Identifying molecular features that distinguish fluvastatin-sensitive breast tumor cells

Statins, routinely used to treat hypercholesterolemia, selectively induce apoptosis in some tumor cells by inhibiting the mevalonate pathway. Recent clinical studies suggest that a subset of breast tumors is particularly susceptible to lipophilic statins, such as fluvastatin. To quickly advance statins as effective anticancer agents for breast cancer treatment, it is critical to identify the molecular features defining this sensitive subset. We have therefore characterized fluvastatin sensitivity by MTT assay in a panel of 19 breast cell lines that reflect the molecular diversity of breast cancer, and have evaluated the association of sensitivity with several clinicopathological and molecular features. A wide range of fluvastatin sensitivity was observed across breast tumor cell lines, with fluvastatin triggering cell death in a subset of sensitive cell lines. Fluvastatin sensitivity was associated with an estrogen receptor alpha (ERa)-negative, basal-like tumor subtype, features that can be scored with routine and/or strong preclinical diagnostics. To ascertain additional candidate sensitivity-associated molecular features, we mined publicly available gene expression datasets, identifying genes encoding regulators of mevalonate production, nonsterol lipid homeostasis, and global cellular metabolism, including the oncogene MYC. Further exploration of this data allowed us to generate a 10-gene mRNA abundance signature predictive of fluvastatin sensitivity, which showed preliminary validation in an independent set of breast tumor cell lines. Here, we have therefore identified several candidate predictors of sensitivity to fluvastatin treatment in breast cancer, which warrant further preclinical and clinical evaluation.Fil: Goard, Carolyn A.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; CanadáFil: Chan Seng Yue, Michelle . University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Mullen, Peter J.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; CanadáFil: Quiroga, Ariel Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. University of Alberta; CanadáFil: Wasylishen, Amanda R.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; CanadáFil: Clendening, James W.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; CanadáFil: Sendorek, Dorota H. S.. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Haider, Syed. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Lehner, Richard. University of Alberta; CanadáFil: Boutros, Paul C.. University Of Toronto; Canadá. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Penn, Linda Z.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; Canad

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

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

BPG: Seamless, automated and interactive visualization of scientific data

Abstract Background We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment. Results This 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. Conclusion BPG 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.genera