My group combines mathematics, computer simulations and genomic information to study evolutionary processes. We aim to understand how a tumour's evolutionary history is reflected in its genome, how evolution can be quantified in individual tumours and how this information predicts future evolution.
Measuring single cell divisions in human cancers from multi-region sequencing data. BioRxiv (2019). doi: https://doi.org/10.1101/560243
Longitudinal Liquid Biopsy and Mathematical Modeling of Clonal Evolution Forecast Time to Treatment Failure in the PROSPECT-C Phase II Colorectal Cancer Clinical Trial. Cancer Discov (2018) 8(10):1270-1285. PMID: 30166348
Identification of neutral tumor evolution across cancer types. Nature Genetics (2016) 48(3):238-244. PMID: 26780609
Reconstructing the in vivo dynamics of hematopoietic stem cells from telomere length distributions. eLife (2015) 4: e08687. PMID: 26468615
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.
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.
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.
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.
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.
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)
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
Exploiting evolutionary steering to induce collateral drug sensitivity in cancer Acar A, Nichol D, Fernandez-Mateos J et al. Nature Communications (2020) 11(1)
Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios Williams MJ, Zapata L, Werner B et al. eLife (2020) 9(7)
The feedback between selection and demography shapes genomic diversity during coevolution Retel C, Kowallik V, Huang W et al. Science Advances (2019) (1)
Abstract 4232: Spatially constrained tumor growth affects the patterns of clonal selection and neutral drift in cancer genomic data Chkhaidze K, Heide T, Werner B et al. (2019) (10) 4232-4232
Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data Chkhaidze K, Heide T, Werner B et al. PLoS Computational Biology (2019) 15(7)
How many samples are needed to infer truly clonal mutations from heterogenous tumours? Opasic L, Zhou D, Werner B et al. BMC Cancer (2019) 19(1)
Evolutionary dynamics of residual disease in human glioblastoma Spiteri I, Caravagna G, Cresswell GD et al. Annals of Oncology (2019) 30(7) 456-463For additional publications, please click here
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.