Cancer network activity associated with therapeutic response and synergism
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| http://data.d4science.org/ctlg/RISIS2OpenData/dedup_wf_001--acce82c22ab71bcd6800804fdf3bdaab |
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Identity
Access Modality
| Field | Value |
|---|---|
| Access Right | Open Access |
Attribution
| Field | Value |
|---|---|
| Author | Eva Gonzalez-Suarez, 0000-0003-0858-8171 |
| Author | Francesca Mateo, 0000-0002-2342-7010 |
| Author | Violeta Serra, 0000-0001-6620-1065 |
| Author | CONXI LAZARO GARCIA, 0000-0002-7198-5906 |
| Author | Esteller M., 0000-0003-4490-6093 |
| Author | Andreas Tjärnberg, 0000-0003-0064-1791 |
| Author | Joaquin Arribas, 0000-0002-0504-0664 |
| Author | Gorka Ruiz de Garibay, 0000-0001-9936-8419 |
| Author | Gema Moreno-Bueno, 0000-0002-5030-6687 |
| Author | Holger Heyn, 0000-0002-3276-1889 |
| Contributor | Universitat de Barcelona |
| Contributor | Fundació La Marató de TV3 |
| Contributor | Generalitat de Catalunya |
| Contributor | Ministerio de Sanidad y Seguridad Social (España) |
| Contributor | Ministerio de Ciencia e Innovación (España) |
| Contributor | European Commission |
| Contributor | Instituto de Salud Carlos III |
Publishing
| Field | Value |
|---|---|
| Collected From | ORCID; Datacite; Diposit Digital de la Universitat de Barcelona; UPF Digital Repository; Microsoft Academic Graph; Publikationer från Linköpings universitet; Diposit Digital de Documents de la UAB; Europe PubMed Central; PubMed Central; Digital.CSIC; UnpayWall; Research Repository of Catalonia; Crossref |
| Hosted By | Europe PubMed Central; Genome Medicine; Digital.CSIC; Diposit Digital de la Universitat de Barcelona; UPF Digital Repository; Research Repository of Catalonia; Publikationer från Linköpings universitet; Diposit Digital de Documents de la UAB |
| Journal | Genome Medicine, 8, 1 |
| Publication Date | 2016-08-24 |
| Publisher | BioMed Central |
Additional Info
| Field | Value |
|---|---|
| Country | Sweden; Spain |
| Description | [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 (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. [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. |
| Description | This study was supported by Generalitat de Catalunya AGAUR SGR 2014 grant 364, Spanish Ministry of Health ISCIII grants PI12/01528, PI15/00854, RTICC D12/0036/0007 and 0008, and PIE13/00022-ONCOPROFILE, Spanish Ministry of Science and Innovation “Fondo Europeo de Desarrollo Regional (FEDER), una manera de hacer Europa”, and the Telemaraton 2014 “Todos Somos Raros, Todos Somos Únicos” grant P35. |
| Description | Peer Reviewed |
| Format | application/pdf; 12 p. |
| Language | English |
| Resource Type | Article; UNKNOWN |
| keyword | Càncer |
| keyword | Terapèutica |
| keyword | Regulació genètica |
| keyword | Cèl·lules canceroses |
| keyword | Cancer; Network; Therapy; Synergy |
| keyword | Càncer de mama |
| keyword | Medicaments antineoplàstics |
| system:type | publication |
Management Info
| Field | Value |
|---|---|
| Source | https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::acce82c22ab71bcd6800804fdf3bdaab |
| Author | jsonws_user |
| Last Updated | 26 December 2020, 09:25 (CET) |
| Created | 26 December 2020, 09:25 (CET) |