TY - JOUR
T1 - A network-based data integration approach to support drug repurposing and multi-Target therapies in triple negative breast cancer
AU - Vitali, Francesca
AU - Cohen, Laurie D.
AU - Demartini, Andrea
AU - Amato, Angela
AU - Eterno, Vincenzo
AU - Zambelli, Alberto
AU - Bellazzi, Riccardo
N1 - Publisher Copyright:
© 2016 Vitali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2016/9
Y1 - 2016/9
N2 - The integration of data and knowledge from heterogeneous sources can be a key success factor in drug design, drug repurposing and multi-Target therapies. In this context, biological networks provide a useful instrument to highlight the relationships and to model the phenomena underlying therapeutic action in cancer. In our work, we applied network-based modeling within a novel bioinformatics pipeline to identify promisingmulti-Target drugs. Given a certain tumor type/subtype, we derive a disease-specific Protein-Protein Interaction (PPI) network by combining different data-bases and knowledge repositories. Next, the application of suitable graph-based algorithms allows selecting a set of potentially interesting combinations of drug targets. A list of drug candidates is then extracted by applying a recent data fusion approach based on matrix tri-factorization.Available knowledge about selected drugs mechanisms of action is finally exploited to identify the most promising candidates for planning in vitro studies.We applied this approach to the case of Triple Negative Breast Cancer (TNBC), a subtype of breast cancer whose biology is poorly understood and that lacks of specific molecular targets. Our "in-silico" findings have been confirmedby a number of in vitro experiments, whose results demonstrated the ability of the method to select candidates for drug repurposing.
AB - The integration of data and knowledge from heterogeneous sources can be a key success factor in drug design, drug repurposing and multi-Target therapies. In this context, biological networks provide a useful instrument to highlight the relationships and to model the phenomena underlying therapeutic action in cancer. In our work, we applied network-based modeling within a novel bioinformatics pipeline to identify promisingmulti-Target drugs. Given a certain tumor type/subtype, we derive a disease-specific Protein-Protein Interaction (PPI) network by combining different data-bases and knowledge repositories. Next, the application of suitable graph-based algorithms allows selecting a set of potentially interesting combinations of drug targets. A list of drug candidates is then extracted by applying a recent data fusion approach based on matrix tri-factorization.Available knowledge about selected drugs mechanisms of action is finally exploited to identify the most promising candidates for planning in vitro studies.We applied this approach to the case of Triple Negative Breast Cancer (TNBC), a subtype of breast cancer whose biology is poorly understood and that lacks of specific molecular targets. Our "in-silico" findings have been confirmedby a number of in vitro experiments, whose results demonstrated the ability of the method to select candidates for drug repurposing.
UR - http://www.scopus.com/inward/record.url?scp=84992413607&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992413607&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0162407
DO - 10.1371/journal.pone.0162407
M3 - Article
C2 - 27632168
AN - SCOPUS:84992413607
SN - 1932-6203
VL - 11
JO - PloS one
JF - PloS one
IS - 9
M1 - e0162407
ER -