MEXPRESS : visualizing expression, DNA methylation and clinical TCGA data

Background: In recent years, increasing amounts of genomic and clinical cancer data have become publically available through large-scale collaborative projects such as The Cancer Genome Atlas (TCGA). However, as long as these datasets are difficult to access and interpret, they are essentially useless for a major part of the research community and their scientific potential will not be fully realized. To address these issues we developed MEXPRESS, a straightforward and easy-to-use web tool for the integration and visualization of the expression, DNA methylation and clinical TCGA data on a single-gene level (http://mexpress.be). Results: In comparison to existing tools, MEXPRESS allows researchers to quickly visualize and interpret the different TCGA datasets and their relationships for a single gene, as demonstrated for GSTP1 in prostate adenocarcinoma. We also used MEXPRESS to reveal the differences in the DNA methylation status of the PAM50 marker gene MLPH between the breast cancer subtypes and how these differences were linked to the expression of MPLH. Conclusions: We have created a user-friendly tool for the visualization and interpretation of TCGA data, offering clinical researchers a simple way to evaluate the TCGA data for their genes or candidate biomarkers of interest

CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules

Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class

BMP2/BMPR1A is linked to tumour progression in dedifferentiated liposarcomas

Bone Morphogenic Protein 2 (BMP2) is a multipurpose cytokine, important in the development of bone and cartilage, and with a role in tumour initiation and progression. BMP2 signal transduction is dependent on two distinct classes of serine/threonine kinase known as the type I and type II receptors. Although the type I receptors (BMPR1A and BMPR1B) are largely thought to have overlapping functions, we find tissue and cellular compartment specific patterns of expression, suggesting potential for distinct BMP2 signalling outcomes dependent on tissue type. Herein, we utilise large publicly available datasets from The Cancer Genome Atlas (TCGA) and Protein Atlas to define a novel role for BMP2 in the progression of dedifferentiated liposarcomas. Using disease free survival as our primary endpoint, we find that BMP2 confers poor prognosis only within the context of high BMPR1A expression. Through further annotation of the TCGA sarcoma dataset, we localise this effect to dedifferentiated liposarcomas but find overall BMP2/BMP receptor expression is equal across subsets. Finally, through gene set enrichment analysis we link the BMP2/BMPR1A axis to increased transcriptional activity of the matrisome and general extracellular matrix remodelling. Our study highlights the importance of continued research into the tumorigenic properties of BMP2 and the potential disadvantages of recombinant human BMP2 (rhBMP2) use in orthopaedic surgery. For the first time, we identify high BMP2 expression within the context of high BMPR1A expression as a biomarker of disease relapse in dedifferentiated liposarcomas

Recurrent patterns of DNA copy number alterations in tumors reflect metabolic selection pressures.

Copy number alteration (CNA) profiling of human tumors has revealed recurrent patterns of DNA amplifications and deletions across diverse cancer types. These patterns are suggestive of conserved selection pressures during tumor evolution but cannot be fully explained by known oncogenes and tumor suppressor genes. Using a pan-cancer analysis of CNA data from patient tumors and experimental systems, here we show that principal component analysis-defined CNA signatures are predictive of glycolytic phenotypes, including 18F-fluorodeoxy-glucose (FDG) avidity of patient tumors, and increased proliferation. The primary CNA signature is enriched for p53 mutations and is associated with glycolysis through coordinate amplification of glycolytic genes and other cancer-linked metabolic enzymes. A pan-cancer and cross-species comparison of CNAs highlighted 26 consistently altered DNA regions, containing 11 enzymes in the glycolysis pathway in addition to known cancer-driving genes. Furthermore, exogenous expression of hexokinase and enolase enzymes in an experimental immortalization system altered the subsequent copy number status of the corresponding endogenous loci, supporting the hypothesis that these metabolic genes act as drivers within the conserved CNA amplification regions. Taken together, these results demonstrate that metabolic stress acts as a selective pressure underlying the recurrent CNAs observed in human tumors, and further cast genomic instability as an enabling event in tumorigenesis and metabolic evolution

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

QuaDMutEx: quadratic driver mutation explorer

Background Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations. Results We propose QuaDMutEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors. Conclusions Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx is a tool that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods. QuaDMutEx is available for download from https://github.com/bokhariy/QuaDMutEx under the GNU GPLv3 license