Integrative methylome-transcriptome analysis unravels cancer cell vulnerabilities in infant MLL-rearranged B cell acute lymphoblastic leukemia
Juan Ramón Tejedor, … , Agustín F. Fernández, Pablo Menéndez
Juan Ramón Tejedor, … , Agustín F. Fernández, Pablo Menéndez
Published May 13, 2021
Citation Information: J Clin Invest. 2021;131(13):e138833. https://doi.org/10.1172/JCI138833.
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Research Article Genetics Oncology

Integrative methylome-transcriptome analysis unravels cancer cell vulnerabilities in infant MLL-rearranged B cell acute lymphoblastic leukemia

Abstract

B cell acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer. As predicted by its prenatal origin, infant B-ALL (iB-ALL) shows an exceptionally silent DNA mutational landscape, suggesting that alternative epigenetic mechanisms may substantially contribute to its leukemogenesis. Here, we have integrated genome-wide DNA methylome and transcriptome data from 69 patients with de novo MLL-rearranged leukemia (MLLr) and non-MLLr iB-ALL leukemia uniformly treated according to the Interfant-99/06 protocol. iB-ALL methylome signatures display a plethora of common and specific alterations associated with chromatin states related to enhancer and transcriptional control in normal hematopoietic cells. DNA methylation, gene expression, and gene coexpression network analyses segregated MLLr away from non-MLLr iB-ALL and identified a coordinated and enriched expression of the AP-1 complex members FOS and JUN and RUNX factors in MLLr iB-ALL, consistent with the significant enrichment of hypomethylated CpGs in these genes. Integrative methylome-transcriptome analysis identified consistent cancer cell vulnerabilities, revealed a robust iB-ALL–specific gene expression–correlating dmCpG signature, and confirmed an epigenetic control of AP-1 and RUNX members in reshaping the molecular network of MLLr iB-ALL. Finally, pharmacological inhibition or functional ablation of AP-1 dramatically impaired MLLr-leukemic growth in vitro and in vivo using MLLr-iB-ALL patient–derived xenografts, providing rationale for new therapeutic avenues in MLLr-iB-ALL.

Authors

Juan Ramón Tejedor, Clara Bueno, Meritxell Vinyoles, Paolo Petazzi, Antonio Agraz-Doblas, Isabel Cobo, Raúl Torres-Ruiz, Gustavo F. Bayón, Raúl F. Pérez, Sara López-Tamargo, Francisco Gutierrez-Agüera, Pablo Santamarina-Ojeda, Manuel Ramírez-Orellana, Michela Bardini, Giovanni Cazzaniga, Paola Ballerini, Pauline Schneider, Ronald W. Stam, Ignacio Varela, Mario F. Fraga, Agustín F. Fernández, Pablo Menéndez

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Figure 6

Integration of DNA methylation and gene expression data.

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Integration of DNA methylation and gene expression data. (A) Schematic d...
(A) Schematic depicting the workflow for integrating DNA methylation and RNA-Seq data using the ELMER algorithm. (B) Barplot depicting the number of gene expression–correlating hyper- or hypomethylated CpGs (absolute Pearson’s correlation > 0.5). (C) Violin plots showing the distribution of gene expression changes (log2 fold change of the indicated groups versus healthy BCPs) for those genes with consistent correlation with dmCpG probes. (D and E) Scatter plots indicating the correlation between DNA methylation and gene expression for the genes LMO2 (D) and BRIP1 (E), respectively. Pearson’s correlation (Cor) score is indicated for each comparison. (F) Scatterplot of Reactome pathway enrichment analyses. Genes with consistent correlation with DNA methylation were used for enrichment calculation versus the background data set, which included all the genes with detectable expression in our RNA-Seq data set (18,668). Color range denotes the significance of the represented ontology (adjusted P value), while dot size indicates the ratio between the number of hits identified and the total number of hits in a given ontology. (G) Heatmap representation of enrichment of TFBSs in gene expression–correlating hyper- or hypomethylated CpGs. Color range indicates enrichment or underrepresentation of a given motif (log2 odds ratio) as calculated by the ELMER algorithm.