DNA damage response genes (DDRG), implicated in several cancers as both predisposing risk factors as well as biomarkers for aggressiveness, have not been fully explored in multiple myeloma (MM). Herein, we analyzed disease associations of pathogenic variations in nine putative candidate genes using 3 446 MM cases and 323 233 cancer-free controls. Increased MM risk was found to be associated with inherited rare pathogenic mutations in TP53, ATM, CHEK2, KDM1A, and ARID1A, with an enrichment of these variants among individuals with early onset or family history of MM. Individuals with TP53 or ATM germline mutations are also likely to have worse overall survival. Our results suggest expansion of the phenotypic spectrum of some of these DDRG to include MM. The identification of these germline predisposition genes opens the avenue for targeted screening of higher risk individuals especially those with young-onset or a family history of plasma cell gammopathies.

Hazard ratios of high vs low risk early treatment failure.

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). 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. 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. 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. 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.

Kaplan-Meier curves of overall survival by spectra and UAMS risk-groups in the CoMMpass data. CoMMpass patients were assigned to high and low risk separately using the OS Spectra score and the UAMS score (See Methods, Comparison to the UAMS risk score). Blue and orange lines: both methods agree on patient's risk category. Purple line: Spectra correctly identified high-risk patients (poorer survival) that are misclassified as low risk by UAMS. Green line: Spectra correctly identified low-risk patients (better survival) that are misclassified as high risk by UAMS.

Classification of genetic variants remains an obstacle to realizing the full potential of clinical genetic sequencing. Because of their ability to interrogate large numbers of variants, multiplexed assays of variant effect and computational tools are viewed as a critical part of the solution to variant classification uncertainty. However, the (joint) performance of these assays and tools on novel variants has not been established. Transformation of the qualitative classification guidelines developed by the American College of Medical Genetics and Genomics (ACMG) into a quantitative Bayesian point system enables empirical validation of strength of evidence assigned to evidence criteria. Here, we derived a maximum-likelihood estimate model that converts frequentist odds ratios calculated from case-control data to proportions pathogenic and applied this model to functional assays, alone and in combination with computational tools across several domains of BRCA1. Furthermore, we defined exceptionally conserved ancestral residues (ECARs) and interrogated the performance of assays and tools at these residues in BRCA1. We found that missense substitutions in BRCA1 that fall at ECARs are disproportionately likely to be pathogenic with effect sizes similar to that of protein-truncating variants. In contrast, for substitutions falling at non-ECAR positions, concordant predictions of pathogenicity from functional assays and computational tools often fail to meet the additive assumptions of strength in ACMG guidelines. Thus, collectively, we conclude that strengths of evidence assigned by expert opinion in the ACMG guidelines are not universally applicable and require empirical validation.

Background:Breast cancer is multifactorial. Focusing on limited risk factors may miss high-risk individuals. Methods:We assessed the performance and overlap of various risk factors in identifying high-risk individuals for invasive breast cancer (BrCa) and ductal carcinoma in situ (DCIS) in 161,849 European-ancestry and 18,549 Asian-ancestry women. Discriminatory ability was evaluated using the area under the receiver operating characteristic curve (AUC). High-risk criteria included: 5-year absolute risk ≥1·66% by the Gail model [GAILbinary]; first-degree family history of breast cancer [FHbinary]; 5-year absolute risk ≥1·66% by a 313-variants polygenic risk score [PRSbinary]; and carriers of pathogenic variants in breast cancer predisposition genes [PTVbinary]. Findings:The 5-year absolute risk by PRS outperformed the Gail model in predicting BrCa (Europeansvs controls: AUCPRS=0·635 [0·632-0·638] vs AUCGail=0·492 [0·489-0·495]; Asiansvs controls: AUCPRS=0·564 [0·556-0·573] vs AUCGail=0·506 [0·497-0·514]). PRSbinary and GAILbinary identified more high-risk European than Asia individuals. High-risk proportions were higher among BrCa (16-26%) and DCIS (20-33%) compared to controls (9-15%) among young Europeans and all Asians. Fewer than 7% of BrCa, 10% of DCIS, and 3% of controls were classified as high-risk by multiple risk classifiers. Overlap between PRSbinary and PTVbinary was minimal (<0·65% Europeans, <0·15% Asians) compared to the proportion at high risk using PTVbinary alone (Europeans: 4·6%, Asians: 4·4%) and PRSbinary alone (Europeans: 13·9%, Asians: 8·5%). PRSbinary and FHbinary uniquely identified 5-6% and 9-11% of young BrCa, respectively. Interpretation:The incomplete overlap between high-risk individuals identified by PRSbinary, GAILbinary, FHbinary, and PTVbinary highlights the need for a comprehensive approach to breast cancer risk prediction.

CONTEXT:DNA damage/repair gene variants are associated with both primary ovarian insufficiency (POI) and cancer risk. OBJECTIVE:We hypothesized that a subset of women with POI and family members would have increased risk for cancer. DESIGN:Case-control population-based study using records from 1995 to 2022. SETTING:Two major Utah academic health care systems serving 85% of the state. SUBJECTS:Women with POI (n = 613) were identified using International Classification of Diseases codes and reviewed for accuracy. Relatives were linked using the Utah Population Database. INTERVENTION:Cancer diagnoses were identified using the Utah Cancer Registry. MAIN OUTCOME MEASURES:The relative risk of cancer in women with POI and relatives was estimated by comparison to population rates. Whole genome sequencing was performed on a subset of women. RESULTS:Breast cancer was increased in women with POI (OR, 2.20; 95% CI, 1.30-3.47; P = .0023) and there was a nominally significant increase in ovarian cancer. Probands with POI were 36.5 ± 4.3 years and 59.5 ± 12.7 years when diagnosed with POI and cancer, respectively. Causal and candidate gene variants for cancer and POI were identified. Among second-degree relatives of these women, there was an increased risk of breast (OR, 1.28; 95% CI, 1.08-1.52; P = .0078) and colon cancer (OR, 1.50; 95% CI, 1.14-1.94; P = .0036). Prostate cancer was increased in first- (OR, 1.64; 95% CI, 1.18-2.23; P = .0026), second- (OR, 1.54; 95% CI, 1.32-1.79; P < .001), and third-degree relatives (OR, 1.33; 95% CI, 1.20-1.48; P < .001). CONCLUSION:Data suggest common genetic risk for POI and reproductive cancers. Tools are needed to predict cancer risk in women with POI and potentially to counsel about risks of hormone replacement therapy.

Clinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyze germline sequencing data of BRCA1 and BRCA2 from 96,691 female breast cancer cases and 302,116 controls from three studies: the BRIDGES study of the Breast Cancer Association Consortium, the Cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observe 11,207 BRCA1 and BRCA2 variants, with 6909 being coding, covering 23.4% of BRCA1 and BRCA2 VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control likelihood ratio (ccLR) evidence is highly consistent with ClinVar assertions for (likely) benign or pathogenic variants; exhibiting 99.1% sensitivity and 95.3% specificity for BRCA1 and 93.3% sensitivity and 86.6% specificity for BRCA2. This approach provides case-control evidence for 787 unclassified variants; these include 579 with strong or moderate benign evidence and 10 with strong pathogenic evidence for which ccLR evidence is sufficient to alter clinical classification.

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.

11570 Background: Data regarding the heritability of rare tumors is limited and may prevent incorporation into genetic testing criteria. This study utilized the Utah Population Database (UPDB) to evaluate cancer risks among LMS cases and their relatives and the prevalence of meeting Chompret criteria (ChC) for LFS genetic testing. Methods: Between 1995-2021, 429 LMS cases with UPDB genealogies were identified from the Utah Cancer Registry. Diagnoses were confirmed, when possible, by pathology reports. Cases were individually age- and sex-matched 1:5 to population controls with similar pedigrees and follow-up (n=2145 controls). Cancers from 1966-2021 were obtained for study subjects and their first- through third-degree relatives. LFS spectrum cancers included breast, soft tissue sarcomas, osteosarcomas, CNS/brain, and adrenocortical. Hazard rate ratio (HRR) estimates of self- and familial relative cancer risks in LMS compared with controls was calculated from a Cox model adjusting for the number of relatives, degree of relatedness, and person-years at risk. Results: A 2.2-fold risk (p<0.001) of a cancer in the LFS spectrum was seen in cases with a non-uterine LMS site (n=323) at any age and a 4.5-fold risk (p<0.001) for developing an LFS cancer at age <50 y. Non-uterine LMS cases had similarly increased risks for developing a non-LFS-spectrum cancer at any age and <50y (Table). Increased risk of LFS or non-LFS-spectrum cancer was not seen in uterine LMS cases (n=106). Although increased cancer risk was not generally observed in relatives of LMS cases compared with control relatives, we observed that non-uterine LMS and their first-degree relatives had an increased risk of colorectal cancer (CRC) (Table). CRC is not an LFS cancer but is known to occur in LFS families. Excluding the LMS diagnosis, non-uterine LMS cases were more likely to meet ChC compared with controls (Table 1). Uterine LMS cases were no more likely to meet ChC than their respective controls. Conclusions: LMS is associated with cancers outside the spectrum, and further studies are needed to determine if LMS is associated with other cancer predisposition genes. Family history should be evaluated broadly and not restricted to ChC. As uterine LMS appears less likely to be associated with genetic predisposition, considering non-uterine and uterine cases separately may be important for future studies of the genetic basis of LMS. Cancer risks in LMS cases and relatives compared with controls. Non-uterine LMS =323, Controls=1615 Uterine LMS =106, Controls=503 Case HRR P FDR HRR P Case HRR P FDR HRR P LFS cancer 2.2 <0.001 1.1 0.68 0.6 0.36 1.3 0.24 LFS cancer <50 4.5 <0.001 1.2 0.72 1.3 0.77 2.1 0.08 Non-LFS cancer 2.2 <0.001 1.2 0.06 1.0 0.98 1.1 0.44 Non-LFS cancer <50 4.1 <0.001 1.2 0.38 1.5 0.52 1.4 0.35 Colorectal 2.7 0.04 1.6 0.02 1.1 0.91 1.4 0.41 HRR=hazard ratio; FDR=first degree relative.

Obesity has been associated with non-Hodgkin lymphoma (NHL), but the evidence is inconclusive. We examined the association between genetically determined adiposity and four common NHL subtypes: diffuse large B-cell lymphoma (DLBCL), follicular lymphoma, chronic lymphocytic leukemia, and marginal zone lymphoma, using eight genome-wide association studies of European ancestry (N = 10,629 cases, 9505 controls) and constructing polygenic scores for body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-hip ratio adjusted for BMI (WHRadjBMI). Higher genetically determined BMI was associated with an increased risk of DLBCL [odds ratio (OR) per standard deviation (SD) = 1.18, 95% confidence interval (95% CI): 1.05-1.33, p = .005]. This finding was consistent with Mendelian randomization analyses, which demonstrated a similar increased risk of DLBCL with higher genetically determined BMI (ORper SD = 1.12, 95% CI: 1.02-1.23, p = .03). No significant associations were observed with other NHL subtypes. Our study demonstrates a positive link between a genetically determined BMI and an increased risk of DLBCL, providing additional support for increased adiposity as a risk factor for DLBCL.

Time to first-line treatment failure covariates and added predictive values.

Hazard ratios of high vs low risk of progression free survival.

Cox regression beta coefficients for overall survival (OS), progression free survival (PFS), and time to treatment failure (TTF). Beta coefficients are by per spectra standard deviation. The quantitative spectra risk score for an outcome is the weighted sum of the spectra retained in its model based on their beta coefficients.

Progression free survival spectra score. A) Quantile-quantile plot. B) Gaussian mixture modeling to identify patients at high and low risk of progression. C) PFS high-risk patients (n = 60, 50 events) had median survival of 9.7 months. PFS low-risk patients (n = 707, 342 events) had median survival of 35.7 months.

Background Breast cancer is multifactorial. Focusing on limited risk factors may miss high-risk individuals. Methods We assessed the performance and overlap of various risk factors in identifying high-risk individuals for invasive breast cancer (BrCa) and ductal carcinoma in situ (DCIS) in 161,849 European-ancestry and 18,549 Asian-ancestry women. Discriminatory ability was evaluated using the area under the receiver operating characteristic curve (AUC). High-risk criteria included: 5-year absolute risk greater or equal to 1.66% by the Gail model [GAILbinary]; first-degree family history of breast cancer [FHbinary]; 5-year absolute risk greater or equal to 1.66% by a 313-variants polygenic risk score [PRSbinary]; and carriers of pathogenic variants in breast cancer predisposition genes [PTVbinary]. Findings The 5-year absolute risk by PRS outperformed the Gail model in predicting BrCa (Europeansvs controls: AUCPRS=0.635 [0.632-0.638] vs AUCGail=0.492 [0.489-0.495]; Asiansvs controls: AUCPRS=0.564 [0.556-0.573] vs AUCGail=0.506 [0.497-0.514]). PRSbinary and GAILbinary identified more high-risk European than Asia individuals. High-risk proportions were higher among BrCa (16-26%) and DCIS (20-33%) compared to controls (9-15%) among young Europeans and all Asians. Fewer than 7% of BrCa, 10% of DCIS, and 3% of controls were classified as high-risk by multiple risk classifiers. Overlap between PRSbinary and PTVbinary was minimal (<0.65% Europeans, <0.15% Asians) compared to the proportion at high risk using PTVbinary alone (Europeans: 4.6%, Asians: 4.4%) and PRSbinary alone (Europeans: 13.9%, Asians: 8.5%). PRSbinary and FHbinary uniquely identified 5-6% and 9-11% of young BrCa, respectively. Interpretation The incomplete overlap between high-risk individuals identified by PRSbinary, GAILbinary, FHbinary, and PTVbinary highlights the need for a comprehensive approach to breast cancer risk prediction. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study is funded by the Agency for Science, Technology and Research (A*STAR) and PRECISION Health Research, Singapore (PRECISE). The breast cancer genome-wide association analyses in BCAC were supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministere de l'Economie, de la Science et de l'Innovation du Quebec through Genome Quebec and grant PSR-SIIRI-701, The National Institutes of Health (U19 CA148065, X01HG007492), Cancer Research UK (C1287/A10118, C1287/A16563, C1287/A10710), and The European Union (HEALTH-F2-2009-223175 and H2020 633784 and 634935). All studies and funders are listed in Additional Materials (BCAC Funding and Acknowledgments). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the A*STAR Institutional Review Board (reference number: 2022-041). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Availability of data and materials The data used in our analyses are available upon reasonable request through BCAC, subject to data access committee approval.

Progression free survival covariates and added predictive values.

Time to first line treatment failure spectra score. A) Quantile-quantile plot. B) Gaussian mixture modeling to identify patients at high and low risk of early treatment failure. C) Patients in the spectra high-risk TTF (n = 31, 25 events) had median TTF of 9.2 months compared to low-risk patients (n = 736, 344 events) with median TTF of 32.8 months.