Applications of personalised signalling network models in precision oncology

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Title: Applications of personalised signalling network models in precision oncology
Authors: Hastings, Jordan F.O'Donnell, Yolonde E. I.Fey, DirkCroucher, David R.
Permanent link: http://hdl.handle.net/10197/12455
Date: Aug-2020
Online since: 2021-09-08T14:48:10Z
Abstract: As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.
Type of material: Journal Article
Publisher: Elsevier
Journal: Pharmacology and Therapeutics
Volume: 212
Copyright (published version): 2020 Elsevier
Keywords: Precision medicineCancer treatmentSystems biologyKinase signalling networksData modellingGenetic instabilityTumour growthPatient response
DOI: 10.1016/j.pharmthera.2020.107555
Language: en
Status of Item: Peer reviewed
ISSN: 0163-7258
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:SBI Research Collection
Medicine Research Collection

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