Unraveling the role of low-frequency mutated genes in breast cancer.

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.

Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions.

Next-generation sequencing (NGS) technologies have deeply changed our understanding of cellular processes by delivering an astonishing amount of data at affordable prices; nowadays, many biology laboratories have already accumulated a large number of sequenced samples. However, managing and analyzing these data poses new challenges, which may easily be underestimated by research groups devoid of IT and quantitative skills. In this perspective, we identify five issues that should be carefully addressed by research groups approaching NGS technologies. In particular, the five key issues to be considered concern: (1) adopting a laboratory management system (LIMS) and safeguard the resulting raw data structure in downstream analyses; (2) monitoring the flow of the data and standardizing input and output directories and file names, even when multiple analysis protocols are used on the same data; (3) ensuring complete traceability of the analysis performed; (4) enabling non-experienced users to run analyses through a graphical user interface (GUI) acting as a front-end for the pipelines; (5) relying on standard metadata to annotate the datasets, and when possible using controlled vocabularies, ideally derived from biomedical ontologies. Finally, we discuss the currently available tools in the light of these issues, and we introduce HTS-flow, a new workflow management system conceived to address the concerns we raised. HTS-flow is able to retrieve information from a LIMS database, manages data analyses through a simple GUI, outputs data in standard locations and allows the complete traceability of datasets, accompanying metadata and analysis scripts.

DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes.

A key challenge in the analysis of cancer genomes is the identification of driver genes from the vast number of mutations present in a cohort of patients. DOTS-Finder is a new tool that allows the detection of driver genes through the sequential application of functional and frequentist approaches, and is specifically tailored to the analysis of few tumor samples. We have identified driver genes in the genomic data of 34 tumor types derived from existing exploratory projects such as The Cancer Genome Atlas and from studies investigating the usefulness of genomic information in the clinical settings. DOTS-Finder is available at https://cgsb.genomics.iit.it/wiki/projects/DOTS-Finder/.