Identify novel radio-sensitising targets for oral squamous cell carcinoma using in silico modelling

Primary supervisor: Jasmin Fisher, University College London
Secondary supervisor: Erik Sahai, The Francis Crick Institute

Project

Radiotherapy is central to the treatment of head and neck cancers, including oral squamous cell carcinoma (SCC). However, responses are frequently not durable. Our goal is to identify strategies that will lead to greater initial tumour volume reduction and more durable responses. We have established a pre-clinical oral SCC model that responds well to radiotherapy, with tumour growth halted following a two-week fractionated course totalling 36Gy. However, after 2-4 months growth resumes and the disease become less responsive to therapy, mirroring the situation in many patients. We will leverage existing longitudinal single cell RNA sequencing, imaging mass cytometry, and in vitro analysis, to build an executable (in silico) model with the goal of identifying the following:

1. Optimal RT-radiosensitiser drug combinations to improve initial responses
2. Signals derived from the microenvironment that confer radio-resistance
3. Acquired chemical vulnerabilities of stable disease following radiotherapy

The in silico model will include oncogenic signalling pathways within the SCC tumour and the crosstalk with the microenvironment. We will use our in silico perturbations tool to simulate the effect of different drug – radiation combinations as well as different treatment sequences, on different genetic backgrounds, to predict optimal combinations that radio-sensitise tumour cells specifically, while sparing healthy tissue.
Selected predictions will be tested initially using in vitro experiments, including co-culture with stromal cells, before further model iteration and identification of optimal strategies to be taken forward in pre-clinical trials. The use of our computational modelling approach will enable exploration of tens of thousands of combinations and scheduling regimes. Crucially, due to the transparency of the computational model, it will also highlight the mechanism by which new strategies will work. The most promising of these will be tested in the final year of the project. The experimental work will be done by a member of the Sahai lab.

Candidate background

This project would suit candidates with a background in cancer biology and an interest in computational biology. Basic programming skills in R, Python and MATLAB would be essential. An ability to work well within a multi-disciplinary team is highly desirable.

Potential Research Placements

1. Erik Sahai, The Francis Crick Institute
2. Jasmin Fisher, University College London
3. Fabian Frohlich, The Francis Crick Institute

References

1. Clarke M.A. et al. Predicting personalised therapeutic combinations in con-small cell lung cancer using in silico modelling. bioRxiv 2025 https://www.biorxiv.org/content/10.1101/2025.01.07.631497v1
2. Howell R. et al. Localized immune surveillance of primary melanoma in the skin deciphered through executable modelling. Science Advances, 9:15, 2023.
3. Clarke M.A. and Fisher J. Executable Cancer Models: Successes and Challenges. Nature Reviews Cancer 20: 343-353, 2020.
4. Kreuzaler P. et al. Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modelling. Proc Natl Acad Sci U S A 116(44): 22399-22408, 2019.
5. Hirata E. and Sahai E. Tumor Microenvironment and Differential Responses to Therapy. Cold Spring Harbor Perspectives in Medicine, 7(7):a026781, 2017.