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International 3Rs Prize now open for applications. £30k prize (£2k personal award) for outstanding science with demonstrable 3Rs impacts.

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PhD Studentship

An ovarian cancer model on a chip

A pink eppendorf rack

At a glance

Completed
Award date
September 2015 - August 2018
Grant amount
£90,000
Principal investigator
Dr Julien Gautrot

Co-investigator(s)

Institute
Queen Mary University of London

R

  • Replacement
Read the abstract
View the grant profile on GtR

Application abstract

Cancers are complex “rogue organs” involving many different cell types (malignant, immune, mesenchymal) that interact with each other as well as their surrounding matrix. The non-malignant cells of the tumour microenvironment (TME) are recruited and corrupted by malignant cells to help all stages of the cancer development. In addition, the vasculature plays an important role in these processes. While mouse cancer models are important in understanding the TME, the best genetically engineered models are time consuming and expensive to generate, may not reflect the genetic variations seen in metastases of human cancer, and are not suitable for testing biological therapies that only act on human cells (e.g. therapeutic antibodies). More complex in vitro human models of cancer development and metastasis would be particularly important to replace or reduce the use of current animal models and improve the accuracy of drug efficacy testing.

Recently, micropatterning platforms and microfluidic systems have been used to build “organs on a chip”, tissue-like structures that can be cultured in vitro, with human cells, and display some of the key features (structure, physiology, response to drugs or disease) of normal tissues. Such human models of tissues and diseases can complement or replace currently used animal models and be more accurate for drug testing. The present project will build for the first time an ovarian cancer model on a chip, using microfluidics and micropatterning of biomaterials. We will validate the accuracy of the model by comparing it to patient data and will establish its predictive potential with drugs currently in the treatment of high grade ovarian cancer (HGSC).