Jevremović, Dragan

Abstract The ZRSR2 gene is a member of the spliceosome gene family which are frequently mutated in myeloid neoplasms. ZRSR2 mutations (ZRSR2m) occur in less than 5% of MDS, CMML, and AML. Our study included 164 ZRSR2m patients (98.8% males) and 98 ZRSR2wt MDS control cohort. In the ZRSR2m group, there were 53 MDS (32%), 39 CCUS (24%), 33 MPN (20%), 23 MDS/MPN overlap (14%), 15 AML (9%), and 1 MPAL (0.6%). Most MDS patients were the low blast subtype (n=37, 70%). Twenty-seven MDS patients (51%), and 23 CCUS patients (59%) had absolute monocyte count ≥0.5 x109/L and 18 of 23 MDS/MPN overlap were CMML (78%). Mutations in ZRSR2 were spread across the entire gene. The median number of co-mutations was 2, with TET2 (51%) and ASXL1 (32%) being the most common. U2AF1 and SRSF2, previously described as mutually exclusive with ZRSR2, were found in 10.4% of patients. Median overall survival (OS) was 51 months, and significantly varied among MNs (p=0.004). ZRSR2m MDS patients had better mOS than the MDS control cohort with ZRSR2wt (35 vs 22 months, p=0.013). ZRSR2m patients with isolated TET2 co-mutation and higher hemoglobin showed improved survival, while patients with RUNX1m, higher WBC count showed worse OS.

7533 Background: Maintenance treatment (rx) with lenalidomide (R) improves overall survival (OS) in multiple myeloma (MM). We report a retrospective cohort of patients (pts) with second primary B-cell acute lymphoblastic leukemia (ALL) diagnosed after R-rx for MM. Methods: We included pts who were exposed to at least 1 yr of R for MM rx prior to ALL dx. After approval by institutional review board, data were collected by chart review at participating centers using Redcap. OS was the primary endpoint. We used BlueSky statistics v10.3.1 for analysis. Results: Analysis included 33 ALL pts diagnosed from 2014-2022 at 7 sites. 52% pts were female. Median follow up was 9.6 yrs (95%CI,7.6-11.8) from MM dx and 3.2 yrs (CI, 2.1-4.7) from ALL dx. Pt characteristics at MM dx are given in Table. Induction rx included: proteasome inhibitor (PI) +immunomodulatory drug (IMiD) (43%), PI+alkylator (24%), IMiD (18%) & others (15%). 50% pts received autologous stem cell transplantation (ASCT). Median OS from MM dx was 11.2 yrs (CI, 8.9-NR). Time from MM dx and initial R rx to ALL dx were 5.0 yrs (range, 2.4-18.5) and 4.4 yrs (2.2-10.6) respectively. Median duration of R exposure before ALL dx was 3.8 yrs (1.6-10.4). Table shows characteristics of pts at B-ALL dx and genetic classification based on karyotyping (n=30), ALL FISH (n=28), chromosomal microarray (n=5) and next-generation sequencing (n=5). 31 pts received ALL-directed rx while 1 pt was initially managed with close observation as the pt attained CR on stopping R ( Lakshman et al; ASH 2019 ). Rx was unknown in 1 pt. Initial rx included: hyper-CVAD+/- rituximab- 49%, pediatric inspired protocols-18%, reduced intensity rx for frail pts- 21% and others- 12%. Response to first line rx were: CR- 56%, CRi- 28%, induction failure- 16%. 32% pts attained measurable residual disease (MRD) negativity. 48% pts underwent SCT for ALL. 14 pts died during follow up. 3-yr OS from ALL-dx was 50% (CI, 33%-76%). 3 pts required rx for MM after ALL diagnosis (2 had allo-SCT for ALL before MM relapse). Conclusions: Most pts had standard risk MM and were exposed to R for several years prior to dx of B-ALL, characterized by high incidence of hypodiploid karyotype; IKZF1 loss and TP53 deletions/mutations were detected in a subset of pts.MRD-adapted timely discontinuation of R in standard risk MM may decrease risk of second primary ALL. [Table: see text]

AbstractSmoldering multiple myeloma (SMM) is a precursor stage that precedes multiple myeloma (MM). SMM is heterogenous with nearly 40% of patients progressing to MM in the first 5 years. The high rate of progression of SMM patients highlights the need for early intervention, which underscores the importance of identifying SMM patients with the highest risk of progression. Several risk stratification models showed utility in identifying high‐risk SMM patients; however, these systems showed limited sensitivity. To date, identifying high‐risk SMM patients remains an important clinical need. In this study, we present the 3‐dimensional telomere profiling as a structural biomarker capable of stratifying SMM patients as a function of genomic instability. Quantifying telomere dysfunction using the TeloView technology showed utility in risk stratification of cancer patients, particularly hematological malignancies. In this study, we analyzed 168 SMM patients. We report an AUC in ROC analysis of 0.8 using a subset of the patients as a training dataset. We then conducted a blind validation on a different cohort and demonstrated a positive predictive value of 85% and negative predictive value of 73%, with sensitivity and specificity of 83% and 76%, respectively. We examined the correlation between the TeloView prediction and the 20‐2‐20 scoring system, and cytogenetic abnormalities. We report a correlation of 53% with the 20‐2‐20 scores and over 60% correlation with cytogenetic abnormalities. The result of this study presents the telomere profiling as an effective biomarker able to stratify SMM patients to their respective risk groups with high sensitivity and specificity.

INTRODUCTION: The Stromal Antigen 2 ( STAG2), on Xq25, is the most common cohesin complex gene mutated in myeloid neoplasms (MN) (5-10%). STAG2 mutations ( STAG2m) were incorporated in the ELN2022 as AML-MDS defining genetic mutations inferring poor prognosis and is a genetic marker in IPSS-M risk stratification. The PHD-finger protein 6 ( PHF6) gene, on Xq26.3, is a tumor suppressor gene, mutated in 3% of MN, and is more common in T-ALL. PHF6m is believed to be a late event in MN with little data on its prognostic implications in MN. Both STAG2m and PHF6m are more prevalent in males, but their implications on the clinical presentation of MN have not been compared prior. METHOD: Our study is a multicenter study at Mayo Clinic (Rochester, Florida, Arizona). After IRB approval, we retrospectively analyzed charts of 7935 patients who had NGS between 2016-2022. STAG2m was found in 96 patients, and PHF6m in 116 (including 3 with concurrent STAG2m and PHF6m). We excluded 4 PHF6m patients who had T-ALL. Data was captured at time of NGS. MDS risk stratification was by IPSS-M and AML by ELN2022. Overall survival (OS) was calculated from date of NGS to date of last follow-up. BlueSky Software was used for statistical analysis. RESULTS: Characteristics: We compared 96 STAG2m vs 112 PHF6m MN patients. Of STAG2m cases, 78% were male vs 76% in PHF6m MN. Median age was 72 vs 73 years, respectively. Diagnoses included AML (29% of STAG2m vs 34% of PHF6m MN), MDS (54% vs 28%), MDS/MPN (10% vs 21%) and MPN (3% vs 9%). MDS was more common among STAG2m MN (p<.001) while MDS/MPN was more common among PHF6m MN (=.05). MDS-IB was the most common subtype in both (69% vs 52% in STAG2m vs PHF6m MDS). MDS-LB and 5q subtypes were only found in PHF6m MDS, and there was more PHF6m MDS-RS (13% vs 2%). Median IPSS-M score was 0.9 vs 0.8 in STAG2m vs PHF6m MDS (both high-risk). Majority of STAG2m AML were adverse risk (96%) while PHF6m AML were intermediate (79%) (p<.001). AML cases included 43% vs 30% secondary AML (sAML) in STAG2m vs PHF6m AML. Among STAG2m and PHF6m MN, 17% of each were tMN. Cytogenetic abnormalities were more common in PHF6m MN (50% vs 31%, p=.005). The median bone marrow (BM) blasts count among STAG2m MN was 7% vs 4% in PHF6m MN (p=.03). Genetic characteristics: STAG2m were nonsense, frameshift and splice site (54%, 36% and 11%). PHF6m were nonsense, frameshift, splice site, missense and start loss variant mutations (44%, 33%, 12%, 10% and 1%). The median variant allele frequency (VAF) of STAG2m MN was 50% compared to 32% in PHF6m MN (p=.008). Co-mutations: The median number of co-mutations was 3 in STAG2m and 4 in PHF6m MN (p=.04). The most common were TET2m (37% in STAG2m MN vs 45% in PHF6m MN), RUNX1m (31% in both), SRSF2m (39% vs 20%), ASXL1m (66% vs 38%), BCORm (19% vs 6%), U2AF1m (13% vs 14%) and IDH2m (18% vs 7%). ASXL1m, SRSF2m, BCORm, IDH2m and SF3B1m were more common among STAG2m MN (p=<.001, .003, .006, .02, .09). JAK2m, IDH1m are more common among PHF6m MN (p=.02, .06). Survival: Of STAG2m and PHF6m MN, 70% and 72% of patients received treatment. HSCT was performed in 23 (24%) and 23 (21%) of STAG2m and PHF6m patients, including AML (n=9, n=10), MDS (n=12, n=8), MDS/MPN (n=2, n=2). OS among STAG2m and PHF6m MN was 20.5 and 22.1 months, respectively. OS among AML cases was 13.5 vs 21 months (p=.6) in STAG2m vs PHF6m, was NR vs 22 months (p=.4) in MDS, 11.7 vs 17.6 months in MDS/MPN (p=.4), and 10.4 vs 27.4 months (p=.5) in MPN. There was no difference between males and females in either STAG2m or PHF6m MN. HSCT led to longer OS among both STAG2m (NR vs 19.9 months, p=.01) and PHF6m MN patients (NR vs 17.6 months, p=.07). Among AML patients, HSCT did not significantly impact OS (18 vs 9.6 months, p=.3 and 25.1 vs 21 months, p=.2, in STAG2m and PHF6m). HSCT improved OS in STAG2m MDS (NR vs 20.5 months, p=.03). In PHF6m MDS, OS was 22.1 vs 13.1 months in HSCT vs non-HSCT (p=.3). CONCLUSION: STAG2m and PHF6m are 2 X-linked mutations that occur in MN. Both commonly occur in elderly males. MDS was the most common diagnosis in STAG2m MN. STAG2m co-occurred particularly with SRSF2m ASXL1m and BCORm, which are associated with poor prognosis. In our cohort, STAG2m AML carried a poorer prognosis than PHF6m AML. Our study confirms the ELN2022 AML risk stratification. MDS risk classification and OS did not differ between STAG2m and PHF6m in our cohort. A larger cohort is required to confirm our findings and may provide stronger validation.

Introduction: Non-Follicular Low-Grade B-Cell Lymphomas (LGBCL) are a subset of lymphomas that share overlapping clinical and histologic features. While most cases of LGBCL have an indolent course, some experience aggressive disease with no definitive biomarkers to identify aggressive cases at diagnosis. Significant advances have been made in the molecular classification of other lymphomas, yet molecular profiling and identification of mechanisms driving aggressive disease in LGBCL remain an unmet need. We previously used a multi-omic approach to characterize the molecular and immune landscapes of LGBCL tumors and identified a transcriptomic signature associated with cell proliferation and inferior overall survival (OS) in two independent cohorts (HR 7.82; 95% CI 2.4-25.4; p < 0.001 & HR 10.07; 95% CI 2.00-50.61; p = 0.005) and cases that transformed to DLBCL (Hopper et al., 2023). Here, we identify the oncoprotein DEK as a novel transcriptional regulator of this signature and use a functional genomic approach to further interrogate the role of DEK in B-cell lymphoma. Methods: Tumors from 64 newly-diagnosed/untreated LGBCL cases enrolled in the University of Iowa/Mayo Clinic Molecular Epidemiology Resource were included in this study (SMZL = 48, NMZL = 6, LPL = 5, B-NOS = 3, EMZL = 2). Tumor RNA-seq and paired tumor-normal WES were used to assess DEK expression and identify copy number alterations (CNAs), respectively. DEK deficient (KO) cell line models were generated using CRIPSR/Cas9. DEK expression was assessed by western blot (WB) and immunofluorescence (IF) using standard techniques. Proliferation was assessed by MTT assay. Results: To identify transcriptional regulators of our previously identified signature, we used the tool MAGIC to mine publicly available ChIP-seq data and identified DEK, an oncoprotein associated with tumorigenesis and poor prognosis in several cancers, as a key regulator of genes in our signature (p = 0.008). RNA-seq indicated that high levels of DEK expression associated with inferior OS in LGBCL (HR 2.99; 95% CI 1.21-7.36; p = 0.02). Additionally, we identified DEK as a target of copy number gains/amps in LGBCL (7% of cases), which corresponded to significantly higher DEK expression (p = 0.04). The association of DEK with aggressive lymphoma was confirmed when we assessed its expression in public cell line RNA-seq data and observed the highest expression in lines derived from aggressive lymphomas, which we further validated at the protein level in a panel of B-cell lymphoma cell lines by WB and IF confocal microscopy. Additionally, we detected nuclear localization of DEK, consistent with its role as a transcriptional regulator. As a model of DEK overexpression for functional characterization, we used the SUDHL6 lymphoma cell line, which has an amplification of DEK at 6p22.3 and expresses high levels of DEK protein. Using CRISPR technology, we generated DEK deficient SUDHL6 and tested the impact of loss of DEK expression. DEK KO cells proliferated significantly slower than WT SUDHL6 cells in vitro (p < 0.0001)(Panel A), indicating DEK plays a role in regulating proliferative capacity in lymphoma. To identify mechanisms responsible for reduced proliferation in DEK KOs, we assessed baseline protein expression levels of apoptosis proteins in WT versus KO cells. We found near complete abrogation of anti-apoptotic protein Bcl-2 and reduction of Bcl-xL expression in DEK KO cells (Panel B). These results indicate DEK may contribute to tumor proliferative capacity by regulating susceptibility to apoptosis. Conclusion: DEK has been implicated in regulation of gene expression, but its precise role has remained elusive, with little known about its role in lymphoma. Using an integrative molecular and functional genomics approach, we identified DEK as a novel oncoprotein in LGBCL that is upregulated in cases with inferior clinical outcome and is regulated by copy number gains/amps. DEK plays a role in regulating tumor proliferative capacity by altering susceptibility to apoptosis through regulation of Bcl-2 and Bcl-xL. Collectively, this data suggests high-risk LGBCL tumors may enrich for cells with DEK overexpression or CNAs as a mechanism driving oncogene addiction, highlighting DEK, and its downstream targets, as potential therapeutic targets in a subset of high-risk LGBCL.

Background: Clonal cytopenia of undetermined significance (CCUS) is characterized by the presence of a clone with somatic mutations in genes associated with myeloid neoplasms (MN) or abnormal cytogenetics, along with unexplained and persistent cytopenias in the absence of significant marrow dysplasia. CCUS is considered a premalignant state with a risk of progression to MN. The clinical outcomes of patients (pts) with high risk CCUS (Weeks et al, NEJM Evidence 2023) may be similar that of some patients with myelodysplastic syndromes (MDS). This study aims to predict the outcomes of CCUS patients using the molecular international prognostic scoring system (IPSS-M) developed for MDS. Methods: A retrospective analysis was conducted in patients diagnosed with CCUS between 2015 and 2023, we included patients harboring pathogenic mutations (MUT) identified through next-generation sequencing (NGS) testing at two institutions: Mayo Clinic and the University of Texas Southwestern Medical Center, Dallas, TX, USA were included. Progression was considered present if patients with CCUS progressed into myeloid neoplasm at follow up. Statistical analysis was performed using JMP® 17.1.0 Software. IPSS-M risk scores were calculated via the IPSS-M Risk Calculator (mds-risk-model.com). Results: A total of 103 patients were included in the study. The median age was 72 years, the majority were male patients (73%), and the median bone marrow blast % at the time of NGS was 1%. Forty-five % had an isolated mutation in a single gene, and 55% had co-mutations in ≥1 gene. The median number of mutations was 2 (range 1 - 5), occurring across 43 different genes. The most common mutations were in TET2 (N=28; 27%), ASXL1 (N=21; 20%), SRSF2 (N=21; 20%), U2AF1 (N=18; 17%), DNMT3A (N=16; 16%), and TP53 (N=12; 12%). Additionally, 25% of patients had an abnormal karyotype. Hematologic and genetic characteristics are summarized in (Table 1A). The median IPSS-M score was -0.9 (-3 to +2.34) and the median IPSS- Revised (R) score was 2 (0 - 6). The majority of patients had low (L) IPSS-M risk category (53%), followed by moderate low (ML) (19%) and very low (VL) (17%). Per IPSS-R, the majority had low-risk category (47%), followed by VL (38%) and intermediate (14%). Overall, 26 (25%) pt died after a median follow-up of 34 months and 21% progressed to MN (MDS 14 & CMML 8), and 1 patient with MDS and 1 with CMML progressed to secondary AML (2%). The median overall survival (mOS) was not reached, with a 2-year estimated (YE) OS and leukemia-free survival (LFS) of 80%. Predictive Power: When utilizing the IPSS-M risk category ( L, M, H) * by combining the risk categories into L *(L+VL), M * (ML+MH), and H *(H+VH) to overcome the low sample size limitation, it significantly predicted OS (p < .0001) ( Figure 1A) and median MN-free survival (FS) (p < .0001) ( Figure 1B). The 2YE-OS rates by IPSS-M risk category were as follows: VL - 93%, L - 87%, ML - 75%, high (H) - 25%, very high (VH) - 0% and the 2YE-OS rates by IPSS-M ( L, M, H) are summarized in Table 1B. IPSS-M hazard ratio (HR) for OS was 1.6 for ( M*) compared to 25 for ( H *) risk category (p < .0001). The ( L *) category, being the largest, was used as the reference level. Conclusion: This study provides the first real-world experience of using the IPSS-M and a modified version of the IPSS-M ( L, M, H) * risk category model to predict survival and progression into MN in CCUS (MUT) pt. The IPSS-M risk category was found to be a significant predictor of OS and MN-free survival. We propose that larger cohorts are needed to validate our findings.

Abstract Objectives Fluorescence in situ hybridization (FISH) for plasma cell neoplasms (PCNs) requires plasma cell (PC) identification or purification strategies to optimize results. We compared the efficacy of cytoplasmic immunoglobulin FISH (cIg-FISH) and fluorescence-activated cell sorting FISH (FACS-FISH) in a clinical laboratory setting. Methods The FISH analysis results of 14,855 samples from individuals with a suspected PCN subjected to cytogenetic evaluation between 2019 and 2022 with cIg-FISH (n = 6917) or FACS-FISH (n = 7938) testing were analyzed. Results Fluorescence-activated cell sorting–FISH increased the detection rate of abnormalities in comparison with cIg-FISH, with abnormal results documented in 54% vs 50% of cases, respectively (P < .001). It improved the detection of IGH::CCND1 (P < .001), IGH::MAF (P < .001), IGH::MAFB (P < .001), other IGH rearrangements (P < .001), and gains/amplifications of 1q (P < .001), whereas the detection rates of IGH::FGFR3 fusions (P = .3), loss of 17p (P = .3), and other abnormalities, including hyperdiploidy (P = .5), were similar. Insufficient PC yield for FISH analysis was decreased between cIg-FISH and FACS-FISH (22% and 3% respectively, P < .001). Flow cytometry allowed establishment of ploidy status in 91% of cases. In addition, FACS-FISH decreased analysis times, workload efforts, and operating costs. Conclusions Fluorescence-activated cell sorting–FISH is an efficient PC purification strategy that affords significant improvement in diagnostic yield and decreases workflow requirements in comparison with cIg-FISH.