Detecting Cancer Gene Networks Characterized by Recurrent Genomic Alterations in a Population

High resolution, system-wide characterizations have demonstrated the capacity to identify genomic regions that undergo genomic aberrations. Such research efforts often aim at associating these regions with disease etiology and outcome. Identifying the corresponding biologic processes that are responsible for disease and its outcome remains challenging. Using novel analytic methods that utilize the structure of biologic networks, we are able to identify the specific networks that are highly significantly, nonrandomly altered by regions of copy number amplification observed in a systems-wide analysis. We demonstrate this method in breast cancer, where the state of a subset of the pathways identified through these regions is shown to be highly associated with disease survival and recurrence

MicroRNA-Gene Association As a Prognostic Biomarker in Cancer Exposes Disease Mechanisms

<div><p>The transcriptional networks that regulate gene expression and modifications to this network are at the core of the cancer phenotype. MicroRNAs, a well-studied species of small non-coding RNA molecules, have been shown to have a central role in regulating gene expression as part of this transcriptional network. Further, microRNA deregulation is associated with cancer development and with tumor progression. Glioblastoma Multiform (GBM) is the most common, aggressive and malignant primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers. To study the transcriptional network and its modifications in GBM, we utilized gene expression, microRNA sequencing, whole genome sequencing and clinical data from hundreds of patients from different datasets. Using these data and a novel microRNA-gene association approach we introduce, we have identified unique microRNAs and their associated genes. This unique behavior is composed of the ability of the quantifiable association of the microRNA and the gene expression levels, which we show stratify patients into clinical subgroups of high statistical significance. Importantly, this stratification goes unobserved by other methods and is not affiliated by other subsets or phenotypes within the data. To investigate the robustness of the introduced approach, we demonstrate, in unrelated datasets, robustness of findings. Among the set of identified microRNA-gene associations, we closely study the example of MAF and hsa-miR-330-3p, and show how their co-behavior stratifies patients into prognosis clinical groups and how whole genome sequences tells us more about a specific genomic variation as a possible basis for patient variances. We argue that these identified associations may indicate previously unexplored specific disease control mechanisms and may be used as basis for further study and for possible therapeutic intervention.</p></div

373 GBM patients were stratified using the described method and analyzed for the association between miR-gene behavior and prognosis.

<p>(A) The set of patients is assigned to different cohorts according to contribution, or lack of, to the correlation metric between MAF and hsa-miR-330-3p (text and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003351#pcbi-1003351-g001" target="_blank">Figure 1</a>). The left hand side panel is composed of the pool of samples that merge to display a strong (negative) correlation, while the right hand side panel displays the pool of samples that merge to give no significant correlation. (B) Kaplan-Meier survival curves of the groups that emerged from the analysis. Group 1 (blue line) has lower survival rates (and a significant negative correlation between the miR and the gene), and Group2 (green line) has higher survival rates. The right-hand panel in (B) also shows how hsa-miR-330-3p itself, without its use in the metric, does not provide any significant stratification.</p

Workflow diagram.

<p>Using standard feature selection algorithms (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003351#s4" target="_blank">Materials and Methods</a>), patients are stratified into two groups. One group is populated by samples with a demonstrated correlation between the miR and the gene. The other group is populated with the samples that together show absent correlation. The algorithm populates these gene-miR correlation groups iteratively; a patient is added to a group according to its contribution to the correlation. Once groups are formed, we quantify their clinical relevance through a Kaplan-Meier survival analysis. Bonferroni correction for multiple hypotheses is performed to choose a significant set of miR-gene pairs. These pairs are then tested in whole genome sequencing data to determine gene-miR binding sites and to identify tumor related modifications in these genomic sites.</p

The list of stratifying pairs in the two unrelated breast cancer datasets.

<p>The table further provides information on predicted binding within the pairs.</p

Distribution of clinical features in the two groups stratified by the miR-gene metric according to the following features: Age, Gender, Tumor longest dimension, endothelial proliferation, Histological type and palisading necrosis.

<p>The figure demonstrates that the two groups display very similar clinical features.</p