Parikh, Sameer A.

Abstract Although Bruton tyrosine kinase inhibitors (BTKis) are generally well tolerated and less toxic than chemotherapy alternatives used to treat lymphoid malignancies, BTKis like ibrutinib have the potential to cause new or worsening hypertension (HTN). Little is known about the optimal treatment of BTKi-associated HTN. Randomly selected patients with lymphoid malignancies on a BTKi and antihypertensive drug(s) and with at least 3 months of follow-up data were sorted into 2 groups: those diagnosed with HTN before BTKi initiation (prior-HTN), and those diagnosed with HTN after BTKi initiation (de novo HTN). Generalized estimating equations assessed associations between time varying mean arterial pressures (MAPs) and individual anti-HTN drug categories. Of 196 patients included in the study, 118 had prior-HTN, and 78 developed de novo HTN. Statistically significant mean MAP reductions were observed in patients with prior-HTN who took β blockers (BBs) with hydrochlorothiazide (HCTZ), (−5.05 mmHg; 95% confidence interval [CI], 10.0 to −0.0596; P = .047), and patients diagnosed with de novo HTN who took either an angiotensin converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB) with HCTZ (−5.47 mmHg; 95% CI, 10.9 to −0.001; P = .05). These regimens also correlated with the greatest percentages of normotensive MAPs. Treatment of HTN in patients taking a BTKi is challenging and may require multiple antihypertensives. Patients with prior-HTN appear to benefit from combination regimens with BBs and HCTZ, whereas patients with de novo HTN appear to benefit from ACEi/ARBs with HCTZ. These results should be confirmed in prospective studies.

Abstract A041202 (NCT01886872) is a phase 3 study comparing bendamustine plus rituximab (BR) with ibrutinib and the combination of ibrutinib plus rituximab (IR) in previously untreated older patients with chronic lymphocytic leukemia (CLL). The initial results showed that ibrutinib-containing regimens had superior progression-free survival (PFS) and rituximab did not add additional benefits. Here we present an updated analysis. With a median follow-up of 55 months, the median PFS was 44 months (95% confidence interval [CI], 38-54) for BR and not yet reached in either ibrutinib-containing arm. The 48-month PFS estimates were 47%, 76%, and 76% for BR, ibrutinib, and IR, respectively. The benefit of ibrutinib regimens over chemoimmunotherapy was consistent across subgroups of patients defined by TP53 abnormalities, del(11q), complex karyotype, and immunoglobulin heavy chain variable region (IGHV). No significant interaction effects were observed between the treatment arm and del(11q), the complex karyotype, or IGHV. However, a greater difference in PFS was observed among the patients with TP53 abnormalities. There was no difference in the overall survival. Notable adverse events with ibrutinib included atrial fibrillation (afib) and hypertension. Afib was observed in 11 patients (pts) on BR (3%) and 67 pts on ibrutinib (18%). All-grade hypertension was observed in 95 pts on BR (27%) and 263 pts on ibrutinib (55%). These data show that ibrutinib regimens prolong PFS compared with BR for older patients with treatment-naïve CLL. These benefits were observed across subgroups, including high-risk groups. Strikingly, within the ibrutinib arms, there was no inferior PFS for patients with abnormalities in TP53, the highest risk feature observed in CLL. These data continue to demonstrate the efficacy of ibrutinib in treatment-naïve CLL.

Abstract Monoclonal B-cell lymphocytosis (MBL) progresses to chronic lymphocytic leukemia (CLL) requiring therapy at 1% to 5% per year. Improved prediction of progression would greatly benefit individuals with MBL. Patients with CLL separate into 3 distinct epigenetic subtypes (epitypes) with high prognostic significance, and recently the intermediate epitype has been shown to be enriched for high-risk immunoglobulin lambda variable (IGLV) 3-21 rearrangements, impacting outcomes for these patients. Here, we employed this combined strategy to generate the epigenetic and light chain immunoglobulin (ELCLV3-21) signature to classify 219 individuals with MBL. The ELCLV3-21 high-risk signature distinguished MBL individuals with a high probability of progression (39.9% and 71.1% at 5 and 10 years, respectively). ELCLV3-21 improved the accuracy of predicting time to therapy for individuals with MBL compared with other established prognostic indicators, including the CLL international prognostic index (c-statistic, 0.767 vs 0.668, respectively). Comparing ELCLV3-21 risk groups in MBL vs a cohort of 226 patients with CLL revealed ELCLV3-21 high-risk individuals with MBL had significantly shorter time to therapy (P = .003) and reduced overall survival (P = .03) compared with ELCLV3-21 low-risk individuals with CLL. These results highlight the power of the ELCLV3-21 approach to identify individuals with a higher likelihood of adverse clinical outcome and may provide a more accurate approach to classify individuals with small B-cell clones.

INTRODUCTION Richter transformation (RT) is the histologic transformation of CLL/SLL into an aggressive lymphoma, most commonly diffuse large B-cell lymphoma (DLBCL). We previously reported an incidence of 0.5% per year of RT in patients with newly diagnosed CLL and an incidence of 1% per year following initiation of therapy (Parikh, BJH, 2013). It is unknown whether these incidences have changed in the novel agent era. METHODS We identified patients with previously untreated CLL/SLL in the Mayo Clinic CLL Database who were seen within 12 months of diagnosis. Only patients with biopsy-proven DLBCL with histopathology confirmation at Mayo Clinic were included. Cumulative incidence methodology was used to display the time to development of RT with death as a competing risk (both from time of initial CLL/SLL diagnosis, and from start of CLL directed therapy). We defined the time period prior to February 2014 (FDA approval of ibrutinib for CLL) as the pre-novel agent era and the time period starting February 2014 as the novel agent era. Using Cox regression analysis, we compared the incidence of RT, both after initial CLL diagnosis and after initiation of CLL therapy, between patients diagnosed with CLL in the pre-novel agent era versus the novel agent era. RESULTS Between 1/2000 and 4/2023, 3347 patients were seen at Mayo Clinic. The median age at diagnosis was 65 years (range 21-97 years), and 66% patients were male. Baseline characteristics of patients at the time of CLL and RT diagnoses between the two treatment eras were similar, including distance in miles of primary residence from Mayo Clinic ( Table 1). The median follow-up for the entire cohort was 5.6 years (9.5 years for the pre-novel agent era cohort and 3.2 years for the novel agent era cohort). A total of 82 patients were diagnosed with RT (69 in the pre-novel agent era and 13 in the novel agent era). Overall, the 1-, 5- and 10-year cumulative incidence rates for RT from the time of CLL/SLL diagnosis were 0.6% (95% CI: 0.4-0.9%), 1.8% (95% CI: 1.3-2.3%), and 3.0% (95% CI: 2.4-3.8%), respectively. Among the patients who received CLL/SLL therapy (n=1382), the 1-, 5-, and 10-year incidence rates for RT from the time of treatment start were 0.3% (95% CI: 0.1-0.8%), 3.3% (95% CI: 2.4-4.5%), and 4.9% (95% CI: 3.7-6.5%), respectively. Among 1981 patients diagnosed with CLL/SLL in the pre-novel agent era, the 1- and 5-year incidence rates for RT from the time of diagnosis were 0.6% (95% CI: 0.4-1.1%) and 2.1% (95% CI: 1.5-2.8%), respectively. Among 1366 patients diagnosed with CLL/SLL in the novel agent era, the 1- and 5-year incidence rates following CLL/SLL diagnosis were 0.5% (95% CI: 0.2-1.1%) and 1.1% (95% CI: 0.6-2.2%), respectively. The RT incidence from time of CLL/SLL diagnosis was not significantly different between the pre-novel agent and novel agent era patients (hazard ratio [HR] 1.6, 95%CI 0.9-3.0, p=0.13). Patients who received CLL/SLL treatment in the pre-novel agent era (n=926) and novel agent era (n=456) had subsequent 1-year incidence rates of RT of 0.3% (95% CI: 0.1-1.0%) and 0.2% (95% CI: 0.0-1.8%), respectively. The 5-year incidence rates for these groups were 3.7% (95% CI: 2.6-5.2%) and 1.9% (95% CI: 0.7-5.0%), respectively. Although an increased risk of RT after CLL/SLL directed therapy was observed in the pre-novel agent era compared to the novel agent era (HR 2.4, 95%CI: 0.9-6.1, p=0.07; Figure 1), this did not cross the threshold of statistial significance. Among patients diagnosed in the pre-novel agent era who developed RT (n=69), CLL treatment prior to RT included: none, n=19 (28%); chemoimmunotherapy only, n=36 (52%); and novel agent, n=14 (20%; includes 11 treated with prior chemoimmunotherapy also). In contrast, among patients diagnosed in the novel agent era who developed RT (n=13), CLL treatment prior to RT included: none, n=8 (62%); and novel agent, n=5 (38%). CONCLUSIONS Patients who received CLL/SLL directed treatment appear to have a lower risk of RT in the novel agent era. This may be driven by the fact that none of the patients who developed RT in the novel agent era received chemoimmunotherapy. Whether novel agent use for CLL/SLL suppresses the development of RT or avoids the risk associated with cytotoxic chemotherapy-induced clonal evolution requires further study. Although limited by length of follow-up, our findings challenge the notion that RT incidence will increase in parallel with CLL patients living longer with novel agent treatments.

Background: Monoclonal B-cell lymphocytosis (MBL) is a precursor condition to chronic lymphocytic leukemia (CLL) characterized by the presence of circulating monoclonal B-cell populations in the peripheral blood of otherwise healthy individuals. MBL increases risk of several adverse clinical outcomes, including development of hematological malignancies. Mosaic chromosomal alterations (mCAs) are a form of clonal hematopoiesis which affect large segments of DNA and include gains, losses, and copy number neutral loss of heterozygosity events. mCAs can be detected from SNP-array genotyping. Prior work has suggested mCAs as a potent risk factor for hematological malignancies. However, the nature of the relationship between mCAs and MBL is unknown. We sought to determine 1) the association between mCAs and MBL and 2) their combined effect on risk of hematological malignancies, in a large, prospectively collected, and well-annotated cohort of individuals with known MBL, mCA, and clinical outcomes. Methods: We examined the prevalence of mCA in a cohort of individuals from the Mayo Clinic Biobank who were screened for MBL using a sensitive eight-color flow cytometry assay. We classified MBL by the size of the clone relative to total B-cells, with high count (HC) defined as those individuals who had a percent clonal B-cell count ≥85% out of total B-cell count and low-count (LC) otherwise. Autosomal mCAs were detected from whole blood DNA collected at the time of MBL screening using a SNP-array-based assay. The MoChA pipeline was used to identify mCA events. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CIs). To examine incident hematological cancers, we identified and validated all hematological cancers through medical record abstraction using the International Statistical Classification of Diseases codes. Cox regression was used to calculate hazard ratios (HR) and 95% CIs, stratified by MBL status or autosomal mCA status. All models were adjusted for age and sex. Results: We evaluated the prevalence of autosomal mCA in 3,764 individuals (792 individuals with MBL of whom 64 had HC-MBL and 2,972 without MBL). The prevalence of autosomal mCA was 7.7% ( n=288) with a median mCA cell fraction of 0.09 (range 0.01-1). Among those with and without MBL overall, the prevalence of mCA was 15.5% ( n=123) and 5.6% ( n=165), respectively (Figure 1). Compared to those without an autosomal mCA, individuals with an autosomal mCA were 2.4-fold more likely to have MBL (95% CI 1.9-3.2; P =2.0x10 -11; Figure 1). CLL-associated mCA (i.e., those commonly altered in CLL including trisomy 12 and deletion of 6q, 11q, 13q, and 17p) were present in 42 individuals, of whom 38 (90.5%) had MBL overall and 30 (71.4%) individuals had HC-MBL. Compared to those without a CLL-associated mCA, individuals with a CLL-associated mCA were 539.39-fold more likely to have HC-MBL (95% CI 190.3-1975.9; P <2.0x10 -16) and 6.7-fold more likely to have LC-MBL (95% CI 2.0-26.5; P =0.0031; Figure 1). Next, we evaluated the relationship between MBL and mCA with risk of developing a hematologic malignancy in 3,009 individuals with follow-up data (median 10 years). Forty-one individuals developed an incident hematological malignancy (68.3% lymphoid). When we evaluated the effect of autosomal mCA stratified by MBL status we observed elevated but non-significant risk of hematological malignancies (Figure 2). When we evaluated the effect of MBL stratified by autosomal mCA status we observed elevated and significant increased risk of developing a hematological malignancy (Figure 2). Conclusions: We found that the presence of an autosomal mCA is highly associated with having MBL. In particular, >90% of CLL-associated mCA carriers had MBL with ~7- and ~540-fold increased association for LC- and HC-MBL, respectively. These results suggest that almost all individuals in large biobanks with CLL-associated mCAs have undiagnosed MBL. However, not all individuals with MBL have an mCA. When evaluating risk of hematological malignancies, we found autosomal mCAs had >2-fold elevated but non-significant effects when stratified by MBL status while MBL remained a significant risk factor regardless of autosomal mCA status. Collectively these results support that MBL remains an important risk factor and suggests that mCAs hold potential to identify a subgroup of individuals at greater risk of developing a hematological malignancy.

Scoring systems designed to improve the accuracy of prognostication in chronic lymphocytic leukemia (CLL) usually rely on discretized/dichotomic values of the clinical and biological variables. Here we analyzed the immunophenotypic and (immuno)genetic profiles of 2,243 CLL patients with Rai stage 0-I-II, by applying unsupervised machine learning methods, elaborating prognostic factors as continuous variables, to identify novel relationships and interactions likely missed in conventional hierarchical models. The study included an internal training cohort (n=863) and two external validation cohorts provided from Cardiff University/Brighton Medical School (n=455) and the Mayo Clinic (Rochester, MN; n=925). Primary endpoint was Time to First Treatment (TTFT). Laboratory-based markers evaluated in the training cohort were: surface antigen expression by flow cytometry (as % of positive cells) of CD20, FMC7, CD49d, CD49c, CD38, CD23, CD43, CD22, ZAP-70; chromosomal aberrations (% of nuclei with abnormal signal) del13q, tris12, del11q and del17p,; mutational status of TP53 by NGS as % VAF; IGHV mutational status as % mutation. 420/863 cases received treatment (median 46 months). Univariate Cox regression identified FISH del11q, del17p and tris12, TP53 and IGHV mutations, CD38 and CD49d expression, as features associated with TTFT (p-value <0.001). An unsupervised k-means algorithm partitioned cases into 6 clusters (C1-C6), as selected by the elbow method, and centroid analysis evaluated which feature most contributed to each cluster (Panel A): C1 (n=275) was heavily IGHV-mutated (mutation range 4.5-22.0%) with low representation of all other features; C2 (n=208) was mostly IGHV-unmutated/intermediate (up to 4.5%) in the absence of other features; C3 (n=169) were CD49d-expressing cases (>50% expression), with low representation of other features, except CD38; C4 (n=127) were cases with trisomy 12 (>28%), also expressing CD49d and CD38; C5 (n=48) were cases with highly clonal del11q (44-98%) and IGHV-unmutated, mutually exclusive with TP53 disruption; C6 (n=34) contained TP53-disrupted cases with high mutation burden (VAF 36-97%) and/or del17p (range 40-96%), with low importance of all the other features. Of note, alternative use of either TP53 mutation or del17p as defining features of cluster 6 did not result in changes of the classification accuracy. Clinically, clusters 3-4-5-6 showed median TTFT of 26, 22, 9, 5 months respectively whereas median TTFT was not reached for clusters 1-2. Hierarchical agglomerative clustering aggregated the 6 clusters in 3 major risk tiers: high (C5-6), intermediate (C2-3-4) and low (C1), with median TTFT of 7, 45 and not reached, respectively (Panel B). Multivariate Cox analysis (MVA) with Rai staging demonstrated that the 3-tier risk score contributed significantly and independently to TTFT estimate (p<0.0001; Harrel's c-index=0.71). To classify novel patients, we designed a hierarchical algorithm, based on each clusters' estimated risk and according to calculated cut-off, i.e. C6, del17p/ TP53>36%; C5, del11q>43%; C4, tris12>28%; C3, CD49d>50%; C2, IGHV≤4.5%; C1, the remaining patients with IGHV>4.5%. This approach was highly robust by an internal training-validation bootstrap (cluster classification accuracy = 0.96±0.02). External samples from two independent cohorts were classified into the six clusters using the cut-offs defined by the hierarchical classifier, and then aggregated into the 3-risk tiers. The classifier performed robustly in both cohorts, again adding independent prognostic information to Rai stage in MVA, with p<0.0001 and c-index of 0.76 for Cardiff/Brighton cohort and p<0.0001 and c-index of 0.74 for Mayo cohort. In conclusion, our machine-learning-driven, laboratory-based classification identifies clusters of patients who at higher risk of requiring early treatment, independent of clinical staging, and may help to better identify patients who may benefit from early treatment or more frequent disease surveillance. For some features, TP53 or IGHV mutational status, our unsupervised approach selected cut-offs not recapitulating the canonical ones, envisioning different biological activities in the TTFT setting. For example, TP53 mutations were clinically relevant only if present in the vast majority of the clone , suggesting the involvement of specific activities of the TP53 mutant in this context.

INTRODUCTION We aim to identify factors that impact outcomes of venetoclax for patients with CLL treated in routine practice at a tertiary center. We report on the use of venetoclax in the most frequently encountered disease scenarios: first-line, relapsed/Bruton tyrosine kinase inhibitor (BTKi)-naïve, and relapsed/BTKi-exposed. METHODS We identified patients who received venetoclax therapy for CLL (between 4/2012-4/2023) from the Mayo Clinic CLL Database. Undetectable measurable residual disease (uMRD) was defined as <1 CLL cell per 10,000 leukocytes using 8-color flow cytometry on peripheral blood (PB) or bone marrow (BM). Overall survival (OS) was defined as the time from venetoclax start until date of death or last known to be alive. Treatment-free survival (TFS) after venetoclax was defined as the time from venetoclax start until the earliest of date of next treatment, or death. Kaplan-Meier was used to display OS and TFS. Multivariable Cox proportional hazards regression models were used to estimate associations of factors with time-to-event outcomes. RESULTS A total of 155 patients received venetoclax: firstline therapy (in combination with obinutuzumab, n=55) and relapsed CLL (n=100; 17 had relapsed/BTKi-naïve CLL, and 83 had previously received BTKi [55 with progression after BTKi], relapsed/BTKi-exposed). The median follow-up for the cohorts of first-line therapy, relapsed/BTKi-naïve, and relapsed/BTKi-exposed was 12.9 months, 37.0 months, and 27.6 months, respectively. Baseline characteristics at the time of venetoclax initiation for all patients are shown in Table 1. The median TFS for the overall cohort was 39.0 months. The median OS was 54.6 months. Among patients treated with venetoclax as first-line therapy (n=55), the 2-year TFS ( Figure 1) and 2-year OS rates were both 91%. MRD testing was performed in 28 patients and was uMRD in 23 (82%) patients (only PB assessed, n=7; only BM assessed, n=2; PB and BM assessed, n=14). Detectable MRD was identified in 3 patients, and 2 patients had discordant results (PB uMRD and BM detectable disease). Among patients treated with venetoclax in the relapsed/BTKi-naïve setting (n=17), the 2-year TFS rate was 73% ( Figure 1) and the 2-year OS rate was 100%. MRD testing was performed in 7 patients and was uMRD in all 7 (100%) (only PB assessed, n=3; only BM assessed, n=2; PB and BM assessed, n=2). The median time to first uMRD result was 14.1 months. Among relapsed/BTKi-exposed venetoclax-treated patients (n=83), the median TFS was 26.9 months ( Figure 1), and the median OS was 39.4 months.In this subgroup, the median TFS for patients with (n=55) and without (n=28) prior disease progression on prior BTKi were 22.3 and 42.3 months, respectively. Median TFS with venetoclax monotherapy (n=30) was 24.0 months, venetoclax in combination with rituximab (n=37) was 26.9 months, and venetoclax in combination with obinutuzumab (n=16) was 39.0 months. BTKi-exposed patients that were chemotherapy-naïve (n=27) and chemotherapy-exposed (n=56) had median TFS of 29.1 and 24.0 months, respectively. MRD testing was performed in 28 patients and was uMRD in 16 (57%) patients (only PB assessed, n=9; only BM assessed, n=2; PB and BM assessed, n=5). Detectable MRD was identified in 11 patients, and 1 had discordant results (PB uMRD and BM detectable disease). The median time to first uMRD result was 11.5 months. TP53 disruption, unmutated IGHV genes, older age, complex karyotype (CK; defined as more than 3 chromosomal aberrations on CpG stimulated karyotype), and disease progression on prior BTKi were associated with shorter TFS in the overall cohort on univariate analysis. TP53 disruption, older age, CK, and disease progression on prior BTKi were associated with shorter OS in the overall cohort on univariate analysis. Multivariable analysis was performed by including only those patients where all variables significant in univariable analysis were available (OS model n=65, TFS model n=53). In these models, only CK was significantly associated with shorter TFS (HR 8.5; 95%CI 2.5-29.1; P<0.001) and shorter OS (HR 4.1; 95%CI 1.2-14; P=0.03). CONCLUSIONS Patients with BTKi-exposed CLL, particularly those with prior disease progression on BTKi, had worse outcomes. Our study identified CK as one of the most important baseline predictors of adverse TFS and OS in the overall cohort of patients, supporting karyotype assessment for prognostication prior to venetoclax treatment.

Introduction: Recent studies have shown that clonal B cells derived from patients with chronic lymphocytic leukemia (CLL) have altered histone modification landscapes compared to B cells from healthy controls. We have also shown that the BCR signaling pathway impacts the chromatin landscapes of CLL B cells, implicating the interaction between oncogenic signaling and epigenetic machinery in CLL (Wang et al., Blood Cancer J. 2022; Wang et al., Cancer Res (2023) 83 (7_Supplement): 4725). However, the majority of the chromatin studies in CLL concentrate on the active enhancers associated with oncogene expression; there is a very limited understanding of lost enhancers in CLL: 1) What is the function of lost enhancers; 2) How are these lost enhancers maintained in CLL; 3) Can these lost enhancers be detected at the precursor state of CLL, monoclonal B-cell lymphocytosis (MBL); 4) if this aberrant chromatin signature affected by CLL treatment. Methods: We systematically mapped the genome-wide histone modification profile (histone marks: H3K4me1, H3K4me3, H3K9me3, H3K27ac, H3K27me3, and H3K36me3) in 22 previously untreated CLL samples, 6 high count MBL samples, and peripheral blood B cells from 6 healthy donors by Cleavage Under Targets and Tagmentation (CUT&Tag). We also analyzed B cells from 8 patients before and after one year of continuous Bruton tyrosine kinase inhibitor (BTKi) ibrutinib treatment. The genomic regions with a decrease in both H3K4me1 and H3K27ac enrichment (analyzed by Deseq2, fold change > 1, p < 0.01) were named lost enhancers. We used CRISPR-mediated epigenetic editing to study the role of gained H3K9me3 on EBF1 gene expression. Results: Compared to normal B cells, the lost enhancers (genomic regions defined by decreased enrichment of H3K4me1 and H3K27ac) in CLL B cells showed increased heterochromatin marks (H3K9me3 and H3K27me3) enrichment. Importantly, these enhancers are associated with genes downregulated in CLL and involved in the immune response, indicating the source of epigenetic impact on impaired immunity in CLL. We further found that this aberrant chromatin structure also exits in clonal B cells from the precursor stage of MBL, implicating the importance of epigenetic silencing in CLL evolution. In particular, we found the lost enhancer signature across the gene locus EBF1, an important transcription factor that defines B cell lineage and regulates normal B cell activation. Using CRISPR-mediated epigenetic editing, we demonstrated that the gain of H3K9me3 is sufficient to silence the EBF1 enhancer and its gene expression. Finally, we found that while BTKi ibrutinib treatment partially inhibits the CLL-gained enhancers in some patients, it failed to restore the lost enhancers in CLL B cells. Conclusion: In summary, we have elucidated an epigenetic silencing mechanism that may contribute to the loss of physiological function of normal B cells, which places the clonal B cells at a selection advantage during CLL evolution and increases the risk of serious infections in CLL and MBL ( Fig 1).

Introduction: The treatment of chronic lymphocytic leukemia (CLL) has evolved over the past two decades, from finite duration chemoimmunotherapy (CIT) to an era of targeted agents such as Bruton Tyrosine Kinase (BTK) inhibitors, BCL2 inhibitors, and anti-CD20 monoclonal antibodies. Little is known about how initial management strategy (watch and wait [W&W] vs. treatment) at diagnosis impacts patient reported outcomes (PROs) in CLL survivors over time, particularly in light of therapeutic advances over the last decade. Methods: Newly diagnosed CLL patients were prospectively enrolled within 9 months of diagnosis in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER1) study between 9/1/02 and 6/30/15, and within 6 months of diagnosis in the MER Phase 2 (MER2) study between 7/1/15 and 5/1/20. PROs were measured using the Functional Assessment of Cancer Therapy-General scale (FACT-G), which generates Physical (PWB, range 0-28), Social/Family (SFWB, 0-28), Emotional (EWB, 0-24), and Functional Well-Being (FWB, 0-28) scores and a Total Score (0-108), with higher scores indicating better WB. In both cohorts, FACT-G was measured at enrollment and at years 1, 2, and 3 after diagnosis, while the timing of later assessments varied slightly by study (MER1: 6 yrs and 9 yrs; MER2: 5 years). We employed a generalized linear mixed model to evaluate change from baseline between groups with groups defined by each participant's initial management strategy (W&W vs. upfront systemic treatment). Initial management strategy was determined by the treating physician. The study was approved by the Mayo Clinic IRB. Results: A total of 722 patients were identified in MER1 and 256 patients in MER2. The median age at diagnosis was 63 years in MER1 and 66 years in MER2 (P<0.05); 66% were male in MER1 and 68% were male in MER2 (P>0.05). More patients initiated upfront systemic therapy in MER1 (n=380, 53%) than in MER2 (n=110, 43%). Other baseline clinical characteristics were similar between the cohorts ( Table 1). There was no statistical difference in the mean FACT-G total score at baseline between MER1 (85.0, SD 12.5) and MER2 (85.2, SD 12.7). In the MER1 cohort, statistically significant lower baseline QOL scores were observed in patients initiating upfront treatment compared to W&W on the FACT-G total score (P=0.002) as well as the PWB (P=0.01), EWB (P=0.03), and FWB (P=0.006) subscales. We also observed that over time, EWB and FWB scores were consistently lower amongst those treated upfront compared to W&W. Further, EWB scores steadily improved over time regardless of initial management ( Figure 1), whereas the FWB and PWB subscales were largely stable over time. Mean SFWB scores dropped amongst patients who received upfront therapy at 3 years, which then steadily improved through year 9 but did not recover back to baseline. SFWB scores were stable among W&W patients. In the MER2 cohort, no statistically significant differences in any of the baseline QOL scores were noted between the W&W vs. initial treatment groups. Despite this, we observed consistently lower mean EWB scores among patients who initiated upfront treatment compared to W&W pts over time. No other statistically significant differences in mean QOL scores over time were observed between groups. Similar to MER1, we did observe a drop in mean SFWB scores amongst patients initiating upfront therapy at 3 years of follow-up that remained decreased at year 5. Conclusion: This is the largest longitudinal study of CLL survivors that spanned over 18 years and two treatment eras. We demonstrated that in the novel treatment era, QOL metrics were similar between initial management strategies (W&W vs. treatment), suggesting that initial management had little to no impact on QOL at baseline or over time. In contrast, in the CIT era, we observed differences in QOL metrics at baseline between groups. In both treatment eras, patients with CLL who required upfront therapy demonstrated a decrease in SFWB from baseline at year 3 of follow-up, and the mean EWB scores were lowest at baseline regardless of initial management strategy (W&W vs. upfront treatment) or treatment era (CIT vs. novel agents), but steadily improved over time. These findings suggest opportunities for interventions to improve QOL in patients with CLL, and additional analyses are ongoing. Further follow-up in the era of novel agents is needed.