Modelling cellular plasticity in colorectal cancer metastasis and therapy relapse

Background

Cancer metastasis and therapy resistance are the major clinical challenges for cancer management and account for the majority of cancer-related deaths. Cellular plasticity, the ability of cells to switch phenotypic states, is implicated as a major driver of both metastasis and relapse following treatment. We have recently identified the chromatin-remodelling enzyme ATRX as an important mediator of cellular plasticity. Loss of Atrx promotes metastasis and leads to loss of colonic identity and emergence of mesenchymal and squamous-like cell states (Cammareri et al., 2025). Interestingly, our subsequent work has demonstrated the same induction of squamous-like plasticity during tumour relapse following chemotherapy suggesting induction of non-canonical cell states drives both tumour relapse and metastasis. However, the mechanisms responsible for this phenomenon are poorly understood, limiting the development of novel therapeutic strategies to target it. 

The aim of this project is to integrate experimental approaches and mathematical models to better understand cancer cell plasticity and predict specific cell state transitions during chemotherapy treatment and relapse and during metastatic dissemination. Using single-cell RNAseq and spatial transcriptomics, we have identified specify markers of cell populations that arise during chemotherapy treatment and metastasis. We have utilized these markers to develop tools to track the fate of these populations over time and in response to different perturbations. This information will be used to identify the critical cell phenotype switches that drive these processes enabling experimental studies to both test these predictions and develop ways of specifically inhibiting them. The project will be built on an existing, well-established collaboration utilizing colorectal cancer models developed in the Myant and Din laboratories and the mathematical expertise of the Fu lab with additional supervision from a Cross Disciplinary Fellow (Dr Rodrigo Garcia-Tejera).

Aims

1. Utilise flow cytometry, lineage tracing and analysis of spatial transcriptomic data to track the fate of plastic cell populations in response to chemotherapy and other exogenous perturbations.
2. Develop mathematical models describing cell proliferation, survival and phenotypic switching. By linking model outputs to the data generated in 1), parameters (e.g., transition rates between cell phenotypic states) will be inferred, generating experimentally testable predictions for more likely paths of phenotypic transitions.
3. Test model predictions using organoid culture models of cell state transitions.
4. Develop and test strategies to target specific cell state transitions and determine effects on tumour relapse and metastasis.

Training outcomes

Wet lab and dry lab expertise. From wet lab side, organoid culturing, flow cytometry, lineage tracing, general analysis techniques. From dry lab, data science and statistical analysis, coding skills and good coding practices, mathematical and mechanistic modelling, computer simulations using High-Performance Computing (HPC) clusters.

The student will acquire cross-disciplinary training, learning to work in interdisciplinary environments and to integrate mathematical modelling with experimental approaches to address complex questions with direct clinical impact. The student will benefit from being integrated in a translational research setting (Myant) with strong links to clinical (Din) and computational (Fu) science. This will allow the student to develop additional skills in communication, science engagement, clinical practice and state-of-the-art computational analysis.