Cerhan, James R.

PURPOSE: This study assessed the long-term quality of life (QOL) of patients with aggressive lymphoma subtypes treated with autologous hematopoietic cell transplant (autoHCT) compared with those without history of transplant. METHODS: Patient-reported QOL measures were prospectively gathered from patients enrolled in the Iowa/Mayo Specialized Program of Research Excellence Molecular Epidemiology Resource cohort with aggressive lymphoma subtypes. QOL was measured using the Functional Assessment of Cancer Therapy-General (FACT-G), Functional Assessment of Chronic Illness Therapy–Fatigue Scale, State-Trait Anxiety Inventory (STAI), and Profile of Mood States instruments and with a numeric rating scale for overall QOL and spiritual QOL. The autoHCT group and no HCT groups were compared at 3 years (FU3) and 6 years (FU6) after lymphoma diagnosis. RESULTS: In total, 980 patients with lymphoma (106 autoHCT and 874 no HCT) diagnosed between 2002 and 2013 were included for analysis. The mean FACT-G total score was similar in the autoHCT and no HCT groups at FU3 (89.9 v 90.1, P = .64) and also at FU6 (91.5 v 89.6, P = .44). No differences between the autoHCT and no HCT groups were identified in the FACT subscales. The STAI identified lower anxiety in the autoHCT group by mean STAI1 (state) at FU3 (30.1 v 33.4, P < .01) and by mean STAI2 (trait) at FU6 (30.1 v 33.5, P = .02). No other clinically meaningful differences were identified between the two groups using the other QOL instruments. CONCLUSION: Patients remaining in remission at 3 and 6 years after diagnosis had a high level of QOL with no significant differences associated with history of treatment with autoHCT.

PURPOSE: Patients' concerns regarding clinical trial (CT) participation include apprehension about side effects, quality of life (QoL), financial burden, and quality of care. METHODS: We prospectively evaluated the experience of patients with multiple myeloma or lymphoma who were treated on CTs (CT group, n = 35) versus patients treated with standard approaches (non-CT group, n = 88) focusing on QoL, financial burden of care, and patients' perception of quality of care over a 1-year period. RESULTS: There were no significant differences in any of the patient-reported outcomes in CT versus non-CT groups. We observed an initial decline in overall QoL in the first 3 months across both groups, driven primarily by physical and functional well-being. QoL gradually improved and was above baseline by month 12. Patients reported highest improvement in the functional well-being subdomain. Patients in both groups reported high satisfaction with the quality of care received, and there were no differences in overall satisfaction, communication with team, or access to care. At baseline, 16%-19% of patients reported financial burden, which increased to a peak of 33% in the CT group and to 49% in the non-CT group over the course of 1 year. There was no significant difference in financial burden in the two groups overall. Most of the patients reported getting all the care that was deemed medically necessary in both groups. However, a significant proportion of patients reported having to make other kinds of financial sacrifices because of their cancer (CT group: 33% of patients at baseline and 21%-40% over 1 year; non-CT group: 19% at baseline and 25%-36% over 1 year). CONCLUSION: Patients treated on CTs reported comparable QoL and quality of care with the non-CT group. A high proportion of patients reported financial burden over time in both groups. Our findings can serve as a guide to educate patients regarding CT participation and highlight the need to address the significant financial burden experienced by patients with cancer.

Abstract Background: A previous International Lymphoma Epidemiology (InterLymph) Consortium evaluation of joint associations between five immune gene variants and autoimmune conditions reported interactions between B-cell response-mediated autoimmune conditions and the rs1800629 genotype on risk of B-cell non–Hodgkin lymphoma (NHL) subtypes. Here, we extend that evaluation using NHL subtype-specific polygenic risk scores (PRS) constructed from loci identified in genome-wide association studies of three common B-cell NHL subtypes. Methods: In a pooled analysis of NHL cases and controls of Caucasian descent from 14 participating InterLymph studies, we evaluated joint associations between B-cell–mediated autoimmune conditions and tertile (T) of PRS for risk of diffuse large B-cell lymphoma (DLBCL; n = 1,914), follicular lymphoma (n = 1,733), and marginal zone lymphoma (MZL; n = 407), using unconditional logistic regression. Results: We demonstrated a positive association of DLBCL PRS with DLBCL risk [T2 vs. T1: OR = 1.24; 95% confidence interval (CI), 1.08–1.43; T3 vs. T1: OR = 1.81; 95% CI, 1.59–2.07; P-trend (Ptrend) &lt; 0.0001]. DLBCL risk also increased with increasing PRS tertile among those with an autoimmune condition, being highest for those with a B-cell–mediated autoimmune condition and a T3 PRS [OR = 6.46 vs. no autoimmune condition and a T1 PRS, Ptrend &lt; 0.0001, P-interaction (Pinteraction) = 0.49]. Follicular lymphoma and MZL risk demonstrated no evidence of joint associations or significant Pinteraction. Conclusions: Our results suggest that PRS constructed from currently known subtype-specific loci may not necessarily capture biological pathways shared with autoimmune conditions. Impact: Targeted genetic (PRS) screening among population subsets with autoimmune conditions may offer opportunities for identifying those at highest risk for (and early detection from) DLBCL.

Introduction: Anthracyclines are a first-line chemotherapeutic agent used in the treatment of lymphoma. Myocardial injury can occur with cumulative doses of 250 to 350 mg/m 2 . Echocardiography is the imaging modality for identifying cardiac dysfunction; however, identifying diastolic changes in patients who received anthracyclines is not well represented in the literature. Variables used to identify diastolic dysfunction include lateral e’, medial e’, mitral E velocity, mitral A velocity, E/A, E/e’, and left atrial volume index (LAVI). This study aims to identify if diastolic abnormalities develop in a prospective cohort of patients with HL or NHL who received anthracycline-based chemotherapy. Methods: Lymphoma patients treated with anthracyclines from 2013 to 2020 were prospectively enrolled. Echocardiography was obtained at 3-time points: before the start of chemotherapy (T1), at 3-6 months (T2), and 9-12 months (T3). Diastolic variables were measured and analyzed based on the total cumulative dose of Doxorubicin per body surface area. Those that received greater than 250 mg/m 2 and greater than 300 mg/m 2 of Doxorubicin were compared to those that did not. Statistical analysis was done using one-way ANOVA, adjusting for age and gender. Results: 151 total patients (90 males, mean age 61 + 16 years); 21 (14%) HL, 130 (85%) NHL. 114 and 66 patients received a total cumulative dose of Doxorubicin greater than 250 mg/m 2 and 300 mg/m 2 , respectively. There was no significant association with 250 mg/m2. Lateral e’ at T3 vs. T1, was lower in those that received greater than 300 mg/ m 2 compared to those received less (0.09 vs 0.10, p < 0.02) (Table 1). Conclusions: Lateral e’ decreased at 1-year follow-up in those that received higher doses of Doxorubicin. These changes in the lateral e’ could reflect early relaxation abnormalities related to anthracycline-based chemotherapy and suggest that changes in lateral e’ should be considered by itself since E/e’ was normal.

Abstract Background: Differences in outcomes between clinical trial and real-world populations in follicular lymphoma (FL) may be partially explained by differences in characteristics between clinical trial and real-world patients. We hypothesized that certain eligibility criteria may be responsible for populations with distinct demographic/clinical characteristics. In this study, we compiled a list of eligibility criteria from front-line FL trials and evaluated their impact on the potentially eligible FL patients derived from two national cohort studies. We also evaluated whether criteria were duplicative and whether liberalizing criteria might enlarge the pool of eligible patients. Methods: Eligibility criteria were abstracted from 21 first-line clinical trials in FL in the FLASH database. We identified FL patients in two prospective cohort studies: Molecular Epidemiology Resource (MER) and Lymphoma Epidemiology of Outcomes (LEO). Descriptive statistics were used to characterize patients included and excluded by various eligibility criteria. Chi-square tests and two-sample t-tests were used to compare categorical and continuous variables, respectively. To study the relative impact of individual criteria, we used a step-wise approach to quantify the number of additional patients excluded with each individual criterion after applying other criteria. Results: Eligibility criteria included in at least one-third of studies included stage (present in all 21 trials), renal function (18), HIV/AIDS status (17), performance status (16), history of other malignancies (16), hepatic function (16), and cardiac function (15), other serious health conditions (13), pregnancy status (10), neuro/psych function (10), age (10), metabolic disease status (9), CNS involvement (9), birth control (8), pulmonary function (8), WBC count (8), HBV status (8), platelet count (7), measurable disease (7), HCV status (7), active infection status (7), and CD20 status (7). We included 738 patients with newly diagnosed FL undergoing first-line therapy from MER and 703 patients from LEO. In MER, the median age was 60 years; 87% were White vs 2% non-White, 80% were non-Hispanic vs 2% Hispanic, and 68% were stage III/IV. In LEO, the median age was 60 years; 88% were White vs 11% non-White, 87% were non-Hispanic vs 11% Hispanic, and 67% were stage III/IV. Exclusion criteria impacting more than 10% of patients included stage (31 in LEO vs 29% in MER), self-reported serious health conditions (17 vs 31%), prior cancer diagnosis (14 vs 11%), performance status (10%), and platelet count (10%). Patients excluded due to the following criteria tended to be older: renal function (median 70 vs 60 years in MER), prior malignancy (67 vs 58 years), and self-reported serious health conditions (66 vs 58 years). This pattern was consistent in LEO (Table). No eligibility criteria significantly impacted race/ethnicity. Given the potential for multiple eligibility criteria to exclude the same patients, we used a step-wise approach to evaluate the impact of individual criteria on patients remaining after exclusion by the larger group of criteria, and whether liberalizing criteria impacted patient numbers and/or demographics. We found that only self-reported serious health conditions significantly impacted numbers (excluding 43% of patients), whereas hematologic parameters and performance status excluded only 5 and 1% of patients, respectively, with no impact on age or race/ethnicity. We also found that liberalizing stage requirement from III-IV to II-IV could increase enrollment by 14% in MER (12% in LEO), and liberalizing platelet requirement from ≥150,000 to ≥100,000 could increase enrollment by 11% (15% in LEO). Moreover, liberalizing eligibility criteria had no impact on age or race/ethnicity of the patient pool or on EFS (Figure). Conclusions: We found that excluding patients with prior malignancies, poor renal function, and self-reported serious health conditions appears to inadvertently exclude older patients. Other serious health conditions appeared to exclude a significant number of patients but did not impact patient demographics. Liberalizing certain criteria, including stage and platelet requirement, could potentially increase trial accrual, but would not likely correct the older patient deficit. These findings may serve in developing consensus eligibility criteria for future first-line FL trials. Figure 1 Figure 1. Disclosures Flowers: Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; National Cancer Institute: Research Funding; Allogene: Research Funding; Biopharma: Consultancy; Cellectis: Research Funding; EMD: Research Funding; Genentech/Roche: Consultancy, Research Funding; Epizyme, Inc.: Consultancy; Genmab: Consultancy; Iovance: Research Funding; Guardant: Research Funding; Burroughs Wellcome Fund: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; Spectrum: Consultancy; Gilead: Consultancy, Research Funding; Amgen: Research Funding; Xencor: Research Funding; Nektar: Research Funding; SeaGen: Consultancy; Ziopharm: Research Funding; TG Therapeutics: Research Funding; 4D: Research Funding; Acerta: Research Funding; Denovo: Consultancy; Novartis: Research Funding; Pfizer: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Morphosys: Research Funding; Adaptimmune: Research Funding; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; BeiGene: Consultancy; AbbVie: Consultancy, Research Funding; Kite: Research Funding; Janssen: Research Funding; Cancer Prevention and Research Institute of Texas: CPRIT Scholar in Cancer Research: Research Funding; Pharmacyclics: Research Funding. Link: MEI: Consultancy; Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding. Friedberg: Acerta: Other: DSMC ; Bayer: Other: DSMC ; Novartis: Other: DSMC . Cohen: Genentech, Takeda, BMS/Celgene, BioInvent, LAM, Astra Zeneca, Novartis, Loxo/Lilly: Research Funding; Janssen, Adaptive, Aptitude Health, BeiGene, Cellectar, Adicet, Loxo/Lilly, AStra ZenecaKite/Gilead: Consultancy. Kahl: AbbVie, Acerta, ADCT, AstraZeneca, BeiGene, Genentech: Research Funding; AbbVie, Adaptive, ADCT, AstraZeneca, Bayer, BeiGene, Bristol-Myers Squibb, Celgene, Genentech, Incyte, Janssen, Karyopharm, Kite, MEI, Pharmacyclics, Roche, TG Therapeutics, and Teva: Consultancy. Lossos: Lymphoma Research Foundation: Membership on an entity's Board of Directors or advisory committees; Stanford University: Patents & Royalties; Seattle Genetics: Consultancy; NCI: Research Funding; Verastem: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; NIH grants: Research Funding; University of Miami: Current Employment. Nastoupil: Bayer: Honoraria; Gilead/Kite: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; ADC Therapeutics: Honoraria; IGM Biosciences: Research Funding; Takeda: Honoraria, Other: DSMC, Research Funding; Janssen: Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; TG Therapeutics: Honoraria, Research Funding; Epizyme: Honoraria, Research Funding; Denovo Pharma: Other: DSMC; Caribou Biosciences: Research Funding; Bristol Myers Squibb/Celgene: Honoraria, Research Funding; MorphoSys: Honoraria. Maurer: Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; BMS: Research Funding. Cerhan: NanoString: Research Funding; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding. Martin: ADCT: Consultancy.

Abstract Background: Event-free survival at 24 months (EFS24) is an important clinical endpoint in newly diagnosed diffuse large B-cell lymphoma and follicular lymphoma, and patients who achieve EFS24 with frontline immunochemotherapy have minimal loss of lifetime compared to age- and sex-matched general population. However, the prognostic role of EFS24 in mantle cell lymphoma (MCL) has not been well studied, possibly due to the perception of continued relapse pattern and poor survival outcome of MCL historically. A recent study from our group demonstrated evolving frontline therapy pattern from 2002-2009 ("Era 1") to 2010-2015 ("Era 2") which was associated with improved EFS and overall survival (OS) in Era 2. In the current study, we sought to explore the prognostic role of EFS24 in the two eras. Methods: Patients with newly diagnosed MCL from 9/2002 through 6/2015 were identified from Molecular Epidemiology Resource (MER), a prospective cohort study of the University of Iowa/Mayo Clinic Lymphoma SPORE. OS was defined as time from diagnosis (or the EFS24 defining event where applicable) to death from any cause and was analyzed using the Kaplan-Meier method. Expected OS accounting for age and sex was generated in R by using the general US population as the reference group. Observed versus expected OS was summarized by using standardized mortality ratio (SMR) and 95% confidence intervals (CI) of observed to expected deaths. Cumulative incidence of lymphoma-specific death was analyzed using Gray's test, with deaths from all other causes treated as competing events. Results: A total of 343 patients were included, 175 from Era 1 (median follow-up 13.0 years) and 168 from Era 2 (median follow-up 6.9 years). Age, sex and simplified MIPI score were similar between the 2 groups. Patients diagnosed in Era 2 had better OS, with a 5-year OS of 68.4% vs 59.2% (simplified MIPI-adjusted hazard ratio 0.68, 95% CI 0.50-0.93). Patients diagnosed in both eras had inferior OS compared to the general population, with an SMR of 3.26 (95% CI 2.70-3.89, P&lt;0.001) in Era 1 and 2.63 (95% CI 2.03-3.36, P&lt;0.001) in Era 2. Patients diagnosed in Era 1 who achieved EFS24 (n=98) still had inferior OS compared to the general population, with an SMR of 2.23 (95% CI 1.67-2.92, P&lt;0.001) (Figure 1A). In contrast, for patients diagnosed in Era 2 who achieved EFS24 (n=99), no evidence of OS difference was found compared to the general population, with an SMR of 1.31 (95% CI 0.78-2.07, P=0.31) (Figure 1B). The primary cause of death after diagnosis was lymphoma-related for patients diagnosed in both eras. The 5-year rate of lymphoma-related death was 28.8% in Era 1 and 20.5% in Era 2. In patients who were diagnosed in Era 1 and achieved EFS24, the primary cause of death after achieving EFS24 remained to be lymphoma-related, with a 5-year rate of 19.8% compared to 6.2% for lymphoma-unrelated causes (Figure 1C). In contrast, in patients who were diagnosed in Era 2 and achieved EFS24, the rate of lymphoma-related death was no longer higher than that of lymphoma-unrelated death, with a 5-year rate of 2.1% vs 5.5%, respectively (Figure 1D). In a sensitivity analysis restricted to only patients who received standard frontline immunochemotherapy, similar results were obtained. For example, in patients who achieved EFS24, the SMR compared to the general population was 2.89 (95% CI 2.00-4.04, P&lt;0.001) in Era 1 and 0.89 (95% CI 0.43-1.64, P=0.82) in Era 2. The 5-year rates of deaths due to lymphoma-related vs lymphoma-unrelated causes were 20.7% vs 6.2% in Era 1 and 1.3% vs 4.9% in Era 2. Conclusions: MCL survival outcome has improved for patients first diagnosed in the more recent era (2010-2015, compared to 2002-2009), likely due to improved frontline therapy as well as better salvage treatments (such as lenalidomide and BTK inhibitors). In the more recent treatment era, patients who achieved EFS24 had survival approaching the age- and sex-matched general population. In addition, these patients had a low risk of dying from lymphoma and were more likely to die from other causes. Longer follow-up (e.g., in Era 2) and external validation in other series are necessary to confirm the prognostic role of EFS24. With more efficacious salvage treatment options and likely continued improvement of OS in MCL, EFS24 may become an important clinical endpoint in frontline therapy of MCL. Figure 1 Figure 1. Disclosures Wang: TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; InnoCare: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; MorphoSys: Research Funding. Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Link: Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding; MEI: Consultancy. Farooq: Kite, a Gilead Company: Honoraria. Paludo: Karyopharm: Research Funding. Witzig: Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding; Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees. Habermann: Seagen: Other: Data Monitoring Committee; Tess Therapeutics: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding; Genentech: Research Funding. Nowakowski: Celgene, NanoString Technologies, MorphoSys: Research Funding; Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Abstract Monoclonal B-cell lymphocytosis (MBL) is one of the most common pre-malignant conditions and is characterized by a circulating population of clonal B-cells with an absolute clonal B-cell count &lt; 5x10 9/L and no evidence of lymphadenopathy, organomegaly, or cytopenias. MBL can be classified by the immunophenotype: CLL-like MBL (CD5+, CD20dim), atypical MBL (CD5+, CD20+), or non-CLL-like MBL (CD5-, CD20+), as well as by the size of the clone (low-count or high-count) with low-count MBL (LC-MBL) defined as clonal B-cell &lt;0.5x10 9/L or percent clonal B-cell count &lt;85% out of total B-cell count. The prevalence of MBL varies depending on detection methods and population tested. In a Spanish screening study of 608 individuals, they identified 73 MBLs (12%) with CLL-like MBL; moreover, these authors also evaluated overall survival (OS) compared to 290 individuals without MBL and found a significantly shorter OS among these predominantly LC-MBLs. Here, we evaluate the prevalence and OS of LC-MBL in a large screening cohort of 8,297 individuals. Study participants from the Mayo Clinic Biobank, a large-scale biorepository of adult patients, who had no prior history of hematologic malignancy, provided blood samples between 7/2009 to 4/2021. Stored peripheral blood mononuclear cells were screened for MBL with an eight-color flow cytometry assay capable of detecting clonal B-cell event to the 0.005% level. We classified each MBL by immunophenotype as CLL-like MBL, atypical MBL, and non-CLL-like MBL. Individuals with more than one immunophenotype were classified into one immunophenotype based on the following hierarchy: CLL-like MBL, then atypical MBL, then non-CLL like MBL. Based on previously published evidence, individuals were also classified by clonal counts using the percent clonal B-cell count &lt;85% out of the total B-cell count to define LC-MBL. Individuals were followed from sample date to the earliest of death, loss to follow-up, or 5/31/2021. OS for LC-MBL and controls (individuals who screened negative for MBL) was assessed by Kaplan-Meier method. Age- and sex-adjusted OS was analyzed using inverse weights methods. Cox regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CIs), adjusted for age and sex. We screened 8,297 individuals 40 years or older (median age 67 years, 39% male) and identified 1,326 (16%) with LC-MBL and 6,651 (80%) controls. Those individuals detected with high-count MBL (N=90, 1%) or individuals who had insufficient cells for flow cytometry interpretation (N=230, 3%) were excluded from subsequent analyses. The prevalence of LC-MBL was higher in males (21%) than females (14%; p&lt;0.01) and significantly increased with age (p&lt;0.01) ranging from 3% among those 40-49 years, 10% among those 50-59 years, 15% among those 60-69 years, 23% among those 70-79 years, and 29% for those 80+ years. Sixty-one individuals had clones with more than one type of immunophenotype. Based on our hierarchy, CLL-like MBL was the most common (N=1,155), followed by non-CLL-like (N=168) and atypical MBL (N=67). The median follow-up time in our cohort was 26 months (range 0-142), and 432 individuals had subsequently died. The 5-year and 10-year OS adjusted for age and sex among LC-MBLs was 95% and 87%, respectively; control estimates were 95% and 84%, respectively. We did not observe a significant difference in OS between those individuals with LC-MBL versus controls (HR=0.98, CI=0.77-1.24, P=0.84, Fig 1a), nor when we stratified individuals by age &lt;65 (P=0.77) or age &gt;= 65 (P=0.68). Among females, OS was longer among LC-MBL compared to controls (HR=0.61, CI=0.38-0.97, P=0.04); but no evidence of a difference in OS among males was observed (HR=1.22, CI=0.92-1.62, P=0.17). When we evaluated OS by MBL immunophenotype, we observed no statistically significant difference (Fig 1b, log rank P=0.44). When we stratified the MBLs by immunophenotype versus controls, we also did not observe a difference: CLL-like (HR=0.91, CI=0.71-1.18, P=0.5), atypical (HR=1.73, CI=0.92-3.27, P=0.09), non-CLL like (HR=0.98, CI=0.58-1.66, P=0.95). In the largest MBL screening cohort to date, LC-MBL was a common condition among adults 40 years or older reaching a prevalence as high as 29% among individuals 80 years of age or older. OS among those with and without LC-MBL was similar, regardless of immunophenotype and age. The longer survival in females with MBL versus controls requires further evaluation. Figure 1 Figure 1. Disclosures Parikh: Pharmacyclics, AstraZeneca, Genentech, Gilead, GlaxoSmithKline, Verastem Oncology, and AbbVie: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics, MorphoSys, Janssen, AstraZeneca, TG Therapeutics, Bristol Myers Squibb, Merck, AbbVie, and Ascentage Pharma: Research Funding. Kay: Juno 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; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Research Funding; Sunesis: Research Funding; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: 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; Acerta Pharma: Research Funding; MEI Pharma: Research Funding; Tolero Pharmaceuticals: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; 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; Rigel: Membership on an entity's Board of Directors or advisory committees. Shanafelt: Genentech, Pharmacyclics: Research Funding.

Abstract Introduction: TP53 aberrations, including mutations and deletion of 17p (del17p), are important adverse prognostic markers in chronic lymphocytic leukemia (CLL). Prevalence of TP53 aberrations ranges from 7-11% in untreated CLL and increases with disease progression and treatment. Among CLL patients with TP53 aberrations, co-occurrence of TP53 mutations with del(17p) is common. CLL patients with TP53 mutations or del(17p) have significantly worse outcomes when compared to wild-type patients. Previous studies, focusing on CLL patients at time of treatment, are mixed as to whether a single or more than one TP53 aberration impacts outcomes. TP53 is less well studied in monoclonal B-cell lymphocytosis (MBL), an asymptomatic pre-malignant state of CLL. Del(17p) occurs in 3-4% of MBL individuals, and a study of 54 MBL individuals reported a 2% mutation frequency in TP53. Here we estimated prevalence and evaluated the impact of TP53 aberrations in a large cohort MBL or untreated CLL individuals. Methods : Patients with CLL or MBL diagnosed between 2000 and 2019 from the Mayo Clinic CLL Resource with pre-treatment peripheral blood mononuclear cells (PBMC) collected within two years of diagnosis were considered. DNA was extracted from PBMCs with purity &gt;80% or with sorted CD5+/CD19+ clonal cells. The entire coding regions of 59 CLL driver genes were paired end sequenced. Median coverage depth was &gt;1000x per nucleotide, allowing for detection of mutations with variant allelic fraction (VAF) as low as 1%. Somatic mutations were called using MuTect2 in tumor-only mode, and high impact mutations (frameshift, nonsense, and splicing variants) and missense mutations in CLL hot spots were selected. Somatic mutations and FISH del(17p) were used to define TP53 state for each patient: 1) wild-type [no TP53 mutations and normal del(17p)], 2) single-hit [one TP53 mutation or del(17p)], or 3) multi-hit [multiple TP53 mutations or TP53 mutation and del(17p)]. Time to first treatment (TTFT) and overall survival (OS) were analyzed by TP53 state. TTFT and OS were defined as time from sample collection to first treatment or death, respectively, or last follow-up date. Median TTFT and OS was estimated by the Kaplan-Meier method. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals for TTFT and OS associations. The models were adjusted for known adverse prognostic factors including clinical diagnosis (CLL or MBL), age at diagnosis, Rai stage, b2 microglobulin, and IGHV mutation status. Results: Individuals with CLL (N=597) or MBL (N=285) were analyzed for prevalence of TP53 mutations and del(17p). We found 58 CLL patients (9.7%) and 15 MBL individuals (5.3%) had TP53 mutations. The median VAF in CLL was 30.9% (&lt;10% VAF, n=21) and in MBL was 13.5% (&lt;10% VAF, n=7). Del(17p) was present in 37 patients with CLL (6.2%) and 7 individuals with MBL (2.7%). In total, 86 CLL/MBL patients had a TP53 mutation and/or del(17p): 56.8% (48 patients) were single-hit and 44.2% (38 patients) were multi-hit (Fig. 1a). Patients with any TP53 aberration had shorter TTFT than wild-type patients (median 2.3 vs 9.4 years). Among patients with TP53 aberrations, median TTFT was shorter in multi-hit patients (20 events, 1.8 years) compared to single-hit patients (24 events, 3.2 years) (Fig. 1b). In Cox regression, single-hit (HR = 1.7 [1.1-2.6]) and multi-hit (HR = 1.8 [1.1-2.9]) patients had shorter TTFT compared to wild-type patients after adjusting for covariates (Fig. 1c). Multi-hit patients also had shorter OS compared to wild-type patients, while OS in single-hit patients did not significantly differ from wild-type patients (Fig. 1d). Median OS was 5.5 years in multi-hit patients (22 deaths) compared to 15.1 years in wild-type patients (196 deaths) and 14.3 years in single-hit patients (15 deaths). In the OS model adjusted for covariates, multi-hit patients had a significant increased risk (HR = 2.6 [1.6-4.1]), but single-hit patients did not (HR = 1.4 [0.8-2.5]) compared with wild-type patients (Fig. 1e). OS HRs remained stable after censoring at time of treatment. Both TTFT and OS HRs were consistent when mutations with VAF &lt; 10% were excluded. Conclusions: This study suggests single versus multi-hit TP53 aberrations may be important for prognostic outcomes in untreated CLL and MBL patients. Prognostic metrics may need to consider single versus multi-hit TP53 aberrations and include TP53 mutations with low VAF. Figure 1 Figure 1. Disclosures Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding; Genentech: Research Funding. Parikh: Pharmacyclics, MorphoSys, Janssen, AstraZeneca, TG Therapeutics, Bristol Myers Squibb, Merck, AbbVie, and Ascentage Pharma: Research Funding; Pharmacyclics, AstraZeneca, Genentech, Gilead, GlaxoSmithKline, Verastem Oncology, and AbbVie: Membership on an entity's Board of Directors or advisory committees. Kay: MEI Pharma: Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Research Funding; 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; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; 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; Behring: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees.

Abstract TMB is used to guide PD-1-directed immunotherapy in solid tumor Oncology. However, it has not been systematically studied in AML, where the focus has been on cytogenetic risk and individual driver gene mutations (GM's). TMB contribution to AML epidemiology is also uncertain. We therefore studied its association with epidemiologic risk factors; driver GM's and somatic mutations (SM's) in AML risk genes which we recently demonstrated (ABCB1; CYP1A1; CYP2B6; EPHX1; ERCC1,2,& 5; MEFV; MTRR; and TERT); clinical and cytogenetic features; and outcome after therapy in the MCAEC, a highly annotated clinical epidemiology cohort of consecutive AML pts [Finn, Cancer Epidemiol 39:1084, 2015]. Methods: We obtained somatic leukemia DNA from remnant diagnostic cytogenetic pellets in 98 MCAEC patients (pts), as previously described [Foran, Blood (2017) 130:570a]. Whole exome sequencing (WES) was performed at depth of ~100 million paired end 100bp reads using Agilent SureSelectXT Human All Exon V5 + UTRs target enrichment kit. Reads were mapped to human genome build hg19 using BWA-MEM. Single nucleotide variants (SNVs) and small INDELs were identified using Mayo Clinic (MC) analytic pipeline GenomeGPS 4.0.1 following Broad GATK variant discovery best practices of alignment, realignment and recalibration, and multi-sample joint genotyping; and filtered using both germline whole exome and genome sequencing of ~1200 MC Biobank samples and public germline variant databases of 1000 genome project, 6500 individuals in exome sequencing project, and HapMap phase 3. Remaining variants were annotated using ANNOVAR, and functional variants of non-synonymous, truncating, frame-shift, and splice-sites were used in the statistical association analyses. TMB was defined as the number of functional mutations per Mb of coding region, heterozygous or homozygous. TMB associations with epidemiologic risk, clinical characteristics, and SM's in AML risk genes (listed above) or driver GM's (occurring in 5 or more pts: ASXL1, BCOR, CEBPA, DNMT3A, FLT3, IDH2, KRAS, MLL2-5, NF1, NPM1, NRAS, PHF6, RUNX1, SF3B1, STAG2, TET2, TP53, U2AF1) were evaluated using linear regression models; a rank transformation of TMB was utilized due to its skewed distribution. Multivariable analysis (MVA) models were adjusted for variables with p-value &lt;0.05 in unadjusted analysis. Regression coefficients ("β") were estimated, interpreted as change in mean TMB rank for specified variables or characteristic. Associations with outcomes were examined using logistic regression or Cox proportional hazards regression models. All statistical tests were two-sided (version 3.6.2; R Foundation for Statistical Computing, Vienna, Austria). Results Median age at AML diagnosis was 70 yrs (Range: 19-94 yrs), and 67 pts were male. Cytogenetic risk group was favorable (7%), intermediate-normal (46%) or abnormal (20%), and poor risk (27%). 40/61 pts (66%) achieved complete remission (CR). With a median follow-up of 8.0 months (Range: 0.1 - 186), 80 pts (82%) died and 20 (20%) underwent allogeneic transplantation (AlloBMT). Median TMB was 18.2 (Range: 15.0-70.1). In MVA, significant associations with increased TMB were observed in pts with history of prior immunosuppressive therapy or solid organ transplantation (β=19.48, P=0.015), and with FLT3 (β=21.12, P=0.015), MLL2 (β=20.91, P=0.001), and MLL3 (β=11.31, P=0.031) GM's. A borderline association was observed for U2AF1 (β=16.14, P=0.057). TMB was also associated with SM's in TERT (β=25.13, P=0.028); a borderline association with SM's in ABCB1 was not confirmed in MVA (β=-17.98, P=0.069). Additionally, cytogenetic risk group was associated with TMB in MVA (P=0.005), being highest in intermediate-normal and lowest in poor risk groups. Body Mass Index was inversely associated with TMB (unadjusted β=-16.99, P=0.005), but unconfirmed in MVA (β=-8.29, P=0.12). There was no association with CR (OR=0.93, P=0.46), use of (HR=0.96, P=0.64) or relapse risk (HR=1.00, P=0.98) following AlloBMT, or survival (HR=0.97, P=0.56) (Figure). Conclusions Measurement of TMB is feasible in this AML epidemiologic cohort, and we observed important associations with AML risk factors, risk gene SM's, cytogenetic risk group, and driver GM's. We acknowledge the relatively small sample size and possibility of type II error, and therefore these observations require validation in a large prospective cohort which is planned. Figure 1 Figure 1. Disclosures Foran: OncLive: Honoraria; certara: Honoraria; actinium: Research Funding; boehringer ingelheim: Research Funding; novartis: Honoraria; abbvie: Research Funding; servier: Honoraria; taiho: Honoraria; pfizer: Honoraria; revolution medicine: Honoraria; gamida: Honoraria; takeda: Research Funding; sanofi aventis: Honoraria; trillium: Research Funding; syros: Honoraria; aptose: Research Funding; bms: Honoraria; kura: Research Funding; h3bioscience: Research Funding; aprea: Research Funding; sellas: Research Funding; stemline: Research Funding. Murthy: CRISPR Therapeutics: Research Funding. Finn: Jazz: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau; BeiGene: Consultancy, Speakers Bureau; ADC Therapeutics: Consultancy, Speakers Bureau. Badar: Pfizer Hematology-Oncology: Membership on an entity's Board of Directors or advisory committees. Cerhan: Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Genentech: Research Funding; NanoString: Research Funding; Regeneron Genetics Center: Other: Research Collaboration.