Vachon, Celine M

1530 Background: Regular breast cancer screening reduces mortality. The American College of Radiology (ACR) recommends annual screening mammography for asymptomatic, average risk 1) transgender and gender diverse (TGD) men (individuals assigned female sex at birth but identify as men) age 40+ and with residual breast tissue and 2) TGD women (individuals assigned male sex at birth but identify as women) age 40+ and on gender-affirming hormone therapy for 5+ years. This nationwide study investigated screening mammography in TGD individuals. Methods: Four cohorts were identified with complex algorithms of medical and pharmacy data in the OptumLabs Data Warehouse, a longitudinal, administrative insurance claims database that includes patients with commercial insurance and Medicare Advantage: 1) TGD men, 2) TGD women, 3) individuals with gender dysphoria not meeting other TGD criteria (NMOT), and 4) cisgender women -- all of whom met ACR screening criteria. The primary endpoint was the percentage of individuals with high adherence, defined as completing >75% of recommended screenings; comparisons were made across the cohorts (Chi-square test). Multivariable logistic regression was used to compare adherence between TGD men and cisgender women with adjustment for demographics and with 1:4 matching (on race/ethnicity, age group, year of healthcare plan enrollment, and duration of follow-up). Results: 10,478 TGD individuals (3,778 TGD men; 1,294 TGD women; and 5,406 with gender dysphoria NMOT) and 6,218,369 cisgender women were identified. For TGD men, TGD women, individuals with gender dysphoria NMOT, and cisgender women, high adherence was observed in 41.1% (95% confidence interval (CI): 39.5, 42.6%); 7.4% (95% CI: 6.0, 8.8%); 11.9% (95% CI: 11.0, 12.7%); and 38.3% (95% CI: 38.3, 38.4%), respectively, p < 0.0001. No screening ever in these same populations was observed in 23.6% (95% CI: 22.2, 24.9%); 81.3% (95% CI: 79.2, 83.4%), 72.8% (95% CI: 71.6, 74%), and 35.9% (95% CI: 35.8, 35.9%), respectively, p < 0.0001. When TGD men were matched and compared to 15,112 cisgender women, a differential association was observed between gender and high adherence by age group (p < 0.0001); compared to cisgender women, the likelihood of high adherence in TGD men ages 40-49 (n = 2,472), 50-59 (n = 852), 60-69 (n = 320), and 70+ (n = 134) were as follows: odds ratios (OR‘s): 1.38 (95% CI: 1.28, 1.50), p < 0.0001; 1.57 (95% CI: 1.35, 1.81), p < 0.0001; 0.812 (95% CI: 0.619, 1.065), p = 0.1316; and 1.277 (95% CI 0.835, 1.951), p = 0.2595, respectively. Conclusions: This study found that TGD men manifest relatively high adherence to screening mammography, whereas TGD women and individuals with gender dysphoria NMOT manifest low adherence. Future research should focus on improving breast cancer screening in these underserved populations.

Abstract Breast cancer risk assessment tools are widely used in clinical practice to guide decisions regarding screening timing and modality, lifestyle interventions, genetic testing, preventive therapy, and risk-reducing surgery. Although a number of tools designed for the general population are used in practice, they face a series of challenges including: (i) modest discriminatory ability due to lack of a unified model that flexibly incorporates a comprehensive set of risk factors; (ii) inability to produce subtype-specific risk estimates, relevant for tailoring screening and/or preventive strategies (e.g. prophylactic endocrine therapy that is effective only for hormone receptor positive tumors); (iii) lack of data from diverse populations to build models that work well for all women; and, (iv) scarcity of opportunities for comparative validation of different models. The Breast Cancer Risk Prediction Project (BCRPP), a collaboration currently including 20 prospective cohorts from the National Cancer Institute’s Cohort Consortium, is addressing these challenges by harmonizing, sharing, and analyzing data on over 1.8 million women, over 115, 000 of whom have developed breast cancer. The BCRPP is developing a comprehensive tool that will predict breast cancer risk, overall and by subtypes, across major racial and ethnic groups in the United States. This tool incorporates known and emerging risk factors (e.g. reproductive history, anthropometry, smoking, alcohol intake, medication use, mammography, family history, and measured genotypes). In parallel to model building and validation efforts, BCRPP investigators are developing privacy-preserving web and mobile interfaces to deliver the risk tools to patients and clinicians. Here we present descriptive data on participating cohorts (e.g. dates of enrollment, ages at enrollment, length of follow up, numbers of cases by subtype), summarize missing data patterns and our approach to model development and validation that accounts for heterogeneity in missingness patterns across cohorts, and describe data governance procedures and data sharing infrastructure (including pooled and federated analyses, to account for policy limitations on sharing individual data). Descriptive information and procedures for requesting and accessing data are available via the BCRPP data platform (https://epidataplatforms.cancer.gov/bcrpp/). Although originally assembled for risk model development, these data on a large and diverse sample of women with extensive harmonized risk factors and covariates can be a resource to address other outstanding questions in breast cancer epidemiology across time, the lifecourse, and diverse populations. Citation Format: Peter Kraft, Laura Beane-Freeman, Julie Palmer, Ellen O'Meara, Jeanine Genkinger, James Lacey, Thomas Rohan, Lauren Teras, Marian L. Neuhouser, Kala Visvanathan, Celine Vachon, Roger Milne, Christopher Haiman, Yu Chen, Heather Eliassen, Cari Kitahara, Katie O'Brien, Emily White, Garnet Anderson, I-Min Lee, Archie Campbell, Renee Fortner, Ylva Lagerros, Sven Sandin, Mia Gaudet, Montserrat Garcia-Closas, Nilanjan Chatterjee. The Breast Cancer Risk Prediction Project: a resource of 1.8 million women with diverse backgrounds from 20 prospective cohorts to improve breast cancer risk prediction and advance breast cancer epidemiology [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 3583.

Abstract Background Most breast biopsies are diagnosed as benign breast disease, with 1.5- to 4-fold increased breast cancer risk. Apart from pathologic diagnoses of atypical hyperplasia, few factors aid in breast cancer risk assessment of these patients. We assessed whether a 313–single nucleotide variation (formerly single-nucleotide polymorphism) polygenic risk score stratifies risk of benign breast disease patients. Methods We pooled data from 5 Breast Cancer Association Consortium case-control studies (mean age = 59.9 years), including 6706 participants with breast cancer and 8488 participants without breast cancer. Using logistic regression, we estimated breast cancer risk associations by self-reported benign breast disease history and strata of polygenic risk score, with median polygenic risk score category among women without benign breast disease as the referent. We assessed interactions and mediation of benign breast disease and polygenic risk score with breast cancer risk. Results Benign breast disease history was associated with increased breast cancer risk (odds ratio [OR] = 1.48, 95% confidence interval [CI] = 1.37 to 1.60; P &lt; .001). Polygenic risk score increased breast cancer risk, irrespective of benign breast disease history (Pinteraction = .48), with minimal evidence of mediation of either factor by the other. Women with benign breast disease and polygenic risk score in the highest tertile had more than twofold increased odds of breast cancer (OR = 2.73, 95% CI = 2.41 to 3.09), and those with benign breast disease and polygenic risk score in the lowest tertile experienced reduced breast cancer risk (OR = 0.79, 95% CI = 0.70 to 0.91) compared with the referent group. Women with benign breast disease and polygenic risk score in the highest decile had a 3.7-fold increase (95% CI = 3.00 to 4.61) compared with those with median polygenic risk score without benign breast disease. Conclusion Breast cancer risks are elevated among women with benign breast disease and increase progressively with polygenic risk score, suggesting that optimal combinations of these factors may improve risk stratification.

Abstract Introduction: Mammographic breast density is among the strongest risk factors for breast cancer. However, breast density is typically assessed subjectively by the radiologist according to the Breast Imaging Reporting and Data System (BI-RADS) based on 2 dimensional (2D) digital mammography (DM) images. Digital breast tomosynthesis (DBT) is quickly replacing DM and allows more detailed volumetric imaging of the breast. Advances in radiomics, the high-throughput extraction of radiologic features, has enabled characterization of breast parenchymal complexity beyond breast density alone. The purpose of this study was to compare the performance of volumetric parenchymal pattern analysis from DBT and DM with conventional breast density measurement with respect to breast cancer risk estimation. Methods: We performed a case control study among women with concurrent DM and DBT screening (Selenia Dimensions, Hologic Inc.) at our institution between 3/2011-12/2014. Cases were diagnosed with breast cancer within 1 year of screening; controls were confirmed negative or benign at 1 year follow-up, matched on race (Black, White, other/unknown) and age (5-year bins). After exclusions for imaging artifacts, craniocaudal (CC) and mediolateral oblique (MLO) views for 187 cases and 737 controls, in six image formats were assessed: 1) raw (“FOR PROCESSING”) DM; 2) processed (“FOR PRESENTATION”) DM; 3) raw DBT central projection; 4) processed DBT central projection; 5) DBT central reconstructed slice; and 6) DBT reconstructed stack. For cases, we analyzed the breast contralateral to cancer diagnosis; for controls the same breast as the matched case. We extracted radiomic features using a lattice-based approach with the publicly available CaPTk software, averaging features for each breast over CC and MLO views. We examined 3 lattice window sizes (6.4, 12.8, and 25.6 mm) and 23 resolutions for image resampling (0.075 - 2mm). We performed PCA on the resulting 487 features for each combination of window size and resolution and built conditional logistic regression models to assess the association of the first 7 principal components with breast cancer, with models including age, BMI, and BI-RADS density. For each image type we calculated the model C-statistic at all window sizes and resolutions, for a total of 2304 experimental conditions. Results: Features from reconstructed DBT scans had on average higher C-statistics across all experimental conditions. A model using only age, BMI, and BI-RADS density had a C-statistic of 0.61. Models using radiomic features plus age, BMI, and BI-RADS density had mean C-statistic of 0.68 (IQR 0.68, 0.69) for reconstructed DBT scans; for all other image types, the mean C-statistic ranged from 0.64 to 0.66. Conclusions: Incorporating volumetric breast parenchymal patterns from DBT improves breast cancer risk estimation beyond markers derived from DM and beyond conventional BI-RADS density. Citation Format: Eric A. Cohen, Omid Haji Maghsoudi, Raymond Acciavatti, Lauren Pantalone, Walter Mankowski, Alex A Nguyen, Christopher G. Scott, Stacey Winham, Andrew D. Maidment, Anne Marie McCarthy, Celine M Vachon, Emily F Conant, Despina Kontos. Volumetric parenchymal pattern analysis for breast cancer risk estimation. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P070.

Abstract As of 2021 November 29, booster vaccination against SARS-CoV-2 infection has been recommended for all individuals aged 18 years and older in the United States. A key reason for this recommendation is the expectation that a booster vaccine dose can alleviate observed waning of vaccine effectiveness (VE). Although initial reports of booster effectiveness have been positive, the level of protection from booster vaccination is unclear. We conducted two studies to assess the impact of booster vaccination, with BNT162b2 or mRNA-1273, on the incidence of SARS-CoV-2 infection between August and December 2021. We first compared SARS-CoV-2 infection incidence in cohorts of 3-dose vaccine recipients to incidence in matched cohorts of 2-dose vaccine recipients (cohort size = 24,539 for BNT162b2 and 14,004 for mRNA-1273). Additionally, we applied a test-negative study design to compare the level of protection against symptomatic infection in 3-dose recipients to that observed in recent 2-dose primary vaccine series recipients. The 3-dose recipients experienced a significantly lower incidence rate of SARS-CoV-2 infection than the matched 2-dose cohorts (BNT162b2 Incidence Rate Ratio: 0.11, 95% CI: 0.09 to 0.13 and mRNA-1273 IRR: 0.11, 95% CI: 0.08 to 0.15). Results from the test-negative study showed the third vaccine dose mitigated waning of VE, with the risk of symptomatic infection in 3-dose recipients being comparable to that observed 7 to 73 days after the primary vaccine series. These results show that 3-dose vaccine regimens with BNT162b2 or mRNA-1273 are effective at reducing SARS-CoV-2 infection and support the widespread administration of booster vaccine doses.

Abstract Background: Breast parenchymal texture features capture the patterns of texture variation on a mammogram. One such texture feature is called V, which reflects the gray scale variation on the image. Recent studies have shown that texture features, including V, are associated with breast cancer risk, independent of mammographic density (MD). However, our knowledge on the genetic basis of texture features and the shared genetic components between these features, MD, and breast cancer is limited. Methods: We conducted a genome-wide association study (GWAS) of breast texture variation, including four different assessments of V, in 7,040 European-ancestry women within the Nurses’ Health Studies and Mayo Mammography Health Study cohorts. Genotype data were imputed to the 1000 Genomes Phase 3 version 5 reference panel. The four V assessments, differ by amount of breast edge erosion and image resolution, were generated from digitized film mammograms using fully automated methods. We used linear regression to test the single-nucleotide polymorphism (SNP)-phenotype associations adjusting for age, body mass index (BMI), MD phenotypes (percent density, dense area, and nondense area), and the top four principal components. Multivariate phenotype association tests were performed to estimate summary test statistics of the four V assessments. We compared our results to GWAS of breast cancer and MD phenotypes, and identified shared susceptibility loci. We calculated genetic correlations of V measures with MD phenotypes, breast cancer, overall and by estrogen receptor status, BMI, childhood body fatness, age at menarche, and age at natural menopause. Aggregate tests were performed to assess the relationship between V measures and the above traits using genome-wide significant SNPs for these traits. Results: We identified three novel genome-wide significant loci that are associated with V measures: rs79670367 (8q24.22) in LINC01591, rs113174754 (12q22) near PGAM1P5, and rs138141444 (6q24.1) in ECT2L. 12q22 is a known locus for both MD (nondense area) and breast cancer. 8q24.22 and 6q24.1 have not previously been associated with MD or breast cancer. We identified four additional loci that are associated with MD or breast cancer, or both and are significantly associated with the V measures at the Bonferroni-corrected thresholds accounting for the number of MD or breast cancer SNPs tested: rs335189 (5q23.2) in PRDM6, rs13256025 (8p21.2) in EBF2, rs11836164 (12p12.1) in LOC105369705, and rs17817449 (16q12.2) in FTO, the directions of association are consistent with MD or breast cancer for all four loci. We observed significant genetic correlations between V measures and dense area (rg = 0.79, p = 6.0 × 10-5), percent density (rg = 0.73, p = 1.0 × 10-4), and BMI (rg = -0.36, p = 3.9 × 10-7). Additional significant relationships were observed for nondense area (z = -4.14, p = 3.5 × 10-5) and childhood body fatness (z = -4.91, p = 9.1 × 10-7) from the aggregate tests. Conclusions: This is the first GWAS of breast parenchymal texture features. These findings provide new insights into the genetic basis of texture variation and their associations with MD, breast cancer risk, and other breast cancer risk factors. Citation Format: Yuxi Liu, Xia Jiang, Constance Turman, Celine M Vachon, Rulla Tamimi, Peter Kraft. A genome-wide association study of mammographic texture variation [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-13-02.

Abstract Background: Monoclonal gammopathy of undetermined significance (MGUS) is a common pre-malignant plasma cell disorder associated with a 1% per year risk of progression to multiple myeloma or related malignancy. Since the risk factors for MGUS are poorly defined, the goal of the present study was to determine if the risk of MGUS is increased in first degree relatives of patients with known multiple myeloma (MM) or MGUS. Methods: MGUS probands (index cases) were recruited from a population-based prevalence study of MGUS in Olmsted County, MN while MM probands were recruited through the Mayo Clinic practice. Consenting probands were asked to provide contact information on all first-degree relatives ages 40 years and older. Serum samples were then collected from first-degree relatives with informed consent and subjected to agaraose-gel electrophoresis and immunofixation. The prevalence of MGUS in first-degree relatives of MM and MGUS probands was compared to population-based rates from Olmsted County using risk ratios (RR). For comparisons to the reference population, only cases detected by protein electrophoresis and confirmed by immunofixation were included so that the diagnostic strategy was identical in the two groups being compared. Results: Serum samples were obtained from 911 relatives of 329 unique families, including 493 siblings, 324 children and 94 parents of patients with MGUS or MM. Using protein electrophoresis, monoclonal protein was detected in the serum of 55 (6%) while immunofixation identified 28 additional relatives (3%), for an age- and sex-adjusted prevalence rate of 8.1% (95%CI: 6.3, 9.8). The age-specific prevalence of MGUS in first-degree relatives increased with age (1.9%, 6.9%, 11.6%, 14.6%, and 21.0% for ages 40–49, 50–59, 60–69, 70–79 and ≥80, respectively; P&lt;0.001). Based on similar MGUS detection methodology as the reference population, there was a significantly higher risk of MGUS in first-degree relatives (age-adjusted RR, 2.6, 95%CI: 1.9, 3.4) compared to the reference population. The increased risk (P&lt;0.001) was seen both in relatives of MM probands (RR, 2.0, 95%CI: 1.4, 2.8), as well as MGUS probands (RR, 3.3, 95%CI: 2.1, 4.8). This association was similar across age of proband, age and gender of relative, and relationship of the first-degree relative. When examining whether the increased risk of MGUS in relatives was specific to probands with a large monoclonal protein concentration or specific monoclonal immunoglobulin isotype (i.e. high-risk MGUS phenotype), there was suggestion of a greater risk for relatives of probands with a high (3 1.5 g/dL) M-protein (RR, 2.8, 95%CI: 2.0,3.8) compared to lower M-protein levels (RR, 1.8, 95%CI: 1.1,2.8) although the difference did not reach statistical significance (P=0.12). The risk of MGUS in relatives did not differ by proband’s isotype (IgG vs. other). Conclusions: First-degree relatives of patients with MM or MGUS have a greater than two-fold risk of MGUS compared to the general population, implying underlying genetic predisposition for these diseases and providing rationale for identifying genetic determinants of MGUS. This study also provides important baseline rates of MGUS in first-degree relatives that impact the clinical care of these patients.

Abstract Abstract 3881 Background: Chronic lymphocytic leukemia (CLL) is largely incurable neoplasm of the blood and has one of the highest familial risks of disease among cancers. Monoclonal B-cell lymphocytosis (MBL) with a CLL immunophenotypic profile (i.e., CLL-like MBL) is a precursor state to CLL. In the general adult population, the prevalence rate of MBL is 5–9% and increases with age. This is in contrast to the 13–18% rate of MBL in first-degree relatives of CLL patients from high-risk families (defined here as families with at least two confirmed living cases of CLL). Few longitudinal studies of MBL incidence exists. One study evaluated MBL status over time in a cohort of individuals with MBL from a rural valley in Northern Italy. Our work herein will be the first to evaluate the status of MBL over time in relatives of CLL patients from high-risk families. Methods: Using a cohort of 41 high-risk CLL families, we evaluated the MBL status and clonal blood B-cell counts in 94 relatives of CLL patients at two time points at least 1 year a part. High sensitivity, multi-color flow cytometry was performed on fresh or previously frozen blood samples in three laboratories: Duke University, the US Food and Drug Administration, and Mayo Clinic. The number of cell events collected for flow cytometry evaluation varied from 1 to 5 x105 cells. To assure concordance across flow cytometry laboratories, 15 samples were evaluated at multiple labs. The concordance of MBL calling was a 100%. We defined three monotypic B-cell subsets as CLL-like MBL (CD5+ CD20dim), atypical MBL (CD5+ CD20+), and non-CLL like MBL (CD5−CD20+). Results: Of the 94 relatives, the median age at consent was 57 years (range 42–93) and 39% were male. At the initial MBL screening, 70 (74%) relatives had normal immunophenotype (NIPT) and 24 (26%) had MBL (1 atypical MBL, 3 non-CLL like MBL, and 20 CLL-like MBL). Seven of the 24 individuals with MBL had monoclonal B-cell counts available at baseline screening; median = 147.4 cells/uL (range 22.7–1663). The 94 relatives underwent repeat evaluation with a median follow up time of 1.8 years (range 1–6). At the second evaluation, we found an increase in the prevalence of MBL, with 11 of the 70 (12%) initially NIPT relatives developing a new MBL clone (10 CLL-like, 1 non-CLL like). These 11 incident MBL cases were from 9 families with 2 families each having 2 relatives changing MBL status. The median number of monoclonal B-cells in these 11 incident MBL cases was 0.58 cells/uL (range 0.38–60.3). The 23 out of the 24 relatives with baseline MBL (20 CLL-like MBL and 3 non-CLL like MBL) had a persistent MBL clone. Only 6 of these 23 individuals had monoclonal B-cell counts available at both time points, and among these 6, the clonal B-cell counts decreased at follow-up screening; median difference −64.1 cells/uL (range −1204.3– 10.5). The second evaluation of the initial atypical MBL relative had too few clonal cells for flow assessment. Conclusion: We found that our CLL-like MBL clones persisted over the period of observation in relatives from high-risk CLL families; this finding is similar to that previously reported using a population-based study conducted in Northern Italy. Notably, 12% of relatives from high-risk CLL families developed incident MBL during just 2 years of follow up. Although these MBL clones are all low count (< 1500 cells/uL), these data provide further evidence that MBL is a marker of inherited predisposition to CLL. It will be important to see how our rate of change of MBL status compares to that of the general population as we continue to follow our cohort of CLL families. Disclosures: No relevant conflicts of interest to declare.

2 Background: Women with atypical hyperplasia (AH) on breast biopsy have an aggregate increased risk of breast cancer (BC), but accurate personalized risk prediction is desirable to facilitate individual clinical management decisions. Currently used models provide poor BC risk prediction for women with AH. Our goal was to develop and validate an improved risk prediction model for women with AH. Methods: From a cohort of 13,538 women with benign breast disease from 1967-2001, pathology review confirmed 699 with AH. Clinical risk factors and histologic features of the tissue biopsy were recorded, and BC events were ascertained from study questionnaires, tumor registry, and review of medical records. Using a lasso approach, 23 variables were assessed for model inclusion. Lasso-identified features were then fit in a Cox regression model to estimate BC risk. Model discrimination was assessed with C-statistics in the model-building set and in a separate external validation set. Calibration was assessed by comparing observed to predicted breast cancer counts. Results: The model-building set comprised 699 women with 142 BC events (median follow-up 8.1 years), and the external validation set comprised 461 women with 114 BC events (median follow-up 11.4 years). The final model included three covariates: age at biopsy, age squared, and number of foci of AH. Model performance was good, with a C-statistic of 0.622 (SE = 0.027) in the model-building set and 0.594 (SE = 0.029) in the external validation set. The model is well-calibrated, with observed to expected numbers of BCs nearly equal across all post-biopsy follow-up years. Conclusions: We propose a new model for predicting BC risk in women with AH based on age at biopsy and number of foci of atypia. This model provides absolute risk estimates for women with AH, has good discriminatory ability, is well-calibrated, and validates in an external cohort.