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Abstract Introduction: Monitoring minimal residual disease (MRD) is critical in multiple myeloma (MM) to predict outcomes and guide therapy. Traditional bone marrow (BM) aspirates for MRD detection are invasive and limited by sample quality. We therefore explored cell-free DNA (cfDNA) fragmentation as a less invasive alternative for MRD detection. Methods: We performed 30-40X whole-genome sequencing (WGS) on peripheral blood cfDNA from 45 MM patients, collecting baseline (n = 45) and follow-up samples (n = 98) from eight Canadian sites (M4 and IMMAGINE studies) and one U.S. site (SPORE study), plus 25 healthy controls. Samples were collected post-induction therapy (n = 13), 100 days post-autologous stem cell transplantation (ACST, n = 36), and during maintenance therapy (n = 49). Multiparameter flow cytometry (MFC) MRD testing was conducted on 77 samples. We evaluated insert size metrics and MM-specific chromatin accessibility (from Ordoñez et al., 2020) using Griffin (described in Doebley et al., 2022). Results: Baseline samples had a higher proportion of short fragments (20-150 bp) than follow-up samples (p = 0.0037) and healthy controls (p = 0.019). A fragment score (FS) based on weighted fragment distribution (per Vessies et al., 2022) was highest at baseline (mean = -0.384), followed by MRD-positive (mean = -0.495) and MRD-negative (mean = -0.550) samples. MRD-negative FS was lower than baseline samples (p < 0.001) but higher than healthy controls (mean = -0.639; p = 0.016). Baseline samples had lower coverage at MM-specific chromatin regions than MRD timepoints (p < 0.05; baseline mean = 0.982; MRD-positive = 0.989; MRD-negative = 0.994; healthy = 0.993), suggesting higher transcriptional activity pre-treatment. MRD-negative samples had higher coverage than MRD-positive cases (p < 0.01). Using logistic regression, we assessed the predictive performance of FS and coverage at MM-specific sites. FS alone achieved an AUC of 0.632 (sensitivity [SN] 39.4%, specificity [SP] 92.1%, accuracy [AC] 67.6%). Coverage yielded an AUC of 0.681 (SN 75.8%, SP 60.5%, AC 67.6%). Combining FS and coverage at MM sites improved performance (AUC = 0.734; SN 48.5%, SP 92.1%, AC 71.8%). We next evaluated whether adjusting the BM tumor cell percentage cutoff by MFC could improve performance. At 0.017% (1 in 5, 789 cells) for MRD positivity, the combined model’s accuracy reached 83% with an AUC of 0.757 (SN 17%, SP 97%). Conclusions: cfDNA fragmentomic analysis achieved high specificity (92-97%) in detecting MRD, providing a less invasive alternative to facilitate serial monitoring. While sensitivity remains an area for improvement, the high specificity of cfDNA makes it valuable for confirming MRD negativity and reducing invasive procedures. Future efforts aim to enhance sensitivity by integrating additional fragmentomic features. Citation Format: Dor D. Abelman, Jenna Eagles, Aimee Wong, Saumil Shah, Stephanie Pedersen, Stephenie Prokopec, David S. Scott, Sarah Bridges, Darrell White, Irwindeep Sandhu, Kevin Song, Esteban Braggio, Alli Murugesan, Anthony Reiman, Suzanne Trudel, Trevor J. Pugh. Tumor-independent monitoring of minimal residual disease in multiple myeloma using cfDNA fragmentomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3250.

Introduction The major primary genetic events in multiple myeloma (MM) include translocations of immunoglobulin heavy chain (IgH) and a hyperdiploidy (HRD) karyotype (Kuehl, 2002). The main drivers of pathogenesis in HRD MM are unknown (Fonseca, 2003), therefore further research is needed in this subgroup. Translational research in HRD MM has relied on analysis of genomic and transcriptomic alterations, however changes at the genome and transcriptome level do not always correlate directionally with protein abundance. Because proteins are considered the main and final effectors of most cellular processes, proteogenomics can improve our understanding of the drivers of HRD MM. We employed it here to identify potential biomarkers in HRD MM and determine whether any given trisomy may lead to a selective advantage. Methods: Protein and mRNA were extracted from CD138+ cells from 47 HRD MM and 14 t(11;14) MM samples. RNA-seq data was processed using MAPR-Seq pipeline using default parameters and gene-wise raw counts were extracted for each sample. Proteomics data was processed using Specranaut and protein-wise intensities were extracted for each sample. A generalized linear model configured with negative binomial distribution was used to analyze gene-wise count data in edgeR software. Genes with an adjusted p-value of <=0.05 were considered significantly differentially expressed. Protein intensity data were normalized using trimmed mean of M-values and log2 transformed. A generalized linear model configured with Gaussian distribution was used to compare protein intensities between the groups. Proteins with an adjusted p-value <= 0.05 were considered significantly differentially expressed. Two separate rank lists for protein and mRNA data were generated for pathway analysis. Results When comparing our small cohort of HRD MM patient samples to those with t(11;14) MM, out of approximately 16,600 total transcripts and 9,700 proteins evaluated we identified approximately 2,100 significantly variant transcripts and 500 significantly variant proteins in HRD MM. GSEA pathway analysis of the transcriptome and proteome revealed significantly upregulated translational and ribosomal processing pathways in HRD MM. Transcriptional MYC target pathways were also significantly upregulated. RNAseq revealed significantly higher expression of MYC (Rank 24.5, p=5.3E-6) in HRD MM. It is known that patients with t(11;14) MM have increased expression of CCND1 (Kuehl and Bergsagel, 2012). As expected, we found that the HRD MM subgroup had significantly lower expression of CCND1 compared to the t(11;14) MM subgroup (Rank -79.9, p=2.8E-21) and this was also true at the protein level (Rank -21.7, p=2.2E-5). The most significantly abundant protein in the HRD MM subgroup compared to t(11;14) MM was BAHD1, which is a protein that targets H3K27me3 and recruits transcriptional co-repressors (Fan, 2020). BAHD1 has been shown to repress erythroid genes and may recruit HDAC5, which plays a role in osteoblast differentiation (Rank 72.3, p=2.9E-20; Xu, 2021). The second most abundant protein in HRD MM was CKS2, which binds to the catalytic subunit of cyclin-dependent kinases (CDKs) and therefore positively regulates cell proliferation, invasion, and migration and has been shown to be implicated in tumor progression in several solid tumors (Rank 72.3, p=2.9E-20; You, 2015). TRIM proteins promote tumorigenesis and cancer development. TRIM14 mediates cancer development through mediating the JAK/STAT, PI3K/AKT, NFkB, and p53 pathways and was found to be significantly higher in HRD MM (Rank 70.6, p=9.0E-20; Huang, 2022). We also found increased protein abundance of LAMP5 in HRD MM (Rank 20.1, p=6.0E-5). LAMP5 may play an important role in MM progression and has been shown to be highly expressed in patients with mixed-lineage leukemia and is associated with a poor prognosis (Wang, 2023; Gracia-Maldonado, 2022). We identified many proteins (not all discussed here) that have not been well described in HRD MM and thus may be biomarkers/targets of disease pathogenesis of this subgroup. Conclusion Proteins are the final effectors of most cellular processes. Proteogenomics can improve our understanding of the drivers of HRD MM. Using a proteogenomic approach we identified proteins that may lead to selective advantage and/or pathogenesis of trisomies in HRD MM.

Background: Patients with relapsed/refractory multiple myeloma demonstrate an increasing incidence of extramedullary disease (EMD), noted in up to 30% of patients. Presence of EMD portends a markedly inferior prognosis with limited response to salvage therapies, including immune effector therapy. Understanding the molecular alterations in EMD is key to exploring novel therapeutic approaches but remains to be well-characterized. Here we describe the mutational landscape and biologic pathways with alterations in a large cohort of patients with EMD. Methods: We included 18 paraffin-embedded tumor tissues from true EMD biopsies (non-bone contiguous disease) for whole exome sequencing (WES); biopsies from paraskeletal/bone-contiguous soft tissue were not considered. Germline sequencing was performed utilizing cryopreserved peripheral blood mononuclear cells (PBMCs) at initial diagnosis from patients without evidence of circulating tumor cells. Additionally, we studied 12 CD138-sorted cryopreserved bone marrow (BM) aspirates collected at the time of development of EMD. Following standard DNA extraction and library construction practices, we performed WES on the EMD, BM and PBMC samples. Variant calling was performed using consensus calls observed in at least 2 out of the 3 callers (Mutect2, VarScan, Strelka2) and these were included for downstream analysis. Synonymous and low-impact variants were filtered. Pathway analysis was performed using the PanCancer driver gene sets and compared using Fisher's exact test. Results: The median age of patients with EMD was 65.6 years (range 46.2-86 years). Seven (39%) patients had de novo (previously untreated) EMD and 11 (61%) had secondary EMD (developed at relapse). The MAPK pathway was frequently mutated, most commonly involving NRAS (33%), KRAS (22%) and BRAF (22%) genes. These mutations were mutually exclusive. Alterations in TP53 were noted in 11% of EMD samples. Single nucleotide variants (SNVs) in epigenetic modulators were frequently noted in EMD, with the most prevalent alterations seen in ARID1A (33%), KMT2C (28%), EP300 (22%) and KMT2B (22%). Both ARID1A and EP300 are postulated to play a role in IRF4 regulation and plasma cell differentiation. Additionally, inhibition of EP300 can be a viable therapeutic strategy in MM through disruption of IRF4 signaling. We noted frequent mutations in the roundabout (ROBO) genes ROBO1 and ROBO2, observed in 22% and 28% of EMD samples, respectively. ROBO1 has been demonstrated to be necessary for myeloma cell adhesion to BM stromal and endothelial cells, with ROBO1 knockout MM cells infused in mice demonstrating a tendency for extramedullary dissemination. Similarly, variants in LRP1B, a frequently altered large putative tumor suppressor gene in the LDL receptor family, were noted in 33 % of EMD samples. Depletion of LRP1B has been demonstrated to promote anchorage-independent growth, cell migration and invasion in vitro. Notably, in the paired BM samples, no SNVs were noted in ARID1A, LRP1B, ROBO1 and ROBO2 genes (q-value not significant). In paired samples, among the shared mutations of interest, we noted expansion of the BRAF clone in 2 EMD samples [variant allele frequency (VAF) of 3% and 13% in BM and 58% and 29% in the paired samples, respectively]. An expansion of a shared KRAS mutation was noted in another EMD sample (VAF of 15% in BM and 29% in EMD). The frequently altered cellular pathways in EMD samples (n=18) included the MAPK pathway (89%), chromatin histone modifier pathway (83%), transcriptional regulation (83%), immune signaling (72%) and RNA abundance (72%). In the cohort of paired EMD and BM samples (n=24), notable enrichment was evident in EMD for MAPK pathway alterations [91% in EMD vs. 25% in BM q=0.02] and chromatin histone modifier pathway (83% in EMD vs. 42% in BM, q <0.01). Conclusions: Patients withEMD demonstrate a near universal presence of alterations in the MAPK pathway. Variants in chromatin modifiers regulating plasma cell differentiation and genes involved in BM stromal adhesion and homing of plasma cells were frequently noted, potentially facilitating the extramedullary spread of myeloma cells. Functional studies are needed to evaluate these hypotheses. Targeting EP300, ARID1A and downstream MEK/ERK inhibition as part of combination therapies may represent promising therapeutic approaches for further exploration in EMD.

Introduction Monoclonal antibodies (MoAbs) directed against CD38 are a mainstay in multiple myeloma (MM). Downregulation of CD38 expression is a resistance mechanism of antigenic escape, however, cell surface expression may return. While this tumor cell plasticity is likely driven by epigenetic mechanisms, it is unknown if MM cells can develop permanent resistance to anti-CD38 MoAb by acquiring genomic events leading to biallelic disruption of CD38 thus precluding re-treatment. Here, we report biallelic loss of CD38 as a recurrent mechanism of resistance, often via convergent evolution where distinct subclones are advantaged towards the same selective pressure. Methods We interrogated two sets of patients (Mayo Clinic; n = 31, Calgary University; n =19) with relapse following anti-CD38 MoAbs. Two additional cases of interest were selected from Heidelberg University. Whole genome sequencing (WGS, 60-100x) or whole exome sequencing (WXS), flow cytometry (flow), and bulk RNAseq were used to validate the effect of genomic events on CD38. Missense mutations predicted to affect CD38 MoAb binding were validated with site-directed mutagenesis of a CD38 plasmid, transduction into K562 MM cells, and CD38 MoAb binding assays. Results The prevalence of biallelic inactivation of CD38 at anti-CD38 MoAb relapse was 6% (3/50). An additional selected biallelic event was noted in the Heidelberg set. Each case is described in detail: MM-19 (Calgary) received daratumumab (Dara), lenalidomide, and dexamethasone (Dex), and then had a second exposure to Dara, following relapse. At this second relapse, WGS was performed on two independent extramedullary disease localizations in the liver. Both samples had unique, large deletions (>3 Mb) on chromosome 4 coupled with additional unique focal deletions involving CD38. Flow confirmed the presence of CD138+/CD38- plasma cell populations in both sites. A second case of convergent evolution was seen in a patient (Mayo) after seventh-line Dara/Len/Dex. WGS revealed a common deletion encompassing CD38. On the remaining allele, one subclone had a structural variation-mediated loss of CD38(cancer cell fraction; CCF 40%) while another harbored an L153H missense mutation (CCF 60%). Flow confirmed presence of two CD138+ clones; one with diminished CD38 expression. L153H was functionally validated to abrogate Dara binding, while maintaining CD38 expression. The third sample was collected after fifth-line Dara and sixth-line Dara/Bortezomib/Dex (Heidelberg) and represents another case of convergent evolution with a common loss of chromosome 4 and two subclones detected by WGS (P98Lfs*12, CCF 39% resulting in biallelic inactivation; R140G, CCF 61%). Bulk RNAseq revealed, in comparison to NDMM from CoMMpass, CD38 expression below the first decile. Notably, the R140G variant reduced binding affinity to Dara but not to Isatuximab (Isa). A fourth case (Mayo) relapsing after 6 months of Dara-based therapy similarly had a focal deletion of CD38 with an L18Sfs*16 frameshift deletion on the remaining allele associated with a 4.35-fold reduction in CD38 expression by RNAseq compared to pre-treatment. Interestingly, in 3/4 cases the CD38-negative clone emerged after a second exposure to anti-CD38 MoAb, suggesting emergence with the first exposure and then selection during the second. One other monoallelic event (C275Y; Heidelberg) was detected post-Isa. Copy number of the alternate allele is not known given targeted sequencing methodology, but the variant was seen to reduce binding affinity to both Dara and Isa. The prevalence of monoallelic loss of CD38 in newly diagnosed MM from MMRF CoMMpass was 7% (50/701) and two patients had mutations in CD38, but there were no cases of biallelic loss suggesting these events are driven by treatment pressure. Conclusion Distinction of the mode of CD38 loss is valuable for predicting outcome of re-challenge to anti-CD38 MoAbs. We see here an estimated 5-10% of anti-CD38 MoAb relapses are characterized by genomic antigen escape. Results here are in line with antigen escape to BCMA- and GPRC5D-directed immunotherapies (Lee et al, Nat Med 2023), suggesting that acquired antigen escape resulting from prolonged exposure to the selective pressures of immunotherapy is a recurrent mechanism of resistance in MM. Moreover, in select cases, non-overlapping resistance mutations provide rationale for switching agents at second exposure.

Background: Monoclonal B-cell lymphocytosis (MBL) is a common premalignant condition that can be classified into chronic lymphocytic leukemia (CLL)-type (CD5, CD23, and CD20 co-expression), and non-CLL-type (CD5-negative) based on flow cytometry immunophenotypes. CLL-type MBL is relatively well characterized with increased risk of hematologic (especially lymphoid) malignancies, serious infections, and decreased response to vaccinations. However, the prevalence, genetic predispositions, and clinical consequences of non-CLL-type MBL remain to be fully elucidated. Herein, we report findings in non-CLL-type MBL in a large screening cohort. Methods: We screened 10,330 individuals (40 years or older with no prior history of hematologic cancers) for MBL with highly sensitive flow cytometry, in the Mayo Clinic Biobank, with patients recruited from primary care-based clinics. Individuals were genotyped by Regeneron Genetics. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). Among those individuals residing in the 27 counties surrounding Rochester, Minnesota, we identified incident hematologic malignancies and melanomas using ICD codes and the Rochester Epidemiology Project, a population based medical records linkage system. All diagnoses were manually reviewed for confirmation. Cox regression was used to calculate hazard ratios (HR) and 95% CIs, adjusted for age and sex. Results: We identified 1,736 individuals with MBL and 8,341 individuals without MBL. Among those with MBL, 246 (14.2%) had non-CLL-type MBL as the major clone. The median age of non-CLL-type MBL was 77 years (range, 47-95). The prevalence of non-CLL-type MBL significantly increased with age, with 0.5% prevalence among those between 40 to 59 years of age, 2.3% prevalence among those between 60 and 79 years, and 7.8% prevalence in those 80 years of age or older. The prevalence of non-CLL-type MBL was significantly higher (P < .001) among males (3.1%) than females (2.0%). Using the genome-wide complex trait analysis tool, the heritability of non-CLL-type MBL was estimated to be ~26%, suggesting inherited genetics plays a role in etiology. To identify common inherited variants, we first evaluated the known CLL susceptibility variants, many of which also associated with CLL-type MBL. Among the 42 variants, none were statistically associated (all P > .05) with non-CLL-type MBL. We next conducted a genome-wide association study to identify novel variants, and the most significant variant resided in 10q26.3 (minor allele frequency [MAF]control = 0.7%, MAFMBL = 3.5%, OR = 6.4, 95% CI 3.6-11.3, P = 5.7 * 10-8). Among the 6,478 individuals (192 with non-CLL-type MBL and 6,286 without MBL) followed for incident hematologic malignancies, 36 developed lymphoid and 23 developed myeloid malignancies after a median of 4.2 years of follow up. Similar to CLL-type MBL, non-CLL-type MBL was associated with a 5.2-fold risk for lymphoid malignancies (95% CI 2.0-13.0, P < .001). Elevated but not significant risks were observed for myeloid malignancies (HR = 2.3, 95% CI 0.5-10.3, P = 0.27). The lymphoid malignancies developed by individuals with non-CLL-type MBL included diffuse large B-cell lymphoma, multiple myeloma, marginal zone lymphoma, and other low-grade lymphomas. We recently reported that individuals with CLL-type MBL are at higher risk for developing melanoma (HR = 1.9, 95% CI 1.3-2.8). Interestingly, non-CLL-type MBL had no evidence of an association with melanoma risk (HR = 0.4, 95% CI 0.1-1.8). We did not observe a significant difference in overall survival between individuals with non-CLL-type MBL vs those without MBL. Conclusions: In the largest screening cohort to date, we evaluated the etiology and clinical outcomes of non-CLL-type MBL, an understudied yet common condition that occurs in ~4 million adults in the United States. Non-CLL-type MBL is more common in men and older individuals, and evidence supports that inherited genetics plays an etiological role with 10q26.3 locus identified as a novel susceptibility locus. Individuals with non-CLL-type MBL had significantly increased risk of lymphoid malignancies, similar to CLL-type MBL, but, distinct from CLL-type MBL, no evidence of an increased risk of melanoma. Our study provides insights into the prevalence, genetic predispositions and clinical consequences of non-CLL-type MBL and how it differs from the classic CLL-type MBL.

Introduction: Multiple myeloma (MM) with t(11;14)(q13;q32), resulting in CCND1;IGH rearrangement, remains the only subset amenable to targeted therapy with the BCL2 inhibitor, Venetoclax. Nevertheless, not all patients with this translocation respond to treatment and some progress early after initial response. The mechanisms behind this clinical heterogeneity are largely unknown. Methods: To investigate mechanisms of heterogeneity in response to Venetoclax, we interrogated 44 whole genome sequencing (WGS) and 24 whole exome sequencing data from a training cohort including 33 patients with t(11;14) MM treated with Venetoclax at the Mayo Clinic. In a validation cohort, we extracted single nucleotide variants (SNV) and indels from 17 MM patients with t(11;14) with available RNAseq data treated with Venetoclax at Emory University. Results: In the training cohort the median number of lines of therapy was 4 and Venetoclax drug regimens included combinations with dexamethasone, proteasome inhibitors, anti-CD38 agents, and immunomodulatory agents. After a median follow up of 30 months, the median progression free survival (PFS) was 9 months, and 13 patients (39%) achieved a complete remission (CR) to therapy, while 9 (27%) were refractory (i.e. no response or progressed within the first 60 days). For our driver discovery analysis, we integrated SNV, indels, copy number variants (CNV) and structural variants (SV). Deletion 17p (del17p) or TP53 mutation, MCL1 gain/amp, and hyper-APOBEC did not correlate with PFS in our cohort (p=0.59, p=0.71, and p=0.05, respectively). In contrast, mutations involving the MAPK pathway (NRAS, KRAS, BRAF, FGFR3) displayed the strongest association with short PFS, which was retained after adjusting for del17p/TP53 mutations, and 1q gain/amp (HR: 4.76, CI: 1.8-12.9, p=0.002). The association between suboptimal response and resistance to Venetoclax in MM patients with MAPK pathway mutations (n=7) was also observed in our validation cohort from Emory University (p=0.046). This is in line with what has been reported in functional studies in acute myeloid leukemia, where RAS pathway promotes MCL1 under the selective pressure of Venetoclax (Zhang et al. Signal Transduct Target Ther 2022). While MAPK mutations explained most cases of refractory patients (86%), the mechanisms through which initially responding patients subsequently progressed remain unclear. To investigate this, we interrogated 10 patients with pre- and post-treatment samples collected at disease progression (PD). We identified genomic events acquired/selected at PD of genes involved in the anti-apoptotic MCL1/BCL2 and MAPK pathways in 6/10 cases. One patient was refractory to Venetoclax and had double minutes involving BCL2, which caused multiple focal amplifications (>30 copies), likely reflecting extrachromosomal DNA. Another patient progressed with an acquired, focal deletion on BCL2L11. In two patients, the progressing clones harbored mutations in MAPK pathways (one BRAF and the other WNK2). Two patients who relapsed after sustained CR had major branching evolution where a previously undetectable focal deletion of PMAIP1 (NOXA) was seen at PD. Importantly, one of these deletions was a biallelic event. Venetoclax sensitivity assays conducted on PMAIP1 knockout in the MM t(11;14) cell line, KMS12PE, demonstrated significantly reduced cell death across all drug concentrations (p<0.05), validating the loss of NOXA as a mechanism of resistance to Venetoclax in MM. Finally, similarly with what was previously reported in chronic lymphocyte leukemia treated with Venetoclax (e.g., Herling et al Nat Comm 2018), CDKN2A/B monoallelic and biallelic focal deletions were selected post-Venetoclax, at PD in 4 patients while TP53 SNV/deletions were selected from subclonal to clonal in 2 patients at PD. Conclusion: In this study, we demonstrate that mutations involving the MAPK pathway identify a population of MM patients with t(11;14)(CCND1;IGH) who are unlikely to respond to Venetoclax. We also show that genomic events involving the MAPK and BCL2/MCL1 pathways are primarily responsible for disease progression after initial response. These findings highlight the critical role of comprehensive genomic profiling through WGS in identifying mechanisms of resistance to targeted therapies in MM, aiding patient selection for Venetoclax, and guiding the development of novel drug combinations.

Background: Immune dysfunction is a hallmark of chronic lymphocytic leukemia (CLL), and several studies suggest that hypogammaglobulinemia at the time of CLL diagnosis may predict shorter time to first therapy (TTFT; Parikh et al, 2015). However, it is unclear if hypogammaglobulinemia can predict TTFT independent of the CLL-International Prognostic Index (CLL-IPI) and the tumor mutational load (TML). We sought to determine if serum immunoglobulin (Ig) levels at diagnosis can independently predict outcomes in patients with CLL and its precursor, high-count monoclonal B-cell lymphocytosis (MBL). Methods: We used the Mayo Clinic CLL Database to identify newly diagnosed CLL/MBL consented within 2 years of diagnosis. Next generation sequencing (NGS) was performed using a 59 gene panel (SureSelect) to detect mutated CLL genes. Serum Ig levels were quantitated by radial immunodiffusion (Immunoplates). Serum Ig levels below the lower limit of normal were defined as hypogammaglobulinemia. We used Chi-squared or Fisher's exact tests to compare discrete variables and the Kruskal Wallis test for continuous variables. TTFT was calculated from date of diagnosis to date of first treatment or date last known to be untreated. We used Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for TTFT and overall survival (OS) associations. Results: We identified 895 newly diagnosed patients (588 CLL and 307 MBL) who had undergone NGS for recurrent somatic mutations and had serum Ig levels available at the time of diagnosis. The median absolute B-cell count was 6.7 x109/L; 613 (69%) were male, 375 (42%) had unmutated IGHV genes, and 81 (9%) had TP53 disruption. Based on CLL-IPI, 412 (46%) patients had low risk, 285 (32%) had intermediate risk, and 198 (22%) had high/very high risk. The most commonly mutated genes were NOTCH1 (14%), SF3B1 (11%), TP53 (10%), NFKBIE (8%), ATM (6%), and BIRC3 (6%). Based on the number of genes with high impact or hotspot mutations, we classified 40%, 29%, and 31% patients with TML scores of 0, 1, or 2+, respectively. At CLL/MBL diagnosis, the median serum IgG, IgA, and IgM was 891 mg/dL (111-2620), 121 mg/dL (1-1150), and 44 mg/dL (5-1320), respectively; 284 (32%) patients had low serum IgG, 128 (17%) had low serum IgA, and 322 (42%) had low serum IgM. Low serum IgA was associated with mutations in NOTCH1, SF3B1, ATM, BIRC3 and NFKBIE, low IgM was associated with SF3B1 and BIRC3 mutations (all comparisons, P < .05), and low IgG was associated with BIRC3 mutation (P = .05). The median follow-up for the cohort was 8.4 years. A total of 359 patients progressed requiring therapy (299 CLL and 60 MBL), and 265 patients died (177 CLL and 88 MBL). The TTFT was significantly shorter for patients with low serum IgG compared to normal serum IgG at diagnosis (median 4.4 vs 10.8 years) and similar findings were observed in individuals with low serum IgA (1.7 vs 10.8 years) and those with low serum IgM (4.6 vs 11.1 years). On univariable analysis, the following were associated with a shorter TTFT: low serum IgG (HR: 1.8, 95% CI 1.4-2.2), low serum IgA (HR: 3.5, 95% CI 2.7-4.4), low serum IgM (HR: 1.9, 95% CI 1.5-2.3), TML score of 1 (HR: 1.7, 95% CI 1.3-2.2), TML of 2+ (HR: 4.4, 95% CI 3.4-5.7), CLL-IPI intermediate risk (HR: 3.7, 95% CI 2.8-4.8), and CLL-IPI high/very high risk (HR: 7.6, 95% CI 5.8-10.1) (all comparisons, P < .0001). In multivariable analyses after adjusting for CLL-IPI and TML, low serum levels of IgG (HR: 1.8, 95% CI: 1.5-2.3), IgA (HR: 2.4, 95% CI: 1.8-3.0), and IgM (HR: 1.8, 95% CI: 1.5-2.3) were independently associated with a shorter TTFT (all comparisons, P < .0001). No significant differences in OS were detected between patients with low and normal serum IgG, IgM, and IgA on univariate analysis. Similar results for TTFT and OS were observed after adding clonal B-cell count to the Cox model. Conclusions: Our study shows that low serum Ig levels at the time of initial diagnosis is predictive of TTFT independently of the CLL-IPI and TML, which can further risk stratify individuals with newly diagnosed CLL/MBL. Additionally, low serum IgA, IgM, and IgG levels at diagnosis are significantly associated with specific somatic mutations in individuals with newly diagnosed CLL/MBL.

Background: Multiple myeloma (MM) is an incurable plasma cell malignancy with several recurrent primary cytogenetic abnormalities including t(11,14)(q13;q32), resulting in increased expression of CCND1. Although 40% of patients with t(11;14) MM respond favorably to the BCL-2 inhibitor venetoclax (ven), 60% fail to achieve an objective response. Previous studies have suggested upregulation of PMAIP1, encoding the pro-apoptotic NOXA protein, in association with ven sensitivity (Leblay, Blood, 2024). Here we sought to evaluate the role for NOXA in ven response in t(11;14) MM. Methods: We analyzed whole exome, whole genome and RNA sequencing data of CD138+ tumor cells from 1,359 patients with MM in the CoMMpass and Mayo Clinic cohorts. The histone mark H3K27ac was assessed in t(11;14)+ KMS12PE and U266B1 human myeloma cell lines using ChIP-seq. Gene expression and protein levels of PMAIP1 (NOXA) were also detected by qRT-PCR and Western blot, respectively. Live cell detection was evaluated by Annexin V and PI using flow cytometry and by YoYo3 dye using IncuCyte following treatment with ven. Results: The t(11;14) MM subtype was associated with a 1.6-5.5-fold increased expression of PMAIP1 compared to non-t(11,14) MM in the CoMMpass and Mayo Clinic cohorts (p<0.0001). These findings are consistent with a significant correlation between CCND1 and PMAIP1 expression in both cohorts (p<0.001). To evaluate whether cyclin D1 is directly associated with increasing expression of PMAIP1 in t(11;14) MM, we generated a cyclin D1 knock out (KO) version of the U266B1 line using CRISPR/cas9. Two independent U266B1 KO cell lines showed reduced expression of PMAIP1 and NOXA protein compared to the WT U266B1 consistent with a role for cyclin D1 in regulating PMAIP1 expression. In addition, the KO lines had a 4.8-fold reduced H3K27ac signal at the PMAIP1 locus in comparison to the WT control suggesting a role for cyclin D1 in promoting H3K27ac. We previously observed increased expression of CCND1 in association with the G allele compared to the A allele of rs9344 in both the CoMMpass and Mayo Clinic cohorts (Leukemia, in press). We next asked whether the G risk allele of rs9344 within CCND1 impacted gene expression and H3k27ac levels at the PMAIP1 locus. To study the allele specific effect of CCND1, we used the CRISPR/cas9 edited KMS12PE WT (rs9344 AA) and Knock In (KI) (rs9344 GG) cell lines. In the KI cells, the G allele was accompanied by an ~2-fold increased expression of PMAIP1 and NOXA protein detected by western blot (<0.05). Increased PMAIP1 expression in KI lines was also accompanied by a 2.2-fold increased H3K27ac further suggesting a role for the G allele of rs9344 in promoting H3K27ac and gene expression at the PMAIP1 locus. Finally, we evaluated the consequence of PMAIP1 expression levels in relation to ven response. Increased expression of PMAIP1 and elevated levels of NOXA protein in the KMS12PE KI lines was associated with increased sensitivity to ven resulting in ~50% increase in cell death of KI vs. WT lines (p<0.02). Conclusion: We demonstrate a role for cyclin D1 in regulating PMAIP1 expression through H3K27ac resulting in increased NOXA protein. Levels of NOXA are associated with the G allele of rs9344 and increased venetoclax response.

Background: Chronic lymphocytic leukemia (CLL) is one of the most common leukemias in the United States (US), with an estimated incidence of 20,700 cases in 2024. Population-based studies conducted in Olmsted County (OC), Minnesota, reported an increase in CLL incidence over a 55-year period (1935 through 1989), which was attributed to increased detection of early-stage cases in individuals over age 50. In an updated study from 2000 to 2010 in OC, we reported no further increase in the incidence of CLL using the 2008 International Workshop on CLL (IWCLL 2008) guidelines. Additionally, when we compared the 1996 National Cancer Institute Working Group (NCI-WG 96) criteria with the IWCLL 2008 guidelines incorporating the monoclonal B-cell lymphocytosis designation, we found that the use of the IWCLL 2008 guidelines reduced the overt CLL incidence, modified the distribution of Rai Stages at diagnosis, and shortened the median time to first treatment (TTFT). Over the last two decades, 5-year survival for CLL has increased due to new therapies and improved supportive care. Herein we provide updated data on the incidence, prevalence, and natural progression of CLL in OC and studied similar features in an expanded 6-county region around Mayo Clinic, Rochester. Methods: We utilized the Rochester Epidemiology Project (REP), a population-based medical records linkage system, to identify CLL cases from 2000 to 2020 using International Classification of Diseases codes. We included individuals residing in OC and the expanded 7-county region (REP-7, including OC), for which data became available starting in 2010. CLL cases were included if they met the IWCLL 2008 criteria. All CLL diagnoses were confirmed by manual review of medical records and data on CLL-related factors were collected, including treatment status, prior history of other cancers, and overall survival (OS). Age- and sex-specific incidence rates were derived from REP census data and adjusted to the 2020 US white population. OS analyses were conducted using the Kaplan-Meier method. TTFT was calculated with death as a competing risk. Cox regression was used to evaluate the effect of decade on TTFT and OS among patients in OC. Results: From 2010-2020, 242 patients were diagnosed with CLL in REP-7, and between 2000-2020, 174 patients were diagnosed in OC. Among the 242 CLL patients in REP-7, the median age at diagnosis was 71 years (range 30-96),158 (65%) were male, 236 (99%) were white, and 74 (31%) had a history of other cancer, with non-melanoma skin cancer being the most common. After age and sex adjustment to the 2020 US white population, the CLL incidence in REP-7 and OC from 2010 to 2020 was 7.8 and 8.1 per 100,000 individuals, respectively. In OC, the age- and sex-adjusted CLL incidence from 2000 to 2020 was 7.5 per 100,000 individuals, showing no significant trend changes over time. Among the 242 patients diagnosed with CLL in REP-7 between 2010 and 2020, 102 received treatment with a median TTFT of 9.8 years. In OC from 2000 to 2020, 79 out of 174 patients received treatment with a median TTFT of 9.4 years. When comparing across two decades in OC, the median TTFT was 12.8 years for the period from 2000 to 2009, decreasing to 5.9 years between 2010 and 2020, but was not significantly different after accounting for age and sex. The median OS for patients diagnosed with CLL in REP-7 from 2010 to 2020 was 11 years. The median OS for patients in OC between 2000 to 2020 was 12.6 years. When comparing across decades in OC, the median OS was 8.9 years for the period from 2000 to 2009, increasing to 13.6 years for those diagnosed between 2010 and 2020, but this trend was not significantly different after accounting for age and sex. Prevalence data for REP-7 and OC were analyzed as of 01/01/2020. Additionally, prevalence data for OC were available for 01/01/2010. In REP-7, the prevalence of CLL was 97 per 100,000 individuals on 01/01/2020. In OC, the CLL prevalence was 86 per 100,000 individuals on 01/01/2010, and 100 per 100,000 individuals on 01/01/2020. Conclusions: We observed no substantial increase in the incidence rates of CLL in REP-7 from 2010 to 2020, nor in the 20 years of available data from OC. However, the prevalence of CLL in OC increased by 16% from 2010 to 2020. Additionally, evidence indicated a longer OS among newly diagnosed CLL cases in OC compared to previous decades, suggesting a potential impact of improved therapeutic approaches on OS.