Cancer

MAGELLAN: Automated Generation of Interpretable Computational Models for Biological Reasoning
Computational models have become essential tools for understanding signalling networks and their non-linear dynamics. However, these …
Matthew A. Clarke, Charlie George Barker, Yuxin Sun, Theodoros I. Roumeliotis, Jyoti S. Choudhary, Jasmin Fisher
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MAGELLAN: Automated Generation of Interpretable Computational Models for Biological Reasoning
Combination therapy
Computatational modelling allows screening of thousands of combinations of drugs to find the most effective treatments. This allows for better protection against the emergence of resistance, as well as rapid drug repurposing e.g. to combat emerging diseases such as COVID-19.
Matthew A. Clarke
Clarke, Barker & Nicholls et al. (2026) Clarke, Barker & Nicholls et al. (2026) Kreuzaler & Clarke et al. (2019) Kreuzaler & Clarke et al. (2019) Howell, Clarke & Reuschl et al. (2022) Howell, Clarke & Reuschl et al. (2022) Howell & Davies et al. (2023) Howell & Davies et al. (2023)
Combination therapy
Virtual Tumors Enable Prediction of Personalized Therapeutic Combinations for Non-Small Cell Lung Cancer
The disease burden from non-small cell lung cancer (NSCLC) adenocarcinoma is substantial, with a million new cases diagnosed globally …
Matthew A. Clarke, Charlie George Barker, Ashley Nicholls, Matt P. Handler, Lisa Pickard, Amna Shah, David Walter, Etienne De Braekeleer, Udai Banerji, Jyoti Choudhary, Saif Ahmed, Ultan McDermott, Gregory J. Hannon, Jasmin Fisher
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Virtual Tumors Enable Prediction of Personalized Therapeutic Combinations for Non-Small Cell Lung Cancer
Predicting Cancer Evolution
Cancers evolve from normal cells, and continue to evolve to evade treatment. Understanding evolution from tumourigenesis will allow for earlier detection of cancer, while predicting evolution of resistance mechanisms is essential for finding treatments that are effective in the long term.
Matthew A. Clarke
Clarke et al. (2019) Clarke et al. (2019) Talarmain et al. (2022) Talarmain et al. (2022)
Predicting Cancer Evolution
Predicted combination treatments for NSCLC using in silico CRISPR screening
Lung cancer is the leading cause of cancer mortality world-wide, with over half of lung cancer patients relying on radiotherapy (RT). …
May 1, 2024 Kennedy Lecture Theatre, UCL Great Ormond Street Institute of Child Health
Matthew A. Clarke, Charles Barker, Ashley Nicholls, Matt Handler, Lisa Pickard, Saif Ahmad, Udai Banerji, Jyoti Choudhary, Gregory J. Hannon, Jasmin Fisher
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Predicted combination treatments for NSCLC using in silico CRISPR screening
DNA Damage Repair and Radiotherapy
Cancers often emerge, in part, due to deficencies in DNA damage repair, making them vulnerable to DNA damaging treatments such as radiotherapy. While radiotherapy efficacy has been increasing, this is mainly due to better targeting of tumour anatomy, but targeted therapy offers the opportunity to radiosensitise tumour cells by targeting vulnerabilities in the tumour biology. We use computational modelling to find such vulnerabilities, and explore potential resistance mechanisms.
Matthew A. Clarke
Clarke, Barker & Nicholls et al. (2026) Clarke, Barker & Nicholls et al. (2026)
DNA Damage Repair and Radiotherapy
Predicted combination treatments for NSCLC using in silico CRISPR screens
Lung cancer is the leading cause of cancer mortality world-wide, with over half of lung cancer patients relying on radiotherapy (RT). …
Feb 27, 2024 — Feb 28, 2024 The Discovery Centre,
Matthew A. Clarke, Charles Barker, Ashley Nicholls, Matt Handler, Lisa Pickard, Saif Ahmad, Udai Banerji, Jyoti Choudhary, Gregory J. Hannon, Jasmin Fisher
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Predicted combination treatments for NSCLC using in silico CRISPR screens
Predicting personalised treatment combinations with in silico CRISPR screens
Targeted therapies can be very effective in the short term, but often are stymied by resistance in the long term. We explore two …
Jan 29, 2024 — Jan 30, 2024 The Francis Crick Institute
Matthew A. Clarke
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Predicting personalised treatment combinations with in silico CRISPR screens
Predicted radio-sensitising combination treatments for NSCLC using in-silico CRISPR screens
Lung cancer is the leading cause of cancer mortality world-wide, with over half of lung cancer patients relying on radiotherapy (RT). …
Nov 3, 2023 Cancer Research UK Cambridge Institute
Matthew A. Clarke, Ashley Nicholls, Charles Barker, Matt Handler, Saif Ahmad, Gregory J. Hannon, Jasmin Fisher
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Predicted radio-sensitising combination treatments for NSCLC using in-silico CRISPR screens
Targeted radio-sensitising combination treatments for NSCLC using in silico CRISPR screens
Lung cancer is the leading cause of cancer mortality world-wide, with over half of lung cancer patients relying on radiotherapy (RT). …
Oct 8, 2023 The Francis Crick Institute
Matthew A. Clarke, Ashley Nicholls, Charles Barker, Matt Handler, Saif Ahmad, Gregory J. Hannon, Jasmin Fisher
Project Project
Targeted radio-sensitising combination treatments for NSCLC using in silico CRISPR screens