Cerhan, James R.

Abstract Background: Lymphoma survivors are at risk for long term and late effects which may negatively impact quality of life (QOL). Few studies have assessed the impact of change in health behaviors (physical activity (PA), alcohol and smoking) on QOL, specifically in lymphoma survivors. Methods: Patients were prospectively enrolled <9 months of diagnosis into the Lymphoma SPORE Molecular Epidemiology Resource cohort. At baseline and 3-year follow-up (FU3), patients self-reported QOL, smoking, alcohol use and PA. At FU3, survivors reported change in health behaviors since diagnosis. The Godin Leisure Score Index (LSI) was calculated as a measure of PA (Pophali et al. ASH 2017); modeled by tertile. QOL was measured via FACT-G; the total and sub-scales [emotional (EWB), functional (FWB), physical (PWB), and social/family well-being (SFWB)] scores were transformed to a 0-100 scale. Patients who completed <80% of QOL questions were excluded. The association of QOL with health behaviors was evaluated using Kruskal-Wallis test (categorical) and Spearman's rank correlation (continuous). Results: At baseline, 2805 participants were evaluable for QOL of which 2025 were evaluable at FU3. The median age at diagnosis was 62 (IQR 52-69) years. Majority were males (N=1148, 56.8%), with advanced stage III-IV disease (N=941, 62%) and ECOG performance status <2 (N=1935, 96%). At baseline, smoking status was current (N=237), former (N=802), never (N=1310) or not available (NA, N= 456). Current smokers had a significantly lower median QOL scores compared to former and never smokers for EWB (75 vs 79 vs 79, p<0.01), FWB (71 vs 79 vs 79, p<0.01), PWB (86 vs 89 vs 89, p=0.02), SFWB (86 vs 90 vs 89, p=0.02) and total FACT-G (67 vs 82 vs 82, p<0.01). The median number of pack years smoked among current &former smokers was 15.6 (range 0.5-126). There was a significant negative correlation of pack years smoked with FWB (p=0.05), and total FACT-G (p=0.04). At FU3, current smokers had significantly lower median scores compared to former and never smokers for FWB (80 vs 86 vs 86, p=0.01), PWB (89 vs 93 vs 93, p<0.01) and total FACT-G (82 vs 86 vs 87, p<0.01). EWB (83 vs 88 vs 88, p=0.12) and SFWB (83 vs 86 vs 86, p=0.12) were also lower than never and former smokers but not statistically significant. Smoking status was decreased (N=30), increased (N=0) and unchanged (N=1439) at FU3. There was no association of change in smoking with change in QOL. At baseline, alcohol use status was current (N=1189), former (N=506), never (N=518) and NA (N=592). Current alcohol users had the highest FWB (79 vs 75 vs 75, p<0.01) vs former and never users. Former users vs current and never users had the lowest PWB (86 vs 89 vs 89, p<0.01), SFWB (88 vs 89 vs 92, p<0.01) and total FACT-G (80 vs 83 vs 82, p<0.01). EWB was similar in all 3 groups. Among current & former alcohol users, the average number of drinks/week was 3.4. There was no association of the number of drinks/week with QOL except positive correlation with PWB (p=0.01). At FU3, alcohol use was reported as increased (N=40), decreased (N=312) and no change (N=875). There was no association between change in alcohol use and change in QOL. At baseline, participants with PA in the high vs low LSI tertile had significantly better median scores for FWB (79 vs 75, p=0.01), SFWB (93 vs 89, p<0.01) and total FACT-G (83 vs 82, p<0.01). At FU3, participants in the high vs low LSI tertile had significantly better scores on all subscales and total FACT-G with a significant positive correlation between the continuous LSI and QOL (FACT-G total and subscales). PA was reported as increased (N=107), decreased (N=394) and no change (N=495). There was a significant association of increase in PA with change in PWB (p<0.01), EWB (p=0.01), FWB (p<0.01) and total FACT-G (p<0.01) from baseline to FU3, but not with change in SFWB (p=0.06). In a sensitivity analysis, QOL was modeled for health behaviors in the subset of patients with QOL assessed prior to treatment; results were consistent with the QOL in all patients. Conclusions: Health behaviors in lymphoma survivors are significantly associated with QOL: better QOL is associated with not smoking, more PA, and moderate alcohol use. Current alcohol consumption, as opposed to former alcohol use, may be related to overall health status. An increase in PA after lymphoma diagnosis is associated with an increase in the QOL. Thus, modification of health behaviors has the potential to impact QOL in lymphoma survivors. Figure. Figure. Disclosures Cerhan: Celgene: Research Funding; Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Monoclonal gammopathies, including multiple myeloma (MM), represent a group of plasma cell (PC) disorders that comprise of mostly incurable hematopoietic malignancies with an increasing incidence in the US. Previous epidemiological studies demonstrated a 2-3 fold higher incidence of monoclonal gammopathy of undetermined significance (MGUS) and a similarly higher incidence of MM along with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). This suggests a possible ancestral-associated genetic predisposition of AAs to the development of monoclonal gammopathies (Landgren, Blood, 2006). When access to care is equal, AAs have a better overall survival compared to EAs suggesting that AAs may have a genetic predisposition that renders them better responders to treatment or have more favorable cytogenetic subtypes of MM (Waxman, Blood, 2010). Previous efforts to understand this disparity have relied on self-reported race rather than genetic ancestry. We hypothesize that quantifying genetic ancestry is necessary to fully understand the genetic mechanisms of racial disparities of monoclonal gammopathies. We describe our study of 881 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from the bone marrow samples was genotyped on the Precision Medicine Research Array. Biogeographical ancestry was assessed using the Geographic Population Structure Origins tool, which provides the ancestral breakdown of 36 admixture components for each individual representing different geographic regions and outputs admixture proportions corresponding to those ancestries (Elhaik, Nat Commun, 2014). We evaluated whether an increase in the percentage of African Ancestry altered the odds of any primary cytogenetic abnormality. We demonstrated that a 10% increase in the percentage of African ancestry was associated with a 6% increase in the odds of detecting either an t(11;14), t(14;16) or t(14;20) (odds ratio = 1.06, 95% CI: 1.02 - 1.11; p-value = 0.05). Although we observed an increase in the odds of each of the individual translocations with 10% increase in African ancestry, l t(11;14) (OR=1.03) t(14;16) (OR=1.11) and t(14;20) (OR=1.10), these three abnormalities were combined to achieve greater statistical power. The differences in cytogenetic abnormalities were most striking among individuals with ≥80.0% African (n=120) compared to individuals with <0.1% African ancestry (n=235). Using these cutoff values comparing the ≥80.0% and <0.1% African ancestry cohorts, there was a higher prevalence of t(11;14), t(14;16) and t(14;20) (51% vs. 33%, respectively, p-value=0.008) and a lower prevalence of trisomies (with or without IgH translocations) (48.3% vs. 61.3%, respectively, p-value=0.066). In addition, the ≥80% African ancestry cohort also displayed a slightly lower prevalence of monosomy 13/13q deletion compared to the cohort with <0.1% African ancestry (34.2% vs. 38.7%, respectively, p-value=0.021). Further, we observed a significantly higher prevalence in the proportion of females with monoclonal gammopathies among the ≥80% African ancestry cohort compared to the cohort with <0.1% African ancestry (56.7% vs. 42.1%, respectively, p-value=0.028). We complement our past studies that relied on self-reported race and characterized patients' demographic and uniformly collected cytogenetic data in relation to genetically defined African ancestry. We demonstrate the prevalence of having one of three specific subtypes (i.e., t(11;14), t(14;16), or t(14;20)) was significantly higher in those with highest (≥80%) compared to lowest (<0.1%) African ancestry demonstrating African ancestry is associated with a subtype distribution of MM that differs from non-African individuals. These differences were only revealed after analysis of individuals with the highest and lowest percentage of African ancestry. Since 38.3% of the individuals with ≥80% African ancestry have the favorable t(11;14) translocation (in comparison to 26.8% of <0.1% African individuals), this finding may explain improved survival with equal access to care. Understanding the genetic mechanisms of this disparity is a fundamental step to understanding differences in incidence and outcomes of patients with monoclonal gammopathies. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding; Celgene: Research Funding. Stewart:Amgen Inc., BMS, Celgene, Takeda, Roche, Seattle Genetics, Janssen, Ono: Consultancy; Amgen Inc., Celgene, Roche, Seattle Genetics: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Kumar:Novartis: Research Funding; KITE: 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; Takeda: Membership on an entity's Board of Directors or advisory committees; AbbVie: 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; 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; 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; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding.

Abstract Background Peripheral T-cell lymphomas (PTCLs) are aggressive non-Hodgkin lymphomas with poor overall survival rates. The genetics of PTCLs are poorly understood. We have described t(6;14)(p25.3;q11.2) translocations in PTCLs involving the T-cell receptor-alpha (TRA@) and interferon regulatory factor-4 (IRF4) genes and overexpressing IRF4 protein (also called MUM1). IRF4/MUM1 is a lymphoid transcription factor involved in differentiation and growth, the expression of which is tightly regulated in normal T cells. However, nearly half of PTCLs constitutively express IRF4/MUM1 and this expression is associated with adverse overall survival in several PTCL subtypes. Because IRF4/TRA@ translocations are rare among PTCLs (<1%), we investigated the presence of other genetic abnormalities of IRF4 in PTCLs and examined their association with IRF4/MUM1 protein expression. Methods Studies were performed on paraffin tissue sections of PTCL specimens diagnosed by standard WHO criteria. IRF4/MUM1 immunohistochemical staining (IHC) was performed using the MUM1p antibody and considered positive if nuclear staining was present in >30% of tumor cell nuclei. Fluorescence in situ hybridization (FISH) was performed using breakapart probes constructed from bacterial artificial chromosome DNA that hybridized to loci flanking the IRF4 locus on 6p25.3. Cases with 3 or more fusion signals were considered to have extra copies of the IRF4 gene. In a subset of cases, PCR and Sanger sequencing was performed on DNA extracted from frozen PTCL tumor tissue with primer sets that amplified exons 1 to 10 of the IRF4 gene. Data were analyzed using the chi-square test or t test. Results PTCLs from 277 patients (175 M:102 F; median age, 61 y) were examined by both IHC and FISH. The subtype distribution was: PTCL, not otherwise specified (NOS), 93; anaplastic large cell lymphoma (ALCL), 89; angioimmunoblastic T-cell lymphoma (AITL), 27; cutaneous T-cell lymphoma (CTCL), 43; and cytotoxic PTCL, 25. IRF4/MUM1 was positive in 116 cases (42%). Extra copies of IRF4 were identified in 18 PTCLs (7%), 83% of which were IRF4/MUM1 protein-positive. In contrast, 39% of cases without extra copies of IRF4 were IRF4/MUM1 protein-positive (p=0.0002). The mean percentage of tumor cells with IRF4/MUM1 staining was 69% in cases with extra copies of IRF4, compared to 30% in cases without extra copies (p=0.0001). Extra copies of IRF4 were seen in 13% of ALCLs, 4% each of PTCLs, NOS and cytotoxic PTCLs, 2% of CTCLs, and 0% of AITLs (p=0.02). Two IRF4/MUM1-positive cases had an IRF4/TRA@ translocation (1%). We selected 30 IRF4/MUM1-positive PTCLs for Sanger sequencing. In this subset, we identified a previously undescribed non-synonymous variant in exon 5 (S309R) in 1 case (3%) without an extra copy of IRF4. Conclusions Extra copies of IRF4 are seen in ∼7% of PTCLs, are significantly associated with IRF4/MUM1 expression, and are preferentially observed in ALCLs. Both IRF4/TRA@ translocations and IRF4 mutations are rare in PTCLs. These data suggest that genetic abnormalities of the IRF4 gene – specifically extra copies of IRF4 – may contribute to IRF4/MUM1 expression in some PTCLs. Since IRF4/MUM1 expression is seen in ∼42% of PTCLs, these data also imply the existence of other regulatory mechanisms. Characterizing these mechanisms may be important clinically, since IRF4/MUM1 has been proposed as a therapeutic target in human cancers but drugs that directly inhibit its function are currently not available. Disclosures: No relevant conflicts of interest to declare.

Abstract Introduction: The addition of Rituximab to chemotherapy has significantly improved the outcome of patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL). Patients treated with immunochemotherapy for DLBCL who achieve EFS24 (event-free for 2 years after diagnosis) have an overall survival equivalent to that of the age- and sex-matched general population. Relapses after achieving EFS24 have been considered to be unusual but have been understudied. We sought to define the rate, clinical characteristics, treatment pattern, and outcomes of such relapses. Methods: 1448 patients with newly diagnosed DLBCL from March 2002 to June 2015 were included. Patients were enrolled in the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma SPORE, treated per physician choice (predominantly R-CHOP immunochemotherapy) and followed prospectively. An event was defined as progression or relapse, unplanned re-treatment after initial therapy, or death from any cause. Cumulative incidence of relapse and non-relapse mortality after achieving EFS24 were analyzed as competing events using Gray's test in the EZR software. Post-relapse survival was defined as time from relapse to death from any cause and analyzed using Kaplan-Meier method in SPSS (V22). Results: Among the 1448 patients, 1260 (87%) had DLBCL alone at diagnosis, and 188 (13%) had concurrent indolent lymphoma (follicular lymphoma 115, marginal zone lymphoma 18, chronic lymphocytic leukemia 14, lymphoplasmacytic lymphoma 4, unspecified 37) at diagnosis. After a median follow-up of 83.9 months, 896 patients achieved EFS24. For all 896 patients who achieved EFS24, the cumulative incidence of relapse (CIR) was 5.7%, 9.3% and 13.2%, respectively, at 2, 5 and 10 years after achieving EFS24. Patients with concurrent indolent lymphoma at diagnosis had a higher CIR compared to those with DLBCL alone at diagnosis (10.2 vs 4.8% at 2 years, 15.7 vs 8.0% at 5 years, 28.8 vs 9.7% at 10 years, P<0.001; Figure 1). There were a total of 84 patients who relapsed after achieving EFS24. The median age at initial diagnosis was 66 years (range 35-92), and 48 (57%) were male. At diagnosis, 11 (13%) had ECOG PS >1, 37 (50%) had LDH elevation, 62 (74%) were stage III-IV, 14 (17%) had more than 1 extranodal site, and 26 (31%) were poor risk by R-IPI score. There were 58 patients with DLBCL alone at diagnosis who relapsed after achieving EFS24, and 38 (75%) relapsed with DLBCL, 13 (25%) relapsed with indolent lymphoma (predominantly follicular lymphoma), and pathology was unknown in 7 patients. In contrast, there were 26 patients with concurrent indolent lymphoma at diagnosis who relapsed after achieving EFS24, and 9 (41%) relapsed with DLBCL, 13 (59%) relapsed with indolent lymphoma, and pathology was unknown in 4 patients. In the 47 patients who relapsed with DLBCL after achieving EFS24, 45% received intensive salvage chemotherapy, 19% received regular intensity chemotherapy, 9% received CNS directed chemotherapy, and 36% went on to receive autologous stem cell transplant (ASCT). In the 26 patients who relapsed with indolent lymphoma after achieving EFS24, 27% were initially observed, 54% received regular intensity chemotherapy, 4% received intensive salvage chemotherapy, and 19% received ASCT after subsequent progression. The median post-relapse survival (PRS) for all patients with a relapse after achieving EFS24 was 38.0 months (95% CI 27.5-48.5). The median PRS for patients who relapsed with DLBCL and indolent lymphoma after achieving EFS24 were 29.9 (19.9-39.9) and 89.9 (NR-NR) months, respectively (P=0.002; Figure 2). Conclusions: Relapses after achieving EFS24 in patients with DLBCL were uncommon in the rituximab era. Patient with DLBCL alone at diagnosis can relapse with either DLBCL or indolent lymphoma (3:1 ratio). Patients with concurrent DLBCL and indolent lymphoma at diagnosis had a significantly higher CIR, and relapses with DLBCL and indolent lymphoma were similar (2:3 ratio). Even with high intensity salvage chemotherapy and consolidative ASCT, patients who relapsed with DLBCL had a significantly worse survival compared to those who relapsed with indolent lymphoma. Late relapses with DLBCL remain clinically challenging, with a median survival of 2.5 years after relapse. Figure 1. Figure 1. Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Ansell:Takeda: Research Funding; Pfizer: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Celldex: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Merck & Co: Research Funding; Bristol-Myers Squibb: Research Funding. Cerhan:Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board; Nanostring: 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 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.

Abstract PURPOSE Urgency to initiate therapy has to be weighed against the time required for screening and consenting to enroll a patient onto a clinical trial. Exclusion of patients with rapidly progressive or symptomatic disease may lead to selection of patients with less aggressive disease. We recently identified that a short diagnosis to treatment interval (DTI) is associated with adverse clinical factors and inferior outcome in patients with diffuse large B-cell lymphoma (DLBCL) (Maurer et al, JCO 2018); shorter time to treatment was also found to be associated with outcome in other aggressive lymphomas, including mantle cell lymphoma (MCL), in the National Cancer Database (Olszewski et al, BJH 2018). Here we assess the association of DTI with clinical variables and outcome in MCL from an observational cohort with validation in patients enrolled on an NCTN clinical trial. PATIENTS AND METHODS Newly diagnosed patients with MCL were prospectively enrolled 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. Patients initially managed with observation were excluded. DTI was defined as the time in days from date of first lymphoma-containing biopsy to the initiation of therapy. Associations of DTI with clinical factors and outcome were examined using a 14 day split based on the previous MER study of DTI in DLBCL. Odds ratios (OR) and Cox model hazard ratios (HR) are reported. Outcome was assessed using event-free survival (EFS) where events were defined as progression/relapse, initiation of new anti-lymphoma therapy, or death due to any cause. A validation cohort was assembled from patients enrolled in CALGB (Alliance) clinical trial 50403. Protocol therapy was high dose chemotherapy with autologous hematopoietic stem cell transplantation (ASCT) consolidation and post-ASCT rituximab followed by bortezomib as either consolidation or maintenance. DTI was defined as time from diagnosis to study registration and outcome was assessed using progression-free survival (PFS) as defined on the clinical trial. RESULTS 271 patients with MCL enrolled in the MER from 2002-2015 and initiated treatment within 100 days of diagnosis. Median age at diagnosis was 64 years (range 36-76), 211 (78%) were male, and 92% were stage III-IV; MIPI was Low (41%), Intermediate (36%) and High (23%). Induction therapy was R-CHOP based (50%), B-R based (16%), HyperCVAD based (8%), 2-CDA based (11%), and other (15%). Median EFS of the cohort was 41 months (95% CI: 34-46). Median DTI was 26 days (IQR: 14-38) and 28% enrolled within 14 days of diagnosis, exhibiting a longer DTI than DLBCL patients enrolled in the MER during the same time period. Short DTI (0-14 days) was associated with adverse clinical factors including ECOG PS 2-4 (OR=5.74, 95% CI: 2.51-13.1, p<0.0001), elevated LDH (OR=2.30 95% CI: 1.25-4.22, p=0.0072), and higher MIPI score (OR=1.48, 95% CI: 1.04-2.07, p=0.025) but was not significantly associated with age, sex, B-symptoms, or stage (all p>0.30). Short DTI was also associated with inferior EFS (HR=1.78, 95% CI: 1.27-2.49, p=0.0008) which remained significant after adjusting for MIPI score (HR=1.72, 95% CI: 1.23-2.41, p=0.0016). In the validation cohort, 144 patients enrolled on 50403 within 100 days of diagnosis from 2006-2010. Median age at diagnosis was 59 years (range 29-69), 77% were male, and 97% were stage III-IV; MIPI was Low (51%), Intermediate (31%) and High (17%). Median PFS of the cohort was 84 months (95% CI: 50-109). Median DTI was 35 days (IQR: 22-46) and 11% enrolled within 14 days of diagnosis. Short DTI (0-14 days) was associated with PS (OR=9.00, 95% CI: 1.18-69.0, p=0.034) and) higher MIPI score (OR=2.09, 95% CI: 1.08-4.04, p=0.029) but not elevated LDH (OR=1.27, 95% CI: 0.43-3.74, p=0.66). Short DTI was associated with inferior PFS (HR=2.21, 95% CI:1.19-4.09, p=0.011) which remained clinically significant after adjusting for MIPI score (HR=1.85, 95% CI: 0.98-3.47, p=0.056). CONCLUSION A short diagnosis-to-treatment interval is strongly associated with adverse clinical factors and poor outcome in newly diagnosed MCL in the MER. Clinical trials are encouraged to facilitate enrollment of patients with short DTI to avoid potential selection bias. Support: U01 CA195568, P50 CA097274, U10CA180821, U10CA180882; ClinicalTrials.gov Identifier: NCT00310037 (CALGB 50403) Figure. Figure. Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Research Funding. Kaplan:Bayer Pharmaceuticals: Consultancy. Cohen:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Pharmacyclics: 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; Bristol-Myers Squibb: Research Funding; AbbVie: 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; 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; BioInvent: Consultancy; Takeda: Research Funding. Martin:Gilead: Consultancy; Kite: Consultancy; Seattle Genetics: Consultancy; AstraZeneca: Consultancy; Janssen: Consultancy; Bayer: Consultancy. Kahl:Abbvie: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy; Acerta: Consultancy; Juno: Consultancy; Celgene: Consultancy; Genentech: Consultancy; Seattle Genetics: Consultancy; CTI: Consultancy; ADC Therapeutics: Consultancy. Bartlett:ImaginAB: Research Funding; Bristol-Meyers Squibb: Research Funding; Novartis: Research Funding; Immune Design: Research Funding; Janssen: Research Funding; Merck & Co: Research Funding; Forty Seven: Research Funding; Novartis: Research Funding; Pharmacyclics: Research Funding; Affimed: Research Funding; Astra Zeneca: Research Funding; Pharmacyclics: Research Funding; Acerta: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Millennium: Research Funding; Genentech: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: 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. Leonard:Karyopharm: Consultancy; Biotest: Consultancy; Bayer: Consultancy; Pfizer: Consultancy; ADC Therapeutics: Consultancy; Novartis: Consultancy; AstraZeneca: Consultancy; Juno: Consultancy; BMS: Consultancy; Sutro: Consultancy; MEI Pharma: Consultancy; Genentech/Roche: Consultancy; Celgene: Consultancy; Gilead: Consultancy; United Therapeutics: Consultancy.

Abstract Background MBL is a precursor state to CLL, with a prevalence of ~5-8% in the general population and 15-18% in CLL families. To date, 41 single nucleotide polymorphisms (SNPs) have been found to be associated with CLL. We previously showed that a PRS of the weighted average of the number of risk alleles of these SNPs is associated with CLL risk using cases and controls from the International Lymphoma Epidemiology (InterLymph) Consortium. We validated this score in an independent sample of CLL cases and controls from the Genetic Epidemiology of CLL (GEC) Consortium, a cohort of families with ≥2 members with CLL. In the CLL families we also reported an association between the PRS and MBL risk, where 93% of MBLs were low-count MBL. Here we evaluate the PRS in an independent sample ascertained from the Mayo Clinic Biobank. Methods The Mayo Clinic Biobank is a large-scale bio-repository of adult patients assembled to provide a wide array of health-related research studies. Biobank participants (N=2530) were screened for MBL using a highly-sensitive, 8-color (CD38, CD45, Kappa, Lambda, CD19, CD23, CD5 and CD20) flow-cytometry assay on stored cryopreserved peripheral blood mononucleotide cells. Low-count and high-count MBL were defined as those individuals who had <85% and ≥85 clonal B-cells out of total B-cells, respectively. Individuals without MBL ("controls") were frequency matched to MBLs based on age and sex, after excluding non-Caucasian and non-interpretable flow results. Participants were genotyped using the Illumina OmniExpress array. Standard genotyping quality-control metrics were applied. We computed the PRS as previously published and categorized it by quintiles based on values previously used with InterLymph controls. The middle quintile served as the reference category. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CI), adjusting for age at consent, sex, and socioeconomic status. In addition, we compared the median PRS by low-count MBL and high count MBL using the Mann-Whitney U test. Results We identified 276 MBLs overall and 2109 controls; 97% (N=267) of MBLs had low-count MBL and only 3% (N=9) had high-count MBL. We then selected 844 controls frequency matched to the MBL cases on age and sex. Family history of leukemia/lymphoma was 11.3% among MBLs overall and 8.8% among controls. The median PRS was 7.88 and 7.46 (Figure) among MBLs overall and controls, respectively. The continuous PRS had a 1.75-fold increased risk for MBL overall (CI:1.5-2.1, P=2.65x10-12). Compared to the middle quintile, the highest quintile had 2.2-fold increased risk for MBL overall (CI:1.5-3.3, P=1.46x10-4) and the lowest quintile had 0.6-fold decreased risk (CI:0.4-0.96, P=0.03). The median PRS for low-count and high count MBL were 7.86 and 8.04, respectively. The continuous PRS had a 1.75-fold increased risk for low-count MBL (CI:1.5-2.1, P=4.81x10-12) and a 1.89-fold increased risk for high-count MBL (CI:0.8-4.2, P=0.13), however, the effect is not statistically significant between high-count and low-count MBL (P=0.89). Conclusion In this independent sample, we validated our previous findings that the CLL PRS is associated with MBL risk. Although the sample size of high-count MBL was limited (N=9), there is evidence of a weak trend towards a higher PRS compared to low-count MBL and controls Larger sample size of high-count MBL is warranted in order to stratify MBL risk by low-count and high-count MBL. These results may help identify individuals at higher risk of MBL including low-count and high-count MBL, beyond the risk associated with age and family history of CLL. Figure: Polygenic risk score distribution by control and MBL status Figure. Figure. Disclosures Parikh: Janssen: Research Funding; Pharmacyclics: Honoraria, Research Funding; Gilead: Honoraria; Abbvie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; MorphoSys: Research Funding. Cerhan:Nanostring: Research Funding; Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board. Kay:Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; 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; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Gilead: 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; Morpho-sys: Membership on an entity's Board of Directors or advisory committees. Shanafelt:Genentech: Research Funding; Pharmacyclics: Research Funding; GlaxoSmithKline: Research Funding; Jansen: Research Funding.