or continuous-valued clinical parameters into individual predictors of pathology. The approach is unbiased and statistically rigorous. Millions of feature combinations were tested using 10-fold cross-validation, and the most informative features were validated in an independent cohort of 130 patients with surgically resected pancreatic cysts.ResultsWe identified combinatorial clinical markers that classified serous cystadenomas with 95% sensitivity and 83% specificity; solid-pseudopapillary neoplasms with 89% sensitivity and 86% specificity; mucinous cystic neoplasms with 91% sensitivity and 83% specificity; and intraductal papillary mucinous neoplasms with 94% sensitivity and 90% specificity. No individual features were as accurate as the combination markers. We further validated these combinatorial markers on an independent cohort of 130 pancreatic cysts, and achieved high and well-balanced accuracies. Overall sensitivity and specificity for identifying patients requiring surgical resection was 84% and 81%, respectively.ConclusionsOur approach identified combinatorial markers for pancreatic cyst classification that had improved performance relative to the individual features they comprise. In principle, this approach can be applied to any clinical dataset comprising dichotomous, categorical, and continuous-valued parameters.

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Dataset Information

A combination of molecular markers and clinical features improve the classification of pancreatic cysts.

Background & aims

Methods

Results

Conclusions

Publications

A combination of molecular markers and clinical features improve the classification of pancreatic cysts.

Similar Datasets

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