Matthew A. Clarke
Matthew A. Clarke
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Lung Cancer
Predicting Personalised Therapeutic Combinations in Non-Small Cell Lung Cancer Using In Silico Modelling
The disease burden from non-small cell lung cancer (NSCLC) adenocarcinoma is substantial, with around a million new cases diagnosed …
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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Project
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
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)
Clarke, Barker & Nicholls et al. (2025)
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. (2025)
Radio-sensitising combination treatments for NSCLC using in silico CRISPR screens
Lung cancer is the leading cause of cancer mortality world-wide, and over half of lung cancer patients rely on radiotherapy (RT). …
Jun 19, 2023
Francis Crick Institute
Matthew A. Clarke
,
Ashley Nicholls
,
Matt Handler
,
Saif Ahmad
,
Gregory J. Hannon
,
Jasmin Fisher
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