Research

Quantitating somatic evolution in cancer

Tumours have tremendous intra-tumour genetic heterogeneity. We do now understand that a stochastic somatic evolutionary process of mutation accumulation and selection can explain these patterns. However, it remains difficult to quantify these evolutionary forces within individual tumours. One of our main goals is the development of methods that can explain and quantitate these processes. To do so we combine mathematical descriptions of somatic evolutionary processes and cancer genomic data.

Theoretical Population Genetics

An important aspect of our work is to develop new theoretical tools rooted in population genetics. We often combine stochastic branching processes and individual based computer simulations to explain and quantitate somatic evolutionary processes.

Resistance Evolution and Treatment Strategies

Exciting new cancer treatments are developed continuously e.g. novel targeted therapies or Immunotherapy. Unfortunately, emerging treatment resistance remains a major challenge. Our aim is to quantitate the process of resistance evolution within single patients. We use ctDNA (cell free tumour DNA) to follow resistance evolution over time, which allows us to forecast relapse times, providing a treatment window of opportunity.

Stochastic Dynamics of Extra Chromosomal DNA Elements

Recent studies have identified extra chromosomal DNA elements (ecDNA) to contribute to tumour evolution and resistance emergence. These elements have a random pattern of inheritance and thus the stochastic dynamics of these elements differs greatly from standard somatic evolutionary dynamics. We develop a theoretical understanding of these dynamics and test these predictions in patient data.

Predator-prey co-evolution

We also have an interest in non-somatic evolutionary processes, in particular, co-evolutionary processes of interacting species and the resulting stochastic dynamics. Our lab has been involved in co-evolutionary experiments in predator-prey systems. Questions involve the understanding of the emergence and maintenance of diversity as well as the interpretation of complicated population genetics data under co-evolutionary processes.

Visit my laboratory website here.

Other Activities

• Member of Society of Mathematical Biology
• Guest Editor in PLoS Computational Biology

Major Funding

• 2022 - Cancer Reserach UK and the National Cancer Institute Cancer Grand Challenge, Team eDyNAmiC, £660,000
• 2022-2026 - UKRI Future Leaders Fellowship, "Quantifying the evolutionary dynamics of extrachromosomal DNA in human cancers," ~£1.4M
• 2021-2024 - Human Frontiers in Science Program, "Asexual evolution in cancer, seagrass and corals," £330,000
• 2021-2024 - European Research council, PhD training network "EvolGamesPlus" £400,000
• 2019-2022- Barts Charity, Mathematics of somatic evolutionary processes

Recent Publications

The mutational landscape of the adult healthy parous and nulliparous human breast Cereser B, Yiu A, Tabassum N et al. Nature Communications (2023) 14(7)

Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors Zapata L, Caravagna G, Williams MJ et al. Nature Genetics (2023) 55(7) 451-460

Phenotypic plasticity and genetic control in colorectal cancer evolution Househam J, Heide T, Cresswell GD et al. Nature (2022) 611(7) 744-753

The evolutionary dynamics of extrachromosomal DNA in human cancers Lange JT, Rose JC, Chen CY et al. Nature Genetics (2022) 54(7) 1527-1533

Quantification of spatial subclonal interactions enhancing the invasive phenotype of pediatric glioma Tari H, Kessler K, Trahearn N et al. Cell Reports (2022) 40(7)

Shining light on dark selection in healthy human tissues Werner B Nature Genetics (2021) 53(7) 1525-1526

Evolution via somatic genetic variation in modular species Reusch TBH, Baums IB, Werner B Trends in Ecology & Evolution (2021) (1)

Measuring single cell divisions in human tissues from multi-region sequencing data Werner B, Case J, Williams MJ et al. Nature Communications (2020) 11(7)

Evolutionary dynamics of neoantigens in growing tumours Graham T, Eszter L, Marc W et al. Nature Genetics (2020) (1)
https://www.biorxiv.org/content/10.1101/536433v1

Subclonal reconstruction of tumors using machine learning and population genetics Graham T, Caravagna G, Heide T et al. Nature Genetics (2020) 52(1) 898-907
https://www.biorxiv.org/content/10.1101/586560v1

For additional publications, please click here

Team

Postdoctoral Researchers

• Dr Nathaniel Mon Père
• Dr Jessica Renton
• Dr Magnus J. Haughey

PhD Students

• Francesco Terenzi
• Alexander Stein

Biography

After I received my Diploma in Physics from the University of Leipzig in 2010 (Germany), I started my PhD (2010-2013) with Arne Traulsen in the Evolutionary Theory Group at the Max Planck Institute for Evolutionary Biology, where I mostly worked on mathematical models of cell population dynamics. I then continued for a brief Post Doc with Arne (April 2013 – January 2015) to work on the dynamics of haematopoietic stem cell and telomere shortening during ageing.

In 2015, I moved to London to become the first Post Doc in the Cancer Evolutionary Genomics & Modelling Group of Andrea Sottoriva at the Institute of Cancer Research. In the next 4 years we worked on many aspects of somatic evolution, with the overall theme of how to combine evolutionary theory and cancer genomic data.

In October 2019 I joined the newly established Centre of Cancer Evolution and Computational Biology at the Barts Cancer Institute to establish my own research group on Evolutionary Dynamics.