Slager, Susan L.

Abstract Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease, and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline, and artifact. In whole-exome sequencing data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome sequencing data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes and predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had < 5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies. The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.

Abstract Most disease-associated single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWASs) reside in noncoding regions. There is increasing evidence that they play regulatory roles through epigenetic mechanisms by altering local chromatin state, 3D chromatin structure, and looping interactions. We and others have identified 42 CLL risk loci from GWASs. At 12 risk loci located outside of gene promoters, we previously demonstrated that functional SNPs are highly enriched in enhancers and super-enhancers that mostly target BCL2 family genes and transcriptional regulators. However, the study was limited by using only public epigenomic data generated in lymphoblastoid cell lines (LCL) and normal immune related cell types. To gain a better understanding of how the functional SNPs alter local chromatin state and modulate target gene expression in CLL, we generated RNA-seq data (n=588) and transposase-accessible chromatin with sequencing (ATAC-seq) data (n=69) in CLL tumor B cells. We performed molecular quantitative trait loci (QTL) analysis with index and correlated SNPs (R2 0.5) at 41 risk loci (excluding the HLA locus). The analyses identified cis expression QTLs (eQTLs), within 1Mb of genes’ transcription start site, at 25 risk loci and chromatin accessibility QTLs (caQTLs, within peak) at 11 risk loci, with both eQTLs and caQTLs at 8 risk loci. We identified a total of 54 eGenes, for which the expression is affected by nearby SNPs within 1Mb, including 40 novel eGenes at 16 CLL risk loci not previously reported. Notably, based on publicly available chromosome interaction data from CLL, LCL, and normal B cells, for over 70% of the novel eGenes, their promoters showed looping interactions with the regions carrying the eQTLs. Further, 13 SNPs were identified as both caQTL and eQTL, with the associated novel eGenes including CD81 at 11p15.5, IPCEF1 at 6q25.2, as well as CXXC1 and MBD1 at 18q21.1. The result suggests that these SNPs play a role in altering chromatin accessibility contributed to gene expression variation. At 11p15.5 risk locus, SNP rs2651823 (A/G) represents a novel caQTL and eQTL. The alternative G allele is associated with increased chromatin accessibility and CD81 expression. Hi-C data from GM12878 and CD20+ B cells supported interaction between the open chromatin region carrying rs2651823 and CD81 promoter. CD81 facilitates the trafficking of CD19 to the surface of activated B cells, and CD81-null mice showed impaired immune response. In a mouse model, CD81 contributes to tumor progression and metastasis in Lewis lung carcinoma. In summary, our molecular QTL analysis revealed that functional SNPs, located in CLL risk loci, modulate chromatin accessibility and expression of downstream target genes that could impact CLL risk. Citation Format: Huihuang Yan, Zhiquan Wang, Mingma Hoel, Cristine Allmer, Chantal McCabe, Daniel O’Brien, Dennis Robinson, James Cerhan, Sameer Parikh, Esteban Braggio, Susan Slager. Molecular QTL analyses identify functional variants at inherited risk loci in chronic lymphocytic leukemia [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 2763.

Abstract Family history of hematologic malignancy (FHHM) is an established risk factor for developing lymphoma, but studies examining associations between self-reported FHHM and lymphoma prognosis during the recent treatment era are limited. Utilizing a prospective cohort study, we evaluated associations between FHHM and prognosis for common lymphoma subtypes, including diffuse large B-cell (DLBCL), follicular (FL), marginal zone (MZL), mantle cell (MCL), Hodgkin (HL), and T-cell (TCL) lymphomas. We used the Mayo Clinic component of the Molecular Epidemiology Resource, a prospective cohort study of newly diagnosed lymphoma patients enrolled from 2002 to 2015. Self-reported FHHM (HL, NHL, leukemia, or multiple myeloma) in a first-degree relative was obtained via questionnaire. Primary outcomes were overall survival (OS) and event-free survival (EFS; time from diagnosis to disease progression, initiation of second-line therapy, or all-cause death). Secondary outcomes were failure to achieve 24 months of event-free survival (EFS24; associated with aggressive disease and poor outcomes) and lymphoma-specific survival (LSS; time from diagnosis to death from lymphoma or lymphoma treatment-related complications). We used multivariable Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CI) for the association of FHHM with OS, EFS, and LSS, and logistic regression to estimate odds ratios (ORs) and 95% CI for the association with failure to achieve EFS24. All models were adjusted for sex and a lymphoma subtype specific prognostic index. The cohort consisted of 650 DLBCL, 679 FL, 291 MZL, 178 MCL, 254 HL, and 196 TCL patients. The prevalence of FHHM ranged from 9.7% in TCL to 14% in FL. FHHM was not associated with OS in any subtype, but MCL patients with FHHM showed an inferior EFS (HR=1.70, 95% CI 1.02-2.85; p=0.04). For EFS24, FHHM was associated with failure to achieve EFS24 only in FL (OR=1.57, 95% CI 0.99-2.48; p=0.05). Similarly, FHHM was associated with inferior LSS in FL patients (HR=1.98, 95% CI 1.11-3.53; p=0.02). In a treatment subgroup analysis of FL patients, the FHHM associations with failure to achieve EFS24 (OR=2.48, 95% CI 1.04-5.74; p=0.04) and inferior LSS (HR=2.68, 95% CI 1.09-6.58; p=0.03) strengthened among those treated with frontline immunochemotherapy (IC). In summary, there were no evidence of associations between FHHM and OS across lymphoma subtypes, but MCL patients with FHHM displayed inferior EFS. FL patients with FHHM were more likely to not achieve EFS24 and to ultimately die of their lymphoma or a lymphoma treatment-related complication. This finding was stronger in more aggressive FL requiring frontline IC. This novel finding would suggest that genetic or environmental factors associated with FHHM may underlie pathways that impact disease aggressiveness and early clinical failure in FL. Citation Format: George Cholack, Raphael Mwangi, Thomas M. Habermann, Verena Hinder, Rosalie Griffen, Dennis Robinson, Andrew L. Feldman, Thomas E. Witzig, Stephen M. Ansell, Brian K. Link, Yucai Wang, Carrie A. Thompson, Susan L. Slager, Matthew Maurer, Anne J. Novak, James R. Cerhan. Family history of hematologic malignancy and prognosis across lymphoma subtypes: Novel association with poor outcomes in follicular lymphoma [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 7344.

Abstract Despite the advent of novel targeted therapies with high overall response rates, chronic lymphocytic leukemia (CLL) is still largely incurable due to the development of resistance. The bone marrow microenvironment (BMME) plays an important role in the survival and drug resistance in CLL. BM stromal cells (BMSCs) are considered a key component of BMME. Our long-term goal is to gain further insights of BMSCs-CLL crosstalk influencing both survival and drug resistance in CLL. To understand the global biologic changes that occur in leukemic CLL cells when in contact with BMSCs, we assessed RNA-seq in paired CLL cells isolated from untreated blood and BM and in untreated patients’ CLL cells cultured alone or with BMSCs. CLL cells from blood/BM and cultured alone/with BMSCs were also examined by Immunoprecipitation (IP), Western blot (WB), untargeted metabolomics (LCMS+GCMS), and preclinical drug sensitivity assays. We detected only 13 upregulated genes that overlap between CLL cells from BM and cocultured with BMSCs (p&lt0.05, fold change &gt1.5). When we analyzed the expression of these 13 genes in CLL cells from a cohort of 162 untreated patients by RNA-seq, we found associations between 4 genes (PNP, C16orf54, MOB3A, CDK2AP2) and clinical outcomes including overall survival (OS), progression free survival (PFS), and time to first treatment (TTFT) [multivariable Cox proportional hazards models, p&lt0.05]. Of the 4 genes, purine nucleoside phosphorylase (PNP), an enzyme in the purine pathway, had the most significant association with OS, PFS, and TTFT. Metabolomic profiling indicated an increased level of purine pathway metabolites adenosine, adenine, inosine, and hypoxanthine in cocultured CLL cells. WB analysis showed variable baseline PNP protein expression with 2 cohorts: low /no (L) and high (H) PNP using CLL blood cells. An induction in PNP protein levels were noticed in CLL cells expressing only LPNP when cocultured. Given the association of HPNP with adverse clinical outcomes, we tested for L vs. HPNP cohorts drug sensitivity to known BTK inhibitors(is) or Bcl2is. Treatment of CLL cells from untreated patients with Bcl2is venetoclax, S55746, and LP-118 (Bcl2-Bclxli, Newave) showed increased sensitivity to the Bcl2/Bclxlis in HPNP but not in LPNP cells. In addition, BTKis showed no difference in drug sensitivity associated with CLL PNP levels. To explain this discrepancy, WB and IP analyses were conducted in L vs. HPNP leukemic cells for apoptotic profiles, where we found increased phosphorylated-Bcl2(S70) and an increased association of Bcl2 with Bax in LPNP-expressing CLL cells compared to HPNP. These findings support increased resistance to apoptosis afforded by inhibiting Bcl2 for LPNP vs. HPNP-expressing CLL cells. Our future goals are to continue to understand the prognostic significance of the 4 genes with adverse clinical outcomes and to investigate how best to exploit the peculiar, enhanced sensitivity to Bcl2/Bclxlis found in the HPNP CLL cells. Citation Format: Sutapa Sinha, Weiguo Han, Zhiquan Wang, Brianna M. Finke, Heather C. Darby, Kari G. Rabe, Susan L. Slager, Chantal E. McCabe, Daniel R. O'Brien, Sameer A. Parikh, Esteban Braggio, Yi Chen, Fenlai Tan, Stephen P. Anthony, Yu Chen, Bing Dai, Yue Shen, Neil E. Kay. CLL drug resistance- A multifaceted mechanism [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 4423.

Abstract Monoclonal gammopathy of undetermined significance (MGUS) is a precursor to multiple myeloma (MM). Clonal hematopoiesis of indeterminate potential (CHIP) is a precursor to either myeloid or lymphoid malignancy, depending on whether the mutation is in a gene associated with lymphoid (L-CHIP) or myeloid (M-CHIP) outcome. Little is known about the association between CHIP and MGUS and their combined effect on risk of MM. We examined this question within Mayo Clinic Biobank cohort of participants aged 50 years or older with no prior history of hematological malignancies. Using whole blood, individuals were screened for MGUS using a matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry assay. M-CHIP and L-CHIP were defined based on mutations in 56 genes associated with myeloid malignancies or mutations in 235 genes associated with lymphoid malignancies, respectively, using whole-exome-sequencing. Incident MM was identified using International Classification of Diseases (ICD) codes and confirmed via medical record review. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals for the association of CHIP and MGUS. Cox proportional hazards regression was used to estimate hazard ratios (HR), with time defined as date between blood sample and the first incident MM, date of death, or 12/31/2023. All models were adjusted for age and sex. Of the 16, 479 individuals (median age 65 years, 45% male) screened, 12.3% were positive for MGUS, 9.8% were positive for M-CHIP, and 1.5% were positive for L-CHIP. There was no evidence of an association between M-CHIP and MGUS (OR=0.97, 95% CI:0.83-1.12) nor L-CHIP and MGUS (OR=1.12, 95% CI:0.78-1.57), including with any MGUS isotype (IgA, IgG, or IgM; P-values>0.22). Next, we investigated the association of these precursors on risk of MM. Median follow-up was 11.7 years (range 0.1-15.1), and 46 individuals developed incident MM. Both MGUS (HR=29.7, 95% CI: 14.1-62.4) and L-CHIP (HR=4.1, 95% CI: 1.3-13.2) were significantly associated with incident MM, but not M-CHIP (HR-1.0, 95% CI: 0.4-2.5). Investigating the combined effect of MGUS and L-CHIP, we found those with MGUS alone had a 27.5-fold (95% CI: 13.0-58.2) increased risk of incident MM compared to those with no precursors. No individuals with L-CHIP only developed MM, but those with both L-CHIP and MGUS had a 129.4-fold (95% CI: 34.4-486.4) increased risk of incident MM. In the largest cohort with screened MGUS and CHIP, we found no evidence of an association between either definition of CHIP with MGUS. However, individuals with both L-CHIP and MGUS were at the highest risk of developing MM. Citation Format: Nicholas J. Boddicker, Cristine Allmer, Danelle H. Moonen, Aaron D. Norman, Angelica Macauda, Alyssa Clay-Gilmour, Janet E. Olson, Mrinal S. Patnaik, Vincent Rajkumar, Esteban Braggio, David L. Murray, Shaji Kumar, Susan L. Slager, Celine M. Vachon. The association between MGUS and CHIP and their impact on multiple myeloma [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 4917.

Abstract Introduction: With over 80, 000 new cases of non-Hodgkin's lymphoma (NHL) diagnosed annually, modifiable risk factors remain unclear. Circadian disruption, linked to cancers like breast and prostate, affects immune cells (e.g., natural killer cells and T-helper cells) by altering their trafficking and proliferation. Some studies (PMID: 30566672, 27611440) suggest long sleep may increase overall NHL risk, including a Mendelian randomization (MR) study (PMID: 32895918). However, no research has examined the causal relationship between sleep traits, including long sleep, and NHL risk, by subtypes. We hypothesize that sleep traits are causally associated with NHL subtypes in European populations, testing this using MR with data from the International Lymphoma Epidemiology Consortium (InterLymph). Methods: We conducted two-sample MR using genome-wide association studies (GWAS) summary data for sleep traits and NHL subtypes. Only independent, genome-wide significant (p < 5×10-8) Single Nucleotide Polymorphisms (SNPs) were selected as valid instruments, sourced from the UK Biobank. Sleep traits included chronotype (153 SNPs), insomnia (48 SNPs), sleep duration (78 SNPs), excessive daytime sleepiness (37 SNPs), short sleep (27 SNPs), and long sleep (8 SNPs). NHL subtype data were from InterLymph: follicular lymphoma (6, 508 cases / 64, 183 controls), mantle cell lymphoma (1, 169 cases / 61, 603 controls), Waldenstrom macroglobulinemia/lymphoplasmacytic lymphoma (WM/LPL: 1, 697 cases / 59, 333 controls), and chronic lymphocytic leukemia (8, 522 cases / 67, 653 controls). The primary analysis used inverse-variance weighted (IVW) random-effects, with MR-Egger for pleiotropy adjustment. Results: Using the IVW method, we did not identify any statistically significant causal association between sleep traits and the risk of NHL subtypes, but did yield several intriguing trends. Long sleep (sleep duration >= 9 h per night) showed a marginally increased NHL risk [Odds ratio (OR) range: 1.51 - 247.9] across each subtype. Short sleep (sleep duration < 7 h per night) showed a similar trend [OR range: 1.25 - 1.81], except for WM/LPL, where we noticed a negative trend. The confidence limit for long sleep was imprecise due to the small number of variants. We did not notice any significant directional horizontal pleiotropy or weak instrument bias. Conclusions: We found no conclusive evidence of a relationship between sleep traits and four NHL subtypes. However, we see suggestive trends of long and short sleep associated with higher risk of some NHL subtypes. These findings align with the previous studies, which showed long sleep was associated with higher risk of NHL overall. Future directions include expanding our analysis to additional NHL subtypes, generating polygenic risk scores, and conducting stratified analyses. Citation Format: Pankhil Shah, Brittany Crawford, Anwar Merchant, Brenda Birmann, Angelica Macauda, Michelle A. Hildebrandt, Aaron Norman, Neil E. Caporaso, Meredith Yeager, Michael Dean, Immaculata De Vivo, Lynn Goldin, Nicola J. Camp, Rosalie Griffin, Delphine Casabonne, Federico Canzian, Pelin Unal, Elad Ziv, Catherine R. Marinac, Alexandra Nieters, Stephen J. Chanock, Mitchell J. Machiela, Michael Conry, Charlie Zhong, Hanla A. Park, Simon Cheah, Jonathan N. Hofmann, Elizabeth E. Brown, Celine Vachon, Susan Slager, Sonja Berndt, Sophia S. Wang, Vijai Joseph, James McKay, Henrik Hjalgrim, Lara Sucheston-Campbell, Karl Smith-Byrne, Alyssa Clay-Gilmour. Association between sleep traits and risk of non-Hodgkin's lymphoma subtypes: a mendelian randomization study in the International Lymphoma Epidemiology (InterLymph) Consortium [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 7415.

Genome-wide association studies (GWAS) of multiple myeloma (MM) in individuals of genetic European ancestry (EA) have identified 35 susceptibility loci. Co-heritability analyses have revealed a strong genetic correlation between MM and its precursor condition, monoclonal gammopathy of undetermined significance (MGUS). Previous research validated the association between a polygenic risk score (PRS) based on 23 MM risk loci and the risk of MGUS. Went and colleagues recently reported 12 new MM risk loci. We constructed a MM-PRS using all 35 MM risk variants and assessed its association with MGUS. Additionally, among individuals with MGUS, we assessed associations of MM-PRS with established prognostic factors [M-protein (<0.2 g/dL; 0.2-1.5 g/dL; >1.5 g/dL); isotype (IgG, IgA, IgM); free light chain (FLC) ratio (Normal (0.26-1.65), Abnormal (<0.26 or >1.65))]. Our study population included 1,723 individuals with MGUS seen at Mayo Clinic, Rochester, MN and 14,175 controls from the Mayo Clinic Biobank who lived in the 27-county region around Rochester, MN, and screened negative for MGUS using MALDI-TOF MS. The majority of MGUS patients and controls were of EA. We used logistic or multinomial regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs). All models were adjusted for age, sex, and the first three principal components. The MM-PRS was significantly associated with MGUS when assessed continuously (OR, 1.28 per standard deviation (SD); 95% CI, 1.22-1.35). When comparing to the middle quintile of the MM-PRS distribution, individuals in the highest quintile had 1.41-fold increased risk of MGUS (95% CI, 1.21-1.64), and individuals in the lowest quintile had 0.68 decreased risk of MGUS (95% CI, 0.57-0.81). Of the 35 SNPs evaluated, 27 demonstrated consistent directions of effect for risk of MGUS with published risk estimates for MM. The effect of MM-PRS differed by isotype (P = 2.2 x 105), with higher MM-PRS associated with IgA MGUS (OR, 1.40 per SD; 95% CI, 1.08-1.81) and a lower MM-PRS associated with IgM (OR, 0.66 per SD; 95% CI, 0.52-0.84) compared to IgG MGUS. The MM-PRS was higher in MGUS patients with M-protein > 1.5 g/dL compared to M-protein of size 0.2-1.5 g/dL, although not statistically significant (OR, 1.41 per SD; 95% CI, 0.95-2.08); there were no differences in the MM-PRS between MGUS patients with M-protein size <0.2 g/dL compared to 0.2-1.5 g/dL (OR, 1.01 per SD;95% CI, 0.81-1.26). There were also no differences in MM-PRS by FLC ratio, (OR, 0.95 per SD; 95% CI, 0.852-1.10 for abnormal vs. normal). Our findings of an association of the MM-PRS with risk of MGUS provides further evidence for shared heritability between MGUS and MM. Future studies are needed to examine whether the expanded MM-PRS adds to existing models for MGUS progression. Citation Format: Angelica Macauda, Alyssa Clay-Gilmour, Cristine Allmer, Danelle Moonen, Aaron D. Normann, Nicholas J. Boddicker, Joselle M. Cook, Linda Baughn, Elizabeth E. Brown, Vincent S. Rajkumar, Esteban Braggio, David L. Murray, Susan L. Slager, Shaji Kumar, Celine M. Vachon. Implications of multiple myeloma polygenic risk scores (PRS) for MGUS [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 2274.

Abstract Background: Diffuse large B-cell lymphoma (DLBCL) is clinically heterogeneous, and gene expression profiling has identified at least two biologically distinct DLBCL subtypes defined by their cell of origin (COO): germinal center B cell (GCB) and activate B cell (ABC) or non-GCB. We evaluated a variety of putative DLBCL risk factors for etiologic heterogeneity by the COO in a clinic-based study of newly diagnosed DLBCL cases (N = 638) and frequency-matched controls (N = 2,253). Methods: The COO was determined on formalin-fixed, paraffin-embedded tumor tissue, with DLBCL classified as GCB (N = 283), non-GCB (N = 188), or undetermined/missing (N = 167; mainly because of lack of tissue). Multivariable logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). Results: We identified heterogeneity by the COO for low socioeconomic status (SES), which was only associated with non-GCB DLBCL (OR = 1.88 for low vs. average SES; 95% CI, 1.08–3.27); alcohol use, which was only associated with GCB DLBCL (OR = 0.48 for former drinkers; 95% CI, 0.29–0.80 and OR = 0.47 for current drinkers; 95% CI, 0.32–0.71); and borderline heterogeneity for the regular use of regular/extra-strength aspirin, which was only associated with non-GCB DLBCL (OR = 0.36; 95% CI, 0.16–0.85). In contrast, there was no significant heterogeneity by the COO for family history, medical history, or other lifestyle factors. Conclusions: Although requiring confirmation, most risk factors for DLBCL did not show etiologic heterogeneity by the COO, with some notable exceptions including alcohol use, SES, and perhaps regular use of regular/extra-strength aspirin showing associations. Impact: Mechanistically, these findings suggest that most of the DLBCL risk factors evaluated here influence lymphomagenesis prior to differentiation into COO subtypes, with selected factors acting later.

Introduction: WM is a rare subtype of lymphoma, identified by the presence of IgM paraprotein, infiltration of clonal, small lymphoplasmacytic B cells in the bone marrow, and the somatic mutation, MYD88 L265P, which is observed in over 90% of WM patients. We previously identified two rare variants at 6p25.3 (EXOC2) and 14q32.13 (TCL1) that were significantly associated with an increased risk of WM/LPL (PMID: 30305637). However, the genetic basis for WM/LPL predisposition remains largely unknown. We conducted the largest WM/LPL GWAS to discover further germline susceptibility loci for WM/LPL. Methods: Our meta-analysis discovery set included 1,707 cases and 58,328 controls of European ancestry from the InterLymph Consortium, genotyped on a high-density SNP array and imputed to TOPMed, providing over 10 million common variants for association testing. Germline genetic loci with suggestive significance (P<5x10-6) were selected for replication in four independent sets (570 cases and 771,770 controls; UK Biobank, Copenhagen Hospital Biobank, FinnGen, Mayo Clinic), giving us a total of 2,277 WM/LPL cases and 831,098 controls. Conditional and sensitivity analyses were performed considering family history and sex. A transcriptome-wide association study (TWAS) was performed using FUSION with expression quantitative trait loci (eQTL) data from GTEx and Young Finns Study in whole blood, lymphocytes, and other tissues. Functional annotations were done using FUMA, F-MAGMA, FORGE, and OpenTarget. Colocalization was measured using CoLoC with whole blood gene expression data. Results & Discussion: We identified 11 germline genetic loci associated with WM/LPL risk that replicated and reached genome-wide significance (P< 5x10-8), including the two known loci on 6p25.3 and 14q32.13 and nine novel loci in the following regions: 6p25.3 (n=2 independent loci), 2q37.1, 9p13.2, 6p21.32, 10q23.31, 16q21, 1p31.1, and 15q15.1. Four of the discovered variants showed strong associations with WM/LPL [odds ratios (ORs) >10]. Enrichment was observed for enhancer and promoter elements in B-cells (FDR q < 0.01), and gene-based and colocalization analyses identified several potential biological pathways. Colocalization analyses show the intronic PAX5 risk variant on 9p (OR=1.5, P= 2.3x10-25) associated with whole blood eQTL expression of PAX5, which plays an essential role in regulating lymphoid progenitors to the B-lymphocyte lineage, as well as RP11-297B17.3 (RNA gene, antisense to PAX5). In addition, GWAS have shown this variant is in linkage disequilibrium (LD) with GWAS loci for CD20 on IgD+ CD38- B cell that represents a specific immunophenotype that helps identify and characterize B cells involved with WM and other similar conditions. The 2q37.1 (SP140) locus (OR=1.6, P=1.2x10-33) contains variants linked to chronic lymphocytic leukemia (CLL) and autoimmune diseases, including multiple sclerosis and Crohn's disease. This locus is important in chromatin-mediated gene regulation, suggesting a significant role in the immune system. The specific variant influences exon-skipping and potentially protein expression, altering the gene's function. The variants on 10q23.31 in FAS (OR=1.3, P=4.4x10-13) and 15q15.1 (OR=1.2, P=8.5x10-11) in BCL2 modifying factor (BMF), are in strong LD with known CLL susceptibility variants. The 15q variant is also linked with multiple lymphoid leukemia GW significant SNPs. Sex-specific analyses identified one novel locus at 20q13.33 associated with the expression of ZGPAT and UCKL1 in males (OR=1.9, P=9.5x10-10). TWAS identified associations with CRHR1 and CDKN2A; overexpression of both are shown to be associated with an increased risk of WM/LPL. Expression of CDKN2A has been linked to childhood acute lymphoblastic leukemia, while CRHR1 has been associated with breast cancer risk. Conclusions: Our study, the largest GWAS of WM to date, has identified 11 susceptibility loci, including two previously known and nine novel regions. The findings underscore the genetic complexity of WM, with the PAX5 and SP140 loci implicated in both chromatin-mediated gene regulation and immune system functions. These insights not only enhance our understanding of WM predisposition but also pave the way for future research focused on functional understanding of these loci, potentially leading to new therapeutic strategies.