Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia

Abstract

Genetic heterogeneity contributes to clinical outcome and progression of most tumors, but little is known about allelic diversity for epigenetic compartments, and almost no data exist for acute myeloid leukemia (AML). We examined epigenetic heterogeneity as assessed by cytosine methylation within defined genomic loci with four CpGs (epialleles), somatic mutations, and transcriptomes of AML patient samples at serial time points. We observed that epigenetic allele burden is linked to inferior outcome and varies considerably during disease progression. Epigenetic and genetic allelic burden and patterning followed different patterns and kinetics during disease progression. We observed a subset of AMLs with high epiallele and low somatic mutation burden at diagnosis, a subset with high somatic mutation and lower epiallele burdens at diagnosis, and a subset with a mixed profile, suggesting distinct modes of tumor heterogeneity. Genes linked to promoter-associated epiallele shifts during tumor progression showed increased single-cell transcriptional variance and differential expression, suggesting functional impact on gene regulation. Thus, genetic and epigenetic heterogeneity can occur with distinct kinetics likely to affect the biological and clinical features of tumors.

Figure 1

EPM levels at diagnosis compared to normal bone marrow segregate patients into two groups with distinct clinical outcomes. (a) Time to relapse analysis for patients (n = 137) with high (red) or low (black) EPM values at diagnosis compared to normal bone marrow. (b) Time to relapse analysis for patients (n = 137) with high (red) or low (black) EPM values assessed from promoter-annotated eloci (loci in promoters that were shared by at least 75% of patients were included). (c) Time to relapse analysis for patients with high (blue) or low (green) somatic mutation burden in diagnosis samples (n = 48). Mantel-Cox log rank test was used for the survival analysis (ac).

Figure 2

AML is characterized by high epiallele shift and variance. (a) Schematic diagram representing the DNA methylation patterns compared between CD34⁺ normal bone marrow controls (NBM), diagnostic AML and relapsed AML patient samples. (b,c) log₁₀(EPM) values of diagnostic (b) and relapsed (c) patient samples versus NBMs. (d) Violin plot of the EPM values between the AML patient samples and NBMs and intra-patient relapse versus diagnosis (Wilcoxon rank sum tests: ***P < 0.001). (e) log₁₀(EPM) values between AML diagnosis and relapse samples.

Figure 3

Disease stage-specific epiallele patterns define unique subsets of AML patients. (a) Proportions of eloci that are diagnosis-specific (light green), shared (green), or relapse-specific (dark green) are shown for each cluster defined using K-means clustering. (b) Proportions of somatic mutations that were diagnosis-specific (light blue), shared (blue), or relapse-specific (dark blue) are shown for the subset of patients with exome-sequencing data within each cluster defined by the abundance of eloci in (a). (c,d) Number of somatic mutations (log10) for each eloci cluster at diagnosis (c; P = 0.048) or relapse (d; P = 0.008). (e,f) Proportion of somatic mutations whose variant allele frequency are increased (e; P = 0.367) or decreased (f; P = 0.0012) by 10% or more at relapse compared to diagnosis. (cf) Wilcoxon rank sum tests: *P < 0.05; **P < 0.01; NS = not significant. ID = patient counts.

Figure 4

Assessment of epiallele shift and genetic changes in serial samples from a single patient. ERRBS and WGS were performed in serial samples from a single patient (AML_130: diagnosis (T1) and four relapse collections: T2T5). (a) Epiallele shift (EPM) compared to NBMs (n = 14) at each time point (error bars are the standard error of the mean). (b) Somatic mutation burden at each time point. (c) The number of eloci that are shared and unique between all time points. (e) Density plot of the dominant epiallele frequency detected at eloci across all time points. (f) Density plot of the tumor variant allele frequencies detected at each time point.

Figure 5

Transcriptional variance is associated with high epiallele shift at promoters. (a) Density plot of log₂ fold change of transcript levels of genes with eloci within their promoters (red), and genes without eloci in their promoters (blue) as measured from bulk cell populations (n = 19 paired patient samples). (b) Violin plot of the log₂ fold change variance in transcript expression from genes with or without eloci in their promoters in bulk cell populations (Wilcoxon signed rank test; P = 3.82×10⁶). (c) Violin plot of the percentage of genes that are differentially expressed (DEGs: absolute log fold change > 1; Wilcoxon signed rank test: P = 3.82×10⁶) with or without eloci in their promoters in bulk cell populations (Wilcoxon signed rank test). (d) Violin plots of transcript expression level variance as measured by single cell RNA-sequencing (AML_130 relapse sample) and association (ANOVA test, P < 2.2×10¹⁶) with low (< 0.05), intermediate (0.050.2) and high (0.21) epiallele shift within respective gene promoters. Wilcoxon signed rank tests and ANOVA test: ^***P < 0.001.