TY - JOUR
T1 - Comparative Network Reconstruction using mixed integer programming
AU - Bosdriesz, Evert
AU - Prahallad, Anirudh
AU - Klinger, Bertram
AU - Sieber, Anja
AU - Bosma, Astrid
AU - Bernards, René
AU - Blüthgen, Nils
AU - Wessels, Lodewyk F.A.
PY - 2018
Y1 - 2018
N2 - Motivation Signal-transduction networks are often aberrated in cancer cells, and new anti-cancer drugs that specifically target oncogenes involved in signaling show great clinical promise. However, the effectiveness of such targeted treatments is often hampered by innate or acquired resistance due to feedbacks, crosstalks or network adaptations in response to drug treatment. A quantitative understanding of these signaling networks and how they differ between cells with different oncogenic mutations or between sensitive and resistant cells can help in addressing this problem. Results Here, we present Comparative Network Reconstruction (CNR), a computational method to reconstruct signaling networks based on possibly incomplete perturbation data, and to identify which edges differ quantitatively between two or more signaling networks. Prior knowledge about network topology is not required but can straightforwardly be incorporated. We extensively tested our approach using simulated data and applied it to perturbation data from a BRAF mutant, PTPN11 KO cell line that developed resistance to BRAF inhibition. Comparing the reconstructed networks of sensitive and resistant cells suggests that the resistance mechanism involves re-establishing wild-type MAPK signaling, possibly through an alternative RAF-isoform. Availability and implementation CNR is available as a python module at https://github.com/NKI-CCB/cnr. Additionally, code to reproduce all figures is available at https://github.com/NKI-CCB/CNR-analyses. Supplementary information Supplementary data are available at Bioinformatics online.
AB - Motivation Signal-transduction networks are often aberrated in cancer cells, and new anti-cancer drugs that specifically target oncogenes involved in signaling show great clinical promise. However, the effectiveness of such targeted treatments is often hampered by innate or acquired resistance due to feedbacks, crosstalks or network adaptations in response to drug treatment. A quantitative understanding of these signaling networks and how they differ between cells with different oncogenic mutations or between sensitive and resistant cells can help in addressing this problem. Results Here, we present Comparative Network Reconstruction (CNR), a computational method to reconstruct signaling networks based on possibly incomplete perturbation data, and to identify which edges differ quantitatively between two or more signaling networks. Prior knowledge about network topology is not required but can straightforwardly be incorporated. We extensively tested our approach using simulated data and applied it to perturbation data from a BRAF mutant, PTPN11 KO cell line that developed resistance to BRAF inhibition. Comparing the reconstructed networks of sensitive and resistant cells suggests that the resistance mechanism involves re-establishing wild-type MAPK signaling, possibly through an alternative RAF-isoform. Availability and implementation CNR is available as a python module at https://github.com/NKI-CCB/cnr. Additionally, code to reproduce all figures is available at https://github.com/NKI-CCB/CNR-analyses. Supplementary information Supplementary data are available at Bioinformatics online.
UR - http://www.scopus.com/inward/record.url?scp=85054127368&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/bty616
DO - 10.1093/bioinformatics/bty616
M3 - Article
C2 - 30423075
AN - SCOPUS:85054127368
VL - 34
SP - i997-i1004
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 17
ER -