Mutagenesis and its biomedical consequences
Lead Research Organisation:
University of Edinburgh
Department Name: UNLISTED
Abstract
We are working to understand what causes changes to the DNA sequence in our cells. New DNA changes are called mutations. Some of these mutations drive the development of cancer, some cause inherited disease and they may contribute to the ageing of our bodies. But many, perhaps most, have no effect.
Mutations can come about through natural processes in our cells, or they can be caused by exposure to damaging environments such as ultraviolet radiation in sunlight, dangerous chemicals in tobacco smoke, and as a side-effect of some medical treatments. Our work reveals the mechanisms that lead to these mutations.
Learning about the causes of mutations tells us a great deal about the processes in our cells that copy and repair DNA. This can suggest new ways to treat cancers. The patterns of mutations we see in a cancer can reveal defects that may be allowing the cancer to grow, but might also be used to target treatments specifically to that cancer.
Working out the patterns of new mutations helps us find those rare ones that have an important effect. This can help understand disease or target therapies. We specialise in making the most of data that already exists, which extracts the maximum insight from past investments and minimises the use of animals. Where the data we need doesn’t exist, we work with other groups in the MRC Human Genetics Unit, and international collaborators to efficiently generate it.
Mutations can come about through natural processes in our cells, or they can be caused by exposure to damaging environments such as ultraviolet radiation in sunlight, dangerous chemicals in tobacco smoke, and as a side-effect of some medical treatments. Our work reveals the mechanisms that lead to these mutations.
Learning about the causes of mutations tells us a great deal about the processes in our cells that copy and repair DNA. This can suggest new ways to treat cancers. The patterns of mutations we see in a cancer can reveal defects that may be allowing the cancer to grow, but might also be used to target treatments specifically to that cancer.
Working out the patterns of new mutations helps us find those rare ones that have an important effect. This can help understand disease or target therapies. We specialise in making the most of data that already exists, which extracts the maximum insight from past investments and minimises the use of animals. Where the data we need doesn’t exist, we work with other groups in the MRC Human Genetics Unit, and international collaborators to efficiently generate it.
Technical Summary
Genetic mutations provide the raw material for evolution, they are responsible for heritable disease, drive the development of cancer and are implicated in somatic ageing. This programme strives to understand the molecular mechanisms that give rise to new mutations and to be able to interpret their consequences.
We previously discovered the process of lesion segregation, in which mutagenic DNA lesions can persist for multiple cellular generations resulting in strand asymmetric mutation patterns and generating genetic diversity within a clonally expanding cell population. In this project we will capitalise on these insights to explore the mechanisms of mutagenesis and DNA repair – with strand-specificity, at genome-wide scale, and single base resolution.
Understanding the patterns of mutation occurrence also helps us establish the functional and selective consequences of mutations. We will generate reliable null expectations of mutation occurrence against which we will compare observed mutations, allowing the inference of selection. This strategy will be applied across scales from between species evolution, to human population variation, and down to the cellular scale studying the clonal expansions of cells both in vitro and in vivo (zebrafish and mouse models). Through innovative high-throughput analyses of existing and collaboratively generated data, we will identify mutations that perturb gene expression and demonstrate how clonally accumulated genetic heterogeneity influences cellular phenotype and clonal selection. This is directly relevant to the accrual of somatic mosaicism through development, and the initiation and progression of cancer.
Leading with computational analysis, our research extracts maximum insight from pre-existing datasets, and then benefits from collaboration and infrastructure within the MRC Human Genetics Unit to generate key missing data. The results of this research will (1.) reveal why mutations occur where they do, (2.) give mechanistic insights that could both guide cancer treatment and identify therapeutic targets, and will (3.) advance understanding of clonal selection, particularly in the context of combinatorial genetic diversity.
We previously discovered the process of lesion segregation, in which mutagenic DNA lesions can persist for multiple cellular generations resulting in strand asymmetric mutation patterns and generating genetic diversity within a clonally expanding cell population. In this project we will capitalise on these insights to explore the mechanisms of mutagenesis and DNA repair – with strand-specificity, at genome-wide scale, and single base resolution.
Understanding the patterns of mutation occurrence also helps us establish the functional and selective consequences of mutations. We will generate reliable null expectations of mutation occurrence against which we will compare observed mutations, allowing the inference of selection. This strategy will be applied across scales from between species evolution, to human population variation, and down to the cellular scale studying the clonal expansions of cells both in vitro and in vivo (zebrafish and mouse models). Through innovative high-throughput analyses of existing and collaboratively generated data, we will identify mutations that perturb gene expression and demonstrate how clonally accumulated genetic heterogeneity influences cellular phenotype and clonal selection. This is directly relevant to the accrual of somatic mosaicism through development, and the initiation and progression of cancer.
Leading with computational analysis, our research extracts maximum insight from pre-existing datasets, and then benefits from collaboration and infrastructure within the MRC Human Genetics Unit to generate key missing data. The results of this research will (1.) reveal why mutations occur where they do, (2.) give mechanistic insights that could both guide cancer treatment and identify therapeutic targets, and will (3.) advance understanding of clonal selection, particularly in the context of combinatorial genetic diversity.
Organisations
- University of Edinburgh (Lead Research Organisation)
- Yale University (Collaboration)
- EMBL European Bioinformatics Institute (EMBL - EBI) (Collaboration)
- German Cancer Research Center (Collaboration)
- Institute for Research in Biomedicine (IRB) (Collaboration)
- Cancer Research UK Cambridge Institute (Collaboration)
- RIKEN (Collaboration)
- Medical Research Council (MRC) (Collaboration)
Publications
Aitken S
(2025)
Genetic background sets the trajectory of cancer evolution
Anderson CJ
(2024)
Strand-resolved mutagenicity of DNA damage and repair.
in Nature
Ginno PA
(2024)
Single-mitosis dissection of acute and chronic DNA mutagenesis and repair.
in Nature genetics
Nicholson M
(2024)
DNA lesion bypass and the stochastic dynamics of transcription-coupled repair
in Proceedings of the National Academy of Sciences
| Description | Cancer Grand Challenges |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Contribution to a national consultation/review |
| Impact | Prioritising major research investments for cancer. |
| URL | https://www.cancergrandchallenges.org/ |
| Description | Dissection and targeting of novel pathogenic mechanisms mediating PIK3CA-driven overgrowth |
| Amount | £654,436 (GBP) |
| Funding ID | MR/Z506321/1 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 07/2024 |
| End | 08/2027 |
| Description | Duncan Odom |
| Organisation | German Cancer Research Center |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | Co lead of collaboration along with Duncan Odom, DKFZ. Experimental design. Genomic data analysis following burst mutagenesis. |
| Collaborator Contribution | Experimental design. Primary data generation. |
| Impact | Outputs are currently in review. |
| Start Year | 2022 |
| Description | FANTOM6 Consortium |
| Organisation | RIKEN |
| Department | Institute of Physical and Chemical Research (RIKEN) |
| Country | Japan |
| Sector | Public |
| PI Contribution | Planning of large scale systematic study on lncRNA and their effect on gene regulation. Planning and initiating analysis of the resulting data. |
| Collaborator Contribution | Planning, coordination and primary data generation. |
| Impact | Project is ongoing - no impact yet. |
| Start Year | 2015 |
| Description | Liver Cancer Evolution Consortium |
| Organisation | Cancer Research UK Cambridge Institute |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Computational analysis of tumor whole genome and transcriptome sequence data to profile mutation patterns. |
| Collaborator Contribution | Generation, histological profiling and whole genome and transcriptome sequencing of carcinogen induced tumors in rodents. |
| Impact | No published outcomes yet, less that 1 year into project and data generation still under way. Scientific discovery & insight. Manuscript writing, project coordination. |
| Start Year | 2017 |
| Description | Liver Cancer Evolution Consortium |
| Organisation | EMBL European Bioinformatics Institute (EMBL - EBI) |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Computational analysis of tumor whole genome and transcriptome sequence data to profile mutation patterns. |
| Collaborator Contribution | Generation, histological profiling and whole genome and transcriptome sequencing of carcinogen induced tumors in rodents. |
| Impact | No published outcomes yet, less that 1 year into project and data generation still under way. Scientific discovery & insight. Manuscript writing, project coordination. |
| Start Year | 2017 |
| Description | Liver Cancer Evolution Consortium |
| Organisation | German Cancer Research Center |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | Computational analysis of tumor whole genome and transcriptome sequence data to profile mutation patterns. |
| Collaborator Contribution | Generation, histological profiling and whole genome and transcriptome sequencing of carcinogen induced tumors in rodents. |
| Impact | No published outcomes yet, less that 1 year into project and data generation still under way. Scientific discovery & insight. Manuscript writing, project coordination. |
| Start Year | 2017 |
| Description | Liver Cancer Evolution Consortium |
| Organisation | Institute for Research in Biomedicine (IRB) |
| Country | Spain |
| Sector | Academic/University |
| PI Contribution | Computational analysis of tumor whole genome and transcriptome sequence data to profile mutation patterns. |
| Collaborator Contribution | Generation, histological profiling and whole genome and transcriptome sequencing of carcinogen induced tumors in rodents. |
| Impact | No published outcomes yet, less that 1 year into project and data generation still under way. Scientific discovery & insight. Manuscript writing, project coordination. |
| Start Year | 2017 |
| Description | Sarah Aitken |
| Organisation | Medical Research Council (MRC) |
| Department | MRC Toxicology Unit |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Joint supervision of ECAT PhD student John Connelly. Machine learning based analysis of cancer histology whole slide images. |
| Collaborator Contribution | Joint supervision of ECAT PhD student John Connelly. Qualified pathologist evaluation of cancer histology whole slide images. |
| Impact | Multidisciplinary: Computer machine learning, mathematical modelling, histopathology, oncology, genomics. |
| Start Year | 2021 |
| Description | Sarah Aitken - Yale |
| Organisation | Yale University |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Collaborative research on DNA damage and mutagenesis |
| Collaborator Contribution | Data provision, expert opinion, collaborative writing. |
| Impact | Publication: Anderson et al, 2024. Publication: Nicholson et al, 2024. Publication: Ginno et al, 2024. Preprint: Aitken et al, 2025. |
| Start Year | 2024 |
| Description | CRUK Grand Challenges |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Policymakers/politicians |
| Results and Impact | Cancer Grand Challenges strategy meeting. 20 International experts reporting to major funders on research opportunities and priorities for major investment programme. |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.cancergrandchallenges.org/ |
| Description | Conference organisation MITS |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Established an international conference "Mutations in Time and Space", held in Edinburgh in 2024, Boston US in 2025. |
| Year(s) Of Engagement Activity | 2024,2025 |
| URL | https://www.mutationmeeting.com/ |
