Raab, Marc S.

Abstract Studies have reported conflicting findings regarding the contribution of germline variants or somatic genomic drivers to racial disparities in multiple myeloma. To comprehensively investigate somatic drivers in relation to inherited genetics in multiple myeloma, we combined newly sequenced whole-genome sequencing data with publicly available datasets (total n = 1,286). Overall, we did not identify germline or somatic genomic differences that explain the different risk of developing multiple myeloma between patients with genetic similarity to African (AFR) or European (EUR) reference populations. A difference in the detectability and timing of APOBEC-associated and germinal center mutational activity was observed. Integrating epidemiologic data and mutational signature-based temporal estimates, we challenge the assumption that individuals in the AFR group develop multiple myeloma at a younger age. Finally, we demonstrate that, with equal access to efficacious therapies, patients in the AFR and EUR groups have equivalent clinical outcomes. Significance: Multiple myeloma is reported to occur at higher rates in individuals who self-identify as non-Hispanic Black. In this large dataset, genomic drivers occur at the same rate among ancestry groups, except for APOBEC mutagenesis. With equivalent therapy, clinical outcomes did not differ for patients grouped by genetic ancestry similarity.

Abstract Background: Bispecific T cell engagers (BTCEs) have transformed the treatment of multiple myeloma (MM) and B cell malignancies by redirecting cytotoxic T cells toward malignant targets. While clinical responses can be deep and durable, the immunological determinants of BTCE efficacy, and mechanisms of eventual immune escape, remain poorly understood. Regulatory T cells (Tregs), enriched early in MM, are classically immunosuppressive, but whether they are modulated by BTCEs and contribute to therapeutic outcomes has not been systematically investigated. Methods We performed longitudinal single-cell RNA/TCR/ATAC-sequencing and immune profiling of 17 newly diagnosed multiple myeloma (NDMM) patients across induction therapy. 11 patients were treated with teclistamab plus daratumumab, lenalidomide, and dexamethasone (Tec-DRD, n = 11) in the ongoing MajesTEC-5 (HD10/DSMMXX) trial (NCT05695508). As controls, patients receiving standard-of-care DRD (n = 6) were included. Functional validation used newly established NDMM plasma cell-T cell co-cultures, real-time killing assays, cytokine ELISAs, intracellular flow cytometry and arrayed quantitative PCRs of key T cell signaling pathways. Mechanistic studies employed CRISPR-mediated perturbations and combination treatments with IMiDs. Generalizability was tested using three BTCEs in vitro: teclistamab (BCMA×CD3), blinatumomab (CD19×CD3), and glofitamab (CD20×CD3) to redirect primary human Tregs towards MM (MM.1S), lymphoma (Raji), and B-ALL (NALM-6) cell lines. Non-targeting BTCEs as well as anti-CD3 antibodies were used as controls. Preclinical in vivo studies used an immunocompetent, transplantable Bcma+ Vk*MYC tumor model treated with murinized BTCE surrogates and IMiDs. Results BTCE therapy induced robust transcriptional and epigenetic reprogramming of bone marrow Tregs into a FOXP3lowIL2RAlow cytotoxic phenotype (“exTregs”) expressing NKG7, GZMA/B, TBX21, and CCL5. In contrast, this Treg phenotype conversion was absent in DRD-treated controls. Although comprising only ~2% of marrow-infiltrating T cells, exTregs mediated direct plasma cell lysis in vitro, strictly dependent on BTCE-mediated antigen-specific synapse formation and not recapitulated by CD3 stimulation alone. IMiDs synergized with BTCEs by destabilizing the suppressive Treg phenotype via Helios (IKZF2) downregulation, augmenting granzyme and cytokine production. Single-cell ATAC-seq of Tregs isolated from Tec-DRD-exposed trial participants revealed increased chromatin accessibility at AP-1 motifs and we confirmed BATF/JUNB-driven AP-1 signaling as a central transcriptional axis orchestrating Treg effector conversion in vitro. This cytotoxic reprogramming was conserved across BTCEs targeting BCMA, CD19, and CD20, and validated in immunocompetent mouse models, indicating a generalizable, BTCE-driven mechanism of Treg repurposing, rather than target- or engager-specific biology. Conclusion Our findings uncover a novel mechanism by which BTCEs actively convert suppressive Tregs into cytotoxic, pro-inflammatory effectors that contribute to tumor clearance. We identified the BATF/JUNB transcriptional axis and IMiD-mediated Helios destabilization as targetable regulators of this process. These results redefine Treg function under BTCE pressure, support rational combination regimens with IMiDs, and suggest exTreg signatures as biomarkers and therapeutic targets to enhance T cell-redirecting immunotherapies across hematological malignancies.

PURPOSE This analysis aimed to contextualize the symptoms of peripheral neuropathy (PN) experienced by patients with triple-class refractory (TCR) multiple myeloma (MM) treated with elranatamab in the MagnetisMM-3 clinical trial with those experienced by patients with TCR MM receiving a new line of physician's choice of therapy in the real-world MagnetisMM-14 study. METHODS MagnetisMM-3 included 123 patients and MagnetisMM-14 included 35 who had TCR MM and were enrolled under the same eligibility criteria. Patient-reported symptoms of PN were collected monthly using the Chemotherapy-Induced Peripheral Neuropathy-20 item (CIPN20) questionnaire and compared for clinically meaningful differences across sensory, motor, and autonomic domains. Higher scores indicated worse patient-reported PN symptoms. RESULTS Baseline demographics and clinical characteristics for patients in MagnetisMM-3 and MagnetisMM-14 were generally similar. Patients in MagnetisMM-3 had a median of five ( v four in MagnetisMM-14) prior lines of therapy. In MagnetisMM-14, common index therapies included elotuzumab + pomalidomide (23%), carfilzomib + pomalidomide (19%), or carfilzomib + cyclophosphamide (19%), all with corticosteroids. Mean scores for sensory (15.8 v 14.8) and motor (14.4 v 12.2) domains of the CIPN20 were similar between MagnetisMM-3 and MagnetisMM-14 at baseline. Mean scores for the autonomic domain were 14.0 versus 21.3 at baseline, respectively. No clinically meaningful differences for any domain were found between month 6 scores or changes in scores from baseline to month 6 between MagnetisMM-3 and MagnetisMM-14 populations. CONCLUSION These findings suggest that elranatamab treatment was not associated with a worsening of patient-reported PN symptoms across the sensory, motor, and autonomic domains of the CIPN20. Moreover, PN symptoms reported with elranatamab treatment were similar to those reported with other common therapies used in real-world clinical practice for the management of MM. MagnetisMM-3 ClinicalTrials.gov identifier: NCT04649359 .

Abstract Monoclonal antibodies targeting CD38 are a therapeutic mainstay in multiple myeloma (MM). Although they have contributed to improved outcomes, most patients still experience disease relapse, and little is known about tumor-intrinsic mechanisms of resistance to these drugs. Antigen escape has been implicated as a mechanism of tumor-cell evasion in immunotherapy. Yet, it is unknown whether MM cells can develop permanent resistance to anti-CD38 antibodies by acquiring genomic events leading to biallelic disruption of the CD38 gene locus. Here, we analyzed whole-genome and whole-exome sequencing data from patients 701 newly diagnosed MM, 67 patients at relapse with naivety to anti-CD38 antibodies, and 50 patients collected at relapse after anti-CD38 antibodies. We report a loss of CD38 in 10 of 50 patients (20%) after CD38 therapy, 3 of whom exhibited a loss of both copies. Two of these cases showed convergent evolution in which distinct subclones independently acquired similar advantageous variants. Functional studies on missense mutations involved in biallelic CD38 events revealed that 2 variants, L153H and C275Y, decreased binding affinity and antibody-dependent cellular cytotoxicity of the commercial antibodies daratumumab and isatuximab. However, a third mutation, R140G, conferred selective resistance to daratumumab, while retaining sensitivity to isatuximab. Clinically, patients with MM are often rechallenged with CD38 antibodies after disease progression and these data suggest that next-generation sequencing may play a role in subsequent treatment selection for a subset of patients.

Despite significant improvements in survival of patients with multiple myeloma (MM), outcomes remain heterogeneous, and a significant proportion of patients experience suboptimal outcomes. Importantly, traditional prognostic factors based on data from patients treated with older therapies no longer capture prognosis accurately in the contemporary era of novel triplet or quadruplet therapies. Therefore, risk stratification requires refinement in the context of available and investigational treatment options in routine practice and clinical trials, respectively. The current identification of high-risk MM (HRMM) in routine practice is based on the Revised International Staging System, which stratifies patients using a combination of widely available serum biomarkers and chromosomal abnormalities assessed via fluorescence in situ hybridization. In recent years, a substantial body of evidence concerning additional clinical, biological, and molecular/genomic prognostic factors has accumulated, along with new MM risk stratification tools and consensus reports. The International Myeloma Society, along with the International Myeloma Working Group, convened an Expert Panel with the primary aim of revisiting the definition of HRMM and formulating a practical and data-driven consensus definition, based on new evidence from molecular/genomic assays, updated clinical data, and contemporary risk stratification concepts. The Panel proposes the following Consensus Genomic Staging (CGS) of HRMM which relies upon the presence of at least one of these abnormalities: (1) del(17p), with a cutoff of >20% clonal fraction, and/or TP53 mutation; (2) an IgH translocation including t(4;14), t(14;16), or t(14;20) along with 1q+ and/or del(1p32); (3) monoallelic del(1p32) along with 1q+ or biallelic del(1p32); or (4) β2 microglobulin ≥5.5 mg/L with normal creatinine (<1.2 mg/dL).

Abstract Accurate prediction of risk of progression from smoldering (SMM) to active multiple myeloma (MM) is paramount to individualized early therapeutic strategies with minimum risk of overtreatment. Current risk stratification models do not account for evolving biomarker trajectories. We assembled the largest cohort to date of 2,270 SMM patients from six international centers with longitudinal clinical and biological data to train and validate the PANGEA 2.0 risk models. Four evolving biomarkers were significantly associated with shorter time-to-progression: M-protein increase ≥0.2g/dL, involved:uninvolved serum free light chain ratio increase ≥20, creatinine increase >25%, and hemoglobin decrease ≥1.5g/dL. PANGEA 2.0 outperforms established models including the 20/2/20 and IMWG models by more accurately predicting progression (C-statistics=0.69–0.84), even without biomarker history (C-statistics=0.69–0.83) or recent bone marrow biopsy. PANGEA 2.0 is an easy-to-use, open-access tool (https://ghobrial.shinyapps.io/pangea_2_calculator) to improve and individualize SMM risk stratification. Validation tools are available to compare PANGEA 2.0 to established models (https://ghobrial.shinyapps.io/pangea_validation).

Introduction Monoclonal antibodies (MoAbs) directed against CD38 are a mainstay in multiple myeloma (MM). Downregulation of CD38 expression is a resistance mechanism of antigenic escape, however, cell surface expression may return. While this tumor cell plasticity is likely driven by epigenetic mechanisms, it is unknown if MM cells can develop permanent resistance to anti-CD38 MoAb by acquiring genomic events leading to biallelic disruption of CD38 thus precluding re-treatment. Here, we report biallelic loss of CD38 as a recurrent mechanism of resistance, often via convergent evolution where distinct subclones are advantaged towards the same selective pressure. Methods We interrogated two sets of patients (Mayo Clinic; n = 31, Calgary University; n =19) with relapse following anti-CD38 MoAbs. Two additional cases of interest were selected from Heidelberg University. Whole genome sequencing (WGS, 60-100x) or whole exome sequencing (WXS), flow cytometry (flow), and bulk RNAseq were used to validate the effect of genomic events on CD38. Missense mutations predicted to affect CD38 MoAb binding were validated with site-directed mutagenesis of a CD38 plasmid, transduction into K562 MM cells, and CD38 MoAb binding assays. Results The prevalence of biallelic inactivation of CD38 at anti-CD38 MoAb relapse was 6% (3/50). An additional selected biallelic event was noted in the Heidelberg set. Each case is described in detail: MM-19 (Calgary) received daratumumab (Dara), lenalidomide, and dexamethasone (Dex), and then had a second exposure to Dara, following relapse. At this second relapse, WGS was performed on two independent extramedullary disease localizations in the liver. Both samples had unique, large deletions (&gt;3 Mb) on chromosome 4 coupled with additional unique focal deletions involving CD38. Flow confirmed the presence of CD138+/CD38- plasma cell populations in both sites. A second case of convergent evolution was seen in a patient (Mayo) after seventh-line Dara/Len/Dex. WGS revealed a common deletion encompassing CD38. On the remaining allele, one subclone had a structural variation-mediated loss of CD38(cancer cell fraction; CCF 40%) while another harbored an L153H missense mutation (CCF 60%). Flow confirmed presence of two CD138+ clones; one with diminished CD38 expression. L153H was functionally validated to abrogate Dara binding, while maintaining CD38 expression. The third sample was collected after fifth-line Dara and sixth-line Dara/Bortezomib/Dex (Heidelberg) and represents another case of convergent evolution with a common loss of chromosome 4 and two subclones detected by WGS (P98Lfs*12, CCF 39% resulting in biallelic inactivation; R140G, CCF 61%). Bulk RNAseq revealed, in comparison to NDMM from CoMMpass, CD38 expression below the first decile. Notably, the R140G variant reduced binding affinity to Dara but not to Isatuximab (Isa). A fourth case (Mayo) relapsing after 6 months of Dara-based therapy similarly had a focal deletion of CD38 with an L18Sfs*16 frameshift deletion on the remaining allele associated with a 4.35-fold reduction in CD38 expression by RNAseq compared to pre-treatment. Interestingly, in 3/4 cases the CD38-negative clone emerged after a second exposure to anti-CD38 MoAb, suggesting emergence with the first exposure and then selection during the second. One other monoallelic event (C275Y; Heidelberg) was detected post-Isa. Copy number of the alternate allele is not known given targeted sequencing methodology, but the variant was seen to reduce binding affinity to both Dara and Isa. The prevalence of monoallelic loss of CD38 in newly diagnosed MM from MMRF CoMMpass was 7% (50/701) and two patients had mutations in CD38, but there were no cases of biallelic loss suggesting these events are driven by treatment pressure. Conclusion Distinction of the mode of CD38 loss is valuable for predicting outcome of re-challenge to anti-CD38 MoAbs. We see here an estimated 5-10% of anti-CD38 MoAb relapses are characterized by genomic antigen escape. Results here are in line with antigen escape to BCMA- and GPRC5D-directed immunotherapies (Lee et al, Nat Med 2023), suggesting that acquired antigen escape resulting from prolonged exposure to the selective pressures of immunotherapy is a recurrent mechanism of resistance in MM. Moreover, in select cases, non-overlapping resistance mutations provide rationale for switching agents at second exposure.

Introduction: Recent efforts have shed light on the clinical and laboratory factors associated with toxicity and response to BCMA-directed CAR-T therapy in patients with relapsed/refractory multiple myeloma (RRMM). However, a major limitation of current scoring systems lies in their assessment at time of lymphodepletion. Because they are not determined before apheresis, they are ill-equipped to guide patient selection, anticipate level-of-care, or plan pre-emptive toxicity management strategies. We therefore aimed to characterize early predictors of CAR-T toxicity and response. Study Design: In this multicenter observational study, we examined demographic, clinical, and laboratory data across three distinct timepoints: indication (e.g., treatment decision), apheresis, and pre-lymphodepletion (LD). CRS and ICANS were graded according to ASTCT criteria. Hematotoxicity (ICAHT) was graded using EHA/EBMT criteria. Early infections (day 0-100) were defined based on microbiologic data or as a clinical infection syndrome, and graded per Hill et al, Blood 2018. Kaplan-Meier estimates of progression-free (PFS) and overall survival (OS) were compared by log-rank test. Cox proportional hazard models were applied to explore associations between pre-apheresis risk factors and key toxicity and survival outcomes. The CAR-HEMATOTOX (HT) score - encompassing markers of inflammation (e.g., CRP, ferritin) and marrow reserve (e.g., hemoglobin, ANC, platelet count) - was determined at each timepoint (Rejeski et al, Blood 2021). Results: We included 530 RRMM patients treated with standard-of-care cilta-cel (n=184) or ide-cel (n=346) across nine centers. Median age was 65 (range 31-88), median ECOG was 1 (IQR 0-1), and patients received a median of 5 prior lines of therapy (IQR 4-7). The median time from indication to apheresis was 16 days (IQR 7-52), while the median time from apheresis to infusion was 56 days (IQR 46-69). The proportion of high-risk patients by HT score did not change between the pre-apheresis and LD timepoints (19% vs. 20%, p=0.82). Most patients stayed either low-risk (n=388, 73.2%) or high-risk (n=68, 12.8%). However, 35 patients (6.6%) shifted from high-to-low risk, while 39 patients (7.4%) progressed from low-to-high risk - reflecting their response to bridging therapies. We noted considerable differences in morbidity and mortality based on the pre-apheresis HT score. Compared to their low-risk counterparts (score 0-2, n=427), high-risk patients (score ≥3, n=103) were hospitalized longer (12 vs. 15 days, p=0.009) and required more frequent ICU admissions (6% vs. 12%, p=0.05). They exhibited more severe early cytopenias (G3+: 8% vs. 25%, p&lt;0.001) and late cytopenias (G3+: 7% vs. 21%, p&lt;0.001), resulting in increased use of TPO agonists and stem cell boosts. Grade 3 or higher ICANS was also more common in HThigh patients (3% vs. 11%, p=0.006), as were severe infections (14% vs. 27%, p=0.007). We found increased non-relapsed mortality in HThigh patients (1-yr NRM 4.6% vs. 16.1%, p&lt;0.001), but no significant difference in the occurrence of secondary malignancies (p&gt;0.9). With a median follow-up of 12.4 months, HThigh patients showed inferior PFS (1-yr PFS 29% vs. 63%, p&lt;0.0001) and OS (1-yr OS 49% vs. 83%, p&lt;0.0001). Transitioning from a low-to-high score was linked to poor survival (1-yr PFS 26%), while the shift from high-to-low risk was associated with favorable outcomes (1-yr PFS 51%). In a multivariable Cox regression model of pre-apheresis features, a high HT score (aHR 2.52, p&lt;0.0001), prior anti-BCMA therapy (aHR 1.55, p=0.04), history of extramedullary disease (aHR 1.42, p=0.02), and penta-refractoriness (aHR 1.45, p=0.02) were independently associated with PFS. Notably, the presence of more than 3 of these high-risk features defined an ultra-high-risk cohort of 28 patients (5.3%) with particularly dismal survival (1-year PFS 15%). Conclusions: Leveraging granular pre-apheresis data from a large international cohort, we identify early markers of toxicity and response in CAR-T-treated RRMM patients. The HT score - reflecting baseline inflammation and hematopoietic reserve - identified high-risk patients likely to benefit from pre-emptive toxicity mitigation strategies such as prophylactic stem cell collection. Most importantly, we characterize a small yet ultra-high-risk group of RRMM patients in need of more effective bridging and therapeutic options.

Introduction The pathogenic role of somatic non-coding (nc) single nucleotide variants (SNVs) is underexplored in multiple myeloma (MM) and additional characterization of these regions has the potential to provide significant new insights into disease progression. To investigate the role of ncSNVs and their relationship with MM pathogenesis, we have characterized a novel series of WGS from 294 cases of MGUS, SMM, and MM using computational tools optimized to identify functional driver SNVs in these regions. We hypothesize the ncSNVs that co-localize to sites of super-enhancers (SEs) active in plasma cells (PC) are sites of high functional impact SNVs and these mutations lead to the dysregulated expression of genes that should be silent at the terminally differentiated stage of PCs, providing a selective advantage that shapes the fitness of the MM genomic landscape. Methods We analyzed 294 high-coverage matched tumor/normal pairs (80x tumor and 40x normal) derived from 21 MGUS, 86 SMM, 167 NDMM and 40 RRMM. In addition to previously sequenced samples, newly sequenced patients were obtained from NYU Langone Health, Memorial Sloan Kettering Cancer Center, University of Alabama at Birmingham and University of Miami following IRB-approved informed consent. DNA was extracted from CD138+ purified bone marrow plasma cells using standard approaches. We used the MGP1000 (https://github.com/pblaney/mgp1000) to process sequencing data which employs a union-consensus mechanism for determining the final set of variants. We collected enhancer elements (E/SEs) active in GC B-cells, ABC-DLBCL and GBC-DLBCL (Bal, et al. Nature, 2022) and PC specific E/SEs (Loven, et al. Cell 2013). FunSeq2 was used to score ncSNVs. GRAM was used to predict how variants alter TF binding and gene expression in a subset of 6 cases with paired WGS in our series and will expand this analysis on a validation set of 263 NDMM and 93 RRMM cases from University-Hospital Heidelberg enrolled in the GMMG-HD6 clinical trial. Results We identified 1,952,232 high-quality SNVs with 18,199 (0.93%) accumulating in 280 focal sites we termed “mutationally enriched regions” or MERs. To discern SNVs in MERs with the highest potential for imparting a selective advantage, we clustered mutations based on the density of FunSeq2 impact scores which uses reference data contexts on sequence conservation, modification of transcription factor (TF) binding, and gene linkage to regulatory elements and network hubs from pan-cancer studies. We show that MERs are enriched for ncSNVs with high impact scores (mean=2.57) compared to random similarly-sized regions of the genome (Fisher's exact test P&lt;9.7e-12, OR=10.10 [3.88-37.40]). The high impact ncSNVs (hi-ncSNVs) in MERs (n=220) account for 8.09% of all hi-ncSNVs and are present in 46.3% of cases (136/294) with some patients having up to 7. The proportion of cases carrying a MER hi-ncSNVs was similar across disease stages (MGUS=42.9%, SMM=46.5%, MM=40.7%, and RR=47.5%). Analysis of the VAF of MER hi-ncSNVs shows a minor shift towards more clonal versus subclonal mutations (53.6% vs 46.4% VAF&gt;=0.25) suggesting some high impact mutations are acquired in the GC but additional hi-ncSNV are acquired later in PC development, providing evidence that hi-ncSNVs accumulate in MERs throughout disease progression. We mapped these hi-ncSNVs to associated genes then intersected the loci with E/SEs active in GCBs and PCs to determine their functional context. This approach identified 12 genes affected by hi-ncSNVs including the previously identified nc-drivers NEAT1, MIR142, ZCCHC7, and IGLL5 as well as the PC differentiation genes IRF8, BACH2, proliferation genes RHOH, SPRED2, known coding MM drivers CCND1, ZFP36L1, and chr1q genes BTG2 and CROCC. We predicted the effect of ncSNVs on expression through modulation of TF binding at this set of putative nc drivers in MM and identified a trend towards increase in expression of SPRED2, CCND1, NEAT1, and IGLL5 compared to non-mutated cases (P=0.089). We will present data on the validation of this set of genes on a larger set of data derived from the Heidelberg group. Conclusions We identified a set of 12 putative nc driver genes in MM that modify expression and have the potential to significantly alter the pathogenesis of MM by providing a selective survival advantage. We will present the impact of these by clinical stage and on outcome.

PURPOSE Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years. METHODS To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data. RESULTS Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression–based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922 ) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited. CONCLUSION Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.