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Genome Med. 2016 Aug 24;8(1):88. doi: 10.1186/s13073-016-0340-x.

Cancer network activity associated with therapeutic response and synergism.

Author information

1
Breast Cancer and Systems Biology Lab, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain.
2
Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, Barcelona, 08028, Catalonia, Spain.
3
Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Dr. Rockville, Bethesda, MD, 20850, USA.
4
Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology (VHIO), Cellex Center, Natzaret 115-117, Barcelona, 08035, Catalonia, Spain.
5
Cancer Epigenetics and Biology Program (PEBC), IDIBELL, Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain.
6
Department of Physiological Sciences II, School of Medicine, University of Barcelona, Feixa Llarga s/n, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain.
7
Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Catalonia, Spain.
8
Department of Biochemistry, Autonomous University of Madrid (UAM), Biomedical Research Institute "Alberto Sols" (Spanish National Research Council (CSIC)-UAM), Hospital La Paz Institute for Health Research (IdiPAZ), Arzobispo Morcillo 4, Madrid, 28029, Spain.
9
MD Anderson International Foundation, Arturo Soria 270, Madrid, 28033, Spain.
10
The Centre for Individualized Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, 58183, Sweden.
11
Hereditary Cancer Program, ICO, IDIBELL, Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain.
12
Preclinical Research Program, VHIO, Cellex Center, Natzaret 115-117, Barcelona, 08035, Catalonia, Spain.
13
Department of Biochemistry and Molecular Biology, Medical School Building M, Autonomous University of Barcelona, Bellaterra, 08193, Catalonia, Spain.
14
Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Dr. Rockville, Bethesda, MD, 20850, USA. marc.ferrer@nih.gov.
15
Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, Barcelona, 08028, Catalonia, Spain. paloy@irbbarcelona.org.
16
Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Catalonia, Spain. paloy@irbbarcelona.org.
17
Breast Cancer and Systems Biology Lab, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain. mapujana@iconcologia.net.

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.

KEYWORDS:

Cancer; Network; Synergy; Therapy

PMID:
27553366
PMCID:
PMC4995628
DOI:
10.1186/s13073-016-0340-x
[Indexed for MEDLINE]
Free PMC Article

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