• CRISPR interference screening and patient epigenetic analysis reveal regulators of CD38 surface expression including XBP1 and SPI1.

  • CD38 knockdown does not lead to broad myeloma cell surface remodeling.

Abstract

CD38 is a surface ectoenzyme expressed at high levels on myeloma plasma cells and is the target for the monoclonal antibodies (mAbs) daratumumab and isatuximab. Pretreatment CD38 density on tumor cells is an important determinant of mAb efficacy. Several small molecules have been found to increase tumor surface CD38, with the goal of boosting mAb efficacy in a cotreatment strategy. Numerous other CD38-targeting therapeutics are currently in preclinical or clinical development. Here, we sought to extend our currently limited insight into CD38 surface expression by using a multiomics approach. Genome-wide CRISPR interference screens integrated with patient–centered epigenetic analysis confirmed known regulators of CD38, such as RARA, while revealing XBP1 and SPI1 as other key transcription factors governing surface CD38 levels. CD38 knockdown followed by cell surface proteomics demonstrated no significant remodeling of the myeloma “surfaceome” after genetically induced loss of this antigen. Integrated transcriptome and surface proteome data confirmed high specificity of all-trans retinoic acid in upregulating CD38, in contrast to the broader effects of azacytidine and panobinostat. Finally, unbiased phosphoproteomics identified inhibition of MAP kinase pathway signaling in tumor cells after daratumumab treatment. Our work provides a resource to design strategies to enhance efficacy of CD38-targeting immunotherapies in myeloma.

Harnessing the immune system to treat myeloma has rapidly become the most exciting therapeutic frontier in this disease. The first such immunotherapy agent to achieve the US Food and Drug Administration approval was the monoclonal antibody (mAb) daratumumab.1 Daratumumab targets CD38, a cell surface ectoenzyme highly expressed on myeloma plasma cells. Daratumumab is currently used as either monotherapy or combination therapy in the relapsed/refractory setting or frontline therapy in combination with other small molecule agents.1 A second mAb targeting CD38, isatuximab, was also recently approved for relapsed/refractory myeloma; at least 15 additional CD38-targeting agents are in development.2 Extensive and encouraging clinical data have already been obtained with daratumumab, although resistance appears to inevitably occur.3,4 Biologically, this process appears to be quite complex, with determinants of resistance ranging from alteration of surface antigens on tumor cells4-6 to dysfunction of the tumor immune microenvironment.7,8 Although it remains unclear whether CD38 downregulation on tumor cells after mAb treatment is a marker of resistance5,9 or, instead, successful therapy,10 compelling preclinical and clinical data suggest that CD38 surface antigen density before treatment strongly correlates with mAb efficacy.5,11 

This latter observation has led to numerous efforts to identify small molecules that can increase tumor surface antigen density of CD38, representing potential cotreatments with CD38-targeting mAbs. The first such example of a CD38-boosting small molecule was all-trans retinoic acid (ATRA).12 Subsequent studies identified the pan-histone deacetylase (HDAC) inhibitor panobinostat,13 the thalidomide analog lenalidomide,14 the JAK inhibitor ruxolitinib,15 and the DNA methyltransferase (DNMT) inhibitor azacytidine (Aza)16 as agents that could lead to myeloma surface CD38 increase. A clinical trial combining ATRA with daratumumab has led to encouraging outcomes in patients previously refractory to daratumumab.17 

Although these published strategies suggest ways to improve CD38 mAb outcomes, they also leave many questions unanswered. Most notably, we do not yet have a broad global sense of the transcriptional or posttranscriptional networks that most strongly affect CD38 expression. Bispecific and trispecific antibodies18 and chimeric antigen receptor T cells19 targeting CD38 are also in clinical development. As seen for similar modalities against other targets,20 efficacy of these novel agents, in addition to mAbs, is likely to also be affected by CD38 antigen density on tumor cells. Furthermore, prior studies showed that CD38 downregulation after daratumumab treatment was accompanied by increases in the complement inhibitors CD55 and CD59.5 Are there other features of myeloma surface remodeling driven by CD38 downregulation? For the small molecules noted above, it is unknown how they more generally affect the makeup of the myeloma cell surface proteome beyond CD38. The tumor cell surface not only harbors opportunities for immunotherapeutic targeting but also is the interface for communication between tumor and microenvironment, potentially leading to other alterations in myeloma biology after changes in surface proteomic profile. To address these questions, here, we have taken advantage of CRISPR interference (CRISPRi)–based functional genomic screens, cell surface proteomics, epigenetic analyses, and phosphoproteomics to provide a multiomic perspective on CD38 regulation and tumor cell consequences of targeting CD38 in myeloma.

CRISPRi screening and hit validation

Genome-wide CRISPRi screening was performed as described previously.21 Briefly, RPMI-8226 cells stably expressing dCas9-KRAB were transduced with a genome-wide library comprising 5 single guide RNAs (sgRNAs) per protein-coding gene. After 14 days, cells were stained for surface CD38 and flow sorted to enrich for populations of cells expressing low or high cell surface levels of CD38. Cell populations were then processed for next-generation sequencing as previously described22 and sequenced on a HiSeq-4000 (Illumina). Reads were analyzed by using the Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout pipeline as previously described.23 Further validation was performed by knockdown with individual sgRNAs extracted from the genome-wide library with conformation by flow cytometry or western blotting. Antibody-dependent cytotoxicity assays were performed using NK92-CD16 cells as described previously.16 Additional details are provided in supplemental Methods.

Epigenetic analysis and machine learning for CD38 transcriptional regulation

Publicly available Assay for Transposase-Accessible Chromatin with sequencing (ATAC-Seq) data from primary myeloma samples24 were analyzed with the Homer tool findPeaks. Motif binding in the identified ATAC peak regions was called with PROMO tool.25 Newly diagnosed patient tumor RNA-sequencing (RNA-seq) data in the Multiple Myeloma Research Foundation CoMMpass trial were used to correlate expression of predicted transcription factors with CD38 expression. To build a predictive model for CD38 expression as a function of transcription factor expression, we developed an XGBoost (Extreme Gradient Boosting) model with randomized search with crossvalidation to find optimal parameters. Eighty percent of CoMMpass data were used for training and the remainder for model testing. Additional details are provided in supplemental Methods.

Cell surface proteomics and phosphoproteomics

Cell surface proteomics was performed using an adapted version of the N-glycoprotein cell surface capture26 method, as we have described previously.27 Unbiased phosphoproteomics was performed using immobilized metal affinity chromatography using methods described previously.28 All samples were analyzed on a Thermo Q-Exactive Plus mass spectrometer with data processing in MaxQuant.29 Additional details are provided in supplemental Methods.

Institutional review board approval was obtained for all animal studies in this work (approval number UCSF IACUC: AN194778-01). No human subjects research is included.

A CRISPRi-based screen reveals regulators of CD38 surface expression

We first sought to use an unbiased approach to identify regulators of surface CD38 in myeloma tumor cells. We specifically used genome-wide screening with CRISPRi, which leads to much higher specificity of knockdown than short hairpin RNA while avoiding potential toxicity of double-strand breakage with CRISPR deletion.30 We recently used this approach to characterize regulators of surface B-cell maturation antigen (BCMA) in myeloma.21 Here, we used an RPMI-8226 cell line with the dCas9-KRAB machinery, required for CRISPRi, as described previously.21 We confirmed that this RPMI-8226 cell line robustly expressed CD38 (supplemental Figure 1A).

The genome-wide screen was performed as shown in Figure 1A. Briefly, RPMI-8226 cells were transduced with a pooled genome-wide sgRNA library. After 14 days, the cells were then stained with fluorescently labeled anti-CD38 antibody and flow sorted into low- and high-CD38 populations. Frequencies of cells expressing each sgRNA was quantified using next-generation sequencing. As an important positive control, increasing confidence in the screen results, we first noted that knockdown of CD38 itself strongly decreased surface CD38 expression (Figure 1B). On the contrary, several dozen genes, when repressed, did indeed lead to increased surface CD38 (right side of volcano plot in Figure 1B; supplemental Table 1). As another positive control, one of these top hits included RARA, whose degradation is catalyzed by ATRA treatment to drive CD38 increase.12 

Figure 1.

CRISPRi screening reveals genetic determinants of surface CD38 regulation. (A) Schematic of CRISPRi screen design. (B) Results of CRISPRi screen demonstrating genes that, when knocked down, regulate surface CD38 in RPMI-8226 cells. The x-axis indicates phenotype (epsilon) from MAGeCK31 statistical analysis. Dashed line indicates cutoff for significant change at false discovery rate (FDR) <0.05. Genes of interest are specifically labeled. 4000 negative control nontargeting sgRNAs are in gray. (C) Gene ontology (GO) Biological Process and KEGG analysis of all genes that when knocked down lead to significant CD38 upregulation. (D) Follow-up flow cytometry validation of CRISPRi screen hits using 2 individual sgRNAs per gene demonstrates TLE3 knockdown drives increased CD38, whereas SPI1 knockdown leads to CD38 decrease.

Figure 1.

CRISPRi screening reveals genetic determinants of surface CD38 regulation. (A) Schematic of CRISPRi screen design. (B) Results of CRISPRi screen demonstrating genes that, when knocked down, regulate surface CD38 in RPMI-8226 cells. The x-axis indicates phenotype (epsilon) from MAGeCK31 statistical analysis. Dashed line indicates cutoff for significant change at false discovery rate (FDR) <0.05. Genes of interest are specifically labeled. 4000 negative control nontargeting sgRNAs are in gray. (C) Gene ontology (GO) Biological Process and KEGG analysis of all genes that when knocked down lead to significant CD38 upregulation. (D) Follow-up flow cytometry validation of CRISPRi screen hits using 2 individual sgRNAs per gene demonstrates TLE3 knockdown drives increased CD38, whereas SPI1 knockdown leads to CD38 decrease.

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To find pathways that may be useful for pharmacologic targeting, we first applied gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to the list of genes that, when inhibited, significantly increased CD38 (Figure 1C). We were intrigued to find that many of the strongest effects appeared to be driven by transcriptional or other epigenetic factors. These specifically included pathways such as “DNA replication,” “messenger RNA (mRNA) processing,” “DNA-templated transcription,” and “spliceosome.”

We considered whether any hits associated with these pathways may be “druggable,” with the goal of expanding our repertoire of small molecules that enhance surface CD38 in myeloma. SS18, a component of the BAF (BRG1/BRM associated factor) chromatin remodeling complex, scored highly as a hit. However, treatment with the proposed BAF inhibitor caffeic acid phenol ester32 did not lead to consistent increases in surface CD38 (supplemental Figure 1B). Similarly, the lysine demethylase KDM4A was a prominent hit, but treatment with the inhibitory metabolite (R)-2-hydroxyglutarate33 also had no effect (supplemental Figure 1B).

The strongest hits for genes whose knockdown increased surface CD38 were 2 transcription factors, HEXIM1 and TLE3. Validation studies using individual sgRNA knockdown confirmed increased surface CD38 (Figures 1D and 2A; supplemental Figure 2A), as well as functional impact in natural killer (NK)–cell antibody-dependent cellular cytotoxicity (ADCC) assays with daratumumab (Figure 2B). However, these proteins are known to be widespread negative regulators of transcription,34,35 suggesting little scope for specific therapeutic targeting at the CD38 locus.

Figure 2.

Validation of CRISPRi screen hits as functionally affecting daratumumab efficacy. (A) Knockdown of HEXIM1 and TLE3 with 2 independent sgRNAs per gene (AMO1 myeloma cells, n = 3) followed by flow cytometry shows significant surface CD38 increase with TLE3_i2 sgRNA and trend toward increased CD38 with HEXIM1_i1 sgRNA. (B) Results from ADCC assays with AMO1 cells stably expressing the noted sgRNAs and incubated with the indicated concentration of daratumumab or isotype control antibody (1:20 myeloma:NK ratio; 20 hours; n = 2). The percent lysis by ADCC was calculated using the following formula: % lysis = (signal in presence of daratumumab – signal in presence of IgG1 control antibody) ×100/signal in presence of IgG1 control antibody. At 10 μM daratumumab, both HEXIM1 and TLE3 knockdown led to significant increase in ADCC. (C) Similar to panel A, sgRNA knockdown of NFKB1, NFKB2, and SPI1 with fold-change in CD38 by flow cytometry (RPMI-8226 cells, n = 3). (D) Similar to panel B, knockdown with the most effective sgRNA for each gene show significant decreases in NK-cell ADCC at 10 μM daratumumab in the RMPI-8266 cells (n = 3). (E) In vivo validation of SPI1 knockdown driving daratumumab resistance. NOD scid gamma mice were IV implanted with CRISPRi RPMI-8226 cells stably expressing both luciferase and noted sgRNA, then treated with 200 μg daratumumab on the noted schedule. Bioluminescence imaging measurement of tumor burden demonstrates significantly increased fold-change in tumor burden (normalized to predaratumumab intensity) with either CD38 or SPI1 knockdown compared with scramble sgRNA. (A-E) ∗P < .05; ∗∗P < .01, by 2-tailed t test. conc, concentration; I.P., intraperitoneal; MFI, mean fluorescence intensity; NSG, NOD scid gamma; Scri, nontargeting control sgRNA.

Figure 2.

Validation of CRISPRi screen hits as functionally affecting daratumumab efficacy. (A) Knockdown of HEXIM1 and TLE3 with 2 independent sgRNAs per gene (AMO1 myeloma cells, n = 3) followed by flow cytometry shows significant surface CD38 increase with TLE3_i2 sgRNA and trend toward increased CD38 with HEXIM1_i1 sgRNA. (B) Results from ADCC assays with AMO1 cells stably expressing the noted sgRNAs and incubated with the indicated concentration of daratumumab or isotype control antibody (1:20 myeloma:NK ratio; 20 hours; n = 2). The percent lysis by ADCC was calculated using the following formula: % lysis = (signal in presence of daratumumab – signal in presence of IgG1 control antibody) ×100/signal in presence of IgG1 control antibody. At 10 μM daratumumab, both HEXIM1 and TLE3 knockdown led to significant increase in ADCC. (C) Similar to panel A, sgRNA knockdown of NFKB1, NFKB2, and SPI1 with fold-change in CD38 by flow cytometry (RPMI-8226 cells, n = 3). (D) Similar to panel B, knockdown with the most effective sgRNA for each gene show significant decreases in NK-cell ADCC at 10 μM daratumumab in the RMPI-8266 cells (n = 3). (E) In vivo validation of SPI1 knockdown driving daratumumab resistance. NOD scid gamma mice were IV implanted with CRISPRi RPMI-8226 cells stably expressing both luciferase and noted sgRNA, then treated with 200 μg daratumumab on the noted schedule. Bioluminescence imaging measurement of tumor burden demonstrates significantly increased fold-change in tumor burden (normalized to predaratumumab intensity) with either CD38 or SPI1 knockdown compared with scramble sgRNA. (A-E) ∗P < .05; ∗∗P < .01, by 2-tailed t test. conc, concentration; I.P., intraperitoneal; MFI, mean fluorescence intensity; NSG, NOD scid gamma; Scri, nontargeting control sgRNA.

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We were surprised that other targets proposed to increase CD38 expression after pharmacologic inhibition, such as HDACs,3 or catalyzed degradation, such as IKZF1/3,25 did not appear as prominent hits (Figure 1B). However, this result may reflect a limitation of functional genomic screens. A pharmacologic agent may inhibit multiple members of a protein class to drive a phenotype, whereas, with single gene knockdown, functional redundancy may prevent this phenotype from appearing36 (ie, multiple HDACs may need to ablated at once, or both IKZF1 and IKZF3 degraded simultaneously, to drive increased CD38). We speculate this is the case with DNMTs. We previously showed that treatment with the DNMT inhibitor Aza, which promotes degradation of all cellular DNMTs,37 could robustly increase surface CD38.16 Here, however, we found that knockdown of any individual DNMT only led to minor CD38 increase (Figure 1B).

Given these findings, we therefore shifted our focus to genes that, when knocked down, led to CD38 decrease (left side of volcano plot in Figure 1B). We reasoned this approach could still reveal important biological inputs that regulate the surface expression of CD38. Examining specific genes, we found that the transcription factor SPI1 was the strongest hit besides CD38 that, when knocked down, repressed surface CD38 expression (supplemental Figure 2B). We also noted that NFKB1 and NFKB2 knockdown appeared to drive CD38 decrease. This finding was intriguing given the known importance of NF-κB signaling in myeloma proliferation and survival.38 KEGG and gene ontology analysis of genes whose knockdown significantly decreased CD38 showed enrichment for MAPK pathway and protein phosphorylation more broadly (supplemental Figure 1C), suggesting key roles for intracellular signaling in regulating surface CD38.

Validation experiments with individual sgRNAs confirmed that SPI1 knockdown strongly decreased CD38 surface expression by flow cytometry, with a lesser decrease in surface CD38 with NFKB2 knockdown (Figures 1D and 2C; supplemental Figure 2D-E). These alterations also led to functional impacts. RPMI-8226 cells with knockdown of these genes showed significantly decreased NK-cell lysis in ADCC assays (Figure 2D). We further probed this dynamic in vivo, finding that RPMI-8226 cells with SPI1 knockdown were relatively resistant to daratumumab in a murine model (Figure 2E; supplemental Figure 2F). We note that we attempted to expand these results to additional cell lines. However, our 4 other myeloma cell lines harboring the CRISPRi machinery21 all express extremely low levels of SPI1 (supplemental Figure 2C), and attempted knockdown in 2 of them (AMO1 and KMS12PE) did not elicit any phenotype (not shown). Therefore, this finding suggests that SPI1 may play an important role in regulating CD38 expression in some myeloma tumors, but it is less likely to be a universal regulator.

Epigenetic analysis suggests XBP1 as a key determinant of CD38 in primary myeloma tumors

Our CRISPRi results suggest that epigenetic and/or transcriptional regulation is a critical driver of surface CD38 levels. However, we do not know whether these specific hits in a myeloma cell line will extend to primary myeloma tumors. We therefore took a complementary approach to find potential transcriptional regulators of CD38. Using ATAC-seq data from 24 primary myeloma tumor samples,24 we extracted open chromatin motifs near the CD38 promoter (supplemental Figure 3A) to identify a list of 46 transcription factors with potential binding sites at this locus (supplemental Table 2). We then correlated expression (via Pearson R) of these transcription factors with CD38 expression across 664 primary patient tumors at diagnosis in the Multiple Myeloma Research Foundation CoMMpass database (release IA13). In this analysis, we found the transcription factor most negatively correlated with CD38 expression was RARA (Figure 3A), consistent with our CRISPRi screen data and underscoring the promise of ATRA as a cotreatment to increase CD38. Intriguingly, the transcription factor with the strongest positive correlation was XBP1 (Figure 3A-B), a central driver of plasma cell identity.39,XBP1 also showed strong positive correlations with CD38 in 2 other patient tumor gene expression data sets40,41 (supplemental Table 2).

Figure 3.

Patient–centered epigenetic analysis and machine learning predicts the most potent transcriptional regulators of CD38. (A) A total of 46 transcription factors predicted to bind to the CD38 locus were derived from motif analysis of published ATAC-seq data (see supplemental Figure 3). Gene expression of each transcription factor (TF) was correlated with CD38 expression in the Multiple Myeloma Research Foundation (MMRF) CoMMpass database (release IA13), with RNA-seq data from CD138+ enriched tumor cells at diagnosis (n = 664 patients). Top predicted positive and negative regulators are shown based on Pearson correlation (R). (B) CoMMpass RNA-seq data illustrate strong positive correlation between XBP1 and CD38 expression. (C) XGBoost machine learning model was used to extract features of TF gene expression that best-model CD38 expression in CoMMpass tumors (shown in log2 TPM [transcripts per million]); 80% of data were used as test set, with 20% left out as a training set. Coefficient of variation (R2) for predictive model = 0.49 after five-fold cross-validation. (D) Shapley additive explanations (SHAP) analysis indicates transcription factors whose expression most strongly affects CD38 expression levels in CoMMpass tumors. FPKM, fragments per kilobase million; TPM, transcripts per million.

Figure 3.

Patient–centered epigenetic analysis and machine learning predicts the most potent transcriptional regulators of CD38. (A) A total of 46 transcription factors predicted to bind to the CD38 locus were derived from motif analysis of published ATAC-seq data (see supplemental Figure 3). Gene expression of each transcription factor (TF) was correlated with CD38 expression in the Multiple Myeloma Research Foundation (MMRF) CoMMpass database (release IA13), with RNA-seq data from CD138+ enriched tumor cells at diagnosis (n = 664 patients). Top predicted positive and negative regulators are shown based on Pearson correlation (R). (B) CoMMpass RNA-seq data illustrate strong positive correlation between XBP1 and CD38 expression. (C) XGBoost machine learning model was used to extract features of TF gene expression that best-model CD38 expression in CoMMpass tumors (shown in log2 TPM [transcripts per million]); 80% of data were used as test set, with 20% left out as a training set. Coefficient of variation (R2) for predictive model = 0.49 after five-fold cross-validation. (D) Shapley additive explanations (SHAP) analysis indicates transcription factors whose expression most strongly affects CD38 expression levels in CoMMpass tumors. FPKM, fragments per kilobase million; TPM, transcripts per million.

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To further extend this analysis, we sought to build a predictive model that could estimate CD38 transcript level as a function of transcription factor expression. We used an XGBoost method applied to CoMMpass mRNA-seq data to find weights of transcription factor expression that most influence CD38 levels in patient tumors. We first tested this analysis on 80% of patient data as a training set, with 20% left out as test set. We found this model, solely based on transcription factor expression, could predict about half of the variance (coefficient of variation R2 = 0.49 using fivefold cross validation) in test set CD38 levels (Figure 3C). Using model weights and Shapley additive explanations analysis (see supplemental Methods) to determine transcription factors that have the greatest impact, either positive or negative, on CD38 expression, we found that XBP1 played the strongest role overall. Other strong hits from both of our analyses included IRF2, ATF1, and STAT1. SPI1 also appeared in the top 10 most relevant transcriptional regulators (Figure 3D; supplemental Figure 3B), consistent with our CRISPRi results, suggesting that SPI1 may play a key role in regulating CD38 in a subset of tumors.

We further evaluated XBP1, given its prominent role in these 2 complementary bioinformatic analyses. In a prior data set of shRNA knockdown of XBP1 in myeloma plasma cells,42 CD38 mRNA was decreased approximately threefold after XBP1 silencing (supplemental Figure 3C). This finding was consistent with results in our CRISPRi screen, in which XBP1 knockdown led to significant surface CD38 decrease (Figure 1B). We further validated this relationship by using a doxycycline-inducible sgRNA construct for CRISPRi vs XBP1, finding a clear dose-response between degree of XBP1 knockdown and loss of surface CD38 by flow cytometry (supplemental Figure 3D-G). Supporting relevance of this link, a recent myeloma tumor whole-genome sequencing study found that deletion of XBP1 was one of the strongest determinants of clinical response to daratumumab.43 We attempted to perform promoter activation assays to directly link XBP1 binding to CD38 expression but were unable to successfully generate a reporter construct that reflected all 8 loci in which XBP1 may bind at CD38 regulatory elements (supplemental Figure 3H). Taken together, these results nominate XBP1 as a particularly strong determinant of surface CD38 in myeloma plasma cells, although future investigation will be required to validate a direct or indirect relationship to CD38 transcription.

No consistent large-scale remodeling of the myeloma surface proteome after CD38 downregulation

We next evaluated CD38 surface regulation from the perspective of mAb therapy. In clinical samples, CD38 loss after daratumumab was accompanied by increases in CD55 and CD59, which may inhibit complement-dependent cytotoxicity and contribute to daratumumab resistance.5 In preclinical studies, macrophage trogocytosis has been proposed as a mechanism contributing to CD38 loss after mAb treatment, which also leads to alterations in other surface antigens, including CD138/SDC1.7 However, we hypothesized that, given its enzymatic activity and role as a cellular differentiation marker,44 loss of CD38 on its own may influence surface expression of other myeloma antigens. Such alterations may reveal new biology or (immuno)therapeutic vulnerabilities of CD38 mAb–treated disease.

To test this hypothesis, we used a method we recently developed termed “antigen escape profiling.”27 We used CRISPRi to transcriptionally repress CD38 in RPMI-8226, AMO-1, and KMS12-PE myeloma cell lines, using this genetic approach to partially mimic the loss of surface antigen seen after mAb therapy (Figure 4A). We then performed cell surface capture proteomics26,27 to uncover surface proteome alterations in a relatively unbiased fashion. Across cell lines, analyzed in biological triplicate with CD38 knockdown vs nontargeting sgRNA, we quantified 897 proteins annotated as membrane-spanning in Uniprot (minimum of 2 peptides per protein; supplemental Table 3). As a positive control, in all lines, we found that the strongest signature was the decrease of CD38 itself (supplemental Figure 4A-C). However, when aggregating proteomic data, we found no significant alterations in any surface antigens beyond CD38 itself (Figure 4B). Integration with RNA-seq data revealed only THY-1/CD90 as upregulated more than threefold at both the mRNA and surface proteomic level after CD38 knockdown (Figure 4C). Intriguingly, CD90 is known as a marker of “stemness” in early hematopoietic lineage cells that is lost when CD38 expression is increased.45 CoMMpass analysis also confirmed increased THY1 expression in tumors with lower CD38 (supplemental Figure 4D). However, further validation as to whether CD90 is truly altered after CD38 mAb will require pretreatment and posttreatment clinical specimens, beyond the scope of our work here. Overall, we conclude that loss of CD38 in isolation leads to limited remodeling of the myeloma surface proteome.

Figure 4.

Minimal alterations of the myeloma cell surface proteome after CD38 loss. (A) Schematic of “antigen escape profiling” approach to reveal new cell surface therapeutic vulnerabilities in the context of CD38 downregulation. (B) Cell surface capture proteomics comparing CD38 knockdown vs nontargeting sgRNA control, with aggregated data across 3 cell lines (CRISPRi-expressing RPMI-8226, AMO1, and KMS12-PE; n = 3 replicates per cell line per sgRNA) reveals minimal changes in the cell surface proteome beyond CD38 knockdown at significance cutoff of P value <.05 and log2 fold-change >|1.5|. (C) Integrated analysis of cell surface proteomics and mRNA-seq (n = 2 per cell line per guide) across 3 cell lines reveals the only consistent change at both protein and transcript level after CD38 knockdown is THY1/CD90 upregulation. Log2 fold-change cutoff = |1.5|.

Figure 4.

Minimal alterations of the myeloma cell surface proteome after CD38 loss. (A) Schematic of “antigen escape profiling” approach to reveal new cell surface therapeutic vulnerabilities in the context of CD38 downregulation. (B) Cell surface capture proteomics comparing CD38 knockdown vs nontargeting sgRNA control, with aggregated data across 3 cell lines (CRISPRi-expressing RPMI-8226, AMO1, and KMS12-PE; n = 3 replicates per cell line per sgRNA) reveals minimal changes in the cell surface proteome beyond CD38 knockdown at significance cutoff of P value <.05 and log2 fold-change >|1.5|. (C) Integrated analysis of cell surface proteomics and mRNA-seq (n = 2 per cell line per guide) across 3 cell lines reveals the only consistent change at both protein and transcript level after CD38 knockdown is THY1/CD90 upregulation. Log2 fold-change cutoff = |1.5|.

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Integrated surface proteomic and transcriptional analysis suggests ATRA is highly specific in CD38 upregulation

Data from our group16 and others12-15 have suggested that several small molecules can increase myeloma surface CD38. However, the broader impacts of these agents on membrane antigens beyond CD38 have not been directly compared. We performed integrated cell surface proteomics and transcriptional analysis of RPMI-8226 cells treated with 10 nM ATRA, 2 μM Aza, and 10 nM panobinostat, all treated for 72 hours, in comparison with 0.1% dimethyl sulfoxide (supplemental Table 4). These doses are chosen because they have been previously published to significantly increase myeloma surface CD38 by flow cytometry.12,13,16 Notably, the magnitude of the increase in surface CD38 after 10 nM ATRA treatment, as measured by surface proteomics, was consistent with that which we previously observed by flow cytometry.16 In this integrated analysis, we found much broader impacts of Aza and panobinostat than ATRA on the “surfaceome” of plasma cells, beyond increasing CD38 (Figure 5A). These results suggest that, at doses driving CD38 upregulation, for Aza or panobinostat, altering CD38 is just a small component of their impact on myeloma tumor cells, whereas ATRA is much more specific in driving CD38 upregulation.

Figure 5.

ATRA drives CD38 upregulation with limited additional cellular impact, whereas Aza leads to a broad interferon-mediated response. (A) Integrated mRNA-seq (n = 2 per drug treatment) and cell surface proteomics (n = 2 per drug treatment) across RPMI-8226 treatment with 10 nM ATRA, 2 μM Aza, and 10 nM panobinostat (Pano). All plots are in comparison with control replicates treated with 0.1% DMSO. Doses chosen are based on those previously published to lead to CD38 upregulation for each agent. Data points shown are for proteins and genes corresponding to Uniprot-annotated membrane-spanning proteins. Log2 fold-change cutoffs shown at |0.5| for ATRA and |2.0| for Aza and Pano to increase clarity of plots given many fewer changed genes with ATRA treatment. (B) RNA-seq for same samples with ATRA or Aza treatment vs DMSO but here showing all mapped genes, not just those annotated as membrane-spanning. Significance cutoff at P value <.05 with log2 fold-change cutoff set at |0.8| to illustrate prominent differences above this level in transcriptome alteration after either ATRA or Aza treatment. (C) KEGG analysis of genes from RNA-seq data set meeting cutoff criteria of P value <.05 and log2 fold-change >0.8 after Aza treatment. DMSO, dimethyl sulfoxide.

Figure 5.

ATRA drives CD38 upregulation with limited additional cellular impact, whereas Aza leads to a broad interferon-mediated response. (A) Integrated mRNA-seq (n = 2 per drug treatment) and cell surface proteomics (n = 2 per drug treatment) across RPMI-8226 treatment with 10 nM ATRA, 2 μM Aza, and 10 nM panobinostat (Pano). All plots are in comparison with control replicates treated with 0.1% DMSO. Doses chosen are based on those previously published to lead to CD38 upregulation for each agent. Data points shown are for proteins and genes corresponding to Uniprot-annotated membrane-spanning proteins. Log2 fold-change cutoffs shown at |0.5| for ATRA and |2.0| for Aza and Pano to increase clarity of plots given many fewer changed genes with ATRA treatment. (B) RNA-seq for same samples with ATRA or Aza treatment vs DMSO but here showing all mapped genes, not just those annotated as membrane-spanning. Significance cutoff at P value <.05 with log2 fold-change cutoff set at |0.8| to illustrate prominent differences above this level in transcriptome alteration after either ATRA or Aza treatment. (C) KEGG analysis of genes from RNA-seq data set meeting cutoff criteria of P value <.05 and log2 fold-change >0.8 after Aza treatment. DMSO, dimethyl sulfoxide.

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Toward understanding how CD38 is modulated after drug treatment, in our previous work,16 we noted that the mechanism of CD38 increase after Aza treatment was unclear. We thus further investigated the global transcriptional response (ie, not limited to membrane) after Aza (supplemental Table 4). Prior studies have suggested that Aza antitumor effect is largely mediated by reactivation of endogenous retroviruses stimulating a tumor-autonomous interferon response.46,47 Consistent with this work, we found a pronounced increase in interferon-responsive genes after Aza, but not ATRA, including IRF1, IFITM1, IFITM2, and IFITM3 (Figure 5B). KEGG analysis also confirmed this effect (Figure 5C). Given evidence across multiple systems that interferon upregulates CD38 expression,48,49 our transcriptional profiling thus also supports an interferon-based mechanism driving surface CD38 increase in plasma cells after Aza treatment.

Plasma cell proliferative signaling pathways are inhibited by mAb binding to CD38

In our final set of experiments related to targeting surface CD38, we were intrigued as to whether binding of a therapeutic mAb leads to specific cellular phenotypes within myeloma plasma cells. For example, isatuximab is known to directly lead to apoptosis of plasma cells,50 and daratumumab can do so after crosslinking.51 However, the mechanism underlying this transduction of extracellular mAb binding to intracellular phenotype remains unclear. In addition, our CRISPRi screen data (Figure 1B; supplemental Figure 1C) suggest that surface CD38 expression may be strongly affected by intracellular phospho-signaling pathways.

Therefore, we used unbiased phosphoproteomics by mass spectrometry to probe intracellular signaling effects driven by CD38 mAb binding. In RPMI-8226 cells, we compared 20 μM daratumumab treatment vs IgG1 isotype control. This supraphysiological dose of daratumumab was chosen to maximize signal-to-noise in the downstream phosphoproteomics assay. We chose a time point of 20 minutes of treatment, given the known rapid alterations in signaling pathways in similar phosphoproteomic experiments.52 In total, across triplicate samples, we quantified 5430 phosphopeptides (supplemental Table 5; supplemental Figure 5A). Analyzing phosphopeptide changes by kinase substrate enrichment analysis,53 we were intrigued to find downregulation of phosphorylation motifs consistent with both cyclin-dependent kinases as well as several kinases of the MAP kinase pathway (Figure 6A). Downregulation of phosphorylation on several central nodes in the MAP kinase as well as AKT pathway was also apparent via KEGG analysis (supplemental Figure 5B). Across a time course, we further confirmed effects on MAP kinase pathway (reported by phosphorylation of MAPK [ERK1/2], a key node in this response) and AKT signaling after daratumumab treatment via western blotting in RPMI-8226 and MM.1S cell lines, respectively (Figure 6B). Although the absolute value of changes in MAPK signaling are modest, both by phosphoproteomics and western blot, these results indicate that daratumumab binding to CD38 can at least partially inhibit this central proliferative pathway within myeloma tumor cells and thus may form a component of daratumumab’s antitumor effect.

Figure 6.

Unbiased phosphoproteomics reveals downregulation of proliferative signaling after daratumumab treatment. (A) RPMI-8226 cells were treated with 20 μM daratumumab (Dara) or IgG1 isotype control for 20 minutes (n = 3 each) and then harvested for unbiased phosphoproteomics with immobilized metal affinity chromatography enrichment for phosphopeptide enrichment. Plot displays results of kinase substrate enrichment analysis, indicating modest decrease in phosphorylation of numerous predicted substrates of MAPK pathway kinases as well as cyclin-dependent kinases (cutoff, P < .05; log2 fold-change > |0.5|). (B) Western blot in RPMI-8226 of MAPK (ERK1/2) (Thr202/Tyr204) relative to total MAPK demonstrates modest decrease in MAPK phosphorylation after 5, 10, or 15 minutes of Dara treatment; magnitude of change normalized to IgG1 control at each time point (red) appears consistent with phosphoproteomic data. (C) Western blot of MM.1S cells treated with Dara and blotted for p-AKT (Ser473) and total AKT, with quantification of p-AKT relative to total AKT and normalized to IgG1 at each time point. All images representative of 2 independent western blots.

Figure 6.

Unbiased phosphoproteomics reveals downregulation of proliferative signaling after daratumumab treatment. (A) RPMI-8226 cells were treated with 20 μM daratumumab (Dara) or IgG1 isotype control for 20 minutes (n = 3 each) and then harvested for unbiased phosphoproteomics with immobilized metal affinity chromatography enrichment for phosphopeptide enrichment. Plot displays results of kinase substrate enrichment analysis, indicating modest decrease in phosphorylation of numerous predicted substrates of MAPK pathway kinases as well as cyclin-dependent kinases (cutoff, P < .05; log2 fold-change > |0.5|). (B) Western blot in RPMI-8226 of MAPK (ERK1/2) (Thr202/Tyr204) relative to total MAPK demonstrates modest decrease in MAPK phosphorylation after 5, 10, or 15 minutes of Dara treatment; magnitude of change normalized to IgG1 control at each time point (red) appears consistent with phosphoproteomic data. (C) Western blot of MM.1S cells treated with Dara and blotted for p-AKT (Ser473) and total AKT, with quantification of p-AKT relative to total AKT and normalized to IgG1 at each time point. All images representative of 2 independent western blots.

Close modal

Our studies here present a “multiomics” view of therapeutically targeting CD38 in multiple myeloma. Our integrated functional genomics and epigenetic analysis point to the central role of transcriptional regulators in governing CD38 surface expression. Using surface proteomics, we further identify that loss of CD38 in isolation is unlikely to drive large changes in the “surfaceome,” whereas known pharmacologic strategies to increase CD38 have largely divergent impacts on other surface antigens. Finally, unbiased phosphoproteomics reveals that binding of anti-CD38 mAb can impair intracellular proliferative signaling within plasma cells.

Our CRISPRi screen illustrated the central role of numerous transcription factors, such as SPI1, HEXIM1, and TLE3, in regulating surface CD38. This functional genomic study suggests that regulation of surface CD38 largely occurs at the transcriptional, as opposed to protein trafficking, level. This finding was in sharp contrast to our prior CRISPRi results with BCMA, in which we found that posttranscriptional mechanisms, such as proteolytic cleavage by γ-secretase and protein trafficking via the SEC61 translocon, played the strongest roles in determining surface BCMA levels.21 

Another recent study by Anderson and colleagues used genome-wide CRISPR deletion screening to find genes that, when knocked out, could abrogate interleukin-6–mediated downregulation of surface CD38.15 The strongest hits in this prior study included the transcription factors STAT1 and STAT3, demonstrating a role for JAK-STAT signaling in regulating tumor CD38 expression within the bone marrow microenvironment.15 In support of this notion, our integrated epigenetic and machine learning analyses, extracted from bone marrow–derived patient tumor samples, also support a critical role for STAT1 in governing surface CD38. However, in our CRISPRi screen in an in vitro monoculture system, neither STAT1 nor STAT3 affected CD38 surface expression (Figure 1B). This result suggests that JAK-STAT signaling may not play a major role in CD38 regulation in the absence of exogenous tumor stimulation. This finding illustrates the complementary nature of our genome-wide screen to that previously published under the context of interleukin-6 stimulation.15 Even more recently, another study from the same group used CRISPR knockout screening to identify KDM6A as an important regulator of both surface CD38 and daratumumab-mediated ADCC.54 Work by others also recently showed that KDM6A knockout can lead to CD38 transcript downregulation in myeloma models.55 In our studies, KDM6A was not a prominent hit (Figure 1B), possibly due to the differences between partial knockdown via CRISPRi and full knockout via CRISPR nuclease. Intriguingly, KDM4A, another histone demethylase, was one of the strongest hits in our screen that when knocked down led to surface CD38 increase (Figure 1B). These findings raise the possibility of an epigenetic interplay between these enzymes in the context of CD38 regulation.

Toward the goal of finding key regulators of CD38 that were not previously known, our epigenetic and machine learning approaches suggest that XBP1 is a critical regulator of plasma cell CD38. To our knowledge, there are not currently any known pharmacologic mechanisms to potentiate XBP1 activity. Given the important role of XBP1 splicing in myeloma plasma cells,56 future work will investigate the role spliced vs unspliced XBP1 in specifically regulating CD38, because this strategy may provide new avenues for CD38 manipulation. Future work will also investigate the role of XBP1 deletion in determining clinical response to daratumumab.43 The clinical study of Maura et al43 also found that genomic deletions of CYLD were strongly enriched in patient tumors that were nonresponsive to daratumumab. This gene, along with others such as TRAF3, are known to regulate NF-κB signaling in myeloma.57 However, we did not observe significant changes in surface CD38 after knockdown of CYLD or TRAF3 (supplemental Table 1), unlike NFKB1 and NFKB2. We cannot exclude insufficient knockdown of these genes in our assay, or cell-line–specific differences in NF-κB signaling, as explanations for why we found CRISPRi impacts of some, but not all, genes within the NF-κB pathway on surface CD38 (supplemental Figure 1C).

Given that plasma cells demonstrate frequent loss of surface CD38 after daratumumab treatment,5 a pressing question is whether CD38-low, daratumumab-resistant cells have novel immunotherapeutic vulnerabilities. However, our recently described strategy of “antigen escape profiling,”27 which involves CRISPRi knockdown followed by unbiased cell surface proteomics (Figure 4), suggests that other surface antigens on plasma cells do not exhibit consistent changes due to CD38 downregulation alone. That said, although our proteomic analysis after CD38 knockdown showed limited common features across 3 myeloma cell lines, we cannot rule out that major surfaceome remodeling could truly be present but with marked variability in responsive surface proteins from line to line. However, our currently favored explanation for this result is excess experimental noise in the surface proteomics quantification that leads to limited replication in the aggregated data across cell lines. We thus believe these findings support the notion that alterations in surface proteins found after mAb treatment on patent tumors, such as increases in CD55 and CD59,5 are caused by other therapy-induced selective pressure within the tumor microenvironment, not CD38 loss.

Although in vitro assays have suggested a strong relationship between CD38 antigen density and either NK-cell–58 or macrophage-mediated antibody-dependent cell killing,6 these experiments cannot readily take into account the critical role of the immune microenvironment in determining daratumamab response or resistance.8 Furthermore, even with the higher specificity of ATRA, there is the potential to alter CD38 expression on other hematopoietic cells, which may affect clinical responses to daratumumab.17 Notably, current clinical data are most consistent with pretreatment tumor CD38 antigen density positively correlating with daratumumab depth of response.5,11 Surprisingly, analysis of transcriptional data in CoMMpass demonstrate that increased tumor CD38 expression at diagnosis was not associated with improved outcomes in patients treated with daratumumab later in their clinical course (supplemental Figure 6). Pharmacologic manipulation of CD38 density on tumor cells may ultimately be most fruitful before treatment rather than in the context of daratumumab resistance. Similar strategies may also be most beneficial for other CD38-targeting immunotherapeutics.

Furthermore, directly related to mAb therapeutic effects, our unbiased phosphoproteomic results suggest that daratumumab binding to CD38 can directly decrease signaling along the MAP kinase and PI3K-AKT pathways. It remains to be investigated whether this inhibition of central proliferative signaling pathways plays a role in the antitumor effect of daratumumab in patients.

In terms of limitations of our work, the most prominent is that the many of our studies are derived from large-scale “omics” experiments in myeloma cell lines. There may be biological differences between our findings in vitro and primary tumors growing within the bone marrow microenvironment.

Taken together, our multiomic studies comprise a resource that reveals new insight into the genetic, epigenetic, and pharmacologic regulation of surface CD38 in myeloma plasma cells. We anticipate these findings will have utility in deriving new strategies to enhance CD38-targeting therapies in myeloma, including mAbs in current clinical practice as well as emerging antibody and cellular therapies. The technologies described here also comprise a blueprint to comprehensively assess determinants of surface antigen regulation and impacts of associated therapeutic manipulation, which could be applied across targets in hematologic malignancies.

The authors thank Ruilin Tian for assistance in applying MAGeCK scripts for the analysis here.

This work was supported by grants NIH K08 CA184116, NIH R01 CA226851, and the UCSF Stephen and Nancy Grand Multiple Myeloma Translational Initiative (A.P.W.), NCI P30 CA082103 supporting the Preclinical Therapeutics Core facility (managed by V. Steri and B.H.), and NIH K99/R00 CA181494 and a Stand Up to Cancer Innovative Research Grant (M.K.).

Contribution: P.C., P.R., M.K., and A.P.W. conceived and designed the study; P.C., C.K., B.P.-E., A. Kang, A. Kishishita, S.R., J.C.P., O.G., L.S., Y.-H.T.L., P.R., N.P., and M.M. performed experiments; P.C., B.P.-E., N.P., Y.-H.T.L., B.G.B., and P.R. analyzed data; D.W., P.P., V. Steri, and B.H. performed murine studies; H.G. analyzed patient epigenetic data; V. Sarin performed machine learning analysis; and P.C. and A.P.W. wrote the manuscript with input from all authors.

Conflict-of-interest disclosure: P.C. is a shareholder of Genentech/Roche. P.R. is a shareholder of Senti Biosciences. A.P.W. is an equity holder and scientific advisory board member of Indapta Therapeutics, LLC and Protocol Intelligence, LLC. M.K. has filed a patent application related to CRISPRi screening (US patent number PCT/US15/40449); and serves on the scientific advisory boards of Engine Biosciences, Cajal Neuroscience, and Casma Therapeutics. The remaining authors declare no competing financial interests.

The current affiliation for P.C. is Genentech/Roche, South San Francisco, CA.

The current affiliation for P.R. is Senti Biosciences, South San Francisco, CA.

Correspondence: Arun P. Wiita, Department of Laboratory Medicine, University of California San Francisco, 185 Berry St, Ste. 290, San Francisco, CA 94107; email: [email protected].

1.
Syed
YY
.
Daratumumab: a review in combination therapy for transplant-ineligible newly diagnosed multiple myeloma
.
Drugs
.
2019
;
79
(
4
):
447
-
454
.
2.
Bonello
F
,
D'Agostino
M
,
Moscvin
M
,
Cerrato
C
,
Boccadoro
M
,
Gay
F
.
CD38 as an immunotherapeutic target in multiple myeloma
.
Expert opinion on biological therapy
.
2018
;
18
(
12
):
1209
-
1221
.
3.
van de Donk
N
,
Usmani
SZ
.
CD38 antibodies in multiple myeloma: mechanisms of action and modes of resistance
.
Front Immunol
.
2018
;
9
:
2134
.
4.
Saltarella
I
,
Desantis
V
,
Melaccio
A
,
Solimando
AG
,
Lamanuzzi
A
,
Ria
R
, et al
.
Mechanisms of Resistance to Anti-CD38 Daratumumab in Multiple Myeloma
.
Cells
.
2020
;
9
(
1
).
5.
Nijhof
IS
,
Casneuf
T
,
van Velzen
J
,
van Kessel
B
,
Axel
AE
,
Syed
K
, et al
.
CD38 expression and complement inhibitors affect response and resistance to daratumumab therapy in myeloma
.
Blood
.
2016
;
128
(
7
):
959
-
970
.
6.
Overdijk
MB
,
Verploegen
S
,
Bogels
M
,
van Egmond
M
,
Lammerts van Bueren
JJ
,
Mutis
T
, et al
.
Antibody-mediated phagocytosis contributes to the anti-tumor activity of the therapeutic antibody daratumumab in lymphoma and multiple myeloma
.
MAbs
.
2015
;
7
(
2
):
311
-
321
.
7.
Krejcik
J
,
Casneuf
T
,
Nijhof
IS
,
Verbist
B
,
Bald
J
,
Plesner
T
, et al
.
Daratumumab depletes CD38+ immune regulatory cells, promotes T-cell expansion, and skews T-cell repertoire in multiple myeloma
.
Blood
.
2016
;
128
(
3
):
384
-
394
.
8.
Viola
D
,
Dona
A
,
Caserta
E
,
Troadec
E
,
Besi
F
,
McDonald
T
, et al
.
Daratumumab induces mechanisms of immune activation through CD38+ NK cell targeting
.
Leukemia
.
2021
;
35
(
1
):
189
-
200
.
9.
Krejcik
J
,
Frerichs
KA
,
Nijhof
IS
,
van Kessel
B
,
van Velzen
JF
,
Bloem
AC
, et al
.
Monocytes and Granulocytes Reduce CD38 Expression Levels on Myeloma Cells in Patients Treated with Daratumumab
.
Clinical cancer research : an official journal of the American Association for Cancer Research
.
2017
;
23
(
24
):
7498
-
7511
.
10.
Plesner
T
,
van de Donk
N
,
Richardson
PG
.
Controversy in the Use of CD38 Antibody for Treatment of Myeloma: Is High CD38 Expression Good or Bad?
.
Cells
.
2020
;
9
(
2
):
378
.
11.
Kitadate
A
,
Kobayashi
H
,
Abe
Y
,
Narita
K
,
Miura
D
,
Takeuchi
M
, et al
.
Pre-treatment CD38-positive regulatory T cells affect the durable response to daratumumab in relapsed/refractory multiple myeloma patients
.
Haematologica
.
2020
;
105
(
1
):
e37
-
e40
.
12.
Nijhof
IS
,
Groen
RW
,
Lokhorst
HM
,
van Kessel
B
,
Bloem
AC
,
van Velzen
J
, et al
.
Upregulation of CD38 expression on multiple myeloma cells by all-trans retinoic acid improves the efficacy of daratumumab
.
Leukemia
.
2015
;
29
(
10
):
2039
-
2049
.
13.
Garcia-Guerrero
E
,
Gogishvili
T
,
Danhof
S
,
Schreder
M
,
Pallaud
C
,
Perez-Simon
JA
, et al
.
Panobinostat induces CD38 upregulation and augments the antimyeloma efficacy of daratumumab
.
Blood
.
2017
;
129
(
25
):
3386
-
3388
.
14.
Fedele
PL
,
Willis
SN
,
Liao
Y
,
Low
MS
,
Rautela
J
,
Segal
DH
, et al
.
IMiDs prime myeloma cells for daratumumab-mediated cytotoxicity through loss of Ikaros and Aiolos
.
Blood
.
2018
;
132
(
20
):
2166
-
2178
.
15.
Ogiya
D
,
Liu
J
,
Ohguchi
H
,
Kurata
K
,
Samur
MK
,
Tai
YT
, et al
.
The JAK-STAT pathway regulates CD38 on myeloma cells in the bone marrow microenvironment: therapeutic implications
.
Blood
.
2020
;
136
(
20
):
2334
-
2345
.
16.
Choudhry
P
,
Mariano
MC
,
Geng
H
,
Martin
TG
,
Wolf
JL
,
Wong
SW
, et al
.
DNA methyltransferase inhibitors upregulate CD38 protein expression and enhance daratumumab efficacy in multiple myeloma
.
Leukemia
.
2020
;
34
(
3
):
938
-
941
.
17.
Frerichs
KA
,
Minnema
MC
,
Levin
MD
,
Broijl
A
,
Bos
GM
,
Kersten
MJ
, et al
.
Efficacy and Safety of Daratumumab Combined With All-Trans Retinoic Acid in Relapsed/Refractory Multiple Myeloma
.
Blood Adv
.
2021
;
5
(
23
):
5128
-
5139
.
18.
Wu
L
,
Seung
E
,
Xu
L
,
Rao
E
,
Lord
DM
,
Wei
RR
, et al
.
Trispecific antibodies enhance the therapeutic efficacy of tumor-directed T cells through T cell receptor co-stimulation
.
Nat Cancer
.
2020
;
1
(
1
):
86
-
98
.
19.
Zhang
H
,
Liu
M
,
Xiao
X
,
Lv
H
,
Jiang
Y
,
Li
X
, et al
.
A combination of humanized anti-BCMA and murine anti-CD38 CAR-T cell therapy in patients with relapsed or refractory multiple myeloma
.
Leuk Lymphoma
.
2022
;
63
(
6
):
1418
-
1427
.
20.
Choudhry
P
,
Galligan
D
,
Wiita
AP
.
Seeking convergence and cure with new myeloma therapies
.
Trends Cancer
.
2018
;
4
(
8
):
567
-
582
.
21.
Ramkumar
P
,
Abarientos
AB
,
Tian
R
,
Seyler
M
,
Leong
JT
,
Chen
M
, et al
.
CRISPR-based screens uncover determinants of immunotherapy response in multiple myeloma
.
Blood Adv
.
2020
;
4
(
13
):
2899
-
2911
.
22.
Kampmann
M
,
Bassik
MC
,
Weissman
JS
.
Functional genomics platform for pooled screening and generation of mammalian genetic interaction maps
.
Nat Protoc
.
2014
;
9
(
8
):
1825
-
1847
.
23.
Tian
R
,
Gachechiladze
MA
,
Ludwig
CH
,
Laurie
MT
,
Hong
JY
,
Nathaniel
D
, et al
.
CRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons
.
Neuron
.
2019
;
104
(
2
):
239
-
255 e212
.
24.
Jin
Y
,
Chen
K
,
De Paepe
A
,
Hellqvist
E
,
Krstic
AD
,
Metang
L
, et al
.
Active enhancer and chromatin accessibility landscapes chart the regulatory network of primary multiple myeloma
.
Blood
.
2018
;
131
(
19
):
2138
-
2150
.
25.
Messeguer
X
,
Escudero
R
,
Farre
D
,
Nunez
O
,
Martinez
J
,
Alba
MM
.
PROMO: detection of known transcription regulatory elements using species-tailored searches
.
Bioinformatics
.
2002
;
18
(
2
):
333
-
334
.
26.
Wollscheid
B
,
Bausch-Fluck
D
,
Henderson
C
,
O'Brien
R
,
Bibel
M
,
Schiess
R
, et al
.
Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins
.
Nat Biotechnol
.
2009
;
27
(
4
):
378
-
386
.
27.
Nix
MA
,
Mandal
K
,
Geng
H
,
Paranjape
N
,
Lin
YT
,
Rivera
J
, et al
.
Surface proteomics reveals CD72 as a target for in vitro-evolved nanobody-based CAR-T cells in KMT2A/MLL1-rearranged B-ALL
.
Cancer Discov
.
2021
;
11
(
8
):
2032
-
2049
.
28.
Lin
YT
,
Way
GP
,
Barwick
BG
,
Mariano
MC
,
Marcoulis
M
,
Ferguson
ID
, et al
.
Integrated phosphoproteomics and transcriptional classifiers reveal hidden RAS signaling dynamics in multiple myeloma
.
Blood Adv
.
2019
;
3
(
21
):
3214
-
3227
.
29.
Tyanova
S
,
Temu
T
,
Cox
J
.
The MaxQuant computational platform for mass spectrometry-based shotgun proteomics
.
Nat Protoc
.
2016
;
11
(
12
):
2301
-
2319
.
30.
Gilbert
LA
,
Horlbeck
MA
,
Adamson
B
,
Villalta
JE
,
Chen
Y
,
Whitehead
EH
, et al
.
Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation
.
Cell
.
2014
;
159
(
3
):
647
-
661
.
31.
Li
W
,
Xu
H
,
Xiao
T
,
Cong
L
,
Love
MI
,
Zhang
F
, et al
.
MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens
.
Genome Biol
.
2014
;
15
(
12
):
554
.
32.
Dykhuizen
EC
,
Carmody
LC
,
Tolliday
N
,
Crabtree
GR
,
Palmer
MA
.
Screening for inhibitors of an essential chromatin remodeler in mouse embryonic stem cells by monitoring transcriptional regulation
.
J Biomol Screen
.
2012
;
17
(
9
):
1221
-
1230
.
33.
Carbonneau
M
,
L
MG
,
Lalonde
ME
,
Germain
MA
,
Motorina
A
,
Guiot
MC
, et al
.
The oncometabolite 2-hydroxyglutarate activates the mTOR signalling pathway
.
Nat Commun
.
2016
;
712700
.
34.
Michels
AA
,
Bensaude
O
.
Hexim1, an RNA-controlled protein hub
.
Transcription
.
2018
;
9
(
4
):
262
-
271
.
35.
Cinnamon
E
,
Paroush
Z
.
Context-dependent regulation of Groucho/TLE-mediated repression
.
Curr Opin Genet Dev
.
2008
;
18
(
5
):
435
-
440
.
36.
Agrotis
A
,
Ketteler
R
.
A new age in functional genomics using CRISPR/Cas9 in arrayed library screening
.
Front Genet
.
2015
;
6
:
300
.
37.
Gnyszka
A
,
Jastrzebski
Z
,
Flis
S
.
DNA methyltransferase inhibitors and their emerging role in epigenetic therapy of cancer
.
Anticancer Res
.
2013
;
33
(
8
):
2989
-
2996
.
38.
Matthews
GM
,
de Matos Simoes
R
,
Dhimolea
E
,
Sheffer
M
,
Gandolfi
S
,
Dashevsky
O
, et al
.
NF-kappaB dysregulation in multiple myeloma
.
Semin Cancer Biol
.
2016
;
3968-76
.
39.
Reimold
AM
,
Iwakoshi
NN
,
Manis
J
,
Vallabhajosyula
P
,
Szomolanyi-Tsuda
E
,
Gravallese
EM
, et al
.
Plasma cell differentiation requires the transcription factor XBP-1
.
Nature
.
2001
;
412
(
6844
):
300
-
307
.
40.
Mulligan
G
,
Mitsiades
C
,
Bryant
B
,
Zhan
F
,
Chng
WJ
,
Roels
S
, et al
.
Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib
.
Blood
.
2007
;
109
(
8
):
3177
-
3188
.
41.
Shi
L
,
Campbell
G
,
Jones
WD
,
Campagne
F
,
Wen
Z
,
Walker
SJ
, et al
.
The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
.
Nat Biotechnol
.
2010
;
28
(
8
):
827
-
838
.
42.
Leung-Hagesteijn
C
,
Erdmann
N
,
Cheung
G
,
Keats
JJ
,
Stewart
AK
,
Reece
DE
, et al
.
Xbp1s-negative tumor B cells and pre-plasmablasts mediate therapeutic proteasome inhibitor resistance in multiple myeloma
.
Cancer Cell
.
2013
;
24
(
3
):
289
-
304
.
43.
Maura
F
,
Boyle
EM
,
Coffey
D
,
Maclachlan
K
,
Gagler
D
,
Diamond
B
, et al
.
Genomic and immune signatures predict clinical outcome in newly diagnosed multiple myeloma treated with immunotherapy regimens
.
Nat Cancer
.
2023
;
4
(
12
):
1660
-
1674
.
44.
Li
Y
,
Yang
R
,
Chen
L
,
Wu
S
.
CD38 as an immunomodulator in cancer
.
Future Oncol
.
2020
;
16
(
34
):
2853
-
2861
.
45.
Majeti
R
,
Park
CY
,
Weissman
IL
.
Identification of a hierarchy of multipotent hematopoietic progenitors in human cord blood
.
Cell Stem Cell
.
2007
;
1
(
6
):
635
-
645
.
46.
Chiappinelli
KB
,
Strissel
PL
,
Desrichard
A
,
Li
H
,
Henke
C
,
Akman
B
, et al
.
Inhibiting DNA Methylation Causes an Interferon Response in Cancer via dsRNA Including Endogenous Retroviruses
.
Cell
.
2015
;
162
(
5
):
974
-
986
.
47.
Roulois
D
,
Loo Yau
H
,
Singhania
R
,
Wang
Y
,
Danesh
A
,
Shen
SY
, et al
.
DNA-Demethylating Agents Target Colorectal Cancer Cells by Inducing Viral Mimicry by Endogenous Transcripts
.
Cell
.
2015
;
162
(
5
):
961
-
973
.
48.
Bauvois
B
,
Durant
L
,
Laboureau
J
,
Barthelemy
E
,
Rouillard
D
,
Boulla
G
, et al
.
Upregulation of CD38 gene expression in leukemic B cells by interferon types I and II
.
J Interferon Cytokine Res
.
1999
;
19
(
9
):
1059
-
1066
.
49.
Mihara
K
,
Yoshida
T
,
Ishida
S
,
Takei
Y
,
Kitanaka
A
,
Shimoda
K
, et al
.
All-trans retinoic acid and interferon-alpha increase CD38 expression on adult T-cell leukemia cells and sensitize them to T cells bearing anti-CD38 chimeric antigen receptors
.
Blood Cancer J
.
2016
;
6
(
5
):
e421
.
50.
Moreno
L
,
Perez
C
,
Zabaleta
A
,
Manrique
I
,
Alignani
D
,
Ajona
D
, et al
.
The mechanism of action of the anti-CD38 monoclonal antibody isatuximab in multiple myeloma
.
Clin Cancer Res
.
2019
;
25
(
10
):
3176
-
3187
.
51.
Overdijk
MB
,
Jansen
JH
,
Nederend
M
,
Lammerts van Bueren
JJ
,
Groen
RW
,
Parren
PW
, et al
.
The Therapeutic CD38 Monoclonal Antibody Daratumumab Induces Programmed Cell Death via Fcgamma Receptor-Mediated Cross-Linking
.
J Immunol
.
2016
;
197
(
3
):
807
-
813
.
52.
Humphrey
SJ
,
Azimifar
SB
,
Mann
M
.
High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics
.
Nat Biotechnol
.
2015
;
33
(
9
):
990
-
995
.
53.
Casado
P
,
Rodriguez-Prados
JC
,
Cosulich
SC
,
Guichard
S
,
Vanhaesebroeck
B
,
Joel
S
, et al
.
Kinase-substrate enrichment analysis provides insights into the heterogeneity of signaling pathway activation in leukemia cells
.
Sci Signal
.
2013
;
6
(
268
):
rs6
.
54.
Liu
J
,
Xing
L
,
Li
J
,
Wen
K
,
Liu
N
,
Liu
Y
, et al
.
Epigenetic regulation of CD38/CD48 by KDM6A mediates NK cell response in multiple myeloma
.
Nat Commun
.
2024
;
15
(
1
):
1367
.
55.
Dupere-Richer
D
,
Riva
A
,
Maji
S
,
Barwick
BG
,
Roman
HC
,
Sobh
A
, et al
.
KDM6A Regulates Immune Response Genes in Multiple Myeloma
.
bioRxiv
.
2024
.
56.
Mimura
N
,
Fulciniti
M
,
Gorgun
G
,
Tai
YT
,
Cirstea
D
,
Santo
L
, et al
.
Blockade of XBP1 splicing by inhibition of IRE1alpha is a promising therapeutic option in multiple myeloma
.
Blood
.
2012
;
119
(
24
):
5772
-
5781
.
57.
Keats
JJ
,
Fonseca
R
,
Chesi
M
,
Schop
R
,
Baker
A
,
Chng
WJ
, et al
.
Promiscuous mutations activate the noncanonical NF-kappaB pathway in multiple myeloma
.
Cancer Cell
.
2007
;
12
(
2
):
131
-
144
.
58.
Bigley
AB
,
Spade
S
,
Agha
NH
,
Biswas
S
,
Tang
S
,
Malik
MH
, et al
.
FcepsilonRIgamma-negative NK cells persist in vivo and enhance efficacy of therapeutic monoclonal antibodies in multiple myeloma
.
Blood Adv
.
2021
;
5
(
15
):
3021
-
3031
.

Author notes

C.K. and B.P.-E. contributed equally to this study.

A. Kang, A. Kishishita, and S.R. contributed equally to this study.

RNA-sequencing (RNA-seq) and proteomics data have been deposited to public repositories. Proteomics data can be accessed at the Proteomics Identification Database repository (accession number PXD027594) and RNA-seq data at the Gene Expression Omnibus repository (accession number GSE181277). Additional processed data are included in supplemental Tables.

Other experimental data are available on request from the corresponding author, Arun P. Wiita ([email protected]).

The full-text version of this article contains a data supplement.