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Abstract Introduction: Suboptimal response to therapy, early disease relapse, and limited long-term survival remain critical challenges in a substantial number of patients with multiple myeloma (MM). While cross-sectional imaging studies are commonly obtained in clinical practice, only limited information is currently incorporated into risk stratification efforts and clinical decision making. Here, we investigate the use of novel image-based biomarkers to identify patients at high risk for adverse outcomes. Methods: We obtained the pretreatment 18F-FDG PET/CT scans of patients with newly diagnosed MM with available clinical and laboratory data seen at Mayo Clinic between 2006 and 2022. In this analysis, we included 637 patients with detectable metabolic tumor volume on their pretreatment scan. The native DICOM images were pre-processed and subjected to automated image segmentation using nnU-Net (nnU-Net v1.0, inference using a pre-trained hematologic malignancies model). Limitations of this automated segmentation approach include heterogeneity introduced by varying image quality, technical variability in PET reconstruction, and potential inclusion of non-neoplastic FDG-avid lesions in segmented regions. PyRadiomics was used to extract quantitative radiomics features for clinical prediction modeling. We evaluated the predictive power of tumor size-, shape-, and texture-related radiomics features for identifying high-risk disease (advanced ISS stage at diagnosis, the presence of high-risk FISH abnormalities, failure to achieve a stringent complete response [sCR] to first-line therapy by IWMG criteria, and early mortality). All radiomics features were quantile-normalized for downstream modeling. Forward and backward feature selection as well as testing for multicollinearity were used to remove redundant features. Results: Median age at diagnosis of the 637 myeloma patients was 64 years (range 22–90), and 387 patients (61%) were male. After a median follow-up of 4.4 years (95% CI 0.2–11.7), median overall survival (OS) was 7.3 years (95% CI 6.3–8.3). Increased total tumor volume (upper tertile vs lower tertiles), measured by the total tumor surface area, was associated with advanced ISS stage (III vs I+II; OR 1.73, 95% CI 1.24–2.42, p=0.001, n=637). Increased tumor density (dichotomized at the median), measured by the median gray level intensity (OR 1.93, 95% CI 1.94–3.12, p=0.007, n=303), and decreased tumor heterogeneity (lower tertile vs upper tertiles), measured by the variability of gray-level intensities (OR 1.88, 95% CI 1.49–3.12, p=0.012, n=303), were associated with the presence of high-risk cytogenetics. Both increased total tumor volume (4.9% increase per tertile, 95% CI 2.2–7.6, p<0.001, n=600) and tumor density (8.9% increase per tertile, 95% CI 4.5–13.3, p<0.001, n=600) were independently associated with more extensive bone marrow involvement at diagnosis. Increased tumor heterogeneity (upper tertiles vs lower tertile), as measured by rapid changes of gray level intensities, was associated with failure to achieve a stringent complete response to first-line therapy (OR 0.50, 95% CI 0.30–0.82, p=0.006, n=351). Increased tumor volume was associated with shorter OS (HR 1.35 per tertile, 95% CI 1.11–1.63, p=0.002, n=301), independent of age, sex, ISS stage, and the presence of high-risk cytogenetics. A simple additive score consisting of tertiles of total tumor volume, ISS stage, and plasma cell labeling index ≥2% identified patients at risk of mortality within 3 years of diagnosis (OR 2.00, 95% CI 1.40–2.85, p<0.001, n=126), with moderate discrimination (AUC=0.71). Sensitivity analysis demonstrated total tumor volume and texture as most predictive of overall survival in a random forest survival model (C-index 0.712, 95% CI 0.649–0.775) and tumor texture as most predictive of response to therapy in a random forest classification model (C-index 0.693, 95% CI 0.607–0.779). Conclusions: Automated image segmentation and radiomics feature extraction from 18F-FDG PET/CT scans produces biomarkers that capture disease stage, burden, and risk in newly diagnosed MM. Total tumor volume and heterogeneity are strongly associated with important disease characteristics and can be used to identify patients at high risk for therapeutic resistance, early mortality, and inferior long-term outcomes.

Abstract Altered Bone Marrow (BM) Plasma Amino Acid (AA) Composition Reflects Microenvironmental Remodeling in Patients with Multiple Myeloma (MM) compared to Monoclonal Gammopathy of Unknown Significance (MGUS) Michael Egan, Yogesh Chawla, Puja Dey, Dragan Jevremovic, Taro Hitosugi, Shaji Kumar and Wilson Gonsalves BACKGROUND: MYC overexpression, a hallmark of progression from MGUS to MM, drives metabolic reprogramming, including enhanced AA uptake and utilization. However, the contribution of AAs to disease progression from MGUS to MM remains poorly defined. In this study, we comprehensively profiled AA levels in the BM plasma of MGUS and MM patients to determine whether shifts in the availability of specific AAs reflect microenvironmental remodeling and evolving metabolic demands of malignant plasma cells (PCs). METHODS: AA profiles of BM plasma samples—reflective of the BM microenvironment—from two different cohorts of patients with either MGUS or MM (Cohort #1: 49 MGUS and 50 newly diagnosed MM (NDMM) and Cohort #2: 50 MGUS and 64 NDMM) were acquired using liquid chromatography (LC) coupled with tandem mass spectrometry (MS). Individual AA were quantified by MS using single-point quantification with isotopically labeled internal standards of AAs. Individual relative and quantitative AA data were analyzed for differences between the groups using the parametric t-test, and significance was defined as p < 0.05. We utilized a Pearson correlation coefficient method to determine the association of the percentage of clonal PCs in the BM to the levels of the AAs. The areas under the curve (AUC) of receiver operating characteristic (ROC) analyses were created on the web-based tool MetaboAnalyst 6.0 to determine the ability of quantitative AA levels to be utilized as a model to distinguish MGUS vs. MM. RESULTS: In BM plasma samples from cohort #1, using a global untargeted metabolite profiling, we identified 28 AAs and their metabolites out of the 1,089 known metabolites detected. A targeted quantification of these AAs was performed in the same samples to validate any observed differences between the groups. Both aspartate and glycine were found to be more abundant in NDMM compared to MGUS in both untargeted and targeted assays. In contrast,12 AAs (branched chain AAs: valine, leucine and isoleucine, aromatic AAs: tryptophan, kynurenine, tyrosine and phenylalanine; essential AAs: threonine and lysine; non-essential AAs: ornithine, citrulline and asparagine) were found to be less abundant in NDMM compared to MGUS. We then validated the presence of these differences using targeted quantification of AAs in the BM plasma samples from cohort #2. Aspartate was the only AA consistently found to be present at higher levels in NDMM vs. MGUS. Whereas, 8 AAs—threonine, valine, citrulline, tyrosine, tryptophan, isoleucine, phenylalanine, and kynurenine—were consistently lower in NDMM vs. MGUS. We observed a statistically significant positive correlation between the concentration of aspartate and the percentage of clonal PCs present in the BM (r = 0.56). In contrast, there were statistically significant negative correlations between the concentrations of all 8 AAs consistently lower in NDMM vs. MGUS and the percentage of clonal PCs in the BM: threonine (r = -0.41), valine (r = -0.35), citrulline (r = -0.37), tyrosine (r = -0.28), tryptophan (r = -0.31), isoleucine (r = -0.21), phenylalanine (r = -0.19), and kynurenine (r = -0.19). Using only the quantitative concentrations of the 9 AAs that were significantly different between NDMM and MGUS across both patient cohorts, we performed ROC analyses to develop predictive models distinguishing NDMM from MGUS. In cohort #1, the models yielded AUC values ranging from 0.738 to 0.785, while in cohort #2, AUC values ranged from 0.798 to 0.835, depending on the number of AAs included in the model. CONCLUSIONS: Several AAs are present at significantly different levels in the BM plasma of patients with NDMM compared to MGUS. These findings support the emerging role of AA availability, across a diverse set of AAs, in the pathogenesis and progression of MGUS to MM. Future studies are warranted to validate these observations and to determine whether alterations in the BM AA landscape can be leveraged for diagnostic or therapeutic strategies.

Abstract Background: Recent clinical trials of immune checkpoint inhibitors (ICI) in multiple myeloma (MM) demonstrated the need to prioritize patients most likely to benefit from this type of therapy (Richard, et al., Nat. Cancer, 2024). Immune checkpoints in MM function through receptor-ligand (RL) interactions between CD8+ T cells and malignant plasma cells. However, there is a gap in our understanding of the immune checkpoint landscape in newly diagnosed MM (NDMM). To explore this, we mapped known immune checkpoint RL pairs in subjects enrolled in the CoMMpass Study (NCT01454297), a prospective, longitudinal observational trial. Methods Bone marrow aspirates from study participants were collected and separated into CD138 positive and CD138 negative fractions for comprehensive molecular characterization of the tumor (using bulk RNA sequencing and whole genome sequencing) and tumor immune microenvironment (using 3' single cell RNA sequencing (scRNA-seq)). 191 patients had paired bulk tumor and single cell immune expression profiles. To evaluate the co-expression of receptor-ligand pairs, we developed two approaches that combine gene expression values across single cell and bulk RNA-seq. The first approach uses joint percentiles to identify patients with high expression (top 75%) of both receptor and ligand (referred to as high-high). The second approach uses a rank-based interaction score to create a single value for each patient within a distribution defined for each receptor-ligand pair; patients with extreme values have high expression of both receptor and ligand. The two methods identified similar sets of patients with high co-expression of receptor and ligand. We then compared progression free survival (PFS) across receptor-ligand co-expression groups (high-high vs. others) and tested for other clinical associations. From a pre-defined list of 17 receptor-ligand pairs (Liu, et al., Exp. Hematol. Oncol., 2023), we focused on the following receptors (from CD8+ T cells) and ligands (from tumor plasma cells): PD-1 with PD-L1; LAG-3 with MHC-II; TIGIT with CD115; and TIM-3 with phosphatidylserine. Results Overall, 91 patients (91/191, 47.6%) showed high-high co-expression for at least 1 out of 17 receptor-ligand pairs, and 100 patients were high-high for no receptor-ligand pairs. Among patients with at least one high-high pair, there was a median of 2.0 high-high pairs (range: 1-11). Hierarchical clustering of high-high status and rank-based interaction score correlations showed that for a given receptor, patients often showed high-high co-expression across multiple related ligands. For TIM-3 (receptor, CD8+ T cells) and phosphatidylserine (ligand, tumor plasma cells), we identified 16 patients (16/191, 8.4%) with high-high co-expression and significantly worse PFS (median survival 485 vs. 1503 days, log-rank p-value 0.043). For PD-1-PD-L1, we identified 7 patients with high-high co-expression; for LAG-3-MHC-II, 12 patients (6.3%); and for TIGIT-CD115, 13 patients (6.8%). Conclusion ICI therapy remains an emerging area of study in MM. Here, we developed methods to explore immune checkpoint receptor-ligand co-expression from paired immune microenvironment and tumor cell populations. Our approach demonstrates the value of data integration across gene expression modalities, especially when standard sample collection methodologies segregate tumor and immune populations. Co-expression analysis may be one factor in prioritizing patients for immune checkpoint inhibitor therapy.

Abstract Introduction: Bispecific T-cell engagers (TCE) targeting BCMA (teclistamab, elranatamab) and GPRC5D (talquetamab) are important therapeutic options in relapsed/refractory MM (RRMM). However, data on the independent prognostic impact of individual cytogenetic abnormalities and functional high-risk MM on outcomes with TCEs remains limited. Methods We included RRMM patients treated with BCMA TCEs (teclistamab, elranatamab) and talquetamab across 15 centers in the US. Patients where TCEs were used only as bridging therapy to CAR-T were excluded. High-risk cytogenetic abnormalities (HRCA) included del(17p), t(4;14), t(14;16), 1q gain/amplification (1q21+), del(1p) before initiation of TCE. Functional high-risk disease (FHRMM) was defined as PFS <18 months with frontline therapy. Extramedullary disease (EMD) included visceral or soft tissue disease and excluded paraskeletal disease. Progression-free survival (PFS) was estimated from TCE initiation using the Kaplan–Meier method and compared using the log-rank test. Univariate and multivariable Cox proportional hazards models were used to identify predictors of PFS in the entire cohort and the subcohorts of BCMA TCEs and talquetamab (Talq). Results We analyzed 943 treatment initiations with TCEs in RRMM, comprising 577 BCMA-directed TCEs [teclistamab (n=436) and elranatamab (n=141)] and 366 talquetamab initiations. The median follow-up from TCE initiation was 13.4 months (95% CI: 12.5–14.2) and median age was 68 years (interquartile range 61-75 years). Two ore more HRCAs were noted in 30% of the cohort (34% in Talq vs. 28% in BCMA TCEs, p=0.04), EMD was present in 21% of patients (26% in Talq vs. 18% in BCMA TCEs, p=0.002), and 45.5% of patients had FHRMM (47% in Talq vs. 45% in BCMA TCE, p=0.54). The median prior lines of therapy was 6 (interquartile range: 5-8), with 92% being triple-class refractory (TCR); 60% received prior BCMA-directed therapy (85% in Talq vs. 45% in BCMA TCE, p<0.0001) and 45% had prior CAR-T in 45% (57% in Talq vs. 37% in BCMA TCE, p<0.001). The best overall response rate (ORR) was 67.1 % for the entire cohort (66.4% for BCMA cohort and 68.2% for Talq), with 32.5% patients achieving a ≥ complete response. The median PFS was 7.7 months (95% CI: 6.7–8.6) for the entire cohort, with a median PFS of 10.1 months (95% CI: 7.5-13.4) for teclistamab and 10.1 months (95% CI: 3.9-NR) for elranatamab. In the more heavily pretreated talq cohort, the median PFS was 6.4 months (95% CI: 5.1-7.5). In a univariate analysis (UVA), several HRCAs were associated with inferior PFS−del(17p) (HR 1.59), t(4;14) (HR 1.43), del(1p) (HR 1.54), and ≥2 HRCAs (HR 1.64), all p<0.01. Apart from HRCAs, adverse disease characteristics included FHRMM (HR 1.20) and EMD (HR 1.46); all p<0.05. Among laboratory markers, hemoglobin <9 g/dL (HR 2.10), platelet count <75 ×10⁹/L (HR 1.96), and elevated ferritin (≥600 ng/mL; HR 2.17) (all p<0.01) were prognostic for PFS. ECOG ≥2 (HR 1.62), prior BCMA-directed therapy (HR 1.56), and prior CAR-T (HR 1.44) (all p<0.001) were also associated with inferior PFS. In a multivariable analysis (MVA), prior BCMA therapy (HR 1.72, p<0.0001), ferritin >600 ng/mL (HR 1.68, p<0.0001), hemoglobin <9 g/dL (HR 1.53, p=0.001), ECOG PS ≥2 (HR 1.52, p=0.0009), del(17p) (HR 1.44, p=0.0034) and FHRMM (HR 1.26, p=0.047), were associated with inferior PFS. Within the talq cohort (n=366), EMD (HR 1.64), del(17p) (HR 1.42), del(1p) (HR 1.54), FHRMM (HR 1.34), ECOG ≥2 (HR 1.56), ferritin >600 (HR 1.88), hemoglobin <9 g/dL (HR 1.6), platelet count <75 ×10⁹/L (HR 1.36) and leukopenia <0.25 x 10⁹/L (HR 1.52) were adverse prognostic markers on UVA. A MVA confirmed the independent impact of ferritin >600 ng/mL (HR: 1.77), ECOG ≥2 (HR 1.61) and functional high-risk status (HR 1.52) on PFS, whereas del(17p) and EMD were trending toward significance. Within the BCMA TCE cohort (n=577), prior BCMA therapy (HR 1.9), ferritin >600 (HR 1.79), ECOG ≥2 (HR 1.55), Hb <9 g/dL (HR 1.54), and del(17p) (HR 1.52) were independently associated with inferior PFS on a MVA.Conclusions: In this large real-world cohort of TCE, outcomes varied by fitness, disease biology, and treatment history. The ECOG PS, elevated ferritin, functional high-risk status and del(17p) were consistently associated with inferior PFS. Prior BCMA exposure was an independent risk factor for inferior PFS in the BCMA TCE cohort, but not with talquetamab.

Abstract Introduction Cila-cel is associated with a 10-15% risk of delayed neurotoxicity (DNT) with rates of cranial nerve palsies (IEC-NP) and parkinsonism (IEC-PKS) seen at a rate of 10% and 1-4% respectively and can occur as early as 3-4 weeks after infusion. We and others have shown that high post-infusion peak absolute lymphocyte count (ALCpeak) of >3x10^9/L is associated with an increased risk of delayed neurotoxicity (Lim et al., ASCO 2025) whilst the risk was negligible (~1%) when ALCpeak fell below that threshold. Since December 2024, we implemented prophylactic administration of dexamethasone (PPX DEX) when ALC was >3x10^9/L (defined as high ALCpeak). Outcome of these patients are described here. Methods Analysis of clinical outcome included 230 pts treated between Feb 2022 and Nov 2024 as a historical cohort and 102 patients treated between Dec 2024 and May 2025 after the introduction of dexamethasone prophylaxis using an oral dose of 10mg twice a day for at least three days in the high ALCpeak cohort. Results No significant differences were seen between the new and historical cohort in pre-lymphodepletion disease burden (bone marrow plasma cell %, involved light chain or M-spike level), and rates of all/high-grade CRS or ICANS, IEC-HS and rates of tocilizumub use. Median follow-up in the new cohort of 102 pts was 3.6 months (95% CI 3.3-4.2). Eleven (10.8%) developed DNT [3 (2.9%) with IEC-PKS, 8 (7.8%) with IEC-NP]. High ALCpeak of >3x10^9/L was observed in 51 (50%) pts. Among pts with high ALCpeak, DNT was observed in 10 (20%) pts [3 (5.9%) with IEC-PKS, 7 (13.7%) with IEC-NP] whereas among 51 pts with ALCpeak <3x10^9/L, DNT was observed in 1 (2%) pt [1 (2%) with IEC-NP]. In the high ALCpeak cohort, 38 (75%) received DEX [6 (12%) for toxicity management, 32 (63%) PPX DEX]. Of the 38 pts treated with DEX, DNT was observed in 6 (16%) pts (2 (5.3%) IEC-PKS, 4 (10.5%) IEC-CNP). We next compared DNT rates between this new cohort of high ALCpeak pts receiving DEX (n=38) against the historical cohort with high ALCpeak (n=98). Estimated 90-day DNT by Kaplan-Meier method was (19.2% vs 20.5%; p=0.53): IEC-PKS (5.8% vs 7.2%; p=0.60) and IEC-NP (11.7% vs 13.3%; p=0.58). In patients with high ALCpeak, the use of DEX did not appear to significantly reduce the risk of DNT compared to historical controls (HR 0.76, 0.33-1.78), IEC-PKS (HR 0.67, 0.14-3.10) or IEC-NP (0.73, 0.24-2.23). We next compared ALC expansion over the first month post CAR-T infusion (area under the curve, AUC). Among the new cohort of 102 patients, no difference was seen in the AUC of pts who received DEX but did not develop DNT vs those who developed DNT (31.0 vs 57.9, p=0.28); No difference in AUC was seen in the new high ALCpeak cohort receiving DEX vs the historical cohort with high ALCpeak (36.0 vs 43.8, p=0.15) Of the eight cases of IEC-NP, 4 received PPX DEX. No noticeable differences were observed in the severity of presentation, laterality of cranial nerve involvement and rates of multiple cranial nerve involvement between these 4 cases who received PPX DEX and the 16 cases from the historical cohort. At the time of database freeze, 3 of the 4 cases had resolved and 1 was improving. Median time to complete symptom resolution was 38 days vs 69 days in the historical cohort. Of the three cases of IEC-PKS, 2 received PPX DEX. Median time to symptom onset for IEC-PKS was 46 days (vs. 24 days in historical cohort). Both cases had severe symptoms non-responsive to first line high-dose methylprednisolone, second line intrathecal chemotherapy and third line high-dose cyclophosphamide. Both pts subsequently died from sepsis 28 and 67 days post-cyclophosphamide. Conclusion While ALCpeak >3x10^9/L predicts risk of DNT in pts treated with cilta-cel, the use of PPX DEX does not appear to significantly reduce ALC expansion and does not appear to significantly reduce the risk of DNT, IEC-PKS or IEC-NP. Other strategies to reduce ALC or alter T cell functions may need to be considered.

Abstract Background: Systemic AL amyloidosis is an incurable disorder caused by misfolded light chain fibrils. Survival is determined by the extent of cardiac involvement and response to therapy. Mayo 2012 (stage I-IV) and European modified Mayo 2004 (stage I-IIIB) prognostic scores were developed in a historic treatment era, prior to widespread bortezomib and daratumumab use. These no longer discriminate those with the poorest outcomes. We presented a new model sub-stratifying IIIB into a high risk, ‘IIIC’ group, incorporating longitudinal strain (LS) to the European modified score (IIIC: LS >-9% + NT-proBNP >8500ng/L + high-sensitivity troponin T [hs-TnT] >50ng/L) in the bortezomib-era (2015-2019) last year (Khwaja et al, ASH 2024). Here, we present an independent external validation of the IIIC model in the modern treatment era. Methods: The ‘IIIC model’ was derived from a large real-world cohort of adults from the UK National Amyloidosis Centre (2015-2019). Performance was externally validated across Europe (Greece, Italy, Netherlands, Switzerland), USA (2015-2024) and UK (2020-2024). Survival estimates were generated from diagnosis to death or last follow-up using the Kaplan-Meier method and groups compared using Cox regression (using Stata v18). Stage IIIB and IIIC were defined as follows IIIB: LS <-9%, NT-proBNP >8500ng/L, hs-TnT >50ng/L and IIIC: LS >-9%, NT-proBNP >8500ng/L, hs-TnT >50ng/L, respectively. Stage I, II and IIIa were defined as previously described in the European modification of Mayo 2004 staging system. Results: 2493 patients were analysed from 2015-2024 (derivation, n=573; validation: Europe, n=374, USA n=458, UK, n=1088). In the derivation cohort, LS ≥-9% and standard cardiac biomarkers (NT-proBNP 332ng/L and 8500ng/L and hs-TnT>50ng/L) were selected as independent prognostic factors in a multivariable model. In the European and UK validation cohort (n=1462), median age at diagnosis was 67 years (range 35-95) with 1014 (69%) cardiac and 869 (60%) renal involvement. Median difference in involved and uninvolved free light chain (dFLC) was 181 mg/L (range 0-24933), NT-proBNP 1911ng/L (range 42-70000), hs-TnT 45ng/L (range 3-1221) and LS -14.7% (range -3- -28). In the US cohort median NT-proBNP was 1764 (range 13-71000) and hs-TnT 44 (6-687). Applying the new model staging, the patient stages were across Europe and UK were stage I: 224 (15%), II: 627 (43%), IIIA: 396 (27%), IIIB: 139 (10%) and IIIC: 76 (5%), respectively. 592 (41%) were treated with first-line daratumumab-based regimens. In the US dataset stage I: 103 (22%), II: 176 (38%), IIIA: 131 (29%), IIIB: 48 (10%) and IIIC: 16 (3%), respectively. 234 (51%) were treated with first-line daratumumab-based regimens. At a median follow-up of 30 months (95% CI 28-32) in the European and UK validation cohort, estimated median overall survival (OS) was not reached and 2-year OS was 74% (95% CI 72-77). Median survival for stages I-II, IIIA, IIIB and IIIC were not reached (NR), 67, 31 and 10 months, respectively (p<0.001). Hazard ratios for stage II, IIIA, IIIB, IIIC were 2.01 (95% CI 1.32-3.07), 3.51 (95% CI 2.31-5.35), 5.67 (95% CI 3.60-8.91) and 11.12 (95% CI 6.93-17.87), respectively (p<0.001). This was confirmed in the US cohort with a median survival for respective stages were NR, NR, NR, 8 and 2 months (p<0.001). External validation in the European and UK cohorts exhibited good predictive performance: 12-month calibration slope was 1.00 (95% CI 0.81-1.20, p=0.98), Harrel's C was 0.90 (95% CI 0.76-1.00), Royston's D 1.07, R2D 0.21 (95% CI 0.15-0.28). In 1462 patients with dataset available, stage IIIC discriminated the poorest outcome despite a greater proportion treated with first-line daratumumab v IIIB in the validation cohort (59% v 40%, p=0.008). 12-month OS for IIIC v IIIB was 44% (95% CI 32-55) v 69% (95% CI 60-76), specificity for 12-month OS 97% v 92% and diagnostic odds ratio 6.62 v 2.36. Conclusions: This study validates the new staging system from stage I-IIIC with robust identification of an ultra-poor prognostic group (Stage IIIC) in a contemporarily treated patient cohort. Despite novel therapies, the OS remains <12 month in the IIIC group and also poor in stage IIIB with median OS 31 months compared to a median of >5 years for all other stages. This staging system can be considered as a new standard of care for risk stratification of AL amyloidosis. Novel approaches are required for patients with advanced risk.

Abstract Background Chimeric-antigen-receptor (CAR) T-cell therapy has transformed outcomes for relapsed or refractory hematologic malignancies, yet early identification of patients at highest risk for death remains elusive. A prognostic model that leverages only the information routinely available at the time of CAR-T evaluationcould guide pre-infusion counseling, intensity of follow-up, and resource allocation. Methods We analyzed a prospectively maintained cohort of CAR-T recipients treated at Mayo Clinic to develop and test our model. For each patient, data (vital signs, lab results, cardiac assessments [ECG and echocardiography], and PET/CT images) from initial CAR-T evaluation (up to 60 days prior to CAR-T infusion) was considered. Vital signs and labs were left un-imputed, while ECG, echocardiographic parameters, and PET/CT features were forward-filled from the preceding 60 days. Each patient-day was labeled as “outcome present” or “no outcome,” enabling supervised training of gradient-boosting models to predict overall survival (OS) at 6 months post CAR-T infusion. Model performance was evaluated using the area under the ROC curve (AUC) with 10,000-fold bootstrapping with data shuffling between each iteration to derive confidence intervals for stability. To enhance interpretability, we also developed a simplified model using the top 19 features (determined by Shapley value-based importance), aiming to preserve predictive accuracy with a smaller feature set. Results A total of 570 CAR-T cell treated patients were included in this study (476 patients in the training cohort and 94 patients in the validation cohort). The median age was 64 years and 61% males. The primary diagnoses were B-cell lymphoma (324 patients) and multiple myeloma (246 patients). A total of 4,851 individual vital-sign readings, 11,781 laboratory measurements were included. PET/CT images were available in 318 patients. The ML model demonstrated high discriminative ability for predicting OS at 6 months post CAR-T infusion. Vital signs alone produced a mean AUC of 0.63; radiomics features alone produced a mean AUC of 0.71, while laboratory tests alone produced a mean AUC of 0.84. The features most contributory to each single-modality model were then combined into a simplified multimodal model with a total of 19 features, which produced a mean AUC of 0.87, with a 95% CI of 0.73 - 0.97. The 19 features included in the simplified model were: LDH, ferritin, uric acid, eosinophils, fibrinogen, D-dimer, heart rate, mean arterial blood pressure, QTc, QT interval, and the radiomics features of sphericity, first-order Kurtosis, first-order mean, first-order energy, first-order total energy, shape maximum2D diameter (sagittal plane), SUV CoV, SUVp90, shape flatness. This multimodal predictive model was then applied to the validation cohort (uninvolved with model training or testing), which produced an AUC of 0.93, with a 95% CI of 0.84 - 0.98. Conclusions Our ML-driven approach successfully predicts overall survival 6 months post CAR-T infusion, highlighting those individuals at most risk of poor outcomes at the time of CAR-T evaluation. The strong performance of the multimodal version of the model suggests that each data domain contributes complementary resolving power, and by including novel data types, such as PET CT radiomics features, the most accurate predictions could be realized. By providing accurate, near-real-time risk stratification for poor outcomes within 6 months of CAR-T infusion, the model could facilitate proactive patient management. Prospective studies of the integration of this ML model into CAR-T treatment workflows will be crucial to confirm its clinical benefits and to facilitate its adoption in practice.

Abstract Background Etentamig is a second-generation B-cell maturation antigen (BCMA) x CD3 bispecific antibody that has shown promising efficacy and a favorable safety profile in heavily pretreated patients with relapsed/refractory multiple myeloma (RRMM). Etentamig possesses a unique combination of 2 high-affinity BCMA binding domains, coupled to a low-affinity CD3 binding domain that have been shown to reduce the negative impact of soluble BCMA (sBCMA), decrease risk for cytokine release syndrome (CRS), and drive sustained T-cell activation with reduced immune exhaustion in preclinical models of MM (Blood 2023;142 [suppl 1]:4666). Here, we describe correlative biomarker analyses from Phase 1 (NCT03933735) and Phase 1b (NCT05650632) clinical trials of etentamig in RRMM designed to assess the impact of sBCMA on clinical response and pharmacodynamic changes in proinflammatory cytokines, and T-cell redistribution, activation, proliferation, and exhaustion. Methods Peripheral blood and serum samples from patients with RRMM enrolled in Phase 1 and Phase 1b open-label studies who received etentamig monotherapy were collected at baseline, on treatment, and at disease progression. The Phase 1b study consisted of dose-optimization (DO) and dose-expansion (DE) phases. During DO, patients received a single step-up dose (SUD) of intravenous etentamig (2 or 4 mg) on Cycle (C) 1 Day (D) 1, followed by the target dose of 60 mg on D4. In DE, based on DO findings, patients received the recommended etentamig SUD (2 mg) on C1D1, followed by 60 mg on D4 with modified dexamethasone predosing. Samples were analyzed by flow cytometry for immune cell populations, Luminex® for cytokines, and electrochemiluminescence for sBCMA. Peripheral blood mononuclear cells and bone marrow mononuclear cells (BMMC) at baseline and on-treatment were subjected to single-cell Cellular Indexing of Transcriptomes and Epitopes sequencing (scCITEseq) and single-cell T-cell receptors (scTCR) / B-cell receptors clonality analyses. Results While baseline serum sBCMA levels did not correlate with clinical response to etentamig at the selected dose level of 60 mg every 4 weeks (Q4W) with 2 mg SUD, a reduction in sBCMA levels over time was associated with best International Myeloma Working Group response. Etentamig treatment led to a rapid and transient increase of proinflammatory cytokines and promoted T-cell redistribution, activation, and proliferation. Maximum reduction in peak levels of CRS-related cytokines, including (interleukin [IL]-6, IL-8, IL-10, tumor necrosis factor–a), were observed in DE compared with DO and 60 mg Q4W without SUD. Peak induction levels of proinflammatory cytokines during C1 correlated with occurrence of CRS (≥Grade [G] 1) as well as clinical response across dose levels. A modified dexamethasone premedication schedule of 2 mg/60 mg Q4W cohort for C1 resulted in significantly reduced baseline levels of IL-6 and key macrophage-associated cytokine/chemokines (eg, monocyte chemoattractant protein-1 and macrophage inflammatory protein-3 beta). Incidence of CRS was lowest in the 2 mg/60 mg Q4W cohort (26% G1, 4% G2, 0% ≥G3). Higher T-cell to myeloma cell ratio in BMMCs at baseline correlated with better response to etentamig. scCITEseq and scTCR analysis revealed that at baseline, responders also had greater CD8+ T-cell cytotoxic activities, increased TCR clonality, and elevated inflammatory signatures in T-cells from both peripheral blood and bone marrow. Etentamig at a dosage of 60 mg Q4W resulted in significant clonal expansion of CD8+ effector T-cells and enhanced gene expression signatures related to activation and cytotoxicity in both CD8+ T effector and natural killer cells. Along treatment cycles, responders maintained their T-cell fitness, high clonality, and low T-cell exhaustion levels. Frequency of baseline CD4+ or CD8+ T-cell exhaustion (programmed cell death receptor 1+/ T cell immunoglobulin and mucin-domain containing-3 +) did not impact clinical response at optimal dose level of 2 mg/60 mg Q4W etentamig. Conclusions Responses to etentamig were independent of baseline sBCMA levels and resulted in rapid and transient proinflammatory cytokine production that promoted robust T-cell redistribution, activation, and proliferation indicating that the optimal dose of etentamig (2 mg/60 mg Q4W) maximizes its clinical potential as a convenient, safe, and effective therapy for MM.

Abstract Background Central nervous system involvement in multiple myeloma (CNS-MM) is a rare but highly aggressive manifestation. Limited data exists regarding its incidence, biology, treatment patterns, and outcomes. A deeper understanding of the features unique to CNS-MM will help inform risk stratification and management strategies. Methods We conducted a retrospective study of CNS-MM patients at our institution between January 2000 and December 2023. We identified 305 MM patients with pathology-proven extramedullary disease (EMD). CNS involvement was defined as the presence of parenchymal (brain or spine) or leptomeningeal involvement by MM. Univariate analysis was performed to compare the clinical characteristics between EMD patients with and without CNS-MM. Results Out of 305 patients with EMD, we identified 22 patients with CNS-MM. Common symptoms prompting evaluation of CNS-MM were headaches (32%), numbness or tingling (32%), weakness (27%), confusion (23%), falls (23%), and visual disturbances (14%). Among these patients, 17 (77%) had detectable disease in cerebrospinal fluid (CSF), 12 (55%) in the brain parenchyma, 4 (18%) in the spinal cord parenchyma and 6 (27%) in the leptomeninges. Of the CNS-MM patients, the median age at diagnosis of MM was 58.3 years and the median time to CNS-MM involvement was 16.2 months, with 3 (14%) patients having CNS involvement at diagnosis. The predictors of CNS-MM development were high-risk cytogenetics (OR 2.9, p=0.04) and presence of additional visceral organ involvement outside of the CNS (OR 3.4, p=0.03). EMD was multifocal in 20 CNS-MM patients (83%) and visceral organ involvement [most commonly lymphadenopathy (27%), solid organ (18%) and pleural fluid (14%)] was present in 14 (64%) of these patients, compared to 54% in EMD patients without CNS-MM. High-risk cytogenetics [del(17p), 1q gain/amplification, t(4;14), t(14;16), t(14;20)] were seen in 16 (73%) patients, most commonly 1q gain or amplification (40%), t(4;14) (23%), and del(17p) (18%). Five (23%) exhibited a double-hit profile. In comparison, 113 (40%) EMD patients without CNS-MM had high risk cytogenetics. The median overall survival (OS) from diagnosis of CNS-MM was 6 months (95%CI:1.26-36) compared to 15.6 months (95%CI: 12-18) from diagnosis of EMD in EMD patients without CNS-MM (p=0.05). Median OS from diagnosis of MM in the CNS-MM cohort was 36 months (95%CI: 22.8-NA) compared to 55.2 months (95%CI: 45.6-68.4) in EMD patients without CNS-MM (P=0.11). When excluding CAR-T or bispecific therapy recipients, median OS from diagnosis of MM for CNS-MM was 22 months versus 48 months in EMD patients without CNS-MM (P=0.02). Overall response rate in our CNS-MM cohort with first treatment for EMD was 32% compared to 48% in EMD patients without CNS-MM (p=0.04). In patients with CNS-MM, median progression-free survival (PFS) from CNS-MM diagnosis was 3.3 months (95%CI: 0.8-12) versus 3.9 months (95%CI: 3-5.1) in EMD patients without CNS involvement (p=0.2). Six (27%) CNS-MM patients received CAR-T; 3 received cilta-cel, 2 received ide-cel and one received a BCMA CAR-T on trial. Bispecific therapy was used in 4 (18%) patients, with 2 receiving Teclistamab and 2 Talquetamab. Three (14%) CNS-MM patients received both CAR-T and bispecific therapy. The median PFS on CAR-T therapy was 10.5 (95%CI: 5.6-NA) months and 12.7 (95%CI: 10.2-NA) months on bispecific therapy. The median OS from diagnosis of CNS-MM for patients who received CAR-T or bispecific therapy was 36 months (95%CI: 14.0-NA), compared to 1.2 months (95%CI: 0-14.4) for those who did not (p=0.002). Six (27%) patients with CNS-MM, all of whom had CSF involvement of disease, received intrathecal therapy; methotrexate in 3, cytarabine in 2, and one receiving both. Two of these patients demonstrated improvement in cerebrospinal fluid findings following intrathecal chemotherapy, in conjunction with other therapies. Patients who received intrathecal chemotherapy had a median OS measured from development of EMD of 10.8 (95%CI: 3.6-NA) months, compared to 15 months (95%CI: 4.8-NA) in those without IT chemotherapy (p=0.59). Conclusion Central nervous system involvement in MM is relatively uncommon and associated with dismal outcomes. High-risk cytogenetics and involvement of other visceral sites are associated with a higher risk of CNS involvement. Intrathecal therapy offers limited value and immune effector therapies represent efficacious options for treatment.

Abstract Introduction Detecting genetic abnormalities is mandatory during diagnostic workup and for potential individualization of therapy selection in multiple myeloma (MM) and its precursor conditions. At present, this requires invasive bone marrow (BM) biopsies, severely limiting early detection, patient monitoring, and the precise selection of therapy. The current standard for detecting genetic alterations in MM is fluorescence in situ hybridization (FISH), which cannot detect point mutations and other clinically relevant alterations. Consequently, the IMS-IMWG guidelines were recently updated to require next-generation sequencing for the classification of high-risk MM. Moreover, the importance of identifying alterations in therapeutic targets (e.g., BCMA, GPRC5D) for guiding immunotherapies is becoming increasingly recognized. To address this need, we recently launched GenoPredicta, a CLIA-approved Laboratory Developed Test that enables routine monitoring, informing diagnosis, and treatment selection by comprehensively and robustly characterizing MM genomes with whole genome sequencing (WGS) from a minimum of ~50 circulating tumor cells (CTCs) isolated from peripheral blood (PB) or tumor cells from BM. Here, we describe the characterization of clinical and research samples with GenoPredicta, highlighting resistance-conferring genetic alterations that can only be detected by sequencing and that are consistent with patients' clinical histories, while further demonstrating the complete concordance of GenoPredicta with prior FISH results. Methods Tumor cells were isolated from BM and/or PB samples using fluorescence-activated cell sorting (FACS) (after CD138+ enrichment for PB) and subjected to whole genome sequencing (WGS), from which we identified copy number alterations, structural variants, and single nucleotide variants and indels. For a subset of samples, the BM was also characterized by FISH, using a standard clinical panel for characterizing MM; for other samples prior FISH results were available from patients' clinical records. Results The analytical validation of GenoPredicta, conducted on a retrospective cohort of >60 viably frozen samples from >30 patients, showed complete concordance between BM and PB for detected genomic abnormalities, with all clinically relevant events (11 translocations and 125 CNVs) observed in BM recapitulated in PB (100% recall). All events identified by FISH in 14 samples (8 translocations and 30 CNVs) were also identified in the BM (100% recall), with 50 translocations and 30 CNVs not detected by FISH also absent in the WGS data (100% precision). The abnormalities detected in these samples also included multiple high-risk events not assayed by FISH, including biallelic TP53 loss (Del 17p coupled with a deleterious TP53 mutation, observed in 4 patients) and MYC::IGL translocations (2 patients). Moreover, for all fresh peripheral blood clinical samples with FISH results available from patients' histories, GenoPredicta recalled 100% of reported events. Among these clinical samples, we notably observed a case with both a biallelic GPRC5D deletion and a TNFRSF17 (BCMA) deletion combined with a truncating mutation on the remaining allele. This was consistent with the patient's clinical history showing progression after therapy with talquetamab (anti-GPRC5D bispecific), and previous nonresponse to teclistamab (anti-BCMA bispecific) after prior therapy with Cilta-cel (BCMA-directed CAR T). GenoPredicta also detected other mutations likely to confer resistance to immunotherapies, including loss-of-function mutations in IKZF3 and deletions of FCRL5 in frozen peripheral blood research samples. Conclusion We demonstrate that WGS-based characterization of MM from BM or CTCs is a viable replacement for FISH for clinical diagnosis and monitoring, with blood-based measurements enabling comprehensive profiling of the myeloma cancer genome. Importantly, this includes genetic alterations that confer resistance to therapy, allowing for both early detection of such alterations and more precise selection of therapy.