Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer

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...

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Main Authors:	Eng, Kevin H., Ruggeri, Christina
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Public Library of Science 2014
Assuntos:	Research Article
Acesso em linha:	https://ncbi.nlm.nih.gov/pmc/articles/PMC4171104/ https://ncbi.nlm.nih.gov/pubmed/25244152 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0107193
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spelling	pubmed-41711042014-09-25 Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer Eng, Kevin H. Ruggeri, Christina PLoS One Research Article 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[Image: see text]PDGFRA and COL1A1[Image: 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. Public Library of Science 2014-09-22 /pmc/articles/PMC4171104/ /pubmed/25244152 http://dx.doi.org/10.1371/journal.pone.0107193 Text en © 2014 Eng, Ruggeri http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
institution	US NLM
collection	PubMed Central
language	Inglês
format	Artigo
topic	Research Article
spellingShingle	Research Article Eng, Kevin H. Ruggeri, Christina Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
description	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[Image: see text]PDGFRA and COL1A1[Image: 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.
author	Eng, Kevin H. Ruggeri, Christina
author_facet	Eng, Kevin H. Ruggeri, Christina
author_sort	Eng, Kevin H.
title	Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
title_short	Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
title_full	Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
title_fullStr	Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
title_full_unstemmed	Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer
title_sort	connecting prognostic ligand receptor signaling loops in advanced ovarian cancer
publisher	Public Library of Science
publishDate	2014
url	https://ncbi.nlm.nih.gov/pmc/articles/PMC4171104/ https://ncbi.nlm.nih.gov/pubmed/25244152 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0107193
_version_	1800865726011015168

Connecting Prognostic Ligand Receptor Signaling Loops in Advanced Ovarian Cancer

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