Breast Cancer Risk Assessment Research Articles

Abstract 5918: The comprehensive genomic profiling in early-stage (T1-2N0-1M0) breast cancer

Abstract Background: Breast cancer (BC) is one of the three most common cancers worldwide, which is a heterogeneous group of different disease subtypes, each with its own biology and clinical characteristics. Approximately 60% of patients with early-stage BC receive adjuvant treatment, however, only a small number derive a benefit. Recurrence remains a major obstacle. Comprehensive profiling analysis in early-stage (T1-2N0-1M0) BC with different disease subtypes might help understand the tumorigenesis mechanisms and pave the way to evaluate the recurrence risk in early-stage BC. Methods: A total of 418 patients diagnosed with early-stage BC were enrolled, including 82 patients with hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-positive (HER2+) BC, 49 HR-/HER2+ patients, 239 HR+/HER2- patients, and 48 HR-/HER2- patients. Capture-based targeted sequencing was performed on tumor tissue samples using a panel consisting of 520 cancer-related genes. The genomic profiling among different disease subtypes were investigated and tumor mutational burden (TMB) was calculated. Results: Besides HER2, TP53, CDK12, and PIK3CA were the most frequently mutated genes in both HR2-/HER2+ (75.5%, 65.3%, and 36.7%) and HR2+/HER2+ BC (67.1%, 57.3%, and 36.6%). PIK3CA and TP53 were also the most frequently mutated gene in both HR-/HER2- (22.9% and 81.3%) and HR+/HER2- (54% and 23.4%) BC. Moreover, alterations in KRAS (12.5%) and GATA3 (26.8%) were frequently occurred in HR-/HER2- and HR+/HER2- BC, respectively. Furthermore, a comparable TMB level was observed among four subtypes (median TMB: 3.0 mutations/Mb for HR+/HER2- BC; 3.7 mutations/Mb for other subtypes). Next, the genomic profiling was compared between T1-2N0 and T1-2N1 disease in four subtypes, respectively. No different mutation frequencies in cancer-related genes were observed between T1-2N0 and T1-2N1 disease in both HR+/HER2+ and HR-/HER2- BC. In HR+/HER2- BC, T1-2N1 disease had a higher mutation frequency of TRRAP (4% vs. 0%, p<0.05) and a lower mutation frequency in CHD4 (0% vs. 6%, p<0.01), ERBB3 (0% vs.5%, p<0.05), and CTCF (1% vs.6%, p<0.05) than T1-2N0 disease. In HR-/HER2+ BC, patients with T1-2N1 disease had more alterations occurring in HNF1B (23% vs. 0%, p<0.05) and CDK12 (77% vs.48%, p<0.05), and less alterations occurring in NCOR1 (0% vs. 13%, p=0.096) than those with T1-2N0 disease. Moreover, a higher mutation frequency in homologous recombination signaling pathway was associated with the presence of node metastases in HR-/HER2+ BC (52.2% vs. 76.9%, p=0.082). A comparable TMB level was observed between T1-2N0 and T1-2N1 in four subtypes, respectively. Conclusions: Our work reveals the molecular heterogeneity among four subtypes, and between T1-2N0 and T1-2N1 disease in a certain subtype. These findings suggest that molecular features may also need to be taken into consideration for recurrence risk assessment in early-stage BC. Citation Format: Bo Chen, Yingzi Li, Yuchen Zhang, Guochun Zhang, Haiwei Du, Dailin Sun, Junjun Li, Ning Liao. The comprehensive genomic profiling in early-stage (T1-2N0-1M0) breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5918.

Abstract 5047: Self-supervised deep learning to assess breast cancer risk

Abstract Background: Personalized breast cancer (BC) screening adjusts the imaging modality and frequency of exams according to a woman's risk of developing BC. This can lower cost and false positives by reducing unnecessary exams and has the potential to find more cancers at a curable stage. Deep learning (DL) is a class of artificial intelligence algorithms that progressively extracts higher-level representations from raw input. A critical challenge to applying DL for BC risk prediction is that images are needed from exams performed before a possible cancer diagnosis. Large longitudinal datasets with cancer labeling are relatively scarce. Recently, new self-supervised methods have been developed which do not require labeling. Instead, they learn to recognize higher-level features by comparing two augmented images and determining if they are derived from the same original image. Methods: We developed Self-supervised AI for CAncer Risk Assessment (SAICARA), a mammography-based DL model to predict BC risk. We trained SAICARA on mammograms from the Chicago Multiethnic Epidemiologic Cohort (ChiMEC). We used the momentum contrast method in pretraining to train an encoder that produces compact representations of input mammography views. We initialized the encoders with weights obtained from training on the ImageNet dataset. We continued pretraining with 223,415 chest radiographs from the CheXpert database. Finally, we used mammograms from ChiMEC without any requirements on the exam date. We used augmentations from two different mammography views to provide better positive pairs for self-supervised learning. For fine-tuning, we trained with exams from women who were known to be cancer-free with at least 100 days of follow-up, and patients diagnosed with BC at least 30 days following the exam. Optimization was performed using a negative-log likelihood loss function which was discretized by considering quantiles of the event-time distribution. Hyperparameters were tuned using a Bayesian optimization strategy implemented by Weights and Biases. We computed the concordance index and the area under the receiver-operating characteristic curve (AUC) at two years to evaluate the discriminating capacity of the predicted risk of BC. We evaluated our model using 10-fold cross-validation. Results: In the final phase of pretraining, we used 13,194 mammography exams from 2,835 women. For fine-tuning, we used 4,849 exams from 1,418 women who were known to be cancer-free at their last follow-up, and 1,760 exams from 744 women who had exams that were followed by a BC diagnosis. SAICARA achieved a mean concordance index of 0.62 (standard deviation, SD = 0.11) and a mean AUC of 0.61 (SD = 0.09). Conclusion: Self-supervised DL holds promise as a technique for improving the performance of image-based BC risk prediction models. Citation Format: Anna Woodard, Olasubomi J. Omoleye, Rachna Gupta, Fangyuan Zhao, Aarthi Koripelly, Ian Foster, Kyle Chard, Toshio F. Yoshimatsu, Yonglan Zheng, Dezheng Huo, Olufunmilayo I. Olopade. Self-supervised deep learning to assess breast cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5047.

Factors Associated with Mammography Screening Among Women Living in Rural Areas.

AIM:This study aims to assess the factors of mammography screening among women living in rural areas.Method:The study was planned in a cross-sectional type. The sample of the study consisted of 139 women admitted to two family health centers in Istanbul who were aged 40 and over, literate, not pregnant or breastfeeding, and not diagnosed with breast cancer. Data was collected between July and November 2018. Data were collected using interview form, Breast Cancer Health Belief Model Subscales of Susceptibility, health motivation, mammography benefits, mammography barriers, Mammography Self- Efficacy Scale, Breast Cancer Fear Scale, Breast Cancer Fatalism Scale, and Breast Cancer Risk Assessment Form. Data were analyzed with descriptive statistics, Mann–Whitney U test, Kruskal–Wallis, chi-square test, and logistic regression analysis.Results:Most of the women were in the age group of 40–50 years (66%), graduated primary school (69%), were married (91%), and were not working (80%). Most of the women had middle-income (78%) and health insurance (88%). In the study, the rate of having had mammography in the last 2 years was 25%. The rate of mammography screening was significantly higher in women who knew mammography, who had a referral for mammography screening from health professionals, and who had health insurance (p < .05). Perceived benefits to mammography, perceived barriers to mammography, perceived self-efficacy, and perceived health motivation were significantly higher in women having mammography (p < .05).Conclusion:In the study, marital status, receiving health professionals’ referral regarding mammography screening, mammography self-efficacy, perceived barriers to mammography, and breast cancer risk level (p < .05) were factors associated with having mammography.

Urinary concentration of endocrine-disrupting phthalates and breast cancer risk in Indian women: A case-control study with a focus on mutations in phthalate-responsive genes

BackgroundPhthalates are known endocrine-disrupting chemicals used indiscriminately as constituents in consumer products including food processing, and packaging, cosmetics, personal care and household items. Although, few studies have assessed the risk of breast cancer on exposure to phthalates, their association with breast cancer risk in Indian women have not yet been evaluated. MethodsWe conducted a case-control study involving 171 participants. Urinary concentrations of six phthalate dieters; DMP (Dimethyl phthalate), DEP (Diethyl phthalate), DBP (Dibutyl phthalate), BBP (benzyl butyl phthalate), DEHP (Di-2-ethyl-hexyl phthalate), DINOP (Di-n-octyl phthalate) were estimated by GC-MS and geometric means were calculated. Univariate and multivariable logistic regression was performed to assess breast cancer risk on exposure to phthalates. Genes responsive to phthalates were identified through literature search and matched with NGS data, and gene-enrichment analysis was performed. ResultsSignificant associations were observed between urinary phthalate concentrations and increased risk of breast cancer for di-butyl phthalate (OR=1.5, 95% CI; 1.06, 2.11, p = 0.002) and di-2-ethyl-hexyl phthalate (>median vs ≤ median; OR=2.97, 95% CI; 1.18, 7.47, p = 0.005) in multivariable analyses. We also found several phthalate-responsive gene mutations in paired breast tumor tissues, which include PTPRD (76.19%), AR (42.86%), CYP1A1 (42.86%), CYP19A1 (23.81%), AHRR (19.05%), PIK3CA (19.05%), CYP1B1 (9.52%), RB1 (9.52%) and MMP9 (9.52%). Gene-enrichment analysis revealed that these genes form a major part of ER/PR, PPAR and HIF-1α-TGF-β signaling cascades involved in breast cancer ConclusionAlthough the sample size is small, in this first case-control study from India, DBP and DEHP were found to be associated with increased risk of invasive breast cancer and tumor tissues revealed mutations in several phthalate-responsive genes. It is, therefore suggested that human biomonitoring in India and larger studies evaluating the early life genetic and epigenetic alterations on phthalates exposure are required to establish their role in breast carcinogenesis.

Clinical tools and counseling considerations for breast cancer risk assessment and evaluation for hereditary cancer risk.

Breast cancer is a complex and heterogeneous disease. Unfortunately, it is the most common malignancy diagnosed in women in the USA, with 281,550 new cases of invasive breast cancer and 49,290 new cases of noninvasive breast cancer are diagnosed per year. In England, it is currently estimated that approximately 1 in 7 (14%) women will be diagnosed with breast cancer in their lifetime. In the UK in 2017, 54,700 women and 390 men were diagnosed with breast cancer. The risk of breast cancer is influenced by many factors, including but not limited to age, family history, reproductive history, hormonal exposure, proliferative breast lesions, physical activity, alcohol use, tobacco use, breast density, and environmental exposures. Breast cancer risk assessment is a critical part of public health. By identifying women at high risk for breast cancer, personalized recommendations can be deployed with regards to modes of screening, the age to initiate breast screening, and the frequency for completing such screens. In addition, breast cancer risk assessment can assist in determining a woman's eligibility for interventions to reduce risk, either through the use of chemoprevention medications or through surgical means with risk-reducing bilateral mastectomy. This chapter summarizes breast cancer risk assessment models and discusses interventions to reduce breast cancer risk to aid in reducing morbidity and mortality from breast cancer.

Project BRA: Breast cancer risk assessment.

10549 Background: In Kansas, breast cancer (BC) incidence is similar in Black and white women, yet Black women are 42% more likely to die from the disease. In models where screening is equal, there is no difference in survival from early-stage BC. Barriers to BC early detection in Black women include provider lack of knowledge in cancer risk, performing risk assessments and providing culturally sensitive education to patients. Our team developed a didactic and case-based learning intervention using Project ECHO (Extension for Community Health Outcomes) to improve provider knowledge on performing BC risk assessment and enhance risk stratified screening in Community Health Clinics (CHCs). A Community Advisory Board (CAB) was established to address barriers to early detection. Methods: CHCs participated in five 1-hour ECHO sessions June thru August 2021. Session topics focused on calculating BC risk, community resources, and cultural sensitivity and were led by experts in breast oncology, risk, genetics, screening, and health care equity. Pre/post surveys administered to participants assessed knowledge and satisfaction and continuing education credits were offered. A CAB member survey gained insight into organizational characteristics and community reach. Asset mapping identified barriers, resources, and opportunities to promote BC screening. Descriptive statistical analyses and the RE-AIM framework were used to assess reach and scalability of the ECHO. Results: Seventy-seven individuals from 16 CHCs registered to participate with a mean of 26 attendees at each session and 34% attending two or more. Participants were physicians (19%), advanced practice providers (18%), nurses (29%), and allied health professionals (34%). Sixty-three (82%) completed the baseline survey and 10 (13%) completed the post-ECHO survey. At baseline, 32% of participants reported lack of training and time as barriers to performing risk assessment. While post-ECHO survey responses were low, 60% reported their knowledge greatly or moderately improved across all topics. Participants reported clinical practice change in assessing personal history of cancer and collecting family history beyond first degree relatives. CAB members reported a broad range of expertise in community engagement and development (44%), direct patient care (15%), healthcare access (15%) and patient advocacy (26%). CAB education and collaboration led to support for tomosynthesis for women screened in our state funded BC screening program, Early Detection Works (EDW). Ongoing asset mapping identified gaps in access for Black women to the EDW program. Conclusions: Project BRA demonstrated successful participation in a limited series Project ECHO and achieved perceived changes in knowledge on performing BC risk assessment. Next steps include incorporating CAB informed opportunities to expand risk-based screening across Kansas and advocate for improved access to risk stratified screening.

A breast cancer (BC) risk model incorporating Tyrer-Cuzick version 8 (TCv8) and a polygenic risk score (PRS) for diverse ancestries.

557 Background: BC risk assessment is important for guiding personalized screening and risk-reducing interventions. TCv8 is used to estimate BC risk based on age, breast density, family cancer history and other clinical factors. Accuracy may be improved by combining TCv8 with a PRS. We developed and validated a PRS for diverse ancestries based on 149 common genetic variants (PRS-149) comprised of 56 ancestry-informative and 93 BC-associated variants. Here, we describe a BC risk model that combines PRS-149 with TCv8. Methods: Subjects had multigene panel testing for hereditary cancer and were negative for pathogenic variants in known BC susceptibility genes. A combined risk score (CRS), incorporating PRS-149 and TCv8, was developed based on 189,230 women, including 43,444 (23%) with a history of BC. Breast Imaging Reporting and Data System (BI-RADS) breast density measurements were available for 12,363 women. We used multivariable logistic regression to test breast density and PRS-149 for association with risk of BC. An independent test cohort of 6,030 BC-unaffected women with BI-RADS assessment was used to evaluate the effect of PRS-149 on risk stratification. Relative contributions of family history, breast density, other clinical factors in TCv8 and PRS-149 were examined by adding terms sequentially to an ANOVA model. We compared differences in classification of women as high (20%) or low/moderate (20%) remaining lifetime risk according to TCv8 versus CRS. Results: In the development cohort, increased breast density was significantly associated with higher risk of BC (p=3.0x10-6) with an effect size consistent with TCv8. PRS-149 improved BC risk prediction over age, breast density and family history (OR per unit standard deviation: 1.41, 95% CI: 1.37 – 1.46; p: 1.8x10-105). PRS-149 was weakly but significantly correlated with both family history (r=0.09) and breast density (r=0.01). After adjusting for multiple testing, no other factors were correlated with PRS-149. In the independent test cohort, PRS-149 explained 27% of CRS variability after accounting for family history, breast density and other clinical factors. Adding PRS-149 to TCv8 significantly altered risk estimates, with 16.3% (983/6,030) of patients classified differently by CRS versus TCv8. By TCv8 alone, 38.0% (2,289/6,030) of patients were classified as high-risk. Among patients who were high-risk by TCv8, 25.2% (576/2,289) were downgraded by CRS. Conclusions: This is the first BC risk model that includes breast density, family history, and a PRS based on genetically determined ancestry that is validated for diverse populations. Addition of PRS-149 improved risk prediction and substantially modified risk stratification compared to TCv8 alone. Implementation of CRS may therefore lead to improved identification of women who are likely to benefit from increased surveillance and preventive medications.

Cross-ancestry polygenic risk score for breast cancer risk assessment.

10540 Background: Breast cancer (BC) risk is influenced by many common variants with small effects. Polygenic risk scores (PRS) weight these variants based on genome-wide association studies (GWAS) and aggregate them into a single measure. PRS has primarily shown benefit in Caucasian women. We established a cross-ancestry polygenic model (caPRS) which assesses risk of breast cancer across multiple ancestries. Methods: Performance of multiple BC polygenic models, both published and developed in-house, were evaluated for each of five ancestry groups: European, African, South Asian, East Asian, and Admixed American. To account for ancestry-specific mean and variance, we computed principal components (PCs) for all women by projecting their genotypes onto PCs calculated on individuals in the 1000 Genomes Project (1KGP). We next centered each ancestry-specific PRS by subtracting the PRS predicted from a linear regression of PRS against the first four PCs in unaffected individuals. Each centered PRS was then divided by the SD of the corresponding 1KGP population. We defined a cross-ancestry polygenic model as a linear combination of the best performing PRS model for each ancestry group weighted by fractional ancestry. Association of the caPRS with breast cancer risk was tested in a validation cohort of &gt;130,000 women consisting of multiple independent cohorts (the Women’s Health Initiative, the Multiethnic Cohort, the ROOT cohort and the UK Biobank) using a multivariate logistic regression model that included caPRS, age, self-reported ancestry, personal history of ovarian cancer (when available) and first-degree family history of BC. Discrimination was assessed by the odds ratio (OR) per SD and the area under the receiver-operator curve (AUC). Results: This study included women with African/Black, East Asian, Caucasian/White, Hispanic/Latino, South Asian and ‘Other’ self-reported ancestry. The ancestry-specific models included in the caPRS ranged in size from 173 to &gt;800,000 variants. The caPRS was associated with BC risk for women in each self-reported ancestry (Table). The caPRS offered a modest increase in performance over a commonly implemented 313-SNP PRS in non-European ancestries, most significantly in African/Black women where the OR per SD increased from 1.24 (1.08 - 1.43), p-value 2.3x10-3. Conclusions: The caPRS performed well for women of any ancestry and allows flexibility to update ancestry-specific models. These results suggest the caPRS has the potential to improve the clinical utility of existing clinical risk predictors. [Table: see text]

Primary language, race, and location of care as main determining factors of decision to opt out of personalized breast cancer risk assessment.

e18567 Background: Personalized breast cancer risk assessment (CRA) and genetic counseling/testing for women have been shown to improve interventional and clinical outcomes. However, despite CRA being routinely offered to all women at our institution, not all women choose to opt into the program. This study evaluated the demographic distribution of patients that opted out of the CRA to identify potential components influencing this substantial decision. Methods: Our medical center, located in four urban and suburban locations, piloted a clinical framework to provide CRA to all women between the ages of 25 and 75 with a qualifying mammogram. In this single institution retrospective study, we analyzed differences among patients who opted out of the program between July 20, 2020 and July 19, 2021. Data elements extracted from the electronic medical record include race, primary language, location of care, and decision to partake in CRA. Overall Chi-square tests and all pairwise comparisons with Bonferroni correction were used to statistically determine the impact of various demographics on opting out. Results: 18726 women met criteria for inclusion and 2717 (14.5%) declined CRA. Within their respective racial groups, 122 (17.6%) Asian, 1175 (16.3%) Black, 455 (14.2%) Hispanic, 112 (15%) Other, and 853 (12.4%) White opted out. Within their identified primary language, 2370 (13.7%) English, 231 (23.1%) Spanish, 35 (19.7%) Bilingual, and 81 (35.2%) Other opted out. Based on their setting of care, 837 (16.8%) Location A, 1436 (14.2%) Location B, 270 (13.8%) Location C, and 174 (10.6%) Location D opted out. The differences between demographics and opting out were statistically significant for all analyses (p &lt; .0001). Results shown in Table below. Conclusions: The main variables mediating the decision to opt out of CRA are a non-English primary language, non-White race, and location of care, potentially due to inadequate interpretation services, barriers to education, and staffing shortages. Thus, these women are less likely to receive genetic counseling/testing or supplemental screening. Our study is the first to investigate the factors contributing to the uptake of risk assessment. Further study is needed to determine the clinical impact of this striking disparity.[Table: see text]

DNA Methylation of Imprinted Genes KCNQ1, KCNQ1OT1, and PHLDA2 in Peripheral Blood Is Associated with the Risk of Breast Cancer.

Simple SummaryDNA methylation alterations of imprinted genes lead to loss of imprinting, and studies have explored the mechanism of the loss of imprinting in breast cancer development. However, the association between the methylation of imprinted genes in peripheral blood and the risk of breast cancer is largely unknown. Our study is the first to identify the CpG sites of imprinted genes associated with the risk of breast cancer by utilizing HumanMethylation450 data from public datasets. We discovered and validated that peripheral blood DNA methylation of KCNQ1, KCNQ1OT1, and PHLDA2 at chromosome 11p15.4-15.5 is associated with breast cancer susceptibility. Individuals with KCNQ1OT1-region hypermethylation (>0.474) had a lower risk of breast cancer. Additionally, the methylation level of KCNQ1OT1 was also unaffected by leukocyte heterogeneity. In summary, the KCNQ1OT1 region could be a potential biomarker for breast cancer risk assessment.Methylation alterations of imprinted genes lead to loss of imprinting (LOI). Although studies have explored the mechanism of LOI in breast cancer (BC) development, the association between imprinted gene methylation in peripheral blood and BC risk is largely unknown. We utilized HumanMethylation450 data from TCGA and GEO (n = 1461) to identify the CpG sites of imprinted genes associated with BC risk. Furthermore, we conducted an independent case-control study (n = 1048) to validate DNA methylation of these CpG sites in peripheral blood and BC susceptibility. cg26709929, cg08446215, cg25306939, and cg16057921, which are located at KCNQ1, KCNQ1OT1, and PHLDA2, were discovered to be associated with BC risk. Subsequently, the association between cg26709929, cg26057921, and cg25306939 methylation and BC risk was validated in our inhouse dataset. All 22 CpG sites in the KCNQ1OT1 region were associated with BC risk. Individuals with a hypermethylated KCNQ1OT1 region (>0.474) had a lower BC risk (OR: 0.553, 95% CI: 0.397−0.769). Additionally, the methylation of the KCNQ1OT1 region was not significantly different among B cells, monocytes, and T cells, which was also observed at CpG sites in PHLDA2. In summary, the methylation of KCNQ1, KCNQ1OT1, and PHLDA2 was associated with BC risk, and KCNQ1OT1 methylation could be a potential biomarker for BC risk assessment.

Beyond the AJR: Validation of an Artificial Intelligence Breast Cancer Risk Assessment Model Across Diverse International Cohorts

Beyond the <i>AJR</i>: Validation of an Artificial Intelligence Breast Cancer Risk Assessment Model Across Diverse International Cohorts

Latent Class Models of Early-life Trauma and Incident Breast Cancer.

Psychosocial trauma has been hypothesized to influence breast cancer risk, but little is known about how co-occurring traumas-particularly during early life-may impact incidence. We examine the relationship between multiple measures of early-life trauma and incident breast cancer. The Sister Study is a prospective cohort study of US women (n = 50,884; enrollment 2003-2009; ages 35-74). Of 45,961 eligible participants, 3,070 developed invasive breast cancer or ductal carcinoma in situ through 2017. We assessed trauma before age 18 using previously studied measures (cumulative score, individual trauma type, and substantive domain) and a six-class latent variable to evaluate co-occurring traumas. We accounted for missing data using multiple imputation and estimated hazard ratios (HRs) and 95% confidence intervals (CIs) using Cox proportional-hazards models. Approximately 49% of participants reported early-life trauma. Using the latent class variable approach, breast cancer hazard was higher among participants who had sexual trauma or household dysfunction (HR = 1.1; CI = 0.93, 1.3) or moderate (HR = 1.2; CI = 0.99, 1.4) but not high trauma (HR = 0.66; CI = 0.44, 0.99) compared to low trauma. Breast cancer HRs associated with sexual early-life trauma or household dysfunction were elevated for pre- and postmenopausal breast cancer and by estrogen receptor status. We found no effect modification by race-ethnicity. Estimated effects were attenuated with report of constant childhood social support. Breast cancer incidence varied by latent patterns of co-occurring early-life trauma. Models capturing childhood social support and trauma patterning, rather than cumulative or discrete indicators, may be more meaningful in breast cancer risk assessment.

Transgender Mastectomy: Incidence of High-Risk Pathologic Findings and the Need for Postoperative Cancer Surveillance.

Chest masculinization surgeries are one of the most common gender-affirming procedures performed. There is a need for better understanding of the risk of breast cancer and postsurgical screening in female to male (FtM) individuals. This study aimed to evaluate the incidence of high-risk pathologic findings in FtM transgender patients undergoing gender-affirming chest reconstructive surgery. Medical records were reviewed from all FtM patients undergoing gender-affirming chest reconstructive surgery from January 2010 to February 2021 by 3 plastic surgeons at the University of Pittsburgh Medical Center. Relative risk of malignant progression was used to stratify pathologic data. Subsequent management of atypical, in situ, and invasive pathology were recorded. A total of 318 patients were included in this study; the average age at surgery was 24.6 ± 8.1 years. Eighty-six patients (27%) had a family history of breast and/or ovarian cancer. Overall, 21 patients (6.6%) had some increased risk of breast cancer: 17 (5.3%) had proliferative lesions, mean age 38.2 ± 12.4 years; 2 had atypical ductal hyperplasia, ages 33.4 and 38.3 years; and 2 had invasive ductal carcinoma, ages 35.4 and 40.6 years. In this study, we found that 6.6% of FtM transgender patients undergoing top surgery had an elevated risk of breast cancer, with 1.2% of patients having a greater than 2 times risk of breast cancer. These results highlight the importance of appropriate preoperative screening as well as pathological analysis of surgical specimens to help guide clinical care. The authors advocate for a thorough breast cancer risk assessment before surgery for all patients, as well as using pathologic findings to guide postoperative cancer screening and follow-up.

135P Real-time genotyping-based breast cancer risk assessment in MyPeBS, an international randomized trial in the general population comparing risk-stratified to standard breast cancer screening (BCS)

Risk-stratified BCS, integrating personal, familial variables and a polygenic risk score (PRS) is a promising strategy that may improve current BCS outcomes. Real-time risk assessment and field implementation are some of the main challenges for such an approach.

Beyond the AJR: Impact of Routine Breast Cancer Risk Assessment on Screening Mammography Adherence Among Women in Racial and Ethnic Minority Groups.

Beyond the AJR: Impact of Routine Breast Cancer Risk Assessment on Screening Mammography Adherence Among Women in Racial and Ethnic Minority Groups.

Breast cancer risk stratification in women of screening age: Incremental effects of adding mammographic density, polygenic risk, and a gene panel

There is great promise in breast cancer risk stratification to target screening and prevention. It is unclear whether adding gene panels to other risk tools improves breast cancer risk stratification and adds discriminatory benefit on a population basis. In total, 10,025 of 57,902 women aged 46 to 73 years in the Predicting Risk of Cancer at Screening study provided DNA samples. A case-control study was used to evaluate breast cancer risk assessment using polygenic risk scores (PRSs), cancer gene panel (n= 33), mammographic density (density residual [DR]), and risk factorscollected using a self-completed 2-page questionnaire (Tyrer-Cuzick [TC] model version 8). In total, 525 cases and 1410 controls underwent gene panel testing and PRS calculation (18, 143, and/or 313 single-nucleotide polymorphisms [SNPs]). Actionable pathogenic variants (PGVs) in BRCA1/2 were found in 1.7% of cases and 0.55% of controls, and overall PGVs were found in 6.1% of cases and 1.3% of controls. A combined assessment of TC8-DR-SNP313 and gene panel provided the best risk stratification with 26.1% of controls and 9.7% of cases identified at <1.4% 10-year risk and 9.01% of controls and 23.3% of cases at ≥8% 10-year risk. Because actionable PGVs were uncommon, discrimination was identical with/without gene panel (with/without: area under the curve= 0.67, 95% CI= 0.64-0.70). Only 7 of 17 PGVs in cases resulted in actionable risk category change. Extended case (n= 644)-control (n= 1779) series with TC8-DR-SNP143 identified 18.9% of controls and only 6.4% of stage 2+ cases at <1.4% 10-year risk and 20.7% of controls and 47.9% of stage 2+ cases at ≥5% 10-year risk. Further studies and economic analysis will determine whether adding panels to PRS is a cost-effective strategy for risk stratification.

Update on the Recommendations on Breast Cancer Screening by the Cancer Expert Working Group on Cancer Prevention and Screening.

Breast cancer (BC) is the most common cancer among women in Hong Kong. The Food and Health Bureau commissioned The University of Hong Kong (HKU) to conduct the Hong Kong Breast Cancer Study (HKBCS) with the aim of identifying relevant risk factors for BC in Hong Kong and developing a locally validated BC risk assessment tool for Hong Kong Chinese women. After consideration of the most recent international and local scientific evidence including findings of the HKBCS, the Cancer Expert Working Group on Cancer Prevention and Screening (CEWG) has reviewed and updated its BC screening recommendations. Existing recommendations were preserved for women at high risk and slightly changed for women at moderate risk. The following major updates have been made concerning recommendations for other women in the general population: Women aged 44 to 69 with certain combinations of personalised risk factors (including presence of history of BC among first-degree relative, a prior diagnosis of benign breast disease, nulliparity and late age of first live birth, early age of menarche, high body mass index and physical inactivity) putting them at increased risk of BC are recommended to consider mammography screening every 2 years. They should discuss with their doctors on the potential benefits and harms before undergoing mammography screening. A risk assessment tool for local women (eg, one developed by HKU) is recommended to be used for estimating the risk of developing BC with regard to the personalised risk factors described above.

Predicting breast cancer risk in a racially diverse, community-based sample of potentially high-risk women.

Identifying women with high risk of breast cancer is necessary to study high-risk experiences and deliver risk-management care. Risk prediction models estimate individuals' lifetime risk but have rarely been applied in community-based settings among women not yet receiving specialized care. Therefore, we aimed: (1) to apply three breast cancer risk prediction models (i.e., Gail, Claus, and IBIS) to a racially diverse, community-based sample of women, and (2) to assess risk prediction estimates using survey data. An online survey was administered to women who were determined by a screening instrument to have potentially high risk for breast cancer. Risk prediction models were applied using their self-reported family and medical history information. Inclusion in the high-risk subsample required ≥20% lifetime risk per ≥1 model. Descriptive statistics were used to compare the proportions of women identified as high risk by each model. N=1053 women were initially eligible and completed the survey. All women, except one, self-reported the information necessary to run at least one model; 90% had sufficient information for >1 model. The high-risk subsample included 717 women, of which 75% were identified by one model only; 96% were identified by IBIS, 3% by Claus, <1% by Gail. In the high-risk subsample, 20% were identified by two models and 3% by all three models. Assessing breast cancer risk using self-reported data in a community-based sample was feasible. Different models identify substantially different groups of women who may be at high risk for breast cancer; use of multiple models may be beneficial for research and clinical care.

Use and Applicability of the Gail Model to Calculate Breast Cancer Risk: A Scoping Review.

Objective:To perform a scoping review of the applicability of the Gail model in different countries for different ethnicities. Methods:The review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist and search strategies based on the PICOS approach. The reviewed articles were included if they were published between 2013 and 2018 in English, Portuguese, or Spanish; were original articles available in full online; and described the use of the Gail model. The PubMed, Embase, and Web of Science data bases were searched. Results:A total of 38 articles eligible for analysis were identified, of which 16 used the Gail model to assess breast cancer risk in women, eight analyzed the applicability of this tool in their population, seven compared the tool and/or modified it according to the specific risk factors of their population, and seven cited the model in determining eligibility for chemoprevention. Conclusion:The Gail model has different applicabilities Greater effectiveness and breast cancer risk are found in developed countries.

Effect of inpatient breast cancer education and risk assessment on mammography utilization among hospitalized women post discharge

ABSTRACT Objective To evaluate prevalence of nonadherence to breast cancer screening guidelines after bedside educational intervention and informed individualized risk assessment score during an inpatient stay. Methods A prospective intervention study was conducted among 507 cancer-free (except skin cancer) women aged 50–75 years hospitalized to a general medicine service. Study intervention included one-on-one bedside education via handout and videos about breast cancer screening and informed individualized risk assessment using the Gail risk model to predict 5-year risk for breast cancer development. Study outcomes were measured using posthospitalization follow-up survey to determine if intervention resulted in improved adherence to breast cancer screening. Chi-square and unpaired t-tests were utilized to compare population characteristics. Results The mean age for the study population was 60.5 years (SD = 6.9), the mean 5-year Gail risk score was 1.77 (SD = 1), and 36% of women were African American. One hundred sixty nine (33%) hospitalized women were nonadherent to breast cancer screening recommendations. Only 15% of the nonadherent women were reachable for follow-up survey, and 42% of these women self-reported adherence to screening mammography after a mean follow-up period of 27 months. Conclusion This study provides evidence that most women who are nonadherent to breast cancer screening remain nonadherent after hospital discharge despite educational interventions. Our study intervention was only partially successful in enhancing breast cancer screening among hospitalized women who were overdue and at high risk. Further studies need to evaluate strategies to overcome the barriers and improve adherence whenever patients encounter health care system regardless of clinical locale.