Rabe, Kari G.

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.

Background: Richter Transformation (RT) refers to the development of an aggressive lymphoma in the setting of chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL). Small molecule inhibitors (SMI), such as the Bruton tyrosine kinase inhibitors (BTKi) and BCL2 inhibitors (BCL2i), have revolutionized the treatment of CLL/SLL. Prior studies evaluated outcomes of patients (pts) with RT in an era where chemoimmunotherapy (CIT) was typically used to treat CLL/SLL. Now that SMI are standard of care, fewer pts with CLL/SLL receive CIT, therefore we sought to determine which variables predict survival in pts who developed RT without prior CIT exposure for CLL/SLL. Methods: We conducted an international multicenter retrospective study of pts from 11 academic centers. Pts with RT occurring as large B-cell lymphoma who did not previously receive CIT (e.g., fludarabine, cyclophosphamide, bendamustine, chlorambucil) for their CLL/SLL were included. We collected pt, disease, and treatment (tx) characteristics. RT was categorized into 3 groups: “Concurrent RT,” defined as RT and CLL/SLL diagnosed simultaneously (within 3 months [mos]); “RT w/o prior CLL/SLL tx,” defined as RT and CLL/SLL diagnosed >3 mos apart with the CLL/SLL never treated; and “RT with prior non-CIT tx for CLL/SLL” defined as having received prior CLL/SLL tx. Overall survival (OS) was measured from RT diagnosis (Dx) and estimated using the Kaplan-Meier method. Cox regression model was used to identify prognostic factors associated with OS. Results: 242 pts were identified. At CLL/SLL Dx, 66% of pts had unmutated IGHV, 37% had del17p/ TP53 disruption, and 20% had trisomy 12. At RT Dx, 56% of the RT with prior non-CIT tx for CLL/SLL group had a del17p/ TP53 disruption versus 27% for the concurrent RT group and 18% for the RT w/o prior CLL/SLL tx groups. Median time from CLL/SLL Dx to RT Dx was 47 mos (range 3-394) for RT w/o prior CLL/SLL tx and 46 mos (range 4-218) for RT with prior non-CIT tx for CLL/SLL. Pts with prior non-CIT tx for CLL/SLL received a median of 1 (range 1-5) prior tx. 86% of pts previously received a BTKi or a BCL2i for tx of CLL/SLL (54% BTKi only, 5% BCL2i only, 27% both). The remaining 14% had been treated with an anti-CD20 monoclonal antibody (MoAb), steroids, lenalidomide, radiation, or alemtuzumab. 85% of pts developed RT while on active tx with SMI. Median age at RT Dx was 69 years (range 37-92). At RT Dx, 38% and 21% of pts had del17p/ TP53 disruption, and trisomy 12 in CLL/SLL tissue, respectively. At RT Dx, there was no difference in highest SUV on PET (median 16, range 2.9-65.6), LDH (median 325, range 103-8162, ULN range 180-271), largest lymph node in diameter (median 4.9 cm, range 0-17.1), Ki-67 (median 80%, range 5%-100%), rate of GCB subtype (27%), presence of MYC translocation (20%), or CLL/SLL and RT clonal relationship (81%) for those pts tested amongst the 3 groups ( Table). The most common 1 st line tx for RT was CIT with or w/o radiation and/or steroids (76%), followed by CIT plus SMI (13%), SMI single agent or combination (7%), and other (4%, which includes anti-CD20 MoAb). Median follow-up from RT Dx was 42.3 mos; the median OS for the entire cohort was 25.8 mos (95% CI: 16.8-49.1). Pts with RT after non-CIT tx for CLL/SLL had a significantly worse OS (median 8.2 mos [95% CI: 5.6-14.3]) than pts with concurrent RT (median 46.3 mos [95% CI: 23.8-96.9]) and RT w/o prior CLL/SLL tx (median 63.5 mos [95% CI: 28.3-70.3]) ( Figure). From an exploratory univariable analysis, we found RT after non-CIT tx for CLL/SLL compared to concurrent RT (HR 2.32 [95% CI: 1.55-3.46]), RT Dx age (HR for 5-year increase 1.14 [95% CI: 1.05-1.23]), del17p/ TP53 disruption (HR 2.27 [95% CI: 1.45-3.56]), and LDH (HR for 2-fold increase 1.37 [95% CI: 1.20-1.58]) were prognostic for worse OS. Conclusions: This is the largest cohort of pts who developed RT having received only SMI with no CIT for their CLL reported, thus reflecting the current treatment landscape. Despite being CIT-naïve, pts in our series who received tx for their CLL/SLL and then developed RT had a short OS, underscoring the need to develop better therapies for these pts. In contrast, as in other published analyses (Wang Haematologica 2020), pts w/o prior tx for CLL/SLL had more favorable outcomes, with a median OS of approximately 5 years. Further analysis to determine PFS and OS by tx for RT for pts who have been treated with SMI for their CLL/SLL is ongoing.

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.

Presence of deletion17p (del17p) in frontline (1L) and relapsed/refractory (R/R) CLL has been associated with shorter time to treatment initiation, early relapse after therapy and inferior overall survival (OS) (Mato et.al., Haemotologica 2022). However, there exists notable clinical heterogeneity in terms of outcome within this group (Tam et.al., Blood 2009, Stephens et.al; AJH 2015). Previous reports in 1L and R/R CLL have identified percentage of del17p nuclei by fluorescence in situ hybridization (FISH), having TP53 mutations on the second allele, copy number alterations and complex karyotype (CK) as additional risk factors within the del17p population (Yu et al., CCR 2017; Baliakas et al., Blood 2019). Here we profile a cohort of wait and watch (W&W, n=14), 1L treated (n=36) and R/R CLL (n=3) patients by whole genome sequencing (WGS) and RNA sequencing (RNAseq) to gain insights into genomic factors and gene expression pathways contributing to the high-risk nature of del17p across the spectrum of CLL patients. Using the Mayo Clinic CLL Database, we identified 53 patients with del17p (identified by FISH testing in routine clinical practice at diagnosis) who had stored peripheral blood mononuclear cells (PBMCs) available. RNA and DNA from matched peripheral blood (tumor) and DNA from CD19- (germline) samples at diagnosis (n=50) and relapse (n=3) was extracted using Qiagen RNeasy (RNA) and Purene (DNA) kits and subjected to RNAseq and WGS respectively. Additional FISH assays (del11q, trisomy 12, del13q) and CK by chromosomal banding analysis (CBA) were performed. Baseline clinical characteristics, time to first treatment, types of treatment, and survival were abstracted from the CLL Database. The Mayo Clinic IRB approved this study. Copy number analysis identified deletions in 3p, 4p, 8p, 9p, 9q and gains in 2p, 8q, 15q and 17q chromosomal lesions occurring in patients. We explored the association of clinical and genetic factors, and genomic instability signatures with survival to identify high-risk features in del17p patients. Both total number of deletions by WGS and number of chromosomal aberrations by CBA as a continuous variable were significantly associated with unfavorable OS in the entire cohort (n=53) in a univariate analysis (Hazard ratio, HR= 2.29, p= 0.00054 and HR= 2.33, p=0.0054 respectively). However, there was no significant association of number of gained genes by WGS or aneuploidy scores with survival (HR=0.74, p=0.37). In patients with CK data (n=41), deletion burden, but not gains, were highly correlated to chromosomal aberrations by CBA. Using cutpointr package for identifying deletion threshold corresponding to CK≥ 3, we defined patients with deletion burden ≥ 514 as enhanced-high risk (EHR; median OS = 4.83 years) compared to the group with high-risk (HR; median OS=9.94 years) (p=0.025). There was no significant difference in number of deleted genes between WW and treated patients. The EHR group is enriched in patients with high clonal cell fraction of del17p by FISH, unmutated IGHV and CK≥ 3. The EHR group remains significant in a multi-variable analysis with IGHV-mutation status, ZAP70, and age (HR: 5.16, 95% CI: 1.04 - 25.6, p=0.04). Mutational analysis identified 35 known CLL driver mutations with TP53 (86%), ATM (15%), NOTCH1(13%), MGA (13%), CHD2 and MED12(11%) as the top mutated genes. Total number of driver genes mutated was not significantly different between EHR and HR subgroups, but the EHR subset was significantly enriched with mutations in NOTCH1 (21%), and MGA (21%) as compared to no mutations in the HR group in these genes. Deleted genes (≥ 514) was prognostic in bi-allelic TP53 aberration patients as well (p=0.021) Differential expression gene-set enrichment analysis indicated a significant enrichment of DNA repair, cellular response to DNA damage and downregulation of inflammatory responses in the EHR group. CK is a known prognostic marker which includes both chromosomal losses and gains. In this small cohort of del17p we were able to show the prognostic value of deletions only, but not gains, as captured by WGS. Using genome wide sequencing, we identify increasing genomic deletions as a feature of enhanced-high risk del17p. While deletion burden cut-offs identified here are specific to our research method and require further validation in additional independent cohorts, EHR subgroup remained significant after adjusting for other known prognostic variables .

Introduction: Chronic lymphoproliferative disorder of natural killer cells (CLPD-NK) is a rare form of large granular lymphocytic leukemia. The clinical characteristics and outcomes of patients (pts) with CLPD-NK in the literature are limited. We aim to describe the patient characteristics, laboratory features, and clinical outcomes of CLPD-NK by reporting on, to our knowledge, the largest single-center cohort of CLPD-NK in the United States. Methods: We retrospectively identified Mayo Clinic pts with CLPD-NK, defined per the WHO 5 th edition and 2022 International Consensus Classification. We conducted chart review to collect clinical and laboratory data. Overall survival (OS) was plotted using Kaplan-Meier. Time to first treatment (TTFT) was displayed using cumulative incidence methodology accounting for competing risk of death. Univariable Cox regression models investigated associations between patient characteristics and outcomes (OS and TTFT). Results: We identified 45 pts who were diagnosed with CLPD-NK between 1997 and 2022. The median age at diagnosis was 69 years (range: 25-90) and 32 pts (71%) were male. At the time of diagnosis, 10 pts (22%) had a concomitant autoimmune disorder and 9 (20%) had laboratory evidence of a serologic abnormality (monoclonal gammopathy, rheumatoid factor, etc.). In addition, 10 pts (22%) had a prior diagnosis of a hematologic malignancy and 12 (27%) had a prior diagnosis of a solid tumor. Clinically, 4 pts (9%) had B symptoms, 15 (33%) had splenomegaly, and 13 (29%) were transfusion dependent. At diagnosis, median hemoglobin (hgb) was 10.2 g/dL, with 25 pts (56%) showing a hgb <11g/dL and 8 (18%) demonstrating a hgb <8 g/dL. The median platelet count was 202x10^9/L, with 12 pts (27%) demonstrating a platelet count <150x10^9/L. The median white blood cell count was 5.8x10^9/L. The median absolute neutrophil count (ANC) was 1.2x10^9/L, with 29 pts (64%) demonstrating an ANC <1.5x10^9/L. The median absolute lymphocyte count (ALC) was 3.8x10^9/L, with 20 pts (44%) demonstrating an ALC >4x10^9/L. The median absolute abnormal NK cell count per flow cytometry was 2.2x10^9/L. Flow cytometry revealed killer cell immunoglobulin-like receptor (KIR) restriction in all pts, with 27 (61%) lacking KIR expression and 17 (39%) demonstrating KIR restriction in CD158a, CD158b, and/or CD158e. CD7 expression was present in 42 pts (93%; 31% dim/partial), CD8 expression was present in 22 (49%; 42% d/p), CD56 expression was present in 17 (39%; 23% d/p), CD57 expression was present in 26 (59%; 46% d/p), CD94 expression was present in 33 (77%; 7% d/p), and NKG2A expression was present in 21 (50%; 5% d/p). Bone marrow examination (n=30) revealed that the median number of NK infiltrate was 11% (range: 5-40%) of cellularity, with 20 demonstrating an intrasinusoidal infiltrative pattern. The median follow-up was 41 months (mo), and 23 pts required at least one line of treatment. Treatment indications included anemia (39%), neutropenia (4%), bi/pancytopenia (39%), lymphocytosis (4%), or other (13%). The median TTFT was 32 mo (95% CI 2-93 mo). Hgb <11g/dL was associated with a shorter TTFT (9.7 vs 187 mo); however, neutropenia and thrombocytopenia were not associated with TTFT. For first-line treatment, in addition to frequent steroid use, 16 pts (70%) received a methotrexate-based regimen, 6 (26%) received a cyclophosphamide-based regimen, and 1 (4%) received a cyclosporine-based regimen. The median TTFT was not reached. Of those who required systemic treatment, 9 pts required a second-line therapy. The median OS from diagnosis was 162 mo (95% CI: 84-not estimable, NE), with 33 pts alive at last follow-up (Figure 1a). Increasing age (HR 1.2, 95% CI 1.1-1.3, p=0.002) and lack of CD94 expression (HR 5.6, 95% 1.3-25.0, p=0.02) were associated with worse OS. In the 23 pts who required treatment, the median OS from treatment initiation was 84 mo (95% CI: 52-NE) (Figure 1b). Conclusions: In this large cohort of 45 pts, the majority had neutropenia and anemia, and approximately half required systemic treatment. Estimated median OS from diagnosis exceeded a decade for the entire cohort and was seven years after treatment initiation. Anemia was associated with a shorter TTFT, while increasing age and lack of CD94 expression were associated with worse OS. This rare disease warrants additional investigations to understand optimal management and improve outcomes, especially for those requiring treatment.

INTRODUCTION Chronic lymphocytic leukemia (CLL) and SLL represent a single entity in current classifications per the WHO-HAEM5 and ICC. Both are characterized by malignant B cells with an identical immunophenotype, but they are distinguished by the predominant site of disease burden, such as peripheral blood for CLL and nodal compartments for SLL. Most clinical trials and retrospective studies involving CLL/SLL do not separate out SLL patients. In this study, we report reasons for treatment initiation and outcomes in a cohort of SLL patients seen at our institution. METHODS Using the Mayo Clinic CLL Database, we retrospectively identified patients with SLL who received first line therapy between 12/1995 and 2/2023. Patients were classified as having SLL if they met at least one of the following criteria for diagnosis: 1) presence of lymphadenopathy or splenomegaly (either by exam or scans) and an absolute clonal B cell count of < 5 x 109/L; or 2) presence of lymphadenopathy or splenomegaly (either by exam or scans) and an absolute lymphocyte count of < 11 x 109/L (if absolute clonal B cell count was not available). Although the presence of anemia (hemoglobin < 11 g/dL) and thrombocytopenia (platelets < 100 x 109/L) are considered as exclusion criteria for a diagnosis of SLL, patients were included if these cytopenia were due to autoimmune hemolytic anemia (AIHA) or immune thrombocytopenia (ITP) with < 50% bone marrow involvement by SLL (bone marrow biopsy confirmed). Patients treated for SLL with extranodal involvement were also included. Indications for SLL treatment were abstracted by chart review and included one of the following: 1) symptomatic or progressive lymphadenopathy and/or splenomegaly, 2) autoimmune cytopenias, 3) extranodal/direct organ infiltration; and 4) other (e.g., cryoglobulinemia, paraneoplastic, non-infiltrative renal disease such as minimal change disease). Overall survival (OS) was defined as the time from treatment start until date of death or last known to be alive. Treatment-free survival (TFS) was defined as the time from treatment start until the earliest of date of next treatment, or death. Kaplan-Meier method was used to display OS and TFS. Cox proportional hazards regression models were used to estimate associations of factors with time-to-event outcomes. RESULTS We identified 182 patients with SLL who received first line treatment. The median age at treatment start was 66 years, and 132 (73%) were male. Unmutated IGHV was identified in 60/82 (73%) patients and peripheral blood fluorescent in situ hybridization (FISH) assessed pre-treatment start showed del13q in 10%, trisomy 12 in 43%, del11q in 7%, del17p in 8%, and no abnormalities in 33%. TP53 disruption (del17p and/or TP53 mutation) was present in 7/89 (8%) patients prior to first therapy. The median follow-up after diagnosis was 6.9 years. The indications for treatment were grouped as follows: symptomatic or progressive lymphadenopathy/splenomegaly (n=130), extranodal/direct organ infiltration (n=30), AIHA/ITP (n=10), and other (n=12). For the entire cohort, the median TFS was 2.9 years and the median OS was 12.3 years. The median TFS by the treatment indication groups were 3.3, 5.1, 1.6, and 5.3 years, respectively (Figure 1). The median OS by treatment indication groups were 11.1, 10.5, 8.8, and 19.9 years, respectively. Among the 160 patients with symptomatic or progressive lymphadenopathy/splenomegaly or extranodal disease, the treatments administered include: mAb alone (n=35), chemotherapy alone (n=33), chemoimmunotherapy (n=57), targeted agent (n=33; Bruton tyrosine kinase inhibitor [BTKi] based n=28; venetoclax-based n=3, BTKi + venetoclax n=2), and other (n=2). The median TFS by treatment groups were 2.4, 2.0, 3.8, and 6.0 years (Figure 2), respectively (excluding the small “other” subgroup of 2 patients). The median OS for these groups were 9.5, 9.7, 15.0, and not evaluable, respectively. Only older age at treatment start predicted for shorter TFS (HR 1.02; 95%CI 1.002-1.038; p=0.03) and shorter OS (HR 1.05; 95%CI 1.03-1.07; p<0.01). CONCLUSIONS In this study of patients with SLL who received therapy, the most frequent indications for treatment included symptomatic lymphadenopathy and/or splenomegaly, followed by extranodal disease. Trisomy 12 was the most frequently detected abnormality on peripheral blood FISH testing. Treatment with novel agents achieved durable TFS and OS.

Abstract Background: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer. Methods: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes. Results: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88–1.22; MR-Egger OR, 0.89; 95% CI, 0.65–1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90–1.27; MR-Egger OR, 0.93; 95% CI, 0.67–1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample. Conclusions: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk. Impact: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.