Kuvaus

Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.
Alkuperäiskielienglanti
Lehtinpj Systems Biology and Applications
Vuosikerta5
Numero1
ISSN2056-7189
DOI - pysyväislinkit
TilaJulkaistu - 2019
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

Lainaa tätä

@article{80a383db5c68429e8aa304c09fb46e1c,
title = "Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer",
abstract = "Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.",
author = "Jing Tang and Prson Gautam and Abhishekh Gupta and Liye He and Sanna Timonen and Yevhen Akimov and Wenyu Wang and Agnieszka Szwajda and Alok Jaiswal and Denes Turei and Bhagwan Yadav and Matti Kankainen and Jani Saarela and Julio Saez-Rodriguez and Krister Wennerberg and Tero Aittokallio",
year = "2019",
doi = "10.1038/s41540-019-0098-z",
language = "English",
volume = "5",
journal = "npj Systems Biology and Applications",
issn = "2056-7189",
publisher = "Springer Nature",
number = "1",

}

Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer. / Tang, Jing; Gautam, Prson; Gupta, Abhishekh; He, Liye; Timonen, Sanna; Akimov, Yevhen; Wang, Wenyu; Szwajda, Agnieszka; Jaiswal, Alok; Turei, Denes; Yadav, Bhagwan; Kankainen, Matti; Saarela, Jani; Saez-Rodriguez, Julio; Wennerberg, Krister; Aittokallio, Tero.

julkaisussa: npj Systems Biology and Applications, Vuosikerta 5, Nro 1, 2019.

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

TY - JOUR

T1 - Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer

AU - Tang, Jing

AU - Gautam, Prson

AU - Gupta, Abhishekh

AU - He, Liye

AU - Timonen, Sanna

AU - Akimov, Yevhen

AU - Wang, Wenyu

AU - Szwajda, Agnieszka

AU - Jaiswal, Alok

AU - Turei, Denes

AU - Yadav, Bhagwan

AU - Kankainen, Matti

AU - Saarela, Jani

AU - Saez-Rodriguez, Julio

AU - Wennerberg, Krister

AU - Aittokallio, Tero

PY - 2019

Y1 - 2019

N2 - Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.

AB - Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.

U2 - 10.1038/s41540-019-0098-z

DO - 10.1038/s41540-019-0098-z

M3 - Article

VL - 5

JO - npj Systems Biology and Applications

JF - npj Systems Biology and Applications

SN - 2056-7189

IS - 1

ER -