Norman, Aaron D.

Abstract Prognostic indices such as the CLL International Prognostic Index (CLL-IPI) and the International Prognostic Score for Early-stage CLL (IPS-E) can predict time to first treatment (TTFT) in patients with early-stage CLL. Their ability to predict risk of infection - a leading contributor to morbidity and mortality in CLL - remains uncertain. The CLL-TIM is a machine-learning model developed in Europe that integrates clinical, laboratory, and infection-related data to predict TTFT or infection risk within 2-years of diagnosis, Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) 0.74; Agius et al., Nat Comm 11, 363 2020. We conducted the first validation of CLL-TIM in a US cohort of newly diagnosed CLL patients and compared its performance with the CLL-IPI and IPS-E indices. Adults with newly diagnosed CLL (2000-2020) were identified through the Rochester Epidemiology Project using International Classification of Disease (ICD) codes; all diagnoses were confirmed. We replicated the variable selection from the original CLL-TIM model and applied it to our cohort. The primary endpoint was a 2-year composite of either TTFT or incident infection (defined as having blood cultures drawn). Model performance was evaluated using ROC-AUC consistent with the original CLL-TIM methods. CLL-IPI and IPS-E performance was assessed using time-dependent ROC-AUC; pairwise differences in discrimination were tested using DeLong. We identified 454 CLL patients with a median age of 72 years [range, 30-97], 166 (37%) were female, Rai Stage was 0-II in 399 (92%) patients. IGHV genes were unmutated in 130/305 (42%) patients and TP53 disruption (either del17p by FISH or TP53 mutation) was present in 24/329 (8%) patients. At 2-years, 55 (12%) patients received CLL therapy, and 56 (12%) patients had an infection. The 2-year composite endpoint was observed in 94 (21%) patients. The ROC-AUC was 0.74 (95% CI 0.68-0.80) for CLL-TIM, 0.69 for CLL-IPI (95% CI 0.63-0.75, p=0.11 compared to CLL-TIM) , and 0.68 for IPS-E (95% CI 0.62-0.74, p=0.02 compared to CLL-TIM). We also evaluated the ROC-AUC using all the prognostic models for each individual endpoints. The corresponding ROC-AUC for TTFT at 2 years were 0.79, 0.74, and 0.76 for CLL-TIM, CLL-IPI, and IPS-E, respectively; and for infection at 2 years were 0.68, 0.57, and 0.52, respectively. This is the first validation of the CLL-TIM in a US-based cohort of newly diagnosed CLL patients. Although the CLL-TIM model exceeded the minimally meaningful discrimination threshold (AUC >0.7) for individual patient-level risk prediction, there was no statistical difference in AUC between CLL-TIM and CLL-IPI, suggesting either model can predict the composite endpoint at 2 years. Most of the observed discrimination was driven by prediction of TTFT rather than infection, underscoring the need to develop more infection-specific risk models for newly diagnosed CLL. Citation Format: Raphael Mwangi, Tait D. Shanafelt, Soren Basnet, Emily L. West, Owen Keegan, Timothy G. Call, Yao Yuan, Bryan Alexis Vallejo, Paul J. Hampel, Lindsey E. Roeker, Yucai Wang, Saad J. Kenderian, Sara J. Achenbach, Aaron D. Norman, Kari G. Rabe, Neil E. Kay, James R. Cerhan, Curtis A. Hanson, Susan L. Slager, Sameer A. Parikh. Validation of the CLL treatment infection model (CLL-TIM) in patients with newly diagnosed chronic lymphocytic leukemia (CLL) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4228.

Abstract Background. Genome-wide association studies (GWAS) have identified 35 genetic susceptibility single-nucleotide polymorphisms (SNPs) for multiple myeloma (MM) in individuals of European ancestry (EA) and shown strong genetic correlation between MM and its precursor, monoclonal gammopathy of undetermined significance (MGUS). We evaluate the contribution of the 35 MM variants to MGUS susceptibility overall and by prognostic subgroups. Methods. The study included 20,756 participants (14,486 controls, 1,883 with MGUS, and 2,163 with MM) from the Mayo Clinic. Logistic regression assuming an additive model estimated odds ratios (ORs) and 95% confidence intervals (CIs) for individual SNPs and for the MM-PRS, adjusted for age, sex, study, and principal components. The PRS was a weighted sum of 35 SNPs with effect estimates from the largest MM GWAS and was modeled continuously (per SD) and by quintiles (Q1-Q5). SNPnexus annotated variants that replicated in MGUS (P < 0.05 and OR > 1.01) versus those that did not (P > 0.05 and OR < 1.01). Results. The 35-SNP MM-PRS was strongly associated with MM risk and modestly with MGUS. Compared with the middle quintile (Q3), MM odds rose from OR=0.55 (CI=0.46-0.65, P=2.2e-11) in Q1 to OR = 1.82 (CI=1.59-2.09, P=6.2e-18) in Q5. MGUS showed a similar, attenuated pattern (Q1 OR = 0.74, CI=0.64-0.84, P=6.4e-6 / Q5 OR=1.36, CI=1.21-1.53, P=3.4e-7). Each SD increase in PRS corresponded to OR=1.52 (CI=1.45-1.59, P=1.3e-66) for MM and OR = 1.22 (CI=1.18-1.27, P=1.0e-23) for MGUS. Higher PRS values were linked to larger M-protein ((0.1-1.5 g/dL: OR=1.40, CI=1.18-1.65, P=5.8e-12) vs <0.1 g/dL: OR = 1.19, CI=1.13-1.24, P=5.8e-12)) and abnormal free light chain (FLC) ratio ((OR = 1.33, CI=1.21-1.46, P=1.0e-9) vs normal ratio (OR=1.19, CI=1.14-1.25, P=1.4e-13)). Ten risk loci replicated in MGUS, mapping to genes involved in plasma-cell function, immune regulation, and DNA repair and enriched for Rho GTPase signaling, NF-κB-mediated apoptosis, and RNA polymerase II transcription, implicating early plasma-cell activation and transcriptional control. The 12 non-replicating loci, including PHC3, ATG5, and NFIC, mapped to genes involved in chromatin remodeling, autophagy, and SUMOylation, suggesting roles in stress response and genomic maintenance. Conclusions. The MM-PRS captures shared heritability between MM and MGUS and correlates with MGUS subtype and severity. Replicating variants highlight immune and cell-cycle pathways relevant to MGUS onset, whereas non-replicating loci cluster in DNA-repair and stress-response processes, underscoring their potential role in progression. Citation Format: Alyssa Ione Clay-Gilmour, Angelica Macauda, Cristine Allmer, Danelle Moonen, Aaron D. Norman, Nicholas Boddicker, Janet E. Olson, Elizabeth E. Brown, Vincent S. Rajkumar, Esteban Braggio, David Murray, Susan Slager, Shaji Kunnathu Kumar, Celine Vachon. Implications of multiple myeloma polygenic risk scores (PRS) for MGUS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3603.

Abstract Artificial intelligence (AI)-scores estimated from digital mammograms predict future breast cancer (BC) risk. MIRAI is a deep learning BC risk model that provides a continuous 5-year risk of overall BC from four screening full field digital mammograms. Common germline genetic variation in the form of a polygenic risk score (BC-PRS) is associated with increased BC risk and may improve MIRAI’s 5-year risk prediction. Our goal was to develop an updated MIRAI 5-year risk model that incorporates the BC-PRS (MIRAI+PRS) and to evaluate its discriminatory accuracy and calibration for both overall and invasive BC compared to MIRAI alone. We developed the MIRAI+PRS model by multiplying each woman’s 5-year MIRAI risk estimate by their relative risk based on their BC-PRS relative to the population mean. We evaluated the models within the Mayo Clinic Biobank mammography cohort, comprised of 12,307 women without a prior history of BC; 176 invasive and 250 overall BC were diagnosed within 5 years. MIRAI was estimated on screening mammograms closest to enrollment but at least 6 months prior to BC. Discriminatory accuracy, assessed by C-statistic, was high and similar for MIRAI+PRS vs. MIRAI models, for overall BC and invasive BC (Table). Calibration assessed by observed to expected (O/E) ratios was also similar for MIRAI+PRS compared to MIRAI predictions for BC outcomes (Table), although there was improvement in decile-specific O/E ratios across the lowest risk deciles (<1.67%) for MIRAI+PRS. Calibration for invasive cancer was poor for MIRAI with or without PRS. In summary, the MIRAI+PRS risk model did not result in significant difference of discriminatory accuracy or overall calibration compared to the MIRAI model, but there was evidence for improved calibration for women with 5-year risk below 1.67%. For invasive BC, the model had poor calibration regardless of whether PRS was included, underscoring the importance of training AI models for BC outcomes that are associated with a clinical intervention. Citation Format: Christopher G. Scott, Peter Kraft, Imon Banerjee, Ramon Correa Medero, Aaron D. Norman, Fergus J. Couch, Karla Kerlikowske, Stacey J. Winham, Celine M. Vachon. Development and evaluation of a MIRAI 5-year risk model with a breast cancer polygenic risk score [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2779.

Abstract Background Artificial intelligence (AI)–based, mammography breast cancer risk prediction models show improved discriminatory accuracy relative to clinical risk models. However, data on their calibration are limited. This study compared model performance of 3 clinical breast cancer risk models: Gail, Tyrer–Cuzick v8, and Breast Cancer Surveillance Consortium v3 with the MIRAI AI-risk model. Methods Digital mammograms were ascertained from a screening mammography cohort of 12 308 women within the Mayo Clinic Biobank with 250 incident breast cancers (176 invasive) within 5 years. We predicted 5-year breast cancer risk, estimated discriminatory accuracy (C-index), and calibration (observed-to-expected ratio) of both overall and invasive breast cancer and compared estimates using bootstrapping approaches. Results MIRAI demonstrated similar or improved discriminatory accuracy of overall breast cancer (C-index = 0.71, 95% confidence interval [CI] = 0.68 to 0.74) and invasive breast cancer (C-index = 0.71, 95% CI = 0.67 to 0.75) compared with clinical models (overall breast cancer: C-index = 0.59-0.68; invasive breast cancer: C-index = 0.60-0.68). MIRAI’s calibration for risk of overall breast cancer (observed-to-expected ratio = 0.96, 95% CI = 0.85 to 1.08) was improved compared with Gail (observed-to-expected ratio = 1.22, 95% CI = 1.07 to 1.38) and Breast Cancer Surveillance Consortium (observed-to-expected ratio = 1.38, 95% CI = 1.22 to 1.56) but similar to TC with volumetric percent density and polygenic risk score (observed-to-expected ratio = 0.99, 95% CI = 0.87 to 1.13). However, for low-risk women (approximately 50%), MIRAI overestimated risk of overall breast cancer. MIRAI also overestimated risk of invasive breast cancer across the risk spectrum (observed-to-expected ratio = 0.68, 95% CI = 0.58 to 0.78), whereas clinical models had good calibration (observed-to-expected ratio = 0.86–0.99). Conclusion MIRAI demonstrated stronger discriminatory accuracy than clinical models for 5-year overall and invasive breast cancer risk prediction but overestimated risk for both breast cancer endpoints. AI-based risk models should consider discriminatory accuracy and calibration for invasive cancer before implementation.

Abstract Monoclonal gammopathy of undetermined significance (MGUS) is a necessary precursor condition to multiple myeloma (MM). Given the role of autophagy in modulating MM risk, we investigated whether genetic variation in autophagy‐related genes influences susceptibility to MGUS. We analyzed the association of 34,042 common autophagy‐related single nucleotide polymorphisms (SNPs) with MGUS across six independent cohorts, five from Europe and one from North America, comprising 2317 MGUS cases and 282,358 controls. We also assessed their impact on immune parameters, including absolute counts of 91 blood‐derived immune cell subsets and 103 circulating immunological proteins. Meta‐analysis revealed a genome‐wide significant association between the ULK4 rs6599175C allele and increased MGUS risk ( p = 3.35 × 10 −8 ). Carriers of this allele showed reduced counts of memory B cell subsets (IgM+CD38+CD27+ and IgD+IgM+CD27+; p = .0038 and p = .0056, respectively) and natural effector B cells (CD24+CD38+IgD+IgM+ cells; p = .0060). Although these associations were not statistically significant after multiple testing correction, they suggest a role of ULK4 in early B‐cell differentiation. Additionally, the CDKN2A rs2811710 variant showed a suggestive association with MGUS risk ( p = 2.17 × 10 −4 ), affecting transcription factor binding involved in B‐cell proliferation and differentiation, although it lacked association with immune markers. In conclusion, we confirm a genome‐wide significant association of the ULK4 locus and MGUS risk, supporting its role in early B‐cell differentiation, and identify CDKN2A as a candidate susceptibility locus warranting further investigation.

Abstract Background: Artificial intelligence (AI) algorithms based on deep learning approaches show promise in improving breast cancer (BC) detection on mammography and may also improve prediction of future BC risk compared with clinical risk prediction models. Historical clinical risk prediction models underperform among women with BBD; however, AI models for BC detection on mammography have not been tested among women at elevated BC risk due to benign breast disease (BBD). We evaluated if an AI cancer detection algorithm can aid BC risk prediction among women with BBD. Methods: We examined women without a prior BC who had an initial BBD biopsy at Mayo Clinic, Rochester, between 2002 and 2013. Incident BC occurring after BBD was identified using the Mayo Tumor Registry and supplemented by follow-up questionnaires. Only women with a full field digital mammogram at least 2 years prior to BC diagnosis were eligible. A breast pathologist evaluated BBD according to increasing severity, as non-proliferative (NP), proliferative disease without atypia (PDWA) or atypical hyperplasia (AH). AI malignancy scores derived from the Transpara detection algorithm (1-10) and volumetric percent density (VPD) from Volpara (per one standard deviation, SD) were assessed from mammograms close to BBD diagnosis. Cox proportional hazards regression models were applied to estimate hazard ratios (HRs) with 95% Confidence Intervals (CIs), adjusted for age and BMI. C-statistics were estimated to assess the contribution of each factor to BC risk prediction. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance with the addition of AI malignancy score. Results: The BBD cohort with mammography consisted of 3,125 women followed for a median 12.9 years, with 250 incident BC. Of these, 221 BC occurred at least two years after the mammogram and were used in analyses. As expected, increased BC risk was associated with BBD severity [for AH HR=3.18 (95%CI: 2.04, 4.95) and for PDWA HR=1.59 (95%CI: 1.17, 2.14) compared to NP] and with higher VPD [HR=1.24 per SD (95% CI: 1.05, 1.45)] (Table ). The AI-malignancy score alone was also associated with BC risk [HR=1.16 per 1 unit score (95%CI: 1.09,1.23)] and showed similar discriminatory accuracy [C-statistic=0.626 (95%CI: 0.586, 0.665)] as the model with BBD severity and VPD combined [C-statistic=0.627, (95%CI: 0.585, 0.669)] (Table ). In models with BBD severity and VPD, the AI-malignancy score was an independent risk factor for BC [HR=1.14 (95%CI: 1.07, 1.21); PLRT<0.001] but only achieved a marginally significant improvement in discriminatory accuracy [C-statistic=0.651 (0.609, 0.693), ΔC-statistic=0.024 (95% CI: 0.000, 0.047)]. Conclusion: In this preliminary study, an AI BC detection algorithm achieved similar performance in predicting future BC risk compared to established risk factors. Citation Format: C. M. Vachon, S. Winham, M. Jensen, D. Hursh, L. Pacheco-Spann, A. Norman, J. Fischer, S. Schrup, L. Seymour, D. Gehling, S. Nyante, M. Troester, N. Karssemeijer, R. Vierkant, D. Radisky, C. Scott, S. I. Maimone, A. Degnim, M. Sherman. An AI algorithm for breast cancer detection improves future BC risk prediction among women with benign breast disease [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD4-02.

Abstract Background: Monoclonal B-cell lymphocytosis (MBL), a precursor to chronic lymphocytic leukemia (CLL), is an understudied yet common premalignant condition present in ~8 million adults in the United States. Based on flow cytometry, MBL can be classified into two immunophenotypes: CLL-type (CD5, CD23, and CD20 co-expression) and non-CLL-type (CD5-negative). While the majority of the data to date pertains to CLL-type MBL, the available evidence indicates non-CLL-type MBL is also associated with an increased risk of hematologic malignancies. Clonal hematopoiesis of indeterminate potential (CHIP) is another precursor condition associated with hematologic malignancies. The relationship between non-CLL-type MBL and CHIP and their combined effect remains unknown. Herein, we report findings on these two precursor conditions in the largest screening cohort to date. Methods: Using flow cytometry, we screened for MBL in the Mayo Clinic Biobank participants who were 40 years or older with no prior history of hematologic cancers. Using whole-exome sequencing of whole-blood DNA, CHIP was screened on 291 genes related to myeloid (56 genes) or lymphoid malignancies (235 genes). Incident hematologic malignancies were detected during follow-up using International Classification of Diseases (ICD) codes in the medical record. All diagnoses were manually reviewed for confirmation. Multivariable logistic regression was used to evaluate the association between non-CLL-type MBL and CHIP. Time to incident hematologic malignancy was calculated from date of MBL screening to the earliest date of incident malignancy, withdrawal, death, last medical visit, or study end. Overall survival (OS) was calculated from date of MBL screening to date of death or last known alive. Multivariable Cox regression, adjusted for age, sex and residency, was used to investigate associations between non-CLL-type MBL and CHIP with clinical outcomes; with results reported using hazard ratios (HR) and 95% confidence intervals (CI). Results: In total, 8,802 individuals with MBL screening and CHIP data were analyzed. Non-CLL-type MBL was detected in 249 (2.8%) individuals; 121 (49%) were male, and median age was 78 years (range, 47-95). We identified 757 (8.6%) individuals with CHIP in at least one of the 291 genes tested. The most frequently mutated CHIP genes were DNMT3A, TET2, ASXL1, PPM1D, and TP53. A total of 47 (0.5%) individuals had both non-CLL-type MBL and CHIP; the four genes with the highest number of CHIP variants in these individuals were DNMT3A (n=16), TET2 (n=10), ASXL1 (n=3), and ATM (n=3). An association between non-CLL-type MBL and any CHIP (OR=1.6, 95%CI:1.2-2.3, P=0.006) was observed. When analyzing CHIP in genes related to myeloid malignancies, the association with non-CLL-type MBL was also significant (OR=1.6, 95%CI:1.1-2.3, P=0.012); however, the association was not statistically significant for genes related to lymphoid malignancies (OR=1.6, 95%CI:0.8-3.3, P=0.165). With a median follow-up of 5.4 years, 104 individuals developed incident hematologic malignancies (66 lymphoid and 38 myeloid malignancies). Non-CLL-type MBL (HR=3.5, 95%CI:1.9-6.5, P<0.001) and CHIP (HR=3.2, 95%CI:2.1-5.1, P<0.001) were each associated with increased risk of incident hematologic malignancies. Co-existence of both precursor conditions was associated with a substantially higher risk of incident hematologic malignancies (HR=10.3, 95%CI:4.0-26.2, P<0.001) compared to individuals with neither precursor condition. Among individuals with non-CLL-like MBL, any CHIP was a borderline significant predictor for incident hematologic malignancy (HR=3.6, 95%CI:1.0-12.9, P=0.049). Compared to individuals with no precursor conditions, OS was not significantly different in those with non-CLL-type MBL-only (HR=1.1, 95%CI:0.8-1.5, P=0.40), those with CHIP-only (HR=1.2, 95%CI:0.99-1.4, P=0.06), or those with both CHIP and non-CLL-type MBL (HR=1.3, 95%CI:0.8-2.1, P=0.39). Conclusions: In the largest screening cohort to date evaluating the relationship of CHIP and non-CLL-type MBL, we found that non-CLL-type MBL and CHIP are associated with each other. Individuals with non-CLL-type MBL were more likely to have CHIP variants in DNMT3A, TET2, ASXL1, and ATM. Among individuals with non-CLL-type MBL, the presence of any CHIP was associated with increased risk of developing hematologic malignancy.

Abstract Background Mosaic chromosomal alterations (mCAs) are large-scale somatic gains, losses, or copy-neutral loss of heterozygosity (CNLOH) detectable by genotyping of peripheral blood DNA. They represent a form of clonal hematopoiesis and are observed in 0.5-5% of cancer-free individuals. The presence of any autosomal mCA has been associated with >3-fold risk of hematological malignancies. In particular, mCAs occurring in regions related to lymphoid malignancies have an 11-fold increased risk of lymphoid malignancies and a 68.6-fold risk for chronic lymphocytic leukemia (CLL). Monoclonal B-cell lymphocytosis (MBL), the precursor state to CLL, is defined by <5×10⁹/L clonal B cells in otherwise healthy individuals and is classified as low-count (<0.5x109/L) or high-count (0.5-5x10⁹/L) based on clonal B-cell count. MBL is also associated with increased risk of lymphoid malignancy (beyond CLL), with 4.3-fold and 74-fold risk in individuals with low- and high-count MBL, respectively. We previously reported a significant enrichment of autosomal mCAs in MBL, demonstrating a 1.7-fold association in those with low-count MBL and a 50.5-fold association in those with high-count MBL. Whether mCAs independently predict lymphoid malignancy risk after accounting for MBL status is unknown. Here, we evaluated the impact of mCAs on risk of incident lymphoid malignancies in individuals with MBL and in individuals screened negative for MBL. Methods CLL-type MBL (CD5 positive) was identified using flow cytometry assay on peripheral blood. Participants were age ≥40, had no prior hematologic malignancy, and were enrolled through the Mayo Clinic Biobank or the Division of Hematology at Mayo Clinic, Rochester. Participants were followed for incident lymphoid malignancy, including CLL, with diagnoses confirmed by medical record review. mCAs with cell fraction >1% were detected from array-based genotyping of whole blood DNA. Cox proportional hazards models, adjusted for age and sex, estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of developing an incident lymphoid malignancy in individuals with autosomal mCA versus those without an autosomal mCA. Results In 4,331 participants with available MBL and mCA status, the median age was 67 years, and 45% were male. Flow cytometry identified 3,231 individuals who screened negative for MBL, 754 individuals with low-count MBL, and 346 individuals with high-count MBL. Autosomal mCAs were present in 6.5% of individuals without MBL, 7.6% with low-count MBL, and 71.1% with high-count MBL. Over a median follow-up of 6.9 years, 237 individuals developed an incident lymphoid malignancy. The most common types were CLL, multiple myeloma, and diffuse large B-cell lymphoma. Ignoring MBL status, the presence of an autosomal mCA was associated with increased risk of lymphoid malignancy (HR = 2.65; 95% CI: 1.45–4.86; P = 0.002), replicating previous results. However, after excluding individuals with MBL, this association attenuated; those with mCA alone did not have a significantly elevated risk (HR = 1.60; 95% CI: 0.57-4.52; P = 0.38) compared to individuals without MBL or mCA. We next examined the effect of mCAs among individuals with MBL. In those with low-count MBL, the most frequent mCA events were 13q loss (2.1%), 12 gain (1.9%), and 4q CNLOH (1.2%), and individuals with an mCA had a borderline significant risk of developing a lymphoid malignancy (HR = 2.78; 95% CI: 1.01–7.67; P = 0.049) compared to those without an mCA. Similarly, in those with high-count MBL, the most frequent mCA events were 13q loss/CNLOH (31.2%), 12 gain (13.0%), and 11q loss (4.9%), and the presence of an mCA conferred an increased risk compared to those without mCA (HR = 1.51; 95% CI: 1.09-2.08; P = 0.012). Conclusions Autosomal mCAs were more frequent in individuals with high-count MBL compared to those with low-count or no MBL. mCAs showed limited predictive value for lymphoid malignancy among individuals without MBL. In contrast, in individuals with low-count MBL, autosomal mCAs emerged as a potential biomarker for identifying those at risk of developing a lymphoid malignancy. In high-count MBL, mCAs were also a biomarker, but were driven by known chromosomal abnormalities identified through clinical testing. These findings suggest that the utility of mCAs as a predictive marker for lymphoid malignancy is contingent on the presence of MBL and may inform risk stratification and surveillance strategies in this population.

Background/Objectives: Family history is a known risk factor for multiple myeloma (MM) and its precursor condition, monoclonal gammopathy of undetermined significance (MGUS). Previous genome-wide association studies (GWASs) have identified 35 common loci associated with MM risk and 21 associated with MGUS. The objective of this study was to identify less common and rare genetic loci predisposing to MM/MGUS through whole-exome sequencing (WES)-based linkage analysis. Methods:Multipoint linkage analysis was conducted using the Multipoint Engine for Rapid Likelihood Inference (MERLIN) with the Lander–Green algorithm on germline WES data from 79 pedigrees with 2 or more affected relatives (120 MM, 86 MGUS, and 21 unaffected). Genome-wide linkage was evaluated using 12,946 independent single-nucleotide variants (linkage disequilibrium r2 < 0.05). Results: Significant linkage was observed at chromosome 6q22.33–q24.2 by the non-parametric model (logarithm-of-odds (LOD) = 3.3) and suggestive linkage by the dominant parametric model (heterogeneity LOD (HLOD) = 2.5). Fourteen rare variants within this region were prioritized using family-specific partial LOD scores and in silico functional prediction tools. Nine of these variants, REPS1, THEMIS, TAAR6, AHI1, VNN1, VNN3, MTFR2/FAM54A, LAMA2, and PHACTR2, overlapped immune-regulatory regions in blood cell lines and were not previously identified in GWASs. Conclusions: This study demonstrates the utility of applying a linkage analysis framework to familial WES data for identifying genomic regions and candidate genes that may contribute to MM/MGUS predisposition. These findings provide new insight into the inherited risk and etiology of familial MM and MGUS.

Abstract We investigated the influence of 55 583 autophagy-related single-nucleotide polymorphisms (SNPs) on chronic lymphocytic leukemia (CLL) risk across 4 independent populations comprising 5472 CLL cases and 726 465 controls. We also examined their impact on overall survival (OS), time to first treatment (TTFT), autophagy flux, and immune responses. A meta-analysis of the 4 populations identified, to our knowledge, for the first time, significant associations between CDKN2A (rs3731204) and BCL2 (rs4940571, rs12457371, and rs1026825) SNPs and CLL risk, with CDKN2A showing the strongest association (P = 1.57 × 10−12). We also validated previously reported associations for FAS, BCL2, and BAK1 SNPs with CLL risk (P = 4.73 × 10−21 to 3.39 × 10−9). The CDKN2Ars3731204 and FASrs1926194 SNPs associated with increased CDKN2A and ACTA2 messenger RNA expression levels in the whole blood and/or lymphocytes (P = 5.1 × 10−7, P = 1.58 × 10−21, and P = 7.8 × 10−41), although no significant effect on autophagy flux was observed. However, associations were found between CDKN2A, BCL2, and FAS SNPs and various T-cell subsets, cytokine production, and circulating concentrations of interferon gamma, tumor necrosis factor–related apoptosis-inducing ligand, CD40, chemokine ligand 20, and interleukin-2 receptor subunit β proteins (P ≤ .005). No significant association was detected between autophagy variants and OS or TTFT, suggesting that these variants drive disease initiation rather than progression. In conclusion, this study identified 4 novel associations for CLL and provided insights into the biological pathways that influence CLL development.