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Background: Systemic light chain (AL) amyloidosis is a clonal plasma cell disorder characterized by the deposition of misfolded immunoglobulin light chain products in vital organs, leading to significant organ dysfunction. The molecular mechanisms underlying its pathogenesis remain incompletely elucidated. Identifying the molecular mechanisms and novel therapeutic targets is critical for improving treatment strategies and patient outcomes. Our previous research work identified that the PI3K/AKT signaling pathway is overexpressed in AL amyloidosis (Zvida et al., Blood, 2022, Fishov et al., Cancer Med., 2023). This pathway plays a critical role in regulating cellular metabolism. Specifically activated AKT can phosphorylate and regulate various target proteins involved in metabolic processes. One of the significant targets is fatty acid synthase (FASN), an enzyme responsible for the synthesis of fatty acids. AKT can enhance FASN expression and activity, thereby promoting lipid biosynthesis. Additionally, elevated FASN levels can drive inflammation through increased fatty acid synthesis, which activates inflammatory signaling pathways and enhances cytokine production (Acosta-Martinez et al.,IJMC, 2022). Elevated levels of FASN have been associated with hematological malignancies and cell survival (Vanauberg, et al., Oncogenesis, 2023). However, its role in AL amyloidosis is yet to be elucidated. Aim: To determine the role of FASN in the pathology and inflammatory response in AL amyloidosis, and to evaluate the potential of targeting FASN and the PI3K/AKT pathway for therapeutic intervention. Methods: We profiled FASN expression in CD138+ bone marrow (BM) samples from patients with AL amyloidosis (n=20), Multiple Myeloma (MM) (n=20), Monoclonal Gammopathy of Undetermined (MGUS) (n=8) and Healthy Controls (HC) (n=6), using qRT-PCR. To further explore the relationship between the PI3K/AKT pathway and FASN, we treated ALMC-1 cells with Capivasertib (an AKT inhibitor) and assessed FASN protein levels, cell proliferation by WST-1 assay, cell cycle distribution by flow cytometry, light chain secretion by ELISA, and expression of AKT and its downstream targets via western blot analysis. Furthermore, TVB-2640 (FASN inhibitor) was used to evaluate the cell response to FASN inhibition. Results: Our analysis revealed a significant upregulation of FASN in AL amyloidosis BM samples compared to MM, MGUS, and HCs. Capivasertib treatment resulted in a dose-dependent reduction in FASN protein levels. Additionally, Capivasertib treatment inhibited cell proliferation, induced G1 phase cell cycle arrest, reduced light chain secretion, and decreased AKT protein expression and its downstream targets p4EBP pS6 and pERK. Elevated levels of proinflammatory cytokines, including TNF and IP-10 (CXCL-10), were observed in AL amyloidosis serum samples, suggesting the involvement of immune mechanisms and the PI3K/AKT pathway, potentially induced by FASN. Finally, treatment with TVB-2640 reduced cell viability and decreased the protein expression of LIPIN-1, which is one of the key target genes regulated by FASN. Conclusions: Our findings highlight the significant upregulation of FASN in AL amyloidosis and its regulation by the PI3K/AKT pathway. The observed effects of Capivasertib on FASN expression and cell proliferation, along with the upregulation of inflammatory cytokines, underscore the potential for targeting the PI3K/AKT signaling pathway and FASN in developing more specific therapeutic strategies for AL amyloidosis.

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 The detection, isotyping and quantitation of over-expressed immunoglobulin(s) (Igs) (M-protein) is key for diagnosing and monitoring patients with monoclonal gammopathies. Traditionally, our lab has used a combination of agarose gel protein electrophoresis (SPEP) and immunofixation electrophoresis (IFE) for these purposes. In 2018, a mass spectrometric method (Mass-Fix) was implemented to replace IFE for M-protein isotyping. This work documents the validation of a quantitative version (Q-Mass-Fix) to replace both SPEP and IFE. Methods 24,645 samples submitted for M-protein testing between 7/2018 and 9/2023 with quantitative IgG, IgA and IgM measurements, Mass-Fix analysis and SPEP results were utilized for this study. A separate cohort of 100,000 M-protein negative samples were utilized to mathematically model the spectral shapes IgG, IgA and IgM polyclonal background. To detect M-proteins, software was developed that fits the polyclonal Ig portion of the spectra and M-proteins are detected as peaks above the polyclonal background. M-proteins isotypes are determined by matching similar peaks in spectra in 5 sperate immuno enrichments (IgG, IgA, IgM, kappa and lambda). M-protein quantitation was obtained by multiplying total M-protein isotype quantity by the fraction area of the M-protein in the spectra. M-protein dilutional studies were used to examine the linearity and level of detection of Q-Mass-Fix. Results SPEP and Q-Mass-Fix M-protein quantitation had the best agreement between the range of 10 to 40 g/L. Below 10 g/L, Q-Mass Fix had on average, lower M-protein values. Above 40 g/dL, Q-Mass-Fix values were on average, higher than SPEP. The automated isotyping assignment was 98.8 percent consistent with the isotype assigned by visual inspection. Dilutional studies revealed that Q-Mass-Fix quantitation was linear over a range of 70 to 0.04 g/L (slope 1.0, r2 0.99) with an analytical limit of quantitation of 0.1 g/L and a limit of detection of 0.04 g/L. Conclusion Q-Mass-Fix represents a significant improvement in comparison to our current SPEP method. Q-Mass-Fix is more capable of removing the interference of polyclonal Igs below 10 g/L and is not subjective to the dye saturation effect at high M-protein levels. The increased analytical measuring range and improved limit of detection of detection could be beneficial in tracking patient's response. Q-Mass Fix is more automated than SPEP and the workflow in our laboratory.

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.

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: Light-chain (AL) amyloidosis is caused by the secretion of immunoglobulin light chains by monoclonal plasma cells. The increase in the levels of these monoclonal proteins may proceed the development of any clinical symptoms, and their identification in the serum and urine remains an integral part of establishing a diagnosis of AL amyloidosis. This systematic review aims to evaluate the diagnostic test accuracy of serum immunofixation (SIFE), urine immunofixation (UIFE), and free light chain (FLC) ratio for the diagnosis of AL amyloidosis. Methods: As part of the ASH AL Amyloidosis Diagnostic Guideline, we performed systematic reviews for 7 prioritized PICO questions related to the screening, diagnosis and organ involvement evaluation of AL amyloidosis. Electronic searches were conducted on PubMed, Embase, and the Cochrane Central Register of Controlled Trials from inception till January 2024. Two reviewers independently and in duplicate performed title and abstract screening and full text article screening on LASER AI, with conflicts resolved by a third reviewer. Eligible studies were those that included patients who performed one of the three tests (SIFE, UIFE, and κ/λ FLC) and had their AL amyloidosis diagnosis confirmed via a biopsy. Diagnostic test accuracy measures of interest were sensitivity, specificity, positive and negative predictive value. Statistical analysis was performed on OpenMeta[Analyst]. Grading of Recommendations Assessment, Development and Evaluation (GRADE) was used to assess the certainty of evidence. Results: After deduplication, we retrieved 29,237 studies from electronic databases. 31 studies with 3973 patients reported on the use of serum immunofixation (SIFE) with a pooled sensitivity of 72.4% (95% CI 68.1%-76.4%). 27 studies with 2855 patients reported on the use of urine immunofixation UIFE for the diagnosis of AL amyloidosis with a pooled sensitivity of 78.6% (95% CI 74.6%-82%). 29 studies with 5305 patients reported on the use of κ/λ FLC ratio amyloidosis with a pooled sensitivity of 79.9% (95% CI 73.7%-84.9%). 24 studies reported on the use of both SIFE and UIFE with a sample size of 3751 patients and a pooled sensitivity of 90% (95% CI 85.3%-93.3%). 3 studies reported on the use SIFE and κ/λ FLC ratio with 321 patients and a pooled sensitivity of 97.7% (95% CI 95.6%-99.9%). Finally, we identified 10 studies that performed SIFE,UIFE, and κ/λ FLC ratio concurrently with 1926 patients and a pooled sensitivity of 99% (95% CI 97%-99.7%) of all three tests and considering a result abnormal if any of the components had a positive result. We also identified 5 studies with 957 patients reporting on the concurrent use of SIFE, UIFE, and κ/λ FLC ratio for the diagnosis AL amyloidosis and differentiation of AL from ATTR amyloidosis in a population of patients with suspected cardiac amyloid involvement, the pooled sensitivity was found to be 99%(95% CI 99%-100%) and with a pooled specificity of 77%(95% CI 75%-80%). Certainty of evidence moderate for all outcomes. Conclusion: The combined use of SIFE, UIFE, and κ/λ FLC ratio has a high sensitivity for the diagnosis of AL amyloidosis, and the tests remain a valuable initial screening of AL amyloidosis and differentiation from ATTR amyloidosis.

Introduction: In patients with systemic light-chain amyloidosis, cardiac involvement is common. Cardiac involvement (presence and severity) is the most important predictor of mortality in patients with AL amyloidosis. Endomyocardial biopsy remains the gold standard, but it is a costly and invasive test. Consequently, cardiac biomarkers are used for screening for cardiac involvement and have significant prognostic value.In this systematic review, we aim to estimate the diagnostic test accuracy (DTA) of Nt-proBNP, BNP, and high sensitivity troponin for cardiac amyloidosis. Methods: As part of the American Society of Hematology guidelines on the diagnosis of Amyloidosis, we searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials from inception till January 2024 for relevant studies. Two reviewers independently performed title and abstract screening and full-text article screening on LASER Al and extracted relevant data using piloted Excel sheets. The panel judged whether the reference test is acceptable or not. Acceptable reference test is EMB alone or a combination of extracardiac biopsy plus cardiac imaging (CMR or echocardiography) with or without positive cardiac biomarkers (troponin or BNP or NT-proBNP). For statistical analysis, we only included studies reporting on both the sensitivity and specificity of the cardiac biomarker. We used Stata 18.0 software for meta-analysis. We evaluated risk of bias using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of the evidence. We report diagnostic test accuracy as sensitivity (95% confidence interval) and specificity (95% confidence interval). Results: After screening 29,237 studies, a total of 10 studies addressed DTA of Nt-proBNP for cardiac amyloidosis. These studies used a wide variety of cutoffs for diagnosis of cardiac involvement, ranging from 150 to 5000 ng/L and a cutoff of 332 ng/L was the most studied and reported. The pooled estimates for sensitivity and specificity of Nt-proBNP using 332 as cutoffs are 0.99 ( 0.97, 1) and 0.53 (0.3, 0.75), respectively. The pooled estimates were based on 4 studies (n=645) with moderate certainty in the evidence. For BNP, DTA was assessed in 3 studies (n= 257). Each study used a different cutoff for BNP (73, 100, and 412 ng/L). The sensitivity and specificity of BNP in each study were comparable and not significantly affected by the cutoff variation. Pooled sensitivity and specificity across studies, were 0.87 (0.80, 0.92), and 0.79 (0.71, 0.85), respectively. The certainty in evidence for the DTA of BNP was low. Eight studies reported on DTA of high sensitivity troponin, with cutoffs ranging from 14 to 86 ng/L. Meta-analysis of studies reporting on cutoff 14 ng/L yielded sensitivity of 0.94 (0.87, 0.97) and specificity of 0.11 (0.08, 0.16) with moderate certainty evidence. Increasing the cutoff to 35 ng/l led to a decrease in sensitivity to 0.82 (0.66, 0.92) and an increase in specificity to 0.86 (0.74, 0.93) with low certainty evidence. Conclusion: Cardiac biomarkers are highly sensitive, valuable initial tests for increasing suspicion of cardiac involvement in patients with AL amyloidosis. However, they lack specificity and, on their own, are insufficient for a definitive diagnosis of cardiac involvement. These biomarkers may be used in conjunction with other diagnostic tests to accurately identify and assess the extent of cardiac involvement.

Background: Despite substantial improvements in early mortality (EM) for patients with systemic light chain amyloidosis (AL), rates remained high in the bortezomib-era, at approximately 25% (Muchtar et al. Blood. 2017). However, the therapeutic landscape in AL amyloidosis has changed with the ANDROMEDA trial, which led to the approval of daratumumab in combination with bortezomib, cyclophosphamide, and dexamethasone (Dara-VCd) in the frontline setting. The incidence and predictors of EM in the context of daratumumab-based frontline therapy have not been studied. Methods: We performed an international multicenter retrospective cohort study of all consecutive patients with newly diagnosed AL treated with frontline Dara-VCd or Dara-Vd, and followed until 12/31/2023. EM was defined as death from any cause within 6 months of treatment initiation. Our primary objective was to identify the rate and significant predictors of EM. Univariate logistic regression was used to identify significant predictors of EM, which were then included in a multivariate logistic regression model. The receiver operator characteristic (ROC) analysis was used to determine the best threshold values that discriminated EM rates for the following continuous variables: age, dFLC (involved-uninvolved), eGFR, NT-proBNP, albumin, and high-sensitivity Troponin-T (hs-TnT). Other variables tested in univariate analysis were the following: ECOG performance status, NYHA functional class, sex, race, light chain isotype, percent bone marrow plasma cells, status of the following cytogenetic abnormalities-t(11;14), +1q, hyperdiploidy, del(13q), high-risk translocations, and del(17p), and the number of organs involved (2 vs 1). To build the clinical prediction score, each variable was weighted according to the β coefficient (natural logarithm of odds ratio) obtained from the multivariable regression. The β coefficients were rounded to the nearest 0.5 and multiplied by 2 to obtain an even number. Results The study cohort included 335 AL patients treated with daratumumab-based frontline therapy (92.2% Dara-VCd, 7.8% Dara-Vd), with all surviving patients having at least 6 months of follow-up. The median age at diagnosis was 66.5 years (range, 59.4 - 71.3), and 6.7% (n = 22) were Non-Hispanic blacks. Median dFLC at diagnosis was 22.91 mg/dL (range, 0.2-1093.4), and median NT-proBNP was 2415 pg/mL (range, 13-70,000). Mayo 2004 stages IIIA/IIIB represented 44% of the total cohort, with 17% having ECOG ≥ 2 and 25.4% having NYHA Class III/IV symptoms. The cumulative incidence of EM was 10.7%, with the incidence in patients with Mayo 2004 stage I, II, IIIA, and IIIB being 0%, 8%, 12.5%, and 25% respectively. On univariate analysis with unadjusted odds ratio (OR), the following variables significantly predicted EM: Age ≥ 75 (OR=2.8, p=0.013) ECOG ≥ 2, (OR 4.9, p<0.001), NYHA Class ≥ III (OR=4.95, p<0.001), dFLC ≥9 mg/dL (OR, 10.18, p=0.023), eGFR <33 mL/min/1.73 m2 (OR=2.7, p=0.026), NT-proBNP >5276 pg/mL (OR=7.69, p <0.001), hs-TnT >70 ng/L (OR=5.88, p<0.001), and absence of t(11; 14) (OR=2.33, p=0.041). These variables were entered into multivariate regression, except for ECOG and NYHA, which were combined to create a single variable (values ranging from 0-8), and hs-TnT was dropped due to substantial missing data as centers used different troponin assays. NT-proBNP was rounded to 5300 pg/ml for ease of clinical application. Based on the multivariate analysis, the following scores were assigned to each variable: Age ≥75 (2.0), ECOG+NYHA (1.0 x combined score), NT-proBNP >5,300 pg/ml (2.0), and absence of t(11;14) (3.0). This led to a score ranging from 1-15, and two risk-groups: low-risk (score <9; 87.5%) and high-risk (score ≥9; 12.5%). The respective cumulative incidence of EM was 6.1% versus 50% (p<0.001). The c-index of the model was 0.725 (95% CI, 0.676-0.774). Conclusion: Using variables that are readily available in the clinic, we have developed a two-tiered risk-prediction model for EM in AL amyloidosis in the context of daratumumab-based frontline therapy. The model needs to be externally validated in an independent dataset.