Binder, Moritz

Background: There is an increased risk of second primary malignancies including second hematological malignancies (SHM) in multiple myeloma (MM) patients undergoing autologous stem cell transplant (auto-SCT) followed by lenalidomide maintenance. We analyzed the characteristics and outcomes of patients who developed SHM after an auto- SCT for MM. Methods: A retrospective study of all MM patients who underwent auto- SCT at Mayo Clinic, Rochester between January 1, 1990, and 31 August 2023 and later developed SHM. Demographic, clinical and pathologic data were collected retrospectively. Overall survival (OS) was calculated from the date of auto-SCT to the date of last follow up. Statistical analyses were performed using GraphPad prism 9.0. Chi-square test was used to calculate the difference between categorical variables. Kaplan Meier method and log rank test were used to estimate OS. Results: Among 3513 patients who underwent at least 1 auto-SCT, 104 (2.9%) developed a SHM at a median follow up of 59 (range, 0.2-265) months. Patients who developed SHM, more frequently received pretransplant alkylating agents (37.5 vs 24.2 %, odds ratio [OR], 1.87; P=0.003) and topoisomerase II inhibitors (anthracyclines/etoposide) (17.3% vs 10.4%, OR [1.79]; P=0.03) compared to a control group of MM patients without SHM. Further, exposure to lenalidomide (77.8% vs 57.6%, OR[2.59]; P <0.0001) and radiation therapy (32.6% vs 23.1%, [1.54]; P=0.04) were more often seen in the SHM cohort compared to control group. Among the 381 patients who underwent > 1 auto- SCT, 3.1% developed SHM. Therapy related myeloid neoplasms (t-MN) accounted for 90.3% of cases [n=94; t-myelodysplastic syndrome (t-MDS=64), t-acute myeloid leukemia (t-AML=12), t-MDS transformed to AML(n=16), chronic myeloid leukemia (n=1), MDS/myeloproliferative neoplasm overlap syndrome (n=1)]. Eight patients (7.6%) developed therapy related B- cell acute lymphoblastic leukemia (t-ALL); Philadelphia (Ph) negative ALL (n=7) and Ph- like ALL (n=1). One patient each developed mixed phenotype acute leukemia and acute leukemia, not otherwise specified. Sixty patients (57.6%) were males and the median age at diagnosis of SHM was 70 (range, 43-84) years. The median time from auto-SCT to diagnosis of SHM was 58.5 (range, 7-236) months. In a multivariate analysis, receiving an alkylating agent (hazard ratio [95% confidence interval]: 2.51 [1.54 to 4.01]; P = 0.0002) and/or topoisomerase II inhibitor as induction for MM (3.12 [1.59 to 5.88]; P = 0.0006), exposure to lenalidomide (7.08 [3.92 to 13.50]; P = <0.0001) and radiation therapy (1.60 [1.02 to 2.44]; P = 0.03) were independent predictors of developing SHM. At diagnosis, 57% (n=37) of t-MDS and 71% (n=20) with t-AML had high risk disease per R-IPSS and 2022 European Leukemia Net respectively. Clonal abnormality in chromosome 5 and/or 7 was present in 45% (n=42) of patients. Complex and monosomal karyotype was seen in 32% (n=30) with t-MN. The most frequently mutated genes based on next generation sequencing data included TP53 (n=33), DNMT3A (n=9), RUNX1 (n=5), TET2 (n=4) and CEBPA (n=4). In the t-MDS/AML cohort, 3 patients (3.1%) underwent second auto-SCT and 9 (9.5%) received allogenic SCT (allo-SCT) for SHM. After a median follow-up of 27.2 months, the median OS from t-MDS/AML diagnosis was 8.04 months (95%CI, 6.1-11.0 months) and there was no difference in median OS for allo-SCT recipients versus nonrecipients (9.1 vs 6.8 months, P =0.19). In the t-ALL (n=8) cohort, all of them received lenalidomide based induction and five received lenalidomide maintenance. Most of the t-ALL patients had adverse risk cytogenetics [IKZF1 deletion (n=2), complex karyotype (n=2), TP53 mutation (n=1) and P2RY8-CRLF2 fusion (n=1)]. Three patients with t-ALL underwent allo-SCT and the median OS for allo-SCT recipients versus non recipients were 8.3 vs 7.9 months respectively, P=0.50. Among patients with SHM, 92.3% (n = 96) have died with SHM (72%) being the most common cause of death. MM patients who developed SHM had an inferior OS when compared to those without SHM (median OS, 75 vs 90 months, P =0.006). Conclusion: SHM following auto-SCT tends to have aggressive disease biology with poor outcomes. Short term follow up underestimates the risk of SHM and the higher incidence noted in our study may be due to longer follow up. As the OS in MM patients continues to improve, SHM remains an important challenge in improving long term outcomes.

Background: Management of cytokine release syndrome (CRS) with chimeric antigen receptor T cell therapy (CAR-T) has evolved in the last decade. First line treatment tocilizumab (toci) was initially given for grade 2 or higher CRS in clinical trials (Toci/CRS≥2). Earlier use of toci in grade 1 CRS was found to be safe without negative impact on clinical response in cohort 4 of ZUMA-1 study for axicabtagene ciloleucel (axi-cel) for B-cell non-Hodgkin lymphoma (NHL; Toci/CRS1). This practice was also adopted for other CAR-T therapies for other disease indications. Subsequently, FDA also updated the package insert for axi-cel in 2021 to include the use of prophylactic dexamethasone (ppx dex) to be given on days 0, 1, 2 of CAR-T infusion based on the results from cohort 6 of ZUMA-1. We examined CRS and ICANS (immune effector cell associated neurologic syndrome) outcome of patients who received FDA approved axi-cel in standard-of-care (SOC) practice with these evolving practices. Of note, adoption of ppx dex has been variable across CAR-T treatment centers in the US (Khurana et al, Blood 2022). At Mayo Clinic Rochester, patients considered high-risk for severe CRS/ICANS (those who received bridging therapy and had LDH above upper limit of normal and/or with secondary CNS involvement or other CNS pathologies) would be considered for ppx dex. Methods: Medical records of patients who received axi-cel in SOC between 2018 and 2023 at Mayo Clinic Rochester were reviewed. ASTCT guideline was used for CRS and ICANS grading. Categorical variables were compared using Chi-squared test and continuous variables were compared using Kruskal-Wallis test. Results: Among 187 pts receiving SOC axi-cel, the median age was 60 years and 121 (65%) were males, 152 (81%) had CRS, of which 73 (39%) were grade ≥2; 97 (52%) had ICANS, of which 65 (35%) were grade ≥2. Of the 152 CRS pts, 54/152 (36%) pts never received toci or ppx/dex. Of the pts who received Toci, 35/152 (23%) received Toci when CRS is at least grade 2 (Toci/CRS≥2), 49/152 (32%) received Toci/CRS1 without ppx dex (Toci/CRS1), and 14 (9%) received ppx dex with Toci given in CRS1 when pts developed CRS (PPX DEX). Baseline demographics were comparable except age (Toci/CRS≥2: 58y; Toci/ CRS1: 63y; ppx dex: 50y; p=0.001) and pre-LD CRP (Toci/CRS≥2: 34mg/L; Toci/ CRS1: 6mg/L; ppx dex: 16mg/L; p=0.002). Pre-LD LDH and ferritin were also comparable. PPX DEX or Toci/CRS1 had significantly lower rate of maximum grade CRS≥2 (PPX DEX: 4 (29%); Toci/ CRS1: 20 (41%); 35 (100%); p<0.001) and duration of CRS (PPX DEX: 2.5d; Toci/ CRS1: 3d; Toci/CRS≥2: 5d; p<0.001). Time to onset of CRS was not statistically different between the 3 groups. Not surprisingly, Toci/CRS1 group had higher overall rate of toci use, compared to . Interestingly, use rate of ≥2 doses of Toci or siltuximab was significantly higher in Toci/CRS1 or PPX DEX (PPX DEX: 8 (57%); Toci/ CRS1: 26 (53%); 9 (26%); p=0.02). Pt who received PPX DEX or Toci/CRS1 had a significantly lower rate of ICANS (PPX DEX: 6 (43%); Toci/ CRS1: 33 (67%); 32 (91%); p=0.001) and rate of max grade ICANS ≥2 (PPX DEX: 3 (21%); Toci/ CRS1: 23 (47%); 27 (77%); p<0.001). The time to onset and duration of ICANS was not different among the 3 groups. Conclusion: Our real-world study shows that earlier use of Toci in grade 1, compared to its first use in grade 2, can result in lower overall incidence of more severe CRS, duration of CRS, incidence of ICANS and more severe ICANS. While we selected patients at higher risk for having more severe CRS in our PPX DEX cohort, experience to date suggests that these patients, compared to toci/CRS>=2 cohort, could also have reduced severity of CRS and ICANS with this updated management.

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: 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.

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.