Data mining of gene arrays for biomarkers of survival in ovarian cancer

The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two care fully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10−11, the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient’s response to treatment or be used as a novel target for therapy

Basal-like breast cancers: from pathology to biology and back again

Human breast cancers referred to as "basal-like" are of interest because they lack effective therapies and their biology is poorly understood. The term basal-like derives from studies demonstrating tumor gene expression profiles that include some transcripts characteristic of the basal cells of the normal adult human mammary gland and others associated with a subset of normal luminal cells. Elucidating the mechanisms responsible for the profiles of basal-like tumors is an active area of investigation. More refined molecular analysis of patients' samples and genetic strategies to produce breast cancers de novo from defined populations of normal mouse mammary cells have served as complementary approaches to identify relevant pathway alterations. However, both also have limitations. Here, we review some of the underlying reasons, including the unifying concept that some normal luminal cells have both luminal and basal features, as well as some emerging new avenues of investigation

SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis

Because of its clonal evolution a tumor rarely contains multiple genomic alterations in the same pathway as disrupting the pathway by one gene often is sufficient to confer the complete fitness advantage. As a result, many cancer driver genes display mutual exclusivity across tumors. However, searching for mutually exclusive gene sets requires analyzing all possible combinations of genes, leading to a problem which is typically too computationally complex to be solved without a stringent a priori filtering, restricting the mutations included in the analysis. To overcome this problem, we present SSA-ME, a network-based method to detect cancer driver genes based on independently scoring small subnetworks for mutual exclusivity using a reinforced learning approach. Because of the algorithmic efficiency, no stringent upfront filtering is required. Analysis of TCGA cancer datasets illustrates the added value of SSA-ME: well-known recurrently mutated but also rarely mutated drivers are prioritized. We show that using mutual exclusivity to detect cancer driver genes is complementary to state-of-the art approaches. This framework, in which a large number of small subnetworks are being analyzed in order to solve a computationally complex problem (SSA), can be generically applied to any problem in which local neighborhoods in a network hold useful information