Tissue-resident FOLR2+ macrophages associate with CD8+ T cell infiltration in human breast cancer.

Macrophage infiltration is a hallmark of solid cancers, and overall macrophage infiltration correlates with lower patient survival and resistance to therapy. Tumor-associated macrophages, however, are phenotypically and functionally heterogeneous. Specific subsets of tumor-associated macrophage might be endowed with distinct roles on cancer progression and antitumor immunity. Here, we identify a discrete population of FOLR2+ tissue-resident macrophages in healthy mammary gland and breast cancer primary tumors. FOLR2+ macrophages localize in perivascular areas in the tumor stroma, where they interact with CD8+ T cells. FOLR2+ macrophages efficiently prime effector CD8+ T cells ex vivo. The density of FOLR2+ macrophages in tumors positively correlates with better patient survival. This study highlights specific roles for tumor-associated macrophage subsets and paves the way for subset-targeted therapeutic interventions in macrophages-based cancer therapies.

Cancer statistics, 2022.

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes. Incidence data (through 2018) were collected by the Surveillance, Epidemiology, and End Results program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2019) were collected by the National Center for Health Statistics. In 2022, 1,918,030 new cancer cases and 609,360 cancer deaths are projected to occur in the United States, including approximately 350 deaths per day from lung cancer, the leading cause of cancer death. Incidence during 2014 through 2018 continued a slow increase for female breast cancer (by 0.5% annually) and remained stable for prostate cancer, despite a 4% to 6% annual increase for advanced disease since 2011. Consequently, the proportion of prostate cancer diagnosed at a distant stage increased from 3.9% to 8.2% over the past decade. In contrast, lung cancer incidence continued to decline steeply for advanced disease while rates for localized-stage increased suddenly by 4.5% annually, contributing to gains both in the proportion of localized-stage diagnoses (from 17% in 2004 to 28% in 2018) and 3-year relative survival (from 21% to 31%). Mortality patterns reflect incidence trends, with declines accelerating for lung cancer, slowing for breast cancer, and stabilizing for prostate cancer. In summary, progress has stagnated for breast and prostate cancers but strengthened for lung cancer, coinciding with changes in medical practice related to cancer screening and/or treatment. More targeted cancer control interventions and investment in improved early detection and treatment would facilitate reductions in cancer mortality.

Breast cancer statistics, 2019.

This article is the American Cancer Society's biennial update on female breast cancer statistics in the United States, including data on incidence, mortality, survival, and screening. Over the most recent 5-year period (2012-2016), the breast cancer incidence rate increased slightly by 0.3% per year, largely because of rising rates of local stage and hormone receptor-positive disease. In contrast, the breast cancer death rate continues to decline, dropping 40% from 1989 to 2017 and translating to 375,900 breast cancer deaths averted. Notably, the pace of the decline has slowed from an annual decrease of 1.9% during 1998 through 2011 to 1.3% during 2011 through 2017, largely driven by the trend in white women. Consequently, the black-white disparity in breast cancer mortality has remained stable since 2011 after widening over the past 3 decades. Nevertheless, the death rate remains 40% higher in blacks (28.4 vs 20.3 deaths per 100,000) despite a lower incidence rate (126.7 vs 130.8); this disparity is magnified among black women aged <50 years, who have a death rate double that of whites. In the most recent 5-year period (2013-2017), the death rate declined in Hispanics (2.1% per year), blacks (1.5%), whites (1.0%), and Asians/Pacific Islanders (0.8%) but was stable in American Indians/Alaska Natives. However, by state, breast cancer mortality rates are no longer declining in Nebraska overall; in Colorado and Wisconsin in black women; and in Nebraska, Texas, and Virginia in white women. Breast cancer was the leading cause of cancer death in women (surpassing lung cancer) in four Southern and two Midwestern states among blacks and in Utah among whites during 2016-2017. Declines in breast cancer mortality could be accelerated by expanding access to high-quality prevention, early detection, and treatment services to all women.

Making sense of breast cancer risk estimates.

Individual probabilistic assessments on the risk of cancer, primary or secondary, will not be understood by most patients. That is the essence of our arguments in this paper. Greater understanding can be achieved by extensive, intensive, and detailed counseling. But since probability itself is a concept that easily escapes our everyday intuition-consider the famous Monte Hall paradox-then it would also be wise to advise patients and potential patients, to not put undue weight on any probabilistic assessment. Such assessments can be of value to the epidemiologist in the investigation of different potential etiologies describing cancer evolution or to the clinical trialist as a way to maximize design efficiency. But to an ordinary individual we cannot anticipate that these assessments will be correctly interpreted.

Segregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk prediction.

Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20-29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%-5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%-95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%-1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20-29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.

Germline mutations of breast cancer susceptibility genes through expanded genetic analysis in unselected Colombian patients.

BACKGROUND: In Colombia and worldwide, breast cancer (BC) is the most frequently diagnosed neoplasia and the leading cause of death from cancer among women. Studies predominantly involve hereditary and familial cases, demonstrating a gap in the literature regarding the identification of germline mutations in unselected patients from Latin-America. Identification of pathogenic/likely pathogenic (P/LP) variants is important for shaping national genetic analysis policies, genetic counseling, and early detection strategies. The present study included 400 women with unselected breast cancer (BC), in whom we analyzed ten genes, using Whole Exome Sequencing (WES), know to confer risk for BC, with the aim of determining the genomic profile of previously unreported P/LP variants in the affected population. Additionally, Multiplex Ligation-dependent Probe Amplification (MLPA) was performed to identify Large Genomic Rearrangements (LGRs) in the BRCA1/2 genes. To ascertain the functional impact of a recurrent intronic variant (ATM c.5496 + 2_5496 + 5delTAAG), a minigene assay was conducted. RESULTS: We ascertained the frequency of P/LP germline variants in BRCA2 (2.5%), ATM (1.25%), BRCA1 (0.75%), PALB2 (0.50%), CHEK2 (0.50%), BARD1 (0.25%), and RAD51D (0.25%) genes in the population of study. P/LP variants account for 6% of the total population analyzed. No LGRs were detected in our study. We identified 1.75% of recurrent variants in BRCA2 and ATM genes. One of them corresponds to the ATM c.5496 + 2_5496 + 5delTAAG. Functional validation of this variant demonstrated a splicing alteration probably modifying the Pincer domain and subsequent protein structure. CONCLUSION: This study described for the first time the genomic profile of ten risk genes in Colombian women with unselected BC. Our findings underscore the significance of population-based research, advocating the consideration of molecular testing in all women with cancer.

Integrating a Polygenic Risk Score into a clinical setting would impact risk predictions in familial breast cancer.

BACKGROUND: Low-impact genetic variants identified in population-based genetic studies are not routinely measured as part of clinical genetic testing in familial breast cancer (BC). We studied the consequences of integrating an established Polygenic Risk Score (PRS) (BCAC 313, PRS313) into clinical sequencing of women with familial BC in Sweden. METHODS: We developed an add-on sequencing panel to capture 313 risk variants in addition to the clinical screening of hereditary BC genes. Index patients with no pathogenic variant from 87 families, and 1000 population controls, were included in comparative PRS calculations. Including detailed family history, sequencing results and tumour pathology information, we used BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) V.6 to estimate contralateral and lifetime risks without and with PRS313. RESULTS: Women with BC but no pathogenic variants in hereditary BC genes have a higher PRS313 compared with population controls (mean+0.78 SD, p<3e-9). Implementing PRS313 in the clinical risk estimation before their BC diagnosis would have changed the recommended follow-up in 24%-45% of women. CONCLUSIONS: Our results show the potential impact of incorporating PRS313 directly in the clinical genomic investigation of women with familial BC.

Validation of the BOADICEA model in a prospective cohort of BRCA1/2 pathogenic variant carriers.

BACKGROUND: No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of BRCA1/2 PV carriers ascertained through clinical genetic centres. METHODS: We evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1614 BRCA1 and 1365 BRCA2 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 SNPs and family history information. RESULTS: The full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95% CI: 0.92 to 1.24) and in quintiles of predicted risk. Discrimination was maximised when all risk factors were considered (Harrell's C-index=0.70, 95% CI: 0.67 to 0.74; area under the curve=0.79, 95% CI: 0.76 to 0.82). The model performance was similar when evaluated separately in BRCA1 or BRCA2 PV carriers. The full model identified 5.8%, 12.9% and 24.0% of BRCA1/2 PV carriers with 5-year breast cancer risks of <1.65%, <3% and <5%, respectively, risk thresholds commonly used for different management and risk-reduction options. CONCLUSION: BOADICEA may be used to aid personalised cancer risk management and decision-making for BRCA1 and BRCA2 PV carriers. It is implemented in the free-access CanRisk tool (https://www.canrisk.org/).

Risk-reducing decisions regarding germline BRCA pathogenic variant: focusing on the timing of genetic testing and RRSO.

BACKGROUND: In Japan, the public insurance policy was revised in 2020 to cover hereditary breast and ovarian cancer (HBOC), including genetic testing and surveillance, for patients with breast cancer (BC). Consequently, the demand for risk-reducing salpingo-oophorectomy (RRSO) has increased. This study aimed to clarify the changes in the demand and timing of genetic testing and RRSO associated with public insurance coverage for HBOC in Japan. METHODS: This retrospective analysis included 350 women with germline BRCA (gBRCA) pathogenic variants (PVs) who had visited gynaecologists; they received gBRCA genetic testing at 45.1±10.6 (20-74) years. The use of medical testing and preventive treatment was compared between the preinsurance and postinsurance groups using Mann-Whitney U and Fisher's exact tests. RESULTS: The findings indicate that RRSO rates doubled from 31.4% to 62.6% among patients with gBRCA-PV. The implementation rate was 32.4% among unaffected carriers and 70.3% among BC-affected patients. Younger patients received genetic testing with significantly shorter intervals between BC diagnosis and genetic testing and between genetic testing and RRSO. CONCLUSION: Overall, the insurance coverage for HBOC patients with BC has increased the frequency of RRSO in Japan. However, a comparison between the number of probands and family members indicated that the diagnosis among family members is inadequate. The inequality in the use of genetic services by socioeconomic groups is an issue of further concern.

BRCA awareness and testing experience in the UK Jewish population: a qualitative study.

BACKGROUND: 1 in 40 UK Jewish individuals carry a pathogenic variant in BRCA1/BRCA2. Traditional testing criteria miss half of carriers, and so population genetic testing is being piloted for Jewish people in England. There has been no qualitative research into the factors influencing BRCA awareness and testing experience in this group. This study aimed to explore these and inform improvements for the implementation of population genetic testing. METHODS: Qualitative study of UK Jewish adults who have undergone BRCA testing. We conducted one-to-one semistructured interviews via telephone or video call using a predefined topic guide, until sufficient information power was reached. Interviews were audio-recorded, transcribed verbatim and interpreted using applied thematic analysis. RESULTS: 32 individuals were interviewed (28 carriers, 4 non-carriers). We interpreted five themes intersecting across six time points of the testing pathway: (1) individual differences regarding personal/family history of cancer, demographics and personal attitudes/approach; (2) healthcare professionals' support; (3) pathway access and integration; (4) nature of family/partner relationships; and (5) Jewish community factors. Testing was largely triggered by connecting information to a personal/family history of cancer. No participants reported decision regret, although there was huge variation in satisfaction. Suggestions were given around increasing UK Jewish community awareness, making information and support services personally relevant and proactive case management of carriers. CONCLUSIONS: There is a need to improve UK Jewish community BRCA awareness and to highlight personal relevance of testing for individuals without a personal/family history of cancer. Traditional testing criteria caused multiple issues regarding test access and experience. Carriers want information and support services tailored to their individual circumstances.

Excess body weight and postmenopausal breast cancer: Emerging molecular mechanisms and perspectives.

Postmenopausal, obese women have a significantly higher risk of developing estrogen receptor-positive (ER+) breast tumors, that are resistant to therapies and are associated with higher recurrence and death rates. The global prevalence of overweight/obese women has reached alarming proportions and with postmenopausal ER+ breast carcinoma (BC) having the highest incidence among the three obesity-related cancers in females (i.e., breast, endometrial and ovarian), this is of significant concern. Elucidation of the precise molecular mechanisms underlying the pro-cancerous action of obesity in ER+BC is therefore critical for disease prevention and novel treatment initiatives. Interestingly, accumulating data has shown opposing relationships between obesity and cancer in either pre- or post-menopausal women. Excess body weight is associated with an increased risk of breast cancer in postmenopausal women and a decreased risk in pre-menopausal women. Moreover, excess adiposity during early life appears to be protective against postmenopausal breast cancer, including both ER+ and ER negative BC subtypes. Overall, estrogen-dependent mechanisms have been implicated as the main driving force in obesity-related breast tumorigenesis. In the present review we discuss the epidemiologic and mechanistic aspects of association between obesity and breast tumors after menopause, mainly in the context of hormone dependency. Molecular and cellular events underlying this association present as potential avenues for both therapeutic intervention as well as the prevention of BC-promoting processes linked to excess adiposity, which is proving to be vital in an increasingly obese global population.

Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women.

BACKGROUND: Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model). METHODS: Data from the Black Women's Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls. RESULTS: AUCs were 0.577 (95% CI 0.556-0.598) for the BWHS model and 0.584 (95% CI 0.563-0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603-0.644). CONCLUSIONS: This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.

How does weight gain since the age of 18 years affect breast cancer risk in later life? A meta-analysis.

Early life factors are important risk factors for breast cancer. The association between weight gain after age 18 and breast cancer risk is inconsistent across previous epidemiologic studies. To evaluate this association, we conducted a meta-analysis according to PRISMA guidelines and the established inclusion criteria. We performed a comprehensive literature search using Medline (Ovid), Embase, Scopus, Cochrane Library, and ClinicalTrials.gov to identify relevant studies published before June 3, 2022. Two reviewers independently reviewed the articles for final inclusion. Seventeen out of 4,725 unique studies met the selection criteria. The quality of studies was assessed using the Newcastle-Ottawa Scale (NOS), and all were of moderate to high quality with NOS scores ranging from 5 to 8. We included 17 studies (11 case-control, 6 cohort) in final analysis. In case-control studies, weight gain after age 18 was associated with an increased risk of breast cancer (odds ratio [OR] = 1.25; 95% CI = 1.07-1.48), when comparing the highest versus the lowest categories of weight gain. Menopausal status was a source of heterogeneity, with weight gain after age 18 associated with an increased risk of breast cancer in postmenopausal women (OR = 1.53; 95% CI = 1.40-1.68), but not in premenopausal women (OR = 1.01; 95% CI = 0.92-1.12). Additionally, a 5 kg increase in weight was positively associated with postmenopausal breast cancer risk (OR = 1.12; 95%CI = 1.05-1.21) in case-control studies. Findings from cohort studies were identical, with a positive association between weight gain after age 18 and breast cancer incidence in postmenopausal women (relative risk [RR] = 1.30; 95% CI = 1.09-1.36), but not in premenopausal women (RR = 1.06; 95% CI = 0.92-1.22). Weight gain after age 18 is a risk factor for postmenopausal breast cancer, highlighting the importance of weight control from early adulthood to reduce the incidence of postmenopausal breast cancer.

Clinical factors associated with patterns of endocrine therapy adherence in premenopausal breast cancer patients.

INTRODUCTION: Patients with hormone receptor positive breast cancer are recommended at least five years of adjuvant endocrine therapy, but adherence to this treatment is often suboptimal. We investigated longitudinal trends in adjuvant endocrine therapy (AET) adherence among premenopausal breast cancer patients and identified clinical characteristics, including baseline comorbidities and non-cancer chronic medication use, associated with AET adherence. METHODS: We included stage I-III premenopausal breast cancer patients diagnosed during 2002-2011 and registered in the Danish Breast Cancer Group clinical database who initiated AET. We used group-based trajectory modeling to describe AET adherence patterns. We also linked patients to Danish population-based registries and fit multinomial logistic models to compute odds ratios (ORs) and 95% confidence intervals (95% CIs) associating clinical characteristics with AET adherence patterns. RESULTS: We identified three adherence patterns among 4,353 women-high adherers (57%), slow decliners (36%), and rapid decliners (6.9%). Women with stage I disease (vs. stage II; OR: 1.9, 95% CI 1.5, 2.5), without chemotherapy (vs. chemotherapy; OR: 4.3, 95% CI 3.0, 6.1), with prevalent comorbid disease (Charlson Comorbidity Index score ≥ 1 vs. 0; OR: 1.6, 95% CI 1.1, 2.3), and with a history of chronic non-cancer medication use (vs. none; OR: 1.3, 95% CI 1.0, 1.8) were more likely to be rapid decliners compared with high adherers. CONCLUSIONS: Women with stage I cancer, no chemotherapy, higher comorbidity burden, and history of chronic non-cancer medication use were less likely to adhere to AET. Taking steps to promote adherence in these groups of women may reduce their risk of recurrence.

Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case-control study.

BACKGROUND: There is increasing evidence that artificial intelligence (AI) breast cancer risk evaluation tools using digital mammograms are highly informative for 1-6 years following a negative screening examination. We hypothesized that algorithms that have previously been shown to work well for cancer detection will also work well for risk assessment and that performance of algorithms for detection and risk assessment is correlated. METHODS: To evaluate our hypothesis, we designed a case-control study using paired mammograms at diagnosis and at the previous screening visit. The study included n = 3386 women from the OPTIMAM registry, that includes mammograms from women diagnosed with breast cancer in the English breast screening program 2010-2019. Cases were diagnosed with invasive breast cancer or ductal carcinoma in situ at screening and were selected if they had a mammogram available at the screening examination that led to detection, and a paired mammogram at their previous screening visit 3y prior to detection when no cancer was detected. Controls without cancer were matched 1:1 to cases based on age (year), screening site, and mammography machine type. Risk assessment was conducted using a deep-learning model designed for breast cancer risk assessment (Mirai), and three open-source deep-learning algorithms designed for breast cancer detection. Discrimination was assessed using a matched area under the curve (AUC) statistic. RESULTS: Overall performance using the paired mammograms followed the same order by algorithm for risk assessment (AUC range 0.59-0.67) and detection (AUC 0.81-0.89), with Mirai performing best for both. There was also a correlation in performance for risk and detection within algorithms by cancer size, with much greater accuracy for large cancers (30 mm+, detection AUC: 0.88-0.92; risk AUC: 0.64-0.74) than smaller cancers (0 to < 10 mm, detection AUC: 0.73-0.86, risk AUC: 0.54-0.64). Mirai was relatively strong for risk assessment of smaller cancers (0 to < 10 mm, risk, Mirai AUC: 0.64 (95% CI 0.57 to 0.70); other algorithms AUC 0.54-0.56). CONCLUSIONS: Improvements in risk assessment could stem from enhancing cancer detection capabilities of smaller cancers. Other state-of-the-art AI detection algorithms with high performance for smaller cancers might achieve relatively high performance for risk assessment.

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population.

BACKGROUND: Generalizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population. METHODS: Demographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy in an inner-city health system (01/01/2012 to 12/31/2021). Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with fivefold cross validation. RESULTS: pCR was not associated with age, race, ethnicity, tumor staging, Nottingham grade, income, and insurance status (p > 0.05). ER-/HER2+ showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2- (all p < 0.05), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.01). Machine learning models ranked ER+/HER2-, ER-/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74-0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine learning models ranked tumor stage, pCR, nodal stage, and triple-negative subtype as top predictors of OS (AUC = 0.83-0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05). CONCLUSION: Tumor subtypes and imaging characteristics were top predictors of pCR in our inner-city population. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine learning models accurately predicted pCR and OS.

Risk-management decision-making data from a community-based sample of racially diverse women at high risk of breast cancer: rationale, methods, and sample characteristics of the Daughter Sister Mother Project survey.

BACKGROUND: To understand the dynamics that limit use of risk-management options by women at high risk of breast cancer, there is a critical need for research that focuses on patient perspectives. Prior research has left important gaps: exclusion of high-risk women not in risk-related clinical care, exclusion of non-white populations, and lack of attention to the decision-making processes that underlie risk-management choices. Our objective was to create a more inclusive dataset to facilitate research to address disparities related to decision making for breast cancer risk management. METHODS: The Daughter Sister Mother Project survey collects comprehensive information about the experiences of women at high risk of breast cancer. We collected novel measures of feelings about and reactions to cancer screenings; knowledge, barriers, and facilitators of risk-management options; beliefs related to cancer risk and risk management; and involvement with loved ones who had cancer. Eligible individuals were non-Hispanic white and non-Hispanic Black adult women who self-identified as having high risk of breast cancer and had no personal history of cancer. Between October 2018 and August 2019, 1053 respondents completed the online survey. Of these, 717 were confirmed through risk prediction modeling to have a lifetime breast cancer risk of ≥ 20%. Sociodemographic characteristics of this sample were compared to those of nationally representative samples of the US population: the 2019 Health Information National Trends Survey and the Pew Research Center report: Jewish Americans in 2020. RESULTS: The sample of 717 women at objectively high risk of breast cancer was largely (95%) recruited from non-clinical sources. Of these respondents, only 31% had seen a genetic counselor, 34% had had genetic testing specific to breast cancer risk, and 35% had seen at least one breast or cancer care specialist. The sample includes 35% Black respondents and 8% with Ashkenazi Jewish ancestry. Although encompassing a substantial range of ages, incomes, and education levels, respondents are overall somewhat younger, higher-income, and more educated than the US population as a whole. CONCLUSIONS: The DSM dataset offers comprehensive data from a community-based, diverse sample of women at high risk of breast cancer. The dataset includes substantial proportions of Black and Ashkenazi Jewish women and women who are not already in clinical care related to their breast cancer risk. This sample will facilitate future studies of risk-management behaviors among women who are and are not receiving high-risk care, and of variations in risk-management experiences across race and ethnicity.

Differential patterns of reproductive and lifestyle risk factors for breast cancer according to birth cohorts among women in China, Japan and Korea.

BACKGROUND: The birth cohort effect has been suggested to influence the rate of breast cancer incidence and the trends of associated reproductive and lifestyle factors. We conducted a cohort study to determine whether a differential pattern of associations exists between certain factors and breast cancer risk based on birth cohorts. METHODS: This was a cohort study using pooled data from 12 cohort studies. We analysed associations between reproductive (menarche age, menopause age, parity and age at first delivery) and lifestyle (smoking and alcohol consumption) factors and breast cancer risk. We obtained hazard ratios (HRs) with 95% confidence intervals (CIs) using the Cox proportional hazard regression analysis on the 1920s, 1930s, 1940s and 1950s birth cohorts. RESULTS: Parity was found to lower the risk of breast cancer in the older but not in the younger birth cohort, whereas lifestyle factors showed associations with breast cancer risk only among the participants born in the 1950s. In the younger birth cohort group, the effect size was lower for parous women compared to the other cohort groups (HR [95% CI] 0.86 [0.66-1.13] compared to 0.60 [0.49-0.73], 0.46 [0.38-0.56] and 0.62 [0.51-0.77]). Meanwhile, a higher effect size was found for smoking (1.45 [1.14-1.84] compared to 1.25 [0.99-1.58], 1.06 [0.85-1.32] and 0.86 [0.69-1.08]) and alcohol consumption (1.22 [1.01-1.48] compared to 1.10 [0.90-1.33], 1.15 [0.96-1.38], and 1.07 [0.91-1.26]). CONCLUSION: We observed different associations of parity, smoking and alcohol consumption with breast cancer risk across various birth cohorts.

Increased risk of contralateral breast cancer for BRCA1/2 wild-type, high-risk Korean breast cancer patients: a retrospective cohort study.

BACKGROUND: This study aimed to investigate the contralateral breast cancer (CBC) recurrence rate in Korean breast cancer patients according to their BRCA1/2 germline mutation status, focusing particularly on the CBC recurrence risk in BRCA1/2 negative (BRCAx) patients. METHODS: We conducted a retrospective study on 13,107 primary breast cancer patients. The patients were divided into high-risk and low-risk groups for hereditary breast cancer based on the Korean National Health Insurance Service's eligibility criteria for BRCA1/2 germline mutation testing. The high-risk group was further categorized into the BRCA mutation group, the BRCAx group, and the not tested group. We evaluated the overall survival and cumulative risk of developing CBC in these patients. RESULTS: Among 4494 high-risk patients, 973 (21.7%) underwent genetic testing for BRCA1/2 germline mutation, revealing mutations in 158 patients (16.2%). We observed significant overall survival differences across all four groups, with the high-risk, not-tested group demonstrating notably worse overall survival (p < 0.001). However, when adjusted for other prognostic factors, there was no significant differences in hazard ratio of death between the four groups. The cumulative risk of CBC also varied among the groups. Patients with BRCA1/2 mutations showed a 7.3-fold increased risk of CBC compared to the low-risk group (95% CI 4.11-13.0, p < 0.001). Interestingly, BRCAx patients also demonstrated a significantly higher risk of CBC (HR 2.77, 95% CI 1.76-4.35, p < 0.001). The prognostic importance of the BRCAx for CBC recurrence persisted after adjusting for the age and subtype, but became insignificant when the family history of breast cancer was adjusted. CONCLUSION: Breast cancer patients who are at high risk of hereditary breast cancer but with wild-type BRCA 1/2 genes (BRCAx) have increased risk of developing contralateral breast cancer when compared to the low-risk patients. More careful surveillance and follow-up can be offered to these patients especially when they have family history of breast cancer.