Abstract
Understanding cancer cell signal transduction is a promising lead for uncovering therapeutic targets and building treatment-specific markers for epithelial ovarian cancer. To brodaly assay the many known transmembrane receptor systems, previous studies have employed gene expression data measured on high-throughput microarrays. Starting with the knowledge of validated ligand-receptor pairs (LRPs), these studies postulate that correlation of the two genes implies functional autocrine signaling. It is our goal to consider the additional weight of evidence that prognosis (progression-free survival) can bring to prioritize ovarian cancer specific signaling mechanism. We survey three large studies of epithelial ovarian cancers, with gene expression measurements and clinical information, by modeling survival times both categorically (long/short survival) and continuously. We use differential correlation and proportional hazards regression to identify sets of LRPs that are both prognostic and correlated. Of 475 candidate LRPs, 77 show reproducible evidence of correlation; 55 show differential correlation. Survival models identify 16 LRPs with reproduced, significant interactions. Only two pairs show both interactions and correlation (PDGFAPDGFRA and COL1A1CD44) suggesting that the majority of prognostically useful LRPs act without positive feedback. We further assess the connectivity of receptors using a Gaussian graphical model finding one large graph and a number of smaller disconnected networks. These LRPs can be organized into mutually exclusive signaling clusters suggesting different mechanisms apply to different patients. We conclude that a mix of autocrine and endocrine LRPs influence prognosis in ovarian cancer, there exists a heterogenous mix of signaling themes across patients, and we point to a number of novel applications of existing targeted therapies which may benefit ovarian cancer.
Highlights
Signal transduction contains cluses to how abberation in cancer cells may lead to uncontrolled growth and division
We focus on the Cancer Genome Atlas (TCGA) study of ovarian cancer [7] as a discovery set paired with two large, independent studies for validation [8,9]
Global correlation and active autocrine signaling Following Graeber and Eisenberg’s hypothesis, we tested for significant correlation between 475 ligand-receptor pairs and found 96 significant pairs in the TCGA discovery set ( Bonferroni pv0:05)
Summary
Signal transduction contains cluses to how abberation in cancer cells may lead to uncontrolled growth and division. It has been hypothesized that evidence of functional signal transduction can be found by examining the mRNA expression-based correlation structure of known ligand-receptor pairs ( LRPs) [1]. If correlation is found between a pair, one infers that they form a positivefeedback loop, an autocrine signaling relationship. This study continued to develop the idea that differential signaling (a change in autocrine status) might be associated with prognosis. Statistical differential correlation or differential co-expression (DC) techniques have advanced significantly in recent years allowing for the consideration of multivariate associations beyond treating LRPs one at a time [3]. A Gaussian graphical model (GGM) studies the precision matrix (the inverse of the correlation) to infer signaling and statistical work has developed techniques for proper false discovery control [4]
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