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PURPOSE In immunoglobulin light chain (AL) amyloidosis, amyloid fibrils cause organ dysfunction. Anselamimab, a monoclonal antibody, selectively binds to amyloid fibrils, accelerating their clearance. METHODS Newly diagnosed patients with European modification of Mayo 2004 stage IIIa or IIIb AL amyloidosis were randomly assigned to receive anselamimab or placebo with cyclophosphamide, bortezomib, and dexamethasone (with or without daratumumab). The primary end point was a hierarchical composite of time to all-cause mortality (ACM) and frequency of cardiovascular hospitalizations (CVHs), analyzed using the Finkelstein-Schoenfeld test and win ratio estimation. RESULTS Among 406 randomly assigned patients, the primary end point win ratio for anselamimab versus placebo was 1.11 (95% CI, 0.83, 1.50; P = .332) and the hazard ratio (HR) for ACM was 0.80 (95% CI, 0.57, 1.13; P = .290). CVH rates in the anselamimab and placebo groups were 0.59 (95% CI, 0.42, 0.83) and 0.89 (95% CI, 0.55, 1.43), respectively ( P = .145). Among patients with kappa isotype (n = 72), the win ratio was 2.06 (95% CI, 0.98, 4.31; P = .100) and anselamimab reduced ACM by 62% versus placebo (HR, 0.38; 95% CI, 0.17 to 0.86; nominal P = .012), and CVH by 71% (IRR, 0.29; 95% CI, 0.10 to 0.87; nominal P = .028) with improvements in Kansas City Cardiomyopathy Questionnaire-Overall Score. There was no clinical benefit observed in the lambda population. Anselamimab was generally well-tolerated; safety data were comparable between treatment arms overall. CONCLUSION Treatment with anselamimab, an antifibril antibody used alongside antiplasma cell dyscrasia therapy, while not meeting the primary end point in the overall population, significantly improved ACM and CVH in patients with kappa AL, potentially offering a new therapeutic option for this isotype.

The treatment landscape for multiple myeloma (MM) is evolving rapidly and includes several new options. The management must be tailored to the individual. The NCCN Guidelines for MM provide a framework to base decisions regarding diagnosis, workup, treatment, and follow-up of both newly diagnosed and previously treated relapsed/refractory MM. This manuscript outlines the recommendations from NCCN Guidelines for MM specific to newly diagnosed MM (NDMM).

PURPOSE Venetoclax, an oral BCL-2 inhibitor, has efficacy in t(11;14)-positive relapsed/refractory multiple myeloma (RRMM), which is enhanced by dexamethasone, which promotes BCL-2 dependency. METHODS The randomized, open-label, phase III CANOVA study (ClinicalTrials.gov identifier: NCT03539744 ) enrolled adults with t(11;14)-positive RRMM who had received ≥2 previous lines of therapy. Patients were randomly assigned (1:1) to venetoclax-dexamethasone or pomalidomide-dexamethasone until progression or intolerable toxicity. The primary end point was independent review committee assessed–progression-free survival (PFS) in the intention-to-treat population analyzed by stratified log-rank test (two-sided type I error rate, α = .05), with hazard ratio (HR) and 95% CI estimated by stratified Cox proportional hazard model. Secondary end points included response rates, overall survival (OS), minimal residual disease (MRD) negativity rate (<10 –5 ), and safety. RESULTS Overall, 263 patients were randomly assigned (venetoclax-dexamethasone, n = 133; pomalidomide-dexamethasone, n = 130). Median PFS was 9.9 months (95% CI, 6.9 to 12.6) with venetoclax-dexamethasone versus 5.8 months (95% CI, 3.8 to 9.2) with pomalidomide-dexamethasone (HR, 0.823 [95% CI, 0.596 to 1.136]; P = .24). Overall response and very good partial response or better rates were 62% and 39%, respectively, with venetoclax-dexamethasone versus 35% and 14% with pomalidomide-dexamethasone. MRD negativity rate was 8% with venetoclax-dexamethasone and 0% with pomalidomide-dexamethasone. Median OS was 32.4 months (95% CI, 26.4 to 40.7) with venetoclax-dexamethasone and 26.9 months (95% CI, 20.4 to 38.9) with pomalidomide-dexamethasone (HR, 0.856 [95% CI, 0.612 to 1.197]). Grade ≥3 treatment-emergent adverse event rates were 67% with venetoclax-dexamethasone versus 83% with pomalidomide-dexamethasone. There were 16 (12%) treatment-emergent deaths with venetoclax-dexamethasone versus 8 (6%) with pomalidomide-dexamethasone. CONCLUSION The primary end point of PFS was not met. PFS and OS were numerically longer with venetoclax-dexamethasone versus pomalidomide-dexamethasone in t(11;14)-positive RRMM. Consistent with previous studies, infections were associated with venetoclax-dexamethasone; no new safety signals were observed.

Abstract Introduction The monoclonal gammopathies of renal significance (MGRS) are defined by the presence of a nephrotoxic monoclonal protein in the absence of a well-defined malignant entity. Mechanistically it has been postulated that, across the varied subtypes of MGRS, abnormal plasma cell or B-cell clones in the marrow produce nephrotoxic proteins; however, in many cases, no paraprotein is detected by conventional laboratory methods and the bone marrow can be morphologically normal. The genomic underpinnings of MGRS are unexplored and, due to the rarity of this disease, clinicians are often left to unable to determine the exact nature of the clonal cell populations. We carried out a pilot study to characterize ctDNA in MGRS patients (pts). The first phase of the protocol sought to collect peripheral blood samples and, when possible, matched bone marrow aspirates, from MGRS pts to evaluate the feasibility of ultra-low pass whole genome sequencing (ULP-WGS) from peripheral blood to identify and characterize ctDNA. Methods We report ULP-WGS results from peripheral blood samples in the initial phase of the study. Peripheral blood samples were collected from renal biopsy proven non-amyloidosis MGRS pts prior to the initiation of treatment. Cell free DNA (cfDNA) was extracted and quantified. A total of 5-20 ng of cfDNA input was used for ULP-WGS. Libraries were pooled and sequenced using 100-bp paired-end runs to an average genome-wide coverage of 0.5x. After deconvolution and alignment, fragments with size 90-150bp were extracted using samtools. IchorCNA was utilized to calculate tumor fraction based on a panel of normals derived from three plasma samples from healthy controls. Model parameters were optimized for low tumor fraction and a threshold of 3% ctDNA was used to identify positive samples. Renal responses were assessed based on International Kidney and Monoclonal Gammopathy (IKMG) group criteria. Results Nine pts and were consented and gave samples for the study along with 3 healthy controls. Seven pts were sequenced, 4 of which were females and 3 were males. Median age was 58 years. Among sequenced pts, 5 presented with proliferative glomerulonephritis with monoclonal immune deposition (PGNMID) and 2 with light chain deposition disease (LCDD). Both patients with LCDD were kappa predominant and showed detectable plasma clones (8% and 3%). One of these pts displayed t(11:14) on bone marrow FISH testing. Among the 5 pts with PGNMID, 3 had IgG kappa deposition and only one had a detectable plasma clone (5%), which harbored trisomy 9 by FISH. Bone marrow flow cytometry was positive in 2 pts. Median 24-hour urine protein content was 7.11g (0.92 – 10.58), median serum albumin 3.20 g/dL (2.90 – 4.90) and median serum creatinine at the time of diagnosis was 2.15 mg/dL (1.30-6.40). CKD was present in 6 of 7 pts at the time of diagnosis. Four pts had known hypertension. One exhibited a history of malignancy and one had a history of autoimmune disease. ctDNA was detectable at a threshold of &gt; 3.0% of cfDNA in 5 of 7 pts. Median ctDNA content was 4.0% (3.18% - 14.97%) of cfDNA. Four of the 5 samples containing ctDNA were hyperdiploid. Chromosome 19 was the most common site of copy number increase whereas chromosomes 6, 10, and 18 exhibited the most frequent decreases in copy number. Two pts with PGNMID had detectable ctDNA without evidence of a serological or marrow-based clone. All pts received daratumumab, cyclophosphamide, bortezomib and dexamethasone as initial treatment. Five pts responded to therapy. Two patients ultimately underwent autologous stem cell transplant and a single patient received venetoclax as second line therapy. Of the patients exhibiting detectable ctDNA, three did not exhibit renal response to initial treatment. Both pts without ctDNA had PGNMID and responded to frontline treatment. Mass spectrometry analysis and deep targeted sequencing of bone marrow aspirates and peripheral blood will be presented. Conclusions To our knowledge, this is the first study to detect ctDNA in MGRS. ULP-WGS, when combined with targeted sequencing and diagnostic modalities such as mass spectrometry, has the potential to not only to aid in diagnosis and treatment selection on a practical basis, but also to begin to unravel the underlying genomics of this rare disease. This approach has potential to serve as a platform for further genomic exploration and biomarker development in MGRS.

The NCCN Guidelines for Multiple Myeloma (MM) provide recommendations for diagnosis, initial workup, treatment, follow-up, and supportive care for patients with MM. These NCCN Guidelines Insights highlight the important updates and changes specific to systemic therapy for patients with newly diagnosed as well as previously treated MM included in Version 1.2025 of the NCCN Guidelines for MM.

The treatment of Waldenström macroglobulinemia/lymphoplasmacytic lymphoma (WM/LPL) has evolved to include several new options. The NCCN Guidelines for WM/LPL provide a framework on which to base decisions regarding diagnosis, treatment, assessment of response to treatment, and follow-up of both newly diagnosed and previously treated WM/LPL.

The treatment of relapsed/refractory multiple myeloma (MM) has evolved to include several new options. These include new combinations with second generation proteasome inhibitors (PI); second generation immunomodulators, monoclonal antibodies, CAR T cells, bispecific antibodies, selinexor, venetoclax, and many others. Most patients with MM undergo several cycles of remissions and relapse, and therefore need multiple lines of combination therapies. Selecting treatment options for relapsed/refractory MM requires consideration of resistance status to specific classes, and patient-specific factors such as age and other comorbidities should be considered. The NCCN Guidelines for MM provide a framework on which to base decisions regarding workup, treatment, and follow-up of newly diagnosed and previously treated MM. This manuscript outlines the recommendations from NCCN Guidelines for MM specific to relapsed/refractory disease.

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 &lt; 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.

8054 Background: The introduction of immunomodulatory agents, proteasome inhibitors, and autologous stem cell transplantation (ASCT) has improved outcomes for patients with multiple myeloma (MM), but those with high risk MM (HRMM) have a poor long-term prognosis. Herein we provide survival outcomes on the first randomized trial in newly diagnosed HRMM, S1211, to follow-up on the previously reported progression-free survival (PFS) (NCT01668719, Usmani SZ et al, Lancet Haem 2021). Methods: S1211 is a randomized phase II trial comparing 8 cycles of lenalidomide, bortezomib and dexamethasone (RVd) induction followed by dose-attenuated RVd maintenance until disease progression with or without elotuzumab (RVd-Elo). Stem cell collection was allowed, but ASCT was deferred until progression. HRMM was defined by one of the following: gene expression profiling high-risk (GEPhi), t(14;16), t(14;20), del(17p), amplification 1q21, primary plasma cell leukemia (pPCL), or elevated serum LDH (> 2X ULN). Median PFS was the primary endpoint, using a one-sided stratified log-rank test at a one-sided significance level of 0.1. Secondary endpoints included overall response rate (ORR), adverse events (AE), serious adverse events (SAE) and OS. Response was assessed using the IMWG 2009 criteria. Results: S1211 enrolled 103 evaluable patients, RVd n=54, RVd-Elo n=49. 74% had ISS II/III, 48% amp1q21, 38% del(17p), 11% t(14;16), 9% GEPhi, 7% pPCL, 5% t(14;20) and 4% elevated LDH (17% >1 feature). With median follow-up of 72 months (mos.), no difference in median PFS was observed [RVd-Elo=29 mos., RVd= 34 mos., HR = 1.11 (80% CI=0.82, 1.49, p=0.66]. No difference in OS was observed [RVd-Elo = median not reached (NR), RVd= 68 mos., HR = 0.85 (80% CI: 0.59, 1.23), p-value = 0.58]. 76% pts had >Grade 3 AEs, no differences in the safety profile were observed. Amongst patients with gain/amp 1q21, median PFS was [RVd-Elo=31 mos., RVd= 37 mos., HR = 1.48 (80% CI= 0.95, 2.31), p=0.25], median OS was [RVd-Elo = 61 mos., RVd= 68 mos., HR = 1.23 (80% CI: 0.72, 2.10), p-value = 0.63]. In patients with del(17p), median PFS was RVd-Elo=41 mos., RVd= 30 mos.[HR = 0.98 (80% CI= 0.60, 1.58), p=0.95], median OS RVd-Elo = NR, RVd= 72 mos., [HR = 0.77 (80% CI: 0.40, 1.48), p-value = 0.61]. Conclusions: In the first randomized HRMM study reported to date, the addition of Elo to RVd induction and maintenance did not improve PFS and OS with a median follow-up of 6 years. Although the median PFS for Del17p subgroup on RVd-Elo arm is higher than RVd, it did not achieve statistical significance. The PFS and OS observed for gain/amp 1q21 and del17p in the RVd control arm may serve as important benchmarks for future enrichment design HRMM clinical trials. The PFS and OS in both arms of the study exceeded the original statistical assumptions and support the role for PI/IMiD combination induction/maintenance therapy for this population. Clinical trial information: NCT01668719.