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Metabolic reprogramming to optimise breast cancer treatment

Barber, Amy L. (2019) Metabolic reprogramming to optimise breast cancer treatment Doctoral thesis, University of Surrey.

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Abstract

Breast cancer is the most common cancer in women worldwide, accounting for a quarter of all female cancer cases. Despite improvements in early diagnosis and treatment, the mortality rate for breast cancer has remained stubbornly high, with some subtypes unresponsive to current treatments. One reason for this is the metabolic heterogeneity of breast cancer, with some sub-types having poor treatment options. Breast cancer may be considered as a metabolic disease due to the high metabolic demand associated with tumour growth. This raises the possibility of targeting cancer metabolic vulnerabilities. We present a systems biology approach to identify novel drug targets through analysing the metabolism of breast cancer and the exploration of novel combination therapies. We explore the combination of metabolism-modulating drugs Metformin and GW4064 with chemotherapeutic agents in vitro and find some of the combinations are synergistic in breast cancer cell lines. To identify novel drug targets for altering metabolism a novel dynamic computational model representing a breast cancer cell integrating genome-scale metabolism is developed, a gene and signalling regulatory network and a kinetic cell cycle model. Using the congruency approach, we develop context-specific models for breast cancer cell lines and through in silico analysis, identify metabolic choke-points as potential drug targets. Two predictions were selected for in vitro investigation: IMPase and Serotonin signalling. We demonstrate synergy between these targets and traditional cancer chemotherapeutics in causing cell death. In conclusion, a systems biology approach can enhance and accelerate the identification of novel drug targets and aid development of network targeting treatments. The work presented here provides an important proof-of-concept to show that computer modelling can predict novel drug combinations that increase the ability of currently approved drugs to kill cancer cells. As such, it offers new opportunities to optimise breast cancer treatment and move toward the goal of making breast cancer a fully treatable disease.

Item Type: Thesis (Doctoral)
Divisions : Theses
Authors : Barber, Amy L.
Date : 12 February 2019
Funders : Breast Cancer Now
DOI : 10.15126/thesis.00853293
Contributors :
ContributionNameEmailORCID
http://www.loc.gov/loc.terms/relators/THSPlant, Nick J.
Depositing User : Amy Barber
Date Deposited : 07 Feb 2020 15:28
Last Modified : 07 Feb 2020 15:29
URI: http://epubs.surrey.ac.uk/id/eprint/853293

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