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Introduction: Hyaline-vascular type unicentric Castleman disease (HV-UCD) is a classic variant of Castleman disease, characterized by localized lymph node hyperplasia. Due to its rarity, there have been few systematic reports summarizing its clinicopathological features globally. Also, its pathogenesis remains unelucidated. The present study aims to provide a detailed clinicopathological and transcriptomic analysis of HV-UCD to enhance the understanding of its underlying mechanisms at a molecular level. Methods: We conducted a clinicopathological analysis of 51 HV-UCD cases and performed whole transcriptome analysis on frozen lymph node specimens from four cases. Differentially expressed genes (DEGs) and Gene Ontology (GO) analyses were performed to identify significant pathways and biological processes involved in HV-UCD, with reactive lymphoid hyperplasia as control. Results: The mean age of HV-UCD patients was 42.8 years, with no significant gender differences. The most common sites involved were the abdominal cavity (n = 18, 35.3%), mediastinum (n = 11, 21.6%), and retroperitoneum (n = 9, 17.6%), with an average size of the lesions being 4.8 cm. Histologically, 33.3% of cases exhibited “dysplastic follicular dendritic cells (dFDCs)” characterized by multinucleation or large nuclei within and outside follicles, positive for follicular dendritic cells (FDC) markers. DEGs analysis compared to reactive lymphoid hyperplasia revealed significant upregulation of genes related to FDCs and angiogenesis. Notably, the placental growth factor (PGF), a proangiogenic factor and a member of the VEGF family, demonstrated significant overexpression. GO analysis identified significant enrichment of genes involved in angiogenesis, cell adhesion, tissue architecture, and humoral immunity. Discussion: This large cohort of HV-UCD cohort provides potentially novel clinicopathological insights. Gene expression analysis demonstrated increased expression of FDC-related genes, suggesting a pivotal role of FDCs in HV-UCD pathogenesis. The differences in the gene profiles related to angiogenesis may contribute to the distinct mechanisms and histological features of HV-UCD and iMCD-TAFRO, where HV-UCD is associated with hyalinization of vascular walls and iMCD-TAFRO is characterized by angiogenesis with endothelial cell swelling. Conclusion: The present results suggest the crucial role of FDCs in HV-UCD pathogenesis from the molecular features. Currently, there are no objective diagnostic markers for HV-UCD. Identifying these markers may lead to a clearer understanding of the disease pathophysiology and improve diagnostic accuracy for HV-UCD. Further investigation is warranted to verify the findings.

Background: Beside fibril-forming proteins, amyloid deposits contain other proteins which can influence organ toxicity, disease initiation and clearance. We investigated the association between the amyloid plaque proteomic signature of diagnostic renal biopsies from patients achieving deep and sustained hematologic remissions and renal organ response in AL amyloidosis. Methods: Retrospective study of newly diagnosed AL amyloidosis patients between 2010-2020. Patients were eligible if; (1) they had a baseline renal biopsy which had undergone laser micro-dissection and mass spectrometry, (2) were assessable for renal response [RenR, non-selective proteinuria ≥1g /24 hours, predominantly albumin], (3) were not on renal replacement therapy and did not experience renal progression within 3-months of diagnosis and (4) had RenR measurements, at least twice annually in the first year from treatment and annually thereafter. Patients who did not obtain a renal complete response (RenCR) required a hematologic very good partial response (VGPR) or better within 6 months of treatment initiation, sustained for a minimum of 6 months. Hematological and renal responses were graded according to consensus criteria. Overrepresentation analyses of differentially expressed proteins were performed using the WebGestalt 2019 and the reactome pathway database. A false detection rate corrected p value <0.05 was used to identify significantly overrepresented proteins and pathways. Results: We defined two organ response categories, to capture the extreme ends of the spectrum: renal suboptimal responders (n=24), patients for whom the best renal response was ≤partial response (RenPR), and renal complete responders (n=29), patients who obtained a RenCR at best response. Overall, the median follow-up was 6.7 years (IQR: 5.4-9.2), 6.6 years (IQR: 5.4-8.1) for complete responders, and 8.4 years (IQR: 6.1-11.3) for suboptimal responders. Between renal suboptimal responders and complete responders there was no significant difference in median age (63 vs 63 years, p=0.7), lambda light chain isotype (66% vs 79%, p=0.3), first-line therapy received and renal stage (Stage III: 14% vs 8%, p=0.9). The median time to RenCR was 52-months (IQR: 31-63). Protein pathways over-represented in renal complete responders included 14-3-3 proteins (YWHAG, YWHAH, YWHAQ, YWHAZ), early components of the complement cascade and complement regulatory proteins (C3, C4A, C4B, C8A, complement factor H), keratins, cytoskeletal proteins (tubulin, actin, filamen) and collagen IV. Protein pathways over-represented in renal suboptimal responders included terminal/effector components of the complement cascade (C5, C6, C8B, C9), amyloid signature proteins (ApoA1 and E, vitronectin, clusterin), proteoglycans (heparan sulfate proteoglycan), transgelin and metallopeptidase inhibitor 3. Proteins with the highest-fold increase in renal complete responders included serine protease inhibitor (SERPINA3) and F-box and leucine rich repeat protein (FBXL6), 9.0 x 106 & 7.7-fold, respectively. Proteins with the highest-fold increase in renal suboptimal responders included complement 6 and 8B, 3.5 & 3.6-fold, respectively. Conclusion: This is the first study to identify proteomic determinants of organ response in AL amyloidosis. Molecular chaperones involved in protein aggregate homeostasis, such as the 14-3-3 proteins are upregulated in renal complete responders, similar to what we have shown in our prior work. This suggests that organ responders may have a more favorable microenvironment which “primes” them for organ response after the amyloidogenic light chain has been cleared from the circulation. Renal suboptimal responders (≤RenPR) have elevated levels of terminal complement proteins including membrane attack complex proteins and reduced levels of early components or complement regulatory proteins, which may be responsible for reno-toxic and reno-protective effects, respectively.

Introduction: Light chain (AL) amyloidosis is a clonal plasma cell disorder characterized by tissue deposition of misfolded immunoglobulin light chains in the form of amyloid, leading to organ dysfunction. Halting the production of the amyloidogenic free light chains by achieving a deep hematologic response (HemR) is key to improving organ function, however, variation in organ responses exists. In this work, we investigated variables associated with liver response (LivR) in a large cohort of patients with AL amyloidosis. Methods: Retrospective study of newly diagnosed AL amyloidosis patients with liver involvement seen at Mayo Clinic within 90 days of diagnosis between January 2000 and December 2021. Patients were eligible if they (1) had liver involvement, defined as a baseline alkaline phosphatase (ALP) ≥ 1.5 x upper limit of normal (ULN) and (2) had LivR measurements at least twice annually in the first year from treatment and annually afterward. LivR was defined as a >50% reduction in alkaline phosphatase (ALP) from baseline value or normalization of ALP. LivR was recorded at 6, 12, and 24 months from treatment initiation and at the best LivR achieved. Patients who died prior to landmark assessment were considered non-responders at subsequent milestone assessments. Covariates associated with LivR were identified by univariate and multivariable logistic regression analyses and a proportional subdistribution hazard (Fine-Gray) model for competing risk (treating death as a competing event). Multivariable logistic regression models for LivR were created using best subset selection, with calculation of odds ratio (OR) and 95% confidence interval (CI). Results: A total of 130 patients were included, with a median age of 60 years (IQR 53-66), male predominance (69%), and a median follow-up of 8.8 years. Sixty percent of patients had lambda isotype, the median ALP was 368 IU/mL (IQR 279-589), with 48% of patients having ALP ≥3 fold the upper limit of normal (ULN). Median bilirubin was 0.7 mg/dL (IQR 0.5-1.1). Forty-three percent of patients received front-line autologous stem cell transplant (ASCT) with or without prior induction therapy, and 5% received daratumumab-based therapy as their front-line treatment. Liver response rate increased with time, with 36 patients (28%) achieving a LivR at 6 months, 47 (36%) at 12 months, 63 (48%) at 24 months, and 94 (72%) at best response. The median time to liver response was 14 months (7.22-32.67). At the 24-month landmark, age <60 years, ALP ≥3 fold ULN, and front-line ASCT were significantly associated with achieving a LivR on univariate logistic regression. On univariate logistic regression at best liver response, age <60 years, ALP ≥3 fold the ULN, kappa isotype, ≥HemVGPR within 6 months, and front-line ASCT were significantly associated with obtaining a LivR. On multivariable logistic regression, at 24 months, ALP ≥3 folds of ULN (OR=2.9, 95% CI 1.4-6.3, P=0.004) was significantly associated with an increased likelihood of LivR. Achieving ≥HemVGPR and front-line ASCT were also associated with increased LivR likelihood, although not statistically significant (p=0.094, p=0.078, respectively). At best liver response, achieving ≥HemVGPR within 6 months (OR=5.2, 95% CI 1.9-14.8), kappa isotype (OR=3.2, 95% CI 1.1-10.0), ALP ≥3 folds of ULN (OR=3.3, 95% CI 1.3-9.9), and front-line ASCT (OR=5.3, 95% CI 1.9-16.7) were factors significantly associated with LivR. The Fine-Gray model demonstrated kappa isotype (HR=1.7), baseline ALP ≥3 folds of ULN (HR=2.1), ≥HemVGPR within 6 months (HR=1.9), and age <60 years at diagnosis (HR=1.9) to be positively associated with achieving LivR (P<0.05). Conclusions: In this large cohort of liver AL amyloidosis, LivR was achieved in 72% of patients, and amongst responders, the median time to response was 14 months from therapy initiation. Predictors of LivR included kappa isotype, deep hematological response in the first 6 months of initial therapy, ASCT usage, and higher baseline ALP levels. These findings better inform clinicians on the likelihood of achievement of liver response and may enable ways to improve its achievement.

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.