Jevremović, Dragan

Abstract Background: The DEAD-box helicase 41 (DDX41), an RNA helicase, have been described as a component of the RNA spliceosome (Cheah et al. International Journal of Hematology 2017). Although DDX41 mutations predispose to late-onset higher grade myeloid neoplasms (MN), these patients may have a trend toward favorable prognosis and outcomes. In this work, we describe the clinical characteristics and survival outcomes of isolated and co-mutated DDX41 patients (pts). Methods: We retrospectively analyzed 4,524 consecutive pts who underwent next-generation sequencing (NGS) (OncoHeme 42 genes panel, Mayo Clinic) testing and included 32 pts harboring pathogenic DDX41 mutation and one pt with proven DDX41 germline variant of unknown significance (VUS). Chart review of DDX41-mutated (m) cases between 2009 and 2021 was conducted after IRB approval. We compared overall survival (OS) of unmatched 27 t(8;21)AML and 40 inv(16) AML pts with 10 m DDX41-AML pts. JMP® Pro 14.1.0 Software was used for statistical analysis. Results: DDX41 mutations characteristics: Our cohort included 19 (58%) myelodysplastic syndromes (MDS), 10 (30%) acute myelogenous leukemia (AML), 2 (6%) myeloproliferative neoplasms (MPN), one clonal cytopenia of undetermined significance (CCUS) (3%) and one (3%) germline carrier. Germline testing was carried out in 10 pts, 9 of whom (90%) were confirmed to be germline). The start-loss variant (p.M1I) was the most common mutation type (N=10, 31%). Other types were frameshift (N=9, 28%), missense mutation (N=8, 25%), nonsense (N=3, 9%), and splice site mutation (N=2, 2%). Twenty-one (65.6%) DDX41 mutations clustered in the N-terminus (NT), 7 (22%) in the helicase-C domain (HCD), and 4 (12.5%) in the DEAD-box domain. Compared to NT mutations, patients with HCD mutation had no family history of solid tumors and were more likely to have an accompanying additional DDX41-VUS (0% vs 70%; p=.001) and (N=6, 86% vs. N=2,10%; p=.0001); respectively. I solated vs. co-mutated DDX41: Twenty (60%) pts were isolated-DDX41 and 13 (40%) were co-mutated. The median DDX41-VAF was 48% vs. 45% (p= .2) in the isolated compared to the co-mutated cases, respectively. The median number of co-mutations in the 13 co-mutated cases was 1 (range,1-3) with DNMT3A (38%), ASXL1 (30%), JAK2 (N=3, 23%), and EZH2 (N=2, 15%) were the most common co-mutations detected. Isolated DDX41 had more males (85% vs. 54%, p=.05), the p.M1I variant (47% vs. 8%, p=.02), normal cytogenetics (100% vs. 91%, p= .02), and less family history of solid tumors (77% vs. 33%, p= .02) compared to their co-mutated counterparts. However, there was no difference in OS (p=.99). Comparison of clinical characteristics and hematological features of isolated and co-mutated DDX41 pts are reported in (Table 1). Treatment and survival outcomes in DDX41-MDS/AML : Twenty-three (80%) patients were treated, MDS pts received hypomethylating agents (HMA) (N=10, 71%), HMA plus Venetoclax (HMA+VEN) (N=1, 7%), erythropoiesis-stimulating agents (N=2, 14%) and lenalidomide (N=1 ,7%). AML pts were treated with induction chemotherapy (N=6, 67%) and HMA+VEN (N=3, 33%). Overall response rate of MDS/AML patients was 77% and 100% of AML pts achieved complete remission (CR) when treated with induction chemotherapy or HMA+VEN regimen. There was no significant difference in OS between responders vs. non-responders 2-yr-OS (90% vs. 50%; p=.38) and treated vs. untreated 2-yr-OS (83% vs. 100%; p=.52). Comparing m DDX41-AML vs. core binding factor-AML: After a median follow-up of 33.3 months, all m DDX41-AML patients were alive. There was a significantly better OS in mDDX41-AML patients compared to pts with t(8;21) AML with 2-yr-OS (100% vs. 51%; p=.024) and a trend of better survival when compared to inv(16) AML 2-yr-OS (100% vs. 84%; p=.2). Conclusion We describe the characteristics and outcomes of m DDX41 patients. We demonstrated that isolated and co-mutated m DDX41 patients have different features. Isolated DDX41 patients had male predominance, more p.M1I variant, normal cytogenetics and less family history of solid tumors. In this study we found that m DDX41 AML has high response to treatment and has comparable (if not possibly better) OS compared to other "favorable risk" AML. This study, although limited by the small number of patients, supports the universal testing for DDX41 mutation in adults with MN diagnosis. Figure 1 Figure 1. Disclosures Al-Kali: Astex: Other: Research support to institution; Novartis: Research Funding. Foran: abbvie: Research Funding; OncLive: Honoraria; boehringer ingelheim: Research Funding; trillium: Research Funding; pfizer: Honoraria; takeda: Research Funding; revolution medicine: Honoraria; bms: Honoraria; gamida: Honoraria; actinium: Research Funding; aptose: Research Funding; novartis: Honoraria; servier: Honoraria; taiho: Honoraria; syros: Honoraria; sanofi aventis: Honoraria; certara: Honoraria; kura: Research Funding; h3bioscience: Research Funding; aprea: Research Funding; sellas: Research Funding; stemline: Research Funding. Badar: Pfizer Hematology-Oncology: Membership on an entity's Board of Directors or advisory committees. Salama: Mayo Clinic: Current Employment, Other: Mayo Clinic had the contractual work for the central pathology review for this study and I was one of the reviewing pathologists; Constellation Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Litzow: Astellas: Research Funding; Biosight: Other: Data monitoring committee; Amgen: Research Funding; AbbVie: Research Funding; Actinium: Research Funding; Pluristem: Research Funding; Jazz: Other: Advisory Board; Omeros: Other: Advisory Board. Patnaik: Stemline Therapeutics: Membership on an entity's Board of Directors or advisory committees; Kura Oncology: Research Funding; Stemline Therapeutics: Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction: Low-grade B-cell lymphomas (LGBCL), aside from follicular lymphoma and chronic lymphocytic leukemia/small lymphocytic lymphoma, account for approximately 10% of B-cell non-Hodgkin lymphomas and consist of several subtypes. While a majority of LGBCL cases have an overall favorable prognosis, we have previously shown that cases who have an event (relapse or progression, transformation, or re-treatment) within 24 months of diagnosis (EFS24) have an inferior overall survival (OS) compared to those achieving EFS24 (Tracy et al., AJH 2019;94:658-66). However, the underlying biological characteristics associated with early failure and aggressive disease across LGBCL subtypes are unknown. In this study, we used matched transcriptomic, genomic, and immune profiling data from LGBCL cases, the largest cohort to date, and asked whether there were unique biological phenotypes across different LGBCL subtypes and whether we could identify signatures associated with aggressive LGBCL. Validation of the prognostic utility of this signature was performed on a previously published, independent cohort of 63 pre-treatment LGBCL cases. Methods: Tumors from 64 newly diagnosed LGBCL patients from the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence were included in this study (SMZL (n = 48), NMZL (n = 6), LPL (n = 5), B-NOS (n = 3), EMZL (n = 2)). RNA sequencing (RNAseq) data from 61 LGBCL tumors and 5 benign CD19+CD27+ memory B samples was subjected to NMF clustering to define groups. Differential expression and pathway analysis were used to identify biological characteristics of each cluster. CIBERSORT was used to identify immune cells in the tumor microenvironment. Whole exome sequencing (WES) was performed on 61 tumor-normal pairs. Singscore was used to assign a single score per patient representing gene expression of the survival-associated transcriptomic signature identified in this study. Results: NMF analysis of RNAseq data identified 5 clusters of patients, denoted LGBCL1-5 (Fig 1A). Patients from the same diagnostic subtype did not exclusively cluster together, with all LGBCL clusters comprised of patients from multiple subtypes (Fig 1B). Exploring the association between patient cluster and outcome, we observed significantly inferior event-free survival (EFS) (HR 2.24; 95% CI 1.01-4.98) and overall survival (OS) (HR 5.59; 95% CI 2.00-15.63) in LGBCL5 patients compared to LGBCL1-4 (Fig 1C). In addition, 80% of the transformation cases in our cohort were classified as LGBCL5 (Fig 1D). Differential expression and pathway analysis showed distinct processes significantly upregulated in each cluster (FDR < 0.05), with LGBCL5 demonstrating enrichment of cell cycle and mitosis pathways. CIBERSORT identified increased immune cell content in LGBCL3 and LGBCL5 compared to other clusters, with high frequencies of mast cells in both (p = 0.0002), increased CD8 T cells in LGBCL3 (p < 0.0001), and increased T follicular helper cells in LGBCL5 (p = 0.004). WES identified previously reported alterations in NOTCH, NFkB, and chromatin remodeling pathways and novel variants in LGBCL, including mutations in HNRNPK, CLTC, HLA-A, HLA-B and HLA-C. Assessment of alterations by cluster showed significant enrichment of TNFAIP3 (OR 5.54; 95% CI 1.20-28.14) and BCL2 alterations (OR 5.49; 95% CI 1.07-32.02) in LGBCL5 cluster. Finally, we identified a cell cycle-related transcriptomic signature of 108 genes upregulated in LGBCL5 and EFS24 failure cases. Cases with high expression of this signature showed significantly inferior EFS (HR 14.25; 95% CI 4.90-41.38) and OS (HR 7.82; 95% CI 2.40-25.44) compared to cases with low expression in our discovery cohort. This observation was reproduced in an independent validation cohort, where patients with high expression of this signature demonstrated significantly inferior EFS (HR 5.70; 95% CI 1.49-21.79) and OS (HR 10.07; 95% CI 2.00-50.61). Conclusions: In this study, we are the first to define mechanisms of pathogenesis in LGBCL with shared transcriptomic, genomic, and immune profiles present across LGBCL subtypes. We then further defined a gene expression signature associated with inferior patient outcome, with application of this signature to an independent validation cohort demonstrating proof of concept and utility of this signature as a prognostic marker in LGBCL patients. Figure 1 Figure 1. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Paludo: Karyopharm: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Abstract DDX41 mutations (germline and somatic) are associated with late onset myelodysplastic syndromes/acute myeloid leukemia (MDS/AML). Myeloid neoplasms (MN) with germline predisposition was identified as a distinct category in the 2016 WHO classification revision, including MN with germline DDX41 mutation. We retrospectively analyzed the molecular findings and clinical characteristics of thirty-three DDX41-mutated (mDDX41) patients at our institution. We identified 14 distinct pathogenic DDX41 variants in 32 patients and 8 DDX41 variants of unknown significance (VUS) in 9 patients. Five (16%) patients had a second DDX41 somatic mutation p.R525H and 13 (40%) had at least one additional oncogenic co-mutation in other genes. The median age at the time of diagnosis was 66 years, with male predominance (72%) and the majority of patients had normal cytogenetics (91%). Two-year overall survival (OS) was 86% and 6 (21%) MDS/AML patients with relatively preserved hematopoietic function were observed without further intervention. In comparison to AML patients with prognostically more favorable subtypes [t(8;21), n=27 and inv(16), n=40], mDDX41 patients in our cohort showed similarly favorable OS. Our study highlights that mDDX41-MN patients often have an indolent course and mDDX41-AML has comparable OS to favorable-risk AML.

AbstractMultiple myeloma (MM) remains an incurable disease despite incorporation of novel agents. Venetoclax, a B‐cell lymphoma 2 (BCL‐2) inhibitor is approved for some hematologic malignancies but not yet for MM, although clinical trials have shown efficacy in patients with MM, particularly those harboring t(11;14). We reviewed the medical records of relapsed and/or refractory MM patients to study the efficacy and safety of venetoclax used outside of clinical trials at Mayo Clinic between December, 2016 and March, 2019. The data cut‐off date was August 06, 2020. We identified 56 patients of whom 42 (75%) harbored t(11;14). The median number of prior therapies was six (range 1–15) and 14% of patients had received ≥10 prior lines of therapy. Fifty‐three (95%) patients were refractory to an immunomodulatory drug and proteasome inhibitor. Venetoclax was used as monotherapy or doublet, in combination with dexamethasone in 55% (n = 31) and a triplet or quadruplet in 45% of patients. No patient experienced tumor lysis syndrome. Overall response rate in 52 evaluable patients was 44%. The median time to best response was 2 months and median duration of response was 13.6 months. The median PFS for the entire cohort was 5.8 (95% CI 4.9–10.3) months and median OS was 28.4 (95% CI 14.6‐not reached) months. The presence of t(11;14) was associated with improved PFS (median 9.7 months vs. 4.2 months, p = 0.019) and OS (median not reached vs. 10.8 9 months, p = 0.015). Venetoclax demonstrates encouraging activity in heavily‐treated patients with relapsed/refractory MM, particularly the t(11;14) patient‐population.

The accurate diagnosis of chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) subtypes with monocytic differentiation relies on the proper identification and quantitation of blast cells and blast-equivalent cells, including promonocytes. This distinction can be quite challenging given the cytomorphologic and immunophenotypic similarities among the monocytic cell precursors. The aim of this study was to assess the performance of convolutional neural networks (CNN) in separating monocytes from their precursors (i.e., promonocytes and monoblasts). We collected digital images of 935 monocytic cells that were blindly reviewed by five experienced morphologists and assigned into three subtypes: monocyte, promonocyte, and blast. The consensus between reviewers was considered as a ground truth reference label for each cell. In order to assess the performance of CNN models, we divided our data into training (70%), validation (10%), and test (20%) datasets, as well as applied fivefold cross validation. The CNN models did not perform well for predicting three monocytic subtypes, but their performance was significantly improved for two subtypes (monocyte vs. promonocytes + blasts). Our findings (1) support the concept that morphologic distinction between monocytic cells of various differentiation level is difficult; (2) suggest that combining blasts and promonocytes into a single category is desirable for improved accuracy; and (3) show that CNN models can reach accuracy comparable to human reviewers (0.78 ± 0.10 vs. 0.86 ± 0.05). As far as we know, this is the first study to separate monocytes from their precursors using CNN.

Abstract Objectives Flow cytometric detection of T-cell clonality is challenging, particularly in differential diagnosis of immature T-cell proliferations. Studies have shown utility of TRBC1, in conjunction with other T-cell markers, as reliable means to identify T-cell clonality by flow cytometry. One limitation of surface TRBC1 (sTRBC1) evaluation is it cannot be detected in surface CD3 (sCD3)-negative T cells, such as normal or abnormal immature T-cell precursors. Here, we assess surface and cytoplasmic TRBC1 expression patterns in the differential diagnosis of T-lymphoblastic leukemia/lymphoma (T-ALL) vs normal thymocyte expansions. Methods Forty-three samples containing T-ALL, thymoma, normal thymus, and/or indolent T-lymphoblastic proliferation (i-TLBP), were evaluated. Results All 24 cases with normal thymocytes or i-TLBPs revealed a characteristic and reproducible sCD3/sTRBC1 expression pattern indicative of polytypic T-cell maturation. In contrast, all 19 T-ALLs lacked this polytypic maturation pattern and were either completely negative for sCD3/sTRBC1 or showed a minor sCD3-positive subset with a monotypic TRBC1 expression pattern. Cytoplasmic TRBC1 evaluation in 9 T-ALLs demonstrated a monotypic intracellular TRBC1-positive (n = 4) or TRBC1-negative (n = 5) expression, indicative of clonality. Conclusions Our findings demonstrate flow cytometric evaluation of surface and cytoplasmic TRBC1 expression can aid detection of T-cell clonality and differential diagnosis of immature T-cell proliferations.

Background: Clonal cytopenias of Undetermined Significance (CCUS) have a higher risk of progression to myeloid neoplasm (MN) compared to idiopathic cytopenias of undetermined significance (ICUS) (Malcovati et al., Blood, 2017). The implementation of flow cytometric (FC) immune-phenotyping and next generation sequencing (NGS) in clinical practice has improved the diagnosis of these entities but the clinical significance and interaction of these abnormal results are still unknown. In this study we investigated predictive factors that play role in survival and progression to MN in patients with ICUS/CCUS. Methods: Patients (Pts) who had undergone evaluation for unexplained cytopenia and had undergone FC and bone marrow morphological assessment between 3/2015-3/2020 at Mayo Clinic Rochester were included. Pts were excluded if a malignant hematological disorder was diagnosed prior to the time of FC. FC included evaluation of the following parameters: Abnormal expression of HLA-DR/CD13, CD2, CD7, CD45, and CD56 on blasts; and abnormal CD13/CD16 expression and side scatter on granulocytic cells. NGS panel up to 2018 included 34 genes, ASXL1, BCOR, BRAF, CALR, CBL, CEBPA, CSF3R, DNMT3A, ETV6, EZH2, FLT3, GATA1, GATA2, IDH1, IDH2, JAK2, KIT, KRAS, MPL, MYD88, NOTCH1, NPM1, NRAS, PHF6, PTPN11, RUNX1, SETBP1, SF3B1, TERT, TET2, TP53, U2AF1, WT1, and ZRSR2; in 2018, BRAF was omitted and 12 genes were added, ANRD26, DDX41, ELANE, ETNK1, KDM6A, MYD88, NOTCH1, RAD21, SH2B3, SRP72, SMC3, and STAG2. Results: Characteristics: Of 490 consecutive pts, 238 (median age 69 years (range 19-92), 66% males) met our inclusion criteria as ICUS. 86 (36%) pts had CCUS (abnormal cytogenetics and/or tested positive for pathogenic mutations on NGS). After a median follow-up of 28 months (95% CI: 20 to 31 months), 21 (25%) patients developed MN and 23 (27%) died during follow-up. Comparing ICUS vs CCUS: upon comparing CCUS pts to ICUS, several factors were found to be significantly different: CCUS pts were older (p= .02); majority male (p= .04), had more abnormal HLA-DR/CD13 (p <.0001), more side-scattered light by FC (p <.0001), more pancytopenia on follow up (p= .02) and more morphologic atypia (<.0001). Overall survival (OS) outcomes in CCUS: Several covariates were significant in univariate models, and model selection was used to generate a risk score based on abnormal CD7, abnormal CD13/CD16, abnormal HLA-DR/CD13, splenomegaly and history of prior chemotherapy or radiotherapy (range: 0 - 4; HR=2.58, [95 CI: 1.69 - 3.94], p<.0001). Risk scores were grouped as low risk (score=0), intermediate risk (score=1), and high risk (score=2+), with estimated 2-yr-OS of 95%, 84%, 40%, respectively (Figure 1). An extended risk score model including NGS factors added ASXL1 and IDH1 mutation status to the previous model (range: 0 - 6; HR=2.72, [95 CI 1.82 - 4.06], p<0.0001). MN-free survival (MNFS) in CCUS: 37 pts either progressed to MN and/or died with a median follow up time for MNFS of 22.4 months. Similar for OS, model selection approaches yielded a composite risk score: splenomegaly, BCOR mutation status, mutation in RAS signaling pathway, abnormal expression of flow markers CD13/CD16, HLA-DR/CD13 or CD7 (HR=3.23, [95 CI: 1.90 - 5.49], p=<0.0001). Risk scores were grouped as low risk (score=0), intermediate risk (score=1), and high risk (score ≥ 2), with estimated 1-yr MNFS of 84%, 74%, and 31%, respectively (Figure 2). Conclusion: CCUS has unique features compared to ICUS. Several factors, including clinical characteristics, immune-phenotyping by FC, and somatic mutations have important impact on risk of progression to MN and on overall survival. Our findings serve as proof-of-concept that such integrated models could help identify CCUS patients at higher risks for progression to MN or death. They can guide treatment accordingly and should be evaluated further. Disclosures Shah: Dren Bio: Consultancy.

SummaryMeasurable residual disease (MRD) assessment by marrow‐based next‐generation flow cytometry (NGF) following autologous stem cell transplantation (ASCT) may lead to false‐negative results due to patchy marrow involvement and extramedullary disease in patients with multiple myeloma. We assessed the value of simultaneous MRD evaluation with NGF and serum matrix‐assisted laser desorption ionization time‐of‐flight mass spectrometry (MASS‐FIX). Of all 61 complete responders who were NGF‐negative for MRD, around day‐100 post ASCT, 59% were MASS‐FIX‐positive. At median follow‐up of 26 months, 69% of MASS‐FIX(+)/NGF(−) patients were alive and progression‐free versus 96% of MASS‐FIX(‐)/NGF(−) patients, P = 0·02. MASS‐FIX, a simple peripheral blood‐based assay complements marrow‐based NGF to accurately prognosticate patients with myeloma.

Background Clonal cytopenia of undetermined significance (CCUS) is a newly described entity with a high probability to progress into myeloid disorders upon follow-up (Malcovati et al, Blood 2017). Little data is known on how to effectively treat patients with CCUS. We hereby describe a single institution experience in managing symptomatic CCUS patients. Methods Patients who had CCUS and underwent active treatment were identified after receiving IRB approval. CCUS diagnosis was rendered if patients had non-diagnostic bone marrow biopsy evaluation combined with evidence of pathogenic myeloid somatic mutation using our institution next generation sequencing (NGS) panel (OncoHeme, Mayo Clinic). "Symptomatic patients" is determined by patients who need therapy beyond blood transfusion. Treatment type is based on physicians' choice. Clinical and lab results were collected. Response and progression were graded based on the MDS IWG 2006 criteria. The date of treatment initiation was used for overall survival (OS) and progression (either to myeloid neoplasm or worsening cytopenias) free survival (PFS) calculation. Stata 14.1(StatCorp) was used for data analysis. Results 1) Cohort characteristics: Between 2015 and 2020, 24 patients met inclusion criteria with median age of 72 years (range: 24-87). Twenty (83%) were men. 15 (63%) were white, 1 (4%) African American, 1 (4%) Native American and 7 unknown (33%). Eighty percent had ECOG PS≤1. Four (17%) patients had prior other hematology disorders and 3 (13%) had solid tumor. Two (8%) patients had previous radiation therapy, 2 (8%) received chemotherapy and 1 (4%) received allogeneic stem cell transplantation. At the time of treatment, the median of hemoglobin was 8.9 (range 6.8-13.7) g/dL, white blood cell 2.9 (range 1.4-12.3) *109/L, and platelet 76 (range 8-407) *109/L. Red blood cell (RBC) and platelet transfusion dependency rate were 54% and 29%, respectively. We identified 23 different mutations. Most common mutations occurred in SRSF2 (N=5, 21%), TET2 (N=5, 21%), U2AF1 (N=5, 21%), and TP53 (N=4, 17%). Ten (42%) patients had 1 mutation, and 14 (58%) had ≥2 mutations. Treatment included hypomethylating agents (HMA) (N=10, 42%), growth factors (N=11, 46%), steroid (N=4, 17%), testosterone (N=2, 8%), cyclosporine (N=2, 8%), rituximab (N=1, 4%), immunoglobulin (N=1, 4%), vitamin B12 and iron (N=1, 4%). Seven (29%) patients received ≥2 treatments. Median follow up duration was 17.5 months (range 8.9-26.1). Median time from the diagnosis to treatment was 2.1 months (range 0-26.8). 2) Molecular correlation with response The overall response rate (RR) was 50% (hematological or symptomatic improvement). Most responders (67%) were treated with HMA (p=0.013). HMA treatment effect was not significantly associated with DNA methylation genes (DNMT3A, IDH1, IDH2 and TET2, all p>0.05). The RR was 100% for the three patients with IDH1 mutation (treated with HMA, steroid and erythropoietin stimulating agent (ESA), respectively). The RR was 100% for the 3 patients with ZRSR2 mutation (2 HMA, 1 testosterone). The RR was 0% for 3 patients with SF3B1 mutation (1 testosterone and ESA, 1 vitamin B12 and iron, and 1 HMA). The number of mutations had no impact on RR (p=0.41). Table demonstrates the RR for each treatment. 3) Survival and progression outcome At the last day of follow up, RBC and platelet transfusion dependency rate were 38% and 25%, respectively. The median PFS was 16.9 months (95% CI: 7.5-65.3). Six patients (25%) progressed to myeloid malignancy with 4 myelodysplastic syndrome (MDS) and 2 acute myeloid leukemia (AML). Five patients progressed with worsening cytopenias. Among the 4 MDS patients, they had SF3B1, TET2, and ASXL1 mutations at the diagnosis of CCUS. Within the 2 AML pts, 1 had RUNX1 mutation, 1 had BCOR, DNMT3A, PHF6, RUNX1, SF3B1, STAG2, and TET2 mutations at the diagnosis of CCUS. The median OS was not reached with an estimated 2 year OS 77%. Three (13%) patients progressed with cytopenia and died. Their NGS at CCUS diagnosis showed ASXL1, TP53 and U2AF1 mutations, respectively. Conclusion Patients with CCUS responded to available treatments used for myeloid diseases. HMA were effective in this cohort. Mutation profile may have an impact on treatment response. Studies of larger cohorts are needed to validate our findings. Additionally, development of response and progression criteria would be helpful for such patients for reporting treatment outcomes. Disclosures Shah: Dren Bio: Consultancy.