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ASXL1 truncating variants in BOS and myeloid leukemia drive shared disruption of Wnt-signaling pathways but have differential isoform usage of RUNX3

BMC Medical Genomics volume 17, Article number: 282 (2024) Cite this article

Abstract

Background

Rare variants in epigenes (a.k.a. chromatin modifiers), a class of genes that control epigenetic regulation, are commonly identified in both pediatric neurodevelopmental syndromes and as somatic variants in cancer. However, little is known about the extent of the shared disruption of signaling pathways by the same epigene across different diseases. To address this, we study an epigene, Additional Sex Combs-like 1 (ASXL1), where truncating heterozygous variants cause Bohring-Opitz syndrome (BOS, OMIM #605039), a germline neurodevelopmental disorder, while somatic variants are driver events in acute myeloid leukemia (AML). No BOS patients have been reported to have AML.

Methods

This study explores common pathways dysregulated by ASXL1 variants in patients with BOS and AML. We analyzed whole blood transcriptomic and DNA methylation data from patients with BOS and AML with ASXL1-variant (AML-ASXL1) and examined differential exon usage and cell proportions.

Results

Our analyses identified common molecular signatures between BOS and AML-ASXL1 and highlighted key biomarkers, including VANGL2, GRIK5 and GREM2, that are dysregulated across samples with ASXL1 variants, regardless of disease type. Notably, our data revealed significant de-repression of posterior homeobox A (HOXA) genes and upregulation of Wnt-signaling and hematopoietic regulator HOXB4. While we discovered many shared epigenetic and transcriptomic features, we also identified differential splice isoforms in RUNX3 where the long isoform, p46, is preferentially expressed in BOS, while the shorter p44 isoform is expressed in AML-ASXL1.

Conclusion

Our findings highlight the strong effects of ASXL1 variants that supersede cell-type and even disease states. This is the first direct comparison of transcriptomic and methylation profiles driven by pathogenic variants in a chromatin modifier gene in distinct diseases. Similar to RASopathies, in which pathogenic variants in many genes lead to overlapping phenotypes that can be treated by inhibiting a common pathway, our data identifies common pathways for ASXL1 variants that can be targeted for both disease states. Comparative approaches of high-penetrance genetic variants across cell types and disease states can identify targetable pathways to treat multiple diseases. Finally, our work highlights the connections of epigenes, such as ASXL1, to an underlying stem-cell state in both early development and in malignancy.

Key points

• ASXL1-driven transcriptomic and DNA methylation dysregulation highlight upregulation of Wnt-signaling pathways and aberrant posterior HOX gene regulation.

• Differential RUNX3 isoform usage between BOS and AML-ASXL1 distinguishes between normal, in BOS, and abnormal, in AML, hematopoiesis.

• The ASXL1-centric approach demonstrates that ASXL1 variants affect some common pathways and mechanisms and highlight the potential for common therapeutic targets.

Peer Review reports

Introduction

Bohring-Opitz syndrome (BOS, OMIM#605309) [1] and acute myeloid leukemia (AML) are two diseases with distinct clinical presentations; BOS is a pediatric, neurodevelopmental disorder caused by germline variants in the additional sex combs-like 1 (ASXL1) gene [2, 3] while AML is a hematologic malignancy derived from myeloid progenitor and hematopoietic stem cells (HSCs) in the bone marrow in which somatic ASXL1 variants are a common driver variant. These two diseases are driven by the same pathogenic, protein-truncating variants in ASXL1. Early developmental disorders and malignancies share a common central cell type: the stem cell, which drives the ensuing disease and highlights potential common molecular mechanisms and cellular targets across clinically distinct disorders.

BOS is characterized by severe to profound intellectual disability, distinctive facial features, and congenital anomalies that affect multiple organ systems [2, 3]. Patients with BOS are at increased risk of developing Wilms tumor and hepatoblastoma [3, 4], rare embryonic kidney and liver tumors that occur in children, yet there are no reported cases of myeloid leukemias, even into the third decade of life [2, 3]. In contrast, myeloid malignancies– including chronic myelomonocytic leukemia (CMML), myelodysplastic syndromes (MDS), myeloproliferative neoplasms, and both secondary and de novo AML [5,6,7,8], exhibit a diverse genetic mutational landscape of which somatic variants of ASXL1 are frequently observed. The presence of ASXL1 variants in AML (AML-ASXL1) are associated with poor overall survival and therapeutic outcome [5]. However, the role of ASXL1 variants across these distinct disorders has not been compared.

Over fifty years ago, Dr. Beatrice Mintz proposed a connection between development and cancer, positing that genetic anomalies in stem cells can lead to cancer by inducing a reversion to an undifferentiated state [9,10,11]. The class of genes that regulate the epigenome, termed epigenes [12,13,14], direct the epigenome structure and play a pivotal role in stem cell differentiation [15, 16] and cancer pathogenesis [17] through control of RNA expression and complex biological signaling [18]. Variants in epigenes dysregulate developmental programs, resulting in structural anomalies or, in somatic variants, reverting cells to an early state with malignant potential [19]. Although the dual presence of epigene variants in both human development and cancer has been documented across numerous studies [20], the specific pathogenic mechanisms driven by variants in the same gene across distinct diseases remain largely unexplored.

ASXL1 encodes Additional sex combs like 1, which functions as a transcriptional regulator and chromatin remodeler within three polycomb repressive complexes (PRC): PRC1, PRC2, and Polycomb Repressive Deubiquitinase (PR-DUB) complex [21, 22]. Despite the crucial role of ASXL1 in development, the factors that control ASXL1 and its interactions with one or more of these complexes remain largely unknown. Previous studies have demonstrated that ASXL1 modulates the balance between the proliferation and differentiation of stem progenitor cell populations. Variants in ASXL1 can disrupt this equilibrium, favoring a stem-cell identity over differentiation in both BOS and myeloid leukemias [23, 24]. In the context of myeloid leukemia, mutations in ASXL1 have been shown to lead to the loss of ASXL1 expression and a consequent reduction of PRC2-mediated histone 3 lysine 27 tri-methylation (H3K27me3), a histone modification associated with gene repression [25].

ASXL1 associates with the PRC2 complex to mediate several downstream events. ASXL1 interacts with PRC2 core components - such as enhancer of zeste homolog 2 (EZH2), a key PRC2 protein that interacts with DNA methyltransferases to modulate DNA methylation (DNAm) while catalyzing specific histone methylation including H3K27me3 [25, 26]. EZH2 is typically enriched at the posterior end of the homeobox A (HOXA) cluster locus, where PRC2 mediates transcriptional repression. The loss of ASXL1 results in reduced EZH2 enrichment at this locus, indicating that ASXL1 plays an essential role in EZH2-mediated repression of the HOXA locus [25]. These interactions highlight how disruptions in ASXL1 can significantly alter both the gene expression and DNA methylation landscape.

Furthermore, overexpression of ASXL1 variants and mouse knockouts have linked ASXL1 to the regulation of splicing [24, 27] suggesting yet another role of ASXL1 during development. Alternative splicing is thought to play a key regulatory role in modulating transitions between stem cell differentiation, proliferation and tissue development. Our previous work studying BOS patient-derived samples, which harbor germline ASXL1 variants, found that many epigenetic and transcriptomic changes are cell-type specific, but there are clear disruptions that are shared across ASXL1-mutated cells, such as dysregulation of the Wnt-signaling pathways [28].

In this study, we prioritized a gene-centric approach, with the hypothesis that ASXL1 variants disrupt the same core pathways regardless of individual genetic background and clinical disease. While these, as well as sex and age and other factors, can contribute to the variance observed in transcriptome and epigenomic data, ASXL1 drives a clear and shared genetic dysregulation across both diseases. Our recent study across BOS patient-derived blood and fibroblasts identified epigenomic and transcriptomic changes associated with ASXL1 variants across tissues, such as the upregulation of Van Gogh-like 2 (VANGL2) [28], a gene associated with non-canonical Wnt-signaling and migration. By integrating distinct disease datasets that share a common pathogenic variant, we aim to pinpoint key molecular events driven by ASXL1 variants and understand how these drive distinct clinical manifestations.

This study explores the landscape of ASXL1 variants driving two distinct diseases - BOS and AML. By employing a comprehensive integrative approach for RNA-sequencing (RNA-seq), DNAm, and exon usage analyses, we found that BOS and AML-ASXL1 patient-derived samples shared an upregulation of Wnt-signaling and DNAm mediated de-repression of specific HOX genes - HOXB4 and HOXA11. However, there remain differences in isoform expression analysis, with distinct RUNX3 isoforms expressed in blood from BOS compared to AML-ASXL1 patient samples. This is the first study to explicitly link and compare the shared epigenetic and transcriptomic changes initiated by ASXL1 variants and highlights potential therapeutic biomarkers.

Samples and methods

Selection and characterization of BOS and AML samples

Our study included data from patient cohorts with Bohring-Opitz syndrome (BOS) and acute myeloid leukemia (AML). Specifically, we leveraged samples from patients with AML harboring ASXL1 variants (AML-ASXL1, Table 1). Informed consent was obtained from all research participants according to the protocol approved by the Hospital for Sick Children (REB#1000038847) and UCLA (IRB#11-001087). Illumina 450K DNA methylation (DNAm) data was acquired for six AML samples from The Cancer Genome Atlas (TCGA) program [29], available on the Genomic Data Commons (GDC) repository [30]. Among these, three samples had somatic ASXL1 variants, serving as the AML-ASXL1 cohort, and three had somatic variants in other genes, serving as AML controls. Transcriptomic data for AML-ASXL1 bone marrow samples (n = 28, samples with evidence of ASXL1 variant in both DNA and RNA) were sourced from the Beat AML cohort (phs001657.v3.p1) [31] and AML-ASXL1 blood samples (n = 6) from TCGA, with non-AML blood controls (n = 60) from the Genotype-Tissue Expression (GTEx) Portal, and bone marrow controls (n = 8) from the publicly available dataset (GSE120444) [32] (Table 1 and Table S1). Since AML-ASXL1 samples can have multiple variants we listed all other variants in Table 1. Additionally, blood samples for RNA-seq and DNAm data from BOS patients (n = 8 RNA-seq, n = 8 DNAm) and healthy blood controls from REACH biobank (n = 11 RNA-seq, n = 26 DNAm) were collected from our previous studies and are publicly available at GSE230685 and GSE230696 [28, 33]. A subset of patient-derived blood (n = 8 BOS, n = 10 healthy controls, n = 4 AML-ASXL1, n = 6 AML controls) RNA-seq data was used to conduct differential exon usage (DEU) and isoform expression analysis (Table 1).

Table 1 Demographic, clinical, and genetic characteristics of AML-ASXL1 patients
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Sample collection, processing, and sequencing

For DNA and RNA extraction, peripheral blood samples were processed using standardized protocols [28, 33], with EDTA tubes for DNA and PAXgene Blood Tubes (BDBiosciences, 762165) for RNA. Genomic DNA was extracted from peripheral blood and bisulfite converted using the EpiTect Bisulfite Kit (EpiTect PLUS Bisulfite Kit, Qiagen, #59124) before being hybridized to the Illumina Infinium Human Methylation EPIC BeadChip following established methods [33]. Cases and controls were randomly assigned a chip position and run in a single batch to reduce batch effects. REACH Biobank RNA-seq libraries were prepared using TruSeq Stranded TotalRNA Library Prep Gold (Illumina, #20020599) with QiaSelect rRNA and globin depletion (Qiagen, #334376 #334386) following established methods [28]. Pooled libraries were sequenced to 40 million reads per sample on a NovaSeq6000, and are publicly available datasets deposited at GSE230685 and GSE230686 [28]. TCGA samples and samples obtained from GSE120444 [32] were also prepared with TruSeq Stranded Total RNA Library Prep Kit (Illumina). GTEx RNA-seq libraries were prepared with TruSeq RNA Library Prep Kit (Illumina). BEAT-AML samples were prepared using Agilent SureSelect Strand-Specific RNA Library Preparation Kit for polyA(+) RNA [31].

RNA-seq and DNA methylation analysis of BOS and AML samples

Preprocessing and quality control of RNA-seq and DNA methylation data

RNA-seq data was processed using our established pipeline [28]. Briefly, reads were mapped to hg38 using STAR 2.7.0e [34], gene counts were generated using featureCounts 1.6.5 [35] and used a gene set of GenCode hg38 annotation v31, composed of 60,662 genes and differential expression adjusted p-value (padj) and log2 fold change (log2FC) were quantified using DESeq2 v1.24.0 [36], correcting for sex and tissue (Table 1 and Table S1).

DNAm data underwent processing through our previously published pipeline [33]. Briefly, the minfi Bioconductor package in R was used to preprocess data including quality control, Illumina normalization and background extraction, followed by identification and filtering of highly differentially methylated sites (|delta beta (Δβ)| > 5%). Significant CpG sites were identified with FDR < 0.05. Principal component analysis (PCA) and hierarchical clustering utilized the BOS DNAm episignature sites (413 CpG sites) [33].

Integration of RNA-seq and DNA methylation data

Integration of RNA-seq and DNAm data utilized beta values for DNAm [37] and transcript per million (TPM) values for RNA-seq [38], enabling the identification of shared and distinct molecular signatures across the conditions studied.

DEXSeq analysis of bulk RNA-seq datasets

The Bioconductor R package DEXSeq v1.50.0 [39] was used to quantify DEU from RNA-seq data. The reads mapping to a single exon bin were normalized against those mapping to all exon bins within the same gene, where an exon bin is a whole exon or part of an exon that arises when an exonic region occurs in different transcripts with varying boundaries. These exon bins were then compared across conditions to test for DEU. Preprocessing was done using two Python scripts built into the DEXSeq package, with the Python package HTSeq. Samtools v1.20 was used to convert BAM files to SAM files. P-values were adjusted for multiple testing by DEXSeq using the Benjamini-Hochberg method. Significant exon bins with DEU were identified with padj < 0.05.

Pathway mapping using KEGG mapper

KEGG Mapper was used to model and visualize the set of differentially expressed genes (DEGs) in the context of biological pathways and molecular networks [40, 41].

Cell type deconvolution using CIBERSORTx

CIBERSORTx is an online bioinformatics tool that assesses cell type-specific gene expression profiles and cellular composition from RNA-seq data [42]. Bulk RNA-seq datasets were compared against the LM22 deconvolution signature matrix containing marker gene profiles to impute cell type proportions and cell expression profiles [43]. LM22 is a signature matrix file consisting of 547 genes that distinguishes between 22 mature human hematopoietic populations from peripheral blood [44].

Results

Study design and genetic landscape of ASXL1 variants

To compare the epigenetic and transcriptomics effects of ASXL1 variants, we collected data from patients with BOS and AML with ASXL1 variants (AML-ASXL1). For BOS patients and matched controls, patient blood samples were collected and RNA-sequencing (RNA-seq) and DNA methylation (DNAm) analyses were performed (Fig. 1A). For comparison across disease-states, we also collected RNA-seq and DNAm data from AML-ASXL1 patients and tissue-matched controls from dbGAP repositories [31]. We re-processed all samples through our RNA-seq and DNAm pipeline to minimize analytical batch effects (Methods).

Fig. 1
figure 1

Study design and genetic landscape of ASXL1 variants (A) Detailed workflow from the collection of patient samples through to the generation and analysis of RNA-sequencing (RNA-seq) and DNA methylation (DNAm) data. Blood samples were collected from Bohring-Opitz syndrome (BOS) patients, and control individuals, and RNA-seq and DNAm were conducted. We compared our BOS samples with RNA-seq and DNAm data from the BEAT AML and The Cancer Genome ATLAS data of in blood and bone marrow from acute myeloid leukemia with ASXL1 variants (AML-ASXL1) or without ASXL1 variants (AML), RNA-seq and DNAm analysis was conducted to examine differential gene expression, differential CpG methylation, dysregulated gene regulatory networks, differential exon usage, and cell type deconvolution. (B) The ASXL1 gene, on chromosome 20q11, is illustrated, highlighting gene domains and the loci of germline variants that cause BOS (below the gene) and somatic variants that drive AML-ASXL1 (above the gene) in this study. Common variant sites (bold font), illustrate the genetic intersections between BOS and AML-ASXL1, and the bracketed numbers preceding the variant annotation indicates the number of samples in this study with that variant. The majority of variants are in the last two exons of the ASXL1 gene

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The ASXL1 gene is made up of 13 exons and encodes a protein that spans 1541 amino acids. The majority of pathogenic variants in this study disrupt the latter half of the protein-coding region encoded by the last two exons of ASXL1. Figure 1B illustrates the germline BOS variants and somatic AML-ASXL1 variants across the ASXL1 gene in this study. Common variant sites between the two disorders are bolded. Variant details for the AML-ASXL1 blood and bone marrow samples in this study are provided (Table 1). No significant differences were identified between BOS and AML-ASXL1 variant allele frequency (VAF) for the pathogenic variant/s in ASXL1. AML-ASXL1 variants had a slightly lower average VAF compared with BOS due to the fact that AML samples are heterogeneous, harboring both leukemia cells with ASXL1 variants and non-leukemia cells which decreases the proportion of reads mapping to the ASXL1 variant.

ASXL1 variants drive transcriptomic dysregulations across blood and bone marrow in AML

We next asked whether the pathogenic ASXL1 variants might drive differentially expressed genes (DEGs) in AML samples. DEG analysis was conducted for AML-ASXL1 bone marrow (n = 26), and AML-ASXL1 blood samples from TCGA (n = 6), as well as tissue-matched healthy controls of blood from the GTEx portal (n = 60), and bone marrow samples from publicly available dataset GSE120444 (n = 8) samples.

To assess the role of tissue specificity on gene expression, we analyzed the AML-ASXL1 data with the blood and bone marrow combined as well as independently with respective matched controls. Our principal components analysis showed that tissue type explained 17% of the variance along PC1, while the presence of an ASXL1 variant explained 11% along PC2 (Figure S1A). In the subsets analyzed within the same tissue types, ASXL1 variant status drove 34% variance in blood and 64% variance in bone marrow samples. DESeq2 analysis was conducted to identify DEGs and these were visualized using unsupervised clustering (Figure S1B). This showed stronger clustering of samples by condition (ASXL1 variant status) than by tissue type (blood or bone marrow).

DESeq2 analysis identified 9527 DEGs, adjusted for tissue type, of which 7889/9527 met a log2 fold change (log2FC) cutoff of |log2FC| ≥ 0.58. Of these, 4610/7889 were upregulated (58.44%, log2FC > 0) and 3279/7889 were downregulated (41.56%, log2FC < 0) in the AML-ASXL1 samples (Figure S1C). The 15 most upregulated and 15 most downregulated DEGs with the largest absolute log2FC are shown in Table S2. Using clusterProfiler, we conducted gene ontology analyses on the set of DEGs to identify dysregulated biological processes. We identified consistent dysregulation of epigenetic functions (padj=6.51E-06), including histone modification (padj=1.40E-05), chromatin modification (padj=2.35E-05), DNA replication (padj=1.30E-05) and conformational change (padj=7.63E-05), and immune activation (padj=1.30E-03) (Figure S1D).

ASXL1 variants in AML-ASXL1 and BOS drive shared transcriptomic dysregulations

To identify DEGs that are common across different disease-types driven by ASXL1 variants, we integrated these AML-ASXL1 DEGs (AML-ASXL1 compared to tissue-matched controls) with the 2118 significant DEGs previously identified in BOS blood samples (BOS n = 8 compared to healthy controls n = 11) [28].

We found that ASXL1 variants, regardless of disease and germline or somatic status, drove common transcriptional changes. We identified 843 common DEGs between the AML-ASXL1 and BOS datasets, of which 566/843 (67.14%) DEGs were dysregulated in the same direction - either upregulated in both datasets, or downregulated in both datasets (Fig. 2A). We plotted fold change for DEGs from the transcriptomic analyses with AML-ASXL1 compared to their respective controls on the x-axis, and BOS compared to their respective controls on the y-axis. This highlighted 388/843 (46.03%) DEGs with a large effect size, with an absolute fold change greater than 1.5 in both BOS and AML-ASXL1. Of these DEGs, 246/388 (63.40%) were dysregulated in the same direction. The 50 DEGs with the largest effect size in BOS are listed in Table 2, with respective log2FC and padj values.

Fig. 2
figure 2

Transcriptomic alterations driven by ASXL1 variants in BOS and AML-ASXL1 highlight key biomarkers and Wnt signaling dysregulation. (A) Fold change integration plot of all 843 genes that were significantly differentially expressed genes (DEGs, padj < 0.05) in both AML-ASXL1 and BOS compared to their respective controls. (B) Gene ontology of all common DEGs in AML-ASXL1 and BOS revealed enrichment in T cell activation, anterior/posterior pattern specification, urogenital system development, axonogenesis, and beta-catenin-TCF complex assembly, among other pathways. Log2 normalized transcript expression analysis of BOS and AML-ASXL1 samples and their respective controls identified key biomarkers and showed significant (C) upregulation in VANGL2 (D) upregulation in GRIK5, and (E) downregulation in GREM2. Wnt signaling co-receptors (F) LRP5 and (G) LRP6 were also significantly upregulated in BOS and AML-ASXL1 compared to controls. (H) ASXL1 was significantly upregulated in AML-ASXL1 but not in BOS. ns or no stars denote p-value > 0.05, * denote p-value ≤ 0.05, ** denote p-value < 0.01, *** denote p-value < 0.001, **** denote p-value < 0.0001

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Table 2 Comparative differentially expressed genes between AML-ASXL1 (blood + bone marrow) and BOS RNA-seq analysis
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Gene ontology analyses demonstrated enrichment in biological processes such as T-cell activation (GO:0042110, padj=4.35E-06), axonogenesis (GO:007409, padj=4.52E-03), and anterior/posterior pattern specification (GO:009952, padj=5.61E-03) (Table 3). While the common DEGs between the AML-ASXL1 and BOS datasets identified T-cell activation and T-cell differentiation among other T-cell functions as significantly enriched pathways, further analysis identified that the DEGs driving these pathways were upregulated in BOS and downregulated in AML-ASXL1, which is consistent with differential T-cell compositions between blood and bone marrow; T-cells comprise approximately 6% of lymphocytes in the bone marrow and approximately 62% in peripheral blood [45]. BOS samples were derived only from blood samples while AML-ASXL1 samples included both blood and bone marrow samples. On the other hand, dysregulation of anterior/posterior pattern specification was driven by upregulation of genes in both BOS and AML-ASXL1.

Table 3 Gene ontology analysis for common DEGs in AML-ASXL1 (blood + bone marrow) and BOS
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Wnt-signaling pathways are dysregulated across BOS and AML-ASXL1 samples

Of note, the genes associated with beta-catenin-TCF complex assembly, a key component of the canonical Wnt signaling pathway, were significantly dysregulated (padj=2.71E-02) in the DEGs across both datasets (Fig. 2B). We previously identified that the canonical Wnt signaling pathway was aberrantly upregulated in BOS patient-derived samples [28]. We further analyzed this dysregulation of Wnt signaling through comprehensive gene ontology network analysis [46, 47]. We first depicted the consensus of DEGs against the KEGG pathway hsa05200 which represents the kernel regulatory factors that contribute to the initiation and progression of pan-cancer. This identified the KEGG pathway hsa04310, representing Wnt signaling pathway, as one of the most significantly dysregulated of the pan-cancer pathways (Figure S2).

These data highlighted key dysregulated genes shared across different cell types in BOS [28], that are also some of the most highly dysregulated genes in the AML-ASXL1 dataset, revealing potential biomarkers and therapeutic targets. These genes include Vang-like 2 (VANGL2), a member of the planar cell polarity pathway [48], glutamate ionotropic receptor kainate type subunit 5 (GRIK5), a pre- and post-synaptic receptor for glutamate, a crucial excitatory neurotransmitter of the central nervous system [28, 49], and gremlin 2 (GREM2), a bone morphogenetic protein antagonist involved in developmental processes and tissue differentiation [50], as well as the transmembrane low-density lipoprotein receptor-related proteins 5 and 6 (LRP5 and LRP6), which are key components of the Wnt signaling pathway. In particular, we identified significant upregulation of VANGL2 (BOS log2FC = 3.80, AML-ASXL1 log2FC = 4.24) (Fig. 2C) and GRIK5 (BOS log2FC = 3.83, AML-ASXL1 log2FC = 4.67) (Fig. 2D) in both BOS and AML-ASXL1 and significant downregulation of GREM2 in both conditions (BOS log2FC=-2.48, AML-ASXL1 log2FC=-1.67) (Fig. 2E; Table 2). Similarly, we identified significant upregulation of LRP5 (BOS log2FC = 1.64, AML-ASXL1 log2FC = 2.65) (Fig. 2F) and LRP6 (BOS log2FC = 1.63, AML-ASXL1 log2FC = 2.21) (Fig. 2G) in both BOS and AML-ASXL1 samples.

These DEGs were previously established in our study across different tissues in BOS as key biomarkers [28]. An independent study comparing AML-ASXL1 to AML without ASXL1 variants also identified VANGL2, LRP5 and LRP6 as three of the most significantly upregulated genes using a limited microarray probe-set [51], supporting that aberrant Wnt signaling occurs in the presence of ASXL1 pathogenic variants. These expression changes suggest roles for these genes in the pathophysiology of disorders associated with ASXL1 variants and highlight their potential as biomarkers for ASXL1 variants. Interestingly, we identified significant upregulation of ASXL1 expression in AML-ASXL1 samples compared to tissue-matched controls (log2FC = 0.46, padj= 1.11E-02) but no significant dysregulation of ASXL1 expression in BOS (Fig. 2H). Overall, our study demonstrates a clear link between ASXL1 variants driving aberrant Wnt-signaling in both BOS and AML.

Analysis of polycomb group (PcG) target genes in BOS and AML-ASXL1 samples

We examined the expression of Polycomb group (PcG) target genes in BOS and AML-ASXL1 samples to determine whether these genes were differentially expressed in the context of ASXL1 mutations (Table S3). Drawing from the list of PcG target genes identified by Bracken et al. (2006), we found that only two genes were significantly differentially expressed across both diseases: Special AT-rich Sequence Binding Protein 1 (SATB1) and Transcription Factor 7 (TCF7). SATB1 was significantly upregulated in both BOS (log2FC = 0.45) and AML-ASXL1 (log2FC = 1.07). In contrast, TCF7 was significantly upregulated in BOS (log2FC = 0.81) but downregulated in AML-ASXL1 (log2FC = -1.25). While SATB1 and TCF7 were the only two PcG target genes that showed significant dysregulation in BOS, a total of 18 out of the 40 target genes were significantly dysregulated in AML-ASXL1.

Analysis of known protein interactors with ASXL1 in BOS and AML-ASXL1

To investigate potential direct effects of ASXL1 mutations on gene expression, we analyzed publicly available gene sets of known ASXL1 interactors. From the 286 ASXL1 interactions identified in the Biological General Repository for Interaction Datasets (BioGRID) database [52], we identified 151 genes that were significantly dysregulated in AML-ASXL1, compared to only 17 in BOS. Notably, 9 of the 17 significant DEGs in BOS were also dysregulated in AML-ASXL1, with 8 of these genes showing upregulation in both conditions. The commonly upregulated genes included: phosphoglycerate dehydrogenase (PHGDH), androgen receptor (AR), solute carrier family 25 member 15 (SLC25A15), RNA binding fox-1 homolog 2 (RBFOX2), gem nuclear organelle associated protein 4 (GEMIN4), chaperonin containing TCP1 subunit 3 (CCT3), inosine monophosphate dehydrogenase 2 (IMPDH2), and phosphoribosylaminoimidazole carboxylase and phosphoribosylaminoimidazole- succinocarboxamide synthase (PAICS).

Additionally, Li et al. (2017) identified 182 ASXL1-interacting proteins through mass spectrometry in HEK293T cells transfected with full-length ASXL1 and C-terminal truncated ASXL1 [53]. Only 7 of these genes were significantly dysregulated, and 5 of them also showed significant dysregulation in AML-ASXL1 blood and bone marrow. Importantly, all 5 genes were dysregulated in the same direction in both diseases.

Furthermore, our analysis of the PRC2 interactome revealed that 9 out of 15 genes in this gene set were significantly dysregulated in AML-ASXL1, while none reached significance in the BOS samples [54, 55]. Notably, 3 of the genes identified in the PRC2 interactome set overlapped with the ASXL1 interactome set identified by BioGRID.

DNA methylation driven de-repression of HOX genes identified across BOS and AML-ASXL1 samples

Our integrated analyses of DNAm leveraged our previously published BOS-specific DNAm episignature [33] to distinguish pathogenic ASXL1 variants from normotypic matched controls and variants of uncertain significance (VUS) in ASXL1 [33]. To assess whether AML-ASXL1 samples and BOS samples shared DNAm signatures and epigenetic changes, we obtained Illumina 450K DNAm data for AML samples (n = 6) from TCGA on the GDC repository [30, 56]. This comprised individuals harboring somatic variants in ASXL1 (n = 3, AML-ASXL1) or somatic variants in other genes (n = 3, AML).

We compared DNAm episignature profiles of blood samples from healthy controls (n = 26), BOS (n = 8), AML-ASXL1 (n = 3), and AML controls (n = 3). PCA based on 413 CpG sites of the BOS DNAm episignature [33] revealed significant differences between the leukemia subtypes. AML-ASXL1 samples clustered distinctly from other AML samples without ASXL1 variants and, instead, clustered more closely with BOS samples (Fig. 3A). Among the 413 BOS episignature CpGs, 90 CpG sites corresponded to transcriptional start sites (TSS), including regions 200 bp upstream (TSS200) or 1500 bp upstream (TSS1500) (Table S4). Unsupervised clustering of CpG methylation across the 413 BOS episignature CpG sites also showed that AML-ASXL1 clusters alongside BOS samples and remains distinct from AML controls (Fig. 3B). This suggests that ASXL1-driven epigenetic alterations transcend disease type in defining DNAm patterns.

Fig. 3
figure 3

Epigenetic landscape of ASXL1 variants in BOS and AML-ASXL1 highlights de-repression of HOX genes. (A) Principal component analysis (PCA) plot demonstrates the closer clustering of acute myeloid leukemia with ASXL1 variants (AML-ASXL1) samples (green, n = 3) with Bohring-Opitz syndrome (BOS) samples (orange, n = 8) compared to AML samples with somatic variants in other genes (AML, purple, n = 3), and control samples (gray, n = 26), illustrating shared epigenetic landscapes driven by ASXL1 variant status. (B) Heatmap representing DNA methylation patterns (DNAm) using the 413 episites identified in the BOS DNAm episignature depicts a consistent clustering pattern of the unique clustering of AML-ASXL1 samples alongside BOS samples. (C) DNAm β values for HOXB4 3’UTR at CpG site cg04014328 highlight the hypermethylation in both BOS and AML-ASXL1 patients compared to controls and other AML samples. (D) RNA-sequencing (RNA-seq) data demonstrates significant upregulation of HOXB4 in BOS and AML-ASXL1 compared to their respective controls. (E) DNAm β values for HOXA11 TSS1500 at CpG site cg00705992 highlight the hypomethylation in both BOS and AML-ASXL1 patients compared to controls and other AML samples. (F) RNA-seq data demonstrates significant upregulation of HOXA11 in AML-ASXL1 but not in BOS

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We examined the methylation status of posterior homeobox A (HOXA) genes, given that the expression of these genes are known to be regulated by ASXL1 [25, 57,58,59], and HOXB3 and HOXB4 [60] (Table 4). The latter two genes are expressed in hematopoietic stem cells (HSCs) and progenitors as “master genes in early hematopoiesis”, and exhibit lineage and differentiation stage-restricted expression [61,62,63].

Table 4 DNA methylation analysis at HOX gene sites across different sample groups
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The HOXB4 3’UTR was hypermethylated in BOS and AML-ASXL1 samples compared to respective controls (Fig. 3C). Consistent with findings that 3’UTR methylation correlates positively with gene expression [64], RNA-seq data showed significant upregulation of HOXB4 (BOS log2FC = 1.41, AML-ASXL1 log2FC = 1.89) (Fig. 3D). Significant upregulation was also observed in HOXB3 (BOS log2FC = 1.34, AML-ASXL1 log2FC = 2.48) (Figure S3A). While overexpression of HOXB4 has been shown to drive enhanced HSC regeneration, deficiency of HOXB3 or HOXB4 leads to defects in proliferative ability of hematopoietic progenitors [65]. Furthermore, one of the key targets of HOXB4 is Wnt signaling [66]. Our investigation of Wnt signaling revealed significant upregulation of Wnt signaling coreceptors LRP5 and LRP6 in both BOS and AML-ASXL1 samples (Fig. 2F and G).

DNAm analysis further identified hypomethylation at specific CpG sites upstream of the HOXA11 TSS (cg00705992 and cg16038003) in BOS and AML-ASXL1 samples, supporting literature that truncating ASXL1 variants lead to derepression of posterior HOXA genes (Fig. 3E and S4A). Interestingly, while ASXL1 variants drove significant upregulation of HOXA11 transcript expression (log2FC = 3.87, padj=1.33E-06) in AML-ASXL1, there was no significant dysregulation of HOXA11 in BOS. Similar trends were identified in other posterior HOXA genes including HOXA5 (Figure S3B and S4B) and HOXA9 (Figure S3C); there was no significant dysregulation in BOS samples.

These findings underscore the pervasive influence of ASXL1 variants in modulating the epigenetic and transcriptomic landscapes across diseases, promoting abnormal gene expression and signaling pathways crucial for disease pathology.

Transcriptomic and epigenomic differences are not driven by differences in cell-type proportion

To ensure that our epigenetic and transcriptomic findings were not influenced by differential blood cell type distributions, we performed a detailed analysis of cell type proportions in the blood samples from BOS and control individuals. This analysis was essential given the heterogeneous nature of whole blood samples, which comprises multiple immune cell types. In our DNAm (Figure S5A) and RNA-seq data (Figure S5B), we compared the proportions of several key immune cell types between BOS and control samples. Table S4 provides a summary of the CIBERSORTx cell type deconvolution results applied to the RNA-seq data. B cells (padj=1.00), NK cells (padj=1.00), monocytes (padj=1.00), and neutrophils (padj=0.678) showed no significant differences, while T cells had a slight significant difference (padj=4.34E-02). Specifically, this was driven by a significant increase in CD4 + T cells (padj=1.97E-04), which play critical roles in effective anti-tumor immunity [67], and not CD8 + T cells (padj=0.44). However, this difference was not identified in the DNAm data. These findings suggest that the epigenetic and transcriptomic findings are not driven by variations in blood cell type proportions.

Differential RUNX3 isoform expression in BOS and AML-ASXL1

In developmental processes, one of the key regulatory mechanisms is mediated through RNA splicing. Differential isoform usage is thought to be a key mechanism driving cell-specific differentiation and disease [68,69,70]. To assess potential isoforms, we performed differential exon usage (DEU) analysis in BOS blood (n = 8), blood controls (n = 10), AML-ASXL1 blood (n = 4), and AML blood controls (n = 6). Using our mapped RNA-seq data, we estimated exon expression using DEXSeq [39] and performed statistical testing to determine DEU in BOS and AML-ASXL1 (Fig. 4A).

Fig. 4
figure 4

Differential exon usage (DEU) analysis of BOS and AML-ASXL1 blood reveals differential isoform usage of RUNX3 between diseases. (A) Using DEXSeq, we obtained exon counts and performed DEU analysis in ASXL1-variant disease samples compared to controls, isoform usage and gene ontology analysis. An integrated analysis was conducted between BOS and AML-ASXL1 samples. We performed DEU analysis for BOS compared to controls (orange outline). (B) Fold change plot of significant DEUs (padj < 0.05) in BOS patients compared to controls highlights RUNX3 as a key affected gene with multiple DEUs. (C) Fitted expression exon usage plot for RUNX3 in BOS blood samples, with significant DEUs indicated with a pink line. Cases are shown in red and controls in blue. The boxed transcript highlights the primary transcript, the longer p46 transcript, observed in BOS blood. We also performed DEU analysis for AML-ASXL1 (green outline). (D) Fold change plot of significant DEUs (padj < 0.05) in AML-ASXL1 patients compared to controls also highlights RUNX3 as an affected gene with multiple DEUs. (E) Fitted expression exon usage plot for RUNX3 in AML-ASXL1 blood. Cases are shown in red and controls in blue. The boxed transcript highlights the primary transcript, the shorter p44 transcript that does not have the first exon, observed in AML-ASXL1

Full size image

Exon counts were calculated for a total of 597,773 exonic regions in 56,940 genes and analyzed for DEU (Figure S6). A total of 88 significant exonic regions from 63 affected genes were identified in BOS blood (padj < 0.05). Some of the most significant DEU from BOS blood were in the following genes: runt-related transcription factor 3 (RUNX3), plasmolipin (PLLP), ST6 beta-galactoside alpha-2,6-sialyltransferase 1 (ST6GAL1), serpin family F member 1 (SERPINF1), and pleckstrin homology and RhoGEF domain containing G6 (PLEKHG6) (Table S6). Many of these genes were also identified as DEGs in the multi-omics study by Lin et al. (2023) and are involved in embryonic or brain development and neuronal differentiation [28, 71, 72]. In AML-ASXL1 blood, a total of 11,624 significant DEU bins from 4521 affected genes were identified (padj < 0.05). Many of the exons with significant DEU in AML-ASXL1 blood had immune- or cancer-related function, such as TBL1X/Y related 1 (TBL1XR1), DEAD-box helicase 42 (DDX42), Rho associated coiled-coil containing protein kinase 1 (ROCK1), neurobeachin like 2 (NBEAL2), and UDP-glucose pyrophosphorylase 2 (UGP2) (Table S7). Across these two analyses, 15 affected genes were shared, most notably RUNX3.

We next asked whether ASXL1 displayed DEU in BOS or AML-ASXL1. We did not observe significant DEU within ASXL1 itself for BOS blood or fibroblast (Figure S8A-B). Unsupervised clustering of ASXL1 exon usage in BOS blood and fibroblast compared to controls showed grouping by tissue type, indicating that tissue specificity, not disease status, drives exon usage for ASXL1 in BOS (Figure S8D). In AML-ASXL1 blood, while we observed DEU for multiple exons bins of ASXL1, these did not correlate with known ASXL1 isoforms (Figure S8C).

While there are shared cellular pathways, these two disorders have distinct isoform expression, even when comparing the same tissue type. Notably, our DEU analysis identified RUNX3 among the most significant DEUs between ASXL1 mutant and control samples in both BOS blood (Fig. 4B) and AML-ASXL1 blood samples (Fig. 4D). While there were several significant DEUs in both BOS blood and AML-ASXL1 blood, these DEUs did not overlap across disorders. Interestingly, the 5 exons bins that were significantly upregulated in BOS samples corresponded to higher usage of the first exon, exon 1 of RUNX3 (Fig. 4B-C, Table S8). On the contrary, we observed DEU of 6 exon bins in RUNX3 for AML-ASXL1 blood samples (Fig. 4D, Table S7) which corresponded to higher usage of the last exons (Fig. 4D, Table S8), and lower usage of the first exon (Fig. 4E). Therefore, opposite effects on exon usage were observed in BOS and AML-ASXL1 blood, which highlights that there do exist clear isoform differences between these two disease states. Finally, DEU of RUNX3 was also identified in BOS fibroblasts samples (Figure S7A) [28] and unsupervised clustering revealed that this DEU was correlated with disease status over tissue type.

RUNX3 is known to have two main transcripts expressed in blood cells— p46 which is expressed from the distal P1 promoter and includes the first exon of the gene, and p44 which is expressed from the proximal P2 promoter and does not include the first exon [73]. The distal P1 promoter has previously been shown to have a role in CD8 + T-cell function; on the other hand, the proximal P2 promoter is often hypermethylated and epigenetically inactivated in solid tumors, leading to inefficient expression compared to the P1 promoter [74, 73].

Our results suggest that the p44 transcript is more highly expressed in AML-ASXL1 blood, while the p46 transcript is more highly expressed in BOS blood.

Discussion

In this study, we aimed to address a fundamental question: do ASXL1 variants exert common molecular effects across distinct disease types? We examined the epigenetic and transcriptomic landscapes associated with ASXL1 variants in BOS and AML, diseases with different clinical manifestations - BOS as a congenital disorder characterized by developmental delays and multiple malformations, and AML as a bone marrow malignancy - and identified several shared features. Both diseases exhibited aberrant activation of Wnt signaling and disrupted posterior HOX gene expression. Notably, we observed differences in RUNX3 isoform usage; the longer isoform, p46, may act as a tumor suppressor in BOS [75, 76] while a shorter RUNX3 isoform predominates in AML. Targeting the longer isoform of RUNX3 may hold therapeutic potential to mitigate the malignant potential of HSCs. This study highlights the shared molecular disruption driven by high-effect ASXL1 variants and suggests potential therapeutic pathways, offering a rationale for the development of targeted therapies applicable to ASXL1 related diseases.

Epigenetic variations are thought to be a major driver of differentiation and maintenance of cell-specification [77] in these genes could drive divergent effects across cell types and diseases. While traditional approaches often focus on directly correcting or targeting the mutated gene, our data, along with previous studies [28, 33], show that the same genetic variant can converge on common pathways across different diseases and cell types [28]. We propose that targeting these shared pathways presents an alternative approach towards treatment for multiple disorders caused by ASXL1 variants. Understanding the interplay of genetic variants, cell-type, genetic background, and disease state can help identify improved therapeutic biomarkers and precision targeted therapies that supersede clinical disease features. For patients with disorders that affect multiple organ systems, such as BOS, the high barriers to effective gene therapy require parallel approaches that target the shared pathways underlying these conditions. This strategy could lead to more versatile and broadly applicable treatments, and better management of diseases associated with ASXL1 variants.

Differential isoform usage associated with ASXL1 mutations

Our study on DEU serves as a proxy for true isoform presence, and indicates that disease states can be heavily influenced by isoform usage. While elements of ASXL1-driven epigenetic and transcriptomic dysregulation are shared among these disorders, RUNX3 isoform usage appears to be distinct and closely tied to disease pathology. ASXL1 mutations have been shown to induce alternative splicing in mutated cell lines [78], however, determining whether the alternative splicing events of RUNX3 in BOS and AML-ASXL1 are primary effects of ASXL1 mutations is beyond the scope of this study.

One possible hypothesis is that ASXL1 mutations may be accompanied by additional mutations in splicing-related genes that directly control RUNX3 isoform usage. Alternatively, ASXL1 mutations may indirectly affect pathways that result in alternate isoform usage. For example, additional spliceosomal mutations and age-related changes in the expression of RNA-binding proteins and RNA modifications could explain the observed differences in RUNX3 isoform usage between BOS and AML-ASXL1.

Direct transcriptomic effects of ASXL1 mutations across BOS and AML-ASXL1

In our study, we examined the relationship between ASXL1 mutations and the expression of a set of 40 PcG target genes previously identified by Bracken et al. (2006) [79]. The limited number of significant dysregulated genes in BOS (only 2 out of 40) compared to AML-ASXL1 (18 out of 40) may reflect the inherent limitations of a smaller sample size associated with BOS, a rare disease.

One of the polycomb complexes that ASXL1 is known to interact with is PRC2, which plays a critical role in gene regulation through epigenetic mechanisms. Mutations in ASXL1 can disrupt the normal function of PRC2, and loss of function mutations have been shown to cause decreased H3K27me3 levels at target genes. Our analysis of the PRC2 interactome gene set published in previous studies [54, 55] revealed that 9 out of 15 genes showed significant dysregulation in AML-ASXL1 compared to none in BOS. This discrepancy suggests a more pronounced effect of ASXL1 mutations on gene regulation within the context of leukemia.

Notably, SATB1 and TCF7 are significantly dysregulated in both diseases, and may be a result of direct interactions with ASXL1. The consistent upregulation of SATB1 in both BOS and AML-ASXL1 suggests its role as a key regulator of chromatin architecture and gene expression relevant to both conditions. SATB1 is a chromatin organizer and transcription factor which is enriched at gene promoters and enhancers involved in long-range chromatin interactions [80,81,82].

In contrast, the significant upregulation of TCF7 in BOS and downregulation in AML-ASXL1, suggests context-dependent regulatory functions. SATB1 plays a crucial role in maintaining appropriate transcriptional programs within naive CD8 + T cells, and TCF7 is one of the key naive transcription factors targeted by SATB1 [80]. Intriguingly, other key naive transcription factors regulated by SATB1 binding, including BCL6, BCL11B, FOXO1, and LEF1, also exhibited significant downregulation in AML-ASXL1 while being upregulated in BOS. This pattern may reflect distinct cellular environments and the varying influences of ASXL1 mutations in different disease states.

In this study, we also examined dysregulation of gene expression in gene subsets known to interact with ASXL1. The limited number of dysregulated genes in BOS may be a consequence of smaller sample size. Interestingly, 5 out of 7 ASXL1-interacting proteins dysregulated in BOS blood were also differentially expressed in AML-ASXL1, and dysregulated in the same direction, supporting the possibility of shared pathogenic mechanisms, despite the different clinical presentations.

Limitations of this study

There remain some key limitations to our study. First and foremost, AML is a heterogeneous disorder with multiple genetic variants and aberrations present in every sample. We suspect that the heterogeneity, even in the presence of the ASXL1 variant, requires a larger sample size to detect true and consistent effects due to ASXL1 variants. However, we do believe that patient-derived germline variants as seen in BOS provide a clean background for isolation of the genetic effect of ASXL1 variants and prioritization of putative targets that are common to both diseases. Moreover, the presence of epigenetic and transcriptional changes observed in both BOS and AML-ASXL1 samples serve to highlight the strong and consistent effect of ASXL1 on gene expression, which supersedes even the effects of tissue [28] and disease [51, 83].

Research and clinical implications of this study

RASopathies encompass a range of genetic syndromes such as Noonan syndrome, Costello syndrome, and neurofibromatosis type 1, and are characterized by variants in multiple genes that regulate the activity of the RAS/MAPK signaling pathway [84]. These variants lead to hyperactive signaling, resulting in developmental abnormalities, cardiovascular defects, and an increased risk of certain cancers [84]. The successful grouping of these disorders has allowed for targeting of shared pathways and drug repurposing which not only leverages existing drugs with known safety profiles such as lovastatin and everolimus but also accelerates the development of targeted therapies, reducing the time and cost associated with bringing new treatments [85,86,87,88,89,90]. Mouse studies and early clinical trials have suggested that targeted inhibition of the RAS/MAPK pathway can mitigate some of the severe manifestations of these RASopathies [90,91,92,93,94]. We believe that a common approach can be used to ameliorate some of the clinical and molecular effects of chromatinopathies.

One intriguing extension from our analysis is the potential to repurpose or harness novel Wnt inhibitors or chromatin modifying drugs to ameliorate the effects of ASXL1 variants in a range of diseases, including BOS and subtypes of AML. Our analysis highlights the strong influence of ASXL1 variants on transcriptional regulation, with over 500 genes differentially expressed in both AML-ASXL1 and BOS data sets, indicating a shared molecular dysfunction. This shared dysregulation is particularly evident in genes involved in epigenetic regulation, chromatin modification, and the canonical Wnt signaling pathway, which are critical for cell fate determination and proliferation. Our findings suggest a common thread in the molecular mechanisms of ASXL1 variants– through dysregulation of Wnt signaling pathways and posterior HOX gene expression.

Modulating Wnt-signaling in ASXL1-mutated diseases

Our data highlighted the aberrant activation of Wnt signaling associated with ASXL1 variants across disease types. Our transcriptomic integration showed that the Wnt signaling co-receptors LRP5 and LRP6 (Fig. 2A, F and G) and non-canonical Wnt signaling through VANGL2 are all upregulated at the transcriptomic level and, in BOS samples, also at the protein level [28]. VANGL2 is a key transmembrane protein in the planar cell polarity pathway and is thought to drive cellular orientation in 3D space and migration patterns, both of which are pivotal in both oncogenic transformations and developmental anomalies. Our work provides an orthogonal validation of previous RNA-microarray data in AML-ASXL1 that also identified upregulation of LRP6 and VANGL2 [51] using a less sensitive approach. This independent data and analysis highlights that despite the different genomic technologies, disease states, genetic background of the cells and differences in samples, ASXL1 variants drive a shared effect among these and other genes. To our knowledge, there are three human and disease-specific data sets in which transcriptomic, and in the case of BOS, protein data, confirm the abnormal activation of Wnt signaling markers associated with ASXL1 variant [28, 33, 95]. Overall, the consistent dysregulation of key genes across different diseases suggests that these findings are not merely artifacts of disease-specific processes but are potentially pivotal drivers of pathophysiology associated with ASXL1 variants. The discovery of these cross-disease biomarkers offers a promising avenue for further research and development of diagnostic tools and therapeutic strategies. Therapeutics targeting VANGL2 and LRP6 could potentially be used as targets for ASXL1-precision therapies.

The Wnt signaling pathway has been a tantalizing target for drug development in a variety of solid tumors and leukemias, but to date there are no FDA approved drugs that are targeted towards tumors with over-active Wnt-signaling. One challenge is that these treatments often engender significant side effects associated with modulation of this central pathway [96]. Currently, there are multiple phase I and II clinical trials that target different aspects of Wnt pathways such as the beta-catenin destruction complex [96, 97] or direct blockage of Beta-catenin with its binding partners. To date, there are no approved precision therapies for patients with ASXL1 variants, and careful modulation of the canonical Wnt signaling pathway represents a potential therapeutic option for BOS patients and for AML-ASXL1. Studies in preclinical models, such as mice and rats, are needed to understand the interplay between these Wnt-signaling pathways and ASXL1 variants and potential off target effects.

Decreased malignant transformation in BOS hint at potential biomarkers for AML-ASXL1

ASXL1 variants in AML lead to dysregulation of genes involved in patterning in hematopoiesis and myeloid differentiation such as HOXA genes that were both differentially expressed and methylated in AML-ASXL1. These data are consistent with previous work showing that ASXL1 variants disrupt the normal repression of posterior HOXA genes during myeloid cell differentiation [25, 33, 98]. This dysregulation likely drives the proliferation of immature myeloid cells, a hallmark of AML. Conversely, in BOS, while similar DNAm changes were identified, the corresponding transcriptional dysregulation was not observed, suggesting that other additional factors are required for transformation. Clinically, no BOS patients have been diagnosed with AML, but they do have an increased risk of Wilms tumor, a pediatric kidney tumor [99].

The absence of myeloid dysplasia in BOS, despite the presence of ASXL1 variants, suggests that additional factors are necessary to trigger leukemogenesis. These factors could include secondary genetic variants, epigenetic changes, or specific microenvironmental or tissue-specific cues that are absent in BOS patients but present in the context of AML. In AML, where ASXL1 variants are only in the leukemia stem cells (LSCs), there is a potential imbalance in paracrine signaling factors and receptors between the microenvironment (no ASXL1 variant) and the LSC (with ASXL1 variant).

While we observe many similarities in the transcriptional profiles between these two diseases, the genes that are dysregulated in opposite directions might provide therapeutic targets and biomarkers– centered around turning AML-ASXL1 profile closer to that seen in BOS blood, thereby ameliorating the malignant potential. We found that BOS cells primarily expressed the longer p46 isoform of RUNX3, which plays a tumor suppressor role and might explain the decreased transformation in BOS compared with AML-ASXL1 that expresses the shorter p44 isoform. Reactivation of the longer RUNX3 isoform in AML might provide a potential therapeutic strategy in AML-ASXL1.

Mouse models of ASXL1 mutations show defects in HSC proliferation and myeloid differentiation [100, 101] in addition to disrupted development. While these models provide valuable insight, they do not always fully reflect the complexities of human diseases. Notably, mouse models typically feature homozygous deletion mutants, while human variants are heterozygous and truncating variants. Additionally, while there are some similarities in hematopoiesis between humans and mice, there are limited effective mouse models for myeloid leukemogenesis, emphasizing significant differences in disease manifestation. Importantly, ASXL1 mutations in mouse models do not lead to myeloid transformation without the presence of additional mutations [102]. This distinction is particularly evident when considering BOS patients, who are generally much younger compared with the average AML patient. The absence of leukemic transformation in BOS patients is intriguing and may suggest novel therapeutic approaches in AML-ASXL1.

Conclusion

This comparative analysis provides a deeper understanding of the complex molecular underpinnings of ASXL1 variants in BOS and AML, highlighting shared and unique molecular features. By delineating the genetic, epigenetic, and transcriptomic impacts of these variants, our study not only advances the understanding of the molecular pathology of these conditions but also sets the stage for the development of targeted therapeutic strategies that address the specific molecular alterations associated with ASXL1 variants. Novel or repurposed therapies targeted against the effects of ASXL1 can be used regardless of clinical presentation: germline or somatic, and provides a pathway to drug development even for the rarest conditions. Overall, our study advocates for a gene-centric approach in understanding the molecular basis of diseases associated with ASXL1 variants.

Our study contributes to a broader understanding of how genetic variants can influence disease across traditional phenotypic boundaries. This not only challenges existing paradigms of disease classification but also opens new avenues for innovative therapeutic strategies that are driven by molecular commonalities rather than solely clinical features. This gene-centric perspective could redefine therapeutic strategies and promote the development of more precise and effective treatments for genetically driven disorders.

Data availability

The datasets analyzed during the current study were collected from our previous study and are available in the GEO repository, under accession number GSE230685 and GSE230696. A subset of the DNA methylation (DNAm) datasets generated during the current study are not publicly available due to institutional ethical restrictions but are available from the corresponding author on reasonable request to authors.Illumina 450 K DNAm data and RNA-sequencing (RNA-seq) data for AML and AML-ASXL1 blood samples were acquired from The Cancer Genome Atlas (TCGA) program, available on the Genomic Data Commons (GDC) repository, accessible at https://portal.gdc.cancer.gov/projects/TCGA-LAML. Transcriptomic data for AML-ASXL1 bone marrow samples were acquired from the Beat AML cohort, available from dbGaP phs001657.v3.p1. Transcriptomic data for blood controls were obtained from the Genotype-Tissue Expression (GTEx) Portal, accessible at https://www.gtexportal.org/home/downloads/adult-gtex/bulk_tissue_expression. Transcriptomic data for bone marrow controls were obtained from the publicly available dataset by Oetjen et al., 2018, and are available in the GEO repository, under accession number GSE120444.

Abbreviations

BOS:

Bohring-Opitz syndrome

AML:

Acute Myeloid Leukemia

AML-ASXL1 :

Acute Myeloid Leukemia with ASXL1 mutation

CMML:

Chronic Myelomyelocytic Leukemia

MDS:

Myeloid Dysplastic Syndrome

DNAm:

DNA Methylation

RNA-seq:

RNA-Sequencing

DEG:

Differentially Expressed Genes

PRC:

Polycomb Repressive Complex

PR-DUB:

Polycomb Repressive Deubiquitinase

LSC:

Leukemia Stem Cells

DEU:

Differential Exon Usage

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Acknowledgements

We would like to acknowledge the wonderful patients and families that make this work possible.

Funding

This work was supported by the following funding sources awarded to V.A.A.: NIH Office of the Director DP5OD024579, the W.M. Keck Foundation, the Toffler Foundation and ASXL Research Related Endowment Pilot Grant (2020–2022). IL was supported by NIH T32GM008042. AW is was supported by NHGRI F31HG013462. This work was supported by the ASXL Rare Research Endowment (ARRE) (MC2015-16), the Canadian Institutes of Health Research (CIHR) grants (IGH-155182 and MOP-126054), and the Ontario Brain Institute (Province of Ontario Neurodevelopmental Disorders (POND) network (IDS11-02) grants to RW.

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Author notes

  1. Isabella Lin and Zain Awamleh contributed equally to this work.

Authors and Affiliations

  1. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA

    Isabella Lin, Mili Sinvhal, Andrew Wan, Leroy Bondhus, Angela Wei & Valerie A. Arboleda

  2. Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA

    Isabella Lin, Mili Sinvhal, Andrew Wan, Leroy Bondhus, Angela Wei & Valerie A. Arboleda

  3. Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA

    Isabella Lin, Mili Sinvhal, Andrew Wan, Leroy Bondhus, Angela Wei, Bianca E. Russell & Valerie A. Arboleda

  4. Department of Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada

    Zain Awamleh, Angela Wei & Rosanna Weksberg

  5. Interdepartmental Bioinformatics Program, UCLA, Los Angeles, CA, USA

    Valerie A. Arboleda

  6. Department of Human Genetics, Division of Clinical Genetics, UCLA, Los Angeles, CA, USA

    Bianca E. Russell

  7. Department of Pediatrics, Division of Clinical & Metabolic Genetics, The Hospital for Sick Children, Toronto, ON, Canada

    Rosanna Weksberg

  8. Institute of Medical Sciences, Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada

    Rosanna Weksberg

  9. Molecular Biology institute, UCLA, Los Angeles, CA, USA

    Valerie A. Arboleda

  10. Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA

    Valerie A. Arboleda

Authors
  1. Isabella Lin
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  2. Zain Awamleh
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  4. Andrew Wan
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  9. Valerie A. Arboleda
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Contributions

VA, ZA and IL designed and conceptualized the study. BR coordinated sample collection for patient samples. IL, LB, MS, AWa and AWe performed data generation and transcriptomic analysis for patient-derived samples. ML performed analysis for exon usage. AW performed analysis for blood cell proportion. ZA and RW performed data generation and analysis of DNA methylation data. All authors reviewed the manuscript.

Corresponding author

Correspondence to Valerie A. Arboleda.

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Ethics approval and consent to participate

Ethics approval was obtained through the Institutional Ethics Review Boards at UCLA and Hospital for Sick Children. Informed consent was obtained from all research participants according to the protocol approved by the Hospital for Sick Children (REB#1000038847) and UCLA (IRB#11-001087).

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Not applicable.

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The authors declare no competing interests.

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Lin, I., Awamleh, Z., Sinvhal, M. et al. ASXL1 truncating variants in BOS and myeloid leukemia drive shared disruption of Wnt-signaling pathways but have differential isoform usage of RUNX3. BMC Med Genomics 17, 282 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12920-024-02039-7

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Keywords

BMC Medical Genomics

ISSN: 1755-8794

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