Web of Science: 3 citas, Scopus: 4 citas, Google Scholar: citas,
Cancer network activity associated with therapeutic response and synergism
Serra-Musach, Jordi (Institut Català d'Oncologia (ICO))
Mateo, Francesca (Institut Català d'Oncologia (ICO))
Capdevila-Busquets, Eva (Institut de Recerca Biomèdica (IRB Barcelona))
de Garibay, Gorka Ruiz (Institut Català d'Oncologia (ICO))
Zhang, Xiaohu (National Institutes of Health. Medical Center Dr. Rockville. National Center for Advancing Translational Sciences (NCATS))
Guha, Raj (National Institutes of Health. Medical Center Dr. Rockville. National Center for Advancing Translational Sciences (NCATS))
Thomas, Craig J. (National Institutes of Health. Medical Center Dr. Rockville. National Center for Advancing Translational Sciences (NCATS))
Grueso, Judit (Institut d'Oncologia de la Vall Hebron (VHIO))
Villanueva, Alberto (Institut Català d'Oncologia (ICO).)
Jaeger, Samira (Institut de Recerca Biomèdica (IRB Barcelona))
Heyn, Holger (Institut d'Investigació Biomèdica de Bellvitge (IDIBELL))
Vizoso, Miguel (Institut d'Investigació Biomèdica de Bellvitge (IDIBELL))
Pérez, Hector (Institut d'Investigació Biomèdica de Bellvitge (IDIBELL))
Cordero, Alex (Institut d'Investigació Biomèdica de Bellvitge (IDIBELL))
Gonzalez-Suarez, Eva (Institut d'Investigació Biomèdica de Bellvitge (IDIBELL))
Esteller, Manel (Institució Catalana de Recerca i Estudis Avançats (ICREA))
Moreno-Bueno, Gema (Universidad Autónoma de Madrid. Departamento de Bioquímica)
Tjärnberg, Andreas (Linköping University. Departament of Clinical and Experimental Medicine)
Lázaro, Conxi (Institut Català d'Oncologia (ICO))
Serra, Violeta (Institut d'Oncologia de la Vall Hebron (VHIO))
Arribas, Joaquín (Universitat Autònoma de Barcelona. Departament de Bioquímica i Biologia Molecular)
Benson, Mikael (Linköping University. Department of Clinical and Experimental Medicine)
Gustafsson, Mika (Linköping University. Department of Clinical and Experimental Medicine)
Ferrer, Marc (National Institutes of Health. Medical Center Dr. Rockville. National Center for Advancing Translational Sciences (NCATS))
Aloy, Patrick (Institució Catalana de Recerca i Estudis Avançats (ICREA))
Pujana, Miquel Àngel (Institut Català d'Oncologia (ICO))

Fecha: 2016
Resumen: 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. 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 (IC) 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. 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. 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. The online version of this article (doi:10. 1186/s13073-016-0340-x) contains supplementary material, which is available to authorized users.
Nota: Número d'acord de subvenció AGAUR/2014/SGR-364
Nota: Número d'acord de subvenció ISCIII/PI12-01528
Nota: Número d'acord de subvenció ISCIII/PI15-00854
Nota: Número d'acord de subvenció ISCIII/RD12-0036-0007
Nota: Número d'acord de subvenció ISCIII/RD12-0036-0008
Nota: Número d'acord de subvenció MICINN/PIE13-00022
Derechos: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Lengua: Anglès.
Documento: article ; recerca ; publishedVersion
Materia: Cancer ; Network ; Therapy ; Synergy
Publicado en: Genome Medicine, Vol. 8 (August 2016) , ART. 88, ISSN 1756-994X

PMID: 27553366
DOI: 10.1186/s13073-016-0340-x


12 p, 2.5 MB

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 Registro creado el 2018-02-07, última modificación el 2019-05-08



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