Biology subjects

Fox, N. S.

Publications and source records attributed to Fox, N. S..

4 recordsLinked to original sources

bioRxiv2022

Germline determinants of the prostate tumor genome

A persons germline genome strongly influences their risk of developing cancer. Yet the molecular mechanisms linking the host genome to the specific somatic molecular phenotypes of individual cancers are largely unknown. We quantified the relationships between germline polymorphisms and somatic mutational features in prostate cancer. Across 1,991 prostate tumors, we identified 23 co-occurring germline and somatic events in close 2D or 3D spatial genomic proximity, affecting 10 cancer driver genes. These driver quantitative trait loci (dQTLs) overlap active regulatory regions, and shape the tumor epigenome, transcriptome and proteome. Some dQTLs are active in multiple cancer types, and information content analyses imply hundreds of undiscovered dQTLs. Specific dQTLs explain at least 16.7% ancestry-biases in rates of TMPRSS2-ERG gene fusions and 67.3% of ancestry-biases in rates of FOXA1 point mutations. These data reveal extensive influences of common germline variation on somatic mutational landscapes.

cancer biology↗

bioRxiv2021

iSubGen: Integrative Subtype Generation by Pairwise Similarity Assessment

There are myriad types of biomedical data- genetics, transcriptomics, clinical, imaging, wearable devices and many more. When a group of patients with the same underlying disease exhibit similarities across multiple types of data, this is called a subtype. Disease subtypes can reflect etiology and sometimes predict clinical behaviour. Existing subtyping approaches struggle to simultaneously handle multiple diverse data types, particularly when there is missing information, as is common in most real-world clinical datasets. To improve subtype discovery, we exploited changes in the correlation-structure between different data types to create iSubGen, an algorithm for integrative subtype generation. iSubGen can combine arbitrary data types for subtype discovery, such as merging molecular, mutational signature, pathway and micro-environmental data. iSubGen recapitulates known subtypes across multiple diseases, even in the face of substantial missing data. It identifies groups of patients with divergent clinical outcomes, and can combine arbitrary data types for subtype discovery, such as merging molecular, mutational signature, pathway and micro-environmental data. iSubGen can accommodate any feature that can be compared with a similarity-metric, and provides a versatile approach for creating subtypes. It is available at https://CRAN.R-project.org/package=iSubGen.

bioinformatics↗

bioRxiv2017

Prediction of early breast cancer patient survival using ensembles of hypoxia signatures

BackgroundBiomarkers are a key component of precision medicine. However, full clinical integration of biomarkers has been met with challenges, partly attributed to analytical difficulties. It has been shown that biomarker reproducibility is susceptible to data preprocessing approaches. Here, we systematically evaluated machine-learning ensembles of preprocessing methods as a general strategy to improve biomarker performance for prediction of survival from early breast cancer.\n\nResultsWe risk stratified breast cancer patients into either low-risk or high-risk groups based on four published hypoxia signatures (Buffa, Winter, Hu, and Sorensen), using 24 different preprocessing approaches for microarray normalization. The 24 binary risk profiles determined for each hypoxia signature were combined using a random forest to evaluate the efficacy of a preprocessing ensemble classifier. We demonstrate that the best way of merging preprocessing methods varies from signature to signature, and that there is likely no best preprocessing pipeline that is universal across datasets, highlighting the need to evaluate ensembles of preprocessing algorithms. Further, we developed novel signatures for each preprocessing method and the risk classifications from each were incorporated in a meta-random forest model. Interestingly, the classification of these biomarkers and its ensemble show striking consistency, demonstrating that similar intrinsic biological information are being faithfully represented. As such, these classification patterns further confirm that there is a subset of patients whose prognosis is consistently challenging to predict.\n\nConclusionsPerformance of different prognostic signatures varies with pre-processing method. A simple classifier by unanimous voting of classifications is a reliable way of improving on single preprocessing methods. Future signatures will likely require integration of intrinsic and extrinsic clinico-pathological variables to better predict disease-related outcomes.\n\nAbbreviations

bioinformatics↗

bioRxiv2017

BPG: Seamless, Automated and Interactive Visualization of Scientific Data

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

bioinformatics↗