The diagnosis and management of pre-invasive breast disease: Pathological diagnosis – problems with existing classifications

In this review, we comment on the reasons for disagreement in the concepts, diagnosis and classifications of pre-invasive intraductal proliferations. In view of these disagreements, our proposal is to distinguish epithelial hyperplasia, lobular carcinoma in situ and ductal carcinoma in situ, and to abandon the use of poorly reproducible categories, such as atypical ductal hyperplasia or ductal intraepithelial neoplasia, followed by a number to indicate the degree of proliferation and atypia, as these are not practical for clinical decision making, nor for studies aimed at improving the understanding of breast cancer development. If there is doubt about the classification of an intraductal proliferation, a differential diagnosis and the reason for and degree of uncertainty should be given, rather than categorizing a proliferation as atypical

Are triple-negative tumours and basal-like breast cancer synonymous? Authors' response

We read with interest the issues raised by Rakha and colleagues [1] in their response to our recent research article [2], and we are pleased to address them. An important conclusion from our research article is that triple-negative breast cancer can be equated to basal-like breast cancer. In their letter, Rakha and colleagues [1] state that equating triple-negative phenotype (TNP) tumours with basal-like breast cancer is misleading and is not supported by the data we have presented. It is important to realize that, as we have also pointed out in our article [2], the basal-like breast cancer subtype was initially defined based on the gene expression pattern of the so-called ‘intrinsic gene list ’ in only six breast tumours [3]. Since this initial report, the intrinsic gene list that is used to identify basallike breast tumours has been updated multiple times [3-5]

Geostatistical inversion of electromagnetic induction data for landfill modelling

The characterization and monitoring of landfills has become a major concern, not only for assessing the associated environmental impact (e.g., groundwater contamination) but also for evaluating the potential for recovery of secondary resources, in particular for the production of raw materials and energy. For both objectives, it is crucial to have knowledge of the waste composition and the current landfill conditions (e.g. water saturation level). Near-surface geophysical surveys have been proven effective for the non-invasive investigation of landfills, in which different methods have been used depending on the specific survey targets.  Because of its sensitivity to two subsurface physical properties, electrical conductivity (EC) and magnetic susceptibility (MS), frequency-domain electromagnetic (FDEM) induction has been successfully applied to the qualitative characterization of urban and industrial landfills, including mine tailings. Yet, due to the generally complex composition and strongly heterogeneous spatial distribution of waste deposits, reconstructing a reliable landfill model from surface geophysical measurements remains challenging. Geostatistical inversion emerges as powerful tool to improve the landfill modelling from geophysical data, allowing for a more detailed description of the spatial distribution of the properties of interest and the associated uncertainty. Additionally, it provides a flexible framework for integrating data from geophysical surveys and conventional sampling from drilling or trenching.</p><p>In this work, we present a new geostatistical inversion technique able for the simultaneous inversion of FDEM data for EC and MS, which optimize the landfill modelling procedure and is sensitive towards change on the physical properties of interest. This method is based on an iterative procedure where ensembles of subsurface models of EC and MS are generated with stochastic sequential simulation and co-simulation. These simulated models are conditioned locally by existing borehole data for these properties and by a spatial continuity pattern imposed by a variogram model. Synthetic instrument response data, including both the in-phase and quadrature-phase components of the FDEM response, are generated from each model using a forward model connecting the data domain (FDEM data) with the model domain (subsurface physical properties). The misfit between the observed and forward-modelled FDEM data, weighted according to the depth sensitivity of the FDEM response toward changes in EC and MS, is used to drive the generation of a new set of models in the next iteration. We illustrate the inversion procedure with synthetic landfill example data sets which were created based on real data collected at a mine tailing in Portugal and a municipal solid waste landfill in Belgium

Bayesian inversion of loop-loop electromagnetic data using an Ensemble Kalman method

C

P53 Expression in Avian Ovarian Follicles

In the present study, we localized p53 protein in the ovary of the adult Japanese quail using immunohistochemical techniques. The best results were obtained with DO-1 monoclonal antibodies and with a heat-induced epitope retrieval method. Immunostaining was detected in cytoplasm and/or nuclei of granulosa and surface epithelial cells. In atretic follicles, p53 protein was found in a few follicular cells. Immunoreactivity was also detected in leukocytes and in the Balbiani complex of prelampbrush oocytes. It is suggested that p53 protein expression is elevated in proliferating granulosa and surface epithelial cells, and that p53 protein may be involved in granulosa cell differentiation

A novel gene expression signature for bone metastasis in breast carcinomas

Metastatic cancer remains the leading cause of death for patients with breast cancer. To understand the mechanisms underlying the development of distant metastases to specific sites is therefore important and of potential clinical value. From 157 primary breast tumours of the patients with known metastatic disease, gene expression profiling data were generated and correlated to metastatic behaviour including site-specific metastasis, metastasis pattern and survival outcomes. We analysed gene expression signatures specifically associated with the development of bone metastases. As a validation cohort, we used a published dataset of 376 breast carcinomas for which gene expression data and site-specific metastasis information were available. 80.5 % of luminal-type tumours developed bone metastasis as opposed to 41.7 % of basal and 55.6 % of HER2-like tumours. A novel 15-gene signature identified 82.4 % of the tumours with bone metastasis, 85.2 % of the tumours which had bone metastasis as first site of metastasis and 100 % of the ones with bone metastasis only (p 9.99e-09), in the training set. In the independent dataset, 81.2 % of the positive tested tumours had known metastatic disease to the bone (p 4.28e-10). This 15-gene signature showed much better correlation with the development of bone metastases than previously identified signatures and was predictive in both ER-positive as well as in ER-negative tumours. Multivariate analyses revealed that together with the molecular subtype, our 15-gene expression signature was significantly correlated to bone metastasis status (p <0.001, 95 % CI 3.86-48.02 in the training set; p 0.001, 95 % CI 1.54-5.00 in the independent set). The 15 genes, APOPEC3B, ATL2, BBS1, C6orf61, C6orf167, MMS22L, KCNS1, MFAP3L, NIP7, NUP155, PALM2, PH-4, PGD5, SFT2D2 and STEAP3, encoded mainly membrane-bound molecules with molecular function of protein binding. The expression levels of the up-regulated genes (NAT1, BBS1 and PH-4) were also found to be correlated to epithelial to mesenchymal transition status of the tumour. We have identified a novel 15-gene expression signature associated with the development of bone metastases in breast cancer patients. This bone metastasis signature is the first to be identified using a supervised classification approach in a large series of patients and will help forward research in this area towards clinical application

Onderbouwen van een methodiek voor de systematische monitoring van koolstofvoorraden in landbouwbodems

Gedurende het laatste decennium kende het onderzoek naar organische koolstofvoorraden in de bodem (BOC-voorraden) een sterke opgang in Europa en ook in Vlaanderen. Deze hernieuwde interesse in organische stof kwam voornamelijk voort uit de ratificatie van het Kyoto-protocol dat een mogelijkheid voorzag om CO2 ‘sinks’ in te brengen in nationale broeikasgasemissiebalansen. Eerder onderzoek in België toonde een sterk verlies aan BOC aan gedurende de jaren ‘90. Bovendien bleek het potentieel om koolstof op te slaan in de bodem beperkt te zijn voor de intensieve Vlaamse landbouw. Dit maakt onze landbouwbodems kwetsbaar voor verder BOC-verlies en de daaraan verbonden bodemdegradatie. Bijgevolg dienen BOC-voorraden verder opgevolgd te worden om de gevolgen van het huidige en toekomstige landbouwbeleid op de bodemkwaliteit te evalueren. Hoewel België op wereldvlak over de grootste dichtheid aan BOC-data beschikt, is een verdere opvolging van BOC-voorraden momenteel niet georganiseerd in Vlaanderen. Dit onderzoeksproject behelst in hoofdzaak het opstellen van een methodiek voor de opvolging van BOC-voorraden in Vlaanderen. De studie werd onderverdeeld in vier werkpakketen: - WP 1: bespreking van de reeds bestaande BOC-monitoringsystemen in Europa - WP 2: bespreking van de methodologische aspecten bij de bepaling van BOC-voorraden - WP 3: inschatting van de ruimtelijke en temporele variabiliteit van BOC-voorraden in Vlaamse landbouwbodems - WP 4: opstellen van een bemonsteringsstrategie voor de Vlaamse landbouwbodem