Xie, Zhuoer

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.

PURPOSE: The benefit of routine pre-emptive screening for severe acute respiratory syndrome coronavirus 2 infections in patients with cancer before cancer-directed therapies is unclear. Herein, we characterize the outcomes of a cohort of patients with cancer who were diagnosed with COVID-19 by routine screening (RS) in comparison with those diagnosed on the basis of clinical suspicion or exposure history (nonroutine screening [NRS]). METHODS: A multisite prospective observational study was conducted at three major and five satellite campuses of the Mayo Clinic Cancer Center between March 18 and July 31, 2020. The primary outcome was COVID-19–related hospital admission. Secondary outcomes included intensive care unit admissions and all-cause mortality. RESULTS: Five thousand four hundred fifty-two patients underwent RS in the outpatient setting only, and 44 (0.81%) were diagnosed with COVID-19. RS detected 19 additional patients from the scheduled inpatient admissions for surgical or interventional procedures or inpatient chemotherapy. One hundred sixty-one patients were diagnosed with COVID-19 on the basis of NRS. COVID-19–related hospitalization rate (17.5% v 26.7%; P = .14), intensive care unit admission (1.6% v 5.6%; P = .19), and mortality (4.8% v 3.7%; P = .72) were not significantly different between the RS and NRS groups. In the multivariable analysis, age ≥ 60 years (odds ratio, 4.4; P = .023) and an absolute lymphocyte count ≤ 1.4 × 109/L (odds ratio, 9.2; P = .002) were independent predictors of COVID-19–related hospital admission. CONCLUSION: The COVID-19 positivity rate was low on the basis of RS. Comparing the hospital admission and mortality outcomes with the NRS cohort, there were no significant differences. The value of routine pre-emptive screening of asymptomatic patients with cancer for COVID-19 remains low.

PURPOSE: The goal of this study was to assess the impact of an interdisciplinary remote patient monitoring (RPM) program on clinical outcomes and acute care utilization in cancer patients with COVID-19. METHODS: This is a cross-sectional analysis following a prospective observational study performed at Mayo Clinic Cancer Center. Adult patients receiving cancer-directed therapy or in recent remission on active surveillance with polymerase chain reaction–confirmed SARS-CoV-2 infection between March 18 and July 31, 2020, were included. RPM was composed of in-home technology to assess symptoms and physiologic data with centralized nursing and physician oversight. RESULTS: During the study timeframe, 224 patients with cancer were diagnosed with COVID-19. Of the 187 patients (83%) initially managed in the outpatient setting, those who did not receive RPM were significantly more likely to experience hospitalization than those receiving RPM. Following balancing of patient characteristics by inverse propensity score weighting, rates of hospitalization for RPM and non-RPM patients were 2.8% and 13%, respectively, implying that the use of RPM was associated with a 78% relative risk reduction in hospital admission rate (95% CI, 54 to 102; P = .002). Furthermore, when hospitalized, these patients experienced a shorter length of stay and fewer prolonged hospitalizations, intensive care unit admissions, and deaths, although these trends did not reach statistical significance. CONCLUSION: The use of RPM and a centralized virtual care team was associated with a reduction in hospital admission rate and lower overall acute care resource utilization among cancer patients with COVID-19.

7542 Background: 60-70% of patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) can be cured with R-CHOP or R-CHOP-like immunochemotherapy. However, patients ≥80 years of age were either excluded or underrepresented in modern DLBCL trials, and their outcomes are understudied. The aim of this study is to define the survival trends and risk factors for inferior survival in older adult patients with DLBCL. Methods: Patients with newly diagnosed DLBCL were identified from the National Cancer Database (2004-2017, representing the rituximab era). Clinical characteristics, treatment, and outcomes were compared between patients ages ≥ 80, 65-79, and < 65 years. The Kaplan-Meier method and Cox proportional hazards model were used for survival analysis. Results: A total of 231,756 patients with newly diagnosed DLBCL were identified; 46,250 (20%) were ≥80 years, 87,702 (38%) were 65-79 years, and 97,904 (42%) were < 65 years. Patients ≥80 years were more likely to have a higher Charlson-Deyo Comorbidity Index score (CDS) (CDS ≥2, 12% vs 11% vs 8%, p = 0.001), less likely to receive systemic chemotherapy (63% vs 83% vs 89%, p < 0.001), and more likely to receive treatment at a non-academic center (71% vs 65% vs 48%, p < 0.001), compared to patients 65-79 and < 65 years, respectively. Median overall survival (OS) was significantly worse for patients ≥80 years compared to patients 65-79 years (11.6 vs 61.0 months, p = 0.001) and patients < 65 years (11.6 vs 178.1 months, p = 0.001). During the study period, the median OS had only minimally improved for patients ≥80 years (10.6 months in 2004-2007 vs 11.5 months in 2008-2011 vs 12.3 months in 2012-2016, p = 0.006). In contrast, the OS improvement appears more meaningful in patients 65-79 years (median in months: 51 vs 61.2 vs 65.9, p = < 0.001) and patients < 65 years (median in years: 14.6 vs 11.3 vs not reached, p < 0.001) in the prespecified intervals (2004-07, 2008-11, and 2012-16). In multivariate analysis, the most substantial risk factor for worse survival in patients ≥80 years was not receiving systemic therapy (hazard ratio [HR] = 3.26, 95%CI = 3.01-3.54, p = 0.001). Other risk factors associated with worse survival included high-risk IPI score (HR = 2.16, 95%CI = 1.96-2.39, p = 0.001), CDS score ≥2 (HR = 1.56, 95%CI = 1.40-1.73, p = 0.001), male sex (HR = 1.16, 95%CI = 1.09-1.24, p = 0.001), B symptoms at diagnosis (HR = 1.16, 95%CI = 1.08-1.25, p = 0.001), and treatment at a non-academic center (HR = 1.1, 95%CI = 1.01-1.20, p = 0.001). Conclusions: Patients ≥ 80 years of age with DLBCL have a significantly inferior survival which has not meaningfully improved in recent years. More than 1/3 of patients ≥ 80 years did not receive systemic therapy. Older adult patients with DLBCL should be assessed for fitness for chemotherapy using validated geriatric assessment tools. Novel therapeutic strategies with favorable safety profiles are urgently needed for this expanding patient population.

6539 Background: The impact of insurance status on survival in diffuse large B‐cell lymphoma (DLBCL), the most common aggressive lymphoma, has not been evaluated after the implementation of the Affordable Care Act (ACA). The aim of this study is to compare overall survival (OS) in patients across insurance status groups and in the periods before and after the ACA. Methods: Adult patients with newly diagnosed DLBCL were identified from the National Cancer Database. The analysis was restricted to patients 64 years of age or younger as most patients 65 years or older are eligible for Medicare under the ACA. The 2004-2017 period was chosen to represent the immunochemotherapy era preceding and following the ACA. Logistic regression was used to explore associations between abstracted variables and insurance status groups. The Kaplan-Meier method and Cox proportional hazards model were used for survival analysis. Results: 93,692 adults (age < 64 years) with newly diagnosed DLBCL and known insurance status were identified (41.3% female, median age 54 years [range: 18 – 64], 81.8% White and 12.1% Black). 7,211 (7.7%) patients were uninsured, 64,744 (69.1%) had private insurance, 11,936 (12.7%) had Medicaid, and 9,801 (10.5%) had Medicare. When compared to insured patients (private insurance, Medicaid or Medicare), uninsured patients were more likely to have a median household outcome of < $38,000 [OR 1.93 (95% CI 1.79-2.07)], less likely to receive chemotherapy [OR 0.69 (0.64-0.77)], more likely to be male [OR 1.14 (1.07-1.21)], more likely to be non-White [OR 1.30 (1.20-1.40], and more likely to present with stage III or IV disease [OR 1.24 (1.16-1.32)]. Uninsured patients had an inferior OS [HR 1.21 (95% CI 1.15-1.27)] when compared to insured patients after adjustment for baseline comorbidity (Charlson-Deyo score ≥2), advanced stage, treatment with chemotherapy, and sociodemographic factors including sex, age, race, household income, facility type (academic/community), and location (urban/rural). With a median follow-up time of 14.8 years (95% CI 14.6-not reached), median OS was lower in uninsured patients [13.4 years (12.3-not reached) vs 14.8 years (14.7-not reached); p < 0.0001]. Despite the lack of major changes in DLBCL therapies, a diagnosis after the implementation of the ACA (in 2010 or later) was associated with a superior OS when compared with the outcomes of patients diagnosed in 2010 or earlier [HR 0.93 (95% CI 0.90-0.95)]. Similarly, five-year OS was superior in the insured group [HR 0.93 (95% CI 0.89-0.96)]. Conclusions: Uninsured patients with DLBCL and < 64 years old had inferior OS when compared with insured patients, and uninsured status emerged as an independent risk factor for inferior OS. Our data highlight the independent effect of insurance disparities - a potential indicator of variations in access to health care - on survival in DLBCL.

1503 Background: Patients with cancer and COVID-19 are at risk for poor clinical outcomes. An established multi-site remote patient monitoring (RPM) service was rapidly adapted to support a novel, interdisciplinary COVID-19 program for outpatient management of patients at high-risk for severe illness. The goal of this study was to assess the impact of the RPM program on clinical outcomes and acute care utilization in cancer patients diagnosed with COVID-19. Methods: This is a cross-sectional analysis following a multi-site prospective observational study performed at Mayo Clinic Cancer Center (MCCC). All adult patients with active cancer – defined as currently receiving cancer-directed therapy or in recent remission on active surveillance – and PCR-confirmed SARS-CoV-2 infection between March 18 and July 31, 2020 were included. RPM was comprised of in-home technology to assess symptoms and physiologic data with centralized nurse and physician oversight. Results: During the study timeframe 224 cancer patients were diagnosed with COVID-19 at MCCC. Initial management included urgent hospitalization (within 48 hours of diagnosis) in 34 patients (15%). Of the remaining 190 patients (85%) initially managed in the outpatient setting, those who did not receive RPM were significantly more likely to experience hospitalization than those receiving RPM (OR 3.6, 95% CI 1.036 to 12.01, P = 0.044). Following balancing of patient characteristics by inverse propensity weighting, rates of hospital admission for RPM and non-RPM patients were 3.1% and 11% respectively, implying that RPM was associated with an 8% reduction in hospital admission rate (-0.077; 95% CI: -0.315 to -0.019, P = 0.009). Use of RPM was also associated with lower rates of prolonged hospitalization, ICU admission, and mortality, though these trends did not reach statistical significance. Conclusions: In the midst of a global pandemic associated with inpatient bed, ventilator, and PPE shortages, the RPM program provided an effective strategy for outpatient clinical management and was associated with decreased rates of hospitalization, ICU admission, and mortality in cancer patients with COVID-19. This care model enabled simultaneous opportunity to mitigate the increased risks of exposure, transmission, and resource utilization associated with conventional care.

Abstract Abstract Importance: The benefit of routine screening for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in patients with cancer before cancer-directed therapies is unclear. Herein, we characterize the outcomes of a cohort of cancer patients diagnosed with Coronavirus Disease 2019 (COVID-19) by routine screening in comparison with those diagnosed based on clinical suspicion or exposure history (non-routine screening). Objective: To describe and compare the outcomes of cancer patients diagnosed with COVID-19 on routine screening vs. non-routine screening at a multi-site tertiary cancer center. To identify risk factors for COVID-19-related hospital admission. Design: A multi-site prospective observational study was conducted between March 18 and July 31, 2020. Setting: Three major and 5 satellite campuses of the Mayo Clinic Cancer Center. Participants: Adult patients diagnosed with active cancer within the past five years and confirmed SARS-CoV-2 infection were included. Primary Outcomes and Measures: Clinical and laboratory data were assessed as independent variables. The primary outcome was COVID-19-related hospital admission. Secondary outcomes included intensive care unit (ICU) admissions and all-cause mortality. Results: Between March 18 and July 31, 2020, 5452 patients underwent routine screening in the outpatient setting, 44 (0.81%) were diagnosed with COVID-19. Routine screening detected additional 19 patients from inpatient and pre-procedural settings; 161 patients were diagnosed with COVID-19 based on non-routine screening. The median age of the entire cohort at diagnosis was 54 years, and 95 patients (42.2%) were female. COVID-19 related-hospitalization rate (17.5% vs. 26.7%, p=0.14), ICU admission (1.6% vs. 5.6%, p=0.19), and mortality (4.8% vs. 3.7%, p=0.72) were not significantly different between routine screening and non-routine screening groups. In the multivariable analysis, age ≥ 60 years (odds ratio: 4.4, p=0.023) and an absolute lymphocyte count ≤1.4 × 109/L (odds ratio: 9.2, p=0.002) were independent predictors of COVID-19-related hospital admission. Conclusions and Relevance: The COVID-19 positivity rate was low based on routine screening. Comparing the outcome with the non-routine screening cohort, there was no significant difference. These results led to an important practice change at our cancer center. We currently follow a testing strategy based on symptoms, exposure, risk factors, and clinical judgment. Citation Format: Zhuoer Xie, Antoine N. Saliba, Jithma Abeykoon, Umair Majeed, Daniel Almquist, Julia Wiedmeier-Nutor, Evandro Bezerra, Xavier Andrade-Gonzalez, Ashley Hickman, Karl Sorenson, Sagar Rakshit, Christopher Wee, Sri Tella, Anuhya Kommalapati, Nadine Abdallah, Joshua Pritchett, Mariza De Andrade, Dipesh Uprety, Andrew Badley, Joleen Hubbard, Naseema Gangat, Carrie A. Thompson, Thomas Witzig, Robert R. McWilliams, Konstantinos Leventakos, Thorvardur R. Halfdanarson. Outcomes of COVID-19 in patients with cancer: Results of a prospective observational comparison of routine screening strategy versus testing based on clinical suspicion [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr S06-03.

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 &lt;.0001), more side-scattered light by FC (p &lt;.0001), more pancytopenia on follow up (p= .02) and more morphologic atypia (&lt;.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&lt;.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&lt;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=&lt;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.