Evolution forecasting for real-time blood-cancer risk prediction

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Cancer is a disease of evolution that plays out in our bodies over decades. As our cells divide, occasional errors ("mutations") in DNA replication occur which can disrupt the normal function of the cell. If these "mutant clones" survive long enough they can acquire further cancer-causing mutations that eventually drive the total break-down of controlled cell proliferation. The first steps of this evolutionary process begin years before the development of cancer. This raises the possibility that these early events could be used as a bellwether for predicting who is at risk of cancer. However, because it is difficult to measure the evolution taking place inside our bodies over such long timescales, our understanding of the evolutionary dynamics driving early cancer remains cursory. A key gap in our understanding is our inability to identify which mutant clones will progress to lethal cancers and which will remain benign. Serial blood samples collected annually from hundreds of thousands of healthy people give us a superpower that can fill in these gaps in understanding. We can "zoom in" on the people who develop cancer, then "rewind" time by analysing blood samples collected years before the cancer was diagnosed. This provides a detailed "fossil record" of the disease, enabling one to determine when the cancer first arose and to watch the entire evolutionary life-history of the tumour unfold, one DNA error at a time. An attractive initial target for this ambitious vision is the aggressive blood cancer Acute Myeloid Leukemia (AML) because it is characterised by a relatively small number of cancer-causing mutations, which are readily detectable in the blood. This UKRI FLF application sets out an innovative long-term research programme for blood cancer prediction and early detection. To achieve this, first we will combine population genetic theory with the vast amounts of sequencing data from the blood of >50,000 individuals to characterise the evolutionary dynamics that defines "normal". Second, by exploiting an extraordinary set of serial blood samples in hundreds of AML cases and cancer-free controls we will generate a unique dynamic dataset that paints a highly quantitative genetic portrait of how AML evolves from healthy tissue. Third, by mining this rich data resource, we will train a set of mathematical and statistical models that use evolutionary dynamics theory and simulation (which we have previously pioneered) to make probabilistic "forecasts" of leukemia risk from a blood sample. This FLF application will thus establish blood-cancers as a "model system" for early cancer detection by combining unique longitudinal samples, novel sequencing technologies and emerging statistical methods to predict blood cancer risk.