Shain, Kenneth H

Patients with plasma cell leukemia (PCL) are generally excluded from pivotal T-cell redirecting bispecific antibody (BsAb) trials. We evaluated real-world outcomes in a multicenter retrospective study of 122 patients with primary or secondary PCL across 15 academic centers, categorized as 'active' (≥5% circulating plasma cells within 30 days of BsAb initiation) or 'historical'. In a heavily pretreated population, patients received teclistamab (37%), elranatamab (11%), talquetamab as a line of therapy (Tal LOT, 42%), or talquetamab as bridging therapy to CAR-T (Tal Bridge, 11%). Cytokine release syndrome (CRS) was grade 1-2 in 56% and grade 3-4 in 4%, whereas neurotoxicity was grade 1-2 in 16% and grade 3-4 in 5%. Among 110 evaluable patients, the overall response rate (ORR) was 55%, including 61% with Tal LOT, 58% with Tal Bridge, 46% with elranatamab, and 33% with teclistamab. With a median follow-up of 8.3 months, talquetamab demonstrated superior survival. Tal LOT showed a median progression-free survival (mPFS) of 5.5 months and a median overall survival (mOS) of 11.5 months, and both were unreached in the Tal Bridge cohort. In contrast, teclistamab and elranatamab showed a mPFS of 1.2 and 1.6 months and mOS of 8.1 and 3.6 months, respectively (P=0.007, P=0.023). In active PCL, Tal LOT achieved mPFS/mOS of 6.9/12.2 months versus 0.7/1.4 months with teclistamab and 1.0/3.1 months with elranatamab, respectively (P<0.001, P=0.002). Multivariable analysis associated active PCL with worse survival and Tal LOT with improved outcomes. BsAbs were safe in PCL, with talquetamab showing superior outcomes compared with BCMA-directed BsAbs.

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 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.