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Background: Tyrosine kinase inhibitors are the first line standard of care for treatment of metastatic renal cell carcinoma (RCC). Accurate response assessment in the setting of antiangiogenic therapies remains suboptimal as standard size-related response criteria do not necessarily accurately reflect clinical benefit, as they may be less pronounced or occur later in therapy than devascularisation. The challenge for imaging is providing timely assessment of disease status allowing therapies to be tailored to ensure ongoing clinical benefit. We propose that combined assessment of morphological, physiological and metabolic imaging parameters using 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) will better reflect disease behaviour, improving assessment of response/non-response/relapse.Methods/design: The REMAP study is a single-centre prospective observational study. Eligible patients with metastatic renal cell carcinoma, planned for systemic therapy, with at least 2 lesions will undergo an integrated 18F-FDG PET and MRI whole body imaging with diffusion weighted and contrast-enhanced multiphasic as well as standard anatomical MRI sequences at baseline, 12 weeks and 24 weeks of systemic therapy allowing 18F-FDG standardised uptake value (SUV), apparent diffusion co-efficient (ADC) and normalised signal intensity (SI) parameters to be obtained. Standard of care contrast-enhanced computed tomography CT scans will be performed at equivalent time-points. CT response categorisation will be performed using RECIST 1.1 and alternative (modified)Choi and MASS criteria. The reference standard for disease status will be by consensus panel taking into account clinical, biochemical and conventional imaging parameters. Intra- and inter-tumoural heterogeneity in vascular, diffusion and metabolic response/non-response will be assessed by image texture analysis. Imaging will also inform the development of computational methods for automated disease status categorisation.Discussion: The REMAP study will demonstrate the ability of integrated 18F-FDG PET-MRI to provide a more personalised approach to therapy. We suggest that 18F-FDG PET/MRI will provide superior sensitivity and specificity in early response/non-response categorisation when compared to standard CT (using RECIST 1.1 and alternative (modified)Choi or MASS criteria) thus facilitating more timely and better informed treatment decisions.Trial Registration: The trial is approved by the Southeast London Research Ethics Committee reference 16/LO/1499 and registered on the NIHR clinical research network portfolio ISRCTN12114913 . [ABSTRACT FROM AUTHOR]

Background: In cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences. Results: We propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation. Conclusions: We show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest. [ABSTRACT FROM AUTHOR]

Motivation: Multivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that are predictive of the trait. However, in studies involving hundreds of thousands of SNPs and high-dimensional traits, a very large ensemble of trees must be inferred from the data in order to obtain reliable rankings, which makes the application of these algorithms computationally prohibitive. Results: We have developed a parallel version of the RF algorithm for regression and genetic similarity learning tasks in large-scale population genetic association studies involving multivariate traits, called PaRFR (Parallel Random Forest Regression). Our implementation takes advantage of the MapReduce programming model and is deployed on Hadoop, an open-source software framework that supports data-intensive distributed applications. Notable speed-ups are obtained by introducing a distance-based criterion for node splitting in the tree estimation process. PaRFR has been applied to a genome-wide association study on Alzheimer's disease (AD) in which the quantitative trait consists of a high-dimensional neuroimaging phenotype describing longitudinal changes in the human brain structure. PaRFR provides a ranking of SNPs associated to this trait, and produces pair-wise measures of genetic proximity that can be directly compared to pair-wise measures of phenotypic proximity. Several known AD-related variants have been identified, including APOE4 and TOMM40. We also present experimental evidence supporting the hypothesis of a linear relationship between the number of top-ranked mutated states, or frequent mutation patterns, and an indicator of disease severity. [ABSTRACT FROM AUTHOR]

Background: X chromosome inactivation, the mechanism used by mammals to equalise dosage of X-linked genes in XX females relative to XY males, is triggered by chromosome-wide localisation of a cis-acting non-coding RNA, Xist. The mechanism of Xist RNA spreading and Xist-dependent silencing is poorly understood. A large body of evidence indicates that silencing is more efficient on the X chromosome than on autosomes, leading to the idea that the X chromosome has acquired sequences that facilitate propagation of silencing. LINE-1 (L1) repeats are relatively enriched on the X chromosome and have been proposed as candidates for these sequences. To determine the requirements for efficient silencing we have analysed the relationship of chromosome features, including L1 repeats, and the extent of silencing in cell lines carrying inducible Xist transgenes located on one of three different autosomes. Results: Our results show that the organisation of the chromosome into large gene-rich and L1-rich domains is a key determinant of silencing efficiency. Specifically genes located in large gene-rich domains with low L1 density are relatively resistant to Xist-mediated silencing whereas genes located in gene-poor domains with high L1 density are silenced more efficiently. These effects are observed shortly after induction of Xist RNA expression, suggesting that chromosomal domain organisation influences establishment rather than long-term maintenance of silencing. The X chromosome and some autosomes have only small gene-rich L1-depleted domains and we suggest that this could confer the capacity for relatively efficient chromosome-wide silencing. Conclusions: This study provides insight into the requirements for efficient Xist mediated silencing and specifically identifies organisation of the chromosome into gene-rich L1-depleted and gene-poor L1-dense domains as a major influence on the ability of Xist-mediated silencing to be propagated in a continuous manner in cis. [ABSTRACT FROM AUTHOR]

Background Cardiac phenotypes, such as left ventricular (LV) mass, demonstrate high heritability although most genes associated with these complex traits remain unidentified. Genome-wide association studies (GWAS) have relied on conventional 2D cardiovascular magnetic resonance (CMR) as the gold-standard for phenotyping. However this technique is insensitive to the regional variations in wall thickness which are often associated with left ventricular hypertrophy and require large cohorts to reach significance. Here we test whether automated cardiac phenotyping using high spatial resolution CMR atlases can achieve improved precision for mapping wall thickness in healthy populations and whether smaller sample sizes are required compared to conventional methods. Methods LV short-axis cine images were acquired in 138 healthy volunteers using standard 2D imaging and 3D high spatial resolution CMR. A multi-atlas technique was used to segment and co-register each image. The agreement between methods for end-diastolic volume and mass was made using Bland-Altman analysis in 20 subjects. The 3D and 2D segmentations of the LV were compared to manual labeling by the proportion of concordant voxels (Dice coefficient) and the distances separating corresponding points. Parametric and nonparametric data were analysed with paired t-tests and Wilcoxon signed-rank test respectively. Voxelwise power calculations used the interstudy variances of wall thickness. Results The 3D volumetric measurements showed no bias compared to 2D imaging. The segmented 3D images were more accurate than 2D images for defining the epicardium (Dice: 0.95vs0.93, P < 0.001; mean error 1.3mm vs 2.2mm, P < 0.001) and endocardium (Dice 0.95 vs 0.93, P < 0.001; mean error 1.1mm vs 2.0mm, P < 0.001).The 3D technique resulted in significant differences in wall thickness assessment at the base, septum and apex of the LV compared to 2D (P < 0.001). Fewer subjects were required for 3D imaging to detect a 1mm difference in wall thickness (72 vs 56, P < 0.001). Conclusions High spatial resolution CMR with automated phenotyping provides greater power for mapping wall thickness than conventional 2D imaging and enables a reduction in the sample size required for studies of environmental and genetic determinants of LV wall thickness. [ABSTRACT FROM AUTHOR]