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      Network pharmacology and drug repurposing pave the way for precision oncology

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            Abstract

            Genomic profiling has shown that not all cancer patients who share similar macro- and microscopical features harbour the same underlying molecular mechanism. This suggests the urge for matching patients to mechanism-based cancer therapies, independent of their primary tumour location and histology (Bashraheel et al. 2020). Currently, precision oncology trials provide personalised treatments based on the druggable variants found in a patient genetic makeup. Typically, those trials target single genetic variants (Schmidt et al. 2016; Murciano-Goroff et al. 2020) or provide combination therapies targeting single, mechanistically unrelated proteins which have been proven to be ineffective and or insufficient (Choobdar et al. 2019; Lazareva et al. 2021). In parallel, these variants are allocated to so-called canonical signalling pathways, e.g., KEGG pathways, Wiki Pathways. However, these are rather curated mind maps only combining similar signalling proteins or messengers. They do not represent true cellular signalling entities. Alternatively, signalling modules can be constructed in an unbiased manner from the interactome using validated seed proteins, also termed cancer driver genes, resulting in fragments and often mixtures of the above curated pathways (Nogales et al. 2022). These modules likely represent the true cancer mechanism and concerted network modulation with multiple mechanistically related drugs all acting on the same module i.e., network pharmacology, promise to be much more effective than targeting single unrelated variants (Cheng et al. 2019). As complex tumours will require multiple drugs targeting several modules (Sanchez-Vega et al. 2018), we start with low complexity tumours with a low mutational burden, e.g., thyroid cancer and diffuse intrinsic pontine gliomas (DIPG) (Vogelstein et al. 2013). Here, we (i) construct de-novo disease modules to identify drug targets and repurposable drugs, (ii) apply diagnostic assays to detect the patient-specific perturbed modules and (iii) decide on the therapeutic strategy to correct the modules using network pharmacology. Repurposable drugs are ranked based on clinical feasibility and other parameters. This allows a fundamentally new approach to cancer therapy often using low-side effect drugs acting in concert to improve patient survival and quality of life by implementing biology-informed drug interventions.

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            Author and article information

            Conference
            ScienceOpen
            29 August 2022
            Affiliations
            [1 ] Department of Pharmacology and Personalised Medicine, Maastricht University, Maastricht, The Netherlands
            [2 ] Department of Statistical Sciences, Faculty of Computer Engineering, Computer Science and Statistics, Sapienza University of Rome, Rome, Italy
            [3 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
            [4 ] Department of Oncology, University Children’s Hospital, Zurich, Switzerland
            [5 ] Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, Maastricht, The Netherlands
            [6 ] Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
            [7 ] Division of Endocrinology, Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
            Author notes
            Author information
            https://orcid.org/0000-0002-4201-7973
            https://orcid.org/0000-0001-7535-0417
            https://orcid.org/0000-0003-2437-1773
            https://orcid.org/0000-0002-1332-8713
            https://orcid.org/0000-0002-3992-0125
            https://orcid.org/0000-0002-9424-8052
            https://orcid.org/0000-0002-0282-0462
            https://orcid.org/0000-0003-2796-729X
            https://orcid.org/0000-0002-0941-4168
            https://orcid.org/0000-0002-1951-9828
            https://orcid.org/0000-0002-9603-0460
            https://orcid.org/0000-0003-0419-5549
            Article
            10.14293/S2199-1006.1.SOR-.PPPDGTLB.v1
            8dc0302d-2d07-4fb0-a65a-c6f1a3948d37

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            RExPO22
            Maastricht, Netherlands
            2-3 September, 2022
            History
            : 29 August 2022

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Precision oncology,DIPG,Thyroid cancer,Drug repurposing,Module construction,Network pharmacology

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