Jevremović, Dragan

Background: Venetoclax (Ven), a BCL2 inhibitor, has shown promising responses in multiple myeloma (MM) patients with t(11;14) and/or BCL2 expression. However, consistent benefit in clinical trials remain elusive. This report evaluates the efficacy and safety of Ven combination therapies, including in the non-t(11;14) subgroup, and examines the impact of secondary cytogenetic abnormalities (SGA) on outcomes. Methods: This retrospective study included multiple myeloma (MM) patients without concurrent AL amyloidosis treated with Ven-based combinations at Mayo Clinic sites from January 2015 to December 2023. FISH data was collected at diagnosis and before venetoclax initiation to assess SGAs. Refractoriness was defined by progression on or within 60 days of stopping treatment. Patients refractory to at least one proteasome inhibitor (PI), immunomodulatory drug (ImiD), and anti-CD38 antibody were considered triple-class refractory (TCR). Ven combinations were grouped as: Daratumumab/CD38 antibody (Dara)-Ven based if received Dara ± other drugs, PI-Ven-based without Dara, Ven-dexamethasone (Ven-d), and other combinations (Ven-Misc). BCL2 expression in patients without t(11;14) was determined by immunohistochemistry (IHC). Progression-free survival (PFS) was calculated from treatment initiation to disease progression or last follow-up. Results: We included 232 patients in this study. The median age at diagnosis of MM was 62 years (range 32-87), and the median age at initiation was 68 years (range 35-89). The median time from diagnosis of MM to initiation of Ven was 55 months (95% CI 28, 89 months), and the median follow-up from initiation of Ven-based treatment was 17.2 months (95% CI: 14, 22). Patients had received a median of 3 lines (IQR 4-6); 65% (n=152) of patients were TCR, 71% (n=163) had received autologous stem cell transplant and 7% (n=16) patients had received prior immune effector therapy prior to initiation of Ven-based therapy. The majority (83%, n=190) of the patients harbored a t(11;14); in the patients without t(11;14), a BCL2 expression by IHC was tested in 18 patients and positive in 17 patients. The most common Ven-based combinations included: Dara-Ven-d (19%, n=44), PI-Ven-d (30.2%, n=70), Ven-d (48.3%, n=112), Ven-misc (2.6%, n=6). The overall response rate for the entire cohort was 57% [64% for t(11;14) and 26% for non t(11;14), p<0.0001]. The median PFS for the entire cohort was 9.5 months (95% CI 8.5,13), and the duration of response was 18 months (95% CI 15.2-23.3). The median PFS was 11.8 months (95% CI 9.5-16.7) for patients harboring a t(11;14) compared to 2.9 months (95% CI 1.8, 8.4) for patients without a t(11;14), p<0.0001. Among the non-t(11;14) cohort with BCL2 expression, the median PFS was 1.8 months (95% CI: 1.2-4.2). Among the 190 patients with t(11;14), 18% (34/190) harbored deletion 17p and 32% (60/193) harbored a 1q gain/amplification (referred hereforth as 1q21+) at initiation of Ven-based therapy. In the cohort of patients with t(11;14), the presence of either del17p or 1q21+ (n=85/190) was associated with a significantly inferior PFS [median PFS 8.2 months (95% CI: 5.5,13)] vs. 15.1 months (95% CI: 11.6, 22), p=0.03. At the last follow-up,42 patients had ongoing treatment with ven-based combination. Most common reasons for discontinuation were progression (77% N=140), adverse effect (12%, n=22), patient/provider preference(9%, n=17); 2% (n=4) patients died without progression (3 infection, 1 unclear). The most commonly utilized maximum dose of venetoclax was 400 mg (42%); 15% (n=35) of patients required a dose reduction, and 23% (n=54) received a ramp-up. Tumor lysis syndrome was not noted in any patient. Infectious complications were noted in 23% (n=52). The estimated median OS from initiation of ven-based Rx was 34 months (95%CI 25-47). The estimated median OS for patients without a t(11;14) was 6.2 months (95% CI: 3.3-11.3) compared to 46 months (31.4-54.1), p<0.0001. In the t(11;14) subgroup, the 18-month OS rate was 79% for patients without 1q21+/del17p vs. 50% for patients with a 1q21+/del17p (p=0.002). Conclusion: Venetoclax-based regimens represent an important treatment option for patients harboring a t(11;14). The PFS in patients with BCL2 expression without t(11;14) was poor suggesting lack of efficacy of venetoclax-based regimens for that subgroup. Presence of concurrent del17p and/or 1q21+ with t(11;14) portends an inferior PFS.

Background: Flow cytometry (FC) is widely utilized for the identification of minimal residual disease (MRD) in B acute lymphoblastic leukemia (BALL), providing an essential laboratory indicator for prognostic assessment and therapeutic management. Unfortunately, the high level of expertise required, long manual analysis time, and complexity of FC data files limit the offering of this testing to selected reference laboratories. Methods: We developed an artificial intelligence (AI) pipeline to automatically enhance unaltered FC files, minimizing the expertise and manual analysis time needed to detect BALL-MRD on a single-tube 10-color panel (CD10, CD19, CD20, CD22, CD24, CD34, CD38, CD45, CD58, CD66c). Raw FC files corresponding to peripheral blood or bone marrow specimens from 171 BALL MRD positive cases (MRD < 5%, median = 0.11%, 107 patients), and 89 MRD-negative cases (63 patients) were processed through our CCADDAS (Clustering and Classification of All events, Dimensionality reduction, Downsampling and Aberrancy Scaling) pipeline on a cloud environment (Google Vertex AI). Automated processing steps included elimination of acquisition errors (FlowCut), state-of-the-art clustering (PARC), dimensionality reduction (UMAP), cluster-based anomaly detection compared to negative controls (24 bone marrow aspirates and 7 peripheral bloods), and cluster-informed downsampling with preservation of low-event subsets. In addition, a deep neural network (DNN) trained on expert-defined subpopulations from negative controls was included for automatic gating of normal subsets (Tensorflow v2). AI-enhanced data files were analyzed using a general purpose flow cytometry analysis software (Kaluza v 3.5, Beckman Coulter), and %MRD estimates compared to reported results based on expert analysis of original FC files. Results: Cluster-informed downsampling reduced the number of cells per case to be manually analyzed from 1.2 million to 155,884 cells on average (87% cellularity reduction); resulting in a smaller FC data file (from 64.5 MB to 13.7 MB; 79% data reduction). Importantly, low-level MRD events were adequately preserved after downsampling (median 100% retention for both MRD<0.01% and MRD 0.01-1%). True number of events were accurately estimated on gated subsets using an “upsampling factor” parameter [observed # events x mean (upsampling factor)]. Gating of normal subsets was completely automated using a DNN classifier parameter. Identification of MRD was aided by an AI-generated “aberrancy scale” parameter that discriminated BALL (median: 991) from both benign mononuclear cells (median: 486, p < 0.0001) and background B-lymphoid cells (median: 379, p < 0.0001), with a performance (AUC: 0.98 and 0.94 for mononuclear and B-lymphoid cells, respectively) superior to CD20 (0.54 and 0.69), CD38 (0.60 and 0.81), CD45 (0.75 and 0.74), CD58 (0.52 and 0.89), CD66c (0.56 and 0.72) and all other antigens studied. The use of AI-enhanced files reduced manual analysis time from 14.16 minutes (SD = 3.7) to 1.05 minutes (SD = 0.6) per case, on average (p < 0.0001) (93% reduction). BALL MRD was detected in all positive cases above clinically relevant threshold (≥0.01%, as recommended by NCCN guidelines), and in 28 of 32 (88%) cases below this threshold, with excellent quantitative correlation with conventional analysis on original FC files (linear regression slope = 1.18, intercept = 0.04%, R squared = 0.92, P < 0.0001). Conclusion: We introduce a largely unsupervised AI pipeline that transforms raw BALL-MRD FC data into a markedly smaller, AI-annotated and software-agnostic FC file, including comparison to normal controls. This CCADDAS pipeline simplifies and accelerates detection of BALL-MRD in clinical diagnostics, reducing number of cells analyzed by 87% and manual analysis time by 93%, without impacting test performance. Moreover, CCADDAS can be utilized for any labroatory-developed BALL-MRD assay, and its small-sized export is compatible with any clinical FC software, computing platform and analysis strategy. Adoption of CCADDAS is likely to facilitate the implementation of BALL-MRD FC analysis by more clinical laboratories.

Introduction: The current definition of plasma cell leukemia (PCL) is based on peripheral blood smear (PBS) demonstrating ≥5% plasma cells. However, PCL cases are enriched in translocation t(11;14), which often has a lympho-plasmacytoid appearance. Detection and quantification of circulating tumor plasma cells (CTC) through peripheral blood flow cytometry (PBF) has emerged as a promising approach for assessing disease burden and prognosis in multiple myeloma (MM) patients. While PBF is more sensitive, the potential threshold values on PBF that correspond with the PBS detection of circulating plasma cells (PBS-PC) remain undetermined. It is important to understand this comparison better to categorize PCL based on PBF. Patients and Methods: This is a retrospective cross-sectional diagnostic accuracy study. We reviewed results of patients diagnosed with plasma cell dyscrasia at the Mayo Clinic from January 2011 to December 2020. The presence of circulating tumor plasma cells (CTC) in encounters was evaluated using peripheral blood flow cytometry (PBF) and peripheral blood smear (PBS) for plasma cells (PBS-PC). PBS-PC was measured as a percentage of plasma cells (PC) in a 100-cell manual differential smear count, and PBF was expressed as the number of plasma cell events per 150,000 peripheral blood mononuclear cells (PBMCs) (Ficoll-isolated peripheral blood mononuclear cells using antibodies to CD38, CD138, CD19, CD45, kappa, lambda, sensitivity (Sn)=6.7x10-5). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of PBS with respect to PBF were calculated. For threshold analysis, four groups were defined: A = absent PBS-PC, C = ≥1% and <5%, D = ≥5%. Sensitivity and specificity of detecting PBS-PC (y-axis) for groups A vs. B, B vs. C, and C vs. D were plotted against CTC levels on PBF (x-axis) to determine the best cut-off. Area Under the Curve (AUC) was calculated to determine accuracy. Agreement analysis was done for internal validation and reliability assessment. Results: 6066 test records of CTC were reviewed. Circulating tumor plasma cells (CTC) by peripheral blood flow cytometry (PBF) were reported in 1610 (26.5%) instances, and peripheral blood smear for plasma cells (PBS-PC) in 226 (3.7%) tests. Peripheral blood smear (PBS) demonstrated a sensitivity of 9.3%, specificity of 99.5%, positive predictive value (PPV) of 86.7%, and negative predictive value (NPV) of 75.3% when compared to peripheral blood flow cytometry (PBF). The correlation between PBS-PC% and CTC was weakly positive (Spearman's rho=0.27, p<0.01). The median (Q1, Q3) of circulating tumor plasma cells (CTC) for group A (n=5840) was 0 (0, 0), group B (n=82) was 780 (97.75, 4012.75), group C (n=67) was 1801 (61.50, 8723.50), and group D (n=77) was 20518 (2296, 41349) (p-value <0.001). On post-hoc analysis, all pairs had significant differences in CTCs. To distinguish between A and B, a threshold of 32.5 for CTC demonstrated a sensitivity of 81.8% (AUC=87.8%), and to distinguish B and C, 1125 demonstrated a sensitivity of 59.4% (AUC=63.2%). To distinguish between C and D, 5875 for CTC demonstrated a sensitivity of 68.8% (AUC=74.6%). Agreement Analysis: The Bland-Altman plot demonstrates a slight positive bias between the Clonal Event % and %PBS-PC values (mean of difference = 0.28). The variability of differences increases with higher mean values, indicating a potential proportional bias. All predicted thresholds lie within the limits of agreement (LoA) (upper: 6.13, lower: -5.58) and are therefore reliable. The precision range (upper: 0.35, lower: 0.2) is narrower for the predicted thresholds. The methods should not be compared beyond a circulating tumor plasma cell (CTC) count of 9,195. Conclusion: Detection of circulating tumor plasma cells (CTC) by peripheral blood smear (PBS) is very specific but not sensitive and can be improved by peripheral blood flow cytometry (PBF) detection of CTC. Important clinical cutoffs by morphology (presence and ≥5%) have their counterparts by PBF (32 and 5875 out of 150,000 peripheral blood mononuclear cells (PBMC), respectively). The predicted thresholds are reliable. The study is retrospective and may not be generalizable across all demographics and disease severity. Circulating tumor plasma cell (CTC) values aren't absolute counts and may not be generalizable for low white blood cell (WBC) counts.

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.

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 Monoclonal antibodies (MoAbs) directed against CD38 are a mainstay in multiple myeloma (MM). Downregulation of CD38 expression is a resistance mechanism of antigenic escape, however, cell surface expression may return. While this tumor cell plasticity is likely driven by epigenetic mechanisms, it is unknown if MM cells can develop permanent resistance to anti-CD38 MoAb by acquiring genomic events leading to biallelic disruption of CD38 thus precluding re-treatment. Here, we report biallelic loss of CD38 as a recurrent mechanism of resistance, often via convergent evolution where distinct subclones are advantaged towards the same selective pressure. Methods We interrogated two sets of patients (Mayo Clinic; n = 31, Calgary University; n =19) with relapse following anti-CD38 MoAbs. Two additional cases of interest were selected from Heidelberg University. Whole genome sequencing (WGS, 60-100x) or whole exome sequencing (WXS), flow cytometry (flow), and bulk RNAseq were used to validate the effect of genomic events on CD38. Missense mutations predicted to affect CD38 MoAb binding were validated with site-directed mutagenesis of a CD38 plasmid, transduction into K562 MM cells, and CD38 MoAb binding assays. Results The prevalence of biallelic inactivation of CD38 at anti-CD38 MoAb relapse was 6% (3/50). An additional selected biallelic event was noted in the Heidelberg set. Each case is described in detail: MM-19 (Calgary) received daratumumab (Dara), lenalidomide, and dexamethasone (Dex), and then had a second exposure to Dara, following relapse. At this second relapse, WGS was performed on two independent extramedullary disease localizations in the liver. Both samples had unique, large deletions (>3 Mb) on chromosome 4 coupled with additional unique focal deletions involving CD38. Flow confirmed the presence of CD138+/CD38- plasma cell populations in both sites. A second case of convergent evolution was seen in a patient (Mayo) after seventh-line Dara/Len/Dex. WGS revealed a common deletion encompassing CD38. On the remaining allele, one subclone had a structural variation-mediated loss of CD38(cancer cell fraction; CCF 40%) while another harbored an L153H missense mutation (CCF 60%). Flow confirmed presence of two CD138+ clones; one with diminished CD38 expression. L153H was functionally validated to abrogate Dara binding, while maintaining CD38 expression. The third sample was collected after fifth-line Dara and sixth-line Dara/Bortezomib/Dex (Heidelberg) and represents another case of convergent evolution with a common loss of chromosome 4 and two subclones detected by WGS (P98Lfs*12, CCF 39% resulting in biallelic inactivation; R140G, CCF 61%). Bulk RNAseq revealed, in comparison to NDMM from CoMMpass, CD38 expression below the first decile. Notably, the R140G variant reduced binding affinity to Dara but not to Isatuximab (Isa). A fourth case (Mayo) relapsing after 6 months of Dara-based therapy similarly had a focal deletion of CD38 with an L18Sfs*16 frameshift deletion on the remaining allele associated with a 4.35-fold reduction in CD38 expression by RNAseq compared to pre-treatment. Interestingly, in 3/4 cases the CD38-negative clone emerged after a second exposure to anti-CD38 MoAb, suggesting emergence with the first exposure and then selection during the second. One other monoallelic event (C275Y; Heidelberg) was detected post-Isa. Copy number of the alternate allele is not known given targeted sequencing methodology, but the variant was seen to reduce binding affinity to both Dara and Isa. The prevalence of monoallelic loss of CD38 in newly diagnosed MM from MMRF CoMMpass was 7% (50/701) and two patients had mutations in CD38, but there were no cases of biallelic loss suggesting these events are driven by treatment pressure. Conclusion Distinction of the mode of CD38 loss is valuable for predicting outcome of re-challenge to anti-CD38 MoAbs. We see here an estimated 5-10% of anti-CD38 MoAb relapses are characterized by genomic antigen escape. Results here are in line with antigen escape to BCMA- and GPRC5D-directed immunotherapies (Lee et al, Nat Med 2023), suggesting that acquired antigen escape resulting from prolonged exposure to the selective pressures of immunotherapy is a recurrent mechanism of resistance in MM. Moreover, in select cases, non-overlapping resistance mutations provide rationale for switching agents at second exposure.

Introduction: DDX41, DEAD-box RNA helicase 41 gene located on chromosome 5q25.3, is one of the most mutated genes in patients with germline predisposition to myeloid neoplasms. Germline and somatic mutations often have different locations and patterns of mutation, with some hot-spots displaying diversity based on ethnicity. We aimed to explore clinical outcomes in patients with various DDX41 hot-spots. Methods: A retrospective study of patients at Mayo Clinic with DDX41 mutation identified through Next Generation Sequencing (NGS) between 2016 and 2024. Patients classified as germline DDX41 variants or somatic variants based on confirmed germline testing results. Data regarding mutations recorded at time of patients' first in-house NGS. We completed unadjusted comparisons using continuous or categorical variables, and survival rates assessed using Kaplan-Meier method and cox regression analysis. Median overall survival calculated from time of NGS to last follow-up or death using BlueSky Statistics V.10.3.4 Results: Clinical Characteristics: Eighty-two patients analyzed, predominantly male (69.5%), with median age of diagnosis of 68 years (IQR 62-75). Most common diagnosis was MDS (50%), followed by AML (22%) and myeloproliferative neoplasm (MPN) (7.3%). Fourteen percent were DDX41 mutation carriers, 4.9% clonal cytopenia of undetermined significance (CCUS), and 1.2% chronic neutrophilic leukemia (CNL). MDS-IB2 was the prevalent MDS subtype (56.1%), and 83.3% of AML patients were intermediate risk. Cytogenetics normal in 73.8% of patients, with del20 being the most common abnormality (7.5%). Twenty-five (30.5%) of patients underwent allogeneic hematopoietic cell transplantation (alloHCT). Forty-nine patients (59.8%) had pathogenic DDX41 mutation only, 15 (18.3%) had only VUS, and 18 (22.0%) had both. Molecular Characteristic s: Mutation hot-spots included 12.2% with p.Arg525His only, 28% p.M1I only, 9.8% p.Asp140GlyFs*2 only, 9.8% p.Arg525 with co-mutation other than p.M1I/p.Asp140. Two patients (2.4%) had p.M1I co-mutation with non-p.Arg525 and p.Asp140, while 1 patient (1.2%) had p.M1I and p.Arg525, and 1 patient (1.2%) had p.Asp140 and p.Arg525. Lastly, 33 patients (40.2%) had mutations in hot-spots other than specified in this study. Median hemoglobin (Hg) was lowest in patients with p.Arg525 hotspot mutation (9.9 g/dl), compared to median Hg of 11.2 g/dl in group with p.M1I and 10.4 g/dl in p.Asp140 only group (p=0.87). The most common cytogenetics in patients with only p.Arg525 and only p.M1I hot-spot mutation was normal cytogenetics (70% and 90.9%, p=0.5). In the 16 patients with proven germline DDX41 mutation, 7 (43.8%) had mutations in only p.M1I. Lastly, median (m) VAF was significantly lower in the group with only p.Arg525 hot-spot mutation (9.5%, IQR 5.5-13.8), compared to mVAF of 48% in patients with only p.M1I (IQR 48.0-49.5, p<0.001). Co-mutations: The most frequent co-mutation was DNMT3A (11.0%), followed by JAK2 (9.8%).Mean number of co-mutations in p.Arg525 only was 0.6, 0.3 in p.M1I only, and 0.5 in p.Asp140 only (p=0.8). Most common co-mutation in p.M1I was ASXL1, with 13.0% of patients with p.M1I only having ASXL1 co-mutation (p=0.062). The most common co-mutation in p.Asp140 was DNMT3A (p=0.7). Survival: Median overall survival (mOS) was 21.5 months (IQR 12.4-38.6). mOS for p.Arg525 only group was 31.3, compared to 41.2 months in p.M1I only (p=0.197). At 12- and 24-months, survival rate was 88.9% and 59.3% in the p.Arg525 only group, compared to p.M1I group with 100% survival rate at 12 months and 89.5% at 24 months. p.Asp140 had 87.5% survival rate at both 12 months and 24 months. Males in p.M1I group had lower survival rate at 24 months (81.8%), compared to 100% survival in females. p.M1I only group with alloHCT also had lower 24 month survival at 60% compared to 100% in group who did not undergo alloHCT. Conclusions: The p.M1I hot-spot was commonly seen in patients with germline DDX41 mutation, likely resulting in significantly higher VAF percentage. Co-mutations frequency differed by hot spot mutation while p.Arg525 as second mutation was seen mainly in cases other than p.M1I/pArg140Gly. Overall survival appears to be higher in p.M1I/pArg140Gly compared to p.Arg525His hot-spot mutation, though males with p.M1I and those who underwent alloHCT had lower survival. Findings suggest implications for clinical outcomes based on DDX41 mutation hot-spots.

Introduction: Measurable residual disease (MRD) is a well-validated prognostic marker and interim outcomes measure in patients with multiple myeloma (MM). Despite its broad acceptance as a robust prognostic marker, the MRD status is only available down the treatment line, and there is limited information on the markers at diagnosis that can predict achievement of MRD negativity post-transplant. This study explored the relationship between baseline risk factors and post-autologous stem cell transplantation (post-ASCT) MRD status, as well as the combined impact of these baseline factors and post-ASCT MRD status on progression-free survival (PFS) and overall survival (OS). Patients and Methods: We conducted a retrospective, single-center study of patients achieving at least VGPR after ASCT between 01/01/2016 and 12/30/2023, majority (n=814, 98%) received ASCT in the frontline setting. MRD testing was performed between days 60-100 post-transplant using next-generation flow cytometry with a sensitivity of at least 1x10^5 nucleated cells (minimum 2x10^6 cells assessed). Treatment response and disease progression were determined by IMWG criteria. High-risk disease (HRD) was defined as presence of International Staging System ISS stage 3, high lactate dehydrogenase, 1q gain, 17p deletion, or t(4;14) chromosomal abnormalities. We calculated PFS/OS from transplant day 0 to first documented event (progression or death). Categorical variables were compared using chi-square and Fisher's exact tests. Non-parametric tests compared continuous variables, and logistic regression analyses tested associations between clinical/lab variables and MRD status post-ASCT. Time-to-event analyses were performed using Kaplan-Meier and compared using log-rank test. Multivariable analyses were conducted using the Cox regression model. Result: The study cohort included 831 newly diagnosed MM patients. The median follow-up for the cohort was 50 months (95% CI: 48-51). 48% (n=402) achieved MRD negativity (MRD-neg) post-ASCT. Univariate analysis demonstrated that R2-ISS stage, depth of response pre-ASCT, and presence of t(11;14) to be associated with worse MRD-negativity rates. Multivariate analysis revealed that t(11;14) significantly decreased the odds of MRD-negativity (p<0.0001, OR=0.4, 95% CI: 0.3-0.6) and a deeper pre-ASCT response was associated with increase odd of MRD-negativity OR-4.7 (95%CI 3.2-7.4) p<0.0001. Patients with MRD-positive disease post-transplant had an inferior estimated median PFS of 57.6 months (95% CI: 49-69) with a 5-year PFS of 49% (95% CI: 43-56), compared to an estimated median PFS of 83.3 months (95% CI: NA) and a 5-year PFS of 66% in MRD-negative patients (p<0.0001, HR=1.9, 95% CI: 1.5-2.5). Similarly, MRD positivity was associated with an inferior OS [estimated median of 92.6 months (95% CI NA) ,5-year OS 76% (95%CI 72-82) for MRD-pos vs. Median OS NR, 5-year OS 85%(95%CI NA) in MRD-neg; p=0.003, HR=1.69, 95% CI: 1.2-2.38)]. In MRD-neg patients, a baseline bone marrow plasma cells BMPC% >60% was significantly associated with worse PFS (HR=1.9, 95% CI: 1.26-2.99; p<0.01). High-risk disease was not associated with an inferior PFS or OS in patients with MRD-negativity. For MRD-neg patients, estimated median PFS was not reached for those without HRD, 5-year PFS was 66% (95%CI 51-85), compared to 83 months(95%CI 65-NA) for those with HRD, 5-year PFS was 65%(95%CI57-74), p=0.6441. The median OS was not reached in either group (p=0.07). Five-year OS was 94% (95%CI 86-100) in MRD-neg without HRD vs. 81%(95%CI 75-87) with HRD (p=0.07). In MRD-pos patients, median PFS was 81 months (95%CI 58-NA) for those without HRD, 5-year PFS of 59% (95%CI 46-77), compared to PFS of 41 months(95%CI 36-52) for those with HRD ,5-year PFS of 39% (95%CI 31.3-47) , p=0.003. Median OS was not reached in MRD-pos patients without HRD, but it was 59.8 months ( for those with HRD (p=0.0012). Five-year OS was 94% (95%CI 89-99) in MRD-pos without HRD vs. 67%(95%CI 60-74) with HRD (p<0.0001). For every 1 log increase in the residual cell % on MRD testing in MRD-pos patients, the hazard ratio for PFS increased by 42% (p=0.0002, HR=1.42, 95% CI: 1.2-1.7). Conclusion: Patients with t(11;14) have a reduced likelihood of achieving MRD negativity post-ASCT, whereas high-risk cytogenetics did not impact MRD-negativity rates. Achievement of MRD negativity post-ASCT mitigates the impact of high-risk characteristics.

Background: Clonal cytopenia of undetermined significance (CCUS), a precursor of myeloid neoplasms (MN), is characterized by one or more persistent cytopenias, and acquired mutations in myeloid malignancy-associated genes. ZRSR2, located at Xp22.2, is one of the MN-associated genes and a member of the spliceosome family of genes. Aim: Describe the clinical characteristics, pattern of progression, and genetics of ZRSR2 mutation (ZRSR2m)-harboring CCUS patients. Methods: Next-generation sequencing (NGS) was performed for 9320 patients between 2016-2023 at Mayo Clinic. After obtaining IRB approval, clinical characteristics and results of bone marrow morphology, cytogenetics, and molecular studies were abstracted. Overall Survival (OS) was calculated using Kaplan-Meier, from the date of NGS until death or last follow-up. The ZRSR2m MN control cohort included 125 patients with myelodysplastic syndrome (MDS), myeloproliferative neoplasm (MPN), and MDS/MPN overlap. BlueSky Statistics V10.3.1 was used for statistical analysis. Results: ZRSR2m was detected in 39 CCUS patients, all of whom were males. Concurrent hematological neoplasms were identified in 8 patients (21%). Five (13%) had plasma cell proliferative disorders including 3 multiple myeloma, 1 MGUS, and 1 AL amyloidosis. There were also 2 patients with chronic lymphocytic leukemia and 1 with VEXAS syndrome. Prior chemotherapy, with or without radiotherapy, was given to 4 patients (10.3%), whereas 13 patients (33%) had received previous immunotherapy. Abnormal cytogenetics were detected in 11 patients (29%), with the most common abnormality being +8 (n=5, 13%). In 23 patients (59%), an absolute monocyte count of ≥0.5 x 10^9/L was documented which could be classified as clonal cytopenia with monocytosis of unknown significance (CCMUS). ZRSR2 median gender-corrected VAF among CCUS patients was 32% (range, 1-45). Isolated ZRSR2m was found in 6 patients (15%), and 1 (3%) had multiple ZRSR2m. The median number of co-mutations was 1 (range, 0-3). Sequential NGS, available in 13 patients, showed that 1 patient developed 3 ZRSR2 mutations and 2 ZRSR2 VUS. TET2 was the most common co-mutation (n=25, 64%). Additionally, TET2 was an isolated co-mutation in 18 patients (46%). ASXL1 co-mutation was the second most common (n=8, 20.5%), while other less common co-mutations were IDH1, IDH2, SRSF2, SF3B1, TP53, GATA2, and KDM6A. Pre-ZF1 was the most commonly mutated region in ZRSR2 mutations (n=23, 59%). With a median follow-up of 32 months, 9 (23%) of the ZRSR2m CCUS patients progressed to MNs, none progressed to AML. Five (55.6%) progressed to MDS, 4 were subclassified as low blast (LB) and 1 as increased blast-1 (IB1). Four (44.4%) progressed to MDS/MPN overlap syndrome, with 3 subclassified as chronic myelomonocytic leukemia (CMML). None of the patients with isolated ZRSR2m progressed to MDS or CMML. None of the patients who received prior immunotherapy progressed to MDS. Thirty-six (92.3%) patients had high-risk CCUS (Malcovati et al, Blood 2017) while 3 had intermediate risk with no low-risk patients. None of the intermediate-risk patients progressed to MDS, while 5 patients of the high-risk (13.9%) progressed to MDS (p=1). Twelve (31%) patients died, and the median overall survival (mOS) was 56 months. Higher hemoglobin levels improved OS (HR=0.59, p=0.02), while higher adjusted ZRSR2 VAF worsened OS (HR=1.05, p=0.017). Patients with TET2 as isolated co-mutations showed better mOS than the rest of the cohort (not reached vs 41 months, p= 0.03). ZRSR2m CCUS patients showed a trend suggestive of better mOS than ZRSR2m with MNs including AML (56 vs 30 months, p=0.14), and with excluding AML (56 vs 39 months, p=0.24). On multivariate analysis, both Hgb (HR=0.61, p=0.03) and isolated TET2 co-mutation (HR=0.19, p=0.04) retained statistical significance. mOS was not affected by prior history of treatment, number of co-mutations, isolated ZRSR2, ASXL1 co-mutation, or MDS progression. Conclusion: ZRSR2m CCUS cohort was exclusively males. There was a significant number of patients with concurrent hematological malignancy or a history of prior chemo- and/or immuno-therapy (36%). Most patients had high-risk CCUS but showed a low rate of progression to MNs, especially in those with isolated ZRSR2. Additionally, patients with isolated TET2 co-mutation had better survival, indicating that TET2 confers a favorable prognosis among ZRSR2m CCUS patients.