Validation of combined carcinoembryonic antigen and glucose testing in pancreatic cyst fluid to differentiate mucinous from non-mucinous cysts.

BACKGROUND: More accurate diagnosis of mucinous cysts will reduce the risk of unnecessary pancreatic surgery. Carcinoembryonic antigen (CEA) and glucose in pancreatic cyst fluid (PCF) can differentiate mucinous from non-mucinous pancreatic cystic neoplasms (PCN). The current study assessed the value of combined CEA and glucose testing in PCF. METHODS: Cross-sectional validation study including prospectively collected PCF from patients undergoing endoscopic ultrasonography-guided fine-needle aspiration (EUS-FNA) and pancreatic surgery. We performed laboratory measurements for CEA and glucose and measured glucose levels by a hand glucometer. Primary outcome was diagnostic accuracy evaluated by receiver operator curves (ROC), sensitivity, specificity, positive, and negative predictive value (PPV, NPV). RESULTS: Overall, PCF was collected from 63 patients, including 33 (52%) with mucinous and 30 (48%) with non-mucinous PCN. Histopathology (n = 36; 57%), cytopathology (n = 2; 3%), or clinical and/or radiological diagnosis (n = 25; 40%) was used as reference standard. Combined CEA (cut-off ≥ 192 ng/ml) and laboratory glucose testing (cut-off ≤ 50 mg/dL) reached 92% specificity and 48% sensitivity, whereas either positive CEA (cut-off ≥ 20 ng/ml) or glucose testing (cut-off ≤ 50 mg/dL) showed 97% sensitivity and 50% specificity. Sensitivity and specificity were 80% and 68% for CEA ≥ 20 ng/mL versus 50% and 93% for CEA ≥ 192 ng/mL (the conventional cut-off level). Laboratory and glucometer glucose both reached 100% sensitivity and 60% and 45% specificity, respectively. None of the biomarkers and cut-offs reached a PPV exceeding 90%, whereas both glucose measurements had a NPV of 100% (i.e., high glucose excludes a mucinous cyst). CONCLUSION: Combined CEA and glucose testing in PCF reached high specificity and sensitivity for differentiating mucinous from non-mucinous PCN. Glucose testing, whether alone or combined with the new CEA cut-off (≥ 20 ng/mL), reached > 95% sensitivity for mucinous cysts, whereas only glucose reached a NPV > 95%.

Unsupervised class discovery in pancreatic ductal adenocarcinoma reveals cell-intrinsic mesenchymal features and high concordance between existing classification systems.

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis of all common cancers. However, divergent outcomes exist between patients, suggesting distinct underlying tumor biology. Here, we delineated this heterogeneity, compared interconnectivity between classification systems, and experimentally addressed the tumor biology that drives poor outcome. RNA-sequencing of 90 resected specimens and unsupervised classification revealed four subgroups associated with distinct outcomes. The worst-prognosis subtype was characterized by mesenchymal gene signatures. Comparative (network) analysis showed high interconnectivity with previously identified classification schemes and high robustness of the mesenchymal subtype. From species-specific transcript analysis of matching patient-derived xenografts we constructed dedicated classifiers for experimental models. Detailed assessments of tumor growth in subtyped experimental models revealed that a highly invasive growth pattern of mesenchymal subtype tumor cells is responsible for its poor outcome. Concluding, by developing a classification system tailored to experimental models, we have uncovered subtype-specific biology that should be further explored to improve treatment of a group of PDAC patients that currently has little therapeutic benefit from surgical treatment.

Molecularly Defined Adult Granulosa Cell Tumor of the Ovary: The Clinical Phenotype.

The histopathologic features of adult granulosa cell tumors (AGCTs) are relatively nonspecific, resulting in misdiagnosis of other cancers as AGCT, a problem that has not been well characterized. FOXL2 mutation testing was used to stratify 336 AGCTs from three European centers into three categories: 1) FOXL2 mutant molecularly defined AGCT (MD-AGCT) (n = 256 of 336), 2) FOXL2 wild-type AGCT (n = 17 of 336), 3) misdiagnosed other tumor types (n = 63 of 336). All statistical tests were two-sided. The overall and disease-specific survival of the misdiagnosed cases was lower than in the MD-AGCTs (P < .001). The misdiagnosed cases accounted for 71.9% of disease-specific deaths within five years. In the population-based cohort, overall survival of MD-AGCT patients was not different from age-matched, population-based controls. Even though 35.2% of all the MD-AGCT patients in our study experienced a relapse, AGCT is usually an indolent disease. The historical, premolecular data underpinning our clinical understanding of AGCT was likely skewed by inclusion of misdiagnosed cases, and future management strategies should reflect the potential for surgical cure and long survival even after relapse.

Classification of ductal carcinoma in situ by gene expression profiling.

INTRODUCTION: Ductal carcinoma in situ (DCIS) is characterised by the intraductal proliferation of malignant epithelial cells. Several histological classification systems have been developed, but assessing the histological type/grade of DCIS lesions is still challenging, making treatment decisions based on these features difficult. To obtain insight in the molecular basis of the development of different types of DCIS and its progression to invasive breast cancer, we have studied differences in gene expression between different types of DCIS and between DCIS and invasive breast carcinomas. METHODS: Gene expression profiling using microarray analysis has been performed on 40 in situ and 40 invasive breast cancer cases. RESULTS: DCIS cases were classified as well- (n = 6), intermediately (n = 18), and poorly (n = 14) differentiated type. Of the 40 invasive breast cancer samples, five samples were grade I, 11 samples were grade II, and 24 samples were grade III. Using two-dimensional hierarchical clustering, the basal-like type, ERB-B2 type, and the luminal-type tumours originally described for invasive breast cancer could also be identified in DCIS. CONCLUSION: Using supervised classification, we identified a gene expression classifier of 35 genes, which differed between DCIS and invasive breast cancer; a classifier of 43 genes could be identified separating between well- and poorly differentiated DCIS samples.