NSD2 E1099K drives relapse in pediatric acute lymphoblastic leukemia by disrupting 3D chromatin organization

Narang, Sonali; Evensen, Nikki A.; Saliba, Jason; Pierro, Joanna; Loh, Mignon L.; Brown, Patrick A.; Kolekar, Pandurang; Mulder, Heather; Shao, Ying; Easton, John; Ma, **aotu; Tsirigos, Aristotelis; Carroll, William L.

doi:10.1186/s13059-023-02905-0

NSD2 E1099K drives relapse in pediatric acute lymphoblastic leukemia by disrupting 3D chromatin organization

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Published: 04 April 2023

Volume 24, article number 64, (2023)
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NSD2 E1099K drives relapse in pediatric acute lymphoblastic leukemia by disrupting 3D chromatin organization

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Sonali Narang ORCID: orcid.org/0000-0002-1138-0067¹,
Nikki A. Evensen¹,
Jason Saliba¹,
Joanna Pierro²,
Mignon L. Loh³,
Patrick A. Brown⁴,
Pandurang Kolekar⁵,
Heather Mulder⁵,
Ying Shao⁵,
John Easton⁵,
**aotu Ma⁵,
Aristotelis Tsirigos^1,6,7^na1 &
…
William L. Carroll^1,8,9^na1

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Abstract

Background

The NSD2 p.E1099K (EK) mutation is shown to be enriched in patients with relapsed acute lymphoblastic leukemia (ALL), indicating a role in clonal evolution and drug resistance.

Results

To uncover 3D chromatin architecture-related mechanisms underlying drug resistance, we perform Hi-C on three B-ALL cell lines heterozygous for NSD2 EK. The NSD2 mutation leads to widespread remodeling of the 3D genome, most dramatically in terms of compartment changes with a strong bias towards A compartment shifts. Systematic integration of the Hi-C data with previously published ATAC-seq, RNA-seq, and ChIP-seq data show an expansion in H3K36me2 and a shrinkage in H3K27me3 within A compartments as well as increased gene expression and chromatin accessibility. These results suggest that NSD2 EK plays a prominent role in chromatin decompaction through enrichment of H3K36me2. In contrast, we identify few changes in intra-topologically associating domain activity. While compartment changes vary across cell lines, a common core of decompacting loci are shared, driving the expression of genes/pathways previously implicated in drug resistance. We further perform RNA sequencing on a cohort of matched diagnosis/relapse ALL patients harboring the relapse-specific NSD2 EK mutation. Changes in patient gene expression upon relapse significantly correlate with core compartment changes, further implicating the role of NSD2 EK in genome decompaction.

Conclusions

In spite of cell-context-dependent changes mediated by EK, there appears to be a shared transcriptional program dependent on compartment shifts which could explain phenotypic differences across EK cell lines. This core program is an attractive target for therapeutic intervention.

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Background

While the overall outcome with children with acute lymphoblastic leukemia (ALL) has improved dramatically, up to 20% of patients relapse making ALL one of the leading causes of cancer-related death in children [1, 2]. Genome-wide profiling studies have shown enrichment of mutations at relapse including somatic alterations in epigenetic regulators such as MSH6, SETD2, and NSD2 [3]. NSD2 is a key histone methyltransferase involved in monomethylation and dimethylation of lysine 36 of histone 3 (H3K36), a mark associated with active transcription [4]. Specifically, the recurrent gain of function mutation p.E1099K (EK) has been found to be enriched in patients with relapsed ALL [5].

The NSD2 EK mutation results in increased methyltransferase activity leading to a global increase in H3K36me2 levels (active mark) as well as the concomitant inhibition of EZH2-mediated H3K27me3 levels (repressive mark) in pediatric ALL [6, 7]. Our previous studies have shown knockdown of NSD2 in EK mutated B-ALL cell lines resulted in decreased proliferation, decreased clonal growth, and increased sensitivity to cytotoxic chemotherapeutic agents with no effect on NSD2 wildtype lines [5]. Other work also demonstrated changes in growth and clonogenicity as well as cellular adhesion upon CRISPR-mediated reversion of EK to wildtype in NSD2 mutated lines [7]. Interestingly, RNA-seq data from both studies revealed variable transcriptional reprogramming upon loss of NSD2 in different cell line models. While some common pathways were identified, such as cell adhesion and Rap1 signaling, collectively the data suggests cell-context-specific changes occur in response to mutated NSD2. Importantly, we also demonstrated minimal overlap in chromatin accessibility changes upon NSD2 knockdown in the three EK harboring cell lines [5]. Our current work addresses the transcriptional and chromatin accessibility heterogeneity observed as a result of the NSD2 EK mutation.

In this study, we investigate the role 3D genome organization plays in EK-mediated relapse. 3D genome organization refers to the strategic positioning of regulatory elements to regions best suited for the regulation of genome function. The organizational hierarchy is made up of multiscale structural units such as chromosomal territories, A/B compartments, topologically associating domains (TADs), and chromatin loops each of which play an important role in regulating gene expression [8, 9]. At the Mb scale, chromosomes are spatially divided into two major domains, A and B compartments, that correspond to active and inactive chromatins, respectively [10, 11]. At the sub-Mb scale, the genome can be further subdivided into highly self-interacting chromatin units referred to as TADs [10, 12, 13]. TADs play a major role in the regulation of gene expression by restricting the influence of regulatory elements to genes within the same TAD as well as insulating them from interactions with neighboring domains [14].

Dysregulation of higher-order genomic architecture in several disease models has been linked to changes in the epigenetic landscape [15,16,17,Full size image

NSD2 EK-mediated epigenetic changes correlate with 3D genome changes

We previously performed ChIP-seq on the RS4;11 NSD2 Low and NSD2 High cell lines [5]. For this work, we additionally performed ChIP-seq on the RCH and SEM NSD2 Low and NSD2 High cell lines for H3K36me2, H3K27me3, and H3K27ac epigenetic marks. As noted previously, H3K36me2 distribution shifted from predominantly promoter regions to intergenic regions in all three cell lines, most dramatically in the RS4;11 cell line (Fig. 7a). Conversely, H3K27me3 peaks shifted from intergenic regions to promoter regions (Fig. 7a). Notably, H3K27ac was found to be enriched at promoter regions with little difference in distribution (Fig. 7a).

To examine the relationship between alterations in the distribution of H3K36me2 and A/B compartment switches, we categorized the A/B compartment switches into those that are AB, BA, or stable and assessed changes in H3K36me2. H3K36me2 peaks positively correlated with BA switches more significantly than with AB switches in each of the cell lines (Fig. 7b). We also identified that B to A compartment switches were associated with increased H3K36me2 (Fig. 7c). This data suggests that expansion of H3K36me2 as a result of NSD2 EK provides a more open chromatin landscape.

From previous work, we know that NSD2 regulates H3K36me2; therefore, we can reasonably argue that the H3K36me2-related gene expression changes are due to NSD2. Under this assumption, we calculated the percentage of gene changes that can be attributed to both B to A compartment switches/shifts and increases in H3K36me2 (Additional file 1: Fig. S9a). Approximately 16–30% of genes that were upregulated were within a decompacted compartment, while ~16% presented with increased H3K36me2. For the differentially expressed genes that cannot be attributed to NSD2-related compartment switches, we believe these might be the result of indirect effects.

Next, to examine the relationship between alterations in the distribution of H3K36me2 and intra-TAD activity, we categorized the intra-TAD activity changes into those that are decreased, increased, or stable and assessed changes in H3K36me2. Although there were few intra-TAD activity changes, we observed that increased intra-TAD activity predominantly consisted of increased H3K36me2 and that decreased intra-TAD activity predominantly consisted of decreased H3K36me2 (Additional file 1: Fig. S9b).

Lastly, to better understand the impact these changes have on downstream signaling, we performed pathway enrichment analysis for those differentially expressed genes that were associated with a compartment switch from B to A and an increase in H3K36me2 in any of the cell lines (Fig. 7d). Wnt signaling and cell adhesion were identified as the two most significant pathways. Interestingly, we observed a B to A compartment switch, increased H3K36me2 peaks, and increased gene expression at the NCAM1 gene locus in all three cell lines (Fig. 7e), one of the genes that is related to cell adhesion pathways. Importantly, while cell adhesion was identified in several of our pathway analyses, some genes were impacted by both 3D change and epigenetic modifications such as NCAM1, while NEO1 showed a compartment change but no change in H3K36me2. This highlights the convergence of NSD2 EK-mediated epigenetic modifications and 3D genome organization on downstream signaling.

Discussion

Our previous work demonstrated enrichment of the NSD2 EK mutation in pediatric B-ALL patients at relapse and confirmed its role in cell proliferation, clonogenicity, and therapy resistance [5]. The lack of effective agents to inhibit the enzymatic activity of NSD2 has hampered targeted therapy for NSD2 rearranged multiple myeloma and NSD2 E1099K relapsed ALL. Moreover, we and others have demonstrated NSD2 EK activates a transcriptional program that is highly cell-context dependent, possibly endowing cancer cells with a broad repertoire of pathways needed to navigate the selective pressures of therapy. However, the lack of common genes impacted by NSD2 EK makes a single targeted strategy difficult. Herein we show that NSD2 EK plays an essential role in reorganizing the 3D genome through a striking reliance on compartment reorganization and uncover a convergence on pathways vital to cancer progression, indicating the translational relevance of our findings.

Hi-C analysis of NSD2 Low and NSD2 High cell lines revealed significant compartment changes (26.06%). This is consistent with a multiple myeloma model in which 23.00% of compartment changes were identified [15]. To put this finding into context, only 3.00–5.00% compartment shifts were identified in various stages of B-cell differentiation including pre-B to pro-B and pro-B to immature-B (unpublished data). Consistent with NSD2 EK’s role in increased H3K36me2 methyltransferase activity, we observed significantly more B to A compartment switches and shifts, compared with A to B switches and shifts. This is in opposition to the multiple myeloma model in which low expressing vs high expressing cells showed comparable A to B and B to A switches [15].

Hi-C analysis also revealed shared compartment switches across the three cell lines including 219 shared switches from B to A and 50 from A to B (Fig. 1e). Additionally, 22.40% (932/4,160) of B to A switches and 16.20% (269/1,661) A to B switches were shared by at least 2 cell lines. Previous work demonstrating variability in the phenotypic impact of NSD2 EK on proliferation, clonal growth, and sensitivity to cytotoxic chemotherapeutic agents attributed this diversity to cell context. Here, we highlight the existence of a common core of decompacting loci, attributed to the NSD2 EK mutation, that can explain previously described universal features, such as proliferation, as well as serve as targets for therapeutic intervention. While we used NEO1 as an example, several other genes known to have roles in cancer proliferation and various pathway cascades were identified within these shared compartments, including EPS8, FAM92A, IRS1, and SETBP1.

Importantly, those compartments that switched to a less compact state showed increased H3K36me2 and decreased H3K27me3 epigenetic marks, suggesting that NSD2 EK plays a prominent role in chromatin decompaction through enrichment of H3K36me2 epigenetic marks. The expansion of H3K36me2 past gene bodies into more distal intergenic regions was also revealed. These data suggest that NSD2 EK creates a more open, active chromatin environment by spreading the H3K36me2 mark perhaps endowing cells with an increased repertoire of genes to respond effectively to the selective pressures of treatment.

Furthermore, compartment changes and epigenetic changes significantly correlated with genes that were upregulated due to NSD2 EK. For those compartments that became compact (A to B), we did not see a clear association with decreased gene expression. Whether alterations in the epigenetic landscape can impact chromosome organization in a manner that corresponds to alterations in gene expression has been a long-standing question in the field. As Lhoumaud et al. described in multiple myeloma, our data also highlight a bidirectional relationship between 2D and 3D chromatin structure in gene regulation [15]. We propose a model in which the NSD2 EK mutation directly results in the expansion of the H3K36me2 landscape, and this is associated with genome-wide chromatin decompaction as well as concordant changes in gene expression. However, we did observe dysregulation of gene expression in the absence of any 3D or epigenetic change, suggestive of possible indirect or downstream effects of NSD2.

Although EK-mediated changes in transcriptional output as well as chromatin accessibility differed dramatically between cell lines, pathway analysis on differential genes with compartments that changed from B to A demonstrated convergence on similar pathways, such as cell adhesion, Rap1, and calcium-related pathways. Interestingly, these pathways have also come up in our previous analysis with RNA-seq data alone as well as in other models [7]. Cell adhesion genes were identified in BA compartment switches associated with H3K36me2 gain and in clinical samples with NSD2 EK at relapse. Swaroop et al. demonstrated that the NSD2 EK mutation was associated with increased adhesion to stromal cells as well as increased migratory properties [7]. The NEO1 gene was upregulated by a compartment shift in all three cell lines as well as in the patient samples and encodes neogenin-1 a cell surface receptor implicated in a variety of cell processes such as cell survival, angiogenesis, and migration. Neogenin has been associated with cell proliferation and motility in a number of solid tumors but not in hematological malignancies. Recent work indicates a key role in maintaining hematopoietic stem cell quiescence and long-term self-renewal [30]. Likewise, the small GTPase Rap1 is impacted by intracellular calcium in some circumstances, two pathways we also identified, and is known to stimulate integrin activation [31]. FGF5 and GNAQ were two of the genes implicated in Rap1 and calcium signaling in the patient samples but were found to be specific to either RS4;11 or RCH cells, respectively [32,33,34]. FGF5 has been shown to affect proliferation and migration in melanoma and glioblastoma multiforme models while GNAQ activates PKC, FAK, and ERK signaling cascades to promote proliferation and cell survival in uveal melanoma. These examples highlight how the underlying differences in the 3D organization leads to the activation of various genes that ultimately provide a clonal advantage. In addition, WNT and MAPK (Ras-related) pathways were identified in two out of the three lines examined and have also previously been linked to NSD2 in the multiple myeloma model [15]. The identification of common downstream targets among preclinical models and patient samples has important clinical implications for possible future therapeutic interventions.

By examining the regions where we observed compartment decompaction, increased accessibility, and upregulation of gene expression, we identified enrichment of motifs linked to stem cell functionality, including NANOG, SUZ12, SOX2, and MEIS1. In the B-cell lymphoma model, Yusufova et al. also demonstrated that many differentially expressed genes were linked to stem cell functionality as well as enriched for various stem cell signature-related pathways [16]. This data suggests a potential core transcription factor network that may be dependent on compartmentalization.

In contrast to the identification of widespread compartment switches upon, we identified very few changes in intra-TAD activity. This data suggests that NSD2 EK-mediated changes are primarily linked to changes in compartmentalization. Furthermore, this supports the notion that TADs and compartments are established by separate, competing mechanisms. Our findings are also in agreement with a recent paper demonstrating H1 loss-of-function mutations in B-cell lymphomas leading to chromatin decompaction. Few TAD changes were observed and H1 loss led to decompaction through increased H3K36me2, again supporting the idea of NSD2 playing a role in chromatin decompaction [16]. Although few TAD activity changes were identified, decreasing TAD activity was predominantly associated with A to B compartment switches and increasing TADs predominantly associated with B to A switches suggesting that TADs might be susceptible to patterns established by compartments.

Conclusions

Here we have identified a link between NSD2 EK-mediated epigenetic changes and dysregulation of higher-order genomic architecture in B-ALL. Our study revealed NSD2 EK’s prominent role in chromatin decompaction through enrichment of H3K36me2 epigenetic marks. We also demonstrated NSD2 EK’s remarkable reliance on compartment reorganization over TAD activity. Despite transcriptional and chromatin accessibility heterogeneity across the three cell lines, this study highlights the existence of a common core of decompacting loci that can explain previously described universal features, such as proliferation, as well as serve as targets for therapeutic intervention. Ultimately, this study offers a novel mechanism by which NSD2 EK creates a more open, active chromatin state through decompaction allowing for diverse transcriptional reprogramming in response to selective pressures associated with treatment.

Methods

Experimental procedures

Cell lines, RNA-seq, ATAC-seq, and ChIP-seq methods published previously [5]. The B-lineage leukemia cell lines RS4;11, RCH-ACV, and SEM were used for the purposes of this study. RS4;11 was acquired from ATCC (Manassas, VA), RCH-ACV was acquired from DSMZ (Braunschweig, Germany), and SEM was kindly gifted by Jun Yang, St. Jude Children’s Hospital. Each leukemia cell line was validated by short tandem repeat analysis through ATCC except for RCH-ACV which was purchased from DSMZ directly. DSMZ routinely verifies cell lines and provides authentication information with each order. Cell lines were routinely monitored for mycoplasma contamination by PCR using ATCC Universal Mycoplasma Detection Kit (20–1012K). NSD2 Low cell lines were generated in these three B-ALL cell lines because they naturally harbor a heterozygous NSD2 EK mutation. More specifically, a short-hairpin RNA (shRNA: GGAAACTACTCTCGATTTATG) was used to knockdown NSD2 targeting the NSD2 Type II and NSD2 RE-IIBP isoforms both of which contain the SET domain.

Primary patient sample experimental procedures

Cryopreserved PBMC samples from Children’s Oncology Group (COG) B-ALL study AALL-1331 were obtained from the COG Biobank. All subjects provided consent for banking and future research use of these specimens in accordance with the regulations of the institutional review boards of all participating institutions. Samples were resuspended in TRIzol reagent (Life Technologies) then processed using 5prime Phase Lock Gel Heavy tubes (Quantabio) following the manufacturer’s instructions. Following precipitation, the RNA was quantitated using the Qubit RNA BR assay kit (Life Technologies) and evaluated for quality using the Agilent 2100 Bioanalyzer with the RNA nano chip (Agilent). The KAPA RNA HyperPrep Kit with RiboErase (HMR) (Roche) with 100–200ng of total RNA was used for library construction with either Nextflex-96 RNA barcode adapters (BIOO Scientific, Illumina HiSeq 4000 platform), or IDT for Illumina unique dual indices adaptors (Illumina, Novaseq 6000 platform) and 12 PCR cycles of library amplification. Libraries were evaluated on the Bioanalyzer 2100 using the DNA 1000 chip. Libraries were sequenced using either the HiSeq 4000 (Illumina) or NovaSeq 6000 (Illumina) paired-end 2X150 cycle protocol.

Hi-C experimental procedures

Five million cells from actively growing NSD2 High and Low cell lines were crosslinked with 2% PFA and quenched with glycine. Following crosslinking, cells were lysed and DNA digested and proximally ligated following Arima Genomics’ Hi-C kit User Guide for Mammalian Cell Lines by the NYU Langone Genome Technology Center. Hi-C libraries were generated using Arima-HiC Kit along with Swift 2S Plus Kit and Swift Indexing Kit following Arima-HiC Kit Library Preparation using Swift Biosciences(R) Accel-NGS(R) 2S Plus DNA Library Kit User Guide. Libraries were sequenced using Illumina’s NovaSeq 6000.

Computational analysis

Cell line RNA-seq analysis

Cell line RNA-seq fastq files were processed in triplicates using the route “rna-star” and “rna-star-groups-dge” from the Slide-n-Seq (sns) pipeline: https://igordot.github.io/sns/. Processing steps include alignment of paired-end reads to the human reference genome (hg19) using the STAR aligner with default parameters [35]. Counts were obtained using featureCounts [36]. Bigwig tracks were obtained for visualization on individual samples using deeptools (v3.1.0) [37]. Downstream analysis including normalization and differential expression analysis was performed using DESeq2 [38]. Genes were categorized as differentially expressed if abs(L2FC > 0.32, p-value < .05). Pathway analysis was performed using enrichR [39].

Primary patient sample RNA-seq analysis

COG RNA-seq count data was provided to us by Dr. ** quality <30 were removed. Duplicated reads were removed using Sambamba (v0.6.8) [42]. Remaining reads were analyzed by applying the peak-calling algorithm MACS2 (v2.1.1) [43]. Bigwig tracks were obtained for visualization on individual samples using deeptools (v3.1.0) [37]. Differential ATAC-seq analysis was performed using DiffBind [44]. Nearest genes were annotated using ChIPseeker [45]. Enrichment analysis was performed using Bioconductor package LOLA (Locus overlap analysis or enrichment of genomic ranges; R package version 1.24.0) with RStudio (v3.6.1) [46]. Enrichment analysis of genomic regions sets was performed using Bioconductor package LOLA (Locus overlap analysis or enrichment of genomic ranges; R package version 1.24.0) with RStudio (v3.6.1) with the hg19 LOLA core database [46]. LOLA core database is curated from many sources including TF binding sites from Encode and epigenome databases from Cistrome.

ChIP-seq analysis

Processing of cell line ChIP-seq fastq files was performed by the Carroll lab’s previous bioinformatician, Gunjan Sethia, as previously described [5]. Cell line ChIP-seq data was processed with three replicates. Paired-end reads were aligned to the human reference genome (hg19) with Bowtie2 (v2.3.4.1) [41]. Reads with low map** quality <30 were discarded using Trimmomatic (v0.33; ref. 23) [47]. Due to the broad/diffuse peaks created by H3K36me2 and H3K27me3, peaks for these marks were called by SICER that uses a cluster enrichment–based analysis [48]. H3K27ac and H3K9ac peaks were called using MACS2 (–broad; ref. 30) [43]. Bigwig tracks were obtained for visualization on individual samples using deeptools (v3.1.0) [37]. Differential ChIP-seq analysis was performed using DiffBind [44]. Peaks were categorized as differentially accessible if abs(L2FC > 1.0, FDR < .01). Nearest genes were annotated using ChIPseeker [45].

Hi-C analysis

Raw Hi-C sequencing data was processed with the hic-bench platform [19]. Cell line Hi-C data was processed as single replicates. Data was aligned against the human reference genome(GRCh37/hg19) with bowtie2(version 2.3.1) [41]. The reads used for downstream analyses were filtered for by the GenomicTools tools-hic filter command in the hic-bench platform using default parameters. The GenomicTools tools-hic filter command discards reads including multi-mapped reads (“multihit”), read-pairs with only one mappable read (“single sided”), duplicated read-pairs (“ds.duplicate”), read-pairs with a low map** quality of MAPQ < 20, read-pairs resulting from self-ligated fragments (together called “ds.filtered”), and short range interactions resulting from read-pairs aligning within 25kb (“ds.too.short”). Downstream analysis is performed with the accepted intra-chromosomal read- pairs (“ds.accepted intra”). The number of accepted intra-chromosomal read-pairs varied between ~40 and ~140 million for all samples (Chapter 1; Fig. 2). Hi-C interaction matrices were generated for each chromosome separately by the hic-bench platform at 40kb resolution. Filtered read counts were normalized by iterative correction and eigenvector decomposition (ICE) [49]. To account for variances in read counts of more distant loci, distance normalization for each chromosome matrix was performed.

Hi-C contact matrix visualization

To visualize the Hi-C contact matrix for the PRDM8/FGF5 locus, ICE normalized Hi-C contact matrices for the corresponding chromosome was loaded and normalized by the total number of intra-chromosomal interactions for NSD2 Low and NSD2 High samples. The log2FC Hi-C contact matrix was produced by applying the log2 function on the division product of the NSD2 High Hi-C table by the NSD2 Low Hi-C table.

A/B compartments analysis

A/B compartment analysis was performed using Cscore tool. The Cscore tool algorithm was used to assign active (A) and inactive (B) compartments [20]. Cscore assumes that each genomic window, i, has a chance P_i to be in the A compartment. By defining Cscore as C_i=2P_i−1⁠, which ranges between −1 and 1, Cscore deduces a log-likelihood function where n_ij is the observed number of contacts, d_ij is the distance along the genome, H(d_ij) is the scaling factor accounting for the decrease of interactions at longer genomic distance, and B_i and B_j are the bias factors from Hi-C experiments. A maximum-likelihood estimation was performed for the model parameters B, C, and H using an iterative algorithm:

For a bin to be considered a switch from A to B or from B to A, the compartment score sign had to flip in sample 2 (NSD2 High) when compared with the reference sample (sample 1 or NSD2 Low). The absolute difference between the compartment scores had to be higher than the cutoff (default: 1.2). The difference is computed as a relative delta:

delta = (Y − X) / abs(Y) #delta value calculation

X = compartment score of bin in sample 1 (reference)

Y = compartment score of bin in sample 2

For the compartment switch analysis, percent switches in compartments was calculated by quantifying the sum of the total number of bins that switched from A to B and B to A divided by the total number of compartment bins (switching bins + stable bins). For compartment switch and shift analysis, bins were classified as A to B or B to A switches if there was a switch in the compartment Cscore sign. Bins were classified as B to more B, B to less B, A to more A, or A to less A if there was not a switch in the compartment Cscore sign but the delta exceeded a cutoff of delta > 0.2. Additionally, percent switches in compartments was calculated by quantifying the sum of the total number of bins that switched from B to more B, B to less B, A to more A, A to less A, A to B, and B to A divided by the total number of compartment bins (switching bins + shifting bins + stable bins).

Intra-TAD activity analysis

Intra-TAD activity was assessed using the “domains” and “domains-diff” steps within the hic-bench platform [19]. The “domains” step uses the hic-ratio algorithm for TAD calling developed within hic-bench by previous Tsirigos lab member Haris Lazaris in which the average of the normalized interaction scores is calculated for all interactions taking place within a particular TAD. The “domains-diff” step assesses TAD interactivity alterations and was developed by previous lab members Sofia Nomikou and Andreas Kloetgen [17, 19]. To identify TADs with differential TAD interactivity between NSD2 Low and High cell lines, we used the TADs identified in the NDS2 Low sample as a reference to identify common TADs. Once intra-TAD values are obtained from the Hi-C data for NSD2 Low and NSD2 High cell lines, a Wilcoxon two-sided rank sum non-parametric test is performed to determine the p-values for each TAD. Multiple testing is used to correct these p-values by adjusting to the total number of TADs. Lastly, the log2 fold change (log2FC) of intra-TAD activity value is calculated between the samples. Intra-TAD activity alterations were categorized as significant if abs(L2FC > 0.25) and FDR < 0.01.

Integration of compartment data with other datasets

To show the correlation between the compartment changes and the various sequencing datasets, we first calculated the peak intensity fold change or gene expression fold change between NSD2 Low and High cell lines for peaks obtained from H3K36me2, H3K27me3, H3K27ac, ATAC-seq, and the genes obtained from RNA-seq through a differential analysis as described above. Differential peaks and genes were then classified as increased, decreased, or stable according to thresholds described above and then mapped to the AB, BA, or stable regions using the “bedtools intersect” command [50, 51]. Correlation between the compartment changes and the various sequencing datasets were then shown with boxplots or bar plots. Statistical significance was assessed using a paired two-sample t-test.

Availability of data and materials

The cell line Hi-C dataset supporting the conclusions of this article is available in the National Center for Biotechnology Information Gene Expression Omnibus (GEO Series accession number GSE199754: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199754) [52]. The COG patient sample dataset (RNA-seq) supporting the conclusions of this article is available in the National Center for Biotechnology Information database of genotypes and phenotypes (dbGaP accession number phs003195.v1.p1;https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs003195.v1.p1) [53]. The previously generated cell line dataset (ChIP-seq, ATAC-seq, and RNA-seq) supporting the conclusions of this article is available in the National Center for Biotechnology Information Gene Expression Omnibus (GEO Series accession number GSE149159; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149159) [5, 54].

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Acknowledgements

We would like to thank the Genome Technology Center (GTC) for expert library preparation and sequencing, and the Applied Bioinformatics Laboratories (ABL) for providing bioinformatics support and hel** with the analysis and interpretation of the data. This work has used computing resources at the NYU School of Medicine High Performance Computing (HPC) Facility.

Review history

The review history is available as Additional file 14.

Peer review information

Wen**g She was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Funding

A.T. is supported by the NCI/NIH P01CA229086, NCI/NIH R01CA252239, NCI/NIH R01CA260028, and NIH/NCI R01CA140729. W.L.C has received grants from the National Cancer Institute of Health (R01CA140729 and R01CA260028), The Leukemia and Lymphoma Society Specialized Center for Research (7010-14), Perlmutter Cancer Center Arline and Norman M. Feinberg Pilot Grant for Lymphoid Malignancies, HHOW Scholar Hope Grant, and the Perlmutter Cancer Center (P30 CA016087). J.P. received funding from the St. Baldrick’s Foundation (Fellowship Award, 524986), Alex’s Lemonade Stand Foundation (Young Investigator Grant), and Pediatric Cancer Foundation (Fellowship Training Grant). GTC and ABL are shared resources partially supported by the Cancer Center Support Grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center.

Author information

Aristotelis Tsirigos and William L. Carroll jointly supervised this study.

Authors and Affiliations

Perlmutter Cancer Center, NYU Langone Health, Smilow 1211, 560 First Avenue, New York, NY, 10016, USA
Sonali Narang, Nikki A. Evensen, Jason Saliba, Aristotelis Tsirigos & William L. Carroll
Northwell Health, Staten Island University Hospital, Staten Island, NY, USA
Joanna Pierro
Department of Pediatrics, Benioff Children’s Hospital and The Helen Diller Family Comprehensive Cancer Center University of California, San Francisco, San Francisco, CA, USA
Mignon L. Loh
The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Patrick A. Brown
Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA
Pandurang Kolekar, Heather Mulder, Ying Shao, John Easton & **aotu Ma
Department of Pathology, NYU Langone Health, New York, NY, USA
Aristotelis Tsirigos
Perlmutter Cancer Center, NYU Langone Health, Science Building 800, 435 East 30th Street, New York, NY, 10016, USA
Aristotelis Tsirigos
Department of Pediatrics, NYU Langone Health, New York, NY, USA
William L. Carroll
Division of Pediatric Hematology/Oncology, NYU Langone Health, New York, NY, USA
William L. Carroll

Authors

Sonali Narang
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Nikki A. Evensen
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Jason Saliba
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Joanna Pierro
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Mignon L. Loh
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Patrick A. Brown
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Pandurang Kolekar
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Heather Mulder
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Ying Shao
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John Easton
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**aotu Ma
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Aristotelis Tsirigos
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William L. Carroll
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Contributions

Conceptualization and study design, W.C. and A.T.. Investigation, S.N.. Formal analysis, S.N.. Writing—original draft, S.N.. Supervision, N.E., W.C. and A.T.. Funding acquisition, S.N., N.E., W.C., A.T. All author(s) read and approved the final manuscript.

Corresponding authors

Correspondence to Aristotelis Tsirigos or William L. Carroll.

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Ethics approval and consent to participate

The study on the B-ALL diagnosis/relapse patient samples complies with all ethical regulations, including the Helsinki Declaration, and was approved by the Institutional Review Boards of St. Jude Children’s Research Hospital and the Children’s Oncology Group, IRB number 20-0439. All patients or a parent/guardian provided written informed consent.

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Figure S1a.

Hi-C million read counts (left) and read percentage (right) per cell line. Figure S2a. Venn diagram showing overlap of differentially expressed genes (abs(L2FC) > 0.32, p-value < 0.05) between three B-ALL cell lines upon knockdown, decreased and increased genes (left and right respectively). b. Volcano plots demonstrating differentially expressed genes (abs(L2FC) > 0.32, p-value < 0.05) highlighted by compartment switch or shift (A to more A, B to A, B to less B, or stable). c. Scatterplot demonstrating compartments colored by concordance score (percentage of genes with that compartment switch or shift that change in the same direction). Figure S3a. Rate of concordance calculations represent the amount of upregulated and downregulated genes (abs(L2FC) > 0.32, p-value < 0.05) that can be explained by compartment switches and shifts. Figure S4a. Barplot showing number of subcompartment calls with SNIPER for each cell line at NSD2 Low and NSD2 High. b. Barplots presenting fraction of Cscore compartment calls (A or B) overlap** SNIPER subcompartment calls (A1, A2, B1, B2, and B3) c. Barplot showing number of subcompartment calls with SNIPER for each cell line at NSD2 Low and NSD2 High excluding B1 subcompartment calls. d. Barplot presenting number of subcompartment switches from A to B and from B to A from NS2D Low to NSD2 High in each of the cell lines. e. Barplots presenting fraction of SNIPER subcompartment calls overlap** cscore compartment switches and shifts. Figure S5a. Correlation boxplots of SNIPER subcompartments and gene expression changes (abs(L2FC) > 0.32, p-value 0.05) for each cell line from NSD2 Low to NSD2 High. b. Volcano plots demonstrate differentially expressed genes (abs(L2FC) > 0.32, p-value < 0.05) highlighted by subcompartment switches and shifts. Figure S6a. PCA of ATAC-seq peaks for each cell line identifies three distinct clusters which are cell line-specific. b. Heat map representation of ATAC-seq results generated with DiffBind. c. Venn Diagram showing overlap of differentially accessible regions between three B-ALL cell lines decreased and increased accessibility (left and right respectively; (FDR < 0.01, abs(log2(fold change)) > 1). d. Example of B to A compartment shift shared by all three cell lines at the NEO1 locus showing concordance with gene expression and chromatin accessibility. e. Example of B to A compartment shift specific to RS4;11 cell line at the PRDM8 locus showing concordance with gene expression and chromatin accessibility. Figure S7a. Heatmap representation of mean intra-TAD activity per cell line. b. Bar plot showing number of TAD activity changes per cell line (abs(L2FC) > 0.25, FDR < 0.01). c. Venn diagrams demonstrating significant overlap of patient NSD2-High upregulated genes and cell line NSD2-High upregulated genes and insignificant overlap of patient NSD2-High downregulated genes and cell line NSD2-High upregulated genes (one-tailed Fisher’s exact test). The last row presents insignificant overlap of genes upregulated in 9 matched D/R patient pairs with no NSD2 mutation (NSD2 Low) and cell line NSD2-High upregulated genes with a B to A compartment switch or shift. d. Table presenting percentage of patient NSD2-High upregulated genes overlap** a compartment switch/shift from any of the three cell lines. Figure S8a. Table presents patient information associated with the three matched diagnosis-relapse pairs acquired from COG. Figure S9a. Rate of concordance calculations present the number of genes upregulated in expression, increased in H3K36me2 ChIP-seq peaks, and B to A compartment shifts and switches for the RCH, RS4;11, and SEM cell lines. b. Association bar plots showing fraction of differential H3K36me2 chip-seq peaks (decreased, increased, and stable) that overlap changes in intra-TAD activity (increased, decreased, and stable TADs).

Additional file 2.

stable_genomic_loci.bed LOLA Analysis File of stable ATAC-seq peaks.

Additional file 3.

AB_genomic_loci.bed LOLA Analysis File of ATAC-seq peaks with A to B compartment switch.

Additional file 4.

BA_genomic_loci.bed LOLA Analysis File of ATAC-seq peaks with B to A compartment switch.

Additional file 5.

LOLA_enrichment_results.tsv LOLA Analysis Results File.

Additional file 6.

shared_comp_genes_down.csv Expression of decreased genes with shared AB compartment switches.

Additional file 7.

shared_comp_genes_up.csv Expression of increased genes with shared BA compartment switches.

Additional file 8.

TAD_increased.csv Gained TAD activity due to NSD2 coordinates.

Additional file 9.

TAD_decreased.csv Lost TAD activity due to NSD2 coordinates.

Additional file 10.

TAD_expression_cscore_AB.csv Concordant lost TAD activity, decreased gene expression, and AB compartment switch.

Additional file 11.

TAD_expression_cscore_BA.csv Concordant gained TAD activity, increased gene expression, and BA compartment switch.

Additional file 12.

TAD_expression_down.csv Concordant lost TAD activity and decreased gene expression.

Additional file 13.

TAD_expression_up.csv Concordant gained TAD activity and increased gene expression.

Additional file 14.

Review history.

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Narang, S., Evensen, N.A., Saliba, J. et al. NSD2 E1099K drives relapse in pediatric acute lymphoblastic leukemia by disrupting 3D chromatin organization. Genome Biol 24, 64 (2023). https://doi.org/10.1186/s13059-023-02905-0

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Received: 17 March 2022
Accepted: 20 March 2023
Published: 04 April 2023
DOI: https://doi.org/10.1186/s13059-023-02905-0

NSD2 E1099K drives relapse in pediatric acute lymphoblastic leukemia by disrupting 3D chromatin organization

Abstract

Background

Results

Conclusions

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Background

NSD2 EK-mediated epigenetic changes correlate with 3D genome changes

Discussion

Conclusions

Methods

Experimental procedures

Primary patient sample experimental procedures

Hi-C experimental procedures

Computational analysis

Cell line RNA-seq analysis

Primary patient sample RNA-seq analysis

ChIP-seq analysis

Hi-C analysis

Hi-C contact matrix visualization

A/B compartments analysis

Intra-TAD activity analysis

Integration of compartment data with other datasets

Availability of data and materials

References

Acknowledgements

Review history

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Funding

Author information

Authors and Affiliations

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Corresponding authors

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Supplementary Information

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