Li, Marissa

Abstract Background: Age-related clonal hematopoiesis (ARCH) is a precursor state to blood cancers and is associated with epigenetic age acceleration (EpiAgeAccel). While ARCH is associated with increased all-cause mortality and cardiovascular disease, not much is known about these risks with therapy-related clonal hematopoiesis (t-CH) – CH occurring in the context of prior cytotoxic chemotherapy/radiation exposure. Methods: Patients were prospectively recruited for CH and EpiAge assessment from Mayo Clinic between 2022 and 2024. CH status was determined using error corrected sequencing on peripheral blood (PB) DNA (sensitivity 0.5% VAF). Patients with CH were classified as t-CH if they had received chemotherapy or radiation prior to CH detection. All other CH cases were classifed as ARCH. For EpiAge analysis, PB DNA was analyzed using the Illumina MethylationEPIC array. The resulting beta values were used to obtain epigentic age estimates using Horvath’s clock, Hannum’s Clock, PhenoAge, DunedinPACE, and estimated telomere lengths (DNAmTL). Results: 106 (62%) of 170 patients assessed were found to have CH. CH patients were classified into ARCH-63 (59%) and t-CH-43 (41%). While the median chronological age (69 vs 67 years) was similar between ARCH and t-CH, t-CH patients had lower Hgb [10.9g/dL vs. 13g/dL (p=0.0001)], lower platelet [146x109/L vs. 192x109/L (p=0.0132)] and lower WBC [4.4x109/L vs. 5.2x109/L (p=0.0345)]. The 3 most common ARCH mutations were TET2 (20%), DNMT3A (19%), and ASXL1 (14%), while PPM1D (27%), TET2 (18%), and DNMT3A (14%) were most frequent in t-CH. There was no significant difference in median EpiAge via Horvath (68 vs 67.5years p=0.2826), Hannum (65 vs 66 years p=0.9333), or PhenoAge (55 vs 56 years p=0.618) between ARCH and t-CH. However, DunedinPACE of aging was higher in t-CH (1.27 vs 1.18 bioyear/chronoyear p=0.0148) and median DNAmTL was shorter in t-CH (6.35 vs 6.49kb p=0.0247), compared to ARCH (Note patients with telomere biology disorders were excluded). Multivariable regression analysis adjusting for chronologic age, smoking status, and male sex showed that both ARCH and t-CH had EpiAgeAccel via Horvath(5.8years p=0.0001, 3.4 years p=0.051), Hannum(5years p=0.001, 4.5 years p=0.007), and PhenoAge (6.7years p=0.002, 7.6 years p=0.002) clocks. t-CH more than doubled DunedinPACE (0.2 bioyear/chronoyear, p<0.001) of aging compared to ARCH (0.08 bioyear/chronoyear, p=0.026). t-CH also demonstrated a significant decrease in DNAmTL (0.34kb, p<0.001) in comparison to ARCH (0.17kb, p=0.015). With a median follow-up of 20 months, t-CH was associated with worse overall survival in comparison to ARCH, and non-CH patients (p=0.013, 93% vs. 90% alive at 10 months, 87 vs. 72% alive at 20 months). Conclusion: Our prospective CH cohort study validates the association between CH and EpiAgeAccel using several different methylation clocks and for the first time highlights an increased pace of aging and shorter DNAmTL in the context of inferior survival outcomes in t-CH. Citation Format: Marissa Li, Jenna Fernandez, Kristina Kirschner, Tamir Chandra, Kristen McCullough, Sarah Aug, Terra Lasho, Christy Finke, James Foran, Cecilia Arana Yi, Yael Kusne, Talha Badar, Abhishek Mangaonkar, Naseema Gangat, Rachel Simon, Alejandro Ferrer, Mrinal Patnaik. A prospective cohort study of biological aging and epigenetic age acceleration in 106 patients with age and therapy-related clonal hematopoiesis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Methylation, Clonal Hematopoiesis, and Cancer; 2025 Feb 1-4; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2025;85(3 Suppl):Abstract nr A015.

Background: Age-related clonal hematopoiesis (ARCH) is a precursor state to blood cancers and is associated with epigenetic age acceleration (EpiAgeAccel) (Aging Cell, 2021). While ARCH in combination with AgeAccel has the highest risk for all cause mortality and cardiovascular disease, not much is known about context-relevant clonal hematopoiesis (CRCH) - CH occurring in the context of prior chemo/radiation, bone marrow failure and germline predisposition syndromes. CRCH has a unique mutational signature enriched in genes in DNA damage response and repair (TP53 and PPM1D). To answer this question, we assessed EpiAge in a prospective cohort of patients with both ARCH and CRCH. Methods: Patients were prospectively recruited for CH and EpiAge assessment from the Mayo Clinic CH clinic between 2022 and 2024. Demographic and clinical data including chronologic age, BMI, sex, blood counts, and medical history were recorded at the time of enrollment. CH status was determined using error corrected sequencing on peripheral blood (PB) DNA. All somatic variants with a variant allele frequency (VAF) ≥0.5% were considered as CH. For EpiAge analysis PB DNA was analyzed using the Illumina MethylationEPIC array. The resulting beta values were used to obtain epigenetic age estimates using Horvath's clock (Genome Biol., 2013), Hannum's Clock (Mol Cell., 2013), PhenoAge (Aging, 2018), DunedinPACE (eLife, 2022), and predicted telomere length (Aging, 2019). Results: 106 (62%) of 170 patients assessed were found to have CH, with the most frequent mutations being TET2 (19%), PPM1D (17%), DNMT3A (16%), ASXL1 (9%), and TP53 (6%); median VAF 9.5%. Except for a higher median chronologic age (68 vs 60 years, p<0.001) in the CH group, baseline characteristics including BMI, comorbidities, baseline blood counts, sex and tobacco use history were similar between the two groups. The CH group demonstrated significant EpiAgeAccel by all three clocks: Horvath (p=0.001), Hannum (p<0.001), and PhenoAge (p<0.001); with an increase in biological age of 4.9 years (95% CI 2-7.7) by Horvath clock, 4.8 years (95% CI 2.2-7.5) by Hannum clock, and 7 years (95% CI 2.9-10.5) by PhenoAge, after adjusting for chronologic age, sex, smoking status, and cancer history. CH was also associated with increased DunedinPACE of aging by 0.13 biological year per chronologic year (bioyear/chronoyear) (95% CI 0.06-0.2, p<0.001), and shortened telomere lengths by 0.24kb (95% CI 0.11-0.35, p<0.001) after adjusting for chronologic age and excluding patients with telomere biology disorders. CH patients were then further classified into ARCH-63 (59%) and CRCH-43 (41%). While the median chronological age (69 vs 67 years) was similar between the 2 groups, CRCH patients had lower Hgb [10.9g/dL vs. 13g/dL (p=0.0001)] , lower platelet [146x109/L vs. 192x109/L (p=0.0132)] and lower WBC [4.4x109/L vs. 5.2x109/L (p=0.0345)]. The median BMI, comorbidities, and tobacco use history were similar between the 2 groups. The 3 most common ARCH mutations were TET2 (20%), DNMT3A (19%), and ASXL1 (14%), while, PPM1D (27%), TET2 (18%), and DNMT3A (14%) were frequent in CRCH. There was no significant difference found in median EpiAge via Horvath (68 vs 67.5years p=0.2826), Hannum (65 vs 66 years p=0.9333), or PhenoAge (55 vs 56 years p=0.618) between ARCH and CRCH. However, DunedinPACE of aging was higher in CRCH (1.27 vs 1.18 bioyear/chronoyear p=0.0148) and median telomere length was shorter in CRCH (6.35 vs 6.49kb p=0.0247), in comparison to ARCH. Multivariable regression analysis was completed for both ARCH and CRCH compared to non-CH, adjusting for chronologic age, smoking status, and male sex. While both ARCH and CRCH demonstrated EpiAgeAccel via Horvath(5.8years p=0.0001, 3.4 years p=0.051), Hannum(5years p=0.001, 4.5 years p=0.007), and PhenoAge (6.7years p=0.002, 7.6 years p=0.002) clocks, CRCH more than doubled DunedinPACE (0.2 bioyear/chronoyear, p<0.001) of aging compared to ARCH (0.08 bioyear/chronoyear, p=0.026). CRCH also demonstrated double the decrease in telomere length (0.34kb, p<0.001) compared to ARCH (0.17kb, p=0.015). Conclusion: Our prospective CH cohort study validates the association between CH and EpiAgeAccel using several different methylation clocks and for the first time, highlights increased pace of aging and shorter telomere lengths in CRCH versus ARCH. Correlations with cardiovascular disease and mortality outcomes are ongoing.

Abstract Clonal cytopenia of undetermined significance (CCUS) is defined by a myeloid driver mutation in the context of otherwise unexplained cytopenia. CCUS has an inherent risk of progressing to myeloid neoplasm. However, it is unknown how exposure to previous cytotoxic therapy may impact the risk of progression and survival. We stratified patients with CCUS by prior exposure to DNA-damaging therapy. Of 151 patients, 46 (30%) had received cytotoxic therapy and were classified as therapy-related CCUS (t-CCUS), whereas 105 (70%) had de novo CCUS. A lower proportion of t-CCUS had hypercellular marrows (17.8% vs 44.8%, P = .002) but had higher median bone marrow blast percentages. After a median follow-up of 2.2 years, t-CCUS had significantly shorter progression-free survival (PFS, 1.8 vs 6.3 years; hazard ratio [HR], 2.1; P = .007) and median overall survival (OS; 3.6 years vs not reached; HR, 2.3; P = .007) compared with CCUS. Univariable and multivariable time-to-event analyses showed that exposure to cytotoxic therapy independently accounted for inferior PFS and OS. Despite the similarities in clinical presentation between CCUS and t-CCUS, we show that exposure to prior cytotoxic therapies was an independent risk factor for inferior outcomes. This suggests that t-CCUS represents a unique clinical entity that needs more stringent monitoring or earlier intervention strategies.

7059 Background: TP53-mutated ( TP53mut) myeloid neoplasms (MN) are aggressive leukemia with poor survival. A subset of TP53mut MN is preceded by TP53mut CCUS that is conventionally considered to be high-risk premalignant condition, though evidence supporting the notion is lacking. We studied outcomes of TP53mut CCUS compared to TP53 wild-type ( TP53wt) CCUS. Methods: Mayo Clinic Molecular Hematology Database (N=7593) was queried to identify patients (pts) harboring pathogenic variant(s) in exons 4-11 of the TP53 gene with ≥2% variance allele frequency (VAF). CCUS was defined using the 5th edition of the WHO Classification for MN. Results: Of 457 pts with TP53mut, 29 (5.9%) had TP53mut CCUS. The median age at diagnosis was 67 years, 17 (59%) were female, and 20 (69%) had received cytotoxic therapies. One and ≥2 TP53mut were seen in 26 (89.6%) and 3 (10.3%) pts, respectively. Median TP53mut VAF was 9% (range 3-42), 3 had TP53mut VAF ≥20%, and 4 (14%) had non-MDS defining cytogenetic anomalies. Of 16 pts with available follow up evaluation (median interval 22 months, Table), 3 (10.3%) progressed to TP53mut MN at 5, 13, and 14 months from diagnosis. We next compared outcomes of TP53mut CCUS to TP53wt CCUS (n=138). The proportion of pts developing MN was not different between TP53wt and TP53mut CCUS (18.1 vs 10.3%, P= .42). Progression free survival of TP53mut CCUS was comparable to TP53wt CCUS at 1- (77 vs 83%), 2- (67 vs 67%), and 5-years (67 vs 59%, P=.9). Similarly, overall survival of TP53mut vs TP53wt CCUS at 1- (81 vs 89%), 2- (76 vs 76%), and 5-years (76 vs 67%) was comparable ( P= .94). Conclusions: In contrast to the prevalent notion, TP53mut was not associated with a higher risk of MN progression or mortality compared to TP53wt CCUS. Majority of TP53mut clones showed remarkable stability over years. [Table: see text]

Abstract Background: SARS-Cov-2 infections are associated with increased mortality and morbidity, largely due to inflammatory cascades and cytokine release syndrome (CRS). Clonal hematopoiesis of indeterminate potential (CHIP) is defined by the presence and subsequent expansion of somatic, leukemia-associated driver mutations in apparently healthy individuals with normal blood counts. CHIP has been associated with increased inflammation, with cytokines such as IL1-b, IL6 and TNF-a being elevated at baseline in affected individuals. We hypothesized that the presence of CHIP in patients with COVID-19 would result in excessive inflammation-related mortality and morbidity. Methods: We used the Mayo Clinic COVID-19 database to identify patients with COVID-19 on whom peripheral blood mononuclear cells (PBMC) were available for research use (IRB approved). We carried out target-capture next generation sequencing for 220 CH related genes, by previously described methods (1000 x coverage, variant allele fraction/VAF detection limit >0.5%; Kusne Y et al AJH 2021). CHIP was defined by the presence of a CH mutation with a VAF>1% in an individual with normal baseline blood counts. Demographics, blood counts, and inflammatory markers (CRP and cytokine levels- ELISA assay) at COVID-19 diagnosis and during follow-up (as clinically indicated) were collected. COVID-19 disease severity was classified based on the presence and severity of CRS, graded using the Penn Grading Scale (Porter et. al., 2018), and the WHO ordinal scale (WHO Blueprint, 2020). We used Fisher's exact test and the Wilcoxon rank sum test to compare categorical and continuous variables. Survival analysis was performed using the Kaplan-Meier method. We accounted for differences in age and sex using multivariable-adjusted proportional hazards regression models. Results: Seventy-two CHIP mutations were detected in 56 (25%) of the 227 patients with COVID-19 that had PBMC available; median age 69 years (range; 42-99 years), 61% male. Fifteen (26%) patients had 2 CHIP mutations, while 1 patient had 3 CHIP mutations. Common mutations encountered included DNMT3A (32%) , TET2 (19%) , SF3B1 (8%) , ASXL1 (6%) , MPL (5%) , and TP53 (5%; Figure 1A). COVID-19 patients with CHIP were older in age (median 69 vs 57 years; p<0.0001) and had higher baseline MCP-1 (p=0.04) levels. However, there were no differences in sex, comorbidities, blood counts, IL1-b, IL6 and TNF-a levels between the two groups. The median follow-up for the entire cohort was 9 months. The relative change from baseline in blood counts and inflammatory markers (CRP and cytokines) during follow-up was similar in CHIP and non-CHIP patients, with the exception that COVID-19-onset neutropenia was more common in CHIP patients (8% vs 1%; p=0.017) compared to those without CHIP. At last follow up neutropenia had resolved in all patients. Both groups had comparable number of patients with CRS (61% CHIP vs 53% non-CHIP patients, p=0.354, Figure 1B), however, CHIP patients had more severe CRS (median Penn Grade 3 versus 2 in non-CHIP, p=0.018, Figure 1C). Based on the WHO ordinal scale, CHIP patients were more likely to experience hospitalization with severe disease and death (61% versus 45% in non-CHIP, p = 0.049). Moreover, COVID-19 CHIP patients experienced worse overall survival in comparison to patients without CHIP (median 13.1 months vs not reached, p<0.001, Figure 1D). This association remained consistent after adjusting for age and sex at the time of COVID-19 diagnosis (HR 3.15, 95% 1.32 - 7.55, p = 0.010). At last follow-up, 22 deaths were documented: 13 (23%) in patients with CHIP and 9 (5%) in the non-CHIP group (p=0.02), with the primary cause for mortality being hypoxic respiratory failure (62% in CHIP vs 44% non-CHIP, p=0.04). Conclusions: In this study, we observe an age-independent impact of CHIP on COVID-19 associated inflammatory morbidity (CRS) and mortality (hypoxemic respiratory failure). We are currently carrying out detailed single cell (ssDNA, RNA and ATAC-seq) and proteomic studies (O-link PEA assays) to better elucidate this pathophysiology. Figure 1 Figure 1. Disclosures Patnaik: Kura Oncology: Research Funding; StemLine: Research Funding.

Abstract Introduction: Clonal hematopoiesis of indeterminate potential (CHIP) is defined by the presence and subsequent expansion of leukemia-associated driver mutations (MT). These MT predominantly involve epigenetic regulators such as DNMT3A, ASXL1, and TET2 (DAT, 75%). CHIP is a disease of aging and is associated with increased all-cause mortality. Clonal Hematopoiesis (CH), when present in the context of unexplained cytopenias and MT variant allele fractions (VAF) >20%, is termed as clonal cytopenias of undetermined significance (CCUS). Chronic myelomonocytic leukemia (CMML) is a hematological malignancy characterized by TET2, ASXL1, and SRSF2MT as well as sustained monocytosis. It usually arises in the setting of existing CH and clonal monocytosis. Here, we assessed the clonal compositions involving DAT genes in patients (pts) with CHIP, CCUS, and CMML. Methods: We included 1008 pts: 54 (5%) with CHIP, 334 (33%) with CCUS, and 620 (62%) with CMML. Demographics, laboratory parameters, and mutational spectrum at diagnosis were collected. We characterized and compared DAT MT in all three cohorts and assessed correlations with clinical parameters. MT locus and type along with protein sequences were analyzed using ProteinPaint. All statistical analyses were performed using R. Results: Of the 1008 pts, 427 (42%) had DAT MT; 36 had CHIP (67%); 96 had CCUS (29%), and 296 had CMML (48%). The median age was 71 years (IQR 65-76), and 68% were male. CMML pts had higher white blood cell counts, absolute neutrophil counts, absolute monocyte counts, and lower hemoglobin and platelet counts in comparison to CCUS and CHIP pts. The distribution of age and sex were similar among the three groups. After a median follow-up of 11 months (IQR 9.5-12.8), there were no myeloid progressions in the CHIP group and 17 (18%) in the CCUS group (8 of which developed CMML, Table 1). There were 738 protein-altering or splice-site DATMT in 495 unique MT hotspots. Pts with TET2MT were older (71 years, compared to DNMT3AMT: 69 years and ASXL1MT: 69 years , p<0.022). ASXL1MT were more common in males (75%, compared to DNMT3AMT: 52% and TET2MT: 66%, p=0.005). The mean VAF (mVAF) were 15.5% for DNMT3AMT, 35% for ASXL1MT and 39.2% TET2MT, respectively (p<0.001). We then compared DATMT characteristics in CHIP, CCUS, and CMML groups: DNMT3AMT were most common in CHIP, followed by CCUS, and were very rare in CMML (35% vs. 15% vs. 3%, p<0.001), with mVAF of DNMT3AMT increasing across this spectrum (CHIP: 6.5% vs. CCUS: 17.1% vs. CMML: 43.4%, p<0.0001). While DNMT3A missense MTs were most common (n=62, 67.4%), the AML-associated R882 hotspot MT was seen in 26 (28%) pts (CHIP: n=3, 5%, CCUS: n=10, 16%, and CMML n=13, 21%, Figure 1a). None of the DNMT3AMT CHIP and CCUS pts progressed to CMML.ASXL1MT were most frequently seen in CMML (n=178, 28.6%), followed by CHIP (n=4, 7.4%), and CCUS (n=17, 5%, p<0.001). The mVAF of ASXL1MT was significantly lower in CHIP, followed by CCUS, and CMML (11% vs. 34% vs. 36%, p<0.0001). Truncating ASXL1 MTs were most common (n=205, 53%). Mutational hotspots included G646W (n=81, CCUS: n=6, 3%, CMML: n=75, 37%) and E635 (n=27, CHIP: n=1, 0.5%, CCUS: n=1, 0.5%, CMML: n=27, 13%, Figure 1b). Four ASXL1MT CCUS pts transformed to CMML (R965, E635, G646W, Q910*, and Q925*); of which 2 co-mutated with TET2MT, one with SRSF2MT, and one with an additional ASXL1MT.TET2MT were frequently encountered in CMML, followed by CCUS and CHIP pts (CMML: n=205, 33% vs. CCUS: n=88, 26.3% vs. CHIP: n=15, 27.8%, p=0.02). The mVAF of TET2MT progressively increased across the 3 groups (CHIP: 14.1%, CCUS: 31.9%, CMML: 45.5%, p<0.001). TET2MT occurred across the span of the gene, with no clear hotspots identified, with nonsense MT being most frequent (n=150, 37.3%, Figure 1c). Among the nonsense and frameshift TET2MT (n=209), 131 (63%) involved the N terminal region, and 78 (37%) involved the catalytic domain. Among 8 CCUS pts that transformed to CMML, 6 had TET2MT, including 4 with spliceosome co-MT. Conclusion: While CMML, a disease of aging, arises in the background of CH, the most common CH MT, DNMT3AMT, plays a minor role in this process, and in fact, DNMT3AMT tend to occur at younger ages. While DNMT3A MT and ASXL1MT are preferentially observed in hotspots, TET2MT span the entire gene. Clonal compositions largely involving truncating TET2MT and frameshift ASXL1MT, in conjunction with splicing MT, shape the CMML genotype. Figure 1 Figure 1. Disclosures Komrokji: Geron: Honoraria; Novartis: Honoraria; Agios: Honoraria, Speakers Bureau; Abbvie: Honoraria, Speakers Bureau; JAZZ: Honoraria, Speakers Bureau; BMS: Honoraria, Speakers Bureau; Acceleron: Honoraria. Zeidan: Genentech: Consultancy; BeyondSpring: Consultancy; Kura: Consultancy, Other: Clinical Trial Committees; Amgen: Consultancy, Research Funding; Cardiff Oncology: Consultancy, Other: Travel support, Research Funding; Astex: Research Funding; Epizyme: Consultancy; Geron: Other: Clinical Trial Committees; Boehringer Ingelheim: Consultancy, Research Funding; AstraZeneca: Consultancy; Acceleron: Consultancy, Research Funding; Astellas: Consultancy; Novartis: Consultancy, Other: Clinical Trial Committees, Travel support, Research Funding; Agios: Consultancy; Aprea: Consultancy, Research Funding; AbbVie: Consultancy, Other: Clinical Trial Committees, Research Funding; Loxo Oncology: Consultancy, Other: Clinical Trial Committees; Pfizer: Other: Travel support, Research Funding; ADC Therapeutics: Research Funding; Gilead: Consultancy, Other: Clinical Trial Committees; BMS: Consultancy, Other: Clinical Trial Committees, Research Funding; Jazz: Consultancy; Incyte: Consultancy, Research Funding; Ionis: Consultancy; Jasper: Consultancy; Daiichi Sankyo: Consultancy; Janssen: Consultancy; BioCryst: Other: Clinical Trial Committees. Coombs: LOXO: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Genentech: Honoraria; MEI Pharma: Honoraria. Madanat: Blue Print Pharmaceutical: Honoraria; Stem line pharmaceutical: Honoraria; Onc Live: Honoraria; Geron Pharmaceutical: Consultancy. Griffiths: Takeda Oncology: Consultancy, Honoraria; Novartis: Honoraria; Taiho Oncology: Consultancy, Honoraria; Apellis Pharmaceuticals: Research Funding; Alexion Pharmaceuticals: Consultancy, Research Funding; Boston Biomedical: Consultancy; Astex Pharmaceuticals: Honoraria, Research Funding; Celgene/Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Genentech: Research Funding; Abbvie: Consultancy, Honoraria. Lai: Jazz Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees; Astellas: Speakers Bureau; Jazz Pharma: Speakers Bureau; Macrogenics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees. Savona: CTI: Consultancy, Membership on an entity's Board of Directors or advisory committees; Geron: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; BMS-Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; NOVARTIS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Taiho: Consultancy, Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; ALX Oncology: Research Funding; Astex: Research Funding; Incyte: Research Funding. Litzow: AbbVie: Research Funding; Actinium: Research Funding; Pluristem: Research Funding; Amgen: Research Funding; Astellas: Research Funding; Jazz: Other: Advisory Board; Omeros: Other: Advisory Board; Biosight: Other: Data monitoring committee. Al-Kali: Novartis: Research Funding; Astex: Other: Research support to institution. Patnaik: StemLine: Research Funding; Kura Oncology: Research Funding.

Introduction: Mutations in the gene isocitrate dehydrogenase 2 (IDH2) occur in ~12-15% of patients with acute myeloid leukemia (AML). Enasidenib, an oral small molecule selective targeted inhibitor of IDH2 enzymes, was approved forIDH2mutated relapsed/refractory AML (RR-AML) in 2017 (Stein et. al., 2017). There is limited data regarding its efficacy and toxicity outside the context of clinical trials. Using Mayo Clinic's cohort of AML patients, we provide a real-world look at outcomes forIDH2mutated RR-AML patients treated with enasidenib. Methods: A total of 13 patients treated with enasidenib for RR-AML were identified, all of which experienced at least one relapse following induction chemotherapy. Response assessment was based on European Leukemia Net (ELN) 2017 response criteria.Complete blood counts including blast percentage and transfusion dependency were obtained at AML diagnosis, initiation of enasidenib, 2 months and 6 months following initiation of therapy. Transfusion dependency was defined by the need for either a red cell or platelet transfusion within 4 weeks of the designated time frame. We compared the clinical characteristics of patients who achieved complete response (CR) (n=4) to those not achieving CR (n=9), using the Mann-Whitney U test for numerical variables and the Fischer's Exact test for quantitative data. Results: The median age at diagnosis of our 13 patients was 68 years (range 60-82), with a male predilection (n=11) (Table 1). Cytogenetic and molecular genetic findings are provided in Table 2. Complete response (CR) was achieved in 4 (30.7%) patients by cycle 2 and 2 of those patients (50%) proceeded to an allogeneic hematopoietic stem cell transplant (AHSCT). The median time from diagnosis to initiation of treatment for patients in CR was 3.5 months (range 1-7), and for patients not achieving CR was 13 months (range 6-53; p=0.048), with median time of follow up after treatment initiation of 23.5 months (range, 5-36) and 7 months (range, 1-14), respectively. Among 8 patients who were transfusion dependent at baseline, all the CR patients (2/2) achieved red blood cell and platelet transfusion independence 6 months after treatment, instead only (3/6) 50% of those not in CR achieved transfusion independence (p=0.028). An initial trajectory of improvement in counts is noted from the time of diagnosis to the 2 month mark post treatment with 75% of the responder cohort showing improvement in counts which was durable through the 6 month mark, whereas 55% of those that did not respond had worsening counts at month 2 and died prior to 6 month follow up. At the time of data cut-off, the median duration of CR was 14 months (range, 2-33), with 3 of 4 (75%) of responders maintaining CR. Figure 1 details overall responses and outcomes in all 13 patients. None of the patients in our study experienced differentiation syndrome. Five patients were hospitalized in the course of treatment for infectious complications. Two patients were noted to have hyperbilirubinemia however there was no clear indication that this was secondary to enasidenib Median overall survival for all 13 patients was 21 months (range, 6-53 months) with leukemia free survival of 7 months (range, 5-19 months). Furthermore, median overall survival for patients in CR was 27 months (range, 6-42), and those not in CR was 16 months (range, 6-53) (p=0.20). In addition, we observed that 75% of patients who attained CR are alive compared to 11.1% of those not in CR (p = 0.052). Conclusion: We demonstrate a superior CR rate of 30.7% with enasidenib amongstIDH2mutated RR-AML patients compared with clinical trial data showing CR rate of 19.6% (Stein et. al., 2019) despite our patients being heavily pre-treated with median number of two prior therapies (range; 1-5) including prior AHSCT in 2 patients. Moreover, a subset of patients (2 of 4 in CR) were able to undergo ASCHT. Finally, the achievement of CR at 2 months after initiation of therapy serves as an appropriate time point for response assessment. Disclosures No relevant conflicts of interest to declare.