 visitant ::
identificació
|
|||||||||||||||
|
Cerca | Lliura | Ajuda | Servei de Biblioteques | Sobre el DDD | Català English Español | |||||||||
| Pàgina inicial > Articles > Articles publicats > Cancer network activity associated with therapeutic response and synergism |
(Institut Català d'Oncologia) (Institut Català d'Oncologia) (Institut Català d'Oncologia) (Institut d'Investigació Biomèdica de Bellvitge) (Institut d'Investigació Biomèdica de Bellvitge) (Institució Catalana de Recerca i Estudis Avançats) (Universidad Autónoma de Madrid. Departamento de Bioquímica) (Vall d'Hebron Institut d'Oncologia) (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular) (Institució Catalana de Recerca i Estudis Avançats)| Data: | 2016 |
| Resum: | 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. |
| Ajuts: | Agència de Gestió d'Ajuts Universitaris i de Recerca 2014/SGR-364 Instituto de Salud Carlos III PI12-01528 Instituto de Salud Carlos III PI15-00854 Instituto de Salud Carlos III RD12-0036-0007 Instituto de Salud Carlos III RD12-0036-0008 Ministerio de Ciencia e Innovación PIE13-00022 |
| Drets: | 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.  |
| Llengua: | Anglès |
| Document: | Article ; recerca ; Versió publicada |
| Matèria: | Cancer ; Network ; Therapy ; Synergy |
| Publicat a: | Genome Medicine, Vol. 8 (August 2016) , ART. 88, ISSN 1756-994X |
