Loss-of-function studies (LOF) have historically been the main functional approach to identify and study genes which drive the biology of Multiple Myeloma (MM) or other neoplasias. We hypothesized that substantial complementary data can be derived from systematically examining the impact of transcriptional activation of endogenous loci of different genes. We thus performed genome-scale CRISPR gene activation (CRISPRa) screens in 10 genotypically diverse MM cell lines (dCas9-P65-HSF transcriptional activation system; Calabrese genome-scale sgRNA library) to identify genes with significant sgRNA enrichment reflecting enhanced tumor cell fitness. These experiments identified that MM driver genes are highly cell line-dependent, with no universal “hit” but with many recurrent (e.g. in ≥4/10 lines) drivers, including key transcription factors/cofactors (e.g. POU2F2, POU2AF1, IRF4, MYC, HOXA3); growth factor signaling mediators, (e.g. IGF1R, IRS1); Ras family members (e.g. KRAS in MM1S cells); NFκB signaling (e.g. RELA, TIFA), PKC pathway (PRKCE), cell surface receptors (e.g. CD48, HTR1D, IGF1R, UPK3B), solute carrier transporter genes (e.g. SLC38A1). No correlation is currently observed between the pattern of driver genes for the individual MM cell lines and their respective molecular subtype (e.g., t(4;14), t(14;16), t(11;14), etc). Some MM-preferential dependencies identified by CRISPR knockout (KO) studies (de Matos Simoes et al Nat Cancer 2023) were identified by CRISPRa as recurrent MM drivers (e.g. POU2AF1, IRF4) or drivers with limited role in individual line(s) (e.g., IKZF3 and IKZF1 in MM1S cells). Notably, many top MM drivers are not essential for MM cells based on CRISPR KO studies. Most top MM drivers were not identified in similar CRISPRa studies as recurrent drivers for 17 non-MM cell lines from other hematologic neoplasias or solid tumors. With some notable exceptions (e.g., IRF4, POU2AF1), most other MM drivers are not identified in MM patient samples among the molecules with most frequent mutations, gene amplification or overexpression (e.g. vs normal plasma cells) at transcript or protein level. However, transcript upregulation (log2FC≥1.0) of at least one MM driver gene is observed in ~85% of paired MM samples after relapse vs. before treatment (MMRF CoMMpass study), suggesting that many CRISPRa-driver genes can contribute, alone or in concert, to enhanced MM cell fitness in clinical relapses. We validated the functional relevance of several MM driver genes (e.g. POU2F2, EGLN1, SLC38A1) with individual sgRNAs for CRISPR activation and/or cDNA overexpression (vs. isogenic controls) in competition experiments. We further probed the mechanistic basis for the role of POU2F2 as one of the top positive regulators of MM cell fitness and performed RNA sequencing analyses of MM1S cells harboring CRISPRa-based upregulation of POU2F2 expression. We observed upregulation of a distinct cluster of genes (including HOXA3, USP32, HTR1D) that are identified in our genome-scale CRISPRa studies as driver genes specifically in MM1S cells or ≥4 MM lines of our analyses, as well as several genes that are essential for MM survival (e.g., U2AF1, ZNF492, HYPK, PAM16, H2BC11). These results indicate that genome-scale CRISPRa studies provide data which are complementary to those derived from LOF studies. Indeed, our CRISPRa studies validated that MM cell fitness can be enhanced by upregulation of some genes that are prominently essential for MM cells in LOF studies (e.g. IRF4, POU2AF1) but also identified many other promising, previously understudied, regulators of MM cell biology which are not essential for baseline survival/growth of MM cells but can induce growth when further activated. Upregulation of one or more MM driver genes is recurrently observed in MM clinical relapses, which raises intriguing hypotheses about how potential cooperative interactions between these genes contribute to the risk of MM relapses. More broadly, the results indicate that CRISPRa studies can provide novel insights into the biology of MM cells and identify previously understudied genes with therapeutic implications towards suppressing the transition of MM cells to states of advanced biological aggressiveness.

Liposarcomas (LPS) are mesenchymal cell malignancies that are diagnosed in more than 3500 patients in the US each year. LPS are characterized by DNA amplifications of MDM2 (100%) and CDK4 (90+%) genes on neochromosomes, generally with wild-type TP53. Management of metastatic or surgically unresectable LPS remains purely palliative. Recent clinical trials targeting MDM2 or CDK4/6 with small-molecule inhibitors have shown modest activity but have generally failed in phase 3 trials. The development of new therapeutics is greatly needed to improve outcomes for patients with LPS. To identify unique liposarcoma-specific vulnerabilities, we utilized the Dependency Map (DepMap) database to analyze gene-specific dependencies of 9 LPS cell lines as compared to non-LPS cancers and identified CSNK1A1 as one of the top essential genes preferentially for LPS. siRNA-mediated depletion of CSNK1A1 confirmed that CK1α negatively regulates p53 and is critical for the proliferation and survival of LPS cells. DepMap analyses identified CDK7 and CDK9 as two additional top LPS-essential genes. Three CDK9 inhibitors suppressed LPS cell growth and induced apoptosis by decreasing MDM2 levels while inducing expression of p53 and PUMA. The cytotoxic effects of CDK9 inhibitors were enhanced upon CK1α depletion. Furthermore, we quantitatively determined synergism between CDK7 and CDK9 inhibitors in LPS cells. These data led us to examine combined targeting of CK1α and CDK7/9 in LPS with the novel small molecule inhibitor BTX-A51, which has previously also been shown to inhibit CK1α, CDK7, and CDK9 with nanomolar potency in acute myeloid leukemia (AML) models. BTX-A51 treatment significantly reduced expression of MDM2 with marked induction of p53, resulting in increased PUMA expression and profound apoptosis of LPS cells. Through CRISPR/Cas9-mediated TP53 knockout, we established that BTX-A51-treatment-induced apoptosis was in part mediated in a p53-dependent manner, which was further validated transcriptionally using RNA-Seq based pathway analysis. BTX-A51 also reduced expression of the apoptosis inhibitor MCL1 and primed LPS cell lines and PDX-derived cells for BIM/PUMA-induced apoptosis. Importantly, in vivo experiments in two LPS PDX models demonstrated that BTX-A51 is well-tolerated with anti-tumor efficacy. These data have confirmed CK1α, CDK9, and CDK7 as essential for LPS cells and indicate that BTX-A51 has potent preclinical efficacy in LPS, through combined inhibition of CK1α, CDK9, and CDK7. Our data form the scientific foundation for our ongoing pilot clinical trial evaluating BTX-A51 in patients with advanced WDLPS or DDLPS (NCT06414434). Renyan Liu, Nicole L. Solimini, Caoibhne McSweeney, Patrick Bhola, Daryl Griffin, Tim B. Branigan, Michael J. Wagner, Audrey Turchick, Ricardo de Simoes, Joshua M. Dempster, Jie Hao, Xin Wang, Roshen Alharthi, Michael Yorsz, Shaili Soni, Cing-siang Hu, Irit Snir-Alkalay, Francisca Vazquez, Prafulla C. Gokhale, Constantine Mitsiades, Anthony Letai, Yinon Ben-Neriah, George D. Demetri, Geoffrey I. Shapiro. Therapeutic potential of combined targeting of casein kinase 1 alpha (CK1 alpha) and CDK7/9 with the inhibitor BTX-A51 in human liposarcomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2980.

Multiple myeloma (MM) is a plasma cell malignancy that disrupts bone homeostasis by suppressing osteogenesis and promoting osteoclast activity. While most therapeutic interventions to date have focused on targeting tumor cells and reducing osteolysis, we investigate whether osteoinductive strategies can restore bone formation and counteract disease progression. Using a human bone marrow-like scaffold model that enables direct in vivo evaluation of tumor-stroma interactions and human bone formation, we demonstrate that MM-derived mesenchymal stromal cells (MSCs) retain osteogenic potential but are functionally suppressed by MM cells. Transcriptomic profiling of MM-primed MSCs revealed the downregulation of small leucine-rich proteoglycans (SLRPs), ASPN, OGN, and OMD, key mediators of bone morphogenetic protein (BMP) signaling, which governs osteoblast differentiation. Among the BMPs analyzed, BMP6 emerged as a potent inducer of osteogenesis and regulator of the expression of these SLRPs. Notably, BMP6 selectively promoted bone formation without enhancing osteoclastogenesis and attenuated inflammatory and tumor-supportive MSC phenotypes. BMP6 also directly inhibited MM cell proliferation and suppressed IL6-induced growth. These findings highlight BMP6 as a distinct multifunctional regulator warranting further investigation as a potential therapeutic approach, while establishing the humanized model as a valuable platform for dissecting tumor-bone interactions in MM.

Ubiquitin is a small, highly conserved protein that acts as a posttranslational modification in eukaryotes. Ubiquitination of proteins frequently serves as a degradation signal, marking them for disposal by the proteasome. Here we report a novel small molecule from a diversity-oriented synthesis library, BRD1732, that is directly ubiquitinated in cells, resulting in dramatic accumulation of inactive ubiquitin monomers and polyubiquitin chains, which causes broad inhibition of the ubiquitin-proteasome system. Ubiquitination of BRD1732 and its associated cytotoxicity are stereospecific and dependent on two homologous E3 ubiquitin ligases, RNF19A and RNF19B, and their shared E2 conjugating enzyme, UBE2L3. Our finding opens the possibility for indirect ubiquitination of a target through a ubiquitinated bifunctional small molecule and more broadly raises the potential for posttranslational modification in trans.

Background - Methods: Hundreds of cell lines have been examined by genome-sale CRISPR knockout (KO) studies to identify functional drivers of tumors, but only few lines have been systematically examined with CRISPR activation (CRISPRa). We reasoned that CRISPRa studies can identify genes whose activation may suppress tumor cell fitness and serve as negative regulators of tumor cell survival/proliferation (NRSPs). To this end, we performed genome-scale CRISPRa studies (dCas9-P65-HSF transcriptional activation system; Calabrese sgRNA library, typically 6 sgRNAs/gene) in 10 genotypically diverse multiple myeloma (MM) cell lines at multiple timepoints and compared results with similar publicly available or in-house CRISPRa studies in other cancers (n=17; including lymphoma, leukemia, NSCLC, melanoma, ovarian, prostate cancer; with up to 3 lines per tumor type). Results: Based on multiple quantitative metrics, hundreds of genes were recurrently identified as NRSPs (e.g. 500 NRSPs in 5 or more lines). Integrated analyses of our CRISPRa data with molecular annotation for the respective MM lines did not identify any obvious bias in the CRISPRa-based detection of NRSP that relates to e.g., DNA copy number status; chromatin accessibility; or baseline levels of transcript expression. Top recurrent and pronounced NRSPs included several known proapoptotic genes (e.g., death receptors; caspases; BH3 only Bcl-2 family members) or other tumor suppressive molecules (e.g. CDK inhibitors), providing reassuring positive controls. Moreover, many previously unrecognized NRSPs were identified including transcription factors (TFs)/cofactors; chromatin remodeling genes and epigenetic regulators; signaling molecules regulating survival/proliferation; RNA binding proteins; nucleic acid-sensing and antiviral restriction factors; metabolic regulators; E3 ligases and other proteostatic regulators; DNA repair genes; solute carrier transporters, among others. No correlation is currently observed between the pattern of NRSPs for individual MM cell lines and their respective molecular subtype (e.g., t(4;14), t(14;16), t(11;14), etc). Some clusters of NRSPs are shared between MM and non-MM lines; and others are more prominent in MM, e.g. certain plasma cell-related TFs or endoplasmic reticulum regulators. We validated the functional relevance of several NRSPs with individual sgRNAs for CRISPRa; using focused sgRNA libraries for CRISPRa in MM cells in vitro and in NSG mice in vivo. We also performed single cell RNA-seq of MM cells transduced with pools of sgRNAs (CROP-seq) or bulk RNA-seq of MM cells transduced with individual sgRNAs for select NRSPs. Many NRSPs have undetectable/low transcript levels, without recurrent deletions/mutations, in MM cell lines or patient-derived MM cells but are expressed in healthy and malignant cells from different non-hematopoietic tissues. Thus, the NRSP effect of these genes in MM cells may involve lineage-inappropriate activation of molecular programs that are critical for, or at least tolerated in, other lineages but suppress MM cell fitness. Among the top 200 NRSPs with recurrent expression in MM cells, analyses of MMRF CoMMpass data revealed downregulation of ≥1 NRSPs in ~50% of paired MM samples after relapse vs. before treatment, suggesting that suppression of such NRSP genes can contribute, alone or in concert, to enhanced MM cell fitness in clinical relapses. Some NRSPs defined by CRISPRa are potent dependencies by CRISPR KO, including the MM-preferential, lineage-defining, TF PRDM1. CROP-Seq and bulk RNA-seq of MM cells with CRISPRa of PRDM1 (vs. other NRSPs or control sgRNAs) revealed that PRDM1 overexpression is toxic to MM cells by suppressing other dependencies, including IRF4, another lineage-defining TF and master MM dependency. Conclusions: Collectively,our studies have identified a large collection of NRSP genes whose overexpression suppresses tumor cell fitness in MM, with several of them also being relevant for other hematologic neoplasias. Several canonical tumor suppressor genes are also identified by CRISPRa, but most NRSPs have not been previously examined for their potential to suppress tumor cell fitness in MM or other neoplasias. Our study highlights the concept that engineered overexpression of NRSPs has intriguing potential for therapeutic applications in MM and other hematologic neoplasias.

We previously applied genome-scale CRISPR knockout studies in a range of cell lines to identify genes that are more potent and recurrent genetic dependencies for the growth and survival of individual cancer types compared with others (de Matos Simoes et al., Nature Cancer 2023). As part of this effort to determine dependencies with tumor type/lineage-selective roles, we identified 116 genes that are preferentially essential for multiple myeloma (MM) cells compared to other hematologic malignancies or solid tumors. Most of these MM-preferential dependencies were not previously considered therapeutic targets for MM or other neoplasias and, to our knowledge, have not been formally examined in drug screens that could identify ligands for these proteins and serve as lead compounds for potential therapeutic applications. Because of the large number of promising targets identified by our CRISPR studies and to accelerate potential therapeutic programs against those targets, the current study sought to identify and characterize “druggable” pockets in these MM-preferential dependencies and establish an in silico framework to nominate candidate small molecules for development of inhibitors or degraders. Protein identifiers and corresponding primary protein sequences were extracted from the UniProt database. Predicted structures were obtained both from the AlphaFold database and from our own local implementation of AlphaFold2 to cross-validate structural predictions. Each predicted structure was annotated based on AlphaFold-derived confidence scores, visualized through pseudo-GenBank files created with custom Biopython scripts and further annotated (Geneious software) to differentiate structured vs. unstructured protein regions. Experimental structures from the Protein Data Bank (PDB) were annotated for comparison. We observed that 82 of 116 target proteins have at least one domain structure in PDB. For the 34 proteins without any available experimental structures, AlphaFold/ AlphaFold2 predictions for structured regions were further evaluated. We developed a “b-factor filtering” script to isolate candidate structural regions predicted with over 50% confidence, adding three amino acids overhang to ensure robust selection of candidate regions. These filtered structures were analyzed using three pocket identification methodologies: AutoSite (AutoDock Vina suite), FPocket, and the Differential of Gaussians method. Combining these complementary approaches is intended to facilitate reliable identification of consensus pockets. Residues lining pockets with higher score and consensus between all pocket identification programs were used to derive target binding regions for small molecule docking by Autodock Vina. Among the 116 MM-preferential dependencies, 57 unique proteins harbored a total of 80 pockets with significant agreement of the primary sequences of the identified pockets and high druggability scores (≥0.5, based on normalized cumulative distribution function) across the different pocket identification programs. We proceeded by docking ~300,000 molecules from the NCI/DTP Open Chemicals Repository and ~3.6 million drug-like commercially available compounds to each of the best target identified regions. Known inhibitors for individual MM-preferential dependencies (e.g. the p300 inhibitor inobrodib) were also identified as putative binders to “druggable” pockets that were nominated in unbiased manner by our in silico pipeline: such observations further support the notion that our pipeline can reliably identify both ligand-binding pockets for “druggable” targets and compounds binding to those specific pockets. Further docking with expanded libraries is ongoing. Our study highlights the importance of machine learning-based pipelines for assessment of primary sequence regions, 3D structurally-defined binding pockets and their interaction with putative small molecule ligands to identify candidate binders to MM-preferential dependencies. Identification of structurally and functionally important binding regions provides essential groundwork for ongoing docking screens with large chemical libraries and innovative machine learning-based docking approaches that involve both existing compounds and synthetic datasets of theoretically plausible chemical space. We also envision that this pipeline can applied more broadly to nominate potential pharmacological modulators for critical dependencies in other neoplasias beyond MM.

Up to 50% of multiple myeloma (MM) patients harbor mutations of KRAS or NRAS. Pharmacologic inhibitors targeting specific KRAS mutations have clinical activity in solid tumors. We evaluated Ras inhibitors in preclinical models of MM, with emphasis on genome-scale CRISPR studies to define the molecular determinants of response and resistance to these agents. We studied selective inhibitors of KRAS G12C (MRTX-1257) or G12D (MRTX-1133) mutants; the broader spectrum mutant-KRAS inhibitor BI-2865; or the tricomplex pan-Ras inhibitor RMC-6236. These agents were active against human MM lines at clinically relevant concentrations comparable to those achieved in solid tumor patients. RMC-6236 is active against a spectrum of MM lines with KRAS or NRAS mutations, as well as wildtype lines, some of which have upstream lesions (e.g. FGFR3 mutations) that can activate Ras signaling. In vivo treatments (e.g. with MRTX-1257 or MRTX-1133 against diffuse lesions of XG-7 or KP-6 cells, respectively, after IV injections in NSG mice) were active, but tumor escape eventually ensued. Notably, tumor cells harvested at full-blown relapse after KRAS inhibitor treatment as well as cells from vehicle-treated mice were similarly responsive in vitro upon retreatment with the same KRAS inhibitor suggesting that the in vivo microenvironment contributes to relapse. DNA barcode-based clonal tracking studies showed lack of significant barcode enrichment in KRAS inhibitor-treated (vs. DMSO control) MM cells, suggesting that treatment escape was not primarily due to selection of pre-existing drug-resistant cells. Co-culture of human MM cells with bone marrow stromal cells attenuated their response to KRAS G12C inhibitors; and genetically engineered VQmyc (NRASQ61R) mouse MM cells respond to RMC-6236, though less so in the presence of IL-6, again suggesting that the microenvironment and its cytokines can affect sensitivity to Ras inhibitors. To functionally map the landscape of genomic lesions that can influence Ras inhibitor response, we conducted 36 genome-scale CRISPR knockout (KO) or activation (CRISPRa) screens across 11 genotypically diverse MM lines. These studies revealed a heterogeneous, cell line-specific landscape of gene perturbations influencing sensitivity and resistance to Ras inhibition, without obvious association between the patterns of CRISPR “hits” in individual MM cell lines and their respective molecular subtype (e.g. t(4;14), t(14;16), t(11;14)). CRISPR KO vs. CRISPRa studies with the same inhibitor in a given cell line yielded complementary and orthogonal results. For example, CRISPRa identified upstream surface receptors (e.g. EGFR, MET), ABCB1, KRAS itself, or positive regulators of Ras/MAPK signaling (e.g. SHOC2) among the top resistance-associated perturbations. In a complementary way, recurrent resistance-associated hits in CRISPR KO screens included diverse negative regulators of Ras/MAPK (e.g. LZTR1); NF-κB (e.g. TRAF3), or PI3K/Akt (e.g. PTEN) signaling; as well as the oxidative stress sensor KEAP1. Many gene perturbations were identified as hits in CRISPR studies across all 3 classes of Ras inhibitors, but others had distinct roles, e.g. PPIA (cyclophilin A) loss decreased MM cell responses to RMC-6236, consistent with this protein forming a complex with Ras and RMC-6236 to block downstream Ras signaling. Many hits from our screens were shared with those in publicly available CRISPR studies of KRAS inhibitors in solid tumor lines, while others were distinct for MM. Bulk RNA-seq of 6 Ras inhibitor-treated MM lines identified a transcriptional signature of genes concordantly up- or down-regulated across all MM lines tested: most of these genes, including some known Ras pathway inhibitors (e.g. SPRED1, DUSP6) were not prominent hits in our CRISPR studies, indicating that the most pronounced/recurrent regulators of response to these agents may not be directly inferred from transcriptional profiling. Our study reveals that MM cell responses to pharmacological Ras inhibitors can be governed by an interplay of non-genomic adaptations, microenvironment-derived cues, and a diverse and heterogeneous landscape of genomic perturbations. Notably, even MM cells with the same KRAS mutation can display distinct “resistomes”, highlighting the complex functional genomic landscape underlying Ras inhibitor responses. We envision that these results will inform personalized uses of Ras inhibitors in future clinical studies in MM.

Introduction: Therapies for the treatment of multiple myeloma (MM) are primarily based on targeting plasma cell biology, while precision medicine approaches have been disappointing. One exception has been venetoclax in t(11;14) MM. This success is based on the dependence of t(11;14) MM cells on BCL2 for survival. In previous studies we found that all 11 t(4;14)-positive MM cell lines and four out of five patient samples tested were resistant to venetoclax. In this study we set out to better define the dependence of t(4;14) MM on members of the BCL2 family and the role of NSD2 in this process. Methods: BH3 profiling was performed on 11 MM lines [10 t(4;14), one t(11:14)], and on KMS11 cells with a deletion in the NSD2 non-translocated allele (NTKO, NSD2 high) or the translocated allele (TKO, NSD2 low). Dynamic BH3 profiling (DBP) was performed by treating KMS11 or KMS11-NTKO cells for six days with 1 μM of the NSD2 inhibitor NSD2i-1(Excenen), followed by BH3 profiling. Cell death studies were performed by drug treatment for 24-48 h followed by Annexin V/propidium iodide staining and flow cytometry. For patient samples, cells were stained with Annexin V/anti-CD38 and -CD45. RNAseq was performed eight days post treatment with NSD2 inhibitor. Results: BH3 profiling of t(11;14)-positive KMS12PE demonstrated a pattern consistent with venetoclax sensitivity, with strong priming as indicated by cytochrome (cyto) C release induced by broad spectrum peptides, BIM, BID and PUMA, little to no release with the MCL1 binding peptide MS1 and little effect with HRK that binds specifically to BCLXL. In contrast the BCL2/BCLXL binding peptide, BAD, indicated priming. Since HRK did not induce cyto C release, the effect of the BAD peptide can be attributed to BCL2 priming. We next performed BH3 profiling on 10 t(4;14) cell lines. Based on response to broad spectrum peptides we observed a range of overall priming with OPM2, H929 and JIM1 showing the greatest priming (≥60% cyto C release with 100 nM BIM peptide). KMS34, KMS18 and KMS26 were the least primed (≤15% release with 100 nM BIM). Heterogeneity was also observed in the response to the more specific peptides. H929, JIM1 and XG7 were highly primed on MCL1(≥59% release with MS1 peptide) while KMS34 and OPM2 were BCLXL primed (≥67% release with HRK peptide). The remaining lines (KMS18, KMS28, KMS26, KMS11) did not show strong priming on a single BCL2 family member, however combining peptides demonstrated priming, indicating co-dependence on more than one BCL2 family member. To determine the role of t(4;14), we performed BH3 profiling on KMS11 NTKO and TKO cells. NTKO cells displayed a similar pattern of dependency as KMS11 parental cells. In contrast TKO cells showed increased overall priming as evidenced by cyto C release by BIM. This priming appears to be on BCLXL and possibly BCL2 as evidenced by increased cyto C release induced by BAD and HRK peptides. Consistent with these findings, TKO cells were found to be more sensitive to venetoclax, the BCL2/BCLXL inhibitor AZD4320 and the BCLXL PROTAC DT2216. To further validate these findings, we performed DBP on KMS11 and KMS11 NTKO to determine the effect of NSD2 inhibition on mitochondrial priming. Consistent with differences in priming between TKO and NTKO, NSD2 inhibition resulted in an increase in cyto C release by BIM (P=0.0574), BAD (P=0.0012) and HRK (P=0.0026) peptides, indicating a shift to BCLXL and possibly BCL2 dependence. To determine the potential mechanism for these changes, RNAseq analysis was performed following NSD2 inhibition in KMS11 cells. After eight days of treatment there was a total of 626 genes with significant changes in gene expression (FDR ≤ 0.01, fold change ≥1.5). BCL2L1, which encodes BCLXL was significantly upregulated (1.586 fold, FDR=1.7x10-25). Finally, we have previously demonstrated the value of ex vivo testing of venetoclax sensitivity in predicting clinical response. We have now tested 15 t(4;14) samples and surprisingly 5 of them demonstrated ex vivo sensitivity to venetoclax. Importantly the difference in sensitivity could not be explained by differences in chr1 gain/amp. Conclusions: Mitochondrial priming is heterogenous in t(4;14) MM which likely explains the lack of responses to venetoclax. However, inhibition or loss of NSD2 results in increased priming and sensitivity to BCLXL and possibly BCL2 inhibition raising the possibility of combination therapy for targeting this high-risk form of myeloma.

INTRODUCTION Mutated RAS, a key oncogenic driver in multiple myeloma (MM), can be present in up to 50% of primary MM. Several novel pharmacological inhibitors developed to target specific KRAS point mutations, have been successfully incorporated in the treatment of solid cancers, but their potential impact in MM has yet to be determined. Here, we performed genome-scale CRISPR studies to decipher the functional gene perturbation landscape of KRAS-mutated MM after treatment with specific KRAS inhibitors as well as the potential mechanisms of resistance to these molecules. METHODS We performed a total of 11 genome-scale CRISPR gene activation or CRISPR gene editing (knockout, KO) studies in 5 MM lines with distinct KRAS point mutations (KHM-1B and XG-7 [G12C]; KARPAS-620 and KP-6 [G12D]; or MM.1S [G12A]) after treatment with specific KRAS inhibitors (MRTX-1133, MRTX-1257, BI-2865, RMC-6236) in clinically achievable concentration ranges (as used in patients with solid tumors). Potency and mutant-specific in vitro activity of these inhibitors in the respective cell lines was assessed with bioluminescence assays and flow cytometry and confirmed to be similar to KRAS-mutant lines from our own in vitro studies (eg. MIA PaCa-2 [G12C]) as well as from other groups. Functional downstream abrogation of KRAS-dependent growth and survival pathways via MEK/MAPK and PI3K/AKT was determined by immunoblotting to confirm dephosphorylation of ERK and/or AKT. The level of apoptosis induction was assessed with flow cytometry after AnnexinV/PI staining. Cell cycle analyses were performed using DAPI staining. Furthermore, we performed RNA sequencing in 6 KRAS mutant cell lines to investigate their transcriptomic response to KRAS-inhibitor treatment. RESULTS Our genome-scale CRISPR activation and KO studies in 5 MM lines with mutant KRAS (G12A/G12C/G12D) revealed a heterogeneous and cell line-specific pattern of gene perturbations regulating response vs. resistance to KRAS inhibition. CRISPR activation screens identified positive regulators of RAS-MAPK signaling (eg. SHOC2) and upstream surface receptors (eg. EGFR) among the highest scoring genes involved in resistance to KRAS inhibitors. Interestingly, CRISPR activation of KRAS itself was identified as a promoter of resistance, possibly due to reduced target saturation. Notably, many of the top “hit” genes are not recurrently genetically altered or differentially expressed in MM nor do they represent clinical “high-risk” features. In turn, CRISPR gene KO studies pointed to a key role of regulators of cell homeostasis (eg. KEAP1, NF2L1) in conferring KRAS inhibitor sensitization vs. resistance. Moreover, genes encoding for GTPase-activating proteins (GAPs) and negative regulators of RAS/MAPK signaling (eg. LZTR1) were functionally important. Some of the genes identified here are not typically expressed in newly diagnosed MM (eg. EGFR), but their upregulation during KRAS inhibitor treatment may be a candidate biomarker for decreased response or early relapse. We examined the RNA sequencing profile of 6 KRAS-mutant MM lines after short-term treatment with specific KRAS inhibitors. Transcriptional responses to KRAS inhibition were also heterogeneous between cell lines, and included downregulation of RAS effector genes (eg. ETV4, ETV5) and negative regulators of RAS signaling (eg. DUSP6, SPRY4), pointing to a RAS-driven negative feedback loop. Other recurrently differentially expressed genes include cell cycle regulators (e.g., D-type cyclin) and genes involved in apoptosis and DNA repair. Upregulation of gene expression was predominant in genes involved in cell cycle arrest and stress responses. CONCLUSION Our genome-scale CRISPR activation and knockout studies revealed a heterogeneous landscape of genomic perturbations regulating the response vs. resistance of MM cell to mutant-specific KRAS inhibitors. These compounds exhibit potent and specific activity against MM cells with the respective KRAS mutations, but our functional studies point to individual lines, even those harboring the same KRAS mutation, exhibiting their own distinct “resistome” against these inhibitors. These results underscore the complex functional genomics of MM cell sensitivity vs. resistance to KRAS inhibitors and have implications for the choice of potential combination partners of these inhibitors in future preclinical or clinical studies.

The therapeutic targeting of tumor cells with loss of function (LOF) for tumor suppressor genes (TSGs) is challenging across cancers, including hematologic neoplasias, because pharmacological mechanisms to restore the function(s) of such genes are not readily feasible, in contrast to e.g., inhibition of oncogenic drivers. We reasoned, however, that, although LOF for TSGs leads to de-repressed growth of neoplastic hematopoietic cells, it may not necessarily protect them from immune attack. We thus explored the hypothesis that CRISPR-based loss of function of TSGs in cells from multiple myeloma (MM), leukemias or lymphoma may still be associated with substantial response to immune effector cells such as NK cells, which have the advantage to kill tumor cells across HLA barriers. We have conducted CRISPR-based studies in 7 cell lines that represent different hematologic malignancies and different levels of sensitivity to natural killer (NK) cells, namely the B-cell lymphoma (SUDHL4), precursor B cell acute lymphoblastic leukemia (NALM6), multiple myeloma (MM1.S, LP1, KMS11), chronic myeloid leukemia (K562), and acute myeloid leukemia (MOLM14). In these genome-scale or focused CRISPR screens for LOF (CRISPR-based gene editing) or gain of function (GOF, CRISPR activation), the blood cancer lines were exposed to allogeneic donor-derived NK cells (vs. control cultures without NK cells). We evaluated the performance of genes known to represent recurrent TSGs based on genomic data of patient samples or cell lines; as well as candidate TSGs, based on results from genome-scale CRISPR gene editing screens (e.g., CERES or CHRONOS scores >0.4 in multiple DepMap releases and TPM>1 [RNA-seq]) in the same cell lines as the NK cell resistance screens. These analyses sought to identify any TSGs whose LOF may potentially alter the response of blood cancer cells to NK cells. We also evaluated genes identified as top recurrent TSGs in patient samples from MM and other hematologic neoplasias (e.g., based on prior genomic studies). On aggregate, we evaluated a collection of known and recurrent TSGs (e.g., PTEN, TP53, RB1, CDKN2C, CDKN1B, TENT5C/FAM46C) as well as other, previously underappreciated candidate genes with TSG properties in hematologic neoplasias (e.g., HIF1A, DEPDC5). Perturbation of none of these genes was identified to meet criteria for association with significant resistance to allogeneic donor-derived NK cells (e.g., log2FC>1.0, at least 3-4 sgRNAs with enrichment upon CRISPR KO or depletion with CRISPR activation, p-value <0.05, enrichment rank <100, based on rank aggregation algorithm) in any of the MM, leukemia or lymphoma cell lines examined in LOF or GOF CRISPR screens for NK cell resistance. In fact, for a limited set of cases (e.g., PTEN in KMS11 cells), KO of a TSG was associated with sensitization to NK cell treatment. To probe the mechanistic basis for this lack of effect of TSG loss, we examined the molecular sequelae of CRISPR-based KO for one of these recurrent TSGs, PTEN, by performing scRNAseq using the CROP-seq platform. Pools of MM1.S and LP1 cells expressing sgRNAs targeting select hits from our CRISPR screens and also PTEN were co-cultured with NK cells for 24 h or left untreated, followed by scRNA-seq and sgRNA detection, differential gene-expression analysis. We observed in these studies that the transcriptional changes induced in MM cells by KO of PTEN included limited, if any, changes in the expression of key genes involved in regulation of NK cell responses of tumor cells, e.g., ligands for activating or inhibitor receptors in NK cells, death receptors (e.g., TRAIL, Fas) or their downstream effectors/regulators or other molecules identified from our aforementioned genome-scale and focused CRISPR LOF and GOF studies. Notably, our in-house results from studies of NK exposure of cell lines from hematologic neoplasias are concordant with results for these TSGs in CRISPR screens of non-hematological cancer cells treated with cytotoxic T-cells (e.g., 4T1 or RENCA cells; GSE149933). Overall, our observations indicate that MM, leukemia or lymphoma cells that become deficient for diverse TSGs based on CRISPR-based gene editing are equally responsive to NK cells as their TSG-proficient counterparts. NK cell-based therapies may thus be a promising approach to target TSG-deficient hematologic malignancies for which specific pharmacological therapies are not currently available.

Cancer cells can evade natural killer (NK) cell activity, thereby limiting anti-tumor immunity. To reveal genetic determinants of susceptibility to NK cell activity, we examined interacting NK cells and blood cancer cells using single-cell and genome-scale functional genomics screens. Interaction of NK and cancer cells induced distinct activation and type I interferon (IFN) states in both cell types depending on the cancer cell lineage and molecular phenotype, ranging from more sensitive myeloid to less sensitive B-lymphoid cancers. CRISPR screens in cancer cells uncovered genes regulating sensitivity and resistance to NK cell-mediated killing, including adhesion-related glycoproteins, protein fucosylation genes, and transcriptional regulators, in addition to confirming the importance of antigen presentation and death receptor signaling pathways. CRISPR screens with a single-cell transcriptomic readout provided insight into underlying mechanisms, including regulation of IFN-γ signaling in cancer cells and NK cell activation states. Our findings highlight the diversity of mechanisms influencing NK cell susceptibility across different cancers and provide a resource for NK cell-based therapies.

S244the first one with significantly shorter PFS and OS (PFS HR 2.15, CI 1.79-2.58p< 0001, OS HR 2.37, CI 1.86-3.02p< 0.0001).The worse prognostic impact was observed even in the absence of both amp1q and del17p, observed in 38/249 (15%) pts (PFS HR 2.57, CI 1.79-3.70p< 00001, OS HR 2.49, CI 1.57-3.94p=0.0001), compared to 494pts.The risk of relapse defined by the presence of ≥1 ER-CNAs was independent from those conferred both by R-IIS 3 (HR=1.51CI 0.17-2.39-2.2;p=0.01) and by low quality (< stable disease) first-line best clinical response (HR=2.59CI0.33-2.84p=0.004).Notably, the type of induction therapy was not descriptive in this multivariate model, suggesting that ER is strongly related to pts' baseline genomic architecture, and not to induction therapy they were provided to.Conclusions: ML approach allowed to define CNAs-specific dynamic clonality cut-offs, improving the CNAs calls' accuracy to identify MM pts with the highest probability to ER.The use of these ER-related cut-offs pinpointed few CNAs, whose presence at baseline is highly predictive of ER, including amp2p, del2p, del12p, and del19p, whose biological role in MM needs further investigations.As being outcome-dependent, the coMMsol method is dynamic and might be adjusted according to the selected outcome variable of interest (e.g.MRD-negativity) thus providing outcome-specific clonality cut-offs.

Mutations of Cereblon ( CRBN) or members of its pathway (CRBN-pathway genes, CRBNPGs) have been reported to exhibit increased frequency in multiple myeloma (MM) patients (pts) after relapse from regimens containing immunomodulatory thalidomide derivatives (IMiDs)±Dex. However, it is unclear if these mutations cause complete loss-of-function (LOF) of CRBNPGs in a way that can explain these relapses, given that, in preclinical studies, MM cells with only partial CRBN LOF retain substantial IMiD responsiveness. We therefore performed analyses integrating whole exome sequencing, RNA sequencing, copy number variation (CNV) and structural variant data of pts in the MMRF CoMMpass study who were treated with IMiD-based therapies. We included all pts with ≥2 paired bone marrow samples before and after an IMiD-based treatment (lenalidomide [LEN], thalidomide [T], pomalidomide [POM]) (baseline and relapse samples) and analyzed the patterns of molecular lesions of 42 CRBNPGs (based on Sievers et al. Blood 2019 and Jones et al. Leukemia 2021). To assess for alternative causes of relapse, a set of 42 known MM driver genes (defined in Ansari-Pour et al. Blood 2023) was analyzed in parallel. 177 pts of the IA22 CoMMpass dataset fulfilled inclusion criteria; 172, 89, and 15 pts received LEN-, POM-, or T-based treatment, respectively. 30 pts (16.9%) had at least one mutated CRBNPG at any timepoint (pts mut) and overall 24 CRBNPGs were mutated in at least one pt at any time. In 19 pts (63.3%), a CRBNPG mutation was present already at baseline; 11 pts (36.7%) developed a new CRBNPG mutation at relapse; and 2 pts (6.7%) had >1 CRBNPG mutated (2 and 5 genes, respectively). FAM83F (n=4; 12.5%) and IKZF3 (n=3; 9.4%) were the CRBNPGs most frequently affected. CRBN was mutated in 2 pts (6.7%) but without CNV loss, while full-length gene transcript was detected in both pts. In 4 cases, CRBN was proximal to chromosomal translocation breakpoints, but no mutations or absence of wild-type CRBN transcript were observed. In addition, no fusion transcripts involving a CRBNPG were detectable (at fusion fragment per million [FFPM]>5). Interestingly, when we examined an additional set of IA22 pts who did not undergo IMiD-treatment (n=14), the frequency of CRBNPG mutations observed (n=2, 14.3%; SALL4 and DDB1) was similar to the IMiD-treated pts. An increase in variant allele frequencies (VAF) of mutations for any CRBNPG occurred in 12 pts (37.5% of pts mut), however, VAF did not exceed 0.5. A preexisting mutation was no longer detectable at relapse in 8 pts (26.7%) and 3 pts showed a decline in VAF. Assessment of other known MM “driver” genes as possible explanation of relapse revealed mutations in 24 pts (80.0% of pts mut) at relapse: VAF of these genes showed an overall heterogeneous picture, with KRAS, BRAF, ARID1A, TP53 and IRF4 being among the most recurrently affected genes, consistent with their universal MM driver gene function. 10 of 11 pts (90.9%) who developed a new CRBNPG mutation and 10 of 12 pts (83.3%) with VAF increase showed partial response or better to IMiD treatment vs. 3 pts without clinical response (1 stable disease, 2 progressive disease). Only 2 of 19 (10.5%) pts with a CRBNPG mutation present at baseline were non-responsive to IMiD treatment. While 4 pts had a combined loss of one CRBNPG allele and mutation of another (for genes COPS8, COPS7B, DEPDC5, FAM83F), only 2 of these combined events occurred at relapse and with VAF values <0.4, suggesting that complete LOF of these genes was not present in a substantial fraction of the MM cell population at relapse. Most pts received combination treatment of IMiDs+proteasome inhibitor (PI), but we observed that preclinical CRISPR KO of CRBNPGs did not cause sensitization (or resistance) to PIs, suggesting that the genomic results for CRBNPGs in clinical samples were conceivably not skewed by PI use, e.g. via elimination of cells with complete LOF of CRBNPGs. Overall, in this integrated analysis, a minority of MM pts relapsing from IMiD-based treatments harbor genomic defects of CRBNPGs but these do not involve complete LOF for any such gene. Notably, new/enriched mutations in other genes, beyond CRBNPGs, with known roles as MM drivers cannot be excluded as alternative explanations for these relapses. Additional and more complex, genomic or non-genomic, mechanisms may account for relapse from IMiD-based regimens.

Oncogenic RAS mutations are frequent genomic drivers in hematologic neoplasias such as multiple myeloma (MM) but are traditionally considered non-“druggable”. Novel specific inhibitors of G12C and G12D KRAS mutants are clinically active in solid tumors, but their impact on KRAS-mutant lymphoid malignancies has not been explored. Here, we examined the activity of these inhibitors against G12C/D KRAS-mutant cells from MM, B-ALL and T-ALL; the functional sequelae of pharmacological KRAS inhibition; and the potential mechanisms of resistance to these agents. We assessed the activity of novel G12C- (MRTX1257) and G12D- (MRTX1133) specific pharmacological KRAS inhibitors against 2 MM lines with G12C (KHM1B, XG7) and 2 with G12D (KARPAS620, KP6) KRAS mutations; as well as the G12D mutant KOPN-8 (B-ALL) and CCRF-CEM (T-ALL) lines. KRAS inhibitors were tested at concentration ranges achievable in patients with solid tumors. Potent and mutant-specific activity was achieved with both of these KRAS inhibitors in their respective lines, with IC50 in the range of 5-150 nM after 3-7d treatment. This level of in vitro activity was similar to KRAS-mutant lines (e.g., in our in vitro studies and also in the literature) from solid tumors that are clinically responsive to these agents. Furthermore, the KRAS inhibitors effectively abrogated downstream MAPK and AKT pathway activation (e.g. phosphorylation of ERK or AKT by immunoblotting). MRTX1133 led to potent induction of cell death of KARPAS620, KP6 and KOPN8 cells (flow cytometry for Annexin V/PI staining) while MRTX1257 induced cell death in KHM1B and had a predominantly cytostatic effect on XG7 cells. Combinations of MRTX1257 with clinically established anti-MM drugs (incl. melphalan, bortezomib, pomalidomide, trametinib) caused in some cases supra-additive effects, but - more importantly - showed no antagonism with any combination. RNA sequencing revealed that KRAS inhibitor treatment downregulated RAS effector genes (e.g., ETV4, ETV5), and negative regulators of RAS signaling (e.g., DUSP6, SPRY4 reflecting a RAS-driven negative feedback loop controlling expression of these genes), and molecules involved in cell cycle regulation (e.g., D-type cyclin), anti-apoptosis, and DNA replication/repair. Conversely, KRAS inhibition upregulated genes involved in cell cycle arrest and stress responses. Importantly, we performed genome-scale CRISPR activation studies in 4 MM lines with G12C/G12D mutant KRAS to identify genes whose gain-of-function (GOF) enhances or suppresses MM cell response to the respective KRAS inhibitors. Top “hits” from these screens were validated with individual sgRNAs. CRISPR activation of mutant KRAS itself promotes MM cell escape from treatment, likely due to stoichiometric reasons and decreased target saturation at a given drug concentration. Notably, CRISPR activation of positive regulators of RAS-MAPK signaling (e.g., SHOC2), and upstream surface receptors (e.g., EGFR) were among top genes mediating KRAS inhibitor resistance. Beyond KRAS itself, these top “hit” genes are typically not recurrently mutated, amplified, differentially expressed (e.g., compared with normal plasma cells) in MM and are not associated with “high-risk” clinical behavior. Some (e.g., EGFR) of the genes identified from these CRISPR studies are not typically expressed in MM cells at diagnosis, thus these results outline how ectopic expression (e.g., EGFR) or overexpression of these “hit” genes could represent candidate biomarkers for decreased response or early relapse in future clinical studies of KRAS inhibitors in KRAS-mutant MM. Ongoing studies are addressing how these genomic perturbations may affect the in vivo response of KRAS-mutant cells to KRAS inhibitors alone or in combinations with established therapies. Our study documents that novel KRAS G12C and G12D inhibitors exhibit potent and mutant-specific activity against MM and other lymphoid malignant cells at dose levels that are clinically achievable in solid tumor patients. This study also provides functional evidence about genes and molecular pathways that regulate MM cell sensitivity vs. resistance to KRAS inhibitors. These results create a framework for ongoing and future efforts to translate the use of KRAS inhibitors into clinical studies for MM and other lymphoid malignancies.

Introduction: Extensive research has been conducted to elucidate the molecular mechanisms underlying the pathophysiology of Multiple Myeloma.The role of BMPs (Bone Morphogenetic Proteins) and their downstream gene expression regulators, such as Smad (Small Mother Against Decapentaplegic) transcription factors in the molecular-level imbalance of bone metabolism remains uncertain.Most studies were conducted in preclinical models which did not account the effect of the human microenvironment.In our study, we investigated the transcriptional levels of BMP-2, BMP-6 and Smad6 in human bone marrow aspirates.Methods: Our study included 39 patients (19 newly diagnosed patients, 20 refractory/relapsed patients) and 10 controls (patients with other non-Hodgkin lymphomas without bone marrow infiltration).RNA isolation of bone marrow mononuclear cells was conducted using the Omega Biotek® EZNA Total RNA kit, and RNA quantification by using a Nanodrop2000® spectrophotometer.cDNA synthesis was performed using the QuantiTect® Reverse Transcription Kit (50), Qiagen®.The primers for BMP-2, BMP-6, and Smad6 were designed by Qiagen®.B2-microglobulin was the reference gene.The RT-PCR was conducted using the QuantiNova SYBR Green PCR kit from Qiagen®.We validated our PCR products using gel electrophoresis and melt curves.PCR reactions were conducted in duplicate.Differential expression was calculated using the ΔΔCt method and graphs were created using the GraphPad Prism program.Results: There was no significant difference in gene expression of BMP-2 between controls and newly diagnosed patients (p=ns), and relapsed patients (p=ns).On the other hand, BMP-6 was found to be increased by 4.8 times in newly diagnosed patients (p = 0.0004) and 2.56 times in relapse patients (p = 0.0226).Finally, for Smad6, no statistically significant difference was found between controls and newly diagnosed patients (p = 0.2861), but it decreased by 1.5 times in relapsed patients (p = 0.012).Conclusions: BMPs and, to a lesser extent, Smads have been investigated in the past as potent regulators of bone metabolism in Multiple Myeloma, mostly in preclinical setting.Our findings in bone marrow aspirates of myeloma patients suggest that BMP-2 did not show a significant difference between controls and patients, indicating that it may not have a significant impact on bone metabolism.Conversely, the increased BMP-6 levels may indicate either increased fixation in the matrix and an inability to act through a positive feedback mechanism or involvement in the induction of genes that inhibit apoptosis.Smad6 expression is reduced in R/R patients, suggesting that MM cells have developed mechanisms of dependence on BMPs and induces Smad6 repression through alternative signaling pathways or inhibitors.The role of BMPs and Smads still appears unclear; further experiments may elucidate their role on signaling pathways in MM.