High-resolution analysis of copy number alterations and associated expression changes in ovarian tumors

<p>Abstract</p> <p>Background</p> <p>DNA copy number alterations are frequently observed in ovarian cancer, but it remains a challenge to identify the most relevant alterations and the specific causal genes in those regions.</p> <p>Methods</p> <p>We obtained high-resolution 500K SNP array data for 52 ovarian tumors and identified the most statistically significant minimal genomic regions with the most prevalent and highest-level copy number alterations (recurrent CNAs). Within a region of recurrent CNA, comparison of expression levels in tumors with a given CNA to tumors lacking that CNA and to whole normal ovary samples was used to select genes with CNA-specific expression patterns. A public expression array data set of laser capture micro-dissected (LCM) non-malignant fallopian tube epithelia and LCM ovarian serous adenocarcinoma was used to evaluate the effect of cell-type mixture biases.</p> <p>Results</p> <p>Fourteen recurrent deletions were detected on chromosomes 4, 6, 9, 12, 13, 15, 16, 17, 18, 22 and most prevalently on X and 8. Copy number and expression data suggest several apoptosis mediators as candidate drivers of the 8p deletions. Sixteen recurrent gains were identified on chromosomes 1, 2, 3, 5, 8, 10, 12, 15, 17, 19, and 20, with the most prevalent gains localized to 8q and 3q. Within the 8q amplicon, <it>PVT1</it>, but not <it>MYC</it>, was strongly over-expressed relative to tumors lacking this CNA and showed over-expression relative to normal ovary. Likewise, the cell polarity regulators <it>PRKCI </it>and <it>ECT2 </it>were identified as putative drivers of two distinct amplicons on 3q. Co-occurrence analyses suggested potential synergistic or antagonistic relationships between recurrent CNAs. Genes within regions of recurrent CNA showed an enrichment of Cancer Census genes, particularly when filtered for CNA-specific expression.</p> <p>Conclusion</p> <p>These analyses provide detailed views of ovarian cancer genomic changes and highlight the benefits of using multiple reference sample types for the evaluation of CNA-specific expression changes.</p

Masking of Figure-Ground Texture and Single Targets by Surround Inhibition: A Computational Spiking Model

A visual stimulus can be made invisible, i.e. masked, by the presentation of a second stimulus. In the sensory cortex, neural responses to a masked stimulus are suppressed, yet how this suppression comes about is still debated. Inhibitory models explain masking by asserting that the mask exerts an inhibitory influence on the responses of a neuron evoked by the target. However, other models argue that the masking interferes with recurrent or reentrant processing. Using computer modeling, we show that surround inhibition evoked by ON and OFF responses to the mask suppresses the responses to a briefly presented stimulus in forward and backward masking paradigms. Our model results resemble several previously described psychophysical and neurophysiological findings in perceptual masking experiments and are in line with earlier theoretical descriptions of masking. We suggest that precise spatiotemporal influence of surround inhibition is relevant for visual detection