Competitive interactions in heterogeneous cancer cell populations: a multimodal imaging, machine learning and computational modelling approach

Most adult human cancers originate from transformation of a single cell within an epithelial cell sheet. Then, several rounds of mutation and selection are believed to give rise to tumour progenitors. At the molecular level, the sequence of events leading to cancer progression is becoming better characterised. Recent work suggests that transformed cells compete with their less transformed neighbours, although this is poorly understood. The outcome of such competition likely depends on environmental conditions. This project will use long-term microscopy, state-of-the-art image analysis and computational models to characterise competition in cancer.

In this project, we propose to examine how microenvironmental selection forces affect the outcome of competition between different cell lineages. In the simplest configuration, we will consider a competition between two cell types at different stages of cancer progression. We will examine the outcome of this competition by analysing cell proliferation in co-cultures under normal and stressful conditions. We propose to examine how environmental conditions such as nutrient depletion, hypoxia, or exposure to anti-cancer drugs affect competition between cell types. Experiments will be run on a custom built microscope that enables continuous imaging over periods of several days. Cells are seeded either on circular micro-patterns or within microfluidic devices. Images are analysed using a custom-written software pipeline that can track individual cells, recognise cell cycle stage, and generate lineage trees (https://youtu.be/dsjUnRwu33k).

We will interpret our experimental results using game theory to understand competition between cell types under different stress conditions. Here, opposing cell types adopt strategies (for example selfish or regulated growth) intended to maximise their fitness within the environment. Game theoretic constructs such as the Prisoner’s Dilemma, have been used to make theoretical predictions about growth rates in mixed cell populations. Here, we seek to provide direct experimental measurements of cell proliferation under selection pressure, to compare with theoretical predictions, and provide a clearer picture of the earliest events in tumourogenesis.

The project will focus on the following objectives:
1) Develop a multimodal imaging assay to continuously image co-cultures of cells
2) Train and optimise machine-learning algorithms to detect and label cells
3) Characterise the interactions between non-transformed cells and cells with different mutations.
4) Characterise how anti-cancer drugs modulate the outcome of competition
5) Integrate experimental data into the computational modelling framework

This is a collaborative project between:
- Dr Alan R Lowe (London Centre for Nanotechnology and ISMB, http://lowe.cs.ucl.ac.uk/)
- Prof. Guillaume Charras (LCN, https://www.london-nano.com/our-people/academics/guillaume-charras) and
- Dr Shiladitya Banerjee (Department of Physics, http://iris.ucl.ac.uk/iris/browse/profile?upi=SBANE26)


To apply: Either fill out the "Email Now" section below or send an email directly to Dr AR Lowe. Please remember to attach your current CV.