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INTRODUCTION Complete staging via bone marrow assessment is standard of practice for most newly diagnosed non-Hodgkin lymphoma (NHL) patients, though the methods and patterns of bone marrow assessment vary in clinical practice. The 2014 Lugano Classification recommends that in diffuse large B cell lymphoma (DLBCL), positron emission tomography-computed tomography (PET/CT) alone can designated advanced-stage disease in many, but not all cases. For most other NHL subtypes, obtaining bone marrow biopsy (BMB) remains recommended. We aimed to assess clinical practice patterns of marrow assessment and the performance of PET/CT in the clinical setting. METHODS The Lymphoma Epidemiology of Outcomes (LEO) cohort is a prospective cohort from 8 academic medical centers. All patients were enrolled in the cohort within 6 months of diagnosis, and prospectively followed. Patients with lymphoma enrolled in the cohort between July 2015 and June 2017 were included for study. Baseline clinical characteristics at diagnosis, whether BMB and/or PET/CT were performed, and the result of these assessments were extracted from the database. Bone marrow involvement on PET/CT was abstracted via review of radiology reports. No central review of images was utilized. RESULTS In total, 3,251 patients were included, consisting of the following subtypes: DLBCL (n=1,098), follicular lymphoma (FL n=730), mantle cell lymphoma (MCL n=302), T-cell lymphoma (TCL n=278), marginal zone lymphoma (MZL n=269), other NHL (n=505) and composite lymphoma (n=69). Other clinical characteristics are shown in table 1. Staging BMB was obtained in 78% of patients, PET/CT was obtained in 56%, both BMB and PET/CT were obtained in 46% while neither were obtained in 9% (table 1). There were significant differences in lymphoma subtype between patients having any bone marrow assessment vs no bone marrow assessment (p<0.0001). MCL (96%) and DLBCL (94%) most frequently had any bone marrow assessment, while MZL was least frequently assessed (84%). Patients undergoing any bone marrow assessment had higher median lactate dehydrogenase (247 U/L vs 206 U/L, p=0.0002). Among the 8 enrolling medical centers, the frequency of bone marrow biopsy at diagnosis ranged from 53% to 86%, and the frequency of PET/CT at diagnosis ranged from 34% to 94% (p<0.0001, table 1). Among patients with DLBCL who had positive bone marrow by either PET/CT or BMB (N=107), 41% (44/107) were positive by BMB only, 19% (20/107) were positive by PET/CT only, and 40% (43/107) were positive by both. Figure 1 shows results of similar analyses for FL, MCL, MZL, and TCL. Using BMB as a gold standard for marrow involvement, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of PET/CT for marrow assessment in DLBCL is 49%, 90%, 68% and 91%, respectively. Data for other subtypes are shown in table 2. Patients undergoing both PET/CT and BMB had lower median LDH (231 U/L vs 254 U/L, p=0.0032) than those undergoing one or neither assessments. CONCLUSION Overall, 91% of all patients with newly diagnosed NHL had bone marrow assessments via either PET/CT, BMB or both. The significant variation in the frequency of BMB and PET/CT use among the 8 centers reflects a lack of clear consensus on staging practices regarding bone marrow assessment. The sensitivity of PET/CT for detection of marrow involvement was less than 50% across all subtypes. It is possible the sensitivity of PET/CT could be improved via a centralized review process of the images, as is performed in some clinical trials, however practical approaches for implementing such reviews in clinical practice have not been developed. Between 92-100% of all positive bone marrows were detected by BMB, except in cases with DLBCL in which 19% of positive bone marrows were missed by BMB but detected by PET/CT. Thus, in our analysis, BMB more reliably captured marrow involvement that PET/CT in all subtypes in which both assessments were performed. However, neither PET/CT nor BMB alone were able to identify all cases of bone marrow involvement and use of both modalities may be necessary in cases where marrow involvement would have clinically important implications. Disclosures Rutherford: Juno Therapeutics Inc: Consultancy, Honoraria; Janssen Scientific Affairs: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Heron: Consultancy, Honoraria. Kahl:ADC Therapeutics: Consultancy, Research Funding; Seattle Genetics: Consultancy; TG Therapeutics: Consultancy; BeiGene: Consultancy. Lossos:Janssen Scientific: Membership on an entity's Board of Directors or advisory committees; NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Friedberg:Acerta: Other: Data & Safety Monitoring Committee; Bayer: Honoraria, Other: Data & Safety Monitoring Committee. Casulo:Celgene: Research Funding; Roche: Other: Travel, accommodation, expenses; Gilead: Honoraria, Other: Travel, accommodation, expenses. Cohen:Astra Zeneca: Research Funding; Takeda Pharmaceuticals North America, Inc.: Research Funding; Hutchison: Research Funding; Gilead/Kite: Consultancy; Bristol-Meyers Squibb Company: Research Funding; LAM Therapeutics: Research Funding; UNUM: Research Funding; Lymphoma Research Foundation: Research Funding; ASH: Research Funding; Seattle Genetics, Inc.: Consultancy, Research Funding; Janssen Pharmaceuticals: Consultancy; Genentech, Inc.: Consultancy, Research Funding. Nastoupil:TG Therapeutics: Honoraria, Research Funding; Novartis: Honoraria; Spectrum: Honoraria; Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Genentech, Inc.: Honoraria, Research Funding; Gilead: Honoraria; Bayer: Honoraria. Flowers:Denovo Biopharma: Consultancy; Spectrum: Consultancy; National Cancer Institute: Research Funding; Optimum Rx: Consultancy; Burroughs Wellcome Fund: Research Funding; Acerta: Research Funding; Bayer: Consultancy; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; TG Therapeutics: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; Karyopharm: Consultancy; AstraZeneca: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding; Millenium/Takeda: Research Funding; Celgene: Consultancy, Research Funding. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Martin:Sandoz: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; Teneobio: Consultancy; I-MAB: Consultancy; Janssen: Consultancy.

Abstract Backgroud. The treatment landscape of mantle cell lymphoma (MCL) has significantly changed in last decades. Improvement in diagnosis and understanding of disease biology has been coupled with emergence of new therapeutic options, including targeted agents. While MCL outcome data comes primarily from clinical trials (CT), the impact of therapeutic advances on pattern of care (POC) and outcome in MCL in the general population is not well characterized. This study sought to characterize changes in pattern of care and outcomes of patients with MCL in a prospective observational series in the rituximab era. Methods. The study included consenting adult patients with newly diagnosed MCL that were prospectively enrolled into the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE) Molecular Epidemiology Resource, from 09/01/2002 to 06/30/2015. Demographic, clinical and prognostic factors were abstracted, and all patients were actively followed for re-treatment, relapse and death. Since bendamustine-rituximab (BR) regimen starting to be used in 2010, we defined patients enrolled from 09/01/2002 to 12/31/2009 as era 1 and those enrolled 01/01/2010 to 06/30/2015 as era 2. Baseline characteristics and outcomes, evaluated in terms of event free survival (EFS), overall survival (OS) and cause of death, were analyzed and compared between the two eras identified. Results. 348 patients with newly diagnosed MCL were enrolled. Five patients had no available data for front-line treatment and were excluded. The analysis was thus conducted on 343 patients: 169 patients were diagnosed in era 1 with a median follow-up of 131.2 months vs. 174 in era 2 with a median follow-up of 58.9 months. Baseline clinical characteristics and MIPI score were similar across the two eras ( Table 1). Frontline induction treatment was significantly different in the two eras. BR use was 0 vs 49 (28.2% ), R-CHOP/CHOP like regimen in 89 (52.7%) vs 45 (25.9%), high-dose cytarabine (HiDAC)-based therapy in 1 (0.6%) vs 28 (16.1%), intensified regimens (HyperCVAD) in 16 (9.5%) vs 8 (4.5%), other regimens (including R-cladribine, R-fludarabine-mitoxantrone, rituximab monotherapy) in 35 (20.7%) vs 13 (7.5%) patients in era 1 vs era 2 respectively. Non-systemic treatment (observation, surgery or radiation only) was performed in 25 (14.8%) vs 31 (17.8%), while 9 (8%) vs 12 (7.7%) patients were enrolled in clinical trials, in era 1 vs era 2 respectively. Autologous stem cell transplantation (ASCT) as consolidation of first line treatment or use of Rituximab maintenance was not different between Era 1 and Era 2. Among the entire cohort of 343 MCL patients, 3y-EFS and 3y-OS were 51.9% (95% IC 46.7-57.6) and 73.5 (95% CI 68.8-78.4), respectively (Figure 1). 3y-EFS was 45.9% (95% CI 39.0-54.1%) vs 58.4 (95% CI, 51.2-66.6%) in Era 1 vs Era 2 (HR 0.69 (0.51-0.92), p=0.006), 3y-OS was 70.9% (95% CI, 63.4-78.1%) vs 76.1% (95% CI, 69.7-83.1%) in Era 1 vs Era 2 (HR 0.87 (0.61-1.24), p=0.26), respectively (Figure 1). In a univariate analysis, high risk simplified MIPI was prognostic of lower EFS and OS in both the era groups. Conclusion. The BR regimen entered front line therapy of MCL resulting in decline of R-CHOP use in the MER. While rates of ASCT remain similar over the two eras, high dose cytarabine usage in induction therapy has increased. While POC in MCL continue to evolve, the introduction of bendamustine and high-dose citarabine based regimens resulted in an improvement in EFS but not OS in this observational cohort-based analysis. Findings in this study are important for design and planning of future clinical trials incorporating novel agents in induction therapy of MCL. Figure 1. Figure 1. Disclosures Cohen: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Bristol-Myers Squibb: Research Funding; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: 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, Research Funding; Janssen: Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cerhan:Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding.

Abstract Introduction: Richter transformation (RT) refers to transformation of chronic lymphocytic leukemia (CLL) to an aggressive lymphoma, most commonly diffuse large B-cell lymphoma (DLBCL). Most studies on the management of RT were either small retrospective series or early phase non-randomized trials before the era of novel agents. The natural history, prognostication and optimal treatment of patients with RT remain undefined. Here we report the clinical characteristics and outcomes of a large series of RT from a single center. Methods: Biopsy-confirmed RT (limited to non-Hodgkin lymphoma) diagnosed from 4/1993 to 4/2018 were identified from the Mayo Clinic CLL database. Clinical characteristics, treatment information and follow-up data were abstracted by chart review. Overall survival (OS) was defined as time from RT diagnosis to death from any cause and analyzed using the Kaplan-Meier method. Statistical analysis was done in SAS 9.4. Results: A total of 204 patients with CLL who developed RT were identified. The median age at CLL diagnosis was 62 years (range 22-85), and 148 (73%) were male. The median time to transformation was 4.7 years (range 0-34.5). Prior to RT, 68 (33.3%) patients received no treatment for CLL, 109 (53.4%) received chemoimmunotherapy (CIT) only, and 27 (13.2%) received at least one novel agent (idelalisib, ibrutinib, or venetoclax) for CLL. The median lines of CLL therapy prior to RT was 2 (range 0-13). The median age at RT diagnosis was 69 years (range 30-88). Pathology of RT was DLBCL and high grade B-cell lymphoma in 193 (94.6%) and 11 (5.4%) patients, respectively. The median LDH was 306 IU/L (range 99-9000). 62/125 (49.6%) patients had bulky disease (≥ 5 cm), and the median PET SUVmax was 13.9 (range 2.9-30.0). 45/131 (34.4%) patients had del(17p) or TP53 mutation, 12 (9.2%) had del(11q), 21 (16.0%) had trisomy 12, 27 (20.6%) had del(13q), and 25 (19.1%) had normal FISH. The CLL and RT were clonally related in 12/21 (57.1%) patients. For the transformed lymphoma, cell of origin by Han's algorithm was germinal center B cell-like (GCB) and non-GCB in 31/100 (31.0%) and 69/100 (69.0%) patients, respectively. EBV was positive in 14/52 (26.9%) patients. The median Ki-67 was 80% (range 10-100). Myc and Bcl-2 were positive by IHC in 31/43 (72.1%) and 83/103 (80.6%) patients, respectively; 27/56 (48.2%) were double-expressors. MYC, BCL2, and BCL6 rearrangement was positive by FISH in 18/68 (26.5%), 10/34 (29.4%), and 4/31 (12.9%) patients, respectively; 8/66 (12.1%) were double/triple-hit. The most common first-line treatment (Table 1 notes) of RT was R-CHOP-like regimen (n=114, 65.5%). Other treatments included R-EPOCH-like (n=6, 3.4%), high-intensity chemotherapy (n=15, 8.6%), novel agents (eg, ibrutinib, venetoclax, pembrolizumab; n=19, 10.9%), other chemotherapy (n=12, 6.9%), and palliative therapy (n=8, 4.6%). Response to first-line treatment was CR in 57 (38.0%), PR in 33 (22.0%), SD in 18 (12.0%), and PD in 42 (28.0%) patients. The median OS of the entire cohort after RT diagnosis was 12.0 months. The median OS for patients who received no prior CLL treatment, CIT only or at least one novel agent for CLL were 65.5, 7.3, and 12.0 months, respectively (P<0.0001; Figure 1). Of note, in patients who received CIT only for CLL, ~10% and 60% received high-intensity and R-CHOP/R-EPOCH-like chemotherapy, respectively, as first-line RT therapy. In contrast, in patients who had prior novel agents for CLL, 56% and 26% were treated with novel agents and R-CHOP/R-EPOCH-like chemotherapy, respectively, as first-line RT therapy. Patients with or without del(17p)/TP53 mutation had a median OS of 8.3 and 12.8 months, respectively (P=0.046). Patients who were treated with high intensity chemotherapy, R-CHOP/R-EPOCH-like regimens, novel agents, and other therapies for RT had a median OS of 35.1, 14.4, 10.9 and 6.1 months, respectively (P=0.02; Figure 2). OS comparisons by CLL/RT clonal relationship, double expressor or double/triple-hit status are shown in Table 1, with no significant differences noted. Conclusions: Over two thirds of RT were the non-GCB subtype, and about half were Myc/Bcl-2 double expressors. Patients who developed RT without prior CLL therapies had a significantly better OS. In contrast, patients who had received prior CLL therapies had poor outcomes. Myc/Bcl-2 double expressor and MYC/BCL2/BCL6 double/triple hit status had no impact on OS. Disclosures Kenderian: Humanigen: Research Funding; Novartis: Patents & Royalties; Tolero Pharmaceuticals: Research Funding. Kay:Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees. Parikh:Janssen: Research Funding; Abbvie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; MorphoSys: Research Funding; Gilead: Honoraria; Pharmacyclics: Honoraria, Research Funding. Ding:Merck: Research Funding.

Abstract Background: To address the current and long-term unmet health needs of the growing population of patients diagnosed with non-Hodgkin lymphoma (NHL) and NHL survivors, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/) with recruitment at eight centers (Cornell University, Emory University, Mayo Clinic, MD Anderson, University of Iowa, University of Miami, University of Rochester, and Washington University). Here we report on the first two years of enrollment. Methods: All patients ≥18 years with NHL (exclusive of chronic lymphocytic leukemia) newly diagnosed within 183 days of enrollment are eligible. LEO reviews all pathology diagnoses; annotates and harmonizes all cases with clinical, epidemiologic, pathology and treatment data; builds a NHL tumor bank that includes an H&E slide, a formalin-fixed, paraffin-embedded tissue sample and extracted tumor DNA and RNA; collects peripheral blood and banks DNA, serum, plasma and buffy coat in a central biorepository; and prospectively follows patients regularly to ascertain disease progression/relapse, retreatment, transformation, second cancers, survival, updated exposures, and patient-reported outcomes. We examined the demographics, clinical characteristics, and NHL subtypes for patients enrolled in the first two years (2015-2017) in LEO Cohort and compared these characteristics to population-based registry data from the United States Surveillance, Epidemiology, and End Results (SEER) Program for 2011-2015. We also examined baseline body mass index (BMI), comorbidities and quality of life (QOL) measured with the Functional Assessment of Cancer Therapy - General (FACT-G) scores (0-100 scale) overall and by NHL subtype that were collected in LEO but not available in SEER or any other large, prospective US NHL cohort. Results: From 2015-2017, LEO enrolled 3244 NHL patients across 47 states (Figure) with a median age at diagnosis of 62 years (range, 18-94 years) and 55.9% male. Based on self-identified race/ethnicity, 85% of the participants were White, 7.4% were Black/African American, 2.6% were Asian, and 3.5% other or more than one race; 9.9% were Hispanic (regardless of race). The most common subtypes were diffuse large B-cell (DLBCL, 33.9%), follicular (FL, 22.2%), mantle cell (MCL, 9.3%), marginal zone (MZL, 8.3%), T-cell (TCL, 10.9%), and all other NHL subtypes (other NHL, 15.3%). These distributions were all within 5% of SEER data, except that 6.8% of participant in LEO were in the age 80+ years group compared to 18.5% in SEER (Table). The overall prevalence of self-reported comorbidities at enrollment was 13.5% for heart disease (range, 12.1% for FL to 16.6% for MCL), 8.8% for diabetes (range, 7.1% for FL to 9.8% for DLBCL), 4.6% for osteoporosis (range, 3.0% for MCL to 6.3% for MZL), and 5.9% for autoimmune disease (range, 2.0% for MCL to 12.6% for MZL). The median BMI was 27.9 kg/m2 (range, 26.7 kg/m2 for TCL to 28.3 kg/m2 for MZL). A total of 2151 participants completed the FACT-G with a median score of 78.7 (range, 75.9 for TCL to 83.3 for MZL). Complete IPI data were available on 2296 participants: 40.4% IPI 0-1, 32.7% IPI 2, 18.3% IPI 3, 8.7% IPI 4-5. Overall, 73.7% of participants were initially treated with systemic therapy (range, 40.0% for MZL and 91.8% for DLBCL); 14.5% were initially managed with observation (range, 1.4% DLBCL and 31.8% for FL); and 2.0% were initially manage with radiotherapy (range, 0.2% for MCL and 3.8% for DLBCL). Outcomes are being prospectively collected. Conclusions: LEO is a large and diverse cohort of newly diagnosed NHL patients and the first two years of enrollment reflects the broader United States NHL population. Enrollment is ongoing. This unique resource will enable examination of the interactions among a broad array of clinical and molecular factors and their impact on multiple outcomes, to improve prognostication, identify new approaches to improve outcomes and survivorship, and facilitate clinical trials using this infrastructure. Disclosures Flowers: Acerta: Research Funding; Pharmacyclics/ Janssen: Consultancy; OptumRx: Consultancy; Janssen Pharmaceutical: Research Funding; V Foundation: Research Funding; Burroughs Wellcome Fund: Research Funding; Spectrum: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Abbvie: Consultancy, Research Funding; Bayer: Consultancy; Millennium/Takeda: Research Funding; Abbvie: Research Funding; Celgene: Research Funding; Gilead: Consultancy; BeiGene: Research Funding; Karyopharm: Consultancy; Denovo Biopharma: Consultancy; Genentech/Roche: Consultancy; Gilead: Research Funding; Genentech/Roche: Research Funding; National Cancer Institute: Research Funding; TG Therapeutics: Research Funding; Pharmacyclics: Research Funding. Nastoupil:TG Therappeutics: Research Funding; Merck: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Janssen: Research Funding; Spectrum: Honoraria; Gilead: Honoraria; Genentech: Honoraria, Research Funding; Karus: Research Funding; Novartis: Honoraria; Juno: Honoraria. Kahl:Acerta: Consultancy; Genentech: Consultancy; Seattle Genetics: Consultancy; Juno: Consultancy; CTI: Consultancy; ADC Therapeutics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; Abbvie: Consultancy. Casulo:Celgene: Research Funding; Gilead: Honoraria. Friedberg:Bayer: Honoraria. Lossos:Affimed: Research Funding. Martin:Seattle Genetics: Consultancy; Kite: Consultancy; Janssen: Consultancy; Gilead: Consultancy; Bayer: Consultancy; AstraZeneca: Consultancy. Leonard:Bayer: Consultancy; Celgene: Consultancy; Biotest: Consultancy; United Therapeutics: Consultancy; BMS: Consultancy; MEI Pharma: Consultancy; Juno: Consultancy; Sutro: Consultancy; Karyopharm: Consultancy; ADC Therapeutics: Consultancy; Pfizer: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy; Genentech/Roche: Consultancy; Novartis: Consultancy. Bernal-Mizrachi:Kodikaz Therapeutic Solutions: Consultancy, Equity Ownership; Takeda Pharmaceutical Company: Research Funding. Maurer:Morphosys: Research Funding; Nanostring: Research Funding; Celgene: Research Funding. Cerhan:Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding.

Abstract Introduction Multiple myeloma (MM) is a result of a malignant transformation of plasma cells that is preceded by the presence of an asymptomatic clonal plasma cell expansion, a condition referred to as monoclonal gammopathy of undetermined significance (MGUS). We and others have shown familial aggregation of MM and MGUS. Evidence from epidemiologic, family and genome-wide association studies (GWAS) suggests a genetic component underlying MM etiology. GWAS have successfully established 17 common genetic risk loci for MM to date and recently, rare inherited susceptibility variants in the LSD1 / KDM1A and USP45 genes were identified in familial MM / MGUS kindreds. Family-based approaches may be used to elucidate genetic variation contributing to familial MM. Genetic linkage analysis has historically been used to detect the chromosomal location of disease genes. The objective of this study was to conduct a linkage analysis of MM / MGUS families to identify genomic regions for MM / MGUS. Methods Linkage analysis was performed on whole exome sequencing (WES) data generated from germline DNA extracted from peripheral blood from MM / MGUS families ascertained at four sites, of which 79 were selected with 2 or more affected members with any combination of MM or MGUS. Whole exome capture was performed using Agilent SureSelect 38 Mb paired end sequencing and ran on Illumina HiSeq 2000s/2500s; standard analyses for alignment (GRCh37) and quality control were conducted. The Genome Analysis Toolkit's (GATK) HaplotypeCaller in per-sample mode was used to jointly call all the samples together. Quality control included removing variants that had <75% call rate, <8x coverage, or minor allele frequency <0.01 (1KGenomes). The jointly called WES data from the 79 families consisted of 230 individuals, including 119 MM, 93 MGUS and 18 unaffected relatives. Additional early onset (defined as age <60) MM cases (n=400), sporadic MM cases (n=879) and controls (n=2389) were also included in the joint calling to be used for follow-up of linkage regions. We included independent variants defined as linkage disequilibrium (LD) r2 <0.05 using both PLINK 1.07's LD based variant pruner and also MERLIN's pairwise r2 cluster marker-marker modeling to ensure variant independence. After quality control and LD filtering: 12,946 variants remained. We conducted multipoint linkage analysis using MERLIN (Multipoint Engine for Rapid Likelihood Inference using the Lander-Green approach) and considered both non-parametric and parametric (dominant and recessive) models to test for co-segregation of chromosomal regions with MM / MGUS. A logarithm of the odds (LOD) score greater than 3.3 was significant evidence for linkage. Within the same families, we interrogated any suggestive regions by performing gene-level association tests using the PEDGENE R package to calculate burden and kernel statistics, accounting for family relationships. Follow-up of these genes in the early onset and sporadic MM cases and controls and interrogation of single variants driving the associations of these genes is ongoing. Results Among the 79 families, 28 consisted of two or more MM, 41 had two or more MM and MGUS cases and 10 had two or more MGUS. Analyses of the 79 MM / MGUS families identified chromosome 6 at 123420001-149070000 BP region, with evidence for linkage by the non-parametric model (LOD score=3.3) and supportive evidence for linkage by the dominant parametric model (LOD=2.5) (Figure). The chromosome 6 region, which is outside the HLA-region, contains a total of 72 genes. Using gene-level association tests, we identified four genes within the region (MOXD1, TRDN, ADAT2, LTV1) that were associated within MM / MGUS in the family cases (p<0.05). Discussion We found evidence for a locus on chromosome 6 linked to MM / MGUS. Four genes in this region may be associated with MM / MGUS in these families. Follow-up of these genes/variants in high-risk individuals (early-onset / age <60), sporadic cases and controls will help elucidate genetic mechanisms contributing to familial MM / MGUS. In addition, we demonstrate the benefit of applying a linkage analysis framework to familial WES data for discovery of genomic regions influencing MM / MGUS. Disclosures Dumontet: Roche: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria; Sanofi: Honoraria. Kumar:AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Roche: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Background An association has been identified between the diagnosis-to-treatment interval (DTI) and prognostic clinical factors and outcomes in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) (Maurer et al, JCO 2018). This association can result in inadvertent selection bias in clinical trials to exclude patients with aggressive disease due to the inability to delay treatment long enough to fulfill enrollment criteria, compromising the validity of clinical trial study results to the general population. A similar concern exists in the relapsed/refractory (r/r) DLBCL setting regarding patient selection bias against patients with aggressive disease requiring immediate treatment following relapse or progression. Here we examine the time from progression after immunochemotherapy (IC) to the initiation of first salvage chemotherapy and its association with outcome. Patients and Methods: Newly diagnosed patients were prospectively enrolled within 9 months of diagnosis in the University of Iowa/Mayo Clinic Lymphoma SPORE Molecular Epidemiology Resource (MER), now a subcohort of the Lymphoma Epidemiology of Outcomes (LEO) Cohort Study, and followed for progression/relapse, retreatment, and death. This analysis includes patients with their first r/r DLBCL following frontline IC who initiated aggressive salvage chemotherapy as identified and included in a previous publication (Farooq et al, BJH 2017). The progression-to-treatment interval (PTI) was defined as the time in days from date of progression from IC to initiation of salvage therapy. The date of progression was defined as either a) the date biopsy was obtained for patients who had a biopsy to confirm progressive disease or b) the date of the scan or clinical examination indicating progression in patients who did not have a biopsy performed. Event-free survival (EFS) was defined as time from initiation of first salvage therapy to progression or relapse, initiation of new anti-lymphoma therapy, or death due to any cause; overall survival (OS) was defined as time from initiation of salvage therapy until death due to any cause. Results: 162 patients with r/r DLBCL enrolled in the MER from 2002-2012 who initiated aggressive salvage chemotherapy with intent to transplant and had confirmed dates for both progression after IC and start of salvage therapy were evaluated. Median age at first progression for these patients was 64 years (range 36-76) and 104 (64%) were male. Median time from diagnosis to first progression on IC was 6.7 months (IQR: 4.6-12.7). Initial salvage therapy was R-ICE (80%), R-DHAP (8%), rituximab, oxaliplatin, cytosine arabinoside, and dexamethasone (ROAD) (6%), and other (7%). At a median follow-up of 49 months from initiation of salvage therapy (IQR: 33-74), 116 patients had died (72%). Median PTI was 6 days (IQR: 2-13). 110 patients (68%) had biopsy confirmation of disease prior to initiating salvage therapy; median PTI was 2 days (IQR: 1-7) for patients who had biopsy confirmation vs. 7 days (IQR: 3-16) in patients without biopsy confirmation, Wilcoxon p=<0.0001. There was no difference in OS from initial salvage therapy between patients who did not have biopsy confirmation (HR=1.09, 95% CI: 0.74-1.60, p=0.68) compared to patients with biopsy confirmation. Patients with short PTI (0-6 days) had significantly worse overall survival from initiation of salvage therapy (median OS = 7.6 months, HR=2.10 (95% CI: 1.43-3.08) compared to patients who initiated therapy 7 or more days from progression (median OS = 29.7 months), logrank p<0.0001. This association remained consistent (HR=2.20, 95% CI: 1.48-3.26, p<0.0001) after adjusting for age and biopsy confirmation of progression. Short PTI was also associated with inferior response rate to initial salvage therapy (51% vs. 65%, p=0.058), lower rate of proceeding to transplant after initial salvage therapy (33% vs. 55%, p=0.0063), lower rate of being event-free 24 months from initial salvage therapy (16% vs 33%, p=0.011) and lower rate of ever proceeding to transplant (44% vs. 59%, p=0.057). Conclusions: A short progression-to-treatment interval is strongly associated with inability to proceed to transplant and inferior overall survival in r/r DLBCL. These results have implications for the design and interpretation of clinical trials in the relapsed/refractory setting. Figure. Figure. Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Research Funding. Ansell:Trillium: Research Funding; Merck & Co: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Pfizer: Research Funding; Celldex: Research Funding; Takeda: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cerhan:Nanostring: Research Funding; Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Despite recent advances and improvement in treatment options, chronic lymphocytic leukemia (CLL) is still an incurable disease. Recent advancement in parallel sequencing technology has provided better understanding of genetic landscape and clonal architecture underlying disease course. Furthermore, mutation status of TP53, SF3B1, NOTCH1, and BIRC3 has improved current CLL risk stratification. In order to better understand the genetic basis and clonal evolution underlying disease progression we analyzed a homogeneously treated cohort of CLL cases at multiple time points before and after therapy. We performed WES in 30 sequential samples from 12 CLL cases. All 12 cases received PCR (pentostatin, cyclophosphamide and rituximab) based chemoimmunotherapy as initial treatment. Of these 12 cases, two had samples available both >6 months prior to enrollment and at treatment initiation, seven patients who experienced relapse had samples available at both treatment initiation and at and the time of relapse, and three patients had samples >6 months prior to enrollment, at treatment initiation, and at relapse. These cases were previously characterized by aCGH/FISH. R function Mclust and kernel density estimate (KDE) plots were used to identify subclonal heterogeneity. An average of 102-fold depth coverage was obtained with an average of 80% of the targeted regions being covered by at least 30X. Overall, we detected a total of 219 nonsynonymous Single Nucleotide Variants (SNV) and indels (average 19, range 11-33). Del13q (42%) was identified as most prevalent copy number abnormality followed by trisomy 12 (33%), del11q and del17p (17% each). Recurrent mutations were identified in NOTCH1 (33%), DDX3X (25%), TP53, SF3B1 XPO1 and MED12 (17% each). Other tumor implicated genes such as CARD11, NOTCH2, NOTCH4, DIS3, TRAF2, NFKBID, CHK2 and RB1 were found mutated in individual cases. In all 12 cases we identified at least one relevant cytogenetic abnormalities (del11q32, del13q14, del17p13, trisomy 12) and/or a mutated driver gene. Interestingly we found NOTCH1, DDX3X and XPO1 mutated at a significantly higher prevalence than previous studies (10%, 2% and 4%, respectively). Since all cases in our cohort correspond to individuals with progressive disease, a plausible explanation for the higher prevalence of mutations in our cohort is that these mutations are associated with disease progression. By WES we identified multiple subclones in 58% of cases (7 of 12 cases), as compared to previously identified 33% (4 of 12 cases) by CGH/FISH. In 5 chemotherapy naïve cases, no significant increase in genomic complexity was detected with disease progression. In contrast, 5 out of 10 cases with samples analyzed before CIT and at the time of relapse had changes in clonal dynamics, with initially dominant subclones diminishing in response to therapy and the available empty niche being occupied by expansion of another “fitter” subclone at the time of relapse. No acquisition of tumor associated gene mutations was seen post therapy or associated with relapse in 9 of 10 cases. In the remaining case we identified an interesting example of convergent evolution. The pre-therapy sample was comprised of a unique clone characterized with del11q13, mutations in SF3B1 (G742D) and DDX3X (pE196fs). After therapy that clone was either eradicated or reduced to below detection level. Interestingly another clone emerged after relapse that harbored an independent del11q22 and mutations affecting different aminoacids of SF3B1 (K700E) and DDX3X (pL21fs). The strong selection of impairment in these genes during disease progression suggests a key role of these pathways/genes in the pathogenesis of this patient. In conclusion, these studies provide additional insight into the genomic landscape and clonal architecture in a cohort of homogeneously treated CLL cases before and after therapy. Stable clonal architecture was seen in prior to treatment, whereas clonal evolution leading to increased tumor heterogeneity occurred after the under selection selective pressure of therapy in relapsed cases. Early identification of tumoral heterogeneity and underlying genetic abnormalities might provide better targets for therapy to decrease the likelihood of relapse. Disclosures: Fonseca: Medtronic: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; Genzyme: Consultancy; BMS: Consultancy; Lilly: Consultancy; Onyx: Consultancy, Research Funding; Binding Site: Consultancy; Millennium: Consultancy; AMGEN: Consultancy; Cylene: Research Funding; Prognostication of MM based on genetic categorization of the disease: Prognostication of MM based on genetic categorization of the disease, Prognostication of MM based on genetic categorization of the disease Patents & Royalties. Shanafelt:Genentech: Research Funding; Glaxo-Smith-Kline: Research Funding; Cephalon: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Polyphenon E International: Research Funding.

Abstract Background Age has long been recognized as a poor prognostic factor in diffuse large B-cell lymphoma (DLBCL) and more recently, a lower baseline quality of life (QOL) in patients with aggressive lymphomas has been associated with an inferior overall survival (OS) (Thompson et al, 2018). Older patients with DLBCL are a heterogeneous population and commonly have comorbid conditions. A concerted effort to identify prognostic factors specific to this population is needed, including QOL. Event free survival at 24 months (EFS24) is a well validated endpoint in DLBCL (Maurer et al, 2014) and is clinically relevant in older patients. We hypothesized that baseline clinical factors and QOL would be associated with EFS24 this patient group. Methods Newly diagnosed lymphoma patients were prospectively enrolled within 9 months of diagnosis into the University of Iowa/Mayo Clinic Lymphoma SPORE Molecular Epidemiology Resource (MER). Patients ≥70 years of age with DLBCL were utilized in this study. All pathology was reviewed by a lymphoma hematopathologist. Clinical variables were abstracted from the medical record. QOL was assessed at enrollment using a single item Linear Analogue Self-Assessment (LASA) and the 27-item Functional Assessment of Cancer Treatment-General questionnaire (FACT-G), which includes physical, social, emotional, and functional well-being domains. QOL scores were converted to a 0-100 scale to facilitate modeling. QOL was analyzed in patients who completed 80% of the FACT-G questions. Characteristics of the study population were compared using the Wilcoxon rank sum test. Odds ratios (ORs) and 95% confidence intervals (CI) were used to estimate the associations of clinical and continuous QOL variables with failure to achieve EFS24 using logistic regression models, with a stepwise approach to identify parsimonious multivariate models. Results A total of 389 patients ≥70 years were identified with DLBCL in the MER from 2002-2015; patient characteristics are described in the Table. In the entire study population, univariate predictors of EFS24 failure included age (OR per 10 years=1.85, 95% CI: 1.26-2.71), ECOG PS ≥2 (OR=4.78, 95% CI: 2.92-7.83), elevated LDH (OR=3.59, 95% CI: 2.27-5.65), stage III-IV (OR=1.94, 1.25-3.02), bulky disease (OR=2.86, 95% CI: 1.43-5.72), international prognostic index (OR=8.71 for IPI 5 vs IPI 0, 95% CI: 3.04-24.9), and receipt of an anthracycline (OR=0.25, 95% CI: 0.12-0.51), while none of the other variables in the Table, including the QOL variables, were statistically significant. In a stepwise multivariate analysis, only ECOG PS ≥2 (OR= 3.56, 95% CI: 2.09-6.06) and elevated LDH (OR=2.86, 95% CI: 1.78-4.60) remained significant predictors of EFS24 failure. QOL data were available on 220 patients, with 120 completing QOL questionnaires prior to receipt of chemotherapy. Compared to participants who completed their FACT-G before the initiation of chemotherapy, the median FACT-G physical (85.7 v. 71.4, p=0.005) and functional (71.4 vs 57.1, p=0.008) scores were lower in participants completing FACT-G after initiation of therapy, were higher on FACT-G emotional (79.2 vs 83.3, p=0.03), and showed no differences on FACT-G social (89.3 vs 91.7, p=0.60) or FACT-G total score (78.2 vs 74.1, p=0.17). Given these differences, we restricted the QOL analysis to those with pre-treatment data. In univariate analysis, the LASA score (OR= 0.98, 95% CI: 0.84-1.14), the FACT-G total (OR=0.99, 95% CI: 0.96-1.01), physical (OR=0.98, 95% CI: 0.97-1.00), social (OR=1.00, 95% CI: 0.98-1.03), emotional (OR= 1.00, 95% CI: 0.98-1.02), and functional (OR=0.99, 95% CI: 0.98-1.01) scores were not associated with EFS24 failure. In a stepwise multivariate analysis of clinical variables in patients with pretreatment QOL scores, only ECOG PS ≥2 (OR=3.56, 95% CI: 1.30-9.72) and female sex (OR=0.37, 95% CI: 0.16-0.85) were associated with failure to achieve EFS24. Conclusions In patients aged 70 years and older, ECOG PS and LDH were the strongest independent predictors of failure to achieve EFS24. As expected, physical and functional scores were lower in patients who completed their QOL after initiation of treatment. Restricting the analysis to patients with pre-treatment data, QOL was not associated with failure to achieve EFS24. Additional studies are needed to identify the most robust clinical predictors of EFS24, while QOL does not appear to be an important predictor of EFS24. Disclosures Reagan: Seattle Genetics: Research Funding. Maurer:Morphosys: Research Funding; Celgene: Research Funding; Nanostring: Research Funding. Cerhan:Celgene: Research Funding; Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Background Approximately 2-8% patients with chronic lymphocytic leukemia (CLL) will transform to diffuse large B-cell lymphoma (DLBCL, Richter syndrome [RS]). Clinical characteristics and molecular markers at the time of CLL diagnosis are associated with the risk of RS; however, there are no data regarding germline genetic variations and the risk of RS. Genomewide association studies (GWAS) have shown several single-nucleotide polymorphisms (SNPs) that are associated with a higher risk of familial CLL. It is not known whether any of these polymorphisms also predispose to RS. Methods Since 2002, all consecutive patients with newly diagnosed (<9 months diagnosis) CLL at Mayo Clinic were offered enrollment into a prospective genetic epidemiology study. Patients completed extensive epidemiologic questionnaires and baseline clinical, laboratory, and biologic data were abstracted using a standard protocol. Genotyping of germline tissue at diagnosis was performed using an Illumina iSelect panel and Affymetrix 6.0 SNP chip. All patients were prospectively and longitudinally followed at defined time-points with systematic collection of data on treatments, second cancers, and RS. All patients with biopsy-proven DLBCL during follow-up were considered to have undergone transformation into RS. Time to RS was calculated from CLL diagnosis date until RS or until last follow-up date for those with no RS. SNPs were modeled in two ways: ordinal and dominant. Cox regression was used to estimate hazard ratio (HR) for individual SNPs with time to transformation. Results Thirteen of the GWAS-discovered SNPs associated with risk of developing CLL were available and genotyped on 620 CLL patients. Median age at diagnosis of CLL was 62 years (range 27-88), and 428 (69%) were male. Three hundred and ten (51%) patients were low (0) Rai stage, 271 (45%) were intermediate (I-II) Rai stage, and 22 (4%) were advanced (III-IV) Rai stage. The immunoglobulin heavy chain gene was unmutated in 189 (40%) patients; 157 (32%) patients expressed ZAP-70, 163 (29%) expressed CD38 and 104 (31%) expressed CD49d. Fluorescence in-situ hybridization (FISH) revealed that 210 (41%) patients had del13q, 90 (18%) patients had trisomy 12, 37 (7%) had del11q, 23 (5%) had del17p and 141 (28%) had no detectable FISH abnormalities. As of last follow-up, 239 (39%) patients received therapy for CLL. After a median follow-up of 5.9 years (range 0-11), 15 (2.4%) patients developed biopsy-proven RS. The median time to RS in these 15 patients was 4.5 years (range 1.0-8.7 years). The ordinal HR for the 13 SNPs tested, their corresponding genes, and p-values are shown in Table 1. Germline polymorphisms in a single SNP, rs4987852, encoding for BCL2 (chromosome 18), was significantly associated with an increased risk of RS (ordinal HR=3.9; 95% CI=1.6-9.8; p-value=0.004). This allele was present in 48/605 (8%) non-transformed CLL patients compared to 4/15 (27%) of patients with RS. Time to RS according to the Kaplan-Meier analysis for rs4987852 is shown in Figure 1. This SNP is located in a region in which t(14;18) translocation breakpoints commonly occur in follicular lymphoma and overexpression of BCL2 leads to an increased incidence of B-cell lymphomas in mice. Conclusion Our results suggest that inherited genetic polymorphisms predispose CLL patients to develop RS. Specifically, SNP (rs4987852) present on the BCL2 gene on chromosome 18 in CLL is associated with an increased risk of transformation to RS. These observations require replication in other CLL cohorts. Disclosures: Shanafelt: Genentech: Research Funding; Glaxo-Smith-Kline: Research Funding; Cephalon: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Polyphenon E International: Research Funding.

Abstract Clonal hematopoiesis of indeterminate potential (CHIP) is defined as somatic mutations in clonal blood cells of individuals without any other hematological abnormalities and have been identified using whole exome sequencing (WES) from pre-specified genes. CHIP is reportedly associated with aging, reduced overall survival, cardiovascular disease, and hematologic disease, mainly myeloid cancers. Additionally, whole genome sequencing and WES studies in CLL cases have identified putative CLL driver genes with recurrent somatic variants or copy number alterations. Here, we investigate the associations of CHIP and mutations in CLL somatic driver genes with risk of CLL and MBL, a precursor to CLL. Using WES, 571 individuals from 132 CLL families were sequenced from two different sites, Mayo Clinic (92 families) and National Cancer Institute (NCI, 40 families), including 251 CLLs, 74 MBLs, and 246 unaffected relatives. DNA was extracted from buccal cells (Mayo Clinic) and peripheral blood (NCI). Allele counts from gVCF files were extracted for specific genes and analyzed. Variants were annotated using the Variant Effect Predictor in Ensembl, genome build GRCh37 (http://useast.ensembl.org/index.html). Highly-sensitive flow cytometry of the blood was used to screen for MBL. CHIP was defined as in Genovese et al., 2014 as the presence of any of the following: A) disruptive mutations in ASXL1, TET2, or PPM1D; B) missense mutation p.617F in JAK2; C) disruptive or missense mutations in DNMT3Alocalized to exons 7 to 23D) any mutations reported at least 7 times in hematological malignancies in the Catalogue of Somatic Mutations in Cancer (COSMIC); and (E) No variant allelic fraction (VAF) threshold. CLL driver genes included disruptive or missense mutations in ATM, BIRC3, MYD88, NOTCH1, SF3B1, TP53, EGR2, POT1, NFKBIE, XPO1, or FBXW7with VAF greater than 0.1. Mutations from COSMIC and CLL driver genes were excluded if the minor allele frequency in 1000 Genomes was greater than 0.5%. Data was analyzed using logistic regression adjusting for age, sex, and site. Odds ratios (OR) and 95% confidence intervals (CI) were estimated. Median age for CLL, MBL, and unaffected was 63, 61.5, and 54, respectively. The unaffected relatives were significantly younger than both the CLL and MBL groups (p<0.0001), with no difference between CLL and MBL (p=0.46). A total of 70 (12%) individuals from 54 different families had CHIP. CHIP was characterized in the majority of samples as mutations in DNMT3A, ASXL1, and TET2with 43, 9 and 8 individuals, respectively. As expected, CHIP was associated with age (p<0.001) but not sex (p=0.87). CHIP was present in 42 (17%) CLLs, 9 (12%) MBLs, and 19 (8%) unaffected relatives. There was a significant association between the presence of CHIP and CLL risk compared to unaffected relatives (OR= 2.19, 95% CI: 1.20 - 4.13, p=0.013) but not with MBL risk (OR=1.34, 95% CI: 0.54 - 3.11, p=0.51). Using the CLL driver genes, we found mutations in 119 (47%) CLL, 20 (27%) MBL, and 70 (29%) unaffected relatives. Mutations in CLL driver genes were associated with CLL risk compared to unaffected relatives (OR = 2.51, 95% CI: 1.64 - 3.87, p<0.0001), but there was no evidence for increased MBL risk (OR = 1.00). These results held when we subset the analysis to the Mayo only data where the DNA was extracted from buccal cells. This study further supports the association between mutations in CHIP related genes and risk of hematologic malignancies, in particular CLL. This is the first report of the prevalence of CHIP in MBLs. Although MBL and CHIP represent clonal population of blood cells, our data suggest the clones are different. Mutations in CLL driver genes are also associated with CLL risk but show little to no evidence with increased MBL risk. Interestingly, our unaffected relatives of CLL cases also had a high frequency of CLL driver mutations. Future studies are needed to understand these acquired mutations (both in CHIP- and CLL driver- related genes) in the unaffected to MBL to CLL continuum. Disclosures Parikh: Abbvie: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; AstraZeneca: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Research Funding. Ding:Merck: Research Funding. Cerhan:Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding; Nanostring: Research Funding. Shanafelt:Jansen: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding; GlaxoSmithKline: Research Funding. Kay:Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta: Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees.