Tumour genomic and microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study

Clinical prognostic groupings for localised prostate cancers are imprecise, with 30–50% of patients recurring after image-guided radiotherapy or radical prostatectomy. We aimed to test combined genomic and microenvironmental indices in prostate cancer to improve risk stratification and complement clinical prognostic factors

Inferring the properties of transcription factor regulation

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages 93-95).The regulatory targets of transcription factors are costly to directly detect using existing technologies. Many computational models have thus been developed to infer the genes targeted by TFs using gene expression profiles, position weight matrices modeling TF protein binding, histone modifications, and other secondary datasets. We develop a framework for scoring the potential targets of various TFs using models that take the profile of motif hits on the proximity of transcription start sites as input, and describe methods to validate this framework using expression datasets. These models are then extended to include cis-regulatory regions inferred from epigenetic data.by Michal R. Grzadkowski.S.M

A comparative study of survival models for breast cancer prognostication revisited: the benefits of multi-gene models

Abstract Background The development of clinical -omic biomarkers for predicting patient prognosis has mostly focused on multi-gene models. However, several studies have described significant weaknesses of multi-gene biomarkers. Indeed, some high-profile reports have even indicated that multi-gene biomarkers fail to consistently outperform simple single-gene ones. Given the continual improvements in -omics technologies and the availability of larger, better-powered datasets, we revisited this “single-gene hypothesis” using new techniques and datasets. Results By deeply sampling the population of available gene sets, we compare the intrinsic properties of single-gene biomarkers to multi-gene biomarkers in twelve different partitions of a large breast cancer meta-dataset. We show that simple multi-gene models consistently outperformed single-gene biomarkers in all twelve partitions. We found 270 multi-gene biomarkers (one per ~11,111 sampled) that always made better predictions than the best single-gene model. Conclusions The single-gene hypothesis for breast cancer does not appear to retain its validity in the face of improved statistical models, lower-noise genomic technology and better-powered patient cohorts. These results highlight that it is critical to revisit older hypotheses in the light of newer techniques and datasets

BPG: Seamless, automated and interactive visualization of scientific data.

BackgroundWe introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment.ResultsThis open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines.ConclusionBPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general