Jevremović, Dragan

Background: A deeper understanding of the cytogenetic features has enabled robust risk stratification for patients with newly diagnosed multiple myeloma (NDMM). However, heterogeneity persists in the currently available prognostic tools and there is further room for personalization. We assess the utility of PC-PRO, a test to measure the proportion of monotypic plasma cells in the synthetic phase (S-phase) of cell cycle, as a prognostic marker for NDMM. Methods: Patients with active MM diagnosed between January 2013 and January 2023 with a flow cytometry-based S-phase assessment at diagnosis were included. Flow cytometric immunophenotyping was performed using the following antibodies: CD19, CD38, CD45, CD138, cytoplasmic kappa and lambda immunoglobulin, and DAPI (4',6-diamidino-2-phenylindole). The percentage of plasma cells in S-phase was determined by measuring the proportion of cells with DNA content of the monotypic plasma cells between the G0/G1 and G2/M peaks. Plasma cell clonality was detected through demonstrating CD38 and CD138 positivity along with immunoglobulin light chain restriction, abnormality of CD19/ CD45 expression and/or ploidy difference by DAPI staining. High-risk cytogenetic abnormalities (HRCA) by FISH were defined as follows: presence of deletion 17p (del17p), t(4;14), t(14;16), t(14;20), 1q gain/amplification, 1p deletion (when available). Additionally, the second iteration of the revised ISS (R2-ISS), incorporating both laboratory and cytogenetic features as well an adaptation of the recently proposed International Myeloma Working Group (IMWG) Classification for high-risk disease, was utilized for risk stratification. All time-to-event analyses were performed using the Kaplan Meier method and compared using the log-rank test. Univariable and multivariable Cox proportional hazards analyses were performed to assess the independent prognostic impact of S-phase. Results: A total of 823 patients were included in the study. The median follow-up was 6 years [95% Confidence interval (CI): 5.5-6.3 years] and estimated median overall survival (OS) was 8.3 years. The median S-phase value for the cohort was 0.8 % (interquartile range: 0.5-1.4%), with 135 (16%) patients harboring an S-phase of ≥2% at diagnosis. Applying the R2-ISS prognostic model to our cohort, 11% (79/706) patients were stratified to the R2-ISS high-risk cohort; 93 patients (out of 788, 12%) had 2 or more HRCA at diagnosis. Patients with a S-phase ≥2% at diagnosis had a median OS of 3.9 years (95% CI: 2.9-5.7) vs. 9.2 years [95% CI: 7.9-not reached (NR)] for patients with a S-phase <2% [Hazard ratio (HR): 2.2 (95% CI: 1.7-2.8), p<0.0001]. The median OS was inferior for cohorts with an increasing S-phase value: S-phase of 0.1-1%: HR: 1 (standard), S-phase 1.1-3%: HR 1.7 (95% CI: 1.3-2.1), S-phase >3%: HR 3.0 (95% CI: 2.2-4.1)]. On a multivariable analysis including S-phase ≥2%, age at diagnosis of MM (>65 years) and R2-ISS high-risk status, S-phase ≥2% was an independent predictor of inferior OS [hazard ratio (HR) 1.9 (95% CI: 1.4-2.6), p<0.0001]. Age >65 years [HR 1.9 (95% CI: 1.5-2.5), p<0.0001] and R2-ISS high-risk status [HR 1.8 (1.3-2.6), p=0.001] were also independent predictors of OS. Within the sub cohort of patients with 2 or more of the listed high-risk cytogenetic features (n=93, 12%), a S-phase ≥2% (n=24) was associated with a worse median OS of 1.6 years (95% CI: 0.7-NR) vs. 6.2 years (95 % CI:3.3-NR) for S-phase <2% (p=0.002). Conclusion: Flow cytometry-based determination of the proportion of monotypic plasma cells in S-phase is an easy-to-study and powerful prognostic tool for NDMM. A S-phase of ≥2% at diagnosis is a predictor of OS independent of age and R2-ISS high-risk stage. An S-phase of ≥2% was able to identify a distinct group with markedly inferior outcomes even within the sub cohort of patients with 2 or more high-risk cytogenetic features.

Background: Flow cytometry (FC) is widely utilized for the identification of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL), 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 CLL MRD on a single-tube 10-color panel (CD5, CD19, CD20, CD22, CD38, CD43, CD45, CD200, kappa and lambda). Raw FC files corresponding to peripheral blood or bone marrow specimens from 166 CLL MRD positive cases (MRD < 5%, median = 0.17%, 122 patients), and 61 MRD-negative cases (45 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 (15 bone marrow aspirates and 14 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 1.1 million to 165,010 cells on average (85% cellularity reduction); resulting in a smaller FC data file (from 62.1 MB to 15.2 MB; 75% 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. Rapid identification of suspected MRD was aided by an AI-generated “aberrancy scale” parameter that discriminated CLL MRD (mean ± 2SD: 969 ± 144) from background B-lymphoid cells (mean ± 2SD: 395 ± 401) (p<0.0001) (AUC = 0.98), with a performance superior to CD5 (AUC = 0.93), CD20 (AUC = 0.83), CD43 (AUC = 0.8) and CD200 (AUC = 0.7). Immunoglobulin light chain-positive CLL MRD was visually identified on a pre-calculated UMAP plot as a single-colored cluster, after dual coloring for kappa and lambda. The use of AI-enhanced files reduced manual analysis time from 11.6 minutes (SD = 4.5) to 0.8 minutes (SD = 0.4) per case, on average (p < 0.01) (93% reduction). CLL MRD was detected in all positive cases above clinically relevant threshold (≥0.01%, as recommended by NCCN guidelines), and in 17 of 21 (81%) cases below this threshold, with excellent quantitative correlation with conventional analysis on original FC files (linear regression slope = 1.02, intercept = 0.001%, R squared = 0.98, P < 0.0001). Conclusion: We introduce a largely unsupervised AI pipeline that transforms raw CLL-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 CLL-MRD in clinical diagnostics, reducing number of cells analyzed by 85% and manual analysis time by 93%, without impacting test performance. Moreover, CCADDAS can be utilized for any laboratory-developed CLL-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 CLL MRD FC analysis by more clinical laboratories.

Background: SETBP1 gene, located on 18q12.3, is a major oncogene in myeloid neoplasms. GATA2 gene, located on 3q21, is one of the six GATA transcription factors regulating gene expression via two conserved zinc finger domains (ZF). Previous data suggested that in patients with germline GATA2 mutation (m), acquisition of a somatic SETBP1 mutation (m) was associated with leukemic transformation among patients with excess blast MDS and CMML. Aim: To study the characteristics of patients with somatic SETBP1-GATA2 mutated myeloid neoplasms. Methods: This study is a retrospective study at Mayo Clinic. After obtaining IRB approval, we retrospectively reviewed the charts of patients who had myeloid NGS panel results between 2016-2023. All data were recorded at time of first NGS. Cohort 1 was defined as patients with SETBP1m and GATA2m, cohort 2; patients with SETBP1m and GATA2wt, and cohort 3; patients with SETBP1wt and GATA2m. Germline work-up was not performed in the majority of GATA2 cases. We stratified risk based on the European LeukemiaNet 2022 guidelines for AML, IPSS-M for MDS and MDS/MPN, and CPSS-M for CMML. Overall survival (OS) was calculated from date of NGS to date of last follow-up based on Kaplan-Meier estimates and Cox regression analysis. For statistical analysis, we used BlueSky Statistics V10.3.1. Results: General characteristics: 114 patients had SETBP1m (median VAF= 39%), and 63 patients had GATA2m (median VAF= 35%). Nine patients had co-occurring SETBP1m/GATA2m mutations (cohort 1). 4/9 patients in cohort 1 had MDS/MPN (44.4%), while only 13/105 in cohort 2 (12.4%) had MDS/MPN (P=0.02). AML was seen in 2 patients in cohort 1 (22.2%) compared to18 in cohort 2 (17.1%) (p=0.6). The median WBC count was 22.9 x 109 cells/L among cohort 1 compared to 9 x 109 cells/L among cohort 2 cases (p=0.09). BM blasts were higher in cohort 3 (7%) compared to cohort 1 (5%) and cohort 2 (3%) (p=0.09), while peripheral blasts tended to be lower in cohort 2 (1%) in comparison to cohort 1 (6%) and cohort 3 (4%) (p=0.48). Genetic characteristics: In cohort 1,7 GATA2m clustered in zinc finger 2 (ZF2) (77.8%), while the other two mutations were outside the ZF domains (22.2%). This was higher than the ZF2 mutated cases in cohort 3 (46.3%, p=0.1). Abnormal cytogenetics were seen in 66.7% of cohort 1 patients and 45% of cohort 2 (p= 0.3). Two cases with trisomy 8 were seen among patients in cohort 1 (22.2%), compared to only 4.9% in cohort 2 (p=0.09). Two incidences of isochromosome 17q [i(17q)] were seen in cohort 1 (22.2%), versus 5 cases in cohort 2 (4.8%) and 1 case in cohort 3 (1.9%) (p= 0.068). Co-mutations: The median number of co-mutationswas 3 in both cohort 1 and cohort 2 (p= 0.6). In cohort 1, ASXL1 and SRSF2 mutations were the most common (7/9, 77.8%) followed by NRAS (22.2%) and TET2 (22.2%). 77.8% of cohort 1 patients had SRSF2 co-mutation, compared to 44.8% in cohort 2 (p=0.08), and 27.8% in cohort 3 (p= 0.006). No isolated SETBP1m/GATA2m cases were found. Survival: Patients in cohort 1 had a numerically higher median OS compared to those in cohort 2 (19.4 vs 15.4 months, p= 0.3). When comparing with the cohort 3, patients in cohort 1 tended to have better outcomes (median OS= 19.4 vs 13.8 months, p=0.8). Among cohort 1, SRSF2 co-mutation was associated with worse survival outcomes compared to patients with SRSF2wt (median OS= 5.3 vs 19.4 months, p= 0.02); this association was not found in cohort 2 (12.5 vs 18.4 months, p= 0.3) and cohort 3 (13.2 vs 17.1 months, p= 0.4). Allogeneic hematopoietic cell transplantation was associated with favorable survival outcomes in all cohorts (cohort 1: 22.6 vs 7.8 months, p= 0.1; cohort 2: 20.9 vs 12.5 months, p= 0.04; cohort 3: 25.6 vs 13.1 months, p= 0.03). Conclusion: SETBP1m/GATA2m myeloid neoplasms included a significantly higher proportion of MDS/MPN overlap cases in comparison with the SETBP1m/GATA2wt group. The SETBP1m/GATA2m group had a higher proportion of ZF2 GATA2m and SRSF2 co-mutation compared to patients with SETBP1wt/GATA2m. Survival of SETBP1m/GATA2m patients was not worse compared to SETBP1wt/GATA2m or SETBP1m/GATA2wt.

Introduction: Light chain multiple myeloma (LCMM) is a distinct subtype of multiple myeloma (MM) characterized by the exclusive production of immunoglobulin light chains, unlike the common form of MM that produces the complete immunoglobulin. There is a paucity of literature on the specific tumor cell alterations leading to light chain-only disease as opposed to the typical presentation in MM. Additionally, data on treatment response and outcomes in this subgroup of patients is sparse. This study explores the differences in baseline characteristics, including cytogenetic abnormalities and treatment response between LCMM and multiple myeloma with heavy chain involvement. Materials and Methods: We reviewed patient charts to obtain clinical and laboratory information for individuals diagnosed with LCMM and those with heavy chain MM (comparison cohort) from 2004 to 2022. Patients were matched for age, sex, and year of diagnosis. Data collected included treatment assessment, overall survival, and progression-free survival (PFS). Associations between outcomes and categorical variables were analyzed using the chi-square test, while the Mann-Whitney U test was employed to compare medians. Progression-free survival following initiation of initial therapy and overall survival were assessed using Kaplan-Meier analysis. The Mayo Clinic Institutional Review Board (IRB) approved the study. Results: We identified 848 patients, representing 17.1% of newly diagnosed MM cases. The median age of the LCMM cohort was 63 years (range 16-92), with a female-to-male ratio of 1:5. The LCMM cohort had higher levels of lactate dehydrogenase (LDH), beta-2 microglobulin (p<0.0001), higher baseline creatinine (p<0.0001) and calcium (p<0.0001). The two cohorts had no significant difference in the International Staging System (ISS) or revised ISS (R2-ISS). Additionally, hemoglobin (Hb) and white blood cell count at presentation were higher in the LCMM cohort. Chromosome 1 abnormalities were lower in the LCMM cohort: 1q gain (OR-0.7; 95% CI: 0.6-0.9) and 1p deletion (OR-0.7; 95% CI: 0.6-0.9, p=0.02). Likewise, the frequencies of trisomies and hyperdiploidy were lower in the LCMM cohort, with odds ratios of 0.32 (p=0.012) and 0.37 (p<0.001), respectively. The frequencies of translocations t(4;14) (OR-0.35, p<0.001) and t(14;16) (OR-0.6, p=0.03) were lower in LCMM compared to heavy chain MM. In contrast, the frequencies of t(11;14) (OR-3.4) and t(6;14) (OR-4.9) and deletion of 13q(OR-1.3) was higher in the LCMM cohort. There was no significant difference in the occurrence of del(17p)/TP53 abnormalities. The median PFS after the first regimen was shorter in the LCMM cohort (23 months vs. 27 months, p=0.03), and this difference remained significant after adjusting for baseline adverse prognostic indices and treatment regimen. Median PFS in LCMM patients with monoclonal antibody (Mab) as part of their treatment was 37 months vs. 23 months (Proteasome Inhibitor or immunomodulator-based regimen without Mab), though this difference is not statistically significant p=0.87. No significant difference was found in overall survival between the two groups. Conclusion: This study highlights unique features of light chain multiple myeloma, including increased frequencies of specific cytogenetic abnormalities. Further evaluation of observed differences in cytogenetic abnormalities may enhance our understanding of LCMM and potentially guide baseline treatment choices to improve outcome.

ABSTRACT Lymphoma is one of the leading causes of cancer and cancer deaths and yet has not been amenable to population screening. The role of methylated DNA markers (MDMs) in the detection of lymphoma has not been extensively studied. We aimed to discover, validate, and test tissue–derived MDMs of lymphoma in archival plasma specimens. Reduced representation bisulfite sequencing (RRBS) was performed on a discovery set of frozen tissues. MDMs identified were converted to methylation–specific PCR assays and validated on independent formalin–fixed, paraffin‐embedded (FFPE) tissues. Target enrichment long–probe quantitative‐amplified signal (TELQAS) assays were developed and assayed in plasma–extracted, bisulfite‐converted DNA from independent treatment‐naïve lymphoma patients and healthy controls. Prediction of cancer status was modeled using random forest model with in silico cross‐validation. After discovery and validation in tissue, 16 TELQAS assays ( ZNF503 , VWA5B1 , HOXA9 , GABRG3 , ITGA5 , MAX.chr17.7190 , BNC1 , CDK20 , MAX.chr4.4069 , TPBG , DNAH14 , SYT6 , CACNG8 , FAM110B , ADRA1D , and NRN1 ) were selected for testing in plasma. These detected 78% (95% CI, 74%–82%) of lymphoma cases at 90% specificity. Excluding marginal zone and T‐cell lymphomas, sensitivity increased to 84% (80%–88%). MDMs in plasma show promise to detect lymphoma and are candidates for inclusion in multi‐cancer detection studies.

AbstractBackgroundCrystal‐storing histiocytosis (CSH) is a rare form of histiocytosis with intralysosomal accumulations of immunoglobulins or paraproteins that aggregate as crystals. Central nervous system (CNS) involvement of CSH is uncommon but should be considered in cases of persistent parenchymal enhancement on neuroimaging.MethodsWe describe one local case of CNS CSH and 10 additional cases identified by literature review.ResultsAmong 11 CSH patients, lesions involved either the dura (2/11) or brain parenchyma (9/11). Two cases had leptomeningeal enhancement. One case had spinal cord involvement. Two cases were associated with marginal zone lymphoma; one case was associated with an immunoglobulin A‐plasma cell dyscrasia. Eight of 11 cases had outcome data available: 7/8 cases had clinical and/or radiological improvement and 1/8 had radiological stability.ConclusionsCentral nervous system involvement of CSH is rare. Potential cases should be comprehensively evaluated for lymphoma or myeloma with positron emission tomography/computed tomography (CT) of the body or alternatively, CT of the chest, abdomen, pelvis and nuclear bone scan, bone marrow biopsy, serum protein electrophoresis, and cerebrospinal fluid protein electrophoresis. Treatment is targeted toward the underlying malignancy.