Cancer evolution

Matthew A. Clarke
Mar 8, 2023
CC-BY 4.0

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.

Image credit: Adapted from Niida et al., PeerJ 2020. CC-BY 4.0

Evolution Cancer Tumorigenesis Binary Decision Diagram BioModelAnalyzer BMA
Matthew A. Clarke
Matthew A. Clarke
Research Fellow

Research Fellow at PIBBSS (Principles of Intelligent Behavior in Biological and Social Systems) working on mechanistic interpratibility of large language models with sparse autoencoders.

Publications

HOXA9 has the hallmarks of a biological switch with implications in blood cancers
Blood malignancies arise from the dysregulation of haematopoiesis. The type of blood cell and the specific order of oncogenic events …
Laure Talarmain, Matthew A. Clarke, David Shorthouse, Lilia Cabrera-Cosme, David G. Kent, Jasmin Fisher, Benjamin A. Hall
PDF Code Dataset Project DOI
HOXA9 has the hallmarks of a biological switch with implications in blood cancers
Using State Space Exploration to Determine How Gene Regulatory Networks Constrain Mutation Order in Cancer Evolution
Cancer develops via the progressive accumulation of somatic mutations, which subvert the normal operation of the gene regulatory …
Matthew A. Clarke, Steven Woodhouse, Nir Piterman, Benjamin A. Hall, Jasmin Fisher
PDF Code Dataset Project DOI

Events

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
Project Project Project
Predicted combination treatments for NSCLC using in silico CRISPR screening
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
Project Project Project
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
Project Project Project
Predicting personalised treatment combinations with in silico CRISPR screens
Prediction of personalised therapies for triple-negative breast cancer using computational network modelling
May 17, 2023 Kennedy Lecture Theatre, UCL Great Ormond Street Institute of Child Health
Matthew A. Clarke, Ashley Nicholls, Steven Woodhouse, Klara Sinalova, Rashmi Kulkarni, Jean Abraham, Gregory J. Hannon, Graeme Hewitt, Jasmin Fisher
Project Project Project
Prediction of personalised therapies for triple-negative breast cancer using computational network modelling
Predicting personalised therapies for triple-negative breast cancer using computational network modelling
Feb 10, 2023 3:30 PM Clare College, University of Cambridge, UK
Matthew A. Clarke, Ashley Nicholls, Steven Woodhouse, Klara Sinalova, Rashmi Kulkarni, Jean Abraham, Gregory J. Hannon, Graeme Hewitt, Jasmin Fisher
Project Project
Predicting personalised therapies for triple-negative breast cancer using computational network modelling
Executable biology: state-of-the-art methods to enable virtual crispr screens
Jun 14, 2022 9:30 AM The Francis Crick Insitute, London, UK
Matthew A. Clarke, Jasmin Fisher
Project Project Project
Executable biology: state-of-the-art methods to enable virtual crispr screens