Role of Epithelial-Mesenchymal Transition in Pancreatic Ductal Adenocarcinoma: Is Tumor Budding the Missing Link?

Pancreatic ductal adenocarcinoma (PDAC) ranks as the fourth commonest cause of cancer death while its incidence is increasing worldwide. For all stages, survival at 5 years is<5%. The lethal nature of pancreatic cancer is attributed to its high metastatic potential to the lymphatic system and distant organs. Lack of effective therapeutic options contributes to the high mortality rates of PDAC. Recent evidence suggests that epithelial-mesenchymal transition (EMT) plays an important role to the disease progression and development of drug resistance in PDAC. Tumor budding is thought to reflect the process of EMT which allows neoplastic epithelial cells to acquire a mesenchymal phenotype thus increasing their capacity for migration and invasion and help them become resistant to apoptotic signals. In a recent study by our own group the presence and prognostic significance of tumor budding in PDAC were investigated and an association between high-grade budding and aggressive clinicopathological features of the tumors as well as worse outcome of the patients was found. The identification of EMT phenotypic targets may help identifying new molecules so that future therapeutic strategies directed specifically against them could potentially have an impact on drug resistance and invasiveness and hence improve the prognosis of PDAC patients. The aim of this short review is to present an insight on the morphological and molecular aspects of EMT and on the factors that are involved in the induction of EMT in PDAC.

Desmoplastic small round cell tumor: A rare cause of a progressive brachial plexopathy.

Introduction: Desmoplastic small round cell tumor (DSRCT) is an uncommon, embryonic-type neoplasm, typically presenting as an abdominal mass in young men. A single case of DSRCT arising in the peripheral nervous system has been reported. Methods: The clinical course, imaging, electrophysiological, intraoperative, histopathological, molecular findings, and postoperative follow-up are reported. Results: A 43-year-old man presented with slowly progressive right brachial plexopathy. Magnetic resonance imaging revealed an enlarged medial cord with heterogeneous contrast enhancement. Histology showed a "small round cell" neoplasm with a polyphenotypic immunoprofile, including epithelial and mesenchymal markers. A pathognomonic fusion of Ewing sarcoma breakpoint region 1 and Wilms tumor 1 genes (EWSR1/WT1) was present. Treatment involved gross total excision and local radiotherapy. Conclusion: Our findings confirm the occurrence of DSRCT as a primary peripheral nerve tumor. Despite its usually very aggressive clinical course, prolonged recurrence-free survival may be reached. Histomorphology and immunoprofile of DSRCT may lead to misdiagnosis as small cell carcinoma. © 2013 Wiley Periodicals, Inc.

Atopic conditions and brain tumor risk in children and adolescents--an international case-control study (CEFALO)

BACKGROUND A number of epidemiological studies indicate an inverse association between atopy and brain tumors in adults, particularly gliomas. We investigated the association between atopic disorders and intracranial brain tumors in children and adolescents, using international collaborative CEFALO data. PATIENTS AND METHODS CEFALO is a population-based case-control study conducted in Denmark, Norway, Sweden, and Switzerland, including all children and adolescents in the age range 7-19 years diagnosed with a primary brain tumor between 2004 and 2008. Two controls per case were randomly selected from population registers matched on age, sex, and geographic region. Information about atopic conditions and potential confounders was collected through personal interviews. RESULTS In total, 352 cases (83%) and 646 controls (71%) participated in the study. For all brain tumors combined, there was no association between ever having had an atopic disorder and brain tumor risk [odds ratio 1.03; 95% confidence interval (CI) 0.70-1.34]. The OR was 0.76 (95% CI 0.53-1.11) for a current atopic condition (in the year before diagnosis) and 1.22 (95% CI 0.86-1.74) for an atopic condition in the past. Similar results were observed for glioma. CONCLUSIONS There was no association between atopic conditions and risk of all brain tumors combined or of glioma in particular. Stratification on current or past atopic conditions suggested the possibility of reverse causality, but may also the result of random variation because of small numbers in subgroups. In addition, an ongoing tumor treatment may affect the manifestation of atopic conditions, which could possibly affect recall when reporting about a history of atopic diseases. Only a few studies on atopic conditions and pediatric brain tumors are currently available, and the evidence is conflicting.

A Hybrid Model for Multimodal Brain Tumor Segmentation

We present a fully automatic segmentation method for multi-modal brain tumor segmentation. The proposed generative-discriminative hybrid model generates initial tissue probabilities, which are used subsequently for enhancing the classi�cation and spatial regularization. The model has been evaluated on the BRATS2013 training set, which includes multimodal MRI images from patients with high- and low-grade gliomas. Our method is capable of segmenting the image into healthy (GM, WM, CSF) and pathological tissue (necrotic, enhancing and non-enhancing tumor, edema). We achieved state-of-the-art performance (Dice mean values of 0.69 and 0.8 for tumor subcompartments and complete tumor respectively) within a reasonable timeframe (4 to 15 minutes).

Differential effects of interferon-gamma and tumor necrosis factor-alpha on Toxoplasma gondii proliferation in organotypic rat brain slice cultures.

Organotypic slice culture explants of rat cortical tissue infected with Toxoplasma gondii tachyzoites were applied as an in vitro model to investigate host-pathogen interactions in cerebral toxoplasmosis. The kinetics of parasite proliferation and the effects of interferon-gamma (IFN-gamma) and tumor necrosis factor-alpha (TNF-alpha) in infected organotypic cultures were monitored by light microscopy, transmission electron microscopy (TEM), and quantitative polymerase chain reaction (PCR) assay. As assessed by the loss of the structural integrity of the glial fibrillary acidic protein-intermediate filament network, tachyzoites infected and proliferated mainly within astrocytes, whereas neurons and microglia remained largely unaffected. Toxoplasma gondii proliferation was severely inhibited by IFN-y. However, this inhibition was not linked to tachyzoite-to-bradyzoite stage conversion. In contrast, TNF-alpha treatment resulted in a dramatically enhanced proliferation rate of the parasite. The cellular integrity in IFN-gamma-treated organotypic slice cultures was severely impaired compared with untreated and TNF-alpha-treated cultures. Thus, on infection of organotypic neuronal cultures, IFN-gamma and TNF-alpha exhibit largely detrimental effects, which could contribute to either inhibition or acceleration of parasite proliferation during cerebral toxoplasmosis.

Tissue metabolomics of hepatocellular carcinoma: Tumor energy metabolism and the role of transcriptomic classification

Hepatocellular carcinoma (HCC) is one of the commonest causes of death from cancer. A plethora of metabolomic investigations of HCC have yielded molecules in biofluids that are both up- and down-regulated but no real consensus has emerged regarding exploitable biomarkers for early detection of HCC. We report here a different approach, a combined transcriptomics and metabolomics study of energy metabolism in HCC. A panel of 31 pairs of HCC tumors and corresponding nontumor liver tissues from the same patients was investigated by gas chromatography-mass spectrometry (GCMS)-based metabolomics. HCC was characterized by ∼2-fold depletion of glucose, glycerol 3- and 2-phosphate, malate, alanine, myo-inositol, and linoleic acid. Data are consistent with a metabolic remodeling involving a 4-fold increase in glycolysis over mitochondrial oxidative phosphorylation. A second panel of 59 HCC that had been typed by transcriptomics and classified in G1 to G6 subgroups was also subjected to GCMS tissue metabolomics. No differences in glucose, lactate, alanine, glycerol 3-phosphate, malate, myo-inositol, or stearic acid tissue concentrations were found, suggesting that the Wnt/β-catenin pathway activated by CTNNB1 mutation in subgroups G5 and G6 did not exhibit specific metabolic remodeling. However, subgroup G1 had markedly reduced tissue concentrations of 1-stearoylglycerol, 1-palmitoylglycerol, and palmitic acid, suggesting that the high serum α-fetoprotein phenotype of G1, associated with the known overexpression of lipid catabolic enzymes, could be detected through metabolomics as increased lipid catabolism. Conclusion: Tissue metabolomics yielded precise biochemical information regarding HCC tumor metabolic remodeling from mitochondrial oxidation to aerobic glycolysis and the impact of molecular subtypes on this process.

Atlas-based Segmentation of Tumor-bearing Brain Images

In diagnostic neuroradiology as well as in radiation oncology and neurosurgery, there is an increasing demand for accurate segmentation of tumor-bearing brain images. Atlas-based segmentation is an appealing automatic technique thanks to its robustness and versatility. However, atlas-based segmentation of tumor-bearing brain images is challenging due to the confounding effects of the tumor in the patient image. In this article, we provide a brief background on brain tumor imaging and introduce the clinical perspective, before we categorize and review the state of the art in the current literature on atlas-based segmentation for tumor-bearing brain images. We also present selected methods and results from our own research in more detail. Finally, we conclude with a short summary and look at new developments in the field, including requirements for future routine clinical use.

Interactions between Siglec-7/9 receptors and ligands influence NK cell-dependent tumor immunosurveillance

Abstract Alteration of the surface glycosylation pattern on malignant cells potentially affects tumor immunity by directly influencing interactions with glycan-binding proteins (lectins) on the surface of immunomodulatory cells. The sialic acid-binding Ig-like lectins Siglec-7 and -9 are MHC class I-independent inhibitory receptors on human NK cells that recognize sialic acid-containing carbohydrates. Here, we found that the presence of Siglec-9 defined a subset of cytotoxic NK cells with a mature phenotype and enhanced chemotactic potential. Interestingly, this Siglec-9+ NK cell population was reduced in the peripheral blood of cancer patients. Broad analysis of primary tumor samples revealed that ligands of Siglec-7 and -9 were expressed on human cancer cells of different histological types. Expression of Siglec-7 and -9 ligands was associated with susceptibility of NK cell-sensitive tumor cells and, unexpectedly, of presumably NK cell-resistant tumor cells to NK cell-mediated cytotoxicity. Together, these observations have direct implications for NK cell-based therapies and highlight the requirement to consider both MHC class I haplotype and tumor-specific glycosylation.

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Interactive Segmentation of MR Images from Brain Tumor Patients

Medical doctors often do not trust the result of fully automatic segmentations because they have no possibility to make corrections if necessary. On the other hand, manual corrections can introduce a user bias. In this work, we propose to integrate the possibility for quick manual corrections into a fully automatic segmentation method for brain tumor images. This allows for necessary corrections while maintaining a high objectiveness. The underlying idea is similar to the well-known Grab-Cut algorithm, but here we combine decision forest classification with conditional random field regularization for interactive segmentation of 3D medical images. The approach has been evaluated by two different users on the BraTS2012 dataset. Accuracy and robustness improved compared to a fully automatic method and our interactive approach was ranked among the top performing methods. Time for computation including manual interaction was less than 10 minutes per patient, which makes it attractive for clinical use.