Carlyn Rose Tan

Bioinformatic pipeline details

Supplementary Figure 1. Overview of bioinformatic workflow for each module of the Myeloma Genome Pipeline 1000 (MGP1000). Supplementary Figure 2. Bar plot highlighting the cases where the self-identified race did not match the ancestry. Supplementary Figure 3. Comparison of copy number variation (CNV) and single nucleotide variants (SNV) between African-dominant (AFR) and European-dominant (EUR) admixture clusters. Supplementary Figure 4. Comparison of tumor coverage and purity stratified by admixture cluster assignment. Supplementary Figure 5. Comparison of age and total mutational burden (TMB) stratified by admixture cluster assignment. Supplementary Figure 6. The number of kataegis foci are associated with admixture groups. Supplementary Figure 7. Principal component analysis (PCA) plots generated via EIGENSTRAT for the AFR Dominant population. Supplementary Figure 8. Haplotype distribution at each of 6 significantly distributed SNPs from those comprising the PRS. Supplementary Figure 9. Relationship between polygenic risk score (PRS), APOBEC activity and APOBEC SNP genotypes. Supplementary Figure 10. APOBEC (SBS2/SBS13) contribution by Admixture group and pre/post-gain stage.

Supplementary Table 1. Summary of the source of data included in this study. Supplementary Table 2. Summary of demographic and genomic metadata variables collected for patients included in this study. Supplementary Table 3. Comparison of genomic classification events (n = 130) between EUR and AFR Dominant populations in NDMM patients with high ancestry contribution (EUR >0.8 or AFR >0.8). Supplementary Table 4. Comparison of genomic classification events (n = 130) using AFR ancestry as a linear value (linear regression model). Supplementary Table 5. Comparison of genomic classification events (n = 130) between EUR and AFR dominant populations in NDMM patients with high tumor purity (> 0.5) and high coverage (> 40x). Supplementary Table 6. Comparison of genomic classification events between the P1000 EUR group and the GMMG-HD6 cohort. Supplementary Table 7. Odds-ratio comparison for risk of developing multiple myeloma or myeloma precursor conditions for 31 germline SNPs. Supplementary Table 8. Proportion of germline SNPs in APOBEC across AFR and EUR in NDMM. Supplementary Table 9. Subanalysis for comparison of association of APOBEC mutational signatures (SBS2/SBS13) with 31 germline SNPs based on ancestry cluster (EUR, AFR). Supplementary Table 10. The age of multiple myeloma diagnosis from the SEER database.

Bispecific antibody (BsAb) treatment in multiple myeloma can be associated with adverse events including cytokine release syndrome (CRS), cytopenias, and infections. While granulocyte colony-stimulating factors (G-CSF) can be used to treat neutropenia, little is known about their safety during the BsAb initiation period and whether G-CSF can increase the risk of CRS. To assess the impact of G-CSF on risk of CRS, we included all patients with multiple myeloma who received at least one dose (step-up doses [SUD] and first treatment dose [FTD]), of commercial teclistamab, elranatamab, or talquetamab between November 2022 and April 2024 at Memorial Sloan Kettering Cancer Center (N = 186). Exposure to G-CSF was defined as administration of at least one dose of filgrastim from 48 h before up to 7 days after BsAb SUD or FTD, or one dose of peg-filgrastim between 7 days prior to up to 7 days after BsAb SUD or FTD. Of the 186 patients, 22 were classified as G-CSF exposed. CRS occurred in 16/22 (73%) patients with G-CSF exposure vs 76/164 (46%) of non-exposed. All CRS was grade 1 (56%) or grade 2 (44%) and the majority occurred prior to G-CSF was given (16/22 patients). The median absolute neutrophil count (ANC) at time of G-CSF exposure was 0.5 × 10⁹/L (0.4-0.7). There was a trend towards increased risk of CRS after G-CSF exposure, although not significant (hazard ratio [HR] 1.7, 95% confidence interval [CI] 0.6-4.7, p = 0.3). These results suggest that G-CSF administration can be considered for neutropenia during BsAb initiation.

Nonimmune effector cell–associated neurotoxicity syndrome neurotoxicities (NINTs) are serious, atypical toxicities associated with ciltacabtagene autoleucel (cilta-cel), a US Food and Drug Administration chimeric antigen receptor T cell (CAR T cell) therapy approved for relapsed/refractory multiple myeloma (RRMM). Risk factors contributing to the development of NINTs are poorly understood. In a cohort of 109 patients with RRMM treated with cilta-cel, we identify predisposing risk factors and propose strategies to mitigate NINTs. We show that high-peak absolute lymphocyte count is a strong NINT predictor, which directly correlates with flow cytometry–based peripheral blood CAR T cell quantitation. The observed CAR lymphocytosis was polyclonal with a bias toward CD4 ⁺ CAR T cells rich in memory marker expression. We then identified CAR lymphocytosis–associated CD4 ⁺ CAR T cell populations, which exhibited increased inflammatory pathway gene expression. Last, we characterize NINT-associated CD4 ⁺ CAR T cell populations, which are potential therapeutic targets for future exploration.

1. Multiple myeloma (MM) is a hematologic cancer characterized by the accumulation of neoplastic plasma cells in the bone marrow [1,2]. These malignant cells produce monoclonal proteins that are de...

The treatment landscape for relapsed/refractory multiple myeloma has been transformed by the approval of T-cell–redirecting bispecific antibodies (BsAbs) targeting B-cell maturation antigen (teclistamab, elranatamab, and linvoseltamab) and GPRC5D (talquetamab). Although pivotal trials have demonstrated deep and durable responses, the rapid transition of these off-the-shelf agents from controlled clinical settings to routine oncologic practice presents significant operational and clinical challenges. This review focuses on the practical framework required to deliver BsAb therapy safely and effectively across academic and community settings. We address critical components of patient selection and effective implementation strategies, including infrastructure for the management of unique short-term toxicities such as cytokine release syndrome and immune effector cell–associated neurotoxicity syndrome, and long-term infection risks. We discuss models for outpatient implementation of these agents including the use of technology and academic-community partnerships (shared care models). As BsAbs move into earlier lines of therapy and combination regimens, optimizing these practical management strategies is essential to ensuring equitable access and maximizing long-term outcomes for patients with multiple myeloma.

Studies have reported conflicting findings regarding the contribution of germline variants or somatic genomic drivers to racial disparities in multiple myeloma (MM). To comprehensively investigate somatic drivers in relation to inherited genetics in MM, 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 MM 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 epidemiological data and mutational signature-based temporal estimates, we challenge the assumption that individuals in the AFR group develop MM 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.

PURPOSE: Talquetamab is a G protein–coupled receptor class C group 5 member D T-cell–engaging antibody, approved for relapsed/refractory multiple myeloma (RRMM). In the MonumenTAL-1 clinical trial, cytokine release syndrome (CRS) occurred at a frequency of 77%; however, there was no assessment of CRS rates by step-up dose. The primary objective of this study was to characterize CRS rates after each talquetamab step-up dose in a real-world setting. METHODS: Patients with RRMM who completed the talquetamab once a week or once every 2 weeks step-up dosing schedule between September 2023 and November 2024 were identified via the institutional database. CRS rate after each talquetamab step-up dose was compared using the chi-square/Fisher’s exact test. The Kruskal-Wallis test was used to compare difference in median time to the CRS onset. Multivariate logistic regression analysis was performed to identify predictors of CRS. RESULTS: Fifty patients completed the talquetamab step-up dosing phase during the study period; CRS occurred at a rate of 80%. Pairwise comparisons revealed significant differences in CRS rates between the fourth dose (4%) and each of step-up dose 1 (28%, P = .014) and 2 (34%, P = .003). The only CRS event with fourth dose was grade 1. The median time to onset of first CRS did not differ significantly between step-up doses ( P = .441). Previous exposure to T-cell–redirecting therapy had no impact on CRS incidence (odds ratio: 0.20 [95% CI, 0.03 to 1.10]). CONCLUSION: Our findings suggested that the fourth talquetamab dose could be administered in outpatient settings given its high tolerability. The reduced hospitalization period for talquetamab step-up dosing could reduce health care expenses.