Jennifer Ashworth - Targeting invasive heterogeneity in breast cancer using methods from wound healing 2021 Pilot Grant
Lay Summary
Background: The spread of breast cancer through the body is the main factor influencing patient survival. This process, known as metastasis, causes a drop in 5-year survival from 99% to 28%, and is notoriously hard to treat. It is also hard to understand, as each tumour contains a complex mixture of cells, some of which will spread more easily than others. If we could predict early enough which cells will spread, we could identify ways of stopping them before metastasis occurs, dramatically improving patient survival.
Aims: We aim to adapt a method used to study wound healing, to predict which cells from a tumour are most likely to cause metastasis. By comparing this approach with gold standard pre-clinical models, we aim to validate a faster way of predicting which cells are most dangerous, and what drugs can be used to stop them
Techniques and methodology: We will use a method called “cellular sieving” to separate the mixture of cells from a tumour according to how well they move through tissue. We have already pioneered this method for studying cell movement during wound healing. We now aim to adapt this technology to benefit breast cancer patients, by allowing early and rapid prediction of metastasis. Looking at the genetic traits of the most dangerous cells will also help identify treatments to stop them before metastasis can occur.
Impact on breast cancer research: To treat breast cancer most effectively, we need a way of identifying the most dangerous cells. Once we know which cells to target, we can understand how to stop them escaping the initial tumour. Unlike the current gold-standard pre-clinical metastasis models, this approach also has high potential for personalisation. This research therefore provides the first stepping stone towards developing a screening tool for making treatment decisions on a patient-by-patient basis.
Scientific Abstract
Background: Breast cancer is a highly heterogeneous disease, with substantial cellular variability both between and within patients. Of the cells in this heterogenous mix, only a subset will be capable of invading into the surrounding tissue and forming metastases at distant sites. We plan to adapt a technique from regenerative medicine to isolate and study the most invasive cellular subpopulations from patient-derived tumours. Termed “cellular sieving”, this method physically separates a heterogeneous cell population according to their ability to invade through a matrix. This will help us understand how to target the most invasive cells within a tumour, in order to inhibit cancer spread most effectively.
Aims:
1. Adapt the cellular sieving methodology into an assay system specific to breast cancer.
2. Develop a workflow for isolating invasive cells from a heterogeneous population of patient-derived tumour cells.
3. Apply this workflow to identify differences in the transcriptome of invasive vs non-invasive cell populations, using spatial gene expression analysis.
Techniques and methodology: The cellular sieving platform is based on an ice-templated collagen sponge - highly relevant to the collagen-rich breast tissue microenvironment. We will incorporate patient-derived cells into this platform by co-injection with a breast-mimetic hydrogel (previously developed by JA and CM under NC3Rs funding). This will allow spatial separation of different cellular fractions according to their invasive capacity. Downstream analysis by spatial RNASeq will allow us to compare the transcriptome of each cell compartment, identifying therapeutic targets specific to the invasive fraction.
Impact on breast cancer research: We will develop a method for predicting which tumour cells are most likely to invade from a mixed population. Understanding which cells to target, before invasion is detectable in standard diagnostic tests, will lead to faster identification of more effective therapies for breast cancer patients. This pilot study will provide a springboard for developing personalised tools for predicting tumour invasive potential, allowing treatment strategies to be designed on a patient-by-patient basis.