Biology subjects

Schuh, A.

Publications and source records attributed to Schuh, A..

23 records · Page 2Linked to original sources

bioRxiv2019

Differential genomic and transcriptomic events associated with high-grade transformation of Chronic Lymphocytic Leukemia

The transformation of chronic lymphocytic leukemia (CLL) to high-grade diffuse large B-cell lymphoma (DLBCL), also called Richters Syndrome (RS), is a rare cancer with dismal prognosis. Drug discovery for RS is hampered by the lack of suitable experimental models, and effective therapies remain elusive rendering RS an area of high unmet clinical need. We performed whole genome sequencing (WGS) to interrogate paired CLL and RS samples from 17 patients enrolled in a prospective multicenter Phase 2 clinical trial (CHOP-OR) and we found that subclones affected by mutations in MAPK and PI3K pathways show a high expansion probability during transformation. We also demonstrate for the first time that non-coding mutation clusters in a PAX5 enhancer, situated 330kb upstream from the transcription initiation site, correlate with transformation. Finally, we confirm our findings by employing targeted DNA sequencing (TGS) and RNA expression profiling on an extended cohort of 38 patients.\n\nStatement of significanceThrough integrated analysis of WGS, TGS and RNA expression data, we identified drivers of transformation not previously implicated in RS, which can be targeted therapeutically and tested in the clinic. Our results have informed the design of a new clinical platform study, which is now open to recruitment in the UK.

genomics↗

bioRxiv2019

A method for non-invasive prenatal diagnosis of monogenic autosomal recessive disorders

Non-invasive prenatal testing (NIPT) to date is used in the clinic primarily to detect foetal aneuploidy. Few studies so far have focused on the detection of monogenic autosomal recessive disorders where mother and foetus carry the same mutation. In particular, NIPT is currently not available for the detection of Sickle Cell Anaemia (SCA), the most common monogenic disorder world-wide and the most common indication for invasive prenatal testing in high-income countries. Here, we report the clinical validation of a novel diagnostic approach that combines ultra-sensitive amplicon-based sequencing of cell-free DNA (cfDNA) with internal controls and bias factor correction to calculate the probability for the presence of allelic imbalance from maternal plasma without prior knowledge of the paternal genotype. Identification of the foetal genotype was determined using a hierarchical probabilistic model based on the relative number of reads from the sequencing, along with the foetal fraction. NIPT was performed on a cohort of 57 patients, all of whom had previously undergone invasive prenatal testing so that the foetal genotype was known. Overall, NIPT demonstrated 100% sensitivity and negative predictive value for foetal fractions higher than 0.5%, and 100% specificity and positive predictive value for foetal fractions higher than or equal to 4%. Our methodology can be used as a safe, non-invasive screening tool in any clinical scenarios where early prenatal diagnosis of SCA or other recessive disorders is important.

genomics↗

bioRxiv2018

Unbiased construction of a temporally consistent morphological atlas of neonatal brain development

Premature birth increases the risk of developing neurocognitive and neurobe-havioural disorders. The mechanisms of altered brain development causing these disorders are yet unknown. Studying the morphology and function of the brain during maturation provides us not only with a better understanding of normal development, but may help us to identify causes of abnormal development and their consequences. A particular difficulty is to distinguish abnormal patterns of neurodevelopment from normal variation. The Developing Human Connectome Project (dHCP) seeks to create a detailed four-dimensional (4D) connectome of early life. This connectome may provide insights into normal as well as abnormal patterns of brain development. As part of this project, more than a thousand healthy fetal and neonatal brains will be scanned in vivo. This requires computational methods which scale well to larger data sets. We propose a novel groupwise method for the construction of a spatio-temporal model of mean morphology from cross-sectional brain scans at different gestational ages. This model scales linearly with the number of images and thus improves upon methods used to build existing public neonatal atlases, which derive correspondence between all pairs of images. By jointly estimating mean shape and longitudinal change, the atlas created with our method overcomes temporal inconsistencies, which are encountered when mean shape and intensity images are constructed separately for each time point. Using this approach, we have constructed a spatio-temporal atlas from 275 healthy neonates between 35 and 44 weeks post-menstrual age (PMA). The resulting atlas qualitatively preserves cortical details significantly better than publicly available atlases. This is moreover confirmed by a number of quantitative measures of the quality of the spatial normalisation and sharpness of the resulting template brain images.

neuroscience↗

bioRxiv2017

Multimodal Surface Matching with Higher-Order Smoothness Constraints

The accurate alignment of brains is fundamental to the statistical sensitivity and spatial localisation of group studies in brain imaging, and cortical surface-based alignment is generally accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of cortical areas relative to these folds, which makes aligning cortical areas based on folding alone a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that using areal features such as resting state-networks and myelin maps to drive cross-subject surface alignment improves group task fMRI statistics and map sharpness. However, the initial implementation of MSMs regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits unless the regularisation strength was increased, in which case this prevented the algorithm from fully maximizing surface alignment. Here, we propose a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multi-modal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints to enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modeling of cortical development for neonates (born between 32 and 45 weeks) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population-based analysis relative to other spherical methods.

neuroscience↗

bioRxiv2017

The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

neuroscience↗