Understanding the gradients that steer cancer cells

Cancer spread - "metastasis" - is one of the disease’s most feared and destructive features. Cancer cells spread in response to gradients of signalling molecules such as LPA and growth factors, and immune cells locate and destroy their targets using gradients of chemokines and other inflammatory signals. It is well known how important attractant gradients are to the responding cells, but we understand much less well how gradients are established and maintained. We have pioneered the field of "self-generated gradients", in which cells make their own gradients at the same time as responding to them.

This project seeks to discover which types of cancer spread in response to self-generated gradients. We will use existing chemotaxis chambers, and build improved variants, that allow us to examine cancer cells exposed to external gradients of different types, for example serum, and individual signals such as lipids, chemokines and complement. When we have found which cancers contain chemotactic cells, and identified the key attractants, we will test whether they respond better to gradients from outside, or if they generate the gradients themselves. With this overview we will choose the most promising cancers for mechanistic analysis - which enzymes break down the attractants, and can they be stopped? - and detailed in vivo studies.

This project will involve a lot of technical microscopy, cell biology and molecular biology. It may - depending on results - also involve mass spectrometry to measure attractants, and experiments to test whether self-generated gradients can be controlled using novel drugs. The student will interact with computational biologists and mathematical modellers, so an interest in computing and quantitative approaches is desirable but not required.