Quantifying the evolutionary dynamics of extra-chromosomal DNA in human cancers.

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Cancer is a disease of the genome. Cells accumulate errors in their genetic code, which can cause uncontrolled cell proliferation, cancer growth and death. We now understand that the formation and progression of cancer is a natural consequence of an ongoing evolutionary process in an aging body. It is a major goal of the cancer research community to quantify these evolutionary processes, to gain insight ahead of time in the hope of improved early detection and treatment. We made substantial progress to unravel the evolution of the cancer genome in the last decade. Yet, whenever we were getting close to a complete understanding, new discoveries emerge. The most recent surprise is the role of so called extra-chromosomal DNA (ecDNA) in cancer. The healthy genome of a human is partitioned into 23 pairs of chromosomes. In cancer, fragments of chromosomes can break and form ecDNA, ring like circular DNA structures with a length of a few hundred to millions of base pairs. In healthy cells, upon cell division, both daughter cells inherit a complete set of chromosomes. This is made possible by regions on each chromosome called centromeres. ecDNA lack centromeres. Consequently, ecDNA is not segregated equally amongst daughter cells and some cancer cells do not inherit any ecDNA at all. We therefore thought that ecDNA is just a random by-product of distorted cancer genomes with no actual causal function. Strikingly, recent studies show that the opposite is true. Circular extra-chromosomal DNA (ecDNA) are a feature of some of the most difficult to treat cancers, e.g. in brain and lung. Patients with detectable ecDNA have worst prognosis and are less likely to respond to treatment. Yet very little is known about the process of ecDNA evolution within tumours. Here I propose to develop the framework to quantify the evolutionary process of circular ecDNA in human cancers. Because ecDNA do not properly assert during cell divisions, we cannot just apply established tools that measure the evolution of chromosomal DNA in cancer cells. We need a new theoretical framework and new computational techniques to interpret and quantify experimental and clinical observations. The backbone of the fellowship and my major expertise is to develop a comprehensive theoretical understanding of ecDNA evolutionary dynamics, using mathematics and computer simulations. In preliminary work, I showed that this is feasible within a rigorous mathematical analysis (Pichugin, Huang & Werner, BioRxiv 2019) and the continuation of this programme will provide us tools to identify individual patients whose tumours are caused by ecDNA, predict how ecDNA adapt to treatment and how treatment strategies can be adjusted to optimise response. Furthermore, it is important to integrate our theory with experimental data. I will continue my collaborations with experts in the UK, USA and Germany (see attached support letters) in order to integrate theory with cutting-edge experimental techniques (see Figures for proof of concept and preliminary data). Secondly, during the duration of the fellowship I will establish the expertise for independent experiments on ecDNA evolution within my group that will allow me to develop my own dry-wet capacity. I will be closely supported by my mentors, who run cancer genomics programmes within our Centre and via exchanges and visits of my students and technicians to the labs of my international collaborators. In summary, extra-chromosomal DNA drives some of the most difficult to treat cancers with worst prognosis. Yet, little is known about the evolutionary process of ecDNA. I propose to develop the mathematical/computational tool-box to describe the evolution of ecDNA and test predictions of these theories in existing and newly generated cancer genomic and imagine data. Quantifying ecDNA evolution is essential for the interpretation of cancer genomes, informed forecasting and optimal design of therapeutic strategies.