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Title: Cancer network activity associated with therapeutic response and synergism
Author: Serra-Musach, Jordi
Mateo González, Francesca
Capdevila Busquets, Eva
Ruiz de Garibay, Gorka
Zhang, Xiaohu
Guha, Raj
Thomas, Craig J.
Grueso, Judit
Villanueva Garatachea, Alberto
Jaeger, Samira
Heyn, Holger
Vizoso, Miguel
Pérez-Montoyo, Hector
Cordero, Alex
González Suárez, Eva
Esteller, Manel
Moreno-Bueno, Gema
Tjärnberg, Andreas
Lázaro García, Conxi
Serra, Violeta
Arribas, Joaquín V. (Vicente)
Benson, Mikael
Gustafsson, Mika
Ferrer, Marc
Aloy, Patrick, 1972-
Pujana Genestar, M. Ángel
Keywords: Càncer
Cèl·lules canceroses
Regulació genètica
Càncer de mama
Medicaments antineoplàstics
Cancer cells
Genetic regulation
Breast cancer
Antineoplastic agents
Issue Date: 24-Aug-2016
Publisher: BioMed Central
Abstract: Background: Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. Methods: A measure of "cancer network activity" (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC50) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. Results: The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. Conclusions: Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations.
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It is part of: Genome Medicine, 2016, vol. 8, p. 88
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ISSN: 1756-994X
Appears in Collections:Articles publicats en revistes (Ciències Fisiològiques)
Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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