Citations

Abstract MBL is a common pre-malignant condition characterized by circulating clonal B-cells with an absolute B-cell count <5x109/L and no lymphadenopathy, organomegaly, or cytopenias. MBL is the precursor to CLL. The incidence of cutaneous SCC in CLL is significantly higher compared to controls. The incidence of SCC in MBLs has not yet been determined. Study participants from the Mayo Clinic Biobank who had no prior history of hematologic malignancy, were 40 years or older, and were Olmsted County residents completed a health questionnaire and provided blood samples between 7/2009 and 12/2020. Stored peripheral blood mononuclear cells were screened for MBL using flow cytometry. We defined three MBL immunophenotypes: CLL-like MBL (CD5+, CD20dim), atypical MBL (CD5+, CD20+), and non-CLL-like MBL (CD5-, CD20+). MBL individuals were also classified by cell count into low-count MBL (LC-MBL) and high-count MBL (HC-MBL), with HC-MBL having a percent clonal B-cell count ≥85%. Data on newly diagnosed SCC was abstracted from the medical records, and prior history of skin cancer before sample collection was ascertained from patient questionnaires. Individuals were followed from sample date to the earliest of SCC, death, loss to follow-up, progression, or 12/31/2020. Cumulative SCC incidence was adjusted for competing risk of death. Cox regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CIs) adjusted for age and sex. A total of 5,470 participants were screened for MBL and included 949 (17%) with LC-MBL, 63 (1%) with HC-MBL, and 4,458 (81%) controls (negative for MBL). Individuals with HC-MBL (median age 75 years) or LC-MBL (median age 73 years) were significantly older (P<0.001) than controls (median age 66 years). There were 41 (65%) males among HC-MBL, 447 (47%) among LC-MBL, and 1,551 (35%) among controls. Prior skin cancer history was highest among HC-MBLs (N=17, 27%) or LC-MBLs (N=236, 25%) compared to controls (N=767, 17%). After a median follow-up of 18 months (range 0-138), 154 of the 5,470 individuals were identified to have incident SCC following MBL screening. At least one SCC was observed in 3 individuals with HC-MBL, 33 individuals with LC-MBL, and 118 controls. The 5- and 10-year cumulative incidence of SCC in individuals with MBLs was 7% and 16%, respectively; control estimates were 4% and 8%, respectively. However, after adjusting for age and sex, we observed no evidence of an association between MBL and risk of incident SCC (HR=0.95, CI=0.65-1.40, P=0.80), nor when we stratified individuals by age or by sex (all P>0.05). In the largest MBL screening cohort to date, individuals with MBL do not have an increased risk of incident SCC compared to controls. In contrast to individuals with CLL, these individuals with screening MBL do not need increased dermatologic examination for skin cancer, which is important given the high prevalence of MBL (18% of the population above age 40). Citation Format: Ahmed Nadeem Ansari, Sara J. Achenbach, Sameer A. Parikh, Geffen Kleinstern, Aaron D. Norman, Kari G. Rabe, Connie E. Lesnick, Timothy G. Call, Janet E. Olson, James R. Cerhan, Neil E. Kay, Celine M. Vachon, Esteban Braggio, Curtis A. Hanson, Tait D. Shanafelt, Christian L. Baum, Susan L. Slager. Incidence of squamous cell carcinoma (SCC) in a large screening cohort of monoclonal B-cell lymphocytosis (MBL) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5256.

Abstract Chronic lymphocytic leukemia (CLL) is characterized by multiple copy number abnormalities (CNVs) with prognostic value. Identifying these structural variations is central to defining CLL pathogenesis, risk stratification, and therapeutic approaches. Fluorescence in situ hybridization (FISH) is the clinical gold standard in detecting prognostic CNVs in CLL. However, next-generation sequencing (NGS) techniques have become more readily available for clinical genomic applications and can also be used to identify CNVs. Here we present bioinformatic methods to accurately identify CNVs in CLL using NGS data. We used the CNV-calling algorithm PatternCNV to detect clinically relevant CNVs: deletion 17p13 [del(17p)], deletion 11q23 [del(11q)], deletion 13q14 [del(13q)], and trisomy 12. PatternCNV was run on 2274 samples (1500 somatic and 774 germline samples) from six different sequencing batches, screened using a targeted sequencing panel that covers all exons of 59 recurrently CLL mutated genes and additional amplicons covering the minimal affected regions of relevant CNVs. To correct for potential batch effects, PatternCNV was initially run to quantify exon coverage behavior without the chromosomes containing recurrent CNV events, 11, 12, 13, and 17. Principal component analyses and correlation matrices were analyzed, grouping the samples into four distinct clusters that contain similar exon coverage patterns. Samples in each of the four clusters were then independently re-run through PatternCNV using all chromosomes. Visual analysis of CNV plots revealed a bias in normalization. To correct this, the log2ratios were corrected to center the log ratio on the median coverage. Sample noisiness was calculated from the difference in the median absolute deviation (DiffMAD) and samples with a DiffMAD score greater than 0.3 were excluded. All CNV analyses were blinded to clinical FISH results. The effectiveness of our CNV calling was evaluated in 522 CLL patients who had FISH conducted within three months of the sample date. We excluded samples with low tumor metrics identified by FISH (less than 20% of cells with either del(17p), del(11q), trisomy 12 or del(13q)). When we compared our CNV analyses with the FISH data, we found high concordance 99.6% for del(17p), 97.5% for del(11p), 99.1% for trisomy 12, and 93.7% for del(13q). N=46 total discordant pairs were identified, with the highest discordance for del(13q), N=28, followed by del(11q), N=12. These novel bioinformatic methods allow for accurate detection of CNVs across NGS sequencing batches. The high concordance in detecting CNVs between targeted NGS and the gold standard of FISH, suggest NGS is an accurate tool for calling CNVs in CLL. Further, NGS can infer clinically relevant CNVs in genomic locations not targeted by FISH. Citation Format: Chantal E. McCabe, Erik Jessen, Daniel R. O'Brien, Julia E. Wiedmeier-Nutor, Susan L. Slager, Esteban Braggio. Identifying copy number variations in chronic lymphocytic leukemia using targeted next generation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3352.

AbstractIdentifying biomarkers associated with disease progression and drug resistance are important for personalized care. We investigated the expression of 121 curated genes, related to immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs) responsiveness. We analyzed 28 human multiple myeloma (MM) cell lines with known drug sensitivities and 130 primary MM patient samples collected at different disease stages, including newly diagnosed (ND), on therapy (OT), and relapsed and refractory (RR, collected within 12 months before the patients’ death) timepoints. Our findings led to the identification of a subset of genes linked to clinical drug resistance, poor survival, and disease progression following combination treatment containing IMIDs and/or PIs. Finally, we built a seven‐gene model (MM‐IMiD and PI sensitivity‐7 genes [IP‐7]) using digital gene expression profiling data that significantly separates ND patients from IMiD‐ and PI‐refractory RR patients. Using this model, we retrospectively analyzed RNA sequcencing (RNAseq) data from the Mulltiple Myeloma Research Foundation (MMRF) CoMMpass (n = 578) and Mayo Clinic MM patient registry (n = 487) to divide patients into probabilities of responder and nonresponder, which subsequently correlated with overall survival, disease stage, and number of prior treatments. Our findings suggest that this model may be useful in predicting acquired resistance to treatments containing IMiDs and/or PIs.

Abstract Herein, we present the long-term follow-up of the randomized E1912 trial comparing the long-term efficacy of ibrutinib–rituximab (IR) therapy to fludarabine, cyclophosphamide, and rituximab (FCR) and describe the tolerability of continuous ibrutinib. The E1912 trial enrolled 529 treatment-naïve patients aged ≤70 years with chronic lymphocytic leukemia (CLL). Patients were randomly assigned (2:1 ratio) to receive IR or 6 cycles of FCR. With a median follow-up of 5.8 years, median progression-free survival (PFS) is superior for IR (hazard ratio [HR], 0.37; P < .001). IR improved PFS relative to FCR in patients with both immunoglobulin heavy chain variable region (IGHV) gene mutated CLL (HR: 0.27; P < .001) and IGHV unmutated CLL (HR: 0.27; P < .001). Among the 354 patients randomized to IR, 214 (60.5%) currently remain on ibrutinib. Among the 138 IR-treated patients who discontinued treatment, 37 (10.5% of patients who started IR) discontinued therapy due to disease progression or death, 77 (21.9% of patients who started IR) discontinued therapy for adverse events (AEs)/complications, and 24 (6.8% of patients who started IR) withdrew for other reasons. Progression was uncommon among patients able to remain on ibrutinib. The median time from ibrutinib discontinuation to disease progression or death among those who discontinued treatment for a reason other than progression was 25 months. Sustained improvement in overall survival (OS) was observed for patients in the IR arm (HR, 0.47; P = .018). In conclusion, IR therapy offers superior PFS relative to FCR in patients with IGHV mutated or unmutated CLL, as well as superior OS. Continuous ibrutinib therapy is tolerated beyond 5 years in the majority of CLL patients. This trial was registered at www.clinicaltrials.gov as #NCT02048813.

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 < 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 <0.5x10 9/L or percent clonal B-cell count <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 <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<0.01) and significantly increased with age (p<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 <65 (P=0.77) or age >= 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 >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 >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% (<10% VAF, n=21) and in MBL was 13.5% (<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 < 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 Venetoclax (VTX) is a selective small-molecule inhibitor of BCL-2 that exhibits antitumoral activity against MM cells presenting lymphoid features and those with translocation t(11;14). Despite its impressive clinical activity, VTX therapy for a prolonged duration can lead to drug resistance. Therefore, it is important to understand the underlying mechanisms of resistance in order to develop strategies to prevent or overcome resistance. In the present study, we established four VTX resistant human myeloma cell lines (HMCLs) from four sensitive HMCLs, including three with t(11;14), in culture with a stepwise increase in treatment dose with VTX. To identify the molecular basis of acquired VTX resistance, whole exon sequencing (WES), mRNA-sequencing (mRNAseq), and protein expression assays were performed in the four isogenic VTX-sensitive/resistant HMCLs and three MM patients with samples collected before VTX administration and after clinical resistance to the drug. Compared with sensitive cell lines and patient samples collected before VTX administration, mRNAseq analysis identified downregulation of BIM and upregulation of BCLXL in both resistant cell lines and MM cells from relapse patients. Other transcriptional changes detected included upregulation of AURKA, BIRC3, BIRC5, and IL32. Enrichment analysis of differentially expressed genes suggested involvement of PI3K and MAPK signaling, likely associated with cytokines, growth factors (EGF, FGF and IGF family members), and receptor tyrosine kinase (EGF and FGF). Western blot analysis was performed to compare BCL2 family expression in resistant cell lines versus sensitive cell lines and it showed upregulation of BCL2 survival members (such as MCL-1 and BCLXL), and downregulation of pro-apoptotic BH3 members (such as BIM and PUMA). BIM expression was completely lost in one resistant cell line, and introduction of exogenous BIM into this cell line enhanced VTX sensitivity. Interestingly, BCL2 was upregulated in some resistant cell lines generated after a long-term treatment with VTX, suggesting BCL2 expression level may not be suitable as a marker of VTX sensitivity for acquired resistance. Unlike in CLL, BCL2 mutations were not identified through WES in any resistant cell lines or primary patient sample harvested after relapse. While 8 genes were mutated in two resistant samples , no clear mutational pattern emerged . Based on the above, we further tested some specific inhibitors in in vitro or ex vivo cell models to help understanding resistant mechanism and identify strategies to overcome VTX resistance. We found that inhibition of MCL-1, with the compound S68345, substantially enhanced VTX sensitivity in three resistant HMCLs and in primary cells from one relapsed MM patient. A BCLXL inhibitor (A155463) only significantly enhanced VTX sensitivity in one resistant cell line after co-treatment with VTX. Co-treatment of the other three resistant cell lines with VTX, S68345 and A155463 resulted in the most synergistic anti-myeloma activity, suggesting those cell lines are co-dependent on MCL-1, BCLXL, and BCL2 for survival, although they are more dependent on MCL-1. We also found that inhibition of PI3K signaling, IGF1, RTK (EGF and FGF) and AURKA significantly increased VTX sensitivity, partially through downregulation of MCL-1, and BCLXL, and upregulation of BIM. Conventional anti-MM drugs such as dexamethasone, bortezomib and lenalidomide, were shown to have little activity on augmenting VTX sensitivity in most resistant cell lines. In summary, we find that acquired resistance to VTX in MM is largely associated with BCL2 family regulation, including upregulation of survival members such as MCL-1, BCLXL, BCL2, and downregulation of pro-apoptotic members, especially BIM. Our study also indicates that upstream signaling involved in BCL2 family regulation during acquired resistance is likely related to cytokine, growth factor, and/or RTK-induced cell signaling such as PI3K. Co-inhibition of MCL-1, or BCLXL, as well as the upstream PI3K, RTK (FGF and EGF), IGF-1 mediated signaling were effective in overcoming VTX resistance. Disclosures Fonseca: Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Juno: Consultancy; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction: CLL is the most common leukemia in the U.S. and characterized by constitutively activated BCR signaling pathway, which has a crucial role both in normal B cell development and B cell malignancies. The biological events controlled by BCR signaling in CLL are not fully understood. Active BCR signaling is mediated through activation of the downstream kinase Bruton tyrosine kinase (BTK), which has become a key therapeutic target to inhibit BCR signaling for the treatment of B cell malignancies. We reasoned that blood samples from CLL patients before and after Bruton's tyrosine kinase inhibitors (BTKi) treatment would provide a valuable resource in the study of BCR modulation of epigenetic machinery in leukemic B cells. Methods: We obtained blood samples from CLL patients before and after BTKi ibrutinib treatment and used them to study BCR signaling regulated genes (n = 8 patients, after one-year of continuous ibrutinib treatment). Gene expression profile of CLL B cells from patients before and after one-year ibrutinib treatment was analyzed by mRNA-seq seq. Genome wide Histone H3K4me1, H3K27ac, and H3K4me3 profile was determined by CUT&tag. Chromatin accessibility was determined by ATAC-seq. Putative enhancers were deleted by CRISPR-Cas9. Results: Notably BTKi treatment led to the reduction of expression in genes associated with single strand DNA deamination (Fig. 1A) The BTKi regulated genes involved in this process mainly contains the APOBEC3 family genes (APOBEC3C, APOBEC3D APOBEC3F, APOBEC3G, APOBEC3H), and their expression levels showed a consistent reduction in CLL B cells from ibrutinib treated patients (Fig. 1B). We then confirmed the reduction of APOBEC3 levels by western blot in CLL B cells from four patients before and after one-year of continuous ibrutinib treatment (Fig. 1C). We hypothesized that BCR signaling regulates APOBEC3 expression by modifying the local chromatin around the APOBEC3 gene cluster and performed CUT&Tag to map the histone marks including H3K4me1, H3K4me3, H3K27ac and ATAC-seq. This approach permitted us to examine the chromatin accessibility of the leukemic cells from CLL patients before and then after one-year of continuous ibrutinib treatment. We found that BTKi treatment caused reductions of H3K4me1, H3K27ac, and chromatin accessibility at these regions in ibrutinib treated patients ( 7 of the 8 samples tested), however, there was no change of the promoter marker H3K4me3 (Fig. 1D), which indicated that BTKi treatment leads to APOBEC3 genes expression change via the regulation of their enhancer regulation (APOBEC3 enhancers, AEs). Based on the enrichment of H3K4me1, H3K27ac and chromatin accessibility, AE regions of the ibrutinib native samples contain three active enhancer modules, we designated these modules as AE1, AE2, and AE3 (Fig. 1D). To assess the functional activity of these enhancers on the expression of APOBEC3 genes, we investigated the consequence of deletion of each one of these AEs in the MEC1 cell line by CRISPR-Cas9. PCR analysis showed very robust deletion of AE1, AE2, and AE3 (Fig. 1E). Both deletion of AE1 or AE2 reduced the expression of APOBEC3 genes, while AE3 deletion suppressed the expression of APBEC3C, APOBEC3D, APOBEC3F and APOBEC3G, but not APOBEC3H, which is in closest proximity to AE3 (Fig. 1F, G). Together, we identified the BCR signaling dependent enhancers that regulate APOBEC3 expression. Since APOBEC3 deaminates ssDNA, we reasoned that APOBEC3 in CLL B cells may also contribute to replication stress and DNA instability. We found that MEC1 cells have a high level of spontaneous DNA damage in the S phase cells (Fig. 1H). which is associated with replication stress. However, AE2 deleted MEC cells showed decreased γH2Ax in the S phase cells (Fig. 1H). Edu/PI assay showed that MEC1 cells had a fraction of S phase cells with low Edu incorporation during S phase, also indicating DNA replication stress (Fig. 1I); however, AE2 deletion greatly increased Edu incorporation (Fig. 1I). Taken together, these data suggest that increased expression of APOBEC3 may be involved in DNA replication stress and drives genomic instability in malignant B cells. Conclusion: We demonstrate a novel mechanism for BTKi suppression of APOBEC3 expression via direct enhancer regulation in CLL B cells, implicating BCR signaling as a potential regulator of leukemic genomic instability. Figure 1 Figure 1. Disclosures 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: 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; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sunesis: Research Funding; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees; Acerta Pharma: Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Research Funding; Tolero Pharmaceuticals: Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Janssen: 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; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction Monoclonal B-cell lymphocytosis (MBL), monoclonal gammopathy of undetermined significance (MGUS), and clonal hematopoiesis (CH) are three common premalignant hematological conditions. MBL is a precursor to chronic lymphocytic leukemia (CLL), MGUS is a precursor to both multiple myeloma (MM) and Waldenström macroglobulinemia (WM), and CH is a possible precursor to myeloid disease. All three conditions are independently associated with aging. Furthermore, there is evidence that all three conditions are associated with increased risk of infection. The relationship among these conditions, the prevalence with which they co-exist, and the association between the number of premalignant conditions and risk of infection is unknown. Methods Study participants from the Mayo Clinic Biobank, a large-scale biorepository of adult patients, provided a peripheral blood sample between 7/14/2009 to 06/18/2015. Individuals were screened for MBL using eight-color flow cytometry capable of detecting clonal B-cells to the 0.005% level. MGUS was screened using a matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) assay. CH was screened using a targeted sequencing panel of 42 CH-associated genes with at least 1000x read depth and the ability to detect variant allele fractions down to 1%. All study participants resided in Olmsted County, Minnesota, the county that includes Mayo Clinic. Serious infection, defined as hospitalization due to infection, was abstracted from medical records from the time of sample collection through 12/31/2020 or date of death. The medical record abstractor collecting data was blinded to condition. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between each of the conditions. Cox regression was used to estimate hazard ratios (HR) and 95% CI to assess the association between number of premalignant conditions with risk of infection. All analyses were adjusted for age at sample collection and sex. Results A total of 407 individuals (55% male, median age 68 years) were screened for all three conditions. The prevalence was 19.9% (n=81) for MBL, 14.5% (n=59) for MGUS, and 25.6% (n=104) for CH. When we evaluated the association between each of the conditions, we found no evidence of an association between the prevalence of CH and risk of MBL (OR=0.79, 95% CI: 0.43-1.42), CH and risk of MGUS (OR=1.24, 95% CI: 0.66-2.27), or MGUS and risk of MBL (OR=1.32, 95% CI:0.66-2.52). The prevalence of co-existence of these conditions was assessed by grouping individuals based on their condition or conditions (Figure 1A). We found that 197 (48.4%) study participants had at least one premalignant condition and 10.6% had two or more. Individuals with 2 or more conditions were the oldest (median age 74 years), followed by 1 condition (median 69 years) and 0 conditions (median 67 years, P-trend <0.001). Sex was not significantly different across the groups, with 55%, 58%, and 47% male for 0, 1, and 2 conditions, respectively. Medical record abstraction identified 97 serious infection events in 80 individuals. Medical records were not available on 7 individuals. The most common infections were urinary tract (20 events), pneumonia (14 events), and cellulitis (14 events). Median follow-up time was 10.6 years (range 0.1-11.5 years). Compared to individuals with no condition, MBL (HR=1.75, 95% CI: 1.06-2.88) and MGUS (HR=1.75, 95% CI: 1.04-2.93) were associated with increased risk of serious infection. We found no evidence of an association with CH (HR=1.15, 95% CI: 0.71-1.87). When grouped by the number of conditions, we observed a significant trend (P-trend <0.01, Figure 1B) in risk of serious infections by the number of conditions: individuals with 1 condition had a 35% increased risk of infections (HR=1.35, 95% CI:0.83-2.20) and individuals with 2 or more conditions had a 2.6-fold increased risk (HR=2.58, 95% CI: 1.37-4.84). Discussion At least one premalignant condition was present in nearly 50% of the study population, with CH being the most prevalent. No evidence of a relationship between any of the three conditions was observed. However, we found increased susceptibility to serious infections as the number of conditions increased with individuals with at least two conditions had 2.6-fold increased risk of infections. 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. Dispenzieri: Takeda: Research Funding; Alnylam: Research Funding; Pfizer: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding. Murray: Mayo Clinic: Other: Has received patents for the Mass-Fix technology which has been licensed to the Binding Site with potential royalties.. Kumar: Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Oncopeptides: Consultancy; Beigene: Consultancy; Novartis: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Roche-Genentech: Consultancy, Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Kay: TG Therapeutics: Research Funding; Morpho-sys: 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; 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; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; 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; Acerta Pharma: Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Research Funding; Genentech: Research Funding; Celgene: 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; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees. Shanafelt: Genentech, Pharmacyclics: Research Funding.