Glucagon receptor gene mutations with hyperglucagonemia but without the glucagonoma syndrome

Pancreatic neoplasms producing exclusively glucagon associated with glucagon cell hyperplasia of the islets and not related to hereditary endocrine syndromes have been recently described. They represent a novel entity within the panel of non-syndromic disorders associated with hyperglucagonemia. This case report describes a 36-year-old female with a 10 years history of non-specific abdominal pain. No underlying cause was evident despite extensive diagnostic work-up. More recently she was diagnosed with gall bladder stones. Abdominal ultrasound, computerised tomography and magnetic resonance imaging revealed no pathologic findings apart from cholelithiasis. Endoscopic ultrasound revealed a 5.5 mm pancreatic lesion. Fine needle aspiration showed cells focally expressing chromogranin, suggestive but not diagnostic of a low grade neuroendocrine tumor. OctreoScan(®) was negative. Serum glucagon was elevated to 66 pmol/L (normal: 0-50 pmol/L). Other gut hormones, chromogranin A and chromogranin B were normal. Cholecystectomy and enucleation of the pancreatic lesion were undertaken. Postoperatively, abdominal symptoms resolved and serum glucagon dropped to 7 pmol/L. Although H and E staining confirmed normal pancreatic tissue, immunohistochemistry was initially thought to be suggestive of alpha cell hyperplasia. A count of glucagon positive cells from 5 islets, compared to 5 islets from 5 normal pancreata indicated that islet size and glucagon cell ratios were increased, however still within the wide range of normal physiological findings. Glucagon receptor gene (GCGR) sequencing revealed a heterozygous deletion, K349_G359del and 4 missense mutations. This case may potentially represent a progenitor stage of glucagon cell adenomatosis with hyperglucagonemia in the absence of glucagonoma syndrome. The identification of novel GCGR mutations suggests that these may represent the underlying cause of this condition

Pancreatic acinar cell carcinoma : an analysis of cell lineage markers, P53 expression, and Ki-ras mutation

In a series of 22 pancreatic acinar cell carcinomas, including two acinar cystadenocarcinomas, cellular differentiation was analyzed by immunocytochemistry and electron microscopy. In addition, overexpression of p53 protein and Ki-ras codon 12 mutation was studied. Four of the 20 noncystic acinar cell carcinomas showed a pure acinar pattern, nine an acinar-solid, and seven a solid pattern. All tumors stained for at least one of the following pancreatic acinar markers: trypsin (21 of 22), lipase (19 of 22), chymotrypsin (13 of 22), phospholipase A2 (nine of 22), and pancreatic stone protein (19 of 22). One-third of the tumors expressed neuroendocrine markers (synaptophysin, eight of 22; chromogranin A, six of 21) and duct cell markers (CA19.9, nine of 21; B72.3, six of 21). Cellular coexpression of trypsin and synaptophysin was demonstrated in one tumor. Electron microscopy revealed zymogen granules (nine of nine). In only one of 16 tumors a Ki-ras mutation at codon 12 was found, whereas in none of 19 tumors could overexpression of p53 protein be demonstrated. The results suggest that acinar cell carcinomas show obvious capacity to differentiate into several directions, but nevertheless constitute an entity different from ductal adenocarcinomas or endocrine tumors

A multi-contrast MRI study of microstructural brain damage in patients with mild cognitive impairment.

OBJECTIVES: The aim of this study was to investigate pathological mechanisms underlying brain tissue alterations in mild cognitive impairment (MCI) using multi-contrast 3 T magnetic resonance imaging (MRI). METHODS: Forty-two MCI patients and 77 healthy controls (HC) underwent T1/T2* relaxometry as well as Magnetization Transfer (MT) MRI. Between-groups comparisons in MRI metrics were performed using permutation-based tests. Using MRI data, a generalized linear model (GLM) was computed to predict clinical performance and a support-vector machine (SVM) classification was used to classify MCI and HC subjects. RESULTS: Multi-parametric MRI data showed microstructural brain alterations in MCI patients vs HC that might be interpreted as: (i) a broad loss of myelin/cellular proteins and tissue microstructure in the hippocampus (p ≤ 0.01) and global white matter (p < 0.05); and (ii) iron accumulation in the pallidus nucleus (p ≤ 0.05). MRI metrics accurately predicted memory and executive performances in patients (p ≤ 0.005). SVM classification reached an accuracy of 75% to separate MCI and HC, and performed best using both volumes and T1/T2*/MT metrics. CONCLUSION: Multi-contrast MRI appears to be a promising approach to infer pathophysiological mechanisms leading to brain tissue alterations in MCI. Likewise, parametric MRI data provide powerful correlates of cognitive deficits and improve automatic disease classification based on morphometric features

The search for the primary tumor in metastasized gastroenteropancreatic neuroendocrine neoplasm.

Gastroenteropancreatic neuroendocrine tumors (NETs) often present as liver metastasis from a carcinoma of unknown primary. We recently showed that primary NETs from the pancreas, small intestine and stomach as well as their respective liver metastases differ from each other by the expression profile of the three genes CD302, PPWD1 and ABHB14B. The gene and protein expression of CD302, PPWD1, and ABHB14B was studied in abdominal NET metastases to identify the site of the respective primary tumors. Cryopreserved tissue from NET metastases collected in different institutions (group A: 29, group B: 50, group C: 132 specimens) were examined by comparative genomic hybridization (Agilent 105 K), gene expression analysis (Agilent 44 K) (groups A and B) and immunohistochemistry (group C). The data were blindly evaluated, i.e. without knowing the site of the primary. Gene expression analysis correctly revealed the primary in the ileum in 94 % of the cases of group A and in 58 % of group B. A pancreatic primary was predicted in 83 % (group A) and 20 % (group B), respectively. The combined sensitivity of group A and B was 75 % for ileal NETs and 38 % for pancreatic NETs. Immunohistochemical analysis of group C revealed an overall sensitivity of 80 %. Gene and protein expression analysis of CD302 and PPWD1 in NET metastases correctly identifies the primary in the pancreas or the ileum in 80 % of the cases, provided that the tissue is well preserved. Immunohistochemical profiling revealed CD302 as the best marker for ileal and PPWD1 for pancreatic detection

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample