Biology subjects

Neville, D.

Publications and source records attributed to Neville, D..

4 recordsLinked to original sources

bioRxiv2026

Menin regulates oncogenic cell identity transcriptional networks in multiple myeloma

Multiple myeloma (MM) is a heterogenous cancer that remains mostly incurable. A unifying feature of MM cells is a highly interconnected network of transcription factors and co-factors that coordinate the activity of super-enhancers to enforce myeloma cell identity. Targeting this network offers a promising avenue for therapeutic intervention across genetically diverse myeloma sub-types. Using integrated molecular and functional genomic approaches we identified Menin as a key driver of oncogenic gene expression in MM cells. Menin and its co-factor KMT2A bind at super-enhancers and maintain expression of essential myeloma genes, including IRF4. We demonstrate that Menin inhibitors, which have recently been approved for treatment of acute myeloid leukaemia, are highly active in myeloma cell lines and in vivo models. Combining Menin inhibitors with other super-enhancer targeting therapies such EP300/CREBBP inhibitors or immunomodulatory drugs (IMiDs) overcomes epigenetic plasticity in cells resistant to single agent treatment.

Cancer Biology↗

bioRxiv2024

Benchmarking Stroke Outcome Prediction through Comprehensive Data Analysis - NeuralCup 2023

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover optimal recipes for predicting each domain. The differences in these optimal recipes reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com

neuroscience↗

bioRxiv2024

Continuous lesion images drive more accurate predictions of outcomes after stroke than binary lesion images

Current medicine cannot confidently predict who will recover from post-stroke impairments. Researchers have sought to bridge this gap by treating the post-stroke prognostic problem as a machine learning problem. Consistent with the observation that these impairments are caused by the brain damage that stroke survivors suffer, information concerning where and how much lesion damage they have suffered conveys useful prognostic information for these models. Much recent research has considered how best to encode this lesion information, to maximise its prognostic value. Here, we consider an encoding that, while not novel, has never before been formally examined in this context: continuous lesion images, which encode continuous evidence for the presence of a lesion, both within and beyond what might otherwise be considered the boundary of a binary lesion image. Current state of the art models employ information derived from binary lesion images. Here, we show that those models are significantly improved (i.e., with smaller Mean Squared Error between predicted and empirical outcome scores) when using continuous lesion images to predict a wide range of cognitive and language outcomes from a very large sample of stroke patients. We use further model comparisons to locate the predictive advantage to the provision of continuous lesion evidence beyond the boundary of binary lesion images. The continuous lesion images thus provide a straightforward way to incorporate details of both lesioned and non-lesioned tissue when predicting outcomes after stroke.

neuroscience↗

bioRxiv2023

CRISPR-ChIP delineates a Menin-dependent oncogenic DOT1L complex in MLL-leukaemia

The regulation of all chromatin-templated processes involves the selective recruitment of chromatin factors to facilitate DNA repair, replication, and transcription. Chromatin immunoprecipitation (ChIP) is a critical experimental method used to provide spatiotemporal evidence for the coordination of these chromatin-based events including the dynamic regulation of chromatin modifications at cis-regulatory elements. However, obtaining a global appreciation of all the factors that influence a specific chromatin event has remained challenging. Here, as a proof of concept we demonstrate the utility of coupling unbiased functional genomics with ChIP to identify the factors associated with active transcription. Specifically, we use this method to identify the major chromatin factors associated with the catalysis of two evolutionarily conserved histone modifications; H3K4me3 present at the transcriptional start site and H3K79me2 present through the gene body of actively transcribed genes. With CRISPR-ChIP, we identify all the non-redundant COMPASS complex members required for H3K4me3 and demonstrate that RNA polymerase II is dispensable for the maintenance of H3K4me3. As H3K79me2 has a putative oncogenic function in leukaemia cells driven by MLL-translocations, using CRISPR-ChIP we reveal a functional partitioning of H3K79 methylation into two distinct regulatory units. An oncogenic DOT1L complex, where the malignant driver directs the catalytic activity of DOT1L at MLL-Fusion target genes and a separate endogenous DOT1L complex, where catalytic activity is directed by MLLT10. This functional demarcation provides an explanation for the observed synergy with Menin and DOT1L inhibitors and why Menin inhibition surprisingly controls methylation of H3K79 at a critical subset of genes that sustain MLL-fusion leukaemia. Overall, CRISPR-ChIP provides a powerful tool for the unbiased interrogation of the mechanisms underpinning chromatin regulation.

molecular biology↗