Extended Data Fig. 6: EVOFLUx captures lymphoid cancers’ evolutionary histories and methylation epimutation dynamics.
From: Fluctuating DNA methylation tracks cancer evolution at clinical scale
a-d: Boxplots (whiskers extending to ±1.5×IQR) showing the distribution of inferred growth rate (a), effective population size (b), time since the most recent common ancestor (c), and mean epigenetic switching rate (i.e. mean of μ, ν, γ, ζ; d) by disease. For interpretability, only a subset of the pairwise p values are annotated (Mann-Whitney U tests, hs correction). e: Linear regression between the growth rate and mean epigenetic switching rates separated by cancer types. There is a positive association in B-ALL (P = 2.4e-98, R2 = 0.44) and T-ALL (P = 5.9e-06, R2 = 0.22), a weak negative association in MM (P = 1.6e-05, R2 = 0.18) and no association in CLL (P = 0.060, R2 = 0.005) or the other entities. f-k: Linear regressions between the patient age at sampling and the mean inferred epigenetic switching rate in B cell acute lymphoblastic leukaemia (B-ALL, f), T cell acute lymphoblastic leukaemia (T-ALL, g), chronic lymphocytic leukaemia (CLL, h), mantle cell lymphoma (MCL, i) multiple myeloma (MM, j) and diffuse large B cell lymphoma (DLBCL, k). l: Linear regression between the EVOFLUx inferred initial evolutionary growth rate and the estimate of a linear model of the number of historical lymphocyte counts with the sampling dates of patients with at least 10 sample timepoints before treatment (P = 2e-5, Supplementary Fig. 10).
