title: Connecting prognostic ligand receptor signaling loops in advanced ovarian cancer.
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 (PDGFA[Formula: see text]PDGFRA and COL1A1[Formula: see text]CD44) 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.
authors.affiliation: Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, New York, United States of America.
authors.forename: Kevin H
authors.initials: KH
authors.lastname: Eng
authors.affiliation: Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, New York, United States of America.
authors.forename: Christina
authors.initials: C
authors.lastname: Ruggeri
mesh_terms: D018572:Disease-Free Survival; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008962:Models, Theoretical; D010051:Ovarian Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000401:mortality*; D011379:Prognosis; D012720:Severity of Illness Index; D015398:Signal Transduction / Q000502:physiology*; D015996:Survival Rate