Rajkumar, S. Vincent

Background: Multiple myeloma (MM) involves the proliferation of monoclonal plasma cells (PCs) in the bone marrow. The plasma cell labeling index (PCLI), indicating the percentage of PCs in the S-phase, is a key prognostic factor in newly diagnosed multiple myeloma (NDMM) patients. Methods: A retrospective study at Mayo Clinic analyzed bone marrow Affymetrix microarray datasets from 136 NDMM patients (2001-2012). Data normalization and quality assessment were followed by differential gene expression analysis using limma-voom, adjusted for age, BMPC percentage, and disease stage (RISS). Selected genes were dichotomized by median and subjected to logistic regression for high/low PCLI. Data from 594 patients with a Proliferation Index at baseline was pooled from the MMRF (CoMMpass) dataset for validation after normalization across assays. Results: The baseline characteristics of the patients are as follows: 62 (46%) were female and 74 (54%) were male, with a mean age at diagnosis of 61.6 ± 11.5 years. RISS staging included 36 patients (26.5%) at stage 1, 32 (23.5%) at stage 2, 29 (21.32%) at stage 3, and 39 patients (28.67%) with unknown stage. PCLI distribution showed 113 patients (83.7%) with <2%, and 23 (16.3%) ≥2%. Differential Analysis: The differential analyses reveal several critical insights. In the primary continuous differential analysis, 21,367 genes were analyzed, identifying 654 as differentially expressed with an FDR-adjusted p-value ≤0.05. Of these, 481 genes were upregulated (top 5: SPC25, TRAT1, PBK, E2F8, ASPM) and 173 were downregulated (top 5: SPARC, CD24, MYOF, CXCL12, VCAM1). Univariate and multivariate stratified analyses showed significant findings: 668 genes in age stratification, 395 genes in BMPC% stratification, and 536 genes in RISS stratification. In multivariate analysis, 408 genes were significant with an FDR-adjusted p-value <0.05 for PCLI and ≥0.05 for BMPC%, age, and RISS. 3-Fold Internal Validation: 240 significant genes were identified in fold 1, 25 in fold 2, and 167 in fold 3, with 21 genes common across all folds (e.g., MCM10, GTSE1, UBE2S, NCAPG, FOXM1, STMN1, NEK2, CENPF, MND1, HJURP, E2F8, TOP2A, SKA1, CCNB2, CCNF, CHAF1A, PBK, EXO1, AURKB, CDCA2, DHFR). 19 genes were common in the sensitivity analysis by excluding RISS 3. 20 genes were common in categorical analysis (Categories <1%, 1-2%, 2-3%, 3-4%, >4%). 3 Gene-Based Proliferation Score (3GPS): On univariate logistic regression analysis of 21 genes for high/low PCLI, 17 significant genes were identified. In the step-wise multivariate analysis, CDCA2 (OR=11.56), DHFR (OR=9.6), and CENPF (OR=6.11) were significant. Log-odds were used as weights. For the high expression(median and above) of gene as 1 and low expression as 0: 3GPS=2.4*CDCA2+ 2.3*DHFR +1.8*CENPF A cut-off of 2.4 was chosen as CDCA2 had the highest positive concordance rate (90.91%) versus DHFR (86.36%) and CENPF (77.27%). External Validation: In the CoMMpass dataset, the sensitivity and specificity for the presence of high expression of any of the 3 genes with ≥2 Proliferation Index (PI) were 92.4% and 73.8%, respectively (AUC=83.1%). The median 3GPS was 0 for <2 PI (n=238) and 6.5 for ≥2 PI (n=356). The sensitivity and specificity for the 3GPS in predicting ≥2 PI were 82.6% and 93.6%, respectively (AUC=88.1%). Pathway Analysis: Significant pathways using Reactome include polo-like kinase mediated events in G2/M transition, condensation of prometaphase chromosomes in metaphase, resolution of sister chromatid cohesion in anaphase, and APC-Cdc20 mediated degradation of Nek2A for regulation of the cell cycle. p53 signaling, cancer pathways, and microtubule regulation were also mapped. Survival Analysis: In our cohort, for CDCA2 quartiles (Q)3 and Q4, for CENPF Q3 and Q4, and for DHFR Q4 were associated with inferior overall survival. 3GPS ≥2.4 was associated with inferior survival of 60.28 months versus 111.5 months for <2.4 (p<0.01). In the validation dataset, median survival was not reached at 100 months (vs. high 79.7, p<0.01). The multivariate Cox model found 3GPS score ≥2.4 as an independent predictor (HR=1.64, 95% CI: 1.23 - 2.19), along with ECOG status, ISS stage, gender, age, and whether the patient underwent Stem Cell Transplant. Conclusion: CDCA2, DHFR, and CENPF are associated with ≥ 2% PCLI. The 3GPS score can serve as a measure of gene-based proliferation index. Prospective validation is needed for clinical practice.

Background: AL amyloidosis is a potentially fatal plasma cell dyscrasia. In a randomized phase 3 study (Kastritis E et al, NEJM 2021;385:46-58), the addition of daratumumab to cyclophosphamide, bortezomib and dexamethasone (Dara-CyBorD) significantly improved hematological and organ responses, and prolonged event-free survival (EFS). There was no overall survival (OS) difference at a median follow-up of 11.4 months. Methods: A retrospective study of AL amyloidosis patients seen at our institution between January 2018 and December 2022 who received upfront Dara-CyBorD or CyBorD. We excluded patients who received other forms of upfront therapy (including modifications of the above regimens, such as bortezomib-dexamethasone or daratumumab-dexamethasone). We compared the hematologic and organ responses, EFS and OS between the two treatment groups. Hematologic response was assessed at 2 and 6 months from therapy initiation, while cardiac and renal responses at 6- and 12-month landmarks. In all response evaluations, we included responses up to autologous stem cell transplantation (ASCT) if used or second-line therapy for any reason. For EFS analysis, an event was defined as death of any cause, hematological progression, end-stage cardiac or renal failure, or need for subsequent anti-plasma cell therapy in the absence of hematological progression. OS was calculated from the time of treatment initiation to death or last follow-up. Results: Among 641 newly diagnosed patients seen at our center between 2018 and 2022, 361 patients (56%) were included on intention-to-treat analysis [Dara-CyBorD =147 (41% of the cohort); CyBorD=214 (59%)]. There was no difference in the baseline characteristics between the treatment groups, including age, sex, difference between involved and uninvolved light chains, bone marrow plasma cells percentage, NT-proBNP and 24-hour proteinuria. The 2-month hematologic complete response (hemCR; 15.3% vs 4.4%, P<0.001) and very good partial response or better (≥hemVGPR; 62.2% vs 32.5%, respectively; P<0.001) were higher with Dara-CyBorD compared to CyBorD. At 6 months, the hemCR and ≥hemVGPR were again in favor of the Dara-CyBorD group compared to CyBorD (32.0% vs 13.7%, and 72.8% vs 46.7%, respectively; P≤0.002). The 6- and 12-month cardiac response rate in the Dara-CyBorD and CyBorD groups were 40.2% vs 22.6% (P=0.01) and 44.1% vs 23.2%, respectively (P=0.006). Cardiac ≥VGPR at the 6- and 12-month landmarks were statistically higher with Dara-CyBorD compared to CyBorD and increased in both arms between 6 and 12 months [20.5 % vs 9.3% (P=0.03) and 34.3 % vs 20.0% (P=0.04), respectively]. The 6-month and 12-month renal response to first-line therapy were numerically higher with Dara-CyBorD compared to CyBorD but were not statistically significant (48% vs 31.3%, P=0.10; 54.1% vs 30.1%, P=0.05). The 6 month and 12 months, renal ≥VGPR were numerically higher with Dara-CyBorD compared to CyBorD, again without statistical significance (26% vs 19.6%, P=0.63; 35.7% vs 17.2%, P=0.11, respectively). Treatment with Dara-CyBorD was significantly associated with lower 6-month mortality rate compared to CyBorD (8.8% vs 16.3%, P=0.04). With a median follow-up of 43.3 months (30 vs 59.4 months for Dara-CyBorD vs CyBorD, respectively; P<0.001), the two-year EFS was superior in the Dara-CyBorD group compared to CyBorD (69.1% vs 30.3%, hazard ratio (HR) 0.36, 95% confidence interval (CI), 0.26 to 0.48, P<0.001). The 2-year OS was also statistically higher in the Dara-CyBorD group compared to CyBorD (82.0% vs 69.6%, HR 0.53, 95% CI, 0.36 to 0.79, P=0.003). 148 patients (40.9%) proceeded to second-line therapy with a median of 5.1 months from first line therapy. To adjust for difference in follow-up time between the two treatment groups, we analyzed the second line therapy utilization at the 12-month mark which was statistically higher with CyBorD compared to Dara-CyBorD (42.9% vs 14.9%, P<0.001). Also, at 12 months, a higher proportion of patients treated with CyBorD received ASCT compared to Dara-CyBorD (24.3% vs 13.6%, P= 0.01). Conclusions: In this cohort of newly diagnosed AL amyloidosis patients in a large referral center, we confirmed higher hematological and organ response rates with Dara-CyBorD compared to CyBorD. We have also demonstrated that these higher response rates translate into a survival advantage of Dara-CyBorD over CyBorD.

Introduction The tumor ecosystem in multiple myeloma (MM) involves complex interactions among various cell types, including clonal plasma cells (PCs) and immune cells. Previous research has indicated that the bone marrow (BM) microenvironment's metabolite and lipid profiles differ between MM patients and those with precursor conditions. This study aims to explore if differences in cell composition, metabolic phenotype of clonal PCs, and the spatial organization of various cell types are linked to the early versus late progression of high-risk smoldering MM (SMM) to MM. Methods We identified high-risk SMM patients using the IMWG 20-2-20 criteria, focusing on early progressors (EP), who progressed to MM within one year, and later progressors (LP), who progressed after more than four years. Using a high-dimensional single-cell spatial imaging platform, CODEX (CO-Detection by indEXing), we identified clonal PCs and immune cell types as well as their metabolic phenotypes at the single-cell level, in paired FFPE BM samples at SMM diagnosis and at the time of progression to MM. Our validated panel included markers such as CD3e, CD4, CD8, CD14, CD20, CD31, CD38, CD45, CD66a, CD68, CD138, HLA-DR, ASCT2, pNRF2, GLUT1, LDHA, ATPA5, CPT1A, and Citrate synthase. We employed single-cell segmentation, artifact cleanup, signal thresholding, and spatial analysis. Repeated measure models accommodated multiple regions of interest (ROIs) per patient and accounted for potential intrapatient correlation, while Poisson models estimated incidence rate ratios (RR) of cell types. Spatial statistical models evaluated cell type segregation and integration at the time of SMM diagnosis. Results Single cell events were acquired from 6 unique SMM patients who had paired FFPE BM samples from the time of their SMM diagnosis and at the time of their progression to MM. There were 3 EP SMM patients (Time to Progression (TTP):8, 8, 10 mos) and 3 LP SMM patients (TTP: 51, 58, 117 mos). The median number of single cell events identified per patient slide sample was 104,390 (Range: 24,357 - 314,877). We identified 8 main cell types: PCs, CD4+ T-cells, CD8+ T-cells, B-cells, granulocytes, Monocyte/Macrophages and two groups of other immune cells. The PCs and the remainder of the non-PCs were further classified based on the expression of the following metabolic markers: ASCT2 (Glutamine importer), CPT1A (Fatty acid metabolism), GLUT1 (Glucose importer), LDHA (Glycolysis), Citrate synthase and ATPA5 (Oxidative Phosphorylation). There was a higher RR of granulocytes (RR 2.1, CI 1.6-2.8, p<0.001) in EP compared to LP samples at the time of SMM diagnosis, but a lower RR of CD4 T cells (RR 0.40, CI 0.3-0.6, p< 0.001). Focusing on metabolic phenotypes of the PCs, we observed higher incidence RR of most metabolic markers: ASCT2 (RR=1.77; p=0.032), ATPA5 (RR=1.25; p<0.001), CPT1a (RR=2.42; p=0.015), and LDHA (RR 2.13; p<0.001) in EP compared to LP samples at the time of SMM diagnosis. However, GLUT1 had a lower incidence RR in EP patients (RR=0.79; p<0.001). The same modeling for non-PCs at the time of SMM diagnosis yielded higher expression of LDHA (RR=1.98; p<0.001) and ASCT2 (RR=3.3; p=0.002). Using the x and y coordinates and the corresponding phenotype classifications, we performed spatial statistical modeling based on spatial point processes to generate aggregated measures of how different types of cells of interest interacted. CD4 T cells were more isolated in LPs compared to EPs (p =0.046). Segregation indices for PCs and CD4 T cells indicated that they were more integrated/collocated in EPs. Granulocytes were more isolated in EPs although the difference was not as pronounced (p= 0.07). PCs were more integrated with granulocytes (p=0.06) and B cells (p=0.07) in LP patients. Further analyses of paired samples at the time of progression to MM will be reported at the time of the presentation. Conclusion This study is the first to provide whole-slide, single-cell, spatial proteomic characterization of clonal PCs and immune cells in the FFPE BMs of high-risk SMM patients with varying progression times to MM. Distinct metabolic pathway markers in clonal PCs and their spatial relationships with immune cell subsets are closely associated with the risk of progression to MM. These findings illuminate the complex interactions between the metabolic states of clonal PCs and their spatial orientation relative to immune cells in the pathogenesis of MM.

Background: Patients with relapsed/refractory multiple myeloma demonstrate an increasing incidence of extramedullary disease (EMD), noted in up to 30% of patients. Presence of EMD portends a markedly inferior prognosis with limited response to salvage therapies, including immune effector therapy. Understanding the molecular alterations in EMD is key to exploring novel therapeutic approaches but remains to be well-characterized. Here we describe the mutational landscape and biologic pathways with alterations in a large cohort of patients with EMD. Methods: We included 18 paraffin-embedded tumor tissues from true EMD biopsies (non-bone contiguous disease) for whole exome sequencing (WES); biopsies from paraskeletal/bone-contiguous soft tissue were not considered. Germline sequencing was performed utilizing cryopreserved peripheral blood mononuclear cells (PBMCs) at initial diagnosis from patients without evidence of circulating tumor cells. Additionally, we studied 12 CD138-sorted cryopreserved bone marrow (BM) aspirates collected at the time of development of EMD. Following standard DNA extraction and library construction practices, we performed WES on the EMD, BM and PBMC samples. Variant calling was performed using consensus calls observed in at least 2 out of the 3 callers (Mutect2, VarScan, Strelka2) and these were included for downstream analysis. Synonymous and low-impact variants were filtered. Pathway analysis was performed using the PanCancer driver gene sets and compared using Fisher's exact test. Results: The median age of patients with EMD was 65.6 years (range 46.2-86 years). Seven (39%) patients had de novo (previously untreated) EMD and 11 (61%) had secondary EMD (developed at relapse). The MAPK pathway was frequently mutated, most commonly involving NRAS (33%), KRAS (22%) and BRAF (22%) genes. These mutations were mutually exclusive. Alterations in TP53 were noted in 11% of EMD samples. Single nucleotide variants (SNVs) in epigenetic modulators were frequently noted in EMD, with the most prevalent alterations seen in ARID1A (33%), KMT2C (28%), EP300 (22%) and KMT2B (22%). Both ARID1A and EP300 are postulated to play a role in IRF4 regulation and plasma cell differentiation. Additionally, inhibition of EP300 can be a viable therapeutic strategy in MM through disruption of IRF4 signaling. We noted frequent mutations in the roundabout (ROBO) genes ROBO1 and ROBO2, observed in 22% and 28% of EMD samples, respectively. ROBO1 has been demonstrated to be necessary for myeloma cell adhesion to BM stromal and endothelial cells, with ROBO1 knockout MM cells infused in mice demonstrating a tendency for extramedullary dissemination. Similarly, variants in LRP1B, a frequently altered large putative tumor suppressor gene in the LDL receptor family, were noted in 33 % of EMD samples. Depletion of LRP1B has been demonstrated to promote anchorage-independent growth, cell migration and invasion in vitro. Notably, in the paired BM samples, no SNVs were noted in ARID1A, LRP1B, ROBO1 and ROBO2 genes (q-value not significant). In paired samples, among the shared mutations of interest, we noted expansion of the BRAF clone in 2 EMD samples [variant allele frequency (VAF) of 3% and 13% in BM and 58% and 29% in the paired samples, respectively]. An expansion of a shared KRAS mutation was noted in another EMD sample (VAF of 15% in BM and 29% in EMD). The frequently altered cellular pathways in EMD samples (n=18) included the MAPK pathway (89%), chromatin histone modifier pathway (83%), transcriptional regulation (83%), immune signaling (72%) and RNA abundance (72%). In the cohort of paired EMD and BM samples (n=24), notable enrichment was evident in EMD for MAPK pathway alterations [91% in EMD vs. 25% in BM q=0.02] and chromatin histone modifier pathway (83% in EMD vs. 42% in BM, q <0.01). Conclusions: Patients withEMD demonstrate a near universal presence of alterations in the MAPK pathway. Variants in chromatin modifiers regulating plasma cell differentiation and genes involved in BM stromal adhesion and homing of plasma cells were frequently noted, potentially facilitating the extramedullary spread of myeloma cells. Functional studies are needed to evaluate these hypotheses. Targeting EP300, ARID1A and downstream MEK/ERK inhibition as part of combination therapies may represent promising therapeutic approaches for further exploration in EMD.

Introduction: Ven is a BCL2 inhibitor that has shown clinical efficacy in RRMM patients (pts) particularly those that harbor t(11;14). Ven has been evaluated in clinical trials as a single agent, in combination with dexamethasone (dex), immunomodulatory drugs (IMiD), proteasome inhibitors (PI), and anti-CD38 monoclonal antibodies (MAb) yet its place in the therapeutic landscape remains in question. In this retrospective analysis, we aim to evaluate the real-world efficacy of Ven-based regimens in RRMM pts treated outside of a clinical trial across the 3-site MCCC. Methods: We retrospectively analyzed the medical records of pts with RRMM treated with Ven-based regimens between January 2015 and October 2023 at the MCCC. Refractoriness was defined by progression on or within 60 days of stopping treatment. Categorical variables were compared with Chi-square tests and continuous variables were compared with t-tests. Outcomes were estimated using the Kaplan-Meier method and curves were compared between the groups using log-rank tests. Results: 83 pts were included in the analysis. The median age was 71 (39-93), 63% were male, 93% were white, 27% had ISS III, 18% had high risk cytogenetics (HRCyto), 76% had t(11;14), the median prior lines of therapy (LOT) was 4 (1-15), 68% received ≥ 4 prior LOT, 87% received prior autologous transplant, 58% were triple-class refractory (TCR), 64%/23% were penta-drug exposed/refractory (PDE/R), 8% received prior BCMA-directed therapies (BDT), 58% received Ven+dex, 25% received Ven+PI +/-dex, 6 % received Ven+anti-CD38 MAb+/-dex and 11% received Ven+other. The median follow-up time from start of a Ven-based regimen was 29 months (mo). The median Ven dose was 800mg (20-1600) with 25% of pts receiving Ven ramp-up dosing. The overall response rate (ORR) to a Ven-based regimen was 58% and was 64% for pts with t(11;14) and 40% for pts without t(11;14); p=0.06. Ven-treated pts with HRCyto had inferior PFS (3mo) and OS (8mo) compared to patients who did not have HRCyto who had a PFS of 9mo (p=0.020) and OS of 80mo (p<0.001). The ORR for TCR and PDR pts were 52% and 42%, respectively. No significant ORR differences were noted across the different Ven-based regimens (p=0.32). The median PFS for pts with t(11;14) was 10mo compared to 2mo for pts w/o t(11;14); p=<0.001. The median OS for pts with t(11;14) was 80mo compared to 10mo for pts without t(11;14); p=0.001. Ven-treated pts who received 1-3 prior LOT has superior ORR, median PFS, and median OS; 89%, 25mo, and 99mo, respectively, compared to pts who received ≥4 prior LOT who achieved an ORR of 43% (p<0.001), median PFS of 5mo (p<0.001) and median OS of 25mo (p<0.001). Ven-treated pts that were TCR had inferior PFS (5mo) and OS (26mo) compared to pts who were not TCR who achieved a PFS of 12mo (p<0.001) and a median OS of 80mo (p=0.02).Ven-treated pts that were PDR had inferior PFS (4mo) and OS (22mo) compared to pts who were not PDR who achieved a PFS of 9mo (p<0.001) and a median OS of 80mo (p=0.02).Ven-treated pts who received prior BDT (n=7) achieved an ORR of 29% and had a median PFS and OS of 2mo and 6mo, respectively on a Ven-based therapy. 59 pts received additional therapy in the post Ven-progression setting. The ORR to post-Ven progression therapies was 53% (n=31/59), the median PFS was 9mo and median OS was 35mo. The most common post-Ven progression regimens used were PI-based (n=16; ORR=56%), anti-CD38 MAb-based (n=14; ORR=50%), BDT (n=12; ORR=83%), selinexor-based (n=3; ORR=0%) with BDT resulting in highest ORR (p=0.048) and PFS (p=0.005). Conclusions: Ven-based regimens are effective in heavily pre-treated pts with RRMM however they are more effective when used within 1-3 prior LOT, in pts with t(11;14), and in pts without HRCyto, TCR or PDR MM. BDT appear to be the most efficacious therapies in the post-Ven progression setting.

Introduction: Uniform assessment of response to treatment is crucial to managing myeloma and developing new therapies. Measurement of the monoclonal protein (s) secreted by the myeloma cells forms the cornerstone of disease assessment in MM and has primarily been performed by electrophoretic methods in the serum and urine, as well as assessment of free light chains in the serum. According to international myeloma working group (IMWG) guidelines, serial serum-free light chain (sFLC) is included in MM response assessment to define stringent complete response and for evaluating partial response (PR) or progressive disease (PD) in non/oligo-secretory MM. This study aims to determine the added value of serial sFLC measurements in monitoring response and progression in MM with measurable disease by comparing the kinetics of FLC with M spike measured by electrophoresis in serum or urine. Methods: This retrospective observational study included newly diagnosed MM patients evaluated at Mayo Clinic in Rochester between 2011-2021. All patients had measurable sFLC (involved FLC level ≥ 10mg/dL, with abnormal sFLC ratio) along with measurable M-protein by serum or urine protein electrophoresis at diagnosis. Serial measurements of sFLC and M-protein by electrophoresis (sPEP/uPEP) were prospectively collected from the electronic medical records at different time points. FLC-response and FLC-PD were evaluated based on IMWG criteria. Results: The study cohort included 839 patients, 52% with measurable M protein by electrophoresis in serum, 27% in both serum and urine, and 21% in urine-only. A significant association was observed between sFLC and electrophoretic response during the induction and at the time of best response (p <.001). Serum FLC showed a comparable response rate of 98% (≥ PR) at the time of best electrophoretic response (p <.001). Ninety-nine percent of early FLC responders eventually achieved an overall response by sPEP/uPEP (p <.001). In the urine-only group, 99% achieved at least a PR by uPEP, and all showed at least a PR based on sFLC. We observed comparable median percentage changes in serial dFLC and urine M-protein, with parallel trends (p <0.001). The correlation coefficients between the change of sFLC and urine M-protein showed a moderate/strong correlation with coefficients (r) of 0.791, 0.597, 0598, 0.545, after cycles 1, 2, 3, 4, respectively, (p < .001). The response for all cohorts was detected earlier by dFLC (median time 1.1 months, 95% CI: 1.06-1.17), in comparison with serum M-protein (1.5 months, 95% CI: 1.40-1.62) and urine M-protein (2.8 months, 95% CI: 2.42-3.10) with p < .001. In the urine-only group, earlier detection of response by dFLC (1.1 months, 95% CI: 1.00-1.2) was observed, compared to urine M-protein (2.4 months, 95% CI: 2.00-2.7) (p <.001). After a median follow-up of 52 (IQR 27.6, 72.3) months, 56% had PD: 62% by s-FLC, 56% by serum M-protein, and 24% by urine M-protein. Among patients with PD by uPEP, all except one patient met sFLC-PD. Within patients with PD, sFLC was the only detectable tumor marker in 12% of cases at the time of the second line of treatment. After four cycles of induction, there was no difference in PFS between VGPR response determined by sFLC, sPEP, and uPEP (p =0.538). The median second-PFS for patients with onlysFLC-PD was similar to those with urine M-protein PD with or without sFLC-PD (HR 1.28, 95% CI =0.77-2.13. p 0.334). However, the median overall survival (OS) from the first relapse was significantly better for patients with onlysFLC-PD than those with urine M-protein PD (HR 1.87, 95% CI =1.07-3.27. p 0.026). Conclusions: In this study, achieving sFLC-VGPR has a similar impact on PFS as electrophoretic-VGPR after induction, indicating that serial sFLC can potentially replace the requirement for urine M-protein response (< 100mg in 24-hours urine) as part of the electrophoretic-VGPR criteria, as required by IMWG. A similar response rate between sFLC and urine paraprotein confirms the current cutoffs (90% for urine and 50% for dFLC for VGPR) used for response categories for s-FLC. We also observed that in 12% of patients with PD, sFLC was the only marker of progression, highlighting the value of serial s-FLC in monitoring relapse. In conclusion, our study supports the integration of serial sFLC measurements for monitoring response and progression in MM, even with electrophoretic measurable disease.

Introduction: High-risk smoldering multiple myeloma (SMM) is an asymptomatic precursor disorder to active multiple myeloma (MM) without approved treatment options. However, recent evidence suggests patients at high risk of progression to MM may benefit from early treatment. Daratumumab (DARA) is a human IgGκ monoclonal antibody targeting CD38 with direct on-tumor and immunomodulatory mechanisms of action. DARA is approved as monotherapy for relapsed/refractory MM (RRMM) and in combination with standard-of-care regimens for RRMM and newly diagnosed MM. Based on the encouraging activity and tolerability observed with DARA monotherapy in patients with intermediate- or high-risk SMM in the phase 2 CENTAURUS study, the phase 3 AQUILA study sought to determine if DARA could delay progression to MM versus active monitoring. Here we report the primary analysis from the AQUILA study. Methods: Eligible patients had a confirmed diagnosis of high-risk SMM for ≤5 years, defined as clonal bone marrow plasma cells (BMPCs) ≥10% and ≥1 risk factor (serum M protein ≥30 g/L, IgA SMM, immunoparesis with reduction of 2 uninvolved Ig isotypes, serum involved:uninvolved free light chain ratio ≥8 and <100, and/or clonal BMPCs >50% to <60%). Focal and lytic lesion assessment was performed in screening by CT and MRI and centrally reviewed prior to study enrollment. Patients were randomized 1:1 to receive DARA SC versus active monitoring. DARA (QW in Cycles 1 and 2, Q2W in Cycles 3-6, and Q4W thereafter) was given in 28-day cycles until cycle 39, 36 months, or disease progression, whichever came first. The primary endpoint was progression-free survival (PFS), defined as progression to active MM as assessed by an independent review committee and according to IMWG diagnostic criteria for MM (SLiM-CRAB) or death. Major secondary endpoints included overall response rate (ORR), PFS on first-line MM treatment (PFS2), and overall survival (OS). Results: A total of 390 patients (DARA, n = 194; active monitoring, n = 196) were randomized. Median (range) age (64 [31-86] years) and time from initial SMM diagnosis to randomization (0.72 [0-5.0] years) were balanced between treatment groups. Median treatment duration in the DARA group was 38 cycles (35.0 months). At a median (range) follow-up of 65.2 (0-76.6) months, PFS was significantly improved with DARA versus active monitoring (HR, 0.49; 95% CI, 0.36-0.67; P <0.0001). Median PFS was not reached in the DARA group versus 41.5 months for active monitoring; estimated 60-month PFS rates were 63.1% versus 40.8%, respectively. Prespecified analyses showed generally consistent PFS improvement with DARA versus active monitoring across subgroups. ORR was 63.4% with DARA versus 2.0% with active monitoring (P <0.0001). As of the clinical cutoff, 64 (33.0%) patients in the DARA group and 102 (52.0%) patients in the active monitoring group had started first-line MM treatment. Median time from randomization to the date of first-line MM treatment was not reached with DARA versus 50.2 months with active monitoring (HR, 0.46; 95% CI, 0.33-0.62; nominal P <0.0001). There was a positive trend in favor of DARA for PFS2 (HR, 0.58; 95% CI, 0.35-0.96) and OS (60-month OS rates: DARA, 93.0%; active monitoring, 86.9%; HR, 0.52; 95% CI, 0.27-0.98). A total of 41 deaths were observed, 15 for the DARA group and 26 for the active monitoring group. Grade 3/4 treatment-emergent adverse events (TEAEs) occurred in 40.4% and 30.1% of patients in the DARA and active monitoring groups, respectively. The most common (≥5% in either group) grade 3/4 TEAE was hypertension (DARA, 5.7%; active monitoring, 4.6%). The frequency of TEAEs leading to DARA discontinuation was low (5.7%), as was the incidence of fatal TEAEs in both groups (DARA, 1.0%; active monitoring, 2.0%). Conclusions: DARA monotherapy was well tolerated and demonstrated a clinically meaningful and significant benefit in preventing or delaying progression to active MM compared with active monitoring in patients with high-risk SMM. ORR was significantly higher and time to first-line MM treatment was prolonged with DARA compared with active monitoring. This was accompanied by positive trends for PFS2 and OS in favor of DARA. These results strongly support the benefit of early intervention with DARA monotherapy versus active monitoring, the current standard of care, in patients with high-risk SMM.

Background Multiple myeloma (MM) is a hematological cancer characterized by the proliferation of plasma cells (PC). Progressive disease (PD) criteria are primarily characterized by an increase in (1) serum M-protein (SPEP), (2) urinary M-protein (UPEP), (3) serum free light chains (sFLC), or (4) PC percentage in the bone marrow. An essential aspect of the PD criteria is the requirement for confirmatory results from blood or urine analyses. However, in many cases, patients may be taken off treatment in favor of a new therapy after only one assessment. This can introduce clinical trial endpoint inaccuracies when confirmatory values are missing, resulting in increased censoring and underestimation of the progression rate (if the second assessment is not done). Moreover, in subjects with high tumor burden and rapidly progressing disease, it is often urgent to confirm PD before starting a subsequent line of treatment. This study assessed the necessity of repeating biomarkers to confirm PD if two biomarkers meet the progression criteria. Methods In this retrospective study, between 2002 and 2020, we assessed all consecutive patients with relapsed MM enrolled in any therapeutic clinical trial at Mayo Clinic. We included patients with measurable disease if SPEP and sFLC were available at the time of assessment. Patients required at least two consecutive assessments (by the same method) to be included in our study. PD was based on the IMWG criteria. Relapse characterized by the development of new bone lesions or extramedullary relapse without meeting biochemical progression criteria was an exclusion criterion. Patients starting a new line of treatment after non-confirmed PD were also excluded. Furthermore, patients who did not meet simultaneous progression criteria and had unconfirmed PD (did not meet IMWG criteria at the second assessment) were excluded from the analysis (n=15). The probability of meeting the progression threshold with 1 and 2 consecutive assessments was calculated. We then examined the proportions of patients meeting PD by 2 biomarkers simultaneously at the first assessment. Results We evaluated episodes of treatment among 434 patients enrolled in clinical trials, including 601 episodes: 593 episodes of biochemical confirmed PD and 8 episodes when patients did not progress but who met simultaneous progression criteria at the same time with two different biomarkers. Per measurable disease criteria, MM isotypes were 71.0% IgG, 16.8% IgA, 0.9% IgM, 1.2% IgD, 0.3% biclonal, and 9.8% light chain. According to the standard IMWG response criteria (sequential criteria), 78% and 9% of episodes reached the progression threshold with serum and urine M-components, respectively, with two consecutive assessments. For the FLC, 21% of episodes met the progression criteria. No patients had a second bone marrow examination for confirmation of PD. At the first available assessment, 82%, 19%, and 2% of episodes met progression threshold, respectively, with serum M-protein, urine M-protein, and both (serum + urine M-protein). Additionally, 58%, and 19%, episodes met the progression threshold at the first assessment with FLC and bone marrow examination, respectively. Moreover, 67% of patients with IgA myeloma meet the progression threshold with the total quantitative IgA. With the simultaneous criteria at time of first assessment, 403 episodes meet progression threshold: 45% with SPEP+sFLC (n=269/593), 4% with SPEP+UPEP (n=21/593), 9% with sFLC+UPEP (n=55/593), 11% with BME only (n=66/593), and 0.3% with qIgA +dFLC/UPEP (n=2/593). Thus, among these 413 episodes, which meet progression criteria by 2 simultaneous values at the time of the first assessment, 98% (n=413/(413+8)) of these patients (positive predictive value of the new criteria) subsequently had confirmed progression by sequential value. Conclusions In summary, we showed that the simultaneous criteria allow to identify patients with true progressive disease without overestimating the progression rate of myeloma. Therefore, confirmatory results of progression for relapsed MM patients having two biomarkers meeting criteria of progression at the same time is useless for determining disease progression.

Background: Light chain (AL) amyloidosis is a rare and understudied plasma cell disorder. It is caused by abnormal misfolding of monoclonal immunoglobulin light chains, leading to extracellular deposition of amyloid, ultimately resulting in organ dysfunction and death. Since AL amyloidosis is uncommon, therapies are adapted from the more common plasma cell disorders like multiple myeloma. Daratumumab, a monoclonal antibody targeting CD38, was first tested in several prospective and retrospective studies in AL patients with relapsed refractory disease. The ANDROMEDA study (Kastritis E et al, NEJM 2021) established daratumumab in combination with CyBorD (Cyclophosphamide-Bortezomib-Dexamethasone) followed by daratumumab maintenance as the new standard of care compared to CyBorD alone for newly diagnosed patients. Consequently, daratumumab maintenance has become universally offered to all newly diagnosed AL amyloidosis patients. Notably, there was no second randomization before the initiation of maintenance therapy in ANDROMEDA. Hence, a critical question that remains is whether the superior efficacy observed in the investigational arm of the ANDROMEDA trial is solely a result of the addition of daratumumab during the induction phase or if the maintenance phase contributed to the favorable outcomes in the investigational arm. We hypothesize that a shorter duration of maintenance would prove non-inferior to the standard duration of daratumumab maintenance in patients with AL amyloidosis that have responded to Daratumumab-CyBorD induction. To answer this question we are conducting a pragmatic phase 2 randomized trial assessing the optimal duration of daratumumab maintenance in patients with AL amyloidosis who have responded to Daratumumab-CyBorD induction. Study Design, Patients and Methods: We use broadened eligibility criteria consistent with pragmatic trial design. The estimated total enrollment is up to 96 patients. The main eligibility criteria are patients with biopsy-proven AL Amyloidosis who have completed 6 cycles of Dara-CyBorD-based induction treatment and achieved an adequate response to induction (defined as hematological complete response (CR), hematological very good partial response (VGPR), or hematological partial response (PR) with at least one organ response). Patients in whom bortezomib and/or cyclophosphamide were omitted from induction due to toxicity concerns or adverse effects are allowed. The major exclusion criteria are patients who were concomitantly diagnosed with multiple myeloma based on International Myeloma Working Group (IMWG) criteria. This phase II, open-label, multicenter trial (NCT05898646) randomizes patients who have attained an adequate response to 6 cycles of Dara-CyBorD-based induction in a 1:1 ratio, using a dynamic allocation procedure that balances the marginal distributions of the stratification factors (age, cardiac stage, and t(11;14) status). Randomization is to either single-agent daratumumab maintenance of 6 cycles duration (experimental arm) or 18 cycles of daratumumab maintenance (control arm) (cycle = 28 days). Treatment is allowed to be given at a local facility After completing maintenance treatment, patients will follow up every 3 months for up to 36 months from registration. The primary endpoint of the trial is to evaluate the non-inferiority of a shorter (6 cycles) daratumumab maintenance measured by event-free survival (EFS) compared to the standard maintenance of 18 cycles in newly diagnosed AL amyloidosis who receive 6 cycles of daratumumab-CyBorD based induction. EFS in this study is defined as time from registration to any of the following events: hematological progression, organ progression, subsequent anti-plasma cell therapy in the absence of hematological progression but with persistent organ dysfunction or death, whichever occurs first. Secondary endpoints are depth of hematological and organ response, bone marrow measurable residual disease negativity rate, duration of response, quality of life, and survival. By investigating whether a shorter duration of maintenance is non-inferior to the standard duration, we aim to inform clinical practice and potentially reduce the toxicities associated with prolonged maintenance therapy, including hypogammaglobulinemia, recurrent infections, the need for intravenous gammaglobulin replacement, and financial toxicity.

Introduction: In myeloma, accurate risk stratification remains a challenge due to substantial biological heterogeneity and highly variable survival outcomes. Cross-sectional imaging is commonly obtained at the time of diagnosis, but imaging findings are not currently incorporated into risk stratification efforts in a standardized manner, despite their prognostic significance. Here, we investigate the use of novel image-based prognostic biomarkers obtained by automated image segmentation and radiomics feature extraction using machine learning methods. Methods: We obtained the pre-treatment 18F-FDG PET/CT scans of 443 patients with newly diagnosed myeloma and complete demographic and cytogenetic data (seen at Mayo Clinic between 2005 and 2019). The native DICOM images were pre-processed and subjected to automated image segmentation using 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 differences related to PET reconstruction, and potential inclusion of non-neoplastic FDG-avid lesions in segmented regions. The quality of the automated image segmentation was evaluated by comparing the radiomics features obtained from ground-truth segmentation (manual lesion segmentation) and model segmentation in a separate cohort of patients with hematologic malignancies (n=72). We selected 42 radiomics features that moderately (r≥0.30, p<0.005, n=23) or strongly (r≥0.50, p<0.001, n=19) correlated between ground-truth (manual) and model (automated) segmentation for further evaluation in myeloma patients. PyRadiomics was used to extract discrete radiomics features for clinical prediction modeling. We evaluated the prognostic significance of tumor size-, shape-, and texture-related radiomics features and compared their predictive power to established prognostic factors (International Staging System [ISS], Revised ISS [R-ISS] stage and high-risk FISH cytogenetics). The outcome of interest was overall survival and the predictive power of competing proportional hazards regression models was assessed using Harrell's C-statistic. For modeling purposes, patients without measurable tumor volume were categorized as having undetectable radiomics features (0 for all features). All radiomics features were median dichotomized for downstream modeling. Forward feature selection and testing for multicollinearity were used to remove redundant features. Select radiomics features were divided into quintiles to generate an additive radiomics risk score and associated risk strata. Results: Median age at diagnosis of the 443 myeloma patients was 63 years (32-91) and 270 patients (62%) were men. After a median follow-up of 2.4 years (95% CI 0.1-7.4), median overall survival was 6.5 years (95% CI 5.5-NR). Most patients had detectable metabolic tumor volume (n=309, 70%) on their pre-treatment 18F-FDG PET/CT scan. Several of the 42 radiomics features were intercorrelated and two features were selected for further investigation based on their independent prognostic significance (forward feature selection). Higher maximum axial tumor diameter (HR 1.90, 95% CI 1.31-2.76, p=0.001) and a lower concentration of low gray-level values in the tumor (HR 1.73, 95% CI 1.20-2.49, p=0.004) were independently associated with overall survival in newly diagnosed myeloma. These associations were independent of age at diagnosis, sex, and R-ISS stage. We devised a simple additive risk score representing the sum of quintiles of the two radiomics features and divided the patients into three risk categories akin to the R-ISS. The predictive power of this radiomics risk score (Harrell's C=0.60) was comparable to the ISS (C=0.59) and the R-ISS (C=0.57). Conclusions: Automated image segmentation and radiomics feature extraction from 18F-FDG PET/CT scans is feasible. This approach yields image-based biomarkers that correlate with results obtained by manual imaging segmentation. Several radiomics features such as tumor size and heterogeneity have independent prognostic significance in newly diagnosed myeloma. These novel biomarkers rival the predictive power of the currently established risk stratification methods and may be used to improve risk stratification in myeloma.