Pre-B cell acute lymphoblastic leukemia (ALL) is the most common cancer in children. Although modern treatment protocols cure a majority of patients, relapsed disease remains a leading cause of pediatric death from cancer. Epigenetic dysregulation plays a critical role in ALL therapeutic resistance, with mutations in epigenetic regulators identified at the time of relapse. Epigenetic dysregulation and variability can propel disease evolution by driving leukemia cell heterogeneity and plasticity. To uncover epigenetic drivers of ALL progression, we sought to comprehensively map the DNA methylation landscape in matched diagnosis-relapse ALL specimens using methods that effectively capture epigenetic variability, aiming to identify the regulators and effectors of epigenetic instability in ALL progression. We aimed to construct genome-wide maps encompassing the dynamic DNA methylation landscape in paired diagnosis-relapse samples of a defined genetic subtype of ALL. We carried outwhole genome bisulfite sequencing (WGBS) on 36 samples representing paired diagnosis and relapse specimens from 18 patients with t(12;21) ETV6-RUNX1 pre-B ALL treated on modern Children's Oncology Group protocols. This represents the most common chromosome translocation in pre-B ALL which is typically associated with favorable prognosis, highlighting the importance of identifying epigentic drivers of treatment failure and relapse. We analyzed WGBS data using the informME method, an information-theoretic approach that effectively captures methylation stochasticity and has been previously applied to the identification of non-mutated epigenetically altered driver genes in ALL (Koldobskiy et al., Nature Biomedical Engineering 5: 360-76, 2021). This approach allows the use of WGBS reads to encapsulate epigenetic variability, in order to map genomic regions undergoing evolution from diagnosis to relapse. DNA methylation levels measured by WGBS are used to generate DNA methylation probability distributions, and comparisons of these distributions from diagnosis to relapse are accomplished using an information-theoretic measure of Jensen-Shannon distance (JSD) which measures the distance between two probability distributions and thereby includes stochasticity rather than conventional analyses which only evaluate mean methylation differences. Subsequently, we ranked genomic regions and genes with significant JSD between diagnosis and relapse and tested for functional enrichments. Our analysis revealed significant differences in the DNA methylation landscape between ALL diagnosis and relapse, associated with genome-wide DNA hypomethylation and globally increased methylation entropy in the majority of relapse samples compared to their corresponding diagnosis samples. Focusing on specific genomic regulatory regions, we noted that despite the global trend of genome-wide hypomethylation at relapse, bivalent promoters showed hypermethylation at relapse compared to diagnosis. Mapping methylation discordance revealed a striking enrichment among the target genes of Polycomb Repressive Complex 2 (PRC2), a core epigenetic regulator linked to suppression of gene transcription. Notably, among the 500 genes exhibiting the greatest JSD between diagnosis and relapse, the top four enrichments converged on targets of EED and SUZ12, critical components of PRC2. To further evaluate the role of PRC2 in pre-B ALL, we employed CRISPR-Cas9-mediated genome editing to silence EED, in the B-cell leukemia Reh cell line harboring t(12;21) ETV6-RUNX1 to evaluate the influence of PRC2 modulation on gene expression and the leukemic phenotype. We employed a powerful approach that comprehensively analyzes DNA methylation genome-wide and quantifies epigenetic variability to map evolution of the dynamic epigenetic landscape in a common genetic subtype of ALL from diagnosis to relapse. This approach permits the dissection of gene-regulatory mechanisms underlying leukemic progression. Identification of PRC2 targets as the predominant effectors of epigenetic instability in leukemic progression can inform novel therapeutic strategies, such as approaches aimed at restricting epigenetic variability that contributes to therapeutic resistance.
No relevant conflicts of interest to declare.
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