Finally, fCpGs record clonal dynamics over time. In two patients with Richter transformation (RT), the emergence of an altered phenotype with dismal outcomes, we inferred that the RT clone diverged from the non-RT lineage over 30 years prior to its clinical manifestation! (6/7)

In chronic lymphocytic leukaemia (CLL) the high-risk U-CLL subtype had much higher growth rates than the low-risk M-CLL subtype. Stratifying by growth rate within these groups was highly prognostic of the time to first treatment. (5/7)

Across 2000 lymphoid cancer samples, we found staggering heterogeneity between different diseases and molecular subtypes! Paediatric (ALL) grew much more rapidly than adult cancers, and the aggressive 11q23/MLL subtype grew even faster than the other subtypes. (4/7)

We designed a computational method, EVOFLUx, to infer the early evolutionary history of a cancer from these data. For instance: when did the most recent common ancestor emerge and how rapidly was the cancer growing at that time? Is the cancer undergoing a subclonal expansion? (3/7)

We used DNA methylation “evolving barcodes” to record the lineage of cells, which we termed fCpGs. These could be measured using low-cost, bulk methylation arrays. The clonal dynamics of cells are recorded in the patterns of these fCpGs (2/7)

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In B-ALL, MCL and CLL, different clinical subtypes had markedly different growth rates and effective pop sizes. In CLL, the growth rate was highly prognostic of time to first treatment, whilst the pop size was a better predictor of over survival. (6/9)

We also able to infer the phylogenetic relationship between longitudinal samples. In CLL, some patients undergo Richter transformation (RT), the emergence of an aggressive phenotype – this lineage diverged >30 years prior to clinical detection in 2 CLL patients. (5/9)

Applying EVOFLUx to quantify the evolution of 1,976 lymphoid malignancies, we found widespread heterogeneity between and within cancer types. Our subclonal inference was validated with matched deep whole exome sequencing. (4/9)

We developed a new computational method (EVOFLUx) to infer a cancer’s evolutionary history from methylation data. We can learn how quickly a cancer is growing, when its most recent common ancestor existed, its effective pop size and the presence of subclonal expansions. (3/9)

Evolution is a dynamic process, but often clinicians only have a single snapshot of a tumour. In our paper last year, we discovered fluctuating methylation as a natural “evolving barcode”, which encodes the evolutionary history of a set of cells. (2/9)
doi.org/10.1038/s415...

Does the evolutionary history of a cancer predict patient outcome? In our new pre-print, we introduce a powerful new method to measure a cancer’s past using bulk methylation data. In chronic lymphocytic leukaemia (CLL), these inferred histories are highly prognostic! (1/9)
doi.org/10.1101/2023...