Citations

PURPOSE Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown. METHODS We identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance. RESULTS On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures ( P LRT values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065, P = .01) but did not reach statistical significance for interval cancer. CONCLUSION AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.

Abstract Introduction: Benign breast disease (BBD) is an important breast cancer (BC) risk factor, which may be classified as non-proliferative disease (NPD), proliferative disease without atypia (PDWA), or atypical hyperplasia (AH) for risk stratification. Data related to the frequency of specific types of BBD and their relationship to BC risk in the Hispanic American population are limited. To address this knowledge gap, we compared BBD and associated BC risk among Hispanic white (HW) and non-Hispanic white (NHW) in New Mexico (NM). Methodology: A retrospective IRB-approved study was performed of women 19 years or older residing in six counties in NM (Bernalillo, Sandoval, Sante Fe, Socorro, Torrance, Cibola/Valencia) between 1996 and mid-2007 to compare the frequency of BBD subtypes and BC risk among HW and NHW. We excluded women who had a history of BC prior to BBD diagnosis or who were diagnosed with BC within 6 months after BBD biopsy. Race and ethnicity were self-reported by women at the time of biopsy. BBD was categorized as NPD, PDWA, or AH based on medical records. Incident BC (in-situ or invasive) was ascertained via linkage to the NM Surveillance Epidemiology End Results (SEER) Registry and BC risk was assessed using standardized incidence ratios (SIRs), comparing the observed number of BC events to that expected based on the NM SEER six-county race- and ethnicity-specific incidence rates, accounting for age and calendar period. Results: Our analysis included 3,870 HW and 6,996 NHW women with BBD. The HW were younger (47.1 vs. 51 years) compared to NHW. HW women had slightly more NPD (69.4% vs. 66.6%) but less PDWA (26.2% vs. 29.4%) and similar frequency of AH (4.3% vs. 3.9%) as compared to NHW. Over a median post-BBD follow-up period of 13 years (range 6 months-17 years), 644 BCs were observed (4.81% in HW and 6.55% in NHW). The observed BC risk among women with BBD was higher than population-based expected rates (SIR 1.98, 95% CI 1.82-2.13, p&lt;0.001) and showed expected increases in risk with increasing degrees of BBD abnormality: SIR=1.90 for NPD, 2.00 for PDWA, and 3.01 for AH. Comparing BC risk by ethnic subgroups, HW women had an overall risk of BC after BBD that was statistically indistinguishable from NHW women (SIR=2.17, 95% CI 1.86-2.48 in HW women, and SIR=1.91, 95% CI 1.73-2.08 in NHW women). Within the major subgroups of the BBD findings, there were no significant differences in risk of BC after BBD for HW versus NHW women. Conclusions: In this population-based study, benign breast disease subtypes and their associated breast cancer risk were similar among the Hispanic and non-Hispanic white women. Citation Format: Kush Raj Lohani, Andrea M. Nibbe, Robert A. Vierkant, Laura M. Pacheco-Spann, Lisa R. Seymour, Celine M. Vachon, Mark E. Sherman, Amy C. Degnim, Deirdre Hill. Comparison of benign breast disease subtypes and breast cancer risk among Hispanic and non-Hispanic white women in New Mexico. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4213.

Abstract Low count (LC) MBL is characterized by a circulating population of clonal B-cells and is a precursor state to CLL. Most individuals with LC MBL do not progress to CLL and never come to clinical attention. An active area of research is to identify factors that distinguish those individuals with MBL who progress from those who do not. Currently, 41 single nucleotide polymorphisms (SNPs) are known to be associated with CLL risk, and a portion of these SNPs have also been found to be associated with MBL risk. We hypothesize that these CLL-susceptibility SNPs associated with both MBL and CLL risk are SNPs associated with developing a B-cell clone; whereas the remaining SNPs found not to be associated with MBL risk are associated with progression of the B-cell clone (i.e., progression from MBL to CLL). To test this hypothesis, we evaluated the association of these 41 SNPs with risk of progression to CLL using a case-control study of 1,911 CLL cases and 1,579 individuals with MBL from Mayo Clinic. Individuals with MBL were identified through screening using 8-color flow cytometry assay. Individuals with LC MBL (defined by having a clonal B-cell population with the percent clonal B-cells out of total B-cell count &lt;85%) who had an immunophenotype consistent with CLL were included as controls. CLL cases were obtained through the Mayo Clinic hematology practice. DNA was genotyped from whole blood. We analyzed the 41 CLL susceptibility SNPs with risk of progression to CLL using logistic regression and estimated odds ratio (OR) and 95% confidence interval (CI), adjusting for sex and age. We evaluated the CLL polygenic risk score (CLL-PRS), comprised of a weighted average of the 41 SNPs, with risk of CLL. Out of the 41 established CLL susceptibility SNPs, 15 were associated with CLL progression from MBL to CLL (all P&lt;0.05). Nine of these 15 significant SNPs were not previously found to be associated with risk of MBL. The most significant SNPs were rs9880772 (OR=1.23, CI: 1.11-1.36, P=6.1 × 10−5), rs888096 (OR=1.25, CI: 1.12-1.38, P=3.0 × 10−5), and rs305065 (OR=1.28, CI: 1.14-1.43, P=1.5 × 10−5). One SNP (rs77551289) had a suggestive association (OR=1.21, P=0.069). The remaining 25 SNPs had no evidence of association with progression (ORs ranging between 0.9-1.1 and all P&gt;0.1). The CLL-PRS was also associated with CLL risk (continuous OR = 1.25, CI: 1.15 - 1.35, P = 6.7 × 10−8). In our study accounting for the presence of B-cell clonal population, we identified 15 out of 41 CLL-susceptibility SNPs to be significantly associated with progression from LC MBL to CLL. The remaining 25 non-significant CLL-SNPs were not associated with progression and may instead be associated with development or initiation of the B-cell clone. These findings suggest that the established CLL susceptibility SNPs may play differing role (i.e., development or progression of the clone) in the etiology of CLL. Citation Format: Raphael Mwangi, Geffen Kleinstern, Sara J. Achenbach, Dennis Robinson, Aaron D. Norman, Kari G. Rabe, Janet E. Olson, Neil E. Kay, Rosalie G. Waller, Nicholas J. Boddicker, James R. Cerhan, Esteban Braggio, Sameer A. Parikh, Curtis A. Hanson, Celine M. Vachon, Tait Shanafelt, Susan L. Slager. Genetic variants associated with progression from monoclonal B-cell lymphocytosis (MBL) to chronic lymphocytic leukemia (CLL). [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5222.

PURPOSE Image-derived artificial intelligence–based short-term risk models for breast cancer have shown high discriminatory performance compared with traditional lifestyle/familial-based risk models. The long-term performance of image-derived risk models has not been investigated. METHODS We performed a case-cohort study of 8,604 randomly selected women within a mammography screening cohort initiated in 2010 in Sweden for women age 40-74 years. Mammograms, age, lifestyle, and familial risk factors were collected at study entry. In all, 2,028 incident breast cancers were identified through register matching in May 2022 (206 incident breast cancers were found in the subcohort). The image-based model extracted mammographic features (density, microcalcifications, masses, and left-right breast asymmetries of these features) and age from study entry mammograms. The Tyrer-Cuzick v8 risk model incorporates self-reported lifestyle and familial risk factors and mammographic density to estimate risk. Absolute risks were estimated, and age-adjusted AUC model performances (aAUCs) were compared across the 10-year period. RESULTS The aAUCs of the image-based risk model ranged from 0.74 (95% CI, 0.70 to 0.78) to 0.65 (95% CI, 0.63 to 0.66) for breast cancers developed 1-10 years after study entry; the corresponding Tyrer-Cuzick aAUCs were 0.62 (95% CI, 0.56 to 0.67) to 0.60 (95% CI, 0.58 to 0.61). For symptomatic cancers, the aAUCs for the image-based model were ≥0.75 during the first 3 years. Women with high and low mammographic density showed similar aAUCs. Throughout the 10-year follow-up, 20% of all women with breast cancers were deemed high-risk at study entry by the image-based risk model compared with 7.1% using the lifestyle familial-based model ( P < .01). CONCLUSION The image-based risk model outperformed the Tyrer-Cuzick v8 model for both short-term and long-term risk assessment and could be used to identify women who may benefit from supplemental screening and risk reduction strategies.

Abstract Mammographic breast density (MBD) has been shown to be a strong, independent risk factor for breast cancer (BC) irrespective of race/ethnicity. Given the risk association of MBD and its potential to mask tumors on a mammogram, state and federal laws have mandated that women receive information regarding their personal MBD in their mammography reports. However, concerns have been raised regarding the impact of MBD notification on patient anxiety, especially written information for women who experience health disparities such as racial/ethnic minorities, lower health literacy, limited English proficiency and lower socioeconomic status. We performed a randomized controlled clinical trial to examine the impact of three different written and interpersonal approaches to MBD notification on patient anxiety, BC worry, and self-perceived BC risk, among Latinas receiving routine mammography screening at a federally qualified medical center (FQHC). We hypothesized that interpersonal education would reduce anxiety and worry, relative to the written notifications alone. The study was performed at the Baseline Clinic of Mountain Park Health Center, a FQHC in Phoenix, AZ. Women between ages 40 and 74 years presenting for screening mammogram were eligible. After providing signed informed consent, participants were randomized equally to usual care (UC- mailed notification letter); enhanced care (notification letter and MBD educational brochure designed for this study): interpersonal care (notification letter, brochure, promotora education via telephone). A stratified block randomization procedure was used with age &gt; 50 years (yes vs no), ethnicity (Hispanic vs non-Hispanic), and language preference (Spanish vs. English) as strata. Study participants completed surveys at the time of enrollment/pre-intervention (T0), at two weeks to six months after intervention was delivered (T1), and about one year after randomization (T2). Anxiety state was measured using the state anxiety subscale of the State-Trait Anxiety Inventory scale (STAI-S) (range from 20-80). BC worry was probed using the question: “How frequently do you worry about getting breast cancer someday”. The self-perceived lifetime risk of BC was rated between 0% (no chance of BC) to 100% (definitely will get BC). The proportion of participants with an increase or persistence of higher level for each outcome between time points was compared between the three study groups. The study was approved by the Mayo Clinic Institutional Review Board. 1332 Latina women were enrolled and randomized between October 2016 and October 2019. All completed the baseline (T0) survey, 928 completed T1, 632 completed T2, and 516 completed both T1 and T2 surveys. At study entry, majority of the participants were young (64.1% between age 40-49 years), had no family history of breast cancer (81.0%), had less than high school education (68.8%), and were married or partnered (67.8%). At T0, the mean (SD) anxiety STAI score was 32.5 (11.1); 51.8% with moderate or severe anxiety (score &gt; 30). With regard to BC worry, 41.3% reported worrying “sometimes”, “often”, or “almost all the time”. Further, 25.4% reported a self-perceived lifetime risk of developing BC of &gt;10% while only 6.6% had a Gail model estimated lifetime risk score of &gt;10%. There was no significant difference in changes in anxiety, BC worry or self-perceived risk from T0 to either T1 or T2 surveys between the intervention groups. In summary, this study found high levels of baseline anxiety and BC worry (despite the majority being 40-49 years old and having no family history of BC) which persisted regardless of how notification and education were delivered. Future work is needed to improve the understanding of factors that could lower anxiety and BC worry and improve risk perception in this population. Citation Format: Karthik Ghosh, Sarah Jenkins, Jennifer Ridgeway, Jessica D. Austin, Bijan Borah, Bhavika K. Patel, Deborah Rhodes, Aaron Norman, Edna P. Ramos, Matt Jewett, Crystal Gonzalez, Valentina Hernandez, Davinder Singh, Vera Suman, Celine Vachon. Breast Density Notification: Impact on anxiety, breast cancer worry, and self-perceived risk among Latinas at a federally qualified medical center. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-05-38.

Abstract Breast parenchymal patterns on radiologic images are associated with breast cancer risk. Radiomic features have been proposed as quantitative measures of parenchymal patterns. We defined intrinsic imaging phenotypes of breast parenchymal patterns based on radiomic features extracted from full field digital mammography (FFDM) in breast screening populations and assessed whether these phenotypes are associated with breast cancer risk and masking. We selected 30,000 women with 4-view FFDM exams from Hologic machines from three institutions (Hospital of the University of Pennsylvania, Mayo Clinic, and San Francisco Mammography Registry), randomly split into a training (20,000 women) and test set (10,000 women). In total, 390 radiomic features were automatically extracted from each image using a validated software pipeline, standardized, and adjusted for site differences using ComBat. We used two methods, hierarchical clustering and Principal Components (PCs) analysis, to classify significant variation among the features in the training set and replicate among the test set. Next, we applied the replicated clusters and PCs to an independent nested case-control set [1082 invasive breast cancer (BC) cases (of which 151 were Black and 893 White women, 38 other race) matched to 2837 controls (411 Black and 2345 White women, 81 other race) on age, race, timing of images, and site]. We examined associations of the clusters and PCs with invasive breast cancer risk, as well as masking [defined as a false-negative (FN) screen (124 cases and 319 matched controls) and additionally the subset with symptomatic interval cancer (IC) within 12 months of negative screen (88 cases and 223 matched controls)] using conditional logistic regression. We evaluated their association with breast cancer alone, and with adjustment for age, body mass index (BMI) and breast density assessed by Breast Imaging Reporting and Data System (BI-RADS) using likelihood ratio tests. We estimated discrimination using area under the curve (AUC) and compared AUCs for models that included the radiomic clusters and PCs with the model that included only age, BMI and density. We also stratified analyses by race (Black/White). From hierarchical clustering, we defined six statistically significant phenotype clusters (each of at least 1000 women) in the training set which were replicated in the test set. For PC Analysis, we identified six PCs in the training set, explaining 85% of the variation in texture features and reproduced these in the test set. The six radiomic phenotype clusters (P&lt; 0.001) and six PCs (P&lt; 0.001) were both associated with invasive BC, including after adjusting for age, BMI, and density (cluster P=0.004; PCs P&lt; 0.001). Improvements in discrimination of invasive BC with inclusion of PCs or clusters were more pronounced among Black women (Table). Further, the PCs (P&lt; 0.001) and clusters (P&lt; 0.001) were significantly associated with FN overall and for symptomatic IC (PCs P&lt; 0.001; clusters P=0.001), but only PCs remained significant after adjusting for age, BMI and density (PCs P=0.004 for FN; PCs P=0.007 for symptomatic IC). Discrimination of masking also improved with inclusion of both clusters and PCs (Table). We identified reproducible radiomic phenotypes that are associated with invasive BC risk, above and beyond breast density with the strongest associations for invasive BC among Black women and symptomatic interval cancers. Citation Format: Stacey Winham, Anne Marie McCarthy, Aimilia Gastounioti, Christopher Scott, Aaron Norman, Walter C. Mankowski, Lauren Pantalone, Matthew Jensen, Eric A. Cohen, Hannah Horng, Kathleen Brandt, Emily F. Conant, Karla Kerlikowske, Despina Kontos, Celine Vachon. Radiomic phenotypes of breast texture and association with breast cancer risk and masking [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr GS4-06.

Abstract Breast cancer (BC) patients with a germline CHEK2 c.1100delC variant have an increased risk of contralateral BC (CBC) and worse BC-specific survival (BCSS) compared to non-carriers. We aimed to assess the associations of CHEK2 c.1100delC, radiotherapy, and systemic treatment with CBC risk and BCSS. Analyses were based on 82,701 women diagnosed with invasive BC including 963 CHEK2 c.1100delC carriers; median follow-up was 9.1 years. Differential associations of treatment by CHEK2 c.1100delC status were tested by including interaction terms in a multivariable Cox regression model. A multi-state model was used for further insight into the relation between CHEK2 c.1100delC status, treatment, CBC risk and death. There was no evidence for differential associations of therapy with CBC risk by CHEK2 c.1100delC status The strongest association with reduced CBC risk was observed for the combination of chemotherapy and endocrine therapy [HR(95%CI): 0.66 (0.55–0.78)]. No association was observed with radiotherapy. Results from the multi-state model showed shorter BCSS for CHEK2 c.1100delC carriers versus non-carriers also after accounting for CBC occurrence [HR(95%CI) :1.30 (1.09–1.56)]. In conclusion, systemic therapy was associated with reduced CBC risk irrespective of CHEK2 c.1100delC status. Moreover, CHEK2 c.1100delC carriers had shorter BCSS, which appears not to be fully explained by their CBC risk. (Main MS: 3201 words)

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 Nonsteroidal anti-inflammatory agents (NSAID) are associated with modest inconsistent reductions in breast cancer risk in population-based cohorts, whereas two focused studies of patients with benign breast disease (BBD) have found lower risk with NSAID use. Given that BBD includes fibroinflammatory lesions linked to elevated breast cancer risk, we assessed whether NSAID use was associated with lower breast cancer risk among patients with BBD. Participants were postmenopausal women in the Cancer Prevention Study-II (CPS-II), a prospective study of cancer incidence and mortality, who completed follow-up surveys in 1997 with follow-up through June 30, 2015. History of BBD, NSAID use, and covariate data were updated biennially. This analysis included 23,615 patients with BBD and 36,751 patients with non-BBD, including 3,896 incident breast cancers over an average of 12.72 years of follow-up among participants. NSAID use, overall and by formulation, recency, duration, and pills per month was analyzed versus breast cancer risk overall and by BBD status using multivariable-adjusted Cox models; BBD status and NSAID use were modeled as time-dependent exposures. Patients with BBD who reported using NSAIDs experienced lower breast cancer risk (HR, 0.87; 95% CI, 0.78–0.97), with similar effects for estrogen receptor (ER)-positive breast cancers [HR, 0.85; 95% confidence interval (CI), 0.74–0.97] and ER-negative breast cancers (HR, 0.87; 95% CI, 0.59–1.29); among women without BBD, NSAID use was unrelated to risk (HR, 1.02; 95% CI, 0.92–1.13; Pinteraction = 0.04). Associations stratified by age, obesity, menopausal hormone use, and cardiovascular disease were similar. Among patients with BBD, NSAID use appears linked to lower breast cancer risk. Further studies to assess the value of NSAID use among patients with BBD are warranted. Prevention Relevance: We examined whether NSAID use, a modifiable exposure, is associated with breast cancer risk in postmenopausal women from the Cancer Prevention Study-II with self-reported benign breast disease, an often inflammatory condition associated with higher rates of breast cancer.

Abstract Introduction: The relationship between germline pathogenic variants (PVs) in cancer predisposition genes and ductal carcinoma in situ (DCIS) is not well established. The objective of this study is to determine the risks of DCIS and contralateral breast cancer among women with DCIS associated with germline PVs in cancer predisposition genes. Methods: Associations between pathogenic variants in 11 cancer predisposition genes (ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, MSH6, PALB2, RAD51C, and RAD51D) and DCIS were determined in case control analyses of a population-based cohort of 3876 women with DCIS and age-matched unaffected women, and in a clinical cohort of 9887 DCIS cases undergoing clinical genetic testing at Ambry Genetics and unaffected reference controls. The incidence of contralateral breast cancer risk in BRCA1, BRCA2, and PALB2 PV carriers with DCIS was also evaluated in a time-to-event analysis. Results: The mean age at diagnosis of DCIS was 50 years in the clinical testing cohort and 59 years in the population-based cohort. The frequency of PVs in 11 predisposition genes among DCIS cases was 6.9% in the clinical testing cohort and 4.9% in the population-based cohort. PVs in ATM, BRCA1, BRCA2, CHEK2, MSH6, PALB2, and RAD51D were associated with significantly increased risks (Odds Ratio (OR) &gt;2) of DCIS in the clinical testing cohort whereas only PVs in BRCA1, CHEK2, PALB2, and ATM were associated with significantly increased risks of DCIS in the population-based cohort. The cumulative incidence of contralateral breast cancer among BRCA1, BRCA2, and PALB2 PV carriers with DCIS was 11% in 5-years and 20% in 15-years. Conclusions: This study provides new insights into PVs that predispose to DCIS. In addition, it establishes an increased risk of contralateral breast cancer risk among women with DCIS who are carriers of PVs in BRCA1, BRCA2, or PALB2. These findings will guide surveillance and risk reducing strategies in germline PV carriers with DCIS. Citation Format: Huaizhi Huang, Ronan E. Couch, Holly LaDuca, Siddhartha Yadav, Eric C. Polley, Nicholas J. Boddicker, Jie Na, Rohan D. Gnanaolivu, David E. Goldgar, Tina Pesaran, Steven N. Hart, Jill S. Dolinsky, Julie R. Palmer, Lauren Teras, Alpa V. Patel, Kathryn J. Ruddy, Janet E. Olson, Celine M. Vachon, Peter Kraft, Song Yao, Amy Trentham-Dietz, Katherine L. Nathanson, Jeffrey N. Weitzel, Susan M. Domchek, Fergus J. Couch, Chunling Hu. Risks of ductal carcinoma in situ of the breast associated with pathogenic variants in cancer predisposition genes [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr A003.