Previous Article in Journal
What Is Still Unclear or Unresolved in Classic Hodgkin Lymphoma Pathobiology, Diagnosis, and Treatment
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Hemato
Volume 6
Issue 3
10.3390/hemato6030021
hemato-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genome-Wide CRISPR-Cas9 Knockout Screening Identifies NUDCD2 Depletion as Sensitizer for Bortezomib, Carfilzomib and Ixazomib in Multiple Myeloma

by
Sophie Vlayen
1,*,
Tim Dierckx
2,
Marino Caruso
3,
Swell Sieben
3,
Kim De Keersmaecker
3,
Dirk Daelemans
2 and
Michel Delforge
4,*
1
Laboratory of Experimental Hematology, Department of Oncology, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
2
Molecular Genetics and Therapeutics in Virology and Oncology Research Group, Immunology and Transplantation, Department of Microbiology, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
3
Laboratory of Disease Mechanisms in Cancer, Department of Oncology, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
4
Department of Internal Medicine—Hematology, UZ Leuven, Herestraat 49, 3000 Leuven, Belgium
*
Authors to whom correspondence should be addressed.
Hemato 2025, 6(3), 21; https://doi.org/10.3390/hemato6030021
Submission received: 5 May 2025 / Revised: 9 July 2025 / Accepted: 10 July 2025 / Published: 16 July 2025
(This article belongs to the Section Plasma Cell Disorders)
Browse Figures
Review Reports Versions Notes

Abstract

Background/Objectives: The treatment of multiple myeloma (MM) remains a challenge, as almost all patients will eventually relapse. Proteasome inhibitors are a cornerstone in the management of MM. Unfortunately, validated biomarkers predicting drug response are largely missing. Therefore, we aimed to identify genes associated with drug resistance or sensitization to proteasome inhibitors. Methods: We performed genome-wide CRISPR-Cas9 knockout (KO) screens in human KMS-28-BM myeloma cells to identify genetic determinants associated with resistance or sensitization to proteasome inhibitors. Results: We show that KO of KLF13 and PSMC4 induces drug resistance, while NUDCD2, OSER1 and HERC1 KO cause drug sensitization. Subsequently, we focused on top sensitization hit, NUDCD2, which acts as a co-chaperone of Hsp90 to regulate the LIS1/dynein complex. RNA sequencing showed downregulation of genes involved in the ERAD pathway and in ER-associated ubiquitin-dependent protein catabolic processes in both untreated and carfilzomib-treated NUDCD2 KO cells, suggesting that NUDCD2 depletion alters protein degradation. Furthermore, bortezomib-treated NUDCD2 KO cells showed a decreased expression of genes that have a function in oxidative phosphorylation and the mitochondrial membrane, such as Carnitine Palmitoyltransferase 1A (CPT1A). CPT1A catalyzes the uptake of long chain fatty acids into mitochondria. Mitochondrial lipid metabolism has recently been reported as a possible therapeutic target for MM drug sensitivity. Conclusions: These results contribute to the search for therapeutic targets that can sensitize MM patients to proteasome inhibitors.

1. Introduction

Multiple myeloma (MM) is a hematological cancer of plasma cells that mainly affects people over 60 years of age and has a median overall survival (OS) of approximately six years [1,2,3]. Whereas normal plasma cells produce antibodies to eradicate pathogens, cancerous plasma cells produce excessive quantities of monoclonal immunoglobulins, called M-proteins [4]. MM is a highly symptomatic illness characterized by a wide range of disease-related complications, including osteolytic bone lesions, renal failure and hypercalcemia [1,5,6].
In the past two decades, the introduction of novel therapeutic agents has changed the treatment of MM. In addition to historical approaches such as chemotherapy, steroids, and autologous stem cell transplantation, new classes of drugs, including immunomodulatory drugs (IMiDs), proteasome inhibitors (PIs), and more recently monoclonal antibodies, were introduced [1]. These treatments have remarkably improved the progression-free survival (PFS) and OS of MM patients [3]. At present, proteasome inhibition is one of the key strategies for the treatment of both newly diagnosed and relapsed/refractory MM. Three PIs are approved for clinical use: the reversible PIs bortezomib and ixazomib, and the irreversible PI carfilzomib [1].
To maintain homeostasis, MM cells rely heavily on the ubiquitin proteasome system (UPS). The UPS represents the main pathway that degrades misfolded, damaged, or unnecessary proteins in the cell. Target proteins are tagged with poly-ubiquitin by three enzymes, the ubiquitin-activating enzyme (E1), the ubiquitin-conjugating enzyme (E2) and lastly the ubiquitin ligase (E3). Following poly-ubiquitination, the target proteins are degraded into small peptides by the 26S proteasome, which consists of the 20S catalytic core and two 19S regulatory subunits. PIs target the β5 catalytic subunit of the 20S core, which is encoded by the proteasome subunit beta type-5 (PSMB5) gene. This results in the accumulation of poly-ubiquitinated proteins tagged for degradation. Subsequently, endoplasmic reticulum (ER) stress activates the unfolded protein response (UPR). Initially, the UPR is capable of restoring cellular homeostasis; however, ER stress persists, ultimately leading to apoptosis of MM cells [7,8].
Despite significant advances in the treatment of MM, complete eradication of malignant plasma cells is still hard to achieve, and the majority of patients relapse due to drug resistant plasma cell clones. The current data on resistance mechanisms to PIs are mostly limited to preclinical studies with the first-in-class PI bortezomib. Data on resistance mechanisms of second-generation PIs carfilzomib and ixazomib are scarce.
Several mechanisms underlying drug resistance to the different PIs have been described, with first, the most documented resistance mechanism being acquired point mutations in the PSMB5 gene [9]. Second, MM cells can upregulate proteasome activity through the overexpression of proteasome subunits in response to PI treatment [10,11]. Third, MM cells can also dedifferentiate into pre-plasmablasts, showing several hallmarks of tumor stem cells [12]. Fourth, alternative proteolytic pathways, such as exocytosis and autophagy, can be used by MM cells for protein degradation [13,14,15]. Fifth, MM cells can also extrude PIs trough the upregulation of membrane efflux transporters [16,17]. Sixth, MM cells can upregulate pro-oncogenic pathways and can downregulate pro-apoptotic pathways, such as the p53 pathway [10,18,19]. Finally, MM cells can alter their cellular metabolism, such as the serine synthesis pathway, in response to PI treatment [20,21,22]. To further optimize MM treatment, a better understanding of the underlying mechanisms that contribute to resistance and sensitivity to PIs is needed. As for many other cancers, treatment choice and duration can become more successful when it can be personalized to individual patient and disease characteristics rather than on a ‘trial and error’ approach [23]. Unfortunately, this is currently not possible due to a lack of clinically applicable biomarkers predicting a response to several drug classes in MM, including PIs.
Therefore, we employed genome-wide CRISPR-Cas9 screening approaches to identify genetic determinants associated with drug resistance or sensitization to PIs. Recent CRISPR-Cas9-based screening efforts have identified mechanisms that play a role in drug resistance and sensitivity to PIs in MM cell lines [24,25]. For example, knockout (KO) of the mitochondrial pyruvate carrier complex has recently been shown to increase sensitivity to bortezomib [25]. In this study, we performed genome-wide CRISPR-Cas9 KO screens in a human MM cell line, followed by RNA sequencing upon depletion of the top sensitization gene.

2. Materials and Methods

Main methods are described below, and additional method information can be found in Supplementary Methods.

2.1. Cell Culture

The KMS-28-BM cell line was purchased from Japanese Collection of Research Bioresources (JCRB, Neuss, Germany) and cultured in RPMI-1640 (Gibco, Waltham, MA, USA) supplemented with 20% fetal bovine serum (FBS, Brazil) (Gibco) and 1% penicillin–streptomycin (Gibco). HEK293T cells were a gift from Prof. Moffat (University of Toronto, Toronto, ON, Canada) and cultured in DMEM (Gibco) supplemented with 10% heat-inactivated FBS (Biowest, Nuaillé, France) and 20 μg/mL gentamicin (Gibco).

2.2. Compounds

Bortezomib, carfilzomib, and ixazomib were purchased from SelleckChem and dissolved in dimethyl sulfoxide (DMSO).

2.3. Genome-Wide CRISPR-Cas9 Knockout Screening

Ninety-eight million KMS-28-BM cells were transduced with the Brunello human CRISPR knockout library (Addgene, #73179), which is a mixture of single guide RNAs (sgRNAs) targeting each gene in the genome (4 sgRNAs per gene). Cells were transduced at a multiplicity of infection (MOI) of 0.3 and with a coverage of at least 200-fold in 12-well plates with two million cells/well in the presence of eight µg/mL polybrene (Sigma, St Louis, Mo, USA). Following spinfection for 2 h at 2000 RPM (Sorvall ST40 R, rotor TX-1000, Thermo Fisher Scientific, Waltham, Massachusetts, USA) at 37 °C and incubation for 2 h at 37 °C, 5% CO2, cells were washed with phosphate-buffered saline (PBS) (Gibco). The next day, cells were pooled and plated in 150 cm2 flasks. Two days later, puromycin selection (400 ng/mL) (Sigma) was started for six days after which the transduction efficiency and MOI were calculated. Subsequently, six cell populations (three replicates of vehicle-treated cells and three replicates of PI-treated cells) were cultured for at least 20 cell doublings in the presence of a sub-lethal concentration of bortezomib or carfilzomib. To analyze the evolution of the sgRNA representation in PI-treated versus vehicle-treated cells, samples of 30 million cells of each replicate were collected before compound selection and at every medium change. Genomic DNA was extracted with the QIAamp DNA Blood Maxi kit (Qiagen, Hilden, Germany) using RNase A (Qiagen) and further precipitated using ethanol according to the protocol of the Moffat lab [26]. Subsequently, two PCR reactions were performed to enrich the sgRNA target sequence (PCR1) and to integrate the Illumina sequencing adaptors (PCR2). Sequencing was performed on a HiSeq X (150 bp paired-end reads), and results were analyzed using CRISPRCloud2 (CC2) [27]. All raw and processed data were deposited in the Gene Expression Omnibus (GEO) of NCBI (GEO accession numbers: GSE301947 and GSE301948). CC2 uses a beta-binomial model with a modified Student’s t-test to determine sgRNA level significance followed by Fisher’s combined probability test to determine gene level significance [28]. Gene level comparison of the PI-treated group to the vehicle-treated group was calculated as the log2 fold change (log2FC) of the averaged difference in the four sgRNAs targeting the same gene between the two groups. Volcano plots were generated in GraphPad Prism (Version 10.3.0). Genes were sorted according to log2FC. To identify the total amount of resistance and sensitization genes of both screens and subsequently, similarity between both screens, a log2FC cut-off value of 0.5 to identify resistance genes and a log2FC cut-off value of −0.5 to identify the sensitization of genes was used. For the selection of candidate genes for further individual validation, we investigated the top 20 of highest positive and negative log2FC values to identify the most interesting hits. Candidate genes were selected based on the relevance of their function to the proteasome and to critical pathways associated with MM, FDR < 0.05, and at least three out of four sgRNAs had to have a significant log2FC.

2.4. Candidate Hit Validation

For each candidate gene of interest, two sgRNAs from the Toronto KnockOut CRISPR (TKOv3) library were cloned separately into the plentiCRISPRv2*-Procode-GCN4-MAP-Tag54 (backbone plasmid lentiCRISPRv2 obtained from Addgene, #52961). As control, the adeno-associated virus integration site 1 (AAVS1) “safe-harbor” locus was targeted (hereafter referred to as AAVS1 cells). The AAVS1 targeting sgRNA was cloned into pLentiCRISPRv2*-Procode-HA-V5-FLAG. Efficient editing of the target loci was confirmed by Sanger sequencing. Cells containing a KO in a specific gene were pooled 1:1 with cells containing an AAVS1 targeting sgRNA and treated with PIs. To assess the evolution of the two pooled cell populations, cells were analyzed every week by flow cytometry. Further details on candidate hit validation are provided in Supplementary Methods.

2.5. Proteasome Activity Assay

To determine the chymotrypsin-like (CTL), trypsin-like (TL) and caspase-like (CL) activities of the proteasome, the Cell-Based Proteasome-Glo Assay (Promega, Madison, WI, USA) was used.

2.6. RNA Sequencing

RNA sequencing was performed using Illumina NovaSeq 6000 technology. All raw and processed data were deposited in the Gene Expression Omnibus (GEO) of NCBI (GEO accession number: GSE301632). Additional information on sample preparation, sequencing and bioinformatics analyses are provided in Supplementary Methods.

2.7. Statistical Analysis

Statistical analyses were performed using GraphPad Prism. All data are shown as mean ± standard deviation (mean ± SD). Statistical significance testing was performed using a one-way analysis of variance (ANOVA) with Dunnett’s test for correction of multiple testing. Similarity of variance between the compared groups was checked using the Brown–Forsythe and Welch’s test. Individual differences were checked using a one-way ANOVA with Tukey’s post hoc test, or using a two-way ANOVA with Tukey’s post hoc test. Differences were considered statistically significant for p-values < 0.05 (*), <0.01 (**), <0.001 (***), or <0.0001 (****).

3. Results

3.1. Genome-Wide CRISPR-Cas9 Knockout Screens Identify Genetic Determinants for Bortezomib and Carfilzomib Sensitivity in MM Cells

3.1.1. Genome-Wide CRISPR-Cas9 Knockout Screens

To identify genetic determinants associated with resistance or sensitization to PIs, we performed genome-wide CRISPR-Cas9 KO screens under selection with bortezomib or carfilzomib (Figure 1A).
In order to find a suitable human MM cell line for our research, we optimized transduction efficiency on a panel of MM cell lines consisting of KMS-28-BM, KMS-12-BM, KMS-12-PE, KMS-20, KMS-34, U266, and OPM2. The KMS-34, KMS-12-PE, and KMS-28-BM cell lines could be efficiently transduced. The KMS-28-BM cell line achieved the most optimal transduction rates for CRISPR-cas9 screening and hence this cell line was used for our screens.
KMS-28-BM cells were transduced with the Brunello library and subsequently treated with vehicle or a moderate concentration (inhibitory concentration of 50% (IC50)) of bortezomib or carfilzomib for 20 cell doublings. This drug concentration was chosen, as treating with an IC50 allows to identify both resistance and sensitization genes in one KO screen. Cells surviving drug treatment contain sgRNAs targeting genes whose inactivation results in resistance to the treatment drug (i.e., resistance genes), and these sgRNAs become enriched in PI-treated versus vehicle-treated cells. In contrast, cells that were killed by drug treatment, contained sgRNAs targeting genes whose inactivation resulted in more sensitivity to the treatment drug (i.e., sensitization genes), and these sgRNAs become depleted in PI-treated cells versus vehicle-treated cells.
To assess read count distribution of sgRNAs before PI treatment, a frequency distribution histogram was created (Figure S1). Gene level and sgRNA level comparisons were performed with CC2 on samples collected at an intermediate time point (day 28 bortezomib KO screen, day 32 carfilzomib KO screen) and at the final time point (day 42 bortezomib KO screen, day 46 carfilzomib KO screen) (Tables S1–S3, Figures S2 and S3) [27]. In both screens, the log2FC of the identified genes from the intermediate and final time point showed consistent tendency with more pronounced effects at the final time points. At the final time point, we identified 236 resistance genes and 719 sensitization genes in the bortezomib KO screen, and 605 resistance genes and 2264 sensitization genes in the carfilzomib KO screen (Figure S4A).

3.1.2. Gene Set Enrichment Analysis of CRISPR-Cas9 Knockout Screens

To gain more insight into the gene classes that were enriched and depleted in our bortezomib and carfilzomib KO screen data, we performed Gene Set Enrichment Analysis (GSEA) (Figure 1B–D) [29]. GSEA showed that the KO of genes involved in mitochondrial electron transport and oxidative phosphorylation confers resistance to bortezomib or carfilzomib treatment (Figure 1B,C). On the other hand, the KO of genes involved in immune response-related pathways, such as the innate immune response activating cell surface receptor signaling pathway, confers more sensitivity in bortezomib-treated cells. In addition, the KO of genes involved in the ubiquitination of proteins, the NF-κB signaling pathway, and of genes that are part of the proteasome sensitizes cells upon treatment with bortezomib. This is expected, as these are known PI drug targets in MM [30,31], and this thus confirms the ability of our screen to detect modulators of PI response. Furthermore, the KO of genes that are part of the spindle pole, such as NUDCD2, confers more sensitivity in bortezomib-treated cells. Lastly, the KO of genes involved in the positive regulation of mRNA catabolic processes sensitizes carfilzomib-treated cells (Figure 1B,D).

3.1.3. Identification of Top Candidate Hits

Next, the resistance and sensitization genes were sorted according to log2FC and a cut-off value of ±0.5 was implemented to identify resistance and sensitization genes, respectively (Figure 1E,F). Comparison of both screens revealed that 49 genes (e.g., RQCD1, JAGN1, PDCL, and HK2) are associated with resistance to both bortezomib and carfilzomib, while 174 genes (e.g., NUDCD2, ECPAS, RNF181, OSER1, HERC1, HERC2, CHUK, PSMF1, CIC, and DDX17) are associated with sensitization to both bortezomib and carfilzomib (Figure S4A). Gene ontology analysis of common sensitization and resistance genes showed the enrichment of genes involved in protein degradation and the MAPK signaling pathway, respectively (Figure S4B,C). When comparing specifically the top 20 of both screens, E3 ubiquitin protein-ligases and proteasome-related genes are detected in both screens. Interestingly, proteasome-related genes can be either sensitizing or confer resistance to PIs.
Subsequently, we selected candidate genes within the top 20 of highest positive and negative log2FC values for further individual validation (Table S4). The top sensitization hit identified in the bortezomib KO screen was NUDCD2. NUDCD2 regulates the LIS1/dynein complex by acting as a co-chaperone of Hsp90 to stabilize LIS1 [32]. We also identified E3 ubiquitin-protein ligases (RNF181, HERC1, and HERC2) as sensitizers. Other interesting top sensitization hits were ECPAS, encoding the proteasome adapter and scaffold protein ECM29, and PSMF1, which encodes the proteasome inhibitor PI31 subunit [33,34]. Lastly, we identified CHUK, an IKK-α, involved in the activation of the NF-κB pathway [35]. The top resistance hit from the bortezomib KO screen was KLF13, which is a transcription factor that activates the chemokine RANTES in T cells and represses transcription by interaction with a Sin3A-HDAC1 complex [36]. Secondly, we identified genes involved in retrograde and vesicle-mediated transport as resistance hits (SNX27 and JAGN1, respectively) [37,38] (Figure 1E).
The top sensitization hit from the carfilzomib KO screen was OSER1, encoding the oxidative stress-responsive serine-rich protein 1. We also identified STUB1, encoding the E3 ubiquitin-protein ligase CHIP, as a sensitizer [39]. The top two resistance hits from the carfilzomib KO screen were PSME1 and PSME2, encoding PA28α and PA28β, respectively, which are both subunits of the 11S regulatory particle of the immunoproteasome [40,41] (Figure 1F).

3.1.4. Validation of Top Candidate Hits

We performed competition assays for 15 selected candidate hits (6 resistance genes and 9 sensitization genes) from the bortezomib KO screen of which we could validate KLF13 and PSMC4 as resistance genes and NUDCD2, OSER1 and HERC1 as sensitization genes for bortezomib in the KMS-28-BM cell line (Figure 2A,B and Figures S5–S9, Tables S5 and S6). In addition to validating candidate hits modulating bortezomib drug sensitivity, we investigated the effect of knocking out these same genes on carfilzomib (Figure 3A and Figure S10) or ixazomib (Figure 3B and Figure S11) drug sensitivity. KLF13 was confirmed as a carfilzomib and ixazomib resistance gene and PSMC4 was confirmed as a resistance gene for ixazomib; however, it was not for carfilzomib. NUDCD2, OSER1, and HERC1 were confirmed as sensitization genes for both carfilzomib and ixazomib, which is consistent with what we had observed for bortezomib.

3.2. NUDCD2 Knockout Does Not Sensitize Myeloma Cells by Modulating Proteasome Activity

Next, we investigated the top sensitization hit from the bortezomib KO screen, NUDCD2, which is involved in chaperone activity in the LIS1/dynein pathway [32]. To evaluate whether NUDCD2 KO modulates cellular PI sensitivity by affecting proteasome activity, we first assessed the effect of NUDCD2 KO on the three proteasome activities (chymotrypsin-like (CTL), trypsin-like (TL), and caspase-like (CL)). We could not observe a significant difference in any of these proteasome activities in NUDCD2 KO cells compared to the control cells (Figure 4A and Figure S12, Table S7). Next, NUDCD2 KO cells were treated with bortezomib, carfilzomib or ixazomib followed by evaluation of effects on proteasome activities (Figure 4B). As expected, CTL activity was reduced upon treatment with all PIs, as well as CL activity upon treatment with ixazomib. We observed an 11.6% significant higher CTL activity upon treatment with ixazomib for sgRNA 2, a 15.5% higher CL activity upon treatment with ixazomib for sgRNA 2, and a 23.5% higher CL activity upon bortezomib treatment for sgRNA 2. However, we did not observe other significant differences in proteasome activities, suggesting that drug sensitivity is not caused by an altered function of the proteasome.

3.3. RNA Sequencing Identifies Potential Mechanisms of NUDCD2 KO Sensitization

To achieve a better understanding of the molecular mechanism by which NUDCD2 KO modulates PI sensitivity, the transcript level changes upon NUDCD2 KO (untreated and PI drug-treated) were characterized by RNA sequencing (Figure 5, Figure 6, Figures S12 and S13, Table S7).
Principal Component Analysis (PCA) of the RNA sequencing experiment showed that vehicle-treated samples have a gene expression profile that is distinct from those of bortezomib- and carfilzomib-treated cells (Figure 5A). In line with the observations made in the proteasome activity experiments, NUDCD2 KO has little impact on gene expression in the vehicle-treated conditions.
We performed differential expression analysis using DESeq2 comparing untreated, bortezomib-treated and carfilzomib-treated NUDCD2 KO cells to their respective controls (Figure 5B). In all three conditions, the expression of Carnitine Palmitoyltransferase 1A (CPT1A) was significantly downregulated (untreated NUDCD2 KO cells: log2FC = −1.16, adjusted p-value (Padj.) = 1 × 10−4; bortezomib-treated NUDCD2 KO cells: log2FC = −1.19, Padj. = 1.88 × 10−5; carfilzomib-treated NUDCD2 KO cells: log2FC = −1.15, Padj. = 5.55 × 10−5). SYNPO2L and RCAN2 were both significantly upregulated in all three conditions compared to their respective control cells. Heat shock protein HSPA6 and TMEM59L were significantly upregulated in both bortezomib- and carfilzomib-treated NUDCD2 KO cells compared to their respective control cells. Furthermore, untreated NUDCD2 KO cells showed significant upregulation of MAST1. Lastly, bortezomib-treated NUDCD2 KO cells showed a significant decrease in CUBN compared to control cells.
Finally, we performed functional annotation of our differential expression results using GSEA. We identified 39 pathways associated with NUDCD2 KO, 67 pathways associated with carfilzomib-treated NUDCD2 KO, and 213 pathways associated with bortezomib-treated NUDCD2 KO. In what follows, we highlight several pathways of particular interest to MM. NUDCD2 KO cells treated with vehicle showed an increased expression of genes involved in tRNA metabolic processes, while the expression of genes involved in the establishment of protein localization to the ER, the nonsense-mediated decay pathway for nuclear-transcribed mRNAs, the ERAD pathway and ER-associated ubiquitin-dependent protein catabolic processes were downregulated (Figure 6A, Figures S14 and S15A). Similarly, these terms were also downregulated in carfilzomib-treated NUDCD2 KO cells compared to their respective control cells (Figure 6B, Figures S14 and S15B). Bortezomib-treated NUDCD2 KO cells showed downregulation of genes involved in ribosomal processes and components, the establishment of protein localization to the ER, the nonsense-mediated decay pathway for nuclear-transcribed mRNAs, RNA processes, oxidative phosphorylation and of genes that are part of the mitochondrial membrane, such as CPT1A. On the other hand, genes involved in NIK-NFκB signaling, the proteasome complex, the innate immune response activating cell surface receptor signaling pathways, and the regulation of cellular amino acid metabolic processes were upregulated (Figure 6C, Figures S14 and S15C).

4. Discussion

In the past 20 years, significant therapeutic progress has been achieved in the management of MM due to the introduction of new drug classes. PIs have become a corner stone in the treatment of MM. However, full disease eradication remains exceptional for the majority of patients because of relapse, partly due to the emergence of drug-resistant clones. Moreover, little is known about the underlying mechanisms of drug resistance to PIs [1,2,3]. Better insights into the drug response of myeloma cells to specific PIs would be a major step forward towards a more tailored disease management. In addition, given the high cost and potential toxicities of several anti-myeloma drugs, the concept of long-term or even ‘continuous’ drug treatment should also be critically evaluated in the perspective of the risk for inducing drug resistance. As such, further optimization of PI-based regimens in MM through a better understanding of mechanisms of drug resistance is necessary.
In this study, we identified resistance and sensitization genes to PIs using CRISPR-Cas9 screening methodology. Several groups have already reported CRISPR-Cas9 screening results in MM cells in the presence of PIs. Stewart et al. performed a genome-wide CRISPR-Cas9 KO screen in the presence of bortezomib to identify resistance genes. They showed that loss of PSMC6 caused resistance to bortezomib and carfilzomib. Although validation results were fully reproduced in a second MM cell line, clinical validation is also lacking in this study [24]. Orthwein et al. performed genome-wide CRISPR-Cas9 KO screens and identified loss of MPC1 to cause sensitization to bortezomib. They investigated the clinical relevance of their findings with three RNA sequencing datasets and tested an MPC1 inhibitor together with bortezomib in patient samples [25].
We performed multiple genome-wide CRISPR-Cas9 KO screens under selection of bortezomib or carfilzomib in a human MM cell line, followed by individual candidate hit validation. We showed that KLF13 and PSMC4 gene knockout decreased drug response, while gene knockout of NUDCD2, OSER1, and HERC1 increased drug sensitivity against three clinically approved PIs, bortezomib, carfilzomib, and ixazomib. These observations define a new role for these genes in drug resistance and sensitivity to bortezomib, carfilzomib, and ixazomib. Identifying E3 ubiquitin-protein ligases as sensitizers, such as HERC1, is not surprising as these are known drug targets in MM and myeloma-targeting agents like IMiDs and cereblon E3 ligase modulators (CELMoDs) being modulators of the cereblon E3 ligase complex [42,43,44]. Moreover, our data confirmed the results of Driessen et al., who previously reported that KO of ECPAS and OSER1 sensitizes bortezomib-resistant cells to PIs [45]. In addition, functional annotation of our genome-wide CRISPR-Cas9 KO screens showed that KO of genes involved in mitochondrial electron transport and oxidative phosphorylation confer more resistance to bortezomib and carfilzomib treatment, confirming the results of Sharma et al., who showed that mitochondrial stress by inhibition of the electron transport chain promotes resistance to bortezomib. Additionally, GSEA performed on data they retrieved from the CoMMpass trial showed downregulation of complex I biogenesis, the TCA cycle, and OXPHOS-related pathways in poor survival patients [46].
Knockout of PSMC4, which encodes the 26S proteasome regulatory subunit 6B, decreased drug response of myeloma cells to PIs. This result is in line with previous publications addressing that a reduced expression of 19S regulatory subunits promote PI resistance [47,48,49].
We also identified that KO of NUDCD2 sensitized myeloma cells to PIs. The function of NUDCD2 is linked to the function of the Hyaluronan Mediated Motility Receptor (HMMR). HMMR and NUDCD2 are part of the same small gene cluster located on chromosome 5q34. Both genes are involved in dynein motor activity, centrosome function and mitotic spindle integrity. Interestingly, elevated HMMR expression is associated with poor prognosis in a variety of cancers, including MM [50]. NUDCD2 interacts with the E3 ubiquitin-protein ligase HERC2 (protein—protein interaction), which is another validated, strong sensitizing hit from both our CRISPR-Cas9 KO screens [51]. HERC2 has key roles in cell cycle regulation, mitotic spindle formation, mitochondrial functions and DNA damage response [43]. Yang et al. showed that NUDCD2 localizes to centromeres and that NUDCD2 is required for centriole duplication by interacting with and stabilizing HERC2. Additionally, quantitative proteomic analysis also revealed that HERC2 is downregulated in NUDCD2 KO cells [52]. However, our RNA sequencing data did not show a significant downregulation of HERC2 in NUDCD2 KO cells compared to control cells, suggesting a regulation at posttranscriptional level. Several other interaction partners of NUDCD2 were identified as either sensitization or resistance hits in our screens (Figure S16, Table S8) [51]. These results suggest that drug sensitization resulting from NUDCD2 KO could be facilitated by dysregulation of the LIS1/dynein complex, which consequently dysregulates mitotic spindle integrity, more specifically centriole duplication, eventually leading to altered cell division and proliferation. However, as our NUDCD2 KO cells seemed to proliferate normally, this does not provide an explanation for the underlying molecular mechanism of sensitizing MM cells to PIs.
Our RNA sequencing results also showed downregulation of genes involved in the ERAD pathway and ER-associated ubiquitin-dependent protein catabolic processes in both untreated and carfilzomib-treated NUDCD2 KO cells compared to control cells, suggesting that NUDCD2 depletion somehow dysregulates protein degradation. However, we did not observe significant differences in proteasome activity between NUDCD2 KO cells and control cells, suggesting that drug sensitivity is not exclusively caused by an altered function of the proteasome. Further research is required to fully understand the exact underlying mechanism. GSEA showed that bortezomib-treated NUDCD2 KO cells showed a decreased expression of genes that are part of oxidative phosphorylation and the mitochondrial membrane, such as CPT1A. CPT1A is located in the outer membrane of mitochondria and plays a role in fatty acid oxidation (FAO). In particular, CPT1A regulates the uptake of long chain fatty acids (LCFA) into mitochondria by catalyzing the transfer of the acyl group of LCFA-CoA conjugates onto carnitine [53]. Targeting FAO has been described to serve as a new therapeutic target to treat MM [54,55,56]. We show that KO of NUDCD2 downregulates CPT1A, which could explain the increased sensitivity to PIs of NUDCD2 KO cells. In addition, NUDCD2 KO upregulated Microtubule Associated Serine/Threonine Kinase 1 (MAST1) expression. It has been shown that MAST1 interacts with PTEN, a tumor suppressor, and that knockdown of MAST4 leads to decreased PTEN activity and increased activation of the mTOR signaling pathway in MM cell lines. MAST4 has also been described to be upregulated in female patients with MM that have limited osteolytic bone lesions. Additionally, Hsp90 interacts with and stabilizes MAST1, which inhibits the ubiquitination of MAST1 by the E3 ubiquitin-protein ligase CHIP and consequently its degradation [57]. Remarkably, we identified STUB1, which encodes CHIP, as a sensitizer in our carfilzomib KO screen, and as previously mentioned, MAST1 was found to be upregulated in our NUDCD2 KO cells. At first, this might seem contradictory to previous studies; however, further validation is critical to unravel the specific mechanism between NUDCD2 KO and the discovered transcriptional changes. Finally, it should be noted that our study only focused on the KMS-28-BM MM cell line, which is a critical limitation of this study. Therefore, it would be of great interest to perform additional genome-wide CRISPR-Cas9 KO screens and NUDCD2 KO validation screens in other MM cell lines to represent the heterogeneity of different MM subtypes.
To summarize, by employing genome-wide CRISPR-Cas9 KO screening, we identified and validated NUDCD2 KO as a sensitizer for both bortezomib, carfilzomib, and ixazomib. Subsequently, RNA sequencing suggests that NUDCD2 depletion alters protein degradation and mitochondrial lipid metabolism.
We suggest NUDCD2 as a possible therapeutic target to overcome PI resistance and sensitize myeloma cells to PIs. However, in the future, additional experiments will be necessary to further elucidate the precise molecular mechanism of NUDCD2 KO sensitization and to evaluate to what extent reduction in NUDCD2 activity could represent a therapeutic target to sensitize myeloma cells in patients.
We also believe that these results can contribute to the development of novel specific biomarkers, which are currently lacking, to predict drug response in MM and to allow personalization and increase effectiveness of the treatment of patients with MM, provided that the obtained results are validated in myeloma cells from both newly diagnosed and relapsed/refractory patients.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/hemato6030021/s1, Supplementary Figures: Figure S1: Frequency distribution histograms; Figure S2: Correlation plot CRISPR-Cas9 KO screen with bortezomib; Figure S3: Correlation plot CRISPR-Cas9 KO screen with carfilzomib; Figure S4: Global analysis of CRISPR-Cas9 knockout screens; Figure S5: On-target analysis; Figure S6: Flow cytometry gating strategy for candidate hit validation; Figure S7: Individual validation of candidate hits using a competition assay; Figure S8: Individual validation of candidate hits using a competition assay; Figure S9: Individual validation of candidate hits using a competition assay; Figure S10: Individual validation of candidate hits using a competition assay; Figure S11: Individual validation of candidate hits using a competition assay; Figure S12: On-target analysis; Figure S13: NUDCD2 KO cells treated with proteasome inhibitors; Figure S14: Overview of upregulated and downregulated pathways; Figure S15 Enrichment plots of up- and downregulated terms and pathways; Figure S16: Protein interaction network of NUDCD2; Table S1: PCR1 and PCR2 primer sequences; Table S2: Quality control CRISPR-Cas9 KO screen with bortezomib; Table S3: Quality control CRISPR-Cas9 KO screen with carfilzomib; Table S4: Top 20 resistance and sensitization hits from the bortezomib and carfilzomib KO screens; Table S5: SgRNA sequences used in individual candidate hit validation; Table S6: Primers on-target analysis; Table S7: Primers on-target analysis; Table S8: NUDCD2 interaction partners identified as hits in our CRISPR-Cas9 KO screens, Supplementary Methods, T42 DMSO_vs_T42 bortezomib_sgRNA, T46 DMSO_vs_T46 carfilzomib_sgRNA, T42 DMSO_vs_T42 bortezomib_gene, T46 DMSO_vs_T46 carfilzomib_gene [26,29,58,59,60,61,62,63,64,65].

Author Contributions

Conceptualization, S.V., K.D.K., D.D. and M.D.; methodology, S.V., T.D., M.C., K.D.K., D.D. and M.D.; software, T.D. and M.C.; validation, S.V.; formal analysis, S.V., T.D. and M.C.; investigation, S.V. and SS; writing—original draft preparation, S.V.; writing—review and editing, S.V., T.D., M.C., S.S., K.D.K., D.D. and M.D.; visualization, S.V.; supervision, K.D.K., D.D. and M.D.; project administration, S.V.; funding acquisition, S.V., K.D.K., D.D. and M.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a SBO (Strategisch Basis Onderzoek) grant from FWO (Fonds Wetenschappelijk Onderzoek—Vlaanderen), grant number 1S39722N, a Translational and Clinical Research grant from Stichting tegen Kanker, grant number 2255 and a grant from Stichting ME TO YOU.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of UZ/KU Leuven (protocol code: S61669 and date of approval: 19 October 2018).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus [66] and are accessible through GEO Series accession numbers GSE301632 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE301632) (accessed on 8 July 2025), GSE301947 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE301947) (accessed on 8 July 2025), GSE301948 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE301948) (accessed on 8 July 2025).

Acknowledgments

Figure 1A and Figure 2A were created with Biorender.

Conflicts of Interest

Michel Delforge received speaker’s honoraria from Amgen, BMS, Janssen and Takeda. Michel Delforge is a Section Board Member of Hemato. All other authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
MMMultiple myeloma
KOKnockout
CPT1ACarnitine Palmitoyltransferase 1A
OSOverall survival
IMiDsImmunomodulatory drugs
PIsProteasome inhibitors
PFSProgression-free survival
UPSUbiquitin proteasome system
PSMB5Proteasome subunit beta type-5
EREndoplasmic reticulum
UPRUnfolded protein response
JCRBJapanese Collection of Research Bioresources
FBSFetal bovine serum
DMSODimethyl sulfoxide
sgRNAsSingle guide RNAs
MOIMultiplicity of infection
PBSPhosphate-buffered saline
CC2CRISPRCloud2
Log2FCLog2 fold change
TKOv3Toronto KnockOut CRISPR library
AAVS1Adeno-associated virus integration site 1
CTLChymotrypsin-like activities
TLTrypsin-like activities
CLCaspase-like activities
SDStandard deviation
ANOVAOne-way analysis of variance
IC50Inhibitory concentration of 50%
GSEAGene Set Enrichment Analysis
NESNormalized enrichment score
Padj.Adjusted p-values
ECPASProteasome adapter and scaffold protein ECM29
PSMF1Proteasome inhibitor PI31 subunit
OSER1Oxidative stress-responsive serine-rich protein 1
PCAPrincipal Component Analysis
DEGDifferentially expressed gene
NSNon-significant
HMMRHyaluronan Mediated Motility Receptor
FAOFatty acid oxidation
LCFALong chain fatty acids
MAST1Microtubule Associated Serine/Threonine Kinase 1

References

  1. Robak, P.; Drozdz, I.; Szemraj, J.; Robak, T. Drug resistance in multiple myeloma. Cancer Treat. Rev. 2018, 70, 199–208. [Google Scholar] [CrossRef] [PubMed]
  2. Rajkumar, S.V.; Kyle, R.A. Multiple Myeloma: Diagnosis and Treatment. Mayo Clin. Proc. 2005, 80, 1371–1382. [Google Scholar] [CrossRef] [PubMed]
  3. Ludwig, H.; Sonneveld, P.; Davies, F.; Blade, J.; Boccadoro, M.; Cavo, M.; Morgan, G.; de la Rubia, J.; Delforge, M.; Dimopoulos, M.; et al. European Perspective on Multiple Myeloma Treatment Strategies in 2014. Oncologist 2014, 19, 829–844. [Google Scholar] [CrossRef] [PubMed]
  4. Smith, C.J.; Ambs, S.; Landgren, O. Biological determinants of health disparities in multiple myeloma. Blood Cancer J. 2018, 8, 85. [Google Scholar] [CrossRef] [PubMed]
  5. Eslick, R. Multiple myeloma from diagnosis to treatment. Aust. Fam. Physician 2013, 42, 684–688. [Google Scholar] [PubMed]
  6. Landgren, O.; Rajkumar, S.V. New developments in diagnosis, prognosis, and assessment of response in multiple myeloma. Clin. Cancer Res. 2016, 22, 5428–5433. [Google Scholar] [CrossRef] [PubMed]
  7. Hideshima, T.; Richardson, P.G.; Anderson, K.C. Mechanism of Action of Proteasome Inhibitors and Deacetylase Inhibitors and the Biological Basis of Synergy in Multiple Myeloma. Mol. Cancer Ther. 2011, 10, 2034–2042. [Google Scholar] [CrossRef] [PubMed]
  8. Tu, Y.; Chen, C.; Pan, J.; Xu, J.; Zhou, Z.G.; Wang, C.Y. The ubiquitin proteasome pathway (UPP) in the regulation of cell cycle control and DNA damage repair and its implication in tumorigenesis. Int. J. Clin. Exp. Pathol. 2012, 5, 726–738. [Google Scholar] [PubMed]
  9. Franke, N.E.; Niewerth, D.; Assaraf, Y.G.; Van Meerloo, J.; Vojtekova, K.; Van Zantwijk, C.H.; Zweegman, S.; Chan, E.T.; Kirk, C.J.; Geerke, D.P.; et al. Impaired bortezomib binding to mutant β5 subunit of the proteasome is the underlying basis for bortezomib resistance in leukemia cells. Leukemia 2012, 26, 757–768. [Google Scholar] [CrossRef] [PubMed]
  10. Niewerth, D.; Jansen, G.; Assaraf, Y.G.; Zweegman, S.; Kaspers, G.J.L.; Cloos, J. Molecular basis of resistance to proteasome inhibitors in hematological malignancies. Drug Resist. Updat. 2015, 18, 18–35. [Google Scholar] [CrossRef] [PubMed]
  11. Rückrich, T.; Kraus, M.; Gogel, J.; Beck, A.; Ovaa, H.; Verdoes, M.; Overkleeft, H.S.; Kalbacher, H.; Driessen, C. Characterization of the ubiquitin-proteasome system in bortezomib-adapted cells. Leukemia 2009, 23, 1098–1105. [Google Scholar] [CrossRef] [PubMed]
  12. Leung-Hagesteijn, C.; Erdmann, N.; Cheung, G.; Keats, J.J.; Stewart, A.K.; Reece, D.E.; Chung, K.C.; Tiedemann, R.E. Xbp1s-Negative Tumor B Cells and Pre-Plasmablasts Mediate Therapeutic Proteasome Inhibitor Resistance in Multiple Myeloma. Cancer Cell 2013, 24, 289–304. [Google Scholar] [CrossRef] [PubMed]
  13. Yang, Y.; Chen, Y.; Saha, M.N.; Chen, J.; Evans, K.; Qiu, L.; Reece, D.; Chen, G.A.; Chang, H. Targeting phospho-MARCKS overcomes drug-resistance and induces antitumor activity in preclinical models of multiple myeloma. Leukemia 2015, 29, 715–726. [Google Scholar] [CrossRef] [PubMed]
  14. Milan, E.; Fabbri, M.; Cenci, S. Autophagy in Plasma Cell Ontogeny and Malignancy. J. Clin. Immunol. 2016, 36, 18–24. [Google Scholar] [CrossRef] [PubMed]
  15. Franke, N.E.; Kaspers, G.L.; Assaraf, Y.G.; Meerloo Jvan Niewerth, D.; Kessler, F.L.; Poddighe, P.J.; Kole, J.; Smeets, S.J.; Ylstra, B.; Bi, C.; et al. Exocytosis of polyubiquitinated proteins in bortezomib-resistant leukemia cells: A role for MARCKS in acquired resistance to proteasome inhibitors. Oncotarget 2016, 7, 74779–74796. [Google Scholar] [CrossRef] [PubMed]
  16. Grogan, T.M.; Spier, C.M.; Salmon, S.E.; Matzner, M.; Rybski, J.; Weinstein, R.S.; Scheper, R.J.; Dalton, W.S. P-glycoprotein expression in human plasma cell myeloma: Correlation with prior chemotherapy. Blood 1993, 81, 490–495. [Google Scholar] [CrossRef] [PubMed]
  17. Besse, A.; Stolze, S.C.; Rasche, L.; Weinhold, N.; Morgan, G.J.; Kraus, M.; Bader, J.; Overkleeft, H.S.; Besse, L.; Driessen, C. Carfilzomib resistance due to ABCB1/MDR1 overexpression is overcome by nelfinavir and lopinavir in multiple myeloma. Leukemia 2018, 32, 391–401. [Google Scholar] [CrossRef] [PubMed]
  18. Markovina, S.; Callander, N.S.; O’Connor, S.L.; Kim, J.; Werndli, J.E.; Raschko, M.; Leith, C.P.; Kahl, B.S.; Kim, K.; Miyamoto, S. Bortezomib-Resistant NF-κB Activity in Multiple Myeloma Cells. Mol. Cancer Res. 2008, 6, 1356–1364. [Google Scholar] [CrossRef] [PubMed]
  19. Podar, K.; Chauhan, D.; Anderson, K. Bone marrow microenvironment and the identification of new targets for myeloma therapy. Leukemia 2009, 23, 10–24. [Google Scholar] [CrossRef] [PubMed]
  20. Soriano, G.P.; Besse, L.; Li, N.; Kraus, M.; Besse, A.; Meeuwenoord, N.; Bader, J.; Everts, B.; Den Dulk, H.; Overkleeft, H.S.; et al. Proteasome inhibitor-adapted myeloma cells are largely independent from proteasome activity and show complex proteomic changes, in particular in redox and energy metabolism. Leukemia 2016, 30, 2198–2207. [Google Scholar] [CrossRef] [PubMed]
  21. Besse, L.; Besse, A.; Mendez-Lopez, M.; Vasickova, K.; Sedlackova, M.; Vanhara, P.; Kraus, M.; Bader, J.; Ferreira, R.B.; Castellano, R.K.; et al. A metabolic switch in proteasome inhibitor-resistant multiple myeloma ensures higher mitochondrial metabolism, protein folding and sphingomyelin synthesis. Haematologica 2019, 104, e415–e419. [Google Scholar] [CrossRef] [PubMed]
  22. Zaal, E.A.; Wu, W.; Jansen, G.; Zweegman, S.; Cloos, J.; Berkers, C.R. Bortezomib resistance in multiple myeloma is associated with increased serine synthesis. Cancer Metab. 2017, 5, 7. [Google Scholar] [CrossRef] [PubMed]
  23. Sonneveld, P.; Broijl, A. Treatment of relapsed and refractory multiple myeloma. Haematologica 2016, 101, 396–406. [Google Scholar] [CrossRef] [PubMed]
  24. Shi, C.X.; Kortüm, K.M.; Zhu, Y.X.; Bruins, L.A.; Jedlowski, P.; Votruba, P.G.; Luo, M.; Stewart, R.A.; Ahmann, J.; Braggio, E.; et al. CRISPR genome-wide screening identifies dependence on the proteasome subunit PSMC6 for bortezomib sensitivity in multiple myeloma. Mol. Cancer Ther. 2017, 16, 2862–2870. [Google Scholar] [CrossRef] [PubMed]
  25. Findlay, S.; Nair, R.; Merrill, R.A.; Kaiser, Z.; Cajelot, A.; Aryanpour, Z.; Heath, J.; St-Louis, C.; Papadopoli, D.; Topisirovic, I.; et al. The mitochondrial pyruvate carrier complex potentiates the efficacy of proteasome inhibitors in multiple myeloma. Blood Adv. 2023, 7, 3485–3500. [Google Scholar] [CrossRef] [PubMed]
  26. Hart, T.; Tong, A.H.Y.; Chan, K.; Van Leeuwen, J.; Seetharaman, A.; Aregger, M.; Chandrashekhar, M.; Hustedt, N.; Seth, S.; Noonan, A.; et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3 Genes Genomes Genet. 2017, 7, 2719–2727. [Google Scholar] [CrossRef] [PubMed]
  27. Jeong, H.-H.; Kim, S.Y.; Rousseaux, M.W.C.; Zoghbi, H.Y.; Liu, Z. CRISPRCloud2: A cloud-based platform for deconvolving CRISPR screen data. BioRxiv 2018. [Google Scholar] [CrossRef]
  28. Jeong, H.H.; Kim, S.Y.; Rousseaux, M.W.C.; Zoghbi, H.Y.; Liu, Z. Beta-binomial modeling of CRISPR pooled screen data identifies target genes with greater sensitivity and fewer false negatives. Genome Res. 2019, 29, 999–1008. [Google Scholar] [CrossRef] [PubMed]
  29. Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 2005, 102, 15545–15550. [Google Scholar] [CrossRef] [PubMed]
  30. Thakurta, A.; Pierceall, W.E.; Amatangelo, M.D.; Flynt, E.; Agarwal, A. Developing next generation immunomodulatory drugs and their combinations in multiple myeloma. Oncotarget 2021, 12, 1555–1563. [Google Scholar] [CrossRef] [PubMed]
  31. Hideshima, T.; Chauhan, D.; Richardson, P.; Mitsiades, C.; Mitsiades, N.; Hayashi, T.; Munshi, N.; Dang, L.; Castro, A.; Palombella, V.; et al. NF-κB as a therapeutic target in multiple myeloma. J. Biol. Chem. 2002, 277, 16639–16647. [Google Scholar] [CrossRef] [PubMed]
  32. Yang, Y.; Yan, X.; Cai, Y.; Lu, Y.; Si, J.; Zhou, T. NudC-like protein 2 regulates the LIS1/dynein pathway by stabilizing LIS1 with Hsp90. Proc. Natl. Acad. Sci. USA 2010, 107, 3499–3504. [Google Scholar] [CrossRef] [PubMed]
  33. Gorbea, C.; Goellner, G.M.; Teter, K.; Holmes, R.K.; Rechsteiner, M. Characterization of mammalian Ecm29, a 26 S proteasome-associated protein that localizes to the nucleus and membrane vesicles. J. Biol. Chem. 2004, 279, 54849–54861. [Google Scholar] [CrossRef] [PubMed]
  34. Li, X.; Thompson, D.; Kumar, B.; DeMartino, G.N. Molecular and cellular roles of PI31 (PSMF1) protein in regulation of proteasome function. J. Biol. Chem. 2014, 289, 17392–17405. [Google Scholar] [CrossRef] [PubMed]
  35. Mercurio, F.; Zhu, H.; Murray, B.W.; Shevchenko, A.; Bennett, B.L.; Li, J.W.; Young, D.B.; Barbosa, M.; Mann, M.; Manning, A.; et al. IKK-1 and IKK-2: Cytokine-Activated IκB Kinases Essential for NF-κB Activation. Science 1997, 278, 860–866. [Google Scholar] [CrossRef] [PubMed]
  36. Kaczynski, J.; Zhang, J.S.; Ellenrieder, V.; Conley, A.; Duenes, T.; Kester, H.; Van der Burg, B.; Urrutia, R. The Sp1-like Protein BTEB3 Inhibits Transcription via the Basic Transcription Element Box by Interacting with mSin3A and HDAC-1 Co-repressors and Competing with Sp1. J. Biol. Chem. 2001, 276, 36749–36756. [Google Scholar] [CrossRef] [PubMed]
  37. Zastrow, M.V. Membrane Trafficking of Signaling Receptors. Nat. Cell Biol. 2011, 13, 715–721. [Google Scholar] [CrossRef] [PubMed]
  38. Boztug, K.; Järvinen, P.M.; Salzer, E.; Racek, T.; Mönch, S.; Garncarz, W.; Gertz, E.M.; Schäffer, A.A.; Antonopoulos, A.; Haslam, S.M.; et al. JAGN1 deficiency causes aberrant myeloid cell homeostasis and congenital neutropenia. Nat. Genet. 2014, 46, 1021–1027. [Google Scholar] [CrossRef] [PubMed]
  39. Matsumura, Y.; Sakai, J.; Skach, W.R. Endoplasmic reticulum protein quality control is determined by cooperative interactions between Hsp/c70 protein and the CHIP E3 ligase. J. Biol. Chem. 2013, 288, 31069–31079. [Google Scholar] [CrossRef] [PubMed]
  40. Gu, Y.; Barwick, B.G.; Shanmugam, M.; Hofmeister, C.C.; Kaufman, J.; Nooka, A.; Gupta, V.; Dhodapkar, M.; Boise, L.H.; Lonial, S. Downregulation of PA28α induces proteasome remodeling and results in resistance to proteasome inhibitors in multiple myeloma. Blood Cancer J. 2020, 10, 125. [Google Scholar] [CrossRef] [PubMed]
  41. Rouette, A.; Trofimov, A.; Haberl, D.; Boucher, G.; Lavallée, V.P.; D’Angelo, G.; Hébert, J.; Sauvageau, G.; Lemieux, S.; Perreault, C. Expression of immunoproteasome genes is regulated by cell-intrinsic and -extrinsic factors in human cancers. Sci. Rep. 2016, 6, 34019. [Google Scholar] [CrossRef] [PubMed]
  42. Yang, Q.; Zhao, J.; Chen, D.; Wang, Y. E3 ubiquitin ligases: Styles, structures and functions. Mol. Biomed. 2021, 2, 23. [Google Scholar] [CrossRef] [PubMed]
  43. Sala-Gaston, J.; Martinez-Martinez, A.; Pedrazza, L.; Lorenzo-Martín, L.F.; Caloto, R.; Bustelo, X.R.; Ventura, F.; Rosa, J.L. Herc ubiquitin ligases in cancer. Cancers 2020, 12, 1653. [Google Scholar] [CrossRef] [PubMed]
  44. Lopez-Girona, A.; Mendy, D.; Ito, T.; Miller, K.; Gandhi, A.K.; Kang, J.; Karasawa, S.; Carmel, G.; Jackson, P.; Abbasian, M.; et al. Cereblon is a direct protein target for immunomodulatory and antiproliferative activities of lenalidomide and pomalidomide. Leukemia 2012, 26, 2326–2335. [Google Scholar] [CrossRef] [PubMed]
  45. Besse, A.; Besse, L.; Büchler, L.; Stolze, S.; Sobh, A.; Kraus, M.; Nakagami, H.; Driessen, C. P-091: Genome-wide CRISPR/Cas9 screening identifies proteasome-related specific vulnerabilities as potential treatment options of proteasome inhibitor-resistant multiple myeloma. Clin. Lymphoma Myeloma Leuk. 2021, 21, S88–S89. [Google Scholar]
  46. Sharma, A.; Nair, R.; Achreja, A.; Mittal, A.; Gupta, P.; Balakrishnan, K.; Edgar, C.L.; Animasahun, O.; Dwivedi, B.; Barwick, B.G.; et al. Therapeutic implications of mitochondrial stress–induced proteasome inhibitor resistance in multiple myeloma. Sci. Adv. 2022, 8, eabq5575. [Google Scholar] [CrossRef] [PubMed]
  47. Tsvetkov, P.; Sokol, E.; Jin, D.; Brune, Z.; Thiru, P.; Ghandi, M.; Garraway, L.A.; Gupta, P.B.; Santagata, S.; Whitesell, L.; et al. Suppression of 19S proteasome subunits marks emergence of an altered cell state in diverse cancers. Proc. Natl. Acad. Sci. USA 2017, 114, 382–387. [Google Scholar] [CrossRef] [PubMed]
  48. Acosta-Alvear, D.; Cho, M.Y.; Wild, T.; Buchholz, T.J.; Lerner, A.G.; Simakova, O.; Hahn, J.; Korde, N.; Landgren, O.; Maric, I.; et al. Paradoxical resistance of multiple myeloma to proteasome inhibitors by decreased levels of 19S proteasomal subunits. eLife 2015, 4, e08153. [Google Scholar] [CrossRef] [PubMed]
  49. Tsvetkov, P.; Mendillo, M.L.; Zhao, J.; Carette, J.E.; Merrill, P.H.; Cikes, D.; Varadarajan, M.; van Diemen, F.R.; Penninger, J.M.; Goldberg, A.L.; et al. Compromising the 19S proteasome complex protects cells from reduced flux through the proteasome. eLife 2015, 4, e08467. [Google Scholar] [CrossRef] [PubMed]
  50. He, Z.; Mei, L.; Connell, M.; Maxwell, C.A. Hyaluronan Mediated Motility Receptor (HMMR) Encodes an Evolutionarily Conserved Homeostasis, Mitosis, and Meiosis Regulator Rather than a Hyaluronan Receptor. Cells 2020, 9, 819. [Google Scholar] [CrossRef] [PubMed]
  51. Szklarczyk, D.; Kirsch, R.; Koutrouli, M.; Nastou, K.; Mehryary, F.; Hachilif, R.; Gable, A.L.; Fang, T.; Doncheva, N.T.; Pyysalo, S.; et al. The STRING database in 2023: Protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023, 51, D638–D646. [Google Scholar] [CrossRef] [PubMed]
  52. Li, M.; Xu, X.; Zhang, J.; Liu, M.; Wang, W.; Gao, Y.; Sun, Q.; Zhang, J.; Lu, Y.; Wang, F.; et al. NudC-like protein 2 restrains centriole amplification by stabilizing HERC2. Cell Death Dis. 2019, 10, 628. [Google Scholar] [CrossRef] [PubMed]
  53. Liang, K. Mitochondrial CPT1A: Insights into structure, function, and basis for drug development. Front. Pharmacol. 2023, 14, 1160440. [Google Scholar] [CrossRef] [PubMed]
  54. Masarwi, M.; DeSchiffart, A.; Ham, J.; Reagan, M.R. Multiple Myeloma and Fatty Acid Metabolism. JBMR Plus 2019, 3, e10173. [Google Scholar] [CrossRef] [PubMed]
  55. Torcasio, R.; Gallo Cantafio, M.E.; Ikeda, R.K.; Ganino, L.; Viglietto, G.; Amodio, N. Lipid metabolic vulnerabilities of multiple myeloma. Clin. Exp. Med. 2023, 23, 3373–3390. [Google Scholar] [CrossRef] [PubMed]
  56. Tirado-Vélez, J.M.; Joumady, I.; Sáez-Benito, A.; Cózar-Castellano, I.; Perdomo, G. Inhibition of Fatty Acid Metabolism Reduces Human Myeloma Cells Proliferation. PLoS ONE 2012, 7, e46484. [Google Scholar] [CrossRef] [PubMed]
  57. Rumpf, M.; Pautz, S.; Drebes, B.; Herberg, F.W.; Müller, H.A.J. Microtubule-Associated Serine/Threonine (MAST) Kinases in Development and Disease. Int. J. Mol. Sci. 2023, 24, 11913. [Google Scholar] [CrossRef] [PubMed]
  58. Doench, J.G.; Fusi, N.; Sullender, M.; Hegde, M.; Vaimberg, E.W.; Donovan, K.F.; Smith, I.; Tothova, Z.; Wilen, C.; Orchard, R.; et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016, 34, 184–191. [Google Scholar] [CrossRef] [PubMed]
  59. Yu, G.; Wang, L.-G.; Han, Y.; He, Q.-Y. ClusterProfiler: An R package for comparing biological themes among gene clusters. Omi. A. J. Integr. Biol. 2012, 16, 284–287. [Google Scholar] [CrossRef] [PubMed]
  60. Sanjana, N.E.; Shalem, O.; Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 2014, 11, 783–784. [Google Scholar] [CrossRef] [PubMed]
  61. Shalem, O.; Sanjana, N.E.; Hartenian, E.; Shi, X.; Scott, D.A.; Mikkelson, T.; Heckl, D.; Ebert, B.L.; Root, D.E.; Doench, J.G.; et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 2014, 343, 84–87. [Google Scholar]
  62. Wroblewska, A.; Dhainaut, M.; Ben-Zvi, B.; Rose, S.; Park, E.; Amir, E.; Bektesevic, A.; Baccarini, A.; Merad, M.; Rahman, A.H.; et al. Protein Barcodes Enable High-Dimensional Single-Cell CRISPR Screens. Cell 2018, 175, 1141–1155.e16. [Google Scholar] [CrossRef] [PubMed]
  63. Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. 2010. Available online: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 3 July 2023).
  64. Bray, N.L.; Pimentel, H.; Melsted, P.; Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016, 34, 525–527. [Google Scholar] [CrossRef] [PubMed]
  65. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014, 15. [Google Scholar] [CrossRef] [PubMed]
  66. Edgar, R.; Domrachev, M.; Lash, A.E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30, 207–210. [Google Scholar] [CrossRef] [PubMed]
Hemato 06 00021 g001
Figure 1. Genome-wide CRISPR-Cas9 knockout (KO) screen in KMS-28-BM cells. (A) Schematic of the genome-wide CRISPR-Cas9 KO screen; (B–D) Gene Set Enrichment Analysis (GSEA) of genome-wide CRISPR-Cas9 KO screen with bortezomib or carfilzomib; (B) selected resistance and sensitization terms and pathways upon genome-wide KO and treatment with either bortezomib or carfilzomib are shown; (C) enrichment plots of selected resistance terms and pathways; (D) enrichment plots of selected sensitizing terms and pathways. GSEA terms and pathways were selected upon their adjusted p-value < 0.05, NES score and biological relevance. Normalized enrichment scores (NES) and adjusted p-values (Padj.) are shown; (E,F) volcano plot representing change in single guide RNA (sgRNA) abundance in proteasome inhibitor (PI)-treated KMS-28-BM cells compared to vehicle-treated KMS-28-BM cells after selection with (E) bortezomib for 42 days and with (F) carfilzomib for 46 days. Log2 fold change (log2FC) on the x-axis corresponds to the averaged difference in the four sgRNAs targeting the same gene between the two groups. The significance of the change is indicated by the p-value on the y-axis.
Figure 1. Genome-wide CRISPR-Cas9 knockout (KO) screen in KMS-28-BM cells. (A) Schematic of the genome-wide CRISPR-Cas9 KO screen; (B–D) Gene Set Enrichment Analysis (GSEA) of genome-wide CRISPR-Cas9 KO screen with bortezomib or carfilzomib; (B) selected resistance and sensitization terms and pathways upon genome-wide KO and treatment with either bortezomib or carfilzomib are shown; (C) enrichment plots of selected resistance terms and pathways; (D) enrichment plots of selected sensitizing terms and pathways. GSEA terms and pathways were selected upon their adjusted p-value < 0.05, NES score and biological relevance. Normalized enrichment scores (NES) and adjusted p-values (Padj.) are shown; (E,F) volcano plot representing change in single guide RNA (sgRNA) abundance in proteasome inhibitor (PI)-treated KMS-28-BM cells compared to vehicle-treated KMS-28-BM cells after selection with (E) bortezomib for 42 days and with (F) carfilzomib for 46 days. Log2 fold change (log2FC) on the x-axis corresponds to the averaged difference in the four sgRNAs targeting the same gene between the two groups. The significance of the change is indicated by the p-value on the y-axis.
Hemato 06 00021 g001
Hemato 06 00021 g002
Figure 2. Candidate hit validation of the bortezomib CRISPR-Cas9 KO screen with individual sgRNAs in KMS-28-BM cells. (A) Experimental workflow of individual candidate hit validation; (B) individual validation of candidate hits using a competition assay. KMS-28-BM KO cells were treated with four nM bortezomib for 20 cell doublings. DMSO was used as control treatment and these data are shown in Figure S6. Validation of resistance hits KLF13 and PSMC4 and of sensitization hits NUDCD2, OSER1, and HERC1 is shown. The y-axis denotes the percentage of sgRNA-containing cells targeting the gene of interest (blue curves) versus control (black curves) in the population upon treatment with bortezomib. These sgRNA-containing cells were detected by flow cytometry as the percentage of Alexa Fluor 488 positive cells (blue curves, sgRNAs targeting gene of interest), and percentage of Alexa Fluor 405 positive cells (black curves, AAVS1 control). A representative experiment is shown.
Figure 2. Candidate hit validation of the bortezomib CRISPR-Cas9 KO screen with individual sgRNAs in KMS-28-BM cells. (A) Experimental workflow of individual candidate hit validation; (B) individual validation of candidate hits using a competition assay. KMS-28-BM KO cells were treated with four nM bortezomib for 20 cell doublings. DMSO was used as control treatment and these data are shown in Figure S6. Validation of resistance hits KLF13 and PSMC4 and of sensitization hits NUDCD2, OSER1, and HERC1 is shown. The y-axis denotes the percentage of sgRNA-containing cells targeting the gene of interest (blue curves) versus control (black curves) in the population upon treatment with bortezomib. These sgRNA-containing cells were detected by flow cytometry as the percentage of Alexa Fluor 488 positive cells (blue curves, sgRNAs targeting gene of interest), and percentage of Alexa Fluor 405 positive cells (black curves, AAVS1 control). A representative experiment is shown.
Hemato 06 00021 g002
Hemato 06 00021 g003
Figure 3. The effect of individually knocking out KLF13, PSMC4, NUDCD2, OSER1, or HERC1 upon carfilzomib or ixazomib treatment. KMS-28-BM KO cells were treated with (A) five nM carfilzomib or (B) 25 nM ixazomib for 20 cell doublings. DMSO was used as control treatment and these data are shown in Figure S6. Validation of resistance hits KLF13 and PSMC4 and of sensitization hits NUDCD2, OSER1, and HERC1 are shown. The y-axis denotes the percentage of sgRNA-containing cells targeting the gene of interest (blue curves) versus control (black curves) in the population upon treatment with carfilzomib or ixazomib. These sgRNA-containing cells were detected by flow cytometry as the percentage of Alexa Fluor 488 positive cells (blue curves, sgRNAs targeting gene of interest), and percentage of Alexa Fluor 405 positive cells (black curves, AAVS1 control). Representative experiments are shown.
Figure 3. The effect of individually knocking out KLF13, PSMC4, NUDCD2, OSER1, or HERC1 upon carfilzomib or ixazomib treatment. KMS-28-BM KO cells were treated with (A) five nM carfilzomib or (B) 25 nM ixazomib for 20 cell doublings. DMSO was used as control treatment and these data are shown in Figure S6. Validation of resistance hits KLF13 and PSMC4 and of sensitization hits NUDCD2, OSER1, and HERC1 are shown. The y-axis denotes the percentage of sgRNA-containing cells targeting the gene of interest (blue curves) versus control (black curves) in the population upon treatment with carfilzomib or ixazomib. These sgRNA-containing cells were detected by flow cytometry as the percentage of Alexa Fluor 488 positive cells (blue curves, sgRNAs targeting gene of interest), and percentage of Alexa Fluor 405 positive cells (black curves, AAVS1 control). Representative experiments are shown.
Hemato 06 00021 g003
Hemato 06 00021 g004
Figure 4. Proteasome activity in NUDCD2 KO KMS-28-BM cell lines. (A) Effect of NUDCD2 KO on chymotrypsin-like (CTL), trypsin-like (TL) and caspase-like (CL) proteasome activity in KMS-28-BM cells. This figure depicts the percentage of proteasome activity compared to AAVS1. Bars denote mean ± SD. Data are shown from three repeated experiments (n = 3). p-values were calculated with a one-way ANOVA with Tukey’s post hoc test to identify individual differences. No significant differences were observed; (B) effect of PI treatment in NUDCD2 KO KMS-28-BM cells on CTL, TL, and CL proteasome activity. Cells were treated with different concentrations of bortezomib (4 and 7 nM), carfilzomib (5 and 7 nM) or ixazomib (20 and 30 nM) for two hours. This figure depicts the percentage of proteasome activity compared to AAVS1. Bars denote mean ± SD. Data are shown from three repeated experiments (n = 3) and are normalized to DMSO treatment. p-values were calculated with a two-way ANOVA with Tukey’s post hoc test to identify individual differences. p < 0.05 (*), p < 0.01 (**). Standard deviation (SD).
Figure 4. Proteasome activity in NUDCD2 KO KMS-28-BM cell lines. (A) Effect of NUDCD2 KO on chymotrypsin-like (CTL), trypsin-like (TL) and caspase-like (CL) proteasome activity in KMS-28-BM cells. This figure depicts the percentage of proteasome activity compared to AAVS1. Bars denote mean ± SD. Data are shown from three repeated experiments (n = 3). p-values were calculated with a one-way ANOVA with Tukey’s post hoc test to identify individual differences. No significant differences were observed; (B) effect of PI treatment in NUDCD2 KO KMS-28-BM cells on CTL, TL, and CL proteasome activity. Cells were treated with different concentrations of bortezomib (4 and 7 nM), carfilzomib (5 and 7 nM) or ixazomib (20 and 30 nM) for two hours. This figure depicts the percentage of proteasome activity compared to AAVS1. Bars denote mean ± SD. Data are shown from three repeated experiments (n = 3) and are normalized to DMSO treatment. p-values were calculated with a two-way ANOVA with Tukey’s post hoc test to identify individual differences. p < 0.05 (*), p < 0.01 (**). Standard deviation (SD).
Hemato 06 00021 g004
Hemato 06 00021 g005
Figure 5. RNA sequencing identifies potential regulators of NUDCD2 KO sensitization. (A) Principal Component (PC) Analysis (PCA) showing all 27 samples analyzed along PC1 (x-axis) and PC2 (y-axis) axes, which describe 53.3% and 23.7% of the variance, respectively. Each data point represents an individual sample. (B) Heatmap showing differentially expressed genes (DEGs) in untreated NUDCD2 KO cells compared with untreated control cells, carfilzomib-treated NUDCD2 KO cells compared with carfilzomib-treated control cells and bortezomib-treated NUDCD2 KO cells compared with bortezomib-treated control cells. DEGs have both a significant log2FC and Padj.-value. Colors reflect log2FC values between condition and control. p-value is adjusted for multiple testing correction using the Benjamini and Hochberg procedure. Padj. < 0.05 (*), Padj. < 0.01 (**), Padj. < 0.001 (***). Cut-offs: log2FC > 1 and <−1, and Padj. < 0.05).
Figure 5. RNA sequencing identifies potential regulators of NUDCD2 KO sensitization. (A) Principal Component (PC) Analysis (PCA) showing all 27 samples analyzed along PC1 (x-axis) and PC2 (y-axis) axes, which describe 53.3% and 23.7% of the variance, respectively. Each data point represents an individual sample. (B) Heatmap showing differentially expressed genes (DEGs) in untreated NUDCD2 KO cells compared with untreated control cells, carfilzomib-treated NUDCD2 KO cells compared with carfilzomib-treated control cells and bortezomib-treated NUDCD2 KO cells compared with bortezomib-treated control cells. DEGs have both a significant log2FC and Padj.-value. Colors reflect log2FC values between condition and control. p-value is adjusted for multiple testing correction using the Benjamini and Hochberg procedure. Padj. < 0.05 (*), Padj. < 0.01 (**), Padj. < 0.001 (***). Cut-offs: log2FC > 1 and <−1, and Padj. < 0.05).
Hemato 06 00021 g005
Hemato 06 00021 g006
Figure 6. Gene Set Enrichment Analysis (GSEA) of RNA sequencing identifies potential pathways of NUDCD2 KO sensitization. (A) Enrichment plots of selected up- and downregulated terms and pathways in untreated NUDCD2 KO cells compared with untreated control cells, (B) in carfilzomib-treated NUDCD2 KO cells compared with carfilzomib-treated control cells, and (C) in bortezomib-treated NUDCD2 KO cells compared with bortezomib-treated control cells. GSEA terms and pathways were selected upon their adjusted p-value < 0.05, NES score, and biological relevance. NES and Padj. are shown.
Figure 6. Gene Set Enrichment Analysis (GSEA) of RNA sequencing identifies potential pathways of NUDCD2 KO sensitization. (A) Enrichment plots of selected up- and downregulated terms and pathways in untreated NUDCD2 KO cells compared with untreated control cells, (B) in carfilzomib-treated NUDCD2 KO cells compared with carfilzomib-treated control cells, and (C) in bortezomib-treated NUDCD2 KO cells compared with bortezomib-treated control cells. GSEA terms and pathways were selected upon their adjusted p-value < 0.05, NES score, and biological relevance. NES and Padj. are shown.
Hemato 06 00021 g006
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Vlayen, S.; Dierckx, T.; Caruso, M.; Sieben, S.; De Keersmaecker, K.; Daelemans, D.; Delforge, M. Genome-Wide CRISPR-Cas9 Knockout Screening Identifies NUDCD2 Depletion as Sensitizer for Bortezomib, Carfilzomib and Ixazomib in Multiple Myeloma. Hemato 2025, 6, 21. https://doi.org/10.3390/hemato6030021

AMA Style

Vlayen S, Dierckx T, Caruso M, Sieben S, De Keersmaecker K, Daelemans D, Delforge M. Genome-Wide CRISPR-Cas9 Knockout Screening Identifies NUDCD2 Depletion as Sensitizer for Bortezomib, Carfilzomib and Ixazomib in Multiple Myeloma. Hemato. 2025; 6(3):21. https://doi.org/10.3390/hemato6030021

Chicago/Turabian Style

Vlayen, Sophie, Tim Dierckx, Marino Caruso, Swell Sieben, Kim De Keersmaecker, Dirk Daelemans, and Michel Delforge. 2025. "Genome-Wide CRISPR-Cas9 Knockout Screening Identifies NUDCD2 Depletion as Sensitizer for Bortezomib, Carfilzomib and Ixazomib in Multiple Myeloma" Hemato 6, no. 3: 21. https://doi.org/10.3390/hemato6030021

APA Style

Vlayen, S., Dierckx, T., Caruso, M., Sieben, S., De Keersmaecker, K., Daelemans, D., & Delforge, M. (2025). Genome-Wide CRISPR-Cas9 Knockout Screening Identifies NUDCD2 Depletion as Sensitizer for Bortezomib, Carfilzomib and Ixazomib in Multiple Myeloma. Hemato, 6(3), 21. https://doi.org/10.3390/hemato6030021

Article Metrics

Back to TopTop