Biology subjects

Rushton, C. K.

Publications and source records attributed to Rushton, C. K..

3 recordsLinked to original sources

bioRxiv2025

LCR-modules: a collection of workflows for cancer genome analysis

The surge of genomic data from advanced sequencing technologies is outpacing current analytical pipelines. We introduce LCR-modules, an open-source suite of bioinformatics tools designed for flexible and automated cancer genome data analysis. LCR-modules enables reproducible analysis of diverse cancer genomics data at scale. The suite comprises 49 Snakemake-based workflows organized into three levels, facilitating tasks from low-level quality control to complex cohort-level analyses. LCR-modules supports various sequencing types and integrates pipelines such as mutation calling, expression quantification, and cohort-level aggregation, ensuring flexibility and reproducibility. LCR-modules represents a significant advancement in genomic data analysis, reducing barriers in reproducibility and scalability and has already been applied to a combination of exomes and genomes from over 10,800 samples.

bioinformatics↗

bioRxiv2021

Non-coding NFKBIZ 3' UTR mutations promote cell growth and resistance to targeted therapies in diffuse large B-cell lymphoma

Amplifications and non-coding 3' UTR mutations affecting NFKBIZ have been identified as recurrent genetic events in diffuse large B-cell lymphoma (DLBCL). We confirm the prevalence and pattern of NFKBIZ 3' UTR mutations in independent cohorts and determine they are enriched in the ABC subtype as well as the recently described novel BN2/C1/NOTCH2 classes of DLBCL. Presently, the effects of and mechanism by which non-coding mutations can act as cancer drivers has been relatively unexplored. Here, we provide a functional characterization of these non-coding NFKBIZ 3' UTR mutations. We demonstrate that the resulting elevated expression of I{kappa}B-{zeta} confers growth advantage in DLBCL cell lines and primary germinal center B-cells as well as nominate novel I{kappa}B-{zeta} target genes with potential therapeutic implications. The limited responses to targeted treatments in DLBCL, particularly those targeting the NF-{kappa}B axis, led us to investigate and confirm that NFKBIZ 3' UTR mutations affect response to therapeutics and suggest it may be a useful predictive biomarker. Statement of SignificanceThrough functional characterization we reveal that non-coding NFKBIZ 3' UTR mutations are a common driver in DLBCL, and mutation status may be a relevant biomarker to predict poor response to therapeutics targeting the NF-{kappa}B pathway.

cancer biology↗

bioRxiv2020

Robust gene expression-based classification of cancers without normalization

Binary classification using gene expression data is commonly used to stratify cancers into molecular subgroups that may have distinct prognoses and therapeutic options. A limitation of many such methods is the requirement for comparable training and testing data sets. Here, we describe and demonstrate a self-training implementation of probability ratio-based classification prediction score (PRPS-ST) that facilitates the porting of existing classification models to other gene expression data sets. We demonstrate its robustness through application to two binary classification problems in diffuse large B-cell lymphoma using a diverse variety of gene expression data types and normalization methods.

bioinformatics↗