Biology subjects

Grzadkowski, M. R.

Publications and source records attributed to Grzadkowski, M. R..

4 recordsLinked to original sources

bioRxiv2020

Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor cohorts within which they are frequently mutated. We sought to identify the downstream expression effects of these perturbations and to find whether or not this heterogeneity at the genomic level is reflected in a corresponding heterogeneity at the transcriptomic level. Applying a novel hierarchical framework for organizing the mutations present in a cohort along with machine learning pipelines trained on sample expression profiles we systematically interrogated the signatures associated with combinations of perturbations recurrent in cancer. This allowed us to catalogue the mutations with discernible downstream expression effects across a number of tumor cohorts as well as to uncover and characterize a multitude of cases where subsets of a genes mutations are clearly divergent in their function from the remaining mutations of the gene.

bioinformatics↗

bioRxiv2019

Myeloid Lineage Enhancers Drive Oncogene Synergy in CEBPA/CSF3R Mutant Acute Myeloid Leukemia

Acute Myeloid Leukemia (AML) develops due to the acquisition of mutations from multiple functional classes. Here, we demonstrate that activating mutations in the granulocyte colony stimulating factor receptor (CSF3R), cooperate with loss of function mutations in the transcription factor CEBPA to promote acute leukemia development. This finding of mutation-synergy is broadly applicable other mutations that activate the JAK/STAT pathway or disrupt CEBPA function (i.e. activating mutations in JAK3 and Core Binding Factor translocations). The interaction between these distinct classes of mutations occurs at the level of myeloid lineage enhancers where mutant CEBPA prevents activation of subset of differentiation associated enhancers. To confirm this enhancer-dependent mechanism, we demonstrate that CEBPA mutations must occur as the initial event in AML initiation, confirming predictions from clinical sequencing data. This improved mechanistic understanding will facilitate therapeutic development targeting the intersection of oncogene cooperativity.

cancer biology↗

bioRxiv2018

Subnetwork-based prognostic biomarkers exhibit performance and robustness superior to gene-based biomarkers in breast cancer

BackgroundEffective classification of cancer patients into groups with differential survival remains an important and unsolved challenge. Biomarkers have been developed based on mRNA abundance data, but their replicability and clinical utility is modest. Integrating functional information, such as pathway data, has been suggested to improve biomarker performance. To date, however, the advantages of subnetwork-based biomarkers have not been quantified.\n\nResultsWe deeply sampled the population of prognostic gene-based and subnetwork-based biomarkers in a breast cancer meta-dataset of 4,960 patients. Analysing the performance and robustness of 22,000,000 gene biomarkers and 6,250,000 subnetwork biomarkers across twenty different training:testing cohort partitions of the meta-dataset revealed that subnetwork biomarkers exhibit superior overall performance and higher concordance across partitions. We find evidence of an upper bound for optimal biomarker size of [~]200 genes or [~]100 subnetworks. Additionally, with both biomarker feature types, larger biomarkers tend to show less consistency in performance across partitions, suggestive of over-fitting. Finally, an evaluation of varying training cohort sizes quantifies the effects of training cohort size.\n\nConclusionsMany groups are developing techniques for exploiting network-based representations of biological pathways to characterize cancer and other diseases. By considering the distribution of gene- and subnetwork-based biomarkers, we show that pathway data improves performance and replicability, and that smaller biomarkers are more robust across patient cohorts. These insights may facilitate development of clinically useful biomarkers.

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↗