A new diagnostic algorithm for Burkitt and diffuse large B-cell lymphomas based on the expression of CSE1L and STAT3 and on MYC rearrangement predicts outcome

Background Aggressive mature B-cell non-Hodgkin's lymphomas (BCL) sharing features of Burkitt's lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL) (intermediate BL/DLBCL) but deviating with respect to one or more characteristics are increasingly recognized. The limited knowledge about these biologically heterogeneous lymphomas hampers their assignment to a known entity, raising incertitude about optimal treatment approaches. We therefore searched for discriminative, prognostic, and predictive factors for their better characterization. Patients and methods We analyzed 242 cytogenetically defined aggressive mature BCL for differential protein expression. Marker selection was based on recent gene-expression profile studies. Predictive models for diagnosis were established and validated by a different set of lymphomas. Results CSE1L- and inhibitor of DNA binding-3 (ID3)-overexpression was associated with the diagnosis of BL and signal transduction and transcription-3 (STAT3) with DLBCL (P<0.001 for all markers). All three markers were associated with patient outcome in DLBCL. A new algorithm discriminating BL from DLBCL emerged, including the expression of CSE1L, STAT3, and MYC translocation. This ‘new classifier' enabled the identification of patients with intermediate BL/DLBCL who benefited from intensive chemotherapy regimens. Conclusion The proposed algorithm, which is based on markers with reliable staining properties for routine diagnostics, represents a novel valid tool in separating BL from DLBCL. Most interestingly, it allows segregating intermediate BL/DLBCL into groups with different treatment requirement

Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project)

Rationale and Objectives: The objective of this study was to develop and validate a predictive magnetic resonance imaging (MRI) activity score for ileocolonic Crohn disease activity based on both subjective and semiautomatic MRI features. Materials and Methods: An MRI activity score (the “virtual gastrointestinal tract [VIGOR]” score) was developed from 27 validated magnetic resonance enterography datasets, including subjective radiologist observation of mural T2 signal and semiautomatic measurements of bowel wall thickness, excess volume, and dynamic contrast enhancement (initial slope of increase). A second subjective score was developed based on only radiologist observations. For validation, two observers applied both scores and three existing scores to a prospective dataset of 106 patients (59 women, median age 33) with known Crohn disease, using the endoscopic Crohn's Disease Endoscopic Index of Severity (CDEIS) as a reference standard. Results: The VIGOR score (17.1 × initial slope of increase + 0.2 × excess volume + 2.3 × mural T2) and other activity scores all had comparable correlation to the CDEIS scores (observer 1: r = 0.58 and 0.59, and observer 2: r = 0.34–0.40 and 0.43–0.51, respectively). The VIGOR score, however, improved interobserver agreement compared to the other activity scores (intraclass correlation coefficient = 0.81 vs 0.44–0.59). A diagnostic accuracy of 80%–81% was seen for the VIGOR score, similar to the other scores. Conclusions: The VIGOR score achieves comparable accuracy to conventional MRI activity scores, but with significantly improved reproducibility, favoring its use for disease monitoring and therapy evaluation

Heterologous expression of cytotoxic sesquiterpenoids from the medicinal mushroom Lignosus rhinocerotis in yeast

Background: Genome mining facilitated by heterologous systems is an emerging approach to access the chemical diversity encoded in basidiomycete genomes. In this study, three sesquiterpene synthase genes, GME3634, GME3638, and GME9210, which were highly expressed in the sclerotium of the medicinal mushroom Lignosus rhinocerotis, were cloned and heterologously expressed in a yeast system. Results: Metabolite profile analysis of the yeast culture extracts by GC-MS showed the production of several sesquiterpene alcohols (C 15 H 26 O), including cadinols and germacrene D-4-ol as major products. Other detected sesquiterpenes include selina-6-en-4-ol, ß-elemene, ß-cubebene, and cedrene. Two purified major compounds namely (+)-torreyol and a-cadinol synthesised by GME3638 and GME3634 respectively, are stereoisomers and their chemical structures were confirmed by 1 H and 13 C NMR. Phylogenetic analysis revealed that GME3638 and GME3634 are a pair of orthologues, and are grouped together with terpene synthases that synthesise cadinenes and related sesquiterpenes. (+)-Torreyol and a-cadinol were tested against a panel of human cancer cell lines and the latter was found to exhibit selective potent cytotoxicity in breast adenocarcinoma cells (MCF7) with IC 50 value of 3.5 ± 0.58 µg/ml while a-cadinol is less active (IC 50 = 18.0 ± 3.27 µg/ml). Conclusions: This demonstrates that yeast-based genome mining, guided by transcriptomics, is a promising approach for uncovering bioactive compounds from medicinal mushrooms

Epicoccolides: Antimicrobial and antifungal polyketides from an endophytic fungus Epicoccum sp associated with Theobroma cacao.

In the search for new compounds to protect plants from fungi, an endophytic fungus, Epicoccum sp. CAFTBO, obtained from Theobroma cacao was found to produce three polyoxygenated polyketides, namely epicolactone (1) and epicoccolides A (2) and B (3), together with seven known metabolites. The structures of these compounds were elucidated by a combination of detailed spectroscopic and spectrometric analyses as well as DFT calculations of the electronic circular dichroism spectra and the single-crystal X-ray diffraction analysis of 1. Compounds 1-3 showed potent antimicrobial activities and significant inhibitory effects on the mycelial growth of two peronosporomycete phytopathogens, Pythium ultimum and Aphanomyces cochlioides, and the basidiomycetous fungus Rhizoctonia solani

Active learning based segmentation of Crohn's disease using principles of visual saliency

We propose a active learning (AL) approach to segment Crohn's disease (CD) affected regions in abdominal magnetic resonance (MR) images. Our label query strategy is inspired from the principles of visual saliency which has similar considerations for choosing the most salient region. These similarities are encoded in a graph using classification maps and low level features. The most informative node is determined using random walks. Experimental results on real patient datasets show the superior performance of our approach and highlight the importance of different features to determine a region's importance

Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity

Recent large-scale genome analyses of human tissue samples have uncovered a high degree of genetic alterations and tumour heterogeneity in most tumour entities, independent of morphological phenotypes and histopathological characteristics. Assessment of genetic copy-number variation (CNV) and tumour heterogeneity by fluorescence in situ hybridization (ISH) provides additional tissue morphology at single-cell resolution, but it is labour intensive with limited throughput and high inter-observer variability. We present an integrative method combining bright-field dual-colour chromogenic and silver ISH assays with an image-based computational workflow (ISHProfiler), for accurate detection of molecular signals, high-throughput evaluation of CNV, expressive visualization of multi-level heterogeneity (cellular, inter- and intra-tumour heterogeneity), and objective quantification of heterogeneous genetic deletions (PTEN) and amplifications (19q12, HER2) in diverse human tumours (prostate, endometrial, ovarian and gastric), using various tissue sizes and different scanners, with unprecedented throughput and reproducibility