Introduction
Multiple myeloma is an incurable disease characterized by clonal expansion of plasma cells, leading to osteolysis, hypercalcemia, renal failure, and cytopenia. Monoclonal gammopathy of undetermined significance (MGUS) is a precursor lesion with the potential to progress to smoldering multiple myeloma (SMM) and eventually symptomatic multiple myeloma over time ( 1 ). Incorporating CD38 antibodies, proteasome inhibitors, and immunomodulatory agents into first-line therapy improved the depth of response and progression-free survival, but still the majority of patients relapse because of therapy resistance ( 2 ).
Both genetic mutations and epigenetic aberrations play pivotal roles in initiation, progression, prognosis, and treatment of multiple myeloma ( 3 , 4 ). Dysregulated histone posttranslational modifications (PTM) by acetylation or methylation particularly contribute to the development and progression of multiple myeloma ( 5 , 6 ). Histone acetyltransferases (HAT) convert compact chromatin into a relaxed state, thereby facilitating accessibility for transcription factors and enhancing gene expression ( 7 ). Conversely, histone deacetylases (HDAC) promote tighter interactions between histones and DNA, resulting in condensed chromatin and gene silencing ( 7 ). A dynamic equilibrium between HATs and HDACs is essential for regulating gene expression and maintaining cellular functions during genotoxic stress ( 8 ). Acetylated lysine residues are recognized by reader proteins, which possess specialized domains, such as bromodomain and extra-terminal (BET) motifs, composed of BRD2, BRD3, BRD4, and BRDT, to motivate transcriptional initiation and elongation ( 9 ). The equilibrium of histone acetylation and deacetylation gained further attention in multiple myeloma due to the availability of a variety of drugs targeting histone acetylation ( 10 ) and associated reader proteins ( 11 ). This strategy is particularly interesting in MYC -driven/-dependent cancers, including multiple myeloma ( 12 , 13 ), because MYC drives inherent vulnerabilities for BET inhibition ( 14 ). A phase I clinical trial testing a BET inhibitor together with daratumumab in relapsed/refractory multiple myeloma showed limited tolerability ( 15 ). Therefore, predictive biomarkers for optimal patient selection for epigenetic therapies in multiple myeloma are urgently needed ( 16 ).
KAT8 regulatory NSL complex subunit 2 (KANSL2) is a subunit of the highly conserved nonspecific lethal (NSL) complex, which primarily mediates histone acetylation ( 17 , 18 ). Among all the components of the NSL complex, the depletion of KANSL1 , KANSL2 , KANSL3 , and PHF20 has been shown to adversely affect the survival in transgenic models, suggesting that they are conserved core members of the pan-essential NSL complex ( 19 ). Although KANSL2 has oncogenic potential in some cancer entities ( 20 – 22 ), its role in hematologic malignancies remains unresolved.
In this study, we identified KANSL2 as a novel, targetable epigenetic modifier in multiple myeloma. By multiomics profiling of genetically engineered model systems with defined KANSL2 expression, we elucidated the KANSL2-associated biological program in multiple myeloma. Here, we present actionable vulnerabilities to epigenetic inhibitors and a biomarker-informed rationale for HDAC- and BET-directed treatments.
Materials and Methods
Cell culture
Cell lines were obtained from the DSMZ (German Collection of Microoganisms) or the ATCC between 2018 and 2024 and authenticated by short tandem repeat profiling. Media were supplemented with 10% FBS (Gibco, A5256701). HEK293T (RRID: CVCL_0063) and U-2-OS (RRID: CVCL_0042) cell lines were cultured in DMEM (Gibco, 41965-062). The INA-6 (RRID: CVCL_5209) cell line was maintained in RPMI-1640 medium (Gibco, 21875-091) further supplemented with 10 ng/mL IL6 (PeproTech, 200-06). For NCI-H929 (RRID: CVCL_1600) cells, 0.1% β-mercaptoethanol (Gibco, 31350-010) and 1% sodium pyruvate (Gibco, 11360-039) were added. L-363 (RRID: CVCL_1357), JJN-3 (RRID: CVCL_2078), OPM-2 (RRID: CVCL_1625), and P493-6 (RRID: CVCL_6783) cell lines were cultured in RPMI-1640 medium. All cell lines were incubated at 37°C with 95% humidity and 5% CO 2 . Cells were passaged every 2 to 3 days at the ratio of 1:2 to 1:4 according to their growth rates. Mycoplasma testing was performed regularly by comparative PCR analysis.
Chemical compounds
Panobinostat (CAS: 404950-80-7), OTX-015 (CAS: 202590-98-5), and mafosfamide (CAS: 84211-05-2) were purchased from Selleck Chemicals. Doxorubicin (CAS: 23214-92-8) was obtained from the clinical pharmacy at Charité Berlin.
Plasmids
The plasmid pLX_311-KRAB-dCas9 (RRID: Addgene_96918) was used to establish multiple myeloma cell lines with the dCas9-KRAB system. For KANSL2 knockdown (KD), single-guide RNA (sgRNA)-KANSL2 (forward: GTCGTCTCCCACCGCGCTGGCGCCACTAGAGAAGTTTCGAGACGTG and reverse: CACGTCTCGAAACTTCTCTAGTGGCGCCAGCGCGGTGGGAGACGAC) and sgRNA-nontargeting (forward: GTCGTCTCCCACCGTGCACCCGGCTAGGACCGGGTTTCGAGACGTG, reverse: CACGTCTCGAAACCCGGTCCTAGCCGGGTGCACGGTGGGAGACGAC) from the CRISPR library ( 23 ) were ligated into the plasmid pLKO5.sgRNA.EFS.tRFP657 (RRID: Addgene_57823) with the competent bacteria Stbl3. Ectopic KANSL2 was ligated into the lentiviral pHIV-EGFP plasmid (RRID: Addgene_21373) with sequence- and ligation-independent cloning as described in detail by Jeong and colleagues ( 24 ).
HEK293T cells were used to produce lentivirus by transfection with the lentiviral plasmids, envelope plasmid pMD2.G (RRID: Addgene, 12259), packaging plasmid psPAX2 (RRID: Addgene, 12260), and Lipofectamine 2000 (Invitrogen, 10696153) in Opti-MEM I Reduced Serum Medium (Gibco, 31985-062). After 48 to 72 hours, the viral supernatant was collected to infect targeted cells in the addition of 8 µg/mL polybrene (MilliporeSigma, TR-1003), followed by centrifugation at 32°C at 450 × g for 1 hour. The viral supernatant was replaced by fresh medium after 24 hours. Antibiotic selection or fluorescent detection was implemented 72 hours after infection.
qRT-PCR
RNA was isolated with an RNeasy Mini Kit (Qiagen, 74106). Reverse transcription and qPCR reactions were combined into one step using the Luna Universal One-Step RT-qPCR Kit (NEB, E3005X) and Ct values were measured using a StepOnePlus cycler (Applied Biosystems). The relative gene expression was calculated by the ΔΔCt method with β-actin as a reference. Primer efficiencies were tested according to Pfaffl and colleagues ( 25 ). Primer sequences used were CAD (forward: TAGTCCTTGGCTCTGGCGTCTA and reverse: TAGTCGGTGCTGACTGTCTCTG), KANSL2 (forward: ATTCACGTCTTGCCAACCAAT and reverse: TGAGAGCATGGACGATGAGTG), MYC (forward: GTCAAGAGGCGAACACACAAC and reverse: TTGGACGGACAGGATGTATGC), and β-actin (forward: CCTCGCCTTTGCCGATCC and reverse: CGCGGCGATATCATCATCC).
Western blotting
Harvested cell pellets were lysed in RIPA buffer containing protease and phosphatase inhibitor cocktails (Roth) on ice for 30 minutes, followed by centrifugation at 13,000 g at 4°C for 15 minutes. The protein supernatant was collected, quantified with Protein Assay Dye Reagent (Bio-Rad, 5000001), mixed with Laemmli buffer, and denatured by heating at 95°C for 10 minutes. Proteins were separated on SDS-PAGE gels and transferred to polyvinylidene difluoride membranes (Bio-Rad, 1620184). After blocking with 5% BSA (Roth) in Tris-buffered saline with Tween-20 (TBST), membranes were incubated in primary antibodies at 4°C overnight. The next day, membranes were washed three times in TBST before incubation in horseradish peroxidase (HRP)–conjugated secondary antibodies for 1 hour at room temperature. HRP Substrate Peroxide/Luminol Solution (MilliporeSigma, WBKLS0500) and an Electrochemiluminescence Chemocam machine (Intas) were used to develop the membranes. Antibodies are listed in the Supplementary Table S1.
RNA sequencing
Three weeks following transduction of control or KANSL2-KD plasmids, cells were collected for RNA sequencing (RNA-seq). Total RNA was extracted from JJN-3 and INA-6 cell lines (KANSL2-KD and control) using an RNeasy Mini Kit (Qiagen, 74106) based on the manufacturer’s instructions. RNA concentration and quality were evaluated with a NanoDrop 1000 spectrophotometer (Peqlab). Sequencing was conducted by Novogene, and data analysis was performed using Trim Galore (RRID: SCR_011847), Bowtie 2 (RRID: SCR_016368), and htseq-count and DESeq2 (RRID: SCR_000154) in Galaxy ( https://usegalaxy.org/ ), as well as Genetrail ( https://genetrail.bioinf.uni-sb.de/ ), and GraphPad Prism.
Apoptosis assays
Three weeks after transduction and validation of KANSL2-KD, 0.5 × 10 6 cells were washed with PBS, resuspended in 50 μL Annexin V buffer (BD Biosciences) containing 0.3 μg 4′,6-diamidino-2-phenylindole (DAPI; Invitrogen, 2747527) and 0.25 μL Annexin V FITC (BioLegend, 640906) or APC (BioLegend, 640941), and incubated for 15 minutes at room temperature before measurement with the CytoFlex Flow Cytometer (Beckman Coulter).
To visualize apoptotic cells, 1,500 cells were seeded in a PhenoPlate 384-well microplate (PhenoPlate). After fixation and blocking, cells were incubated overnight at 4°C in dark with antibodies against cleaved caspase-3 (Cell Signaling Technology, 9669) and DAPI. Images were acquired and cells were analyzed using an Operetta microscope (Revvity) and Harmony 5.1 PhenoLOGIC (Revvity).
CellTiter-Glo assay
To determine drug sensitivity, cells were seeded equally per well with 100 μL medium containing increasing drug concentrations in Nunc MicroWell 96-well plates (Thermo Fisher Scientific). Three technical and biological replicates were designed for each drug concentration and each cell line. After 72 hours of treatment, cell viability was assessed using CellTiter-Glo 2.0 reagent (Promega, G9243) and a Luminescence Microplate Reader (Berthold Technologies).
Competitive repopulation assay
Approximately 40% of parental cells [red fluorescence protein (RFP) negative] were mixed with 60% of control or KANSL2-KD cells (RFP positive) in 1 mL medium in a 24-well plate 3 days after transduction. Every 48 hours, RFP-positive cells were quantified on a CytoFlex Flow Cytometer (Beckman Coulter), representing the proportion of targeted cells in the competitive pool.
Cell growth curve analysis
To compare cell growth rates, cells were seeded 3 days after transduction at 0.25 × 10 6 cells/mL in a 24-well plate in triplicates. Every 48 hours, 10 μL of cell suspension was mixed with 0.4% trypan blue solution (Sigma, T8154) for cell counting. Cells were then re-seeded at 0.25 × 10 6 cells/mL for continued growth monitoring. The final cell density was the absolute number multiplied by every splitting time.
Immunofluorescence
About 0.1 × 10 6 cells were collected, attached to a coverslip via centrifugation at 200 g for 5 minutes, and transferred to a 12-well plate. After fixation with 4% cold paraformaldehyde and permeabilization with 0.5% Triton-X (Sigma) for 10 minutes, cells were blocked in 5% BSA + 0.25% Triton-X for 30 minutes at room temperature. The coverslip was incubated overnight at 4°C with the primary antibody diluted in 1% BSA with 0.1% Triton-X. The following day, cells were incubated with the secondary antibody in dark at room temperature for 1 hour and mounted on a microscope slide with DAPI-containing mounting medium (Dianova) for confocal microscopy (Zeiss) observation.
Global proteome and acetylome analyses by LC/MS-MS
Global proteomic and acetylomic analyses were performed 3 weeks after transduction by the Berlin Institute of Health Core Unit Proteomics Platform, using a tandem mass tag (TMT)–based approach. Cell pellets were resuspended in SDC lysis buffer, heated for 10 minutes at 95°C, and treated with Benzonase (Merck, 50 units) for 30 minutes at room temperature. Samples were incubated on a shaker at 37°C overnight after adding 10 µg of sequence-grade trypsin (Promega) and LysC (Wako). Peptides were subsequently acidified with 1% trifluoroacetic acid, cleaned with C18 SepPak columns (Waters, 100 mg/cc), and resolved in 50 mmol/L HEPES. Peptides were labeled with 16-plex TMT (Thermo Fisher Scientific) reagents following the vendor’s instructions, pooled, and cleaned up using C18 SepPak columns (Waters, 200 mg/cc). Peptide mixtures of each plex were fractionated by high-pH reversed-phase off-line chromatography (1290 Infinity, Agilent) and pooled into 30 fractions. For global proteome measurements, 5% of the material was used. The remaining 95% was further pooled into 12 fractions and used for Acetyl-Lysine enrichment [PTMScan HS Acetyl-Lysine (Ac-K) Magnetic Immunoaffinity Beads], following the vendor’s instructions. For measurements, peptides were reconstituted in 3% acetonitrile with 0.1% formic acid, separated on a reversed-phase column using a high-performance liquid chromatography system (Thermo Fisher Scientific), and analyzed on the Exploris 480 instrument (Thermo Fisher Scientific) using data-dependent acquisition.
Raw data were analyzed using the MaxQuant software package (v 1.6.10.43) with the UniProt databases for human (UP000005640_2022_03). Further analysis utilized the log 2 -transformed and normalized reporter ion intensities. Abundance differences were calculated using a Student t test. FDR less than 5% was considered differentially expressed.
Assay for transposase-accessible chromatin sequencing and analysis
Two weeks after transduction of KANSL2-KD, bulk assay for transposase-accessible chromatin sequencing (ATAC-seq) was performed using 0.5 × 10 5 cells per sample. Following centrifugation, cells were lysed and tagmented for 30 minutes in buffer (33 mmol/L Tris-acetate, 66 mmol/L potassium acetate, 11 mmol/L magnesium acetate, and 16% dimethylformamide) containing in-house Tn5 transposase. DNA was then isolated using the Biozol Clean & Concentrator-5 kit. Tagmented DNA was amplified, and Illumina-compatible adapters were added during library preparation. Libraries were purified and sequenced on an Illumina NovaSeq X Plus system with a 25B flow cell in paired-end mode. All experiments were performed in biological duplicates. The ATAC-seq data were then processed with an nf-core pipeline ( v/2.1.2/ ). Bowtie 2 was used to map the reads to the human reference genome hg38. Then duplicated reads and reads mapped to the ENCODE blacklist regions v3 were removed from the bam files. Peaks called by MACS2 ( 26 ) were then filtered to the consensus ones within each group of replicates. EdgeR ( 27 ) was used to detect the differentially accessible peaks in the JJN-3-KANSL2-KD versus JJN-3 control samples. Peaks that were annotated as the promoter regions to a gene were integrated with the RNA-seq DESeq2 results.
Samples from patients with primary multiple myeloma and ex vivo drug response profiling
Fresh primary human bone marrow (BM) aspirates were obtained from nine patients with relapsed/refractory multiple myeloma in the Department of Hematology, Oncology, and Cancer Immunology of Charité – Universitätsmedizin Berlin. We obtained written informed consent and protocols were approved by the ethics committee of Charité (EA1/152/10) in accordance with the Declaration of Helsinki. Mononuclear cells were isolated from BM aspirates by Ficoll-Paque density gradient centrifugation. For KANSL2 expression analysis, cells were incubated with anti-CD138 MicroBeads (Miltenyi Biotec) and purified using magnetic-activated cell sorting according to the manufacturer’s protocol, followed by qPCR for KANSL2 and β-actin. The sample with the lowest KANSL2 expression was used as a reference point for normalization (fold change). For ex vivo drug response profiling, BM aspirates containing multiple myeloma and healthy cells were seeded in 384-well plates and treated with DMSO, panobinostat, OTX-015, and their combination for 72 hours. After incubation, cells were washed and stained with LIVE/DEAD Fixable Dye (Invitrogen, 2810942, dilution 1:350), fixed (Cytofix, BD Biosciences), permeabilized (Cytoperm, BD Biosciences), and stained for CD138 (Beckman Coulter, B49219 , dilution 1:600) and DAPI (Invitrogen, 2747527, dilution 1:10,000). Cells were stained in the dark overnight at 4°C. The next day, 25 nonoverlapping fields per well were imaged at 20× magnification using an Operetta CLS High-Content Analysis System (Revvity). Brightfield (650–760 nm), DAPI (435–480 nm), EGFP (500–550 nm), and APC (650–760 nm) channels were acquired with constant LED exposure and power settings, as previously described ( 28 ). Image analysis was performed with Harmony 5.1 software (Revvity). First, all images were corrected for flatfield. Cells were detected by DAPI nuclei stain. To detect different markers, a cell area was defined from the nuclear mask. By linear intensity thresholding of APC and GFP intensity values, cells were assigned to viable and CD138-positive cell populations. The relative myeloma fraction was calculated as viable CD138-positive cells divided by all viable cells, normalized to DMSO control.
Statistical analyses
Statistical analysis was implemented with GraphPad Prism 8 software. Statistical significance was indicated by P < 0.05. Unless otherwise specified, data presented were acquired in three independent experiments and P values were determined by (multiple) Student t test. Two-way ANOVA was used as indicated in respective figure legends. Quantitative data are presented as mean ± SD. Raw data from the CytoFlex Flow Cytometer were analyzed with FlowJo version 10.6.0 software.
Results
High KANSL2 levels are associated with adverse prognosis in multiple myeloma
To identify novel histone-PTM–related oncogenic drivers in multiple myeloma, we leveraged a histone-PTM cancer gene set derived from 1,110 patients across 11 cancer types ( 29 ), a patient-derived multiple myeloma gene set ( GSE6477 ), and a MYC-driven, genome-wide in vivo PiggyBac transposon mutagenesis screen ( 30 ). Candidate cancer genes from the in vivo screen encompassed 957 genes in which transposon integration and subsequent activation or suppression led to the development of lymphoid neoplasms. Overlapping these three data sets yielded five candidates that were examined for an association of high expression with adverse prognosis in patients with multiple myeloma treated with bortezomib-containing regimens ( Fig. 1A and B ; ref. 31 ). Of these, only high KANSL2 expression was significantly associated with shorter overall survival (OS), which we confirmed with data from the CoMMpass database ( NCT01454297 , research.themmrf.org ; Fig. 1C ; Supplementary Fig. S1). Among NSL complex members ( 18 ), only KANSL2 and MCRS1 were associated with shorter OS, whereas PHF20 was associated with longer OS (Supplementary Fig. S2). No significant correlation was found between the expression of other NSL subunits and OS in multiple myeloma (Supplementary Fig. S2). WDR5 expression levels were not sufficient for conducting survival analysis in the Mulligan multiple myeloma dataset.
To analyze the relationship between KANSL2 and MYC expression, we used U-2-OS and P493-6 cell lines, in which MYC expression can be switched on and off, respectively, by a tetracycline-inducible regulatory element. MYC activation induced KANSL2 expression on mRNA and protein levels (Supplementary Fig. S3A and S3B). This finding was further substantiated in JJN-3 and INA-6 multiple myeloma cells, in which KANSL2-KD led to a decrease in MYC RNA and protein (Supplementary Fig. S3C and S3D), and in samples from patients with multiple myeloma at the protein level (Supplementary Fig. S3E; ref. 33 ).
Furthermore, we found that both mRNA and protein expression of KANSL2 were significantly increased in MGUS and SMM samples compared with healthy plasma cells in publicly available RNA expression and proteomic datasets, respectively ( Fig. 1D and E ; refs. 32 , 33 ).
Together, we here identified KANSL2 as a MYC -associated and prognostic candidate gene in multiple myeloma.
Multiple myeloma cells depend on KANSL2
To characterize the biological role of KANSL2 in multiple myeloma, OPM-2, INA-6, JJN-3, L-363, and NCI-H929 multiple myeloma cell lines were selected for in vitro exploration from 34 multiple myeloma cell lines ranked from highest to lowest according to their KANSL2 mRNA expression (Supplementary Fig. S4). We used a dCas9-KRAB CRISPR interference system ( 34 ) for KANSL2 -KD ( Fig. 2A and B ). For gain-of-function studies, we introduced a lentiviral vector containing KANSL2 [KANSL2 overexpression (KANSL2-OE)] into NCI-H929 cells, a cell line with low KANSL2 expression, and into INA-6-KANSL2-KD cells to restore KANSL2 expression ( Fig. 2A ).
To interrogate the effects of KANSL2-KD on multiple myeloma cell growth, we performed competition assays between parental cells (without fluorescence) and control or KANSL2-KD cells (RFP+). The proportions of KANSL2-KD cells decreased significantly compared with control cells across all five multiple myeloma cell lines ( Fig. 2C ), which revealed that multiple myeloma cells depend on KANSL2. In line with this finding, cells harboring KANSL2-KD grew significantly slower compared with their corresponding controls in both JJN-3 and INA-6 groups ( Fig. 2D ). Furthermore, KANSL2-KD extensively increased cell apoptosis in both JJN-3 and INA-6 cells ( Fig. 2E ). In addition, DAPI and cleaved caspase-3 staining showed a lower proportion of apoptotic cells in control compared with KANSL2-KD cells ( Fig. 2F ), which was further supported by a substantial increase of cleaved caspase-3 protein in INA-6-KANSL2-KD cells ( Fig. 2G ).
Collectively, our findings manifest the critical function of KANSL2 in maintaining survival and proliferation of multiple myeloma cells.
KANSL2 is required for maintaining genomic stability in multiple myeloma
Because of the observed phenotypes, we hypothesized that KANSL2 shapes the biological program of multiple myeloma cells. To this end, we performed whole-transcriptome and -proteome in JJN-3 and INA-6 cells with decreased KANSL2 expression. Principal component analysis displayed substantial differences between control and KANSL2-KD groups but minimal variability within each group, indicating that KANSL2 profoundly affects gene transcription with a total of 2,847 and 3,118 differentially expressed genes in JJN-3 and INA-6 (Supplementary Fig. S5A and S5B), respectively. In proteomics, 529 and 582 proteins were downregulated and upregulated, respectively, in the KANSL2-KD setting (Supplementary Fig. S5C).
Overall, KANSL2 is significantly associated with essential cell biological processes, such as cell apoptosis, nuclear stability, and protein modification ( Fig. 3A ). Notably, DNA damage and repair–related pathways were enriched upon KANSL2 loss from Hallmark and GO_BP terms ( Fig. 3B ; Supplementary Fig. S6; Supplementary Data S1). Additionally, the G 2 –M checkpoint was activated ( Fig. 3B ) and, as expected, a subset of Hallmark MYC target genes was among the differentially regulated genes upon KANSL2-KD (Supplementary Fig. S7A and S7C). Filtering for MYC targets revealed that these genes were involved in chromatin organization and regulation of cell cycle, among others (Supplementary Fig. S7B and S7D).
Activation of DNA damage response (DDR) was verified by the accumulation of abnormal nuclear blebs and γH2AX foci, a biomarker of DNA damage ( Fig. 3C ; ref. 35 ), which was consistent with observations in mouse embryonic fibroblasts with KANSL2 knockout ( 36 ), indicating a conserved role for KANSL2 in DDR.
Furthermore, quantitative global proteomic profiling of JJN-3 cells confirmed that KANSL2-KD leads to DDR and cell death ( Fig. 3D ). Immunoblotting further corroborated that in both JJN-3 and INA-6 cell lines, KANSL2-KD increased γH2AX protein level and induced the activation of phosphorylated-checkpoint kinase 1 (p-CHK1), a key effector in cell-cycle arrest for DNA damage repair processes ( 37 ), which was rescued by reconstituted KANSL2 ( Fig. 3E ).
Taken together, these findings underscore that KANSL2 is a crucial regulator of DNA damage checkpoint activation and DNA repair processes in multiple myeloma.
KANSL2 protects multiple myeloma from chemotherapy-induced DNA damage
To test whether KANSL2 protects multiple myeloma cells from DNA damage–inducing chemotherapeutic agents, we treated genetically engineered multiple myeloma cells with different KANSL2 levels using doxorubicin. Indeed, cells with high KANSL2 levels exhibited resistance to doxorubicin, whereas those with low expression were more sensitive ( Fig. 4A ). Additionally, we validated this effect with the active metabolite of cyclophosphamide, mafosfamide, another DNA-damaging agent ( Fig. 4B ). This was also true for multiple myeloma cells with reconstituted KANSL2 expression ( Fig. 4C ).
To elucidate the mechanism by which elevated KANSL2 mediates chemotherapy resistance through the DDR, protein expression of DNA damage checkpoint induced p-CHK1, γH2AX, and cleaved caspase-3, which were assessed after treatments with doxorubicin and mafosfamide. Both agents triggered DNA damage and apoptosis as shown by increased levels of p-CHK1, γH2AX, and cleaved caspase-3 ( Fig. 4D ). Of note, in KANSL2 - high cells, treatment with either drug resulted in impaired checkpoint activation, decreased DNA damage, and apoptosis compared with controls ( Fig. 4D ), suggesting that elevated KANSL2 confers protection against DNA damage–inducing drugs.
In conclusion, enhanced KANSL2 expression leads to chemotherapy resistance by providing protection against DNA damage in multiple myeloma.
KANSL2 modulates histone acetylation and chromatin accessibility in multiple myeloma
To identify a therapeutic strategy to target the oncogenic program associated with KANSL2, we performed quantitative acetylome profiling in JJN-3 and NCI-H929 cell lines. In line with transcriptomics and proteomics, a number of DDR proteins were differentially acetylated ( Fig. 5A ). In addition, KANSL2 depletion decreased acetylation at all common sites of histone H3 and histone H4 ( Fig. 5B ). Although increased KANSL2 expression in NCI-H929 cells did not consistently alter acetylation of all histone H3/H4 sites ( Fig. 5B ), we observed significantly increased H3K27ac, a prominent marker of active chromatin ( Fig. 5B ; Supplementary Fig. S8; ref. 38 ). These results were confirmed by immunoblotting displaying reduction of H3Kac, H4Kac, and H3K27ac levels in JJN-3-KANSL2-KD cells and increased H3K27ac in NCI-H929-KANSL2-OE cells compared with controls ( Fig. 5C ). With decreased H3K27ac, we observed an increase in H3K27me3 and vice versa ( Fig. 5C ). To investigate how KANSL2 influences chromatin accessibility, we performed ATAC-seq and integrated the resulting data with whole-transcriptome profiling following KANSL2-KD in JJN-3 cells ( Fig. 5D ; Supplementary Data S4). A subset of genes, including the long noncoding RNA MIAT , exhibited concordant loss of chromatin accessibility and gene expression, suggesting direct transcriptional repression resulting from impaired KANSL2-dependent chromatin opening. Other loci, including the histone methyltransferase SMYD3 , the 26S proteasome subunit PSMD5 , and the translation elongation factor EEF1A1, displayed altered chromatin accessibility without corresponding changes in transcript levels, suggesting that KANSL2 not only shapes transcriptional programs but might also prime “epigenetic readiness” of stress response pathways. Additionally, genes within all regulatory categories contain genes that have been linked to DDR ( SMYD3 and PSMD5 ), multiple myeloma biology ( MIAT and PSMC1 ), or both.
KANSL2 is a biomarker for sensitivity to HDAC and BET inhibitors
As KANSL2 regulates histone acetylation dynamics in multiple myeloma, we chose to target this pathway as a biomarker-based strategy. Therefore, we applied the previously approved pan-HDAC inhibitor (panobinostat) and the bromodomain inhibitor (OTX-015) targeting readers of acetylated histone marks into multiple myeloma cells with different KANSL2 expression. Indeed, compared with control cells, KANSL2-high cells were significantly more sensitive to both monotherapies of panobinostat and OTX-015 and vice versa ( Fig. 6A and B ; Supplementary Fig. S9A and S9B). Panobinostat treatment resulted in increased acetylation of H3K27, which was further elevated in cells with higher KANSL2 expression ( Fig. 6C ). OTX-015 treatment led to a reduction of BRD4, which was also more pronounced with enforced KANSL2 expression ( Fig. 6D ).
Previous studies have shown that HDAC and BET inhibitors act synergistically to induce cancer cell death by regulating similar genes and pathways in several cancers ( 39 , 40 ). To enhance antimyeloma efficacy, we evaluated the combination of panobinostat and OTX-015 in NCI-H929 (KANSL2-OE vs. control) cells. This co-treatment showed increased efficacy in KANSL2-OE cells compared with control ( Fig. 6E and F ). Importantly, synergism of HDAC and BET inhibition is potentiated by overexpressing KANSL2.
To test whether KANSL2 expression is a clinically actionable biomarker for HDAC/BET inhibitor sensitivity, we performed ex vivo drug profiling in primary BM aspirates of nine patients with relapsed/refractory multiple myeloma at our institution. We developed a customized assay to allow for stratification by KANSL2 expression based on Kropivsek and colleagues ( 28 ). Fresh aspirates containing multiple myeloma and healthy cells were treated with panobinostat, OTX-015, and a combination. The effect on malignant and healthy cells (relative myeloma fraction) was quantified by automated multiplex immunofluorescence ( Fig. 6G ), in which a lower relative myeloma fraction indicates better antimyeloma effect and lower toxicity to healthy cells. KANSL2 mRNA in the multiple myeloma cell fraction was used to stratify patients into KANSL2-low and KANSL2-high groups (Supplementary Fig. S9C). Strikingly, the combination treatment with panobinostat and OTX-015 was significantly more effective in KANSL2-high compared with -low patients with multiple myeloma ( Fig. 6H and I ). In patients with high KANSL2 expression, combined HDAC and BET inhibition was significantly more effective than single-agent treatment. KANSL2 expression was positively correlated with multiple myeloma–specific response ( Fig. 6J ).
Collectively, we discovered that KANSL2 represents vulnerabilities to HDAC and BET inhibitors in multiple myeloma and represents an actionable biomarker for patient stratification.
Discussion
Here, we identified KANSL2 as an oncogene associated with unfavorable prognosis in multiple myeloma by integrating data from an unbiased cancer gene screen with data from patients with multiple myeloma. Phenotypic analyses and multiomics demonstrated the dependency of multiple myeloma cells on KANSL2 for maintaining genome integrity, which is critical for cell survival and proliferation. High KANSL2 expression confers chemotherapy resistance and presents a potential avenue for targeted therapy with epigenetic modifiers.
KANSL2 comprises 492 amino acid residues and includes four zinc-coordinating motifs that are critical for DNA binding ( 17 ). It has been implicated in neurodevelopmental disorders, intellectual disability, organ dysfunction, and cancer ( 21 , 41 – 44 ). Nevertheless, the role of KANSL2 in lymphoid malignancies, particularly malignant plasma cells, is unknown. Our finding that among NSL complex members, KANSL2 showed the strongest association with adverse prognosis in patients with multiple myeloma suggests a biological function of KANSL2 independent of other NSL complex members in malignant plasma cells. In line with our discovery, KANSL2 was reported as a cancer-specific epigenetic regulator in triple-negative breast cancer through CRISPR screening ( 22 ), but no therapeutic strategies targeting KANSL2-associated biological programs are established.
Panobinostat was approved by the FDA in 2015 in combination treatment with bortezomib and dexamethasone for patients with relapsed or refractory multiple myeloma after receiving two prior lines of therapy ( 45 , 46 ). It was subsequently withdrawn from the US market in 2022 because of failure in completing post-marketing trials but remains available in Europe with infrequent utilization in clinical practice ( 47 ). As the tolerability of panobinostat is limited by gastrointestinal toxicity and myelosuppression and its single-agent efficacy is modest, biomarkers for response and rational combinations are needed to support its use in multiple myeloma ( 48 ). A recent phase II trial of panobinostat in combination with lenalidomide and dexamethasone in relapsed multiple myeloma showed an acceptable toxicity profile for this combination and proposed high MAGEA1 expression as a negative prognostic factor ( 49 ). One aspect of the mechanism of HDAC inhibition in multiple myeloma is increasing the expression of silenced tumor suppressor genes by increasing chromatin accessibility ( 50 ). Similarly, histone acetylation by the NSL complex results in open chromatin and transcriptional activation ( 18 ). High KANSL2 expression in multiple myeloma cells could promote an already acetylated and active chromatin state, potentially increasing vulnerability to the enforced hyperacetylation effects caused by panobinostat.
OTX-015, also known as birabresib, is a BET inhibitior that competitively and selectively occupies the acetyl-binding pockets of BRD2, BRD3, and BRD4. OTX-015 was the first-in-class BET inhibitor to enter phase I clinical trials in several hematologic malignancies, including multiple myeloma ( 51 , 52 ), and phase Ib in selected advanced solid tumors ( 53 ). Although single-agent activity is promising, toxicity is a matter of concern, arguing for stringent patient selection. Our data showed that the efficacy of monotherapy with panobinostat and OTX-015 and their combination treatment is dependent on KANSL2 levels in multiple myeloma. HDAC inhibition induces a more relaxed chromatin structure, increasing reliance on BET proteins to facilitate transcription, whereas BET inhibitors prevent the binding of acetylated sites and BET proteins, leading to reduced transcription of oncogenes and constrained cancer growth ( 54 ). As MYC is an essential driver in multiple myeloma, KANSL2 potentially represents a biomarker for a MYC-driven oncogenic program vulnerable to dual HDAC and BET inhibition. This is further supported by the positive correlation of MYC and KANSL2 protein expression we found in proteomes of patients with multiple myeloma. Additionally, MYC-on/off cell line data support the connection of KANSL2/MYC signaling networks, with the limitation of MYC-driven cycle effects as potential confounders. The large number of regulated genes beyond MYC target genes argues for MYC-independent functions of KANSL2 in multiple myeloma. Importantly, the aim of this study was not to establish MYC activity/expression as a targetable biomarker in multiple myeloma but to dissect the potential of KANSL2 to serve as a functional determinant of response to epigenetic therapies.
Of note, we opted for low concentrations of panobinostat (2 nmol/L) and OTX-015 (100–200 nmol/L) that lie below achievable plasma concentrations in patients and found that co-treatment is synergistic in multiple myeloma cells with enforced KANSL2 expression and samples from patients with multiple myeloma ( 53 , 55 ). Selecting patients with relapsed/refractory multiple myeloma based on KANSL2 expression or ex vivo drug response in malignant plasma cells for HDAC/BET inhibitor treatment might be an attractive strategy to enrich for potential responders in future clinical trials. Hence, histone acetylation related–epigenetic drugs are promising biomarker-informed candidate therapies for patients with multiple myeloma with high KANSL2 expression.
Supplementary Material
GSEA
global proteome
acetylome
ATACseq
Kaplan Meier survival curves of top candidate genes
Kaplan-Meier survival curves of NSL complex members
MYC induces KANSL2 RNA and protein expression
KANSL2 mRNA expression in MM cell lines
KANSL2 reshapes the transcriptomic and proteomic program of MM cells
Enrichment analysis of DNA damage and repair related pathways in KANSL2 depleted cells
MYC and KANSL2 co-regulated targets and their functions
KANSL2 influences the level of H3K27ac
Panobinostat and OTX015 act synergistically in high KANSL2 MM cells
List of primary antibodies for WB
Acknowledgments
This work was funded by Else-Kröner-Fresenius Stiftung (EKFS) grant number 2022_EKEA.94, Berliner Krebsgesellschaft (BKG) grant number HAFF202130 MM, and DKTK Berlin Young Investigator Grant to S. Habringer and Deutsche Forschungsgemeinschaft (DFG) grant KE 222/10-1 (project number 494535244) and Deutsche Krebshilfe (project number 70114425, project number 70114724, and project number 70111944) to U. Keller. K. Jiang was supported by Chinese Scholarship Council (CSC) and Charité – Universitätsmedizin Berlin (Promotionsstipendium I).
Footnotes
Note: Supplementary data for this article are available at Molecular Cancer Therapeutics Online ( http://mct.aacrjournals.org/ ).
Data Availability
RNA- and ATAC-seq data were deposited in the NCBI Gene Expression Omnibus database under accession number GSE302047. Mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD066205.
Authors’ Disclosures
J. Krönke reports personal fees from Johnson & Johnson, AbbVie, Amgen, Bristol Myers Squibb, and Pfizer outside the submitted work. S. Habringer reports grants from Berliner Krebsgesellschaft, Else Kröner Fresenius Stiftung (EKFS), and DKTK Berlin during the conduct of the study, as well as other support from Moonlight AI GmbH, nonfinancial support from AbbVie, personal fees and nonfinancial support from SERB, and personal fees from Jazz Pharmaceuticals and Incyte outside the submitted work. No disclosures were reported by the other authors.
Authors’ Contributions
K. Jiang: Conceptualization, data curation, software, formal analysis, validation, investigation, visualization, methodology, writing–original draft, project administration. M. Kirchner: Data curation, formal analysis, visualization, methodology. F. Herzberg: Software, writing–review and editing. Y. Zhao: Formal analysis, visualization, methodology, writing–review and editing. A. Gasper: Data curation, writing–review and editing. F. Baumgartner: Formal analysis, visualization, methodology, writing–review and editing. P. Jung: Formal analysis, visualization, methodology, writing–review and editing. J. Braune: Software. V. Schulze: Methodology. K. Isaakidis: Methodology. P. Mertins: Writing–review and editing. J. Krönke: Writing–review and editing. M. Wirth: Visualization, writing–review and editing. U. Keller: Supervision, writing–review and editing. S. Habringer: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing.
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Associated Data
Supplementary Materials
GSEA
global proteome
acetylome
ATACseq
Kaplan Meier survival curves of top candidate genes
Kaplan-Meier survival curves of NSL complex members
MYC induces KANSL2 RNA and protein expression
KANSL2 mRNA expression in MM cell lines
KANSL2 reshapes the transcriptomic and proteomic program of MM cells
Enrichment analysis of DNA damage and repair related pathways in KANSL2 depleted cells
MYC and KANSL2 co-regulated targets and their functions
KANSL2 influences the level of H3K27ac
Panobinostat and OTX015 act synergistically in high KANSL2 MM cells
List of primary antibodies for WB
Data Availability Statement
RNA- and ATAC-seq data were deposited in the NCBI Gene Expression Omnibus database under accession number GSE302047. Mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD066205.