Kumar, Shaji

Abstract Introduction Surface proteins in cancer not only reflect tumor–microenvironment interactions but also serve as targets for diagnostic flow cytometry and precision therapies, including monoclonal antibodies and CAR-T cell therapies. A better understanding of plasma cell immunophenotypes could inform flow cytometric prognostic assays and development of targeted therapies. Methods RNA-seq and Seq-FISH data from CD138-enriched bone marrow plasma cells, along with corresponding clinical annotations, were obtained from the Multiple Myeloma Research Foundation-CoMMpass Study. A curated list of surface genes was assembled by integrating three sources: the Human Protein Atlas (plasma membrane and cell junction annotations), the Gene-Set Enrichment Analysis membrane-associated gene set (Ontology:0016020), and the Cell Surface Protein Atlas (validated high-confidence category). A total of 4,579 genes coding for cell surface proteins were identified. Genes below the 25th variance percentile were removed. PCA (≥90% variance) followed by k-means (k=2, silhouette) and confirmed by hierarchical clustering. A Plasma Cell Maturity (PBM) score was computed using singscore on 84 mature plasma cell–associated genes to compare cluster phenotypes. Pathway analysis was done using GSEA software for Hallmark pathways. Clinical biomarkers and survival kinetics were compared between the clusters. Results 761 newly diagnosed patients were included. (Age: 27-93 years, median overall survival/OS: 29 months/mo). Clustering revealed two groups which we termed Atypical/Aggressive Cluster 1 (C1, n=105, 13.8%) and Classical Cluster 2 (C2, n=656, 86.2%), based on the patterns and clinical characteristics. Among non-missing cases, high-risk disease per the International Myeloma Society–International Myeloma Working Group (IMS–IMWG) definition was observed in 27.7% of patients in C2 (n = 552) and 24.7% in C1 (n = 90) (p = 0.4).C1 exhibited enrichment for t(14;16)-MAF (OR = 2.6, 95% CI: 1.1–6.1, p = 0.04) and t(11;14)-CCND1 (OR = 1.7, CI: 1.01–2.8, p = 0.04). In contrast, hyperdiploidy was more common in C2 (OR for C1 = 0.6, CI: 0.4–0.9, p = 0.04). Elevated LDH was observed in C1 (OR = 2.6, CI: 1.6–4, p < 0.001). Median serum Beta-2 microglobulin was lower in C1.(p=0.009) Upregulated genes in C1 in decreasing order of log Fold Change(logFC): AHNAK, MT-ND1, EEF2, EZR, HLA-B, TXNIP, HLA-E, TRIB1, MCL1, RHOB, CD74, CFLAR, HLA-A, HERPUD1, RPSA, ERN1, HSP90AB1, RAPGEF2, ADGRE5, and LAPTM5. Downregulated genes in C1 in descending order of |logFC|: B2M, SDC1 (CD138), HSP90B1, SEL1L, CALR, CANX, PECAM1, SLAMF7, STT3A, TNFRSF17 (BCMA), FCRL5, STT3B, PALM2AKAP2, CD53, PARP1, CCR2, RPN1, CD38, EDNRB, and LMAN2. Upregulated pathways in C1: proliferation (MYC, E2F, PI3K/AKT/mTOR, KRAS, Wnt); evasion of apoptosis (p53, TGF-β, Notch, NF-κB, IL6/JAK/STAT3); Epithelial to Mesenchymal Transition (angiogenesis, apical junction); inflammatory (interferon, TNF-α, complement, IL2/STAT5); metabolic reprogramming (oxidative phosphorylation, lipid/cholesterol); and stress (DNA repair, unfolded protein response). The PBM score for C1 was significantly lower, suggesting a less differentiated phenotype. C1 patients had inferior survival (n = 93, median OS 43.9 mo, p = 0.04) compared to those in the C2 cluster (n = 559, median OS 102.5 mo). Patients in the C1 + IMS–IMWG high-risk status had the shortest survival (20.7 mo), compared to C1+low (50 mo), C2/high (68.3 mo), and C2+low (not reached). In a multivariable analysis, C1 status (HR = 1.93; 95% CI: 1.1–3.4, p = 0.02) was an independent predictor, along with older age (1.05), UAMS70 (1.02), EMC-92 (1.4). In C1, early (<18 mo) progression occurred in 76.3% (n=71) (p < 0.001). In patients receiving first line of treatment(LOT1), median PFS was 39.7 mo in C1vs. 47.3 mo in C2 (p=0.8). Among patients who received stem cell transplant in LOT1, the median survival for the C2 was 102.5 mo, compared to 44.5 mo for C1 (n = 52) (p = 0.01). Among non-SCT patients on Immunomodulator + Proteasome Inhibitor therapy, C1 (n=28) had shorter survival of 50.0 vs. 71.7 C2 (n=137; p =0.8). Conclusion Our transcriptomic analysis identified a higher-risk group of patients (C1) exhibiting downregulation of myeloma specific markers (CD38, CD138, BCMA, SLAMF7, FCRL5) and inferior outcomes. It is important to diagnose and find appropriate therapy for these patients as they don't obtain the same benefit from the current standard of therapy as the remaining patients.

Abstract Introduction Ciltacabtagene autoleucel (cilta-cel) has demonstrated unprecedented response rates and progression-free survival (PFS) in relapsed/refractory multiple myeloma (RRMM), but a proportion of patients experience early relapse. Identifying these patients is essential to guide future therapeutic strategies, such as enhanced bridging therapy or consolidation, while avoiding overtreatment in those likely to achieve durable disease control. Methods We conducted a multi-center study of patients treated with cilta-cel at 15 U.S. academic centers. Outcomes were evaluated based on high-risk (HR) features, including functional HR disease (FHR; relapse within 18 months of first-line therapy initiation), true extramedullary disease (EMD), traditional HR cytogenetics as defined in CARTITUDE-1 [deletion 17p, t(4;14), t(14;16)], and a 2024 IMS-IMWG-adapted HR definition (deletion 17p; t(4;14) or t(14;16) with gain 1q or deletion 1p; and gain 1q with deletion 1p). Multivariable Cox regression was used to examine the association of HR features (FHR, adapted IMS-IMWG HR, and EMD, individually and collectively) with PFS while adjusting for baseline ferritin, prior BCMA, and ECOG performance status (PS ≥2). Among patients with ≥18 months of follow-up, we evaluated predictors of early relapse (≤18 months post infusion) using logistic regression. Results Our cohort included 598 patients treated with cilta-cel between 5/2022 and 12/2024 who had available data on all HR features. The median age was 65 years (range 33-83), and the median prior lines of therapy (pLOT) was 5 (range: 1-18, 84% with >3 pLOT); 8% had received prior BCMA-directed therapy, and 27% were penta-refractory. Most patients (88%) received bridging therapy and 30% achieved ≥ partial response. In our cohort, 33% (n=195) had FHR, 43% (n=258) had traditional HR cytogenetics, 42% (n=253) had adapted IMS-IMWG HR disease, and 11% (n=64) had EMD. Median follow up was 10.6 months (range 0.4-34.1). Overall response rate (ORR) and complete response (CR) rates were lower in patients with vs without HR features: FHR (ORR: 89% vs 94%, CR: 61% vs 75%), traditional HR (ORR: 90% vs 94%, CR: 65% vs 74%), adapted IMS-IMWG HR (ORR: 91% vs 94%, CR: 64% vs 75%) and EMD (ORR: 77% vs 94%, CR: 61% vs 71%). Corresponding 12-month PFS was also lower in patients with vs without HR features: FHR (55% vs 79%), traditional HR (63% vs 78%), adapted IMS-IMWG HR (61% vs 80%) and EMD (59% vs 73%). ORR, CR rate, and 12-month PFS in patients without any HR features compared to those with ≥1 HR feature were 96% vs 90%, 81% vs 64%, and 83% vs 65%, respectively. In multivariable models, each HR feature (FHR, adapted IMS-IMWG HR, and EMD) was independently associated with inferior PFS after adjusting for baseline ferritin, prior BCMA, and ECOG PS ≥ 2. However, because early relapse cannot always be identified a priori based on traditional risk factors, we sought to identify predictors of relapse within 18 months of cilta-cel in a subset of patients with ≥18 months of follow-up (N=247; infused by 12/2023). Notably, while FHR (OR=3.04, 95% CI=1.59-5.89) and adapted IMS-IMWG HR (OR=2.07, 95% CI=1.10-3.89) were independently associated with odds of relapse within 18 months, EMD was not (OR=0.90, 95% CI=0.32, 2.40) when accounting for prior BCMA, ECOG PS ≥ 2, and baseline ferritin. We also used a time-varying Cox model to examine each HR feature associated with PFS at two time periods, <18 and ≥18 months, while adjusting for baseline ferritin, ECOG PS, and prior BCMA. We confirmed that FHR and adapted IMS-IMWG HR were both associated with relapse <18 months (HR=2.05, 95% CI=1.48-2.83 and HR=1.56, 95% CI=1.11-2.19) but not in the ≥18-month time period (HR=0.70, 95% CI=0.19-2.59 and HR=0.76, 95% CI=0.24-2.40). The magnitude of the association for EMD with PFS was similar across both time periods (<18 months HR=1.67, 95% CI=1.09-2.57 and ≥18 months HR=2.17, 95% CI=0.45-10.40). Conclusion While cilta-cel leads to high response rates and durable PFS in RRMM, a subset of patients with FHR and adapted IMS-IMWG HR remains at increased risk for early relapse. In this large multi-center cohort, we describe PFS outcomes across high-risk subgroups and identify key predictors of relapse within 18 months of cilta-cel. These findings inform risk-adapted therapeutic strategies and interventional trials, including bridging, consolidation, or maintenance approaches, to improve outcomes in the highest-risk RRMM patients.

Background/Objectives: Family history is a known risk factor for multiple myeloma (MM) and its precursor condition, monoclonal gammopathy of undetermined significance (MGUS). Previous genome-wide association studies (GWASs) have identified 35 common loci associated with MM risk and 21 associated with MGUS. The objective of this study was to identify less common and rare genetic loci predisposing to MM/MGUS through whole-exome sequencing (WES)-based linkage analysis. Methods:Multipoint linkage analysis was conducted using the Multipoint Engine for Rapid Likelihood Inference (MERLIN) with the Lander–Green algorithm on germline WES data from 79 pedigrees with 2 or more affected relatives (120 MM, 86 MGUS, and 21 unaffected). Genome-wide linkage was evaluated using 12,946 independent single-nucleotide variants (linkage disequilibrium r2 < 0.05). Results: Significant linkage was observed at chromosome 6q22.33–q24.2 by the non-parametric model (logarithm-of-odds (LOD) = 3.3) and suggestive linkage by the dominant parametric model (heterogeneity LOD (HLOD) = 2.5). Fourteen rare variants within this region were prioritized using family-specific partial LOD scores and in silico functional prediction tools. Nine of these variants, REPS1, THEMIS, TAAR6, AHI1, VNN1, VNN3, MTFR2/FAM54A, LAMA2, and PHACTR2, overlapped immune-regulatory regions in blood cell lines and were not previously identified in GWASs. Conclusions: This study demonstrates the utility of applying a linkage analysis framework to familial WES data for identifying genomic regions and candidate genes that may contribute to MM/MGUS predisposition. These findings provide new insight into the inherited risk and etiology of familial MM and MGUS.

Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.

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 .