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Therapeutics, Targets, and Chemical Biology

Quantification of Pathway Cross-talk Reveals Novel Synergistic Drug Combinations for Breast Cancer

Samira Jaeger, Ana Igea, Rodrigo Arroyo, Victor Alcalde, Begoña Canovas, Modesto Orozco, Angel R. Nebreda and Patrick Aloy
Samira Jaeger
Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
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Ana Igea
Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
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Rodrigo Arroyo
Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
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Victor Alcalde
Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
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Begoña Canovas
Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
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Modesto Orozco
Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.Department of Biochemistry and Molecular Biology, University of Barcelona, Barcelona, Catalonia, Spain.
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Angel R. Nebreda
Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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Patrick Aloy
Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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  • For correspondence: patrick.aloy@irbbarcelona.org
DOI: 10.1158/0008-5472.CAN-16-0097
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Abstract

Combinatorial therapeutic approaches are an imperative to improve cancer treatment, because it is critical to impede compensatory signaling mechanisms that can engender drug resistance to individual targeted drugs. Currently approved drug combinations result largely from empirical clinical experience and cover only a small fraction of a vast therapeutic space. Here we present a computational network biology approach, based on pathway cross-talk inhibition, to discover new synergistic drug combinations for breast cancer treatment. In silico analysis identified 390 novel anticancer drug pairs belonging to 10 drug classes that are likely to diminish pathway cross-talk and display synergistic antitumor effects. Ten novel drug combinations were validated experimentally, and seven of these exhibited synergy in human breast cancer cell lines. In particular, we found that one novel combination, pairing the estrogen response modifier raloxifene with the c-Met/VEGFR2 kinase inhibitor cabozantinib, dramatically potentiated the drugs’ individual antitumor effects in a mouse model of breast cancer. When compared with high-throughput combinatorial studies without computational prioritization, our approach offers a significant advance capable of uncovering broad-spectrum utility across many cancer types. Cancer Res; 77(2); 1–11. ©2016 AACR.

Footnotes

  • Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).

  • Received January 18, 2016.
  • Revision received October 7, 2016.
  • Accepted October 25, 2016.
  • ©2016 American Association for Cancer Research.
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Published OnlineFirst January 9, 2017
doi: 10.1158/0008-5472.CAN-16-0097

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Quantification of Pathway Cross-talk Reveals Novel Synergistic Drug Combinations for Breast Cancer
Samira Jaeger, Ana Igea, Rodrigo Arroyo, Victor Alcalde, Begoña Canovas, Modesto Orozco, Angel R. Nebreda and Patrick Aloy
Cancer Res January 9 2017 DOI: 10.1158/0008-5472.CAN-16-0097

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Quantification of Pathway Cross-talk Reveals Novel Synergistic Drug Combinations for Breast Cancer
Samira Jaeger, Ana Igea, Rodrigo Arroyo, Victor Alcalde, Begoña Canovas, Modesto Orozco, Angel R. Nebreda and Patrick Aloy
Cancer Res January 9 2017 DOI: 10.1158/0008-5472.CAN-16-0097
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Cancer Research Online ISSN: 1538-7445
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