Gene expression signatures of morphologically normal breast tissue identify basal-like tumors

INTRODUCTION: The role of the cellular microenvironment in breast tumorigenesis has become an important research area. However, little is known about gene expression in histologically normal tissue adjacent to breast tumor, if this is influenced by the tumor, and how this compares with non-tumor-bearing breast tissue. METHODS: To address this, we have generated gene expression profiles of morphologically normal epithelial and stromal tissue, isolated using laser capture microdissection, from patients with breast cancer or undergoing breast reduction mammoplasty (n = 44). RESULTS: Based on this data, we determined that morphologically normal epithelium and stroma exhibited distinct expression profiles, but molecular signatures that distinguished breast reduction tissue from tumor-adjacent normal tissue were absent. Stroma isolated from morphologically normal ducts adjacent to tumor tissue contained two distinct expression profiles that correlated with stromal cellularity, and shared similarities with soft tissue tumors with favorable outcome. Adjacent normal epithelium and stroma from breast cancer patients showed no significant association between expression profiles and standard clinical characteristics, but did cluster ER/PR/HER2-negative breast cancers with basal-like subtype expression profiles with poor prognosis. CONCLUSION: Our data reveal that morphologically normal tissue adjacent to breast carcinomas has not undergone significant gene expression changes when compared to breast reduction tissue, and provide an important gene expression dataset for comparative studies of tumor expression profiles

Somatic cancer genetics in the UK: real-world data from phase I of the Cancer Research UK Stratified Medicine Programme

Introduction: Phase I of the Cancer Research UK Stratified Medicine Programme (SMP1) was designed to roll out molecular pathology testing nationwide at the point of cancer diagnosis, as well as facilitate an infrastructure where surplus cancer tissue could be used for research. It offered a non-trial setting to examine common UK cancer genetics in a real-world context. Methods: A total of 26 sites in England, Wales and Scotland, recruited samples from 7814 patients for genetic examination between 2011 and 2013. Tumour types involved were breast, colorectal, lung, prostate, ovarian cancer and malignant melanoma. Centralised molecular testing of surplus material from resections or biopsies of primary/metastatic tissue was performed, with samples examined for 3–5 genetic alterations deemed to be of key interest in site-specific cancers by the National Cancer Research Institute Clinical Study groups. Results: 10 754 patients (98% of those approached) consented to participate, from which 7814 tumour samples were genetically analysed. In total, 53% had at least one genetic aberration detected. From 1885 patients with lung cancer, KRAS mutation was noted to be highly prevalent in adenocarcinoma (37%). In breast cancer (1873 patients), there was a striking contrast in TP53 mutation incidence between patients with ductal cancer (27.3%) and lobular cancer (3.4%). Vast inter-tumour heterogeneity of colorectal cancer (1550 patients) was observed, including myriad double and triple combinations of genetic aberrations. Significant losses of important clinical information included smoking status in lung cancer and loss of distinction between low-grade and high-grade serous ovarian cancers. Conclusion: Nationwide molecular pathology testing in a non-trial setting is feasible. The experience with SMP1 has been used to inform ongoing CRUK flagship programmes such as the CRUK National Lung MATRIX trial and TRACERx

Epistemic geographies of climate change: science, space and politics

Anthropogenic climate change has been presented as the archetypal global problem, identified by the slow work of assembling a global knowledge infrastructure, and demanding a concertedly global political response. But this ‘global’ knowledge has distinctive geographies, shaped by histories of exploration and colonialism, by diverse epistemic and material cultures of knowledge-making, and by the often messy processes of linking scientific knowledge to decision-making within different polities. We suggest that understanding of the knowledge politics of climate change may benefit from engagement with literature on the geographies of science. We review work from across the social sciences which resonates with geographers’ interests in the spatialities of scientific knowledge, to build a picture of what we call the epistemic geographies of climate change. Moving from the field site and the computer model to the conference room and international political negotiations, we examine the spatialities of the interactional co-production of knowledge and social order. In so doing, we aim to proffer a new approach to the intersections of space, knowledge and power which can enrich geography’s engagements with the politics of a changing climate

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

An unbiased minimum distance estimator of the proportion parameter in a mixture of two normal distributions

An estimator that minimizes an L2 distance used in studies of estimation of the location parameter is shown here to give an explicit formulation for the estimator of proportion in a mixture of two normal distributions when other parameters are known. This can prove to be an advantage over other minimum distance methods and the maximum likelihood estimator. Monte Carlo simulation demonstrates this and highlights good small sample behaviour of the estimator. It is shown that the estimator is also qualitatively robust both empirically and asymptotically, the latter being evidenced by the existence of a Fréchet derivative.mixtures of two normal distributions unbiased estimator Monte Carlo simulation minimum distance estimation Fréchet derivative

On the convergence of Newton's method when estimating higher dimensional parameters

In this paper, we consider the estimation of a parameter of interest where the estimator is one of the possibly several solutions of a set of nonlinear empirical equations. Since Newton's method is often used in such a setting to obtain a solution, it is important to know whether the so obtained iteration converges to the locally unique consistent root to the aforementioned parameter of interest. Under some conditions, we show that this is eventually the case when starting the iteration from within a ball about the true parameter whose size does not depend on n. Any preliminary almost surely consistent estimate will eventually lie in such a ball and therefore provides a suitable starting point for large enough n. As examples, we will apply our results in the context of M-estimates, kernel density estimates, as well as minimum distance estimates.Newton's algorithm M-estimates Kernel density estimates Minimum distance estimates