Chronic FLT3-ITD Signaling in Acute Myeloid Leukemia Is Connected to a Specific Chromatin Signature

Abstract

Acute myeloid leukemia (AML) is characterized by recurrent mutations that affect the epigenetic regulatory machinery and signaling molecules, leading to a block in hematopoietic differentiation. Constitutive signaling from mutated growth factor receptors is a major driver of leukemic growth, but how aberrant signaling affects the epigenome in AML is less understood. Furthermore, AML cells undergo extensive clonal evolution, and the mutations in signaling genes are often secondary events. To elucidate how chronic growth factor signaling alters the transcriptional network in AML, we performed a system-wide multi-omics study of primary cells from patients suffering from AML with internal tandem duplications in the FLT3 transmembrane domain (FLT3-ITD). This strategy revealed cooperation between the MAP kinase (MAPK) inducible transcription factor AP-1 and RUNX1 as a major driver of a common, FLT3-ITD-specific gene expression and chromatin signature, demonstrating a major impact of MAPK signaling pathways in shaping the epigenome of FLT3-ITD AML.

Graphical abstract

Figure 1

FLT3-ITD+ AML Displays a Characteristic mRNA Expression Profile (A) PCR amplification of the transmembrane coding region of the FLT3 gene used to identify ITD mutations. On the left are the sizes of the DNA marker bands (M). Below are the estimated numbers of normal and mutated FLT3 alleles. (B) Hierarchical clustering of Pearson correlation coefficients of mRNA values. For each sample the mutations present in the most commonly mutated genes are indicated as M in the table underneath. (C–F) Identification of genes that differ by at least 2-fold in a comparison of the average log2 mRNA microarray values for a core group of three ITD+ AML samples (ITD1, ITD2, and ITD3) to the equivalent values determined from either the average of two independent PBSC samples (C), the average of four WT FLT3 AML samples (WT2, WT3, WT5, and WT7), for CD14+ bone marrow cells (E), or a second group of ITD+ AML samples (ITD4, ITD6, ITD7, and ITD8) used here for validation of the ITD+ pattern (F). The 134 genes that are consistently expressed at 2-fold higher levels in ITD+ AML in each of the three comparisons in (C) and (E) are shown in red, and the remaining genes that are upregulated in at least (C) are shown in blue. In (C) we also highlight 77 genes with values that are consistently 2-fold lower in ITD+ AML compared to each of PBSCs, WT FLT3 AML, and CD14+ cells in black, and the remaining genes that are also downregulated at least in (B) are shown in green. (D) Average log2 mRNA values for the specific groups of 134 upregulated and 77 downregulated genes in each of the 5 groups used in this analysis, with the SD for mRNA expression shown as error bars.

Figure 2

FLT3-ITD Consistently Deregulates the Same Genes in AML (A–C) Average mRNA microarray values for FLT3-ITD target genes in 2 datasets obtained from 461 (Verhaak et al., 2009) or 200 (Cancer Genome Atlas Research Network, 2013) AML samples. (A) Average profiles for 134 upregulated genes, with a subset of validated genes listed on the right. (B) Average profiles for 77 downregulated genes. (C) Profiles for individual upregulated genes.

Figure 3

FLT3-ITD+ AML Has a Characteristic Chromatin Signature (A) Hierarchical clustering of Pearson correlation coefficients of DHS peaks. For each sample the mutations present in the most commonly mutated genes are indicated as M in the table underneath. (B and C) UCSC Genome Browser views of the DNase-seq patterns (B), and relative mRNA values (C) of genes surrounding an ITD+ AML-specific DHS in MED16 marked by a red oval, and a cluster of adjacent genes upregulated in ITD+ AML. mRNA values are shown as a ratio of the values detected in the AML samples compared to PBSCs (red: up, green: down). (D) Heatmap depicting hierarchical clustering of the relative DNase-seq signals seen in each distal DHS peak in each AML sample relative to PBSCs. (E) Profiles of the DNase-seq signals within each 400-bp window centered on each peak for PBSC and ITD1, with peaks shown in the order of increasing DNase-seq tag count signal for ITD1 relative to PBSC. This analysis includes the union of all peaks present in either ITD1 or in PBSC. Shown to the right of the DNase-seq profiles are the relative mRNA expression values for genes with the nearest transcription start sites (TSS) in ITD1 relative to PBSC, and the DNA methylation signals for the nearest TSS and for the DHS in ITD1 relative to PBSCs.

Figure 4

FLT3-ITD Mutations Are Associated with a Specific Subset of DHSs (A–C) Venn diagrams depicting the overlaps between populations of DHSs that are 2-fold upregulated in AML samples compared to PBSCs (FC > 2). (A) Intersections between ITD1, ITD2, and ITD3. (B) Intersections between ITD1, ITD2, and WT1, which has a NPM1 mutation. (C) Intersections between WT2, WT3, and WT5. (D) Gene ontology analysis of the genes closest to the 1,216 ITD-specific DHSs defined in (A). (E) Plot of the relationship between the log2 mRNA fold change for genes upregulated in ITD+ AML versus the distance to the nearest ITD-specific DHS for all of the 134 ITD-specific genes (red), and the remainder of the 703 upregulated genes (blue) for genes located within 800 kb of an ITD-specific DHS. (F and G) Log2 mRNA microarray values (as in Figure S1) (F) and UCSC Genome Browser views for DNase-seq and RUNX1 ChIP-seq data (G) for the ITD-specific genes SCARA3, WDR86, and CTSG/GZMB. ITD-specific DHSs are enclosed by red ovals.

Figure 5

ITD-Specific DHSs Have a Specific Motif Signature and Bind RUNX1 (A) Result of de novo motif search of 1,216 ITD-specific DHSs using HOMER. (B and C) Alignment of ITD-specific DHS motifs with distal DHSs present in either ITD1 or PBSCs (B) with the rolling averages of motif densities plotted underneath (C). (D–F) Analysis of RUNX1 ChIP-seq data comparing ITD1 (D), ITD4 (E), and PBSCs (F) plotted alongside the ITD1 and ITD4 DHS data. (F) Depiction of the rolling averages of the ChIP signals.

Figure 6

AP-1 and RUNX Pathways Are Activated in ITD+ AML (A) Log2 RUNX1 mRNA microarray values. (B) UCSC Genome Browser view for RUNX1 DNase-seq. (C) Sequence of the ITD-specific RUNX1 +43-kb DHS with the indicated sequence motifs underlined, which underlay the ITD-specific DHS signature. (D) EMSA using nuclear extracts from the indicated cell lines grown in the presence and absence of stimulation for 2 hr with phorbol 12-myristate 13-acetate and calcium ionophore A23187 (PMA/I) to induce AP-1 activity. Some assays include AP-1 or non-specific (NS) DNA competitors or FOS antibodies. (E) Western blot analyses extracts from ITD+ MV4-11 cells treated with FLT3 or mismatch (MM) control siRNA, using the indicated antibodies. (F) RT-QPCR analysis of ITD target gene mRNA expression after treatment of MV4-11 cells with either siRNA against FLT3 (left) or with the MAPK pathway inhibitors PD98059, SP600125, and SB202190 directed against MEK1/2, JNK, and p38, respectively (right), from three independent experiments for each gene. Values were calculated relative to GAPDH. (G) ChIP analyses of FOS and RUNX1 binding to ITD-specific DHSs, with normal IgG used as a control. Data are expressed relative to FOS or RUNX1 binding to a region in the inactive IVL locus. (H) Average FOS ChIP peak profiles obtained from MV4-11 cells treated with either FLT3 or MM siRNA.

Figure 7

ITD-Specific DHS Motifs Are Occupied in ITD+ AML (A) Model depicting the generation of DNase I footprints at DHSs. (B) DNase I cleavage patterns within ITD1-specific footprints predicted by Wellington. Upper strand cut sites are shown in red and lower strand cut sites in green within a 200-bp window centered on each footprint (gap), for all 5,142 ITD-specific footprints, and for those containing AP-1 sites. (C) Analysis of overrepresented binding motifs within each footprint using HOMER. (D) Profiles of motifs in ITD-specific footprints plotted on the same DHS axes as used in Figure 2E. (E) Analysis of statistically significant co-localization of the indicated footprinted motifs within 50 bp of each other using bootstrapping analysis (Z score). (F) Model of an ITD-specific transcriptional network based on our data.