Allmer, Cristine

AbstractThe development of cardiovascular disease (CVD) in long‐term survivors of lymphoma is of increasing importance. Here, we characterize the cumulative incidence and risk factors for CVD in lymphoma patients diagnosed in the current treatment era. From 2002–2015, newly diagnosed lymphoma patients (>18 years) were enrollment into a prospective cohort study that captured incident CVD, consisting of congestive heart failure (CHF), acute coronary syndrome (ACS), valvular heart disease (VHD), and arrhythmia. The cumulative incidence of CVD was calculated with death modeled as a competing risk. We estimated the association of treatment with anthracyclines or radiotherapy and traditional CVD risk factors with incidence of CVD using hazard ratios (HR) and 95% confidence intervals (CI) estimated from Cox regression. After excluding prevalent CVD at lymphoma diagnosis, the study consisted of 3063 patients with a median age of 59 years (range 18–95). The cumulative incidence of CVD at 10‐years was 10.7% (95% CI, 9.5%–12.1%). In multivariable analysis, increasing age (HR = 1.05 per year, p < 0.001), male sex (HR = 1.36, p = 0.02), current smoker (HR = 2.10, p < 0.001), BMI > 30 kg/m2 (HR = 1.45, p = 0.01), and any anthracycline treatment (HR = 1.57, p < 0.001) were all significantly associated with risk of CVD. Anthracyclines were associated with increased risk of CHF (HR = 2.71, p < 0.001) and arrhythmia (HR = 1.61, p < 0.01), but not VHD (HR = 0.84, p = 0.58) or ACS (HR = 1.32, p = 0.24) after adjustment for CVD risk factors. Even in the modern treatment era, CVD remains common in lymphoma survivors and preventive efforts are required that address both treatment and CVD risk factors.

Abstract Background: Most indolent non-Hodgkin lymphomas (NHL) are considered incurable and are characterized by relapse and remission. Quality of life (QOL) can be affected by disease burden, side effects of treatment, and psychosocial effects of living with an incurable cancer. However, little is known about QOL in this population. We sought to describe QOL at diagnosis, change in QOL over time, and determinants of QOL in patients with indolent NHL. Methods: Newly diagnosed lymphoma patients were prospectively enrolled within 9 months of diagnosis in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource. Eligibility criteria included age ≥18 years; exclusion criteria included HIV-positive, non-US residency, and inability to complete surveys in English. All pathology was reviewed by a hematopathologist. Patients with indolent B-cell NHL were included in this analysis. QOL was measured at baseline (BL) and 3 year follow-up (FU3) with the Functional Assessment of Cancer Therapy-General scale (FACT-G), which measures 4 QOL domains: physical, social/family, emotional, and functional well-being (WB), and a single item Linear Analogue Self-Assessment (LASA) for measuring overall QOL. Patients who did not complete ≥80% of FACT-G questions were excluded. Clinical data were abstracted according to a standard protocol and patients were systematically followed for events (progression, re-treatment, and death). CLL patients were excluded for event analysis due to differing definitions of outcome (progression/retreatment vs. time to first treatment). Treatment was defined as any systemic therapy (including chemotherapy, targeted agents, or antibody therapy) or radiation. QOL scores were transformed to a 0-100 scale with 100 representing the best WB or QOL for analysis. Change in QOL was assessed using the raw difference in QOL scores (i.e., FU3-BL) on the 0-100 scale with positive change scores representing improvements; minimally important difference was defined as 7 points on the transformed FACT-G subscales per previously established cutoffs (Yost and Eton, Eval & Health Prof. 2005 Jun;28:172-91). Changes in QOL from BL to FU3 were assessed using one-sample t-test of change scores vs zero. Results: 1050 patients with indolent NHL enrolled between 9/02-12/2012 and with both pre-treatment and 3 year QOL were included. QOL was collected after initiation of therapy in 215 patients (20%) and prior to therapy in 835 (80%). Subtype was 41% CLL/SLL, 32% grade 1-2 FL, 14% extranodal marginal zone, and 13% other (lymphoplasmacytic lymphoma and other). Median age was 62 years and 55% were male. At the FU3 assessment, 577 patients (55%) had received treatment, 42 (4%) transformed, and 53 (25%) had an event. Emotional WB significantly improved from BL to FU3 (mean +5.9, SD 13.3, p&lt;0.001) while social/family WB significantly decreased (mean -4.8, SD 20.5, p&lt;0.001). Functional WB, physical WB, overall FACT-G, and overall QOL LASA were not significantly different at FU3. These findings were similar in those who were treated vs. those who were observed. We also assessed the results stratified by non-CLL patients who had progression or retreatment between BL and FU3. In patients with an event in the first 3 years, emotional WB had a significant improvement from BL to FU3 (mean +3.6, SD 13.6, p=0.002) and overall QOL LASA decreased (mean -4.0, SD 20.0, p=0.01). In patients without an event, improvements were reported in functional WB (mean +2.1, SD 18.2, p=0.01), physical WB (mean +2.5, SD 13.9, p=0.001), emotional WB (mean +7.0, SD 15.0, p&lt;0.001), and overall QOL LASA (mean +1.1, SD 11.4, p=0.03), while social/family WB decreased (mean -6.2, SD 20.3, p&lt;0.001). Sensitivity analysis showed that results were consistent in the subset of patients who had BL QOL collected prior to initiating therapy. While many changes were statistically significant, they were small in magnitude; only the improvement in emotional WB in the entire cohort approached clinical significance and met that criterion in those without an event. Conclusions: In this large sample of patients with indolent lymphoma, QOL generally remained stable from diagnosis to FU3 while emotional WB showed improvement, suggesting psychosocial adaptation by the patient. Mean score changes in functional WB, physical WB, emotional WB, social/family WB, and overall QOL were more pronounced in patients without an event in the first 3 years since diagnosis. Disclosures Shanafelt: Hospira: Research Funding; AbbVie: Research Funding; Celgene: Research Funding; GlaxoSmithKline: Research Funding; Genentech: Research Funding; Jannsen: Research Funding; Pharmacyclics: Research Funding.

Abstract Introduction: Although increased physical activity (PA) is associated with improved quality of life in cancer survivors, knowledge about the impact of PA on survival in lymphoma patients is limited. We studied the impact of usual adult PA and PA at 3 years after diagnosis on overall (OS) and lymphoma-specific (LSS) survival in lymphoma patients. Methods: Lymphoma patients were prospectively enrolled within 9 months of diagnosis into the Lymphoma SPORE Molecular Epidemiology Resource (MER) cohort. At baseline, patients reported their usual level of mild, moderate and strenuous PA during most of their adult life and (for survivors) again at the 3 year follow-up (FU3), which we used to calculate the Godin Leisure Score Index (LSI). At FU3, patients also self-reported change in PA since diagnosis as increase, decrease or no change. LSI was modeled as a continuous score (per 10-point change) and by tertile. Survival was measured as time from diagnosis (for baseline PA) and time from FU3 (for FU3 PA) until death due to any cause and due to lymphoma. We evaluated the association of PA with outcome using Kaplan-Meier curves as well as hazard ratios (HRs) and 95% confidence intervals (CI) from Cox models stratified by lymphoma subtype and adjusted for age and sex (and baseline PA for change models). Results: Of 4087 patients enrolled in the MER at the Mayo Clinic from 2002-2012, 3129 had baseline PA data (3060 evaluable for LSI), and 1845 (1395 evaluable for LSI) had FU3 PA data. In the baseline cohort, 57.6% of the patients were male. The median age at diagnosis was 62 years (range 18-92 years). The majority had ECOG performance status &lt;2 (95%), no B-symptoms (85.8%) and advanced disease (63% stage III-IV) at diagnosis. The subtypes included were CLL/small lymphocytic (25.9%), follicular (18.1%), diffuse large B-cell (16.8%), marginal zone (8.1%), Hodgkin (6.9%), mantle cell (4.8%), T-cell (5%) and other (14.3%) lymphomas. The median LSI was 28 (range 0-57) at baseline and 25 (range 0-67) at FU3 with a median change in LSI of -2 (range -57 to 61). At a median follow-up of 84 months from diagnosis (IQR 59-119 months) in the cohort with baseline PA, there were 806 total deaths (271 in pts with FU3 PA) and 423 lymphoma deaths (104 in pts with FU3 PA). When analyzed as a continuous variable, baseline LSI was associated with OS (HR 0.95, 95% CI 0.91-0.99) and LSS (HR 0.95, 95% CI 0.90-1.01). LSI by tertiles was associated with OS (p&lt;0.0001, Figure 1a), and patients in the high LSI compared to the low LSI tertile had superior OS (HR 0.81, 95% CI 0.68-0.97) and LSS (HR 0.81, 95% CI 0.63-1.04). For prognosis after 3 years, continuous LSI at FU3 was also associated with superior OS (HR 0.84, 95% CI 0.77-0.92) and LSS (HR 0.76, 95% CI 0.65-0.89). LSI at FU3 by tertiles was associated with OS (p&lt;0.0001, Figure 1b), and patients in the high LSI compared to the low LSI tertile had superior OS (HR 0.51, 95% CI 0.34-0.77) and LSS (HR 0.33, 95% CI 0.16-0.67). A change in LSI from baseline to FU3 PA was associated with superior OS (HR 0.84, 95% CI 0.76-0.92) and LSS (HR 0.74, 95% CI 0.64-0.87) after FU3. Change in LSI by tertiles was associated with OS (p=0.0007, Figure 1c). Compared to patients with stable LSI (change of LSI between -10 and 5 points from baseline, n=429), patients who increased LSI by &gt; 5 (n=464) had improved OS (HR 0.67, 95% CI 0.45-1.00) and LSS (HR 0.47, 95% CI 0.23, 0.94), while patients who decreased LSI by &gt;10 (n=477) had no association with OS or LSS. Self-reported change in PA from baseline to FU3 was associated with OS (p&lt;0.0001, Figure 1d); compared to no change, an increase in PA was not associated with OS (HR 0.79, 95% CI 0.42-1.48) or LSS (OR 0.73, 0.26-2.07), while a reduction in PA was associated with inferior OS (HR 1.94, 95% CI 1.49-2.53) and LSS (HR 2.51, 95% CI 1.62-3.88). After excluding patients with any events through FU3, the associations for the PA change and OS remained significant for change in LSI (HR 0.91, 95%CI 0.83-1.00) and self-reported decrease in PA (HR 1.83, 95%CI 1.32-2.53). Conclusions: Patients with a higher level of usual PA during adult life have significantly better OS and LSS after lymphoma diagnosis compared to those who are less physically active. Higher PA in 3-year survivors is also associated with improved survival, while a reduction in physical activity is associated with worse outcomes. These data support an important role for PA in lymphoma survivorship and also provide a strong rationale for a PA intervention trial in lymphoma patients. Figure 1 Figure 1. Disclosures Cohen: LAM Therapeutics, Inc: Research Funding; Takada: Research Funding; Infinity: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bioinvent: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees. Flowers: Spectrum: Consultancy; OptumRx: Consultancy; Acerta: Research Funding; Onyx: Research Funding; Janssen Pharmaceutical: Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; National Cancer Institute: Research Funding; Burroughs Welcome Fund: Research Funding; Genentech/Roche: Consultancy, Research Funding; Gilead: Consultancy; Research to Practice: Research Funding; Millennium/Takeda: Research Funding; National Institutes Of Health: Research Funding; Prime Oncology: Research Funding; Educational Concepts: Research Funding; Abbvie: Consultancy, Research Funding; Pharmacyclics LLC, an AbbVie Company: Research Funding; Seattle Genetics: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Celgene: Consultancy, Research Funding; Infinity: Research Funding; Bayer: Consultancy; Clinical Care Options: Research Funding. Cerhan: Janssen: Other: Scientific Advisory Board (REMICADELYM4001); Janssen: Other: Multiple Myeloma Registry Steering .

Introduction Monoclonal gammopathy of undetermined significant (MGUS) is a premalignant plasma cell disorder that is common in individuals over age 50. MGUS prevalence estimates to date have generally been based on results of serum electrophoresis and immunofixation (for heavy chain [HC] MGUS) and additionally, free light chain (for light chain [LC] MGUS). Mass-spectrometry assays, however, can detect lower levels of monoclonal (M) proteins, identify the isotype and provide accurate quantification of the M-protein. Given the evidence that MGUS is likely present at least 10 years prior to its detection, mass spectrometry may provide more accurate estimates of underlying MGUS. We examined the prevalence of HC-MGUS using matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry among three risk groups: European Americans (EA), African Americans (AA) and first-degree relatives (FDR) of patients with multiple myeloma (MM), MGUS, chronic lymphocytic leukemia (CLL) or non-Hodgkin's lymphoma (NHL). Methods We sampled all AA and a random sample of EA participants ages 50 and older enrolled in the Mayo Clinic Biobank. We also selected unaffected first-degree relatives (limiting to ages 50 and older) from our ongoing family study of hematological malignancies. We screened serum samples by the MALDI-TOF mass spectrometry assay, which is clinically used at Mayo Clinic (known as MASS-FIX) for detection and isotypes of M-proteins. The assay uses isotype-specific nanobody enrichment coupled to MALDI-TOF mass spectrometry. For each of the risk groups, HC- MGUS prevalence was estimated by age (50-59, 60-69, 70+). Age- and sex-adjusted prevalence rates were calculated by direct standardization to the 2010 US population. Ninety-five percent confidence intervals (95% CI) for the prevalence rates and comparisons across the three groups were based on the Poisson distribution. Because free LC information was unavailable, LC-MGUS was not estimated. Results A total 327 AA, 854 EA and 792 FDR were screened for M-proteins using the MASS-FIX assay. All three groups had similar sex (42-44% male) and age distributions (mean[SD] age was 64.5[9.5] yrs., 64.4[8.8] yrs. and 64.9[10.5] yrs. for AA, EA and FDR, respectively). As previously noted using conventional detection methods, the overall prevalence of HC-MGUS was higher in the AA population (16.5% [95%CI: 12.2%, 20.8%]) and FDR (21.5% [95%CI: 18.3%, 24.7%]) than in EA (11.2% [95%CI: 8.8%, 13.5%]), both p-values &lt;0.0001. This pattern was generally seen across age groups (Table 1), with increasing prevalence with age. FDR of MM or MGUS cases had a higher prevalence (22.2% [95%CI: 18.4%, 26.0%]) than FDR of CLL or NHL cases (18.8% [95%CI: 13.3%, 24.4%]), although the difference was not statistically significant (p-value=0.82). Overall prevalence estimates of HC-MGUS using MASS-FIX were at least three-fold higher than estimates using conventional methods alone (i.e. age- and sex-adjusted prevalence in Olmsted County, MN, was 3.1% [95% CI: 2.9%-3.4%]). Conclusion We provided some of the first data on prevalence of HC-MGUS across three risk groups, using a sensitive method for detecting monoclonal proteins. The MASS-FIX assay resulted in substantially higher absolute rates of HC-MGUS (at least 3-fold higher) relative to conventional methods. Further, the prevalence of HC-MGUS among the AA and FDR was significantly higher than the EA population. Thus, the more sensitive assay identified greater absolute numbers of individuals with MGUS, but resulted in similar relationships as seen in prior studies across these risk groups. Our results have implications for future studies of the etiology of MGUS and its progression. Disclosures Dispenzieri: Janssen: Research Funding; Pfizer: Research Funding; Alnylam: Research Funding; Intellia: Research Funding; Celgene: Research Funding; Takeda: Research Funding. Kumar:Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Karyopharm: Consultancy; Genecentrix: Consultancy; Cellectar: Other; Carsgen: Other, Research Funding; Merck: Consultancy, Research Funding; Dr. Reddy's Laboratories: Honoraria; Adaptive Biotechnologies: Consultancy; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Kite Pharma: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Tenebio: Other, Research Funding; Sanofi: Research Funding; MedImmune: Research Funding; Novartis: Research Funding. Murray:The Binding Site: Patents & Royalties: Patent Use of Mass Spec to identify monoclonal proteins licensed to The Binding Site.

Introduction Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant clonal disorder, which progresses at a rate of 1% per year to multiple myeloma (MM), or other plasma-cell or lymphoid disorders. MGUS is common and occurs in approximately 3% and 5% of persons 50 and 70 years of age or older, respectively. Clinical factors reported to be associated with MGUS progression include MGUS isotype, abnormal serum free light-chain ratio (FLCr), and high serum monoclonal protein (M protein) level (&gt;=1.5 g per deciliter). Body mass index (BMI) has also been seen associated with MGUS progression, but has not been evaluated in context of established clinical prognostic factors. Here, we evaluate the contribution of BMI to MGUS progression beyond clinical prognostic factors in a population-based study and examine differential associations by sex. Methods We studied 594 patients residing in Olmsted County Minnesota, who were identified with MGUS in a screening study conducted between1995 through 2003. Patients with light-chain MGUS were not included. Patients were screened for M-protein [normal (&lt;1.5 g/dl) or high (≥1.5 g/dl)], FLCr [normal (0.26-1.65) or abnormal (&lt;0.26 and/or &gt;1.65)], and isotype (IgG, IgM, IgA, bi-clonal). Follow-up time was calculated from date of MGUS screening to date of last follow-up, death, or progression to MM or another plasma-cell or lymphoid disorder. BMI (kg/m2) was calculated using height and weight values reported close to sample date (80% of patients within 2 years), and then categorized into BMI&lt;25 and BMI≥25. We used Cox regression to estimate the association of BMI with risk of MGUS progression to MM or another plasma-cell or lymphoid disorder, univariately, and accounting for clinical factors. Analyses were also stratified by sex. We report hazard ratios (HR) and 95% confidence intervals (CI). Results Of the 594 patients, 51% were male, and the median age at screening date was 73 (range:50-98 years). The immunoglobulin type was IgG in 71% of patients, IgM in 17%, IgA in 10%, and bi-clonal in 2%. High M protein level was observed in 22% and abnormal FLCr in 28%. BMI≥25 was observed in 67% of patients. The median follow-up time was 10.5 years (range:0-25). Over a median of 6,846 person-years, 465 patients (78%) died and 57 patients progressed and developed MM (N=39), amyloidosis (N=8), lymphoma (N=5), or Waldenstrom macroglobulinemia (N=5). The overall rate of progression was 0.87 events (CI:0.67-1.12) per 100 person-years, and higher among males (1.01, CI:0.73-1.42) than in females (0.72, CI:0.48-1.08), however, the difference was not statistically significant (P=0.24). In univariate analyses, BMI≥25 (HR=2.14, CI:1.05-4.36, P=0.04), non-IgG isotype (HR=2.84, CI:1.68-4.80, P=0.0001), high M protein (HR=2.57, CI: 1.50-4.42, P=0.001), and abnormal FLCr (HR=3.39, CI:1.98-5.82, P&lt;0.0001) were associated with increased risk of progression (Table 1.A). These four factors were independently associated with MGUS progression in a multivariable model, with a c-statistic=0.75 (CI:0.68-0.82). When stratifying by sex, associations of BMI with progression were stronger among females than males (Table 1.B) [c-statistic=0.81 (CI:0.72-0.90) vs 0.71 (CI:0.60-0.82) for females vs. males respectively], however, a test for interaction between BMI and sex was not statistically significant (P=0.15). Conclusions In this study we found evidence that high BMI is a prognostic factor independent of isotype, M protein, and FLC ratio in the full cohort, however, this was mainly driven by the effect in females rather than males. Future studies should further investigate sex differences in evaluating prognostic factors among MGUS patients. Disclosures Dispenzieri: Intellia: Research Funding; Pfizer: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Janssen: Research Funding. Kumar:Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Cellectar: Other; Kite Pharma: Consultancy, Research Funding; Tenebio: Other, Research Funding; BMS: Consultancy, Research Funding; Karyopharm: Consultancy; MedImmune: Research Funding; Sanofi: Research Funding; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Merck: Consultancy, Research Funding; Adaptive Biotechnologies: Consultancy; Genecentrix: Consultancy; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Dr. Reddy's Laboratories: Honoraria; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Novartis: Research Funding; Carsgen: Other, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member.

Abstract Immune dysfunction is a hallmark of CLL/MBL; ~25% individuals with newly diagnosed CLL have hypogammaglobulinemia at diagnosis. In this study of newly diagnosed CLL/MBL, we sought to a) investigate the association between serum immunoglobulins and specific mutations identified by next generation sequencing, and b) determine if serum immunoglobulin levels predict TFT, independent of the CLL-International Prognostic Index (CLL-IPI) and tumor mutational load (TML). We used the Mayo Clinic CLL Resource to identify newly diagnosed CLL/MBL individuals consented within 2 years of diagnosis. Baseline clinical characteristics and the CLL-IPI score was ascertained on all individuals. DNA was extracted from peripheral blood CD5+/CD19+ clonal B-cells; and the entire coding regions of 60 somatically recurring mutated CLL driver genes were enriched using a customized Agilent SureSelect gene panel and sequenced in Illumina Hiseq4000. Serum immunoglobulins were quantitated by radial immunodiffusion using Immunoplates. The relationship between serum immunoglobulins was assessed: a) with individual gene mutations (mutated vs. not); and b) using the TML score (using a value of 0, 1, and 2 or greater [2+] mutated genes). The impact of serum immunoglobulin levels on TFT was analyzed for all individuals. Of 356 individuals identified, 80 (22%) had MBL, median age was 63 years (28-87), 261 (73%) were male, and the CLL-IPI distribution was: low risk (n=149, 42%), intermediate risk (n=112, 32%), and high/very high risk (n=94, 26%). We observed 43%, 39%, and 18% individuals with 0, 1, or 2+ mutated genes, respectively. The most commonly mutated genes were NOTCH1 (13%), SF3B1 (13%), TP53 (8%), ATM (8%), BIRC3 (7%), and NFKBIE (7%). Using standard cutoffs, 135 (38%) individuals had low serum IgG, 73 (21%) had low serum IgA, and 154 (44%) had low serum IgM at diagnosis. There were no differences in individual gene mutation frequencies between individuals with low and normal serum IgG. In contrast, individuals with low serum IgA were more likely to have mutations in NOTCH1 (25% vs 10%) and ATM (14% vs 7%) compared to those with normal IgA, and individuals with low serum IgM were more likely to have mutations in NOTCH1 (19% vs 9%) and BIRC3 (11% vs 4%) compared to those with normal IgM (all p-values &lt;0.05). There were no significant differences in individuals with low serum IgG based on TML scores of 0, 1, and 2+ (31%, 35%, 40%, respectively, p=0.28). However, individuals with low serum IgA and low serum IgM were more likely to have a higher TML (8%, 20%, 36% for IgA, p&lt;0.0001; and 35%, 45%, 54% for IgM, p&lt;0.0001, for TML scores of 0, 1, and 2+, respectively). After a median follow-up of 6.9 years, 157 individuals required CLL therapy. After adjusting for CLL-IPI and TML, low serum IgG, low serum IgA, and low serum IgM (hazard ratio [HR]: 1.8, 2.3, and 2.4, respectively, all p-values &lt;0.05) were independently associated with a shorter TFT. Our study shows that individuals with CLL/MBL who harbor mutations in NOTCH1, ATM and BIRC3 are more likely to have low serum IgA and IgM at diagnosis. Serum IgA and IgM (but not serum IgG) levels decrease with an increase in the TML score. Finally, low serum immunoglobulins can predict TFT, independent of the CLL-IPI and TML, which has important implications for risk stratification in newly diagnosed CLL/MBL. Citation Format: Sameer A. Parikh, Cristine Allmer, Geffen Kleinstern, Nicholas J. Boddicker, Shulan Tian, Cecilia B. De Campos, Laura A. Bruins, Daniel R. o"brien, Brian F. Kabat, Kari G. Rabe, Aaron D. Norman, Huihuang Yan, Xing Li, Timothy G. Call, Wei Ding, Jose F. Leis, Saad J. Kenderian, Celine M. Vachon, James R. Cerhan, Neil E. Kay, Susan L. Slager, Esteban Braggio. Serum immunoglobulins are an independent prognostic marker of time to first therapy in newly diagnosed chronic lymphocytic leukemia (CLL) and monoclonal B-cell lymphocytosis (MBL) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6466.

Abstract Background Factors associated with chronic immune stimulation such as transplantation, autoimmune disorders and chronic infections have been linked to the development of lymphoma, while atopic conditions have been associated with a lower risk of lymphoma. Vaccinations, in part due to their ability to induce immune responses, have been hypothesized to play a role in lymphoma etiology, but there are few studies with mixed results and limited data on lymphoma subtypes. Methods We used a clinic-based study of 2461 newly diagnosed lymphoma cases and 2253 frequency-matched controls enrolled from 2002-2015. Participants self-reported lymphoma risk factors and history of vaccinations for hepatitis A, hepatitis B, chicken pox, yellow fever and influenza. Pathology was centrally reviewed, and included 644 chronic lymphocytic leukemia, 528 follicular (FL), 414 diffuse large B-cell (DLBCL), 170 marginal zone, 127 mantle cell, 119 T-cell, 181 Hodgkin, and 278 other lymphoma subtypes. We used polytomous logistic regression to estimate odds ratio (OR) and 95% confidence intervals (CI) of vaccinations with lymphoma risk overall and for subtypes, adjusting for age, sex, socioeconomic status, body mass, alcohol use, smoking history, recreational sun exposure, and family history of hematological malignancies. Results The median age at diagnosis for lymphoma cases was 64 years (range: 18-93) and 58% were male; the median age at enrollment for controls was 62 years (range: 18-91) and 53% were male. The most common vaccinations among controls were influenza (90%) followed by hepatitis B (41%), hepatitis A (30%), chicken pox (20%), and yellow fever (11%). Vaccination for influenza was inversely associated with lymphoma risk overall (OR=0.75, CI:0.61-0.93, P=0.01), with an inverse trend for increasing number of vaccinations (P=0.0001). In subtype analyses, Influenza vaccination was inversely associated with risk of FL (OR=0.59, CI:0.43-0.80, P=0.001), DLBCL (OR=0.64, CI:0.44-0.92, P=0.02) and other lymphoma subtypes (OR=0.66, CI:0.43-1.00, P=0.05). Vaccination for hepatitis A was inversely associated with lymphoma risk (OR=0.79, CI:0.65-0.96, P=0.02), with no significant subtype-specific associations. No evidence of association with vaccination for hepatitis B (OR=0.95, CI:0.79-1.14) and chicken pox (OR=0.83, CI:0.68-1.02) for lymphoma risk overall or for any subtypes. Although no evidence of an association with vaccination for yellow fever (OR=0.81, CI:0.62-1.06) for lymphoma risk, it was inversely associated with DLBCL risk (OR=0.42, CI:0.22-0.80). Conclusion Vaccination for influenza and hepatitis A were inversely associated with overall lymphoma risk. DLBCL was associated with vaccination for influenza and yellow fever. FL was associated with vaccination for influenza. Understanding the underlying mechanisms for a putative protective effect of certain vaccines could provide new clues to the etiology and prevention of lymphoma. Citation Format: Geffen Kleinstern, Dennis P. Robinson, Carrie A. Thompson, Melissa C. Larson, Cristine Allmer, Grzegorz S. Nowakowski, Timothy G. Call, Stephen M. Ansell, Andrew L. Feldman, Neil E. Kay, Thomas M. Habermann, Susan L. Slager, James R. Cerhan. Vaccination history and risk of lymphoma and its major subtypes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1054.

e19112 Background: We sought to study the longitudinal QOL, financial burden and perception of care (PoC) in patients with multiple myeloma (MM) or lymphoma (LYM) enrolled on CTs vs. those receiving standard care (Non-CT). Methods: QOL was evaluated using the FACT-G questionnaire (baseline, 1, 2, 3, 6 & 12 months). Financial burden was evaluated using 10 questions adapted from the MEPS survey and PoC was evaluated using 6 questions adapted from the CAHPS survey (baseline, 3, 6 & 12 months). Results: 35 (28%) of 123 enrolled patients were treated on CTs. Demographics, QOL and financial burden are shown in the Table. Baseline QOL scores were similar in CT vs non-CT groups. Age, gender and self-reported performance status were significantly associated with baseline QOL, while lines of therapy & cancer type were not. There were no significant differences in change in QOL vs. baseline in patients treated on CTs vs. non-CTs at 1, 2, 3, 6 and 12 months from baseline, including the total FACT-G score and the subdomains of functional, physical, emotional and social well-being. Within each group, there was a decline in QOL over the first 3 months, specifically physical WB, which gradually returned to baseline. Baseline answers to financial burden questions were similar, except patients on CTs reported less need for taking extended time-off from work (22% vs. 46%, p = 0.02). The only statistically significant difference in individual Qs over 1 year was ‘worry about having to pay large medical bills’ at the 6-month timepoint (CT: 0%, non-CT: 52%, p = 0.01). Using a composite score from 4 MEPS questions, financial burden over time was lower in the CT group, though the differences were not statistically significant. There were no significant differences in PoC at baseline, 3, 6 and 12 months in the two groups based on answers to each of the six questions adapted from the CAHPS survey. Conclusions: Over 1 year, patients on CTs experienced a similar QOL and perceived their care to be similar as non-CT patients. Financial burden was lower in the CT group, though differences were not statistically significant. [Table: see text]