Motivation: Breast cancer is the most commonly diagnosed malignancy in women and the second cause of cancer death in developed countries. While advancements in early detection and therapeutic options have led to a significant decrease in mortality, response to treatment is affected by the genetic heterogeneity of the disease. Recent genome-wide DNA mutation analyses revealed the existence of hundreds of low-frequency mutated genes, in addition to known cancer drivers: a finding that is prompting research into the impact of these genes on the pathogenesis of the disease. Results: Herein, we describe a strategy towards the characterization of the role of low-frequency mutated genes in breast cancer. Through the combined analyses of publicly available gene expression and mutational datasets, we identified several Cancer Gene Modules (CMs) that we re-organized in Gene Regulatory Networks (GRN) enriched in low-frequency mutated genes. Importantly, these low-frequency mutated genes were mutually exclusive with known cancer drivers. Finally, we provide evidence that gene expression analysis of these mutated GRNs can predict resistance/sensitivity to chemotherapeutic drugs for breast cancer treatment. Availability and implementation: Datasets are available at https://www.ncbi.nlm.nih.gov/geo/ and at https://www.ebi.ac.uk/ega/datasets/. Molecular signatures and GSEA software are available at http://www.gsea-msigdb.org/gsea/index.jsp. Source codes are available at https://github.com/EleonoraLusito/Reverse_Engineering_BC_GRNs. Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: Herein we introduce flowFit, a Bioconductor package designed to perform quantitative analysis of cell proliferation in tracking dye-based experiments. The software, distributed as an R Bioconductor library, is based on a mathematical model that takes into account the height of each peak, the size and position of the parental population (labeled but not proliferating) and the estimated distance between the brightness of a cell and the brightness of its daughter (in which the dye is assumed to undergo a 2-fold dilution). Although the algorithm does not make any inference on cell types, rates of cell divisions or rates of cell death, it deconvolutes the actual collected data into a set of peaks, whereby each peak corresponds to a subpopulation of cells that have divided N times. We validated flowFit by retrospective analysis of published proliferation-tracking experiments and demonstrated that the algorithm predicts the same percentage of cells/generation either in samples with discernible peaks (in which the peaks are visible in the collected raw data) or in samples with non-discernible peaks (in which the peaks are fused together). To the best of our knowledge, flowFit represents the first open-source algorithm in its category and might be applied to numerous areas of cell biology in which quantitative deconvolution of tracking dye-based experiments is desired, including stem cell research. AVAILABILITY AND IMPLEMENTATION: http://www.bioconductor.org/packages/devel/bioc/html/flowFit.html (Bioconductor software page). http://www.bioconductor.org/packages/2.13/bioc/vignettes/flowFit/inst/doc/HowTo-flowFit.pdf (package vignette). http://rpubs.com/tucano/flowFit (online tutorial).

There is a body of literature that describes the geometry and the physics of filopodia using either stochastic models or partial differential equations and elasticity and coarse-grained theory. Comparatively, there is a paucity of models focusing on the regulation of the network of proteins that control the formation of different actin structures. Using a combination of in-vivo and in-vitro experiments together with a system of ordinary differential equations, we focused on a small number of well-characterized, interacting molecules involved in actin-dependent filopodia formation: the actin remodeler Eps8, whose capping and bundling activities are a function of its ligands, Abi-1 and IRSp53, respectively; VASP and Capping Protein (CP), which exert antagonistic functions in controlling filament elongation. The model emphasizes the essential role of complexes that contain the membrane deforming protein IRSp53, in the process of filopodia initiation. This model accurately accounted for all observations, including a seemingly paradoxical result whereby genetic removal of Eps8 reduced filopodia in HeLa, but increased them in hippocampal neurons, and generated quantitative predictions, which were experimentally verified. The model further permitted us to explain how filopodia are generated in different cellular contexts, depending on the dynamic interaction established by Eps8, IRSp53 and VASP with actin filaments, thus revealing an unexpected plasticity of the signaling network that governs the multifunctional activities of its components in the formation of filopodia.