Williams, Louis S.

Abstract Introduction: Teclistamab is the first FDA approved BCMA-directed bispecific antibody for the treatment of relapsed and refractory multiple myeloma (RRMM). With BCMA-targeting CAR-T therapies, cytokine release syndrome (CRS) is a risk factor for cardiotoxicity. Teclistamab was associated with fatal cardiovascular adverse events (CVAEs) in a recent US pharmacovigilance database analysis, but this analysis was limited by reporting bias. Therefore, the aim of our study is to conduct a multicenter study to define the real-world incidence of CVAEs in RRMM patients treated with teclistamab. Method We performed a retrospective multi-center cohort study of patients receiving teclistamab at 5 medical centers. Baseline oncologic and cardiovascular characteristics (including vital sign and echocardiographic data) were collected. Baseline cardiovascular disease (CVD) was defined as presence of coronary artery disease, congestive heart failure, severe or symptomatic valvular heart disease, or stroke at baseline. CVAE was collected and categorized based on CTCAE v5.0 by each institution. Major adverse cardiovascular event (MACE) was a composite outcome with any of the following: congestive heart failure, asymptomatic cardiomyopathy (interval LVEF drop >10% to resultant LVEF less than 50%), coronary artery disease requiring intervention, stroke or cardiovascular death. Arrhythmia was a composite outcome with any of the following: atrial tachyarrhythmia, ventricular tachyarrhythmia or bradyarrhythmia. Median follow-up was calculated through reverse Kaplan-Meier method. Multivariable Cox regression analysis was used to define the relation between MACE and survival. Cause of death was categorized as myeloma-related death or non-myeloma-related deaths (i.e. CRS, infection or cardiovascular death). Cause-specific Cox regression was performed to define relationship between MACE and cause of death. Results In a cohort of 219 patients treated with teclistamab with median follow-up of 20.1 months, 7.3% (N=16) developed MACE, 7.3% (N=16) developed arrhythmia and 12.8% (N=28) developing any MACE or arrhythmia. MACE occurred at a median of 4.5 days (IQR 2.25-15.75 days) after teclistamab step-up. The incidence of MACE was 16.4% in patients with baseline CVD versus 2.7% in those without baseline CVD (p<0.001). Patients who developed MACE had inferior ECOG performance status (PS) (PS >=2: 62.5% vs. 27.7%; p=0.008). Additionally, baseline hemoglobin level (9 vs 10g/dL; p=0.072) was numerically lower in MACE, compared to no MACE. There was no difference in age, sex, bone marrow (BM) plasma cell burden, ferritin, C-reactive protein or LDH levels between MACE vs. no MACE group. Patients who developed MACE had lower baseline diastolic blood pressure (67.7 vs 73.8mmHg; p=0.041) and numerically lower LVEF (51.4 vs 59.3%; p=0.059; baseline echocardiogram performed in 86% of all cohort). Patients who developed MACE had similar incidences of CRS (Grade >=2: 25.0 vs 16.7%; p=0.620) and immune effector cell-associated neurotoxicity (ICANS) grade 2 or higher (12.5 vs 7.5%; p=0.812). In multivariable Cox regression analysis, MACE was independently associated with inferior overall survival (HR 2.72; 95% CI 1.41-5.25; p=0.003), after adjusting for baseline CVD, age, high BM burden, extramedullary disease, and plasma cell leukemia. In cause-specific multivariable Cox regression analysis, MACE was independently associated with non-myeloma-related death (HR: 10.77 95% CI 4.39- 26.41; p<0.001) but not with myeloma-related death (1.54; 95% CI 0.64- 3.70; p=0.336). Conclusion In conclusion, in this large, real-world multicenter study, we observed that MACE occurred in over 7% of patients following teclistamab for RRMM, typically within 2 weeks of initiation. MACE was significantly associated with increased non-myeloma-related mortality. In contrast to CAR-T therapy, CRS or ICANS were not associated with MACE, whereas majority of patients who developed MACE had ECOG PS >=2 or baseline cardiovascular disease. More studies are necessary to define mechanisms and strategies to reduce these MACE and improve survival. Patients with baseline cardiovascular disease or poor performance status may benefit from cardiovascular optimization before and during teclistamab therapy.

Multi-agent regimens incorporating immunomodulatory (IMiD®) agents such as thalidomide, lenalidomide, and pomalidomide have become the preferred standard of care for the treatment of patients with multiple myeloma (MM), resulting in improved survival outcomes. Currently, there are three IMiD agents approved for the treatment of MM: thalidomide, lenalidomide, and pomalidomide. Lenalidomide is commonly used to treat patients with newly diagnosed MM and as maintenance therapy following stem cell transplant or after disease relapse. Pomalidomide, the focus of this review, is approved in patients with relapsed/refractory MM (RRMM). Despite survival benefits, IMiD agents each have different safety profiles requiring consideration both prior to starting therapy and during treatment. Adverse event (AE) management is essential, not only to ensure treatment adherence and thus ensure optimal efficacy but also to maintain patient quality of life. Here, we discuss AEs associated with pomalidomide and present five clinically relevant hypothetical case studies in patients with RRMM to provide scenario-driven guidance regarding treatment selection and AE prevention and management in the clinical setting. Lastly, as new treatment approaches continue to be explored in MM, we also discuss novel cereblon E3 ligase modulator (CELMoD™) agents including iberdomide (CC-220) and mezigdomide (CC-92480).

Introduction: In many cases, treatment decisions for multiple myeloma patients must be made in the absence of high quality randomized controlled clinical trials. As a result, many clinicians lean on prognostic models to drive treatment selection. The most widely used of these models incorporate a limited number of patient related factors that reflect tumor burden, cytogenetic features, or gene expression profiling. These prediction scores are derived from regression models that incorporate these variables at the time of diagnosis. They do not account for variables that change dynamically over time, do not explore the relationship between treatment selection and progression, and imperfectly predict survival outcomes. We designed a prognostic model based on an ensemble machine learning platform to predict progression in NDMM using Extreme Gradient Boosting Machine (XGBoost) combined with accelerated failure time modeling for survival analysis. Methods: We utilized a large retrospective data set containing treatment and response information for 1127 patients with newly diagnosed multiple myeloma (NDMM) treated at the Cleveland Clinic Foundation between 2000 and 2023. Following data preprocessing based on data completeness, 953 patient records were included in our training, testing, and cross validation datasets. For the purposes of our analysis, the initial dataset was randomly split into training (70%), testing (15%) and validation (15%) subsets at the patient level. We also defined the lower and upper bounds for each subset, which is critical due to the right-censored nature of survival data. Hyperparameters for XGBoost were optimized using Bayesian search, minimizing the negative log-likelihood. A random forest method was applied for the imputation of missing data (MissForest). We then trained the XGBoost model using GPU acceleration for enhanced computational efficiency. Finally, a log-rank test on predicted survival times was used to test the model's performance on patients with and without known progression. Results: Our preprocessed data set had 953 patients with a mean age at disease onset of 65 years and a slight male predominance (55%). Approximately 27% of patients harbored high cytogenetic risk disease and 3.5% of the cohort presented with extramedullary disease a diagnosis. At a median follow up of 35 months, 47% of patients had experienced disease progression or death. Induction therapy included an immunomodulatory drug in 40% of patients and proteasome inhibitors in 22%. Frontline autologous stem cell transplantation followed induction in 28% of patients. Median progression free survival in the overall data set was 44 months. These features were consistent across the randomly assigned training, testing, and validation cohorts. Following data preprocessing, 34 independent clinical and genomic variables including selection of first line treatment were assessed as model inputs each available record. Under optimized parameters, the model was trained with a maximum tree depth = 7 and a learning rate ~ 0.22 for the desired output of progression free survival (PFS). We then queried PFS for both the training and validation sets and divided patients into those known to have progressed and those without a progression event. Survival analysis was undertaken using predicted PFS values for patients known to have progressed and those who remained progression free. For the validation data set, our model successfully discerned between progressed and non-progressed cases (log-rank test statistic = 22.07, p < 0.005) (Figure 1). Discussion: We present a machine learning approach based on regularized gradient boosting that accurately discerns between patients who experience progression and those who remain progression free at a median ~35 months of follow-up in a large retrospective data set in patients presenting with NDMM. Further elaboration of our model will allow for the incorporation of large amounts data to predict survival outcomes on the basis of dynamic variables such as depth of response and treatment selection. With the ability to parse outcomes among multiple myeloma patients at high resolution, future clinicians and clinical trialists may be able to overcome limitations in trial design and patient-level therapy selection for both newly diagnosed and relapsed patients.