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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Autores principales:	Kevin H Eng, Christina Ruggeri
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e9780d4f7f184635b96880a58bb966c5
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spelling	oai:doaj.org-article:e9780d4f7f184635b96880a58bb966c52021-11-25T05:59:50ZConnecting prognostic ligand receptor signaling loops in advanced ovarian cancer.1932-620310.1371/journal.pone.0107193https://doaj.org/article/e9780d4f7f184635b96880a58bb966c52014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0107193https://doaj.org/toc/1932-6203Understanding 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.Kevin H EngChristina RuggeriPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e107193 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kevin H Eng Christina Ruggeri 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[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.
format	article
author	Kevin H Eng Christina Ruggeri
author_facet	Kevin H Eng Christina Ruggeri
author_sort	Kevin H Eng
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 (PLoS)
publishDate	2014
url	https://doaj.org/article/e9780d4f7f184635b96880a58bb966c5
work_keys_str_mv	AT kevinheng connectingprognosticligandreceptorsignalingloopsinadvancedovariancancer AT christinaruggeri connectingprognosticligandreceptorsignalingloopsinadvancedovariancancer
_version_	1718414313764421632

Connecting prognostic ligand receptor signaling loops in advanced ovarian cancer.

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