2. Academic Validation
  3. Network analysis of immunotherapy-induced regressing tumours identifies novel synergistic drug combinations

Network analysis of immunotherapy-induced regressing tumours identifies novel synergistic drug combinations

  • Sci Rep. 2015 Jul 21;5:12298. doi: 10.1038/srep12298.
W Joost Lesterhuis 1,  Catherine Rinaldi 1,  Anya Jones 2,  Esdy N Rozali 1,  Ian M Dick 1,  Andrea Khong 1,  Louis Boon 3,  Bruce W Robinson 1,  Anna K Nowak 4,  Anthony Bosco 2,  Richard A Lake 1
Affiliations

Affiliations

  • 1 1] National Centre for Asbestos Related Diseases [2] School of Medicine and Pharmacology, University of Western Australia, The Harry Perkins Institute of Medical Research, 5th Floor, QQ Block, 6 Verdun Street, Nedlands WA 6009, Australia.
  • 2 Telethon Kids Institute, The University of Western Australia, 100 Roberts Road, Subiaco WA 6008, Australia.
  • 3 Bioceros, Yalelaan 46, 3584 CM Utrecht, The Netherlands.
  • 4 1] School of Medicine and Pharmacology, University of Western Australia, The Harry Perkins Institute of Medical Research, 5th Floor, QQ Block, 6 Verdun Street, Nedlands WA 6009, Australia [2] Department of Medical Oncology, Sir Charles Gairdner Hospital, Hospital Ave, Nedlands WA 6009, Australia.
PMID: 26193793 DOI: 10.1038/srep12298
Abstract

Cancer Immunotherapy has shown impressive results, but most patients do not respond. We hypothesized that the effector response in the tumour could be visualized as a complex network of interacting gene products and that by mapping this network we could predict effective pharmacological interventions. Here, we provide proof of concept for the validity of this approach in a murine mesothelioma model, which displays a dichotomous response to anti-CTLA4 immune checkpoint blockade. Network analysis of gene expression profiling data from responding versus non-responding tumours was employed to identify modules associated with response. Targeting the modules via selective modulation of hub genes or alternatively by using repurposed pharmaceuticals selected on the basis of their expression perturbation signatures dramatically enhanced the efficacy of CTLA4 blockade in this model. Our approach provides a powerful platform to repurpose drugs, and define contextually relevant novel therapeutic targets.

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