The variation in breast cancer (BC)-risk factor associations between screen-detected (SD) and non-screen-detected (NSD) tumors has been poorly studied, despite the interest of this aspect in risk assessment and prevention. This study analyzes the differences in breast cancer-risk factor associations according to detection method and tumor phenotype in Spanish women aged between 50 and 69.We examined 900 BC cases and 896 controls aged between 50 and 69, recruited in the multicase–control MCC-Spain study. With regard to the cases, 460 were detected by screening mammography, whereas 144 were diagnosed by other means. By tumor phenotype, 591 were HR+, 153 were HER2+, and 58 were TN. Lifestyle, reproductive factors, family history of BC, and tumor characteristics were analyzed. Logistic regression models were used to compare cases vs. controls and SD vs. NSD cases. Multinomial regression models (controls used as a reference) were adjusted for case analysis according to phenotype and detection method.TN was associated with a lower risk of SD BC (OR 0.30 IC 0.10–0.89), as were intermediate (OR 0.18 IC 0.07–0.44) and advanced stages at diagnosis (OR 0.11 IC 0.03–0.34). Nulliparity in postmenopausal women and age at menopause were related to an increased risk of SD BC . Nulliparity in postmenopausal women was associated with a higher risk of HR+ (OR 1.66 IC 1.15–2.40). Age at menopause was related to a greater risk of HR+ (OR 1.60 IC 1.22–2.11) and HER2+ (OR 1.59 IC 1.03–2.45) tumors.Reproductive risk factors are associated with SD BC, as are HR+ tumors. Differences in BC-risk factor associations according to detection method may be related to prevailing phenotypes among categories.The online version contains supplementary material available at 10.1007/s10552-021-01511-4. Breast cancer (BC) is the most common tumor among women worldwide. An estimated 2,261,419 new cases occurred globally in 2020 .Breast cancer is a heterogeneous disease; it includes several entities with a different natural history and response to treatment. Breast tumors can be classified into phenotypes based on the expression of hormone receptors and human epidermal growth factor receptor 2 in tumor cells . ReproduThrough European Council Recommendation of 2 December 2003 on cancer screening , the EU Tumor characteristics according to detection method have been previously studied. By comparison, non-screen-detected BC  is associated with more advanced stages at diagnosis, aggressive tumors, and poorer prognoses, whereas screen-detected tumors are more frequently diagnosed at an early stage, are of a small size, and express hormone receptors –24.A previous study in the United States has shown differences in breast cancer-risk factor associations by the method of detection . HoweverThus, we aim to analyze the differences in breast cancer-risk factor associations according to method of detection and tumor phenotype in a population of Spanish women aged 50–69.MCC-Spain is a population-based multicase–control study aimed at investigating the influence of environmental and genetic factors on different tumors  . Cases aControls were frequency-matched to cases by age, sex, and region, ensuring that there was at least one control of the same sex and within the same five-year age interval for each case in each region.http://www.mccspain.org) focused on socio-demographic characteristics, lifestyle, medical history, and family history of cancer. Additionally, subjects were provided with a semi-quantitative Food Frequency Questionnaire (FFQ), which was a modified version of a previously validated instrument in Spain [Participants had resided within the hospital catchment areas for at least 6 months. Cases and controls answered an epidemiological questionnaire in a personal interview. The questionnaire . By self-referred detection method, 460 cases were screen-detected either by organized or spontaneous screening mammography , and 144 cases were non-screen-detected . Detection method data were missing and/or inconsistent for 296 cases. The age range was defined in accordance with European Council recommendations on cancer screening programs .Detection method: BC cases were classified into screen-detected and non-screen-detected tumors, based on the answers provided in the epidemiological questionnaire. BC cases were considered screen-detected whenever participants declared that the tumor had been diagnosed by screening (organized or spontaneous screening mammography performed in the absence of symptoms) and, additionally, that this mammogram had been performed in the last 6 months. On the contrary, BC cases were classified as non-screen-detected when patients declared that the tumor had not been diagnosed by mammogram screening and, additionally, that they had not undergone a screening mammography in the last 6 months.Using a simplified version of the St Gallen International Consensus , breast Epidemiological factors included reproductive factors , history of breast cancer in first-degree relatives, and lifestyles factors .A descriptive analysis of tumor characteristics and epidemiological factors was conducted for screen-detected/non-screen-detected cases, total cases/controls, screen-detected cases/controls, non-screen-detected cases/controls, HR+ cases/controls, HER2+ cases/controls, and TN/controls. Frequencies and percentages were calculated, and significant differences were tested using the Chi-square test for categorical variables and the Wilcoxon test for continuous variables.Adjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated using multivariate logistic regression models to analyze the association between epidemiological factors and overall BC risk, as well as to investigate the differences in screen-detected/non-screen-detected cases related to tumor characteristics and epidemiological factors.A multinomial logistic regression model was conducted to examine whether risk factor associations vary according to method of detection. An additional multinomial logistic regression model was used to analyze variation in risk factor associations by tumor phenotype. In both models, controls were used as a reference group. The exponential multinomial logarithmic coefficient provided an estimate of relative risk, expressed here as the relative risk (RR) coefficient.p values were considered statistically significant below 0.05. All statistical analyses were performed using the statistical software R (version 3.6.0). Analyses were adjusted by region, educational level, age, and menopausal status.In order to assess the age-related risk factors of menopause and nulliparity, analyses were performed for the overall sample, for postmenopausal women, and for parous women.Table The multivariate logistic regression analysis for screen-detected/non-screen-detected tumors Table  shows siTable Concerning reproductive factors, the association of nulliparity and age at menopause with breast cancer risk varied by detection method for postmenopausal women only, with a 60%  and 48%  greater risk of screen-detected BC, respectively. Similar results for nulliparity and age at menopause are found in postmenopausal women for overall breast cancer cases, irrespective of the detection method (see the supplementary table).Concerning lifestyle factors, the risk factor association analysis by phenotype Table  reveals Table A possible risk association between lack of physical activity and HER2+ breast cancer is observed in postmenopausal  and parous women , with borderline significant results.Regarding reproductive factors, in postmenopausal women the strength of association of nulliparity varied according to phenotype, with an approximately 60% greater risk of HR+ BC . Age at menopause also changed risk associations depending on phenotype, with an approximately 60% greater risk of HR+ BC  and HER2+ BC . Additionally, in parous women, we also found that age at first birth was positively correlated to TN BC .This study analyzes variation in breast cancer-risk factors according to method of detection and tumor phenotype. The results showed differences between the association of screen-detected and non-screen-detected breast cancer with the reproductive risk factors of age at menopause and nulliparity. In postmenopausal women, these factors were associated with a risk of screen-detected breast cancer. Meanwhile, age at menopause and nulliparity entailed a greater risk of tumors containing positive hormone receptors. Moreover, we found that screen-detected breast cancers were less likely to be TN tumors. In addition, BMI was related to an increased risk of breast cancer, regardless of the detection method and tumor phenotype.. [There is little evidence available on the association of epidemiological risk factors with breast cancer according to method of detection. Unlike the present study, the study conducted by Sprague et al. does not Previous analyses  found thPrevious evidence has demonstrated a risk association between tumors expressing hormone receptors and reproductive factors –5, 33. IConsidering the results according to detection method and phenotype, similarities can be observed in the association between screen-detected breast cancers and HR+ tumors with reproductive factors, specifically for nulliparity and age at menopause. This fact suggests that variation in breast cancer-risk factor associations by method of detection (screen-detected vs. non-screen-detected) could be explained by underlying prevailing phenotypes. Thus, the risk association of screen-detected tumors with these reproductive factors could be explained by the fact that they are predominantly hormone-sensitive tumors, which are in turn related to reproductive factors that modify endogenous hormone exposure , 34–36. Our results do not conclusively confirm associations with other known lifestyle-related risk factors , 4, suchWith regard to self-referred data , there may be a recall bias. Nonetheless, the impact was lessened thanks to the intervention of professional interviewers and the thorough design of the epidemiological questionnaire. Another limitation in this analysis may be the limited number of cases including information on phenotype, which makes stratification difficult.The strengths of our study include its population-based design and the fact that it includes both opportunistic and organized screening, unlike most studies, which refer exclusively to the latter. As far as we know, this is one of the few studies conducted in a European context that analyzes breast cancer-risk factors according to detection method and tumor phenotype. Taking into account these results it would be advisable to consider detection method in future epidemiological studies on breast cancer. Moreover, further research on the differences between SD and NSD tumors, regarding prognostic and predictive tumor characteristics, could provide relevant information for patient management.In conclusion, we found that screen-detected breast cancers are essentially associated with reproductive risk factors, and also with tumors including positive hormone receptors. Our results seem to indicate that the differences found in breast cancer-risk factor associations according to detection method can be attributed to predominating phenotypes, given that screening mammography mostly detects hormone-sensitive tumors which are susceptible to changes caused by reproductive factors.Supplementary file1 (DOCX 17 KB)Below is the link to the electronic supplementary material.


Although mammographic density is strongly linked to the risk of breast cancer, research on the relationship between changes in density over time and the risk of breast cancer has shown conflicting results. We found in the present meta-analysis that increased breast density over time was associated with higher breast cancer risk whereas decreased breast density might be associated with lower breast cancer risk. The results of the meta-analysis constitute a potential opportunity for more individualized screening strategies based on the evolution of breast density during mammography screening.The aim of this meta-analysis was to evaluate the association between mammographic density changes over time and the risk of breast cancer. We performed a systematic literature review based on the PubMed and ISI Web of Knowledge databases. A meta-analysis was conducted by computing extracted hazard ratios (HRs) and 95% confidence intervals (CIs) for cohort studies or odds ratios (ORs) and 95% confidence interval using inverse variance method. Of the nine studies included, five were cohort studies that used HR as a measurement type for their statistical analysis and four were case–control or cohort studies that used OR as a measurement type. Increased breast density over time in cohort studies was associated with higher breast cancer risk  whereas decreased breast density over time was associated with lower breast cancer risk . Similarly, increased breast density over time was associated with higher breast cancer risk in studies presented ORs . Our findings imply that an increase in breast density over time seems to be linked to an increased risk of breast cancer, whereas a decrease in breast density over time seems to be linked to a lower risk of breast cancer. Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death in women globally, with the majority of cases arising in Western Europe and the United States .The presence of mammographically dense breast tissue, based on the amount of fibroglandular tissue in the breast representing the dense area in relation to non-dense area , has beeMammographic density, on the other hand, is a trait that is constantly changing and normally decreases with increasing age, especially during menopause . Other vAlthough the breast density is expected to decrease with increased age, the breast cancer incidence increases with age, resulting in an apparent paradox on the association between breast density and breast cancer risk . A propoSeveral studies have investigated the potential association between changes in breast density over time and breast cancer risk with conflicting results, making the interpretation of current evidence in clinical practice difficult ,20,21,22The aim of the present systematic review and meta-analysis was to gather the current evidence and to investigate the potential association between breast density changes over time and breast cancer risk.We searched the Pubmed and ISI Web of Science databases using the following searching algorithm:(Breast cancer risk OR breast cancer incidence) AND (breast density OR dense breast OR dense breast tissue OR density) AND (change OR breast density change).We considered only studies published in English to be included to our search strategy. The last search was conducted on 30th June.The search strategy was supplemented by checking the reference lists on eligible articles. Inclusion criteria were used to screen the studies based on their title and abstract. Full-text articles were evaluated for eligibility by using the exclusion and inclusion criteria.Two independent researchers (AM and AV) carried out the literature search, and in case of discrepancies, consensus between the researchers was achieved through discussion.The studies that matched the following PICOS format were included in the literature review:Population: Individuals free of breast cancer prior to study start.Intervention: Breast density changes over time.Comparison: Non-dense breast or stable breast density over time.Outcome: Invasive breast cancer (DCIS was not considered as outcome of interest).Study design: prospective or retrospective cohort studies or case–control studies.The exclusion criteria were studies that fulfilled at least one of the following criteria: lack of data regarding density change over time; lack of data regarding breast cancer risk; no multivariate analysis investigating the association between breast density change and breast cancer risk; no adequate data to be extracted for the pooled analyses; and review articles.Data from eligible studies were extracted by two researchers independently, and any discrepancies that arose were solved through conversation. The information that was extracted included: author; year of publication; type of study (prospective/retrospective and cohort/case-control); median follow-up; total number of patients; number of patients with breast cancer; definition of breast density change; change in breast density ; menopausal status; measurement type ; HR/RR/OR in multivariate analysis; 95% confidence interval (CI); and covariates in multivariate analysis.The definition of change in breast density varied among eligible studies. Four studies used BI-RADS classification  ,16,17,20Increased breast density (compared with non-dense breast) defined as A or B converted to C or D (according to BI-RADS classification); changes from <5% or 5–25% to >25–50% or >50%; or a higher percent of change than the fixed cut-off for studies using a fixed cut-off or change from a low-risk to high-risk pattern.The variation in definitions is a source of heterogeneity in the meta-analysis. To minimize this heterogeneity, we considered two different patterns of breast density change:Considering this broad definition, one study with BI-RADS classification (or classification in four categories based on the percent of density) and separate data for changes among different categories can contribute to the meta-analysis with more than one comparison .Decreased breast density (compared with stable breast density) defined as C or D converted to A or B (according to BI-RADS classification); changes from >25–50% or >50% to <5% or 2–25%; or a lower percent of change than the fixed cut-off for studies using a fixed cut-off or change from a high-risk to low-risk pattern.As described above, one study with BI-RADS classification (or classification in four categories based on the percent of density) and separate data for changes among different categories can contribute to the meta-analysis with more than one comparison.The Newcastle–Ottawa Quality Assessment Scale (NOS)  was usedHR or RR, and their accompanying 95% CI from multivariate analyses on the risk for developing breast cancer depending on change in breast density over time was extracted from cohort studies, whereas OR and 95% CI were extracted from case–control studies. HR and RR were treated as similar measures of outcome for the meta-analysis whereas OR was analyzed separately.When the same study presented data on different comparisons, we allowed more than one comparison for each study to be included to the pooled analyses in the following instances: when the different comparisons within the same study presented separate results without overlapping cohorts and when the increased or decreased breast density patterns analyzed were matched with our definitions described above.The logHR (or RR) and standard error (SE) for each comparison was calculated, and then, the inverse variance method was used for meta-analysis of cohort studies. For case–control studies (including one cohort study presenting OR), the logOR and SE for each comparison was calculated and the inverse variance method was used for meta-analysis.2 test and I2 statistics. Indications for significant heterogeneity were p < 0.05 on the Chi2 test and I2 > 50%. The fixed-effects model was selected for calculating the pooled HRs (or ORs for case–control studies) in the absence of statistical heterogeneity; otherwise, the random-effects model was selected.Heterogeneity between the studies was assessed using ChiWe also conducted meta-regression analyses to investigate the impact of study characteristics on the study estimates of relative measures. The explanatory factors in the meta-regression analyses were the sample size, the inclusion of BMI on multivariate analysis (yes vs. no), and the type of measure for breast density .p < 0.05. Publication bias was evaluated by analyzing asymmetry in SE-based funnel plots.The results of the meta-analysis were graphically presented as forest plots and were considered statistically significant if The meta-analysis was conducted using the Review Manager (RevMan) 5.3 software.As described in The inclusion and exclusion criteria were applied to full-text articles to find eligible studies. The following are the grounds for eliminating seven of the records: four studies did not have data regarding density change over time; two studies did not have adequate data for extraction; and one study was a review article. Thus, seven records were excluded and nine were included in the meta-analysis.We analyzed data from nine different studies ,20,21,22The quality of the nine eligible studies was assessed, as shown in Four cohort studies with nine comparisons (using different categories of breast density changes over time) were included in the pooled meta-analysis. The studies showed a higher risk for breast cancer in women with increased breast density compared with women with non-dense breast tissue  showed a pooled OR of 0.90 (95% CI = 0.79–1.03) .p = 0.007).In pooled estimates for the association between increased breast density and breast cancer risk, none of the explanatory factors were found to be statistically significant in the meta-regression analyses except from the presence of BMI in multivariate analysis that was related to lower effect size for the association (regression coefficient (β) = −0.886; p < 0.001; sample size, β = 1.755; p < 0.001; type of measure for breast density, β = −2.192; p = 0.008).In pooled estimates for the association between decreased breast density and breast cancer risk, none of the explanatory factors were statistically significant in the meta-regression analysis from cohort studies whereas all three factors were statistically significant in the meta-regression analysis from case–control studies  and others using qualitative criteria (BI-RADS classification or percent breast density in categories). To limit the risk of heterogeneity due to different definitions, we considered two scenarios. The first scenario included all comparisons among and within studies where a clear definition of increased breast density over time could be extracted. The second scenario included all comparisons among and within studies where a clear definition of decreased breast density over time could be identified. These two broad scenarios enabled us to include several comparisons in pooled estimates and to offer a more general aspect on the potential impact of changes in breast cancer on breast cancer risk. However, this approach can influence the interpretation of results regarding which classification system to use.Looking at study-level results, the use of qualitative classifications systems for breast density seems to be more often associated with breast cancer risk, compared with a quantitative analysis of breast density ,20,21,22An additional challenge with this meta-analysis was the consideration of confounding factors influencing both breast density and breast cancer risk such as BMI, reproductive factors, age, and exogenous hormones. To overcome this potential source of bias, we included in our pooled estimates only comparisons using multivariate analyses considering potential confounders. However, several studies ,20,21,22Two studies ,28 invesA clinically relevant aspect that should be discussed is the fact that all pooled estimates referred to breast cancer patients in general and not to specific subgroups of potential interest. Due to a lack of adequate data, we were unable to perform subgroup analyses based on menopausal status or on family history of breast cancer. Considering the expected changes in breast density between pre- and postmenopausal women, separate analyses would be of interest and should be included in future studies on this topic. In addition, prior studies found that the association between breast density and breast cancer risk was stronger in women with a family history of breast cancer , suggestIn summary, we found a statistically significant and clinically relevant association between changes in breast density and breast cancer risk, mainly in terms of higher risk with increased breast density, where the pooled estimates were consistent in both cohort and case–control studies. Despite the methodological challenges, the results imply that breast density changes over time might be an opportunity for a more individualized screening strategy. Further studies are needed to evaluate whether escalated screening strategies with ultrasound , tomosyn


Cancer diagnosed in patients between the ages of 15 and 39 deserves special consideration. Diagnoses within this cohort of adolescents and young adults include childhood cancers which present at an older age than expected, or an early presentation of cancers that are typically observed in older adults, such as breast cancer. Cancers within this age group are associated with worse disease-free and overall survival rates, and the incidence of these cases are rising. Knowing an individual’s susceptibility to disease can change their clinical management and allow for the risk-testing of relatives. This review discusses the risk factors that contribute to breast cancer in this unique cohort of patients, including inherited genetic risk factors, as well as environmental and lifestyle factors. We also describe risk models that allow clinicians to quantify a patient’s lifetime risk of developing disease.Cancer in adolescents and young adults (AYAs) deserves special consideration for several reasons. AYA cancers encompass paediatric malignancies that present at an older age than expected, or early-onset of cancers that are typically observed in adults. However, disease diagnosed in the AYA population is distinct to those same cancers which are diagnosed in a paediatric or older adult setting. Worse disease-free and overall survival outcomes are observed in the AYA setting, and the incidence of AYA cancers is increasing. Knowledge of an individual’s underlying cancer predisposition can influence their clinical care and may facilitate early tumour surveillance strategies and cascade testing of at-risk relatives. This information can further influence reproductive decision making. In this review we discuss the risk factors contributing to AYA breast cancer, such as heritable predisposition, environmental, and lifestyle factors. We also describe a number of risk models which incorporate genetic factors that aid clinicians in quantifying an individual’s lifetime risk of disease. Adolescents and young adults (AYAs) with cancer, who are defined as those individuals diagnosed with cancer between the ages of 15 and 39 ,3. The iUnderlying hereditary cancer susceptibility syndromes account for 7–10% of cancer diagnoses in children and adolescents ,14,15,16Breast cancer among cis-gender females is very common, affecting approximately 1 in 8 women over their lifetime, with the peak age-specific incidence between the ages of 65 and 69 , when compared to an incidence of 109.2 per 100,000 in those aged under 40 [The risk of breast cancer is dependent on several so-called modifiable factors, which include body weight, use of hormone replacement therapy, and reproductive factors, as well as non-modifiable factors such as age at menarche and menopause, breast density, and a family history of breast cancer. Twin studies demonstrated that 30% of breast cancers are attributed to hereditary factors . Breast Research over the past three decades has identified germline variants at numerous loci with variable associated disease penetrance. The genetic variants that predispose a person to breast cancer are now broadly categorised into three groups: high-, moderate-, and low-risk variants, depending on the relative risk conferred by the variant. Pathogenic high-risk variants in several genes are associated with a greater than four-fold risk of breast cancer than that observed in the general population , and modIn keeping with the rules governing biological fitness, it follows that those variants that are associated with a particularly high risk of early-onset disease are usually rare in the general population when compared to low-risk susceptibility alleles, which may be relatively common . Risk alIndividuals who carry inherited genetic traits that are associated with a high risk of cancer can manage their cancer risk through a combination of surveillance, risk-reducing surgery or other techniques. The exact advice provided to carriers of such traits will depend on the genotype, associated cancer risk and the availability of proven screening or other risk-reducing interventions. The early recognition of a genetic predisposition to disease, as well as the potential barriers to genetic testing where such a predisposition is suspected, is crucial to facilitate cascade testing and early intervention in at-risk relatives .TP53 encodes for the p53 protein, which is commonly referred to as the “guardian of the genome” [TP53 may cause conformational changes in the protein, abrogate DNA binding or suppress interaction with target genes. Deleterious variants may cause a partial or total loss of key functions, and certain deleterious variants may lead to an inappropriate gain of function—such as inappropriate transcriptional activity [TP53—certain TP53 variants may exert dominant negative effects on the wild type allele, for example, by inactivation of the wild-type protein or through dimerization [ genome” . In respactivity . Germlinactivity , behavinrization .TP53 variants are common across a host of cancer types, most commonly colorectal and head and neck cancer, and TP53-mutant cancers demonstrate higher rates of chemo- and or radio-therapeutic resistance, increased risk of relapse, and reduced survival [Disruption of p53 function due to germline or somatic pathogenic variants contributes to virtually all of the hallmark features of cancer . Somaticsurvival ,39.TP53 give rise to heritable TP53-related cancer (hTP53rc) syndrome, of which the most extreme phenotype is traditionally referred to as Li Fraumeni syndrome (LFS) [Heterozygous pathogenic germline variants of me (LFS) . This rame (LFS) .TP53 variants are associated with a higher penetrance than BRCA variants [TP53, is 33 years of age, with approximately one third of this population diagnosed prior to the age of 30 years. Indeed, overall, approximately 50% of the cancers occurring in the carriers of such variants occur before this age. The population frequency of germline pathogenic variants in TP53 is very low, ranging from 1 in 3555 to 1 in 5476 individuals [TP53 variant is 2–8% [TP53 variant is also dependent on the molecular phenotype of the tumour. There is an apparent enrichment for HER2-overexpression, and particularly “triple positive”  breast cancers —compared to the general population, in which HER2-overexpressing breast cancers account for 20–30% of cases, 63–83% of breast cancers in carriers of pathogenic TP53 variants are HER2-amplified [Most pathogenic variants ,42,43, aividuals , althougividuals . In wome is 2–8% . The likmplified ,49. Intemplified . Malignamplified .TP53 variants have an annual clinical screening and a whole-body MRI (WBMRI), with an annual breast MRI beginning at 20 years of age [Given the risk of early-onset cancer, it is advised that carriers of pathogenic s of age . A discus of age .BRCA1) and 2 (BRCA2) genes remain the most clinically significant breast cancer predisposition genes. A multi-functional protein, BRCA1 is involved in a wide array of cellular pathways, including DNA damage repair (DDR), chromatin remodelling, gene expression, and protein ubiquitination [Since their discovery over three decades ago, the breast cancer 1  [BRCA2 variants show more association with the Luminal B subtype, and a higher risk of contralateral disease, but no definitive association has been demonstrated thus far [BRCA variants are associated with more aggressive malignancies at earlier ages, presenting with a higher grade and metastatic potential. Tumours that are associated with a germline variant are also associated with a poorer prognosis than those that acquire a somatic BRCA variant [The cumulative lifetime risk of breast cancer that is conferred by pathogenic ectively . There i50 years , with a 50 years . The majr (TNBC) ,65,66. Tthus far ,68. Germ variant .BRCA2, where the lifetime risk is approximately 8–10% when compared to BRCA1, where the risk is in the order of 1% [BRCA2 variants, with a lifetime risk of approximately 25–30%, and a tendency to a more aggressive phenotype. The risk of prostate cancer in male carriers of BRCA1 variants is approximately equivalent to the general population’s risk, but the phenotype of such cancers, as well as the therapeutic approach, is similar to that of carriers of BRCA2 variants. Female carriers of BRCA1/BRCA2 variants also have the additional high risk of ovarian cancer, with the cumulative lifetime risk in carriers of BRCA1 being 44% (36–53%), compared to BRCA2, which is 17% (11–25%) [BRCA2 variants [The risk of male breast cancer is also increased in the male carriers of such variants, particularly those in er of 1% . The ris(11–25%) . Both fe(11–25%) , and thevariants .BRCA1 or BRCA2, high intensity surveillance with MRIs and/or mammograms (depending on age and breast density) is recommended, starting from 25–30 years depending on family history and local guidelines [BRCA variants who have already developed breast cancer are also predisposed to developing further primary breast cancers in any residual breast tissue. Consequently, affected patients may choose to have contralateral prophylactic surgery during or after therapeutic surgery, although several factors influence the potential survival benefit of this approach, including age and the stage of the first breast cancer, systemic treatments, age at prophylactic surgery, and surveillance [BRCA-associated ovarian cancer may be too short to warrant preventive surgery. However, such surgery may be considered in individuals with occult, or stage I/II ovarian cancer, which is diagnosed before age 55, with at least 10 years of disease-free survival [In unaffected women found to carry pathogenic/likely pathogenic variants in idelines ,74. The idelines ,76,77. Ceillance . The bensurvival .BRCA-deficiency modulates the response of disease to systemic cancer treatment. Loss of BRCA1 or BRCA2 function, as a consequence of biallelic variation or hypermethylation (germline and/or somatic), leads to homologous recombination repair deficiency, which can then be exploited for therapeutic purposes. Such a deficiency is characterised by a unique mutational signature, and a high genomic instability score (GIS), which is derived from an algorithmic measurement of large-scale state transitions, telomeric allelic imbalance, and loss of heterozygosity [BRCA variant carriers demonstrate a higher objective response rate to platinum-based chemotherapy (carboplatin) versus taxane chemotherapy (docetaxel) (68% vs. 33%) [BRCA1 variant carriers who are treated with platinum-based neoadjuvant chemotherapy [BRCA variant screening in patients with TNBC. However, it has been reported that secondary tumours develop resistance to platinum-based therapies via a somatic reversion mutation which restores BRCA function [As well as contributing significantly to the risk of disease, zygosity . Tumoursvs. 33%) . High raotherapy . These dfunction . This saBRCA1 or BRCA2, olaparib treatment has been shown to lead to superior progression-free survival when compared to standard therapy in individuals with HER2-negative metastatic breast cancer [BRCA1/BRCA2 variants, as well as to investigate their potential toxicity, risk of secondary cancer development and any mechanisms of resistance [BRCA1 variants is therefore limited, considering their strong association with ER-negative disease [The use of PARPi in BRCA-deficient tumours has been shown to be particularly effective ,85,86. It cancer , and to t cancer . The efft cancer ,90. Furtsistance ,92. In tsistance . Given tsistance . Their u disease . Other a disease .BRCA1 or BRCA2 is contentious. Annual screening with CA-125 and transvaginal ultrasound has a high sensitivity and positive predictive value, but the majority of cancers detected in this manner are stage III or higher, and an annual screening is interval associated with a poor overall 10-year survival in the carriers of germline BRCA1/BRCA2 variants [BRCA1/BRCA2 is prophylactic bilateral salpingo-oophorectomy. The optimum age at which this is undertaken should consider age-related penetrance, and the adverse impact of premature menopause on heart, brain and bone health. Risk-reducing salpingo-oophorectomy is associated with a 96% reduction in the risk of tubo-ovarian cancer; and may be associated with a reduction in breast cancer risk in the carriers of BRCA2, but not BRCA1, variants [Ovarian cancer screening in individuals with germline variants in variants . The Univariants . This stvariants ,99.PALB2 have been associated with an increased risk of breast cancer which approaches that seen with variants in BRCA2, in the order of 48–63%, with increased risks for those patients with a family history of breast cancer [PALB2 are highest for those under 40 years of age, at 8–9 times the risk of the general population, and while this falls somewhat with age, this risk remains significant, with a 5-fold relative risk in those carriers aged over 60 years [PALB2 variants, but the absolute risk appears very low 0.9 (0.2–5)%. Additionally, female carriers of PALB2 variants have increased risks of ovarian cancer in the order of 5 (2–10)%, and carriers of either gender have an increased risk of pancreatic cancer (approximately 2–3%).The tumour suppressor PALB2 mediates the physical interaction of BRCA2 with the COOH-terminal fragment of BRCA1 . Heterozt cancer ,104,105.60 years ,105. MalPALB2. Depending on the specific variant identified and the patient’s family history, prophylactic risk-reducing breast surgery may be considered, although specific data regarding a survival advantage in carriers of PALB2 variants is lacking [BRCA-equivalent breast surveillance is recommended for the carriers of pathogenic variants in  lacking ,106. At PALB2 variants as for carriers of pathogenic variants in BRCA1/BRCA2 [PALB2 has a critical role in homologous recombination repair, such that tumours which are associated with pathogenic variants in PALB2 demonstrate homologous recombination repair deficiency , and theA1/BRCA2 .RAD51C and RAD51D genes, respectively. The association between germline pathogenic variants in these genes and ovarian cancer has been recognised for many years, but the association with breast cancer risk was only more recently established [RAD51C and RAD51D variants and oestrogen receptor negative or triple-negative breast cancer [Two other proteins that function in the homology-directed repair of double stranded DNA breaks are RAD51C and RAD51D, which are encoded for by the ablished ,109. Pret cancer ,111. TheFANCA, and less frequently due to biallelic variants in any of at least twenty genes, which include BRCA2, PALB2, RAD51C, and very rarely, BRCA1 [BRCA1 were traditionally believed to either be embryonically lethal, or a rare cause of Fanconi anaemia; however, increasing numbers of cases are being reported with phenotypes which overlap between Fanconi anaemia and other chromosome instability syndromes, particularly Nijmegen Breakage Syndrome; this has an associated risk of early-onset breast and/or ovarian cancer predisposition [Fanconi anaemia (FA) is a rare, genetically heterogeneous disorder that is associated with congenital anomalies, skeletal, ophthalmic, and genitourinary malformations, cutaneous manifestations, and a predisposition to malignancies—most typically haematological malignancies and squamous cell cancers. Fanconi anaemia can be inherited in an autosomal recessive manner, most commonly due to biallelic, pathogenic variants in y, BRCA1 . Biallelposition ,115,116.In a proband with suspicious clinical findings, a diagnosis of FA can be determined by chromosome breakage analysis of lymphocytes treated with diepoxybutane (DEB) and mitomycin C , with suBRCA1, BRCA2, PALB2, BRIP1 or RAD51C. However, some FA-associated variants may be hypo-morphic, and cause FA only when found in trans with a different pathogenic variant. Heterozygous carriers of hypo-morphic alleles in cancer predisposition genes may be at an only moderately, or slightly, increased cancer risk [A diagnosis of Fanconi anaemia in a family has wide-ranging implications—each sibling of an individual with autosomal recessive FA has a 25% risk of being affected with the same disorder, and a 50% risk of being a heterozygous carrier of one variant allele, while the parents of such individuals are likely to be heterozygous carriers. Heterozygous carriers of variants in a gene associated with autosomal recessive forms of FA are not at risk of FA, but may be at increased risk of breast, ovarian, and/or other cancers, if the monoallelic variants in question are in cer risk .ATM give rise to ataxia-telangiectasia syndrome. The features of ataxia-telangiectasia syndrome are usually evident from childhood, and include progressive ataxia, cutaneous and ophthalmic telangiectasia, as well as an increased risk of cancer. The most common malignancies in individuals with A-T are haematological, including leukaemias and lymphomas, however, solid organ cancers, including breast cancers, are being increasingly noted as the survival of affected individuals improves, as well as in the carriers of certain ATM haplotypes or hypo-morphic alleles. The treatment of solid organ cancers in individuals with A-T should avoid radiotherapy, as A-T confers exquisite hypersensitivity to ionising radiation, with increased susceptibility to second primary cancers and radiotherapy-related complications. Certain A-T families have also demonstrated adverse chemotherapy-related effects [ATM plays a major role in maintaining genomic stability where it functions in the DNA damage response, but also in controlling the cell cycle and mitotic recombination ,121. Bia effects ,123,124.ATM variant are recommended to have increased breast surveillance starting from the age of 40. The frequency and duration of increased screening may be guided by the genotype and family history. MRI is favoured over mammography in homozygous/compound heterozygous carriers. Depending on the variant and the family history, prophylactic risk reducing surgery may be considered [A-T is very rare, with a prevalence of 1 in 40,000 to 1 in 100,000, and the Hardy Weinberg principle dictates that this translates to a carrier frequency of at least 1 in 100. Monoallelic variants are associated with a moderately increased lifetime risk of breast cancer, over 20%, which is greatest for patients under 50 years of age ,125,126.CHEK2 is a tumour suppressor involved in DNA damage response pathways. While the majority of pathogenic germline variants in CHEK2 are associated with a moderately increased risk of breast cancer, certain variants may confer a higher risk of disease [CHEK2 variants are offered enhanced breast cancer surveillance [CHEK2 are offered at least “moderate” risk screening, with annual mammograms starting from the age of 40 and continuing until the age of 50, with screening thereafter as part of the national breast cancer screening programme. The carriers of truncating variants in CHEK2 that have a strong family history of breast cancer are offered high intensity surveillance, with annual mammograms between the ages of 40–60 years. In families where there is a strong family history of breast cancer, non-carriers of the familial CHEK2 variant may still be offered moderate risk surveillance, because of the potential of other co-existing familial variants that have not been identified. Consideration of “very high risk” breast screening with annual MRI, as well as mammograms, should be considered for biallelic carriers of truncating CHEK2 variants, but this is determined on a case-by-case basis. Missense variants in CHEK2 are associated with a lower disease risk [The checkpoint kinase protein  disease ,129, andeillance ,130,131,ase risk , and a rase risk . ScreeniCHEK2 are associated with bilateral disease and an oestrogen-positive phenotype, and in the affected AYA are associated with reduced disease-free rates and overall survival [Pathogenic variants in survival ,134. Thesurvival . Becausesurvival ,137.BRCA1 hinder the interaction between BARD1 and BRCA1, suggesting that similar complex-destabilising variants in BARD1 may promote tumorigenesis. A recent multicenter association study of more than 113,000 women found that protein-truncating variants in BARD1 were significantly associated with breast cancer, and most strongly with ER-negative and triple-negative disease [BARD1 variants confer a low-moderate risk of breast cancer susceptibility [BRCA1 exists mostly as a stable heterodimer with BARD1 (BRCA1 associated RING domain 1). In this heterodimer form, the E3 ubiquitin ligase activity of BRCA1 is significantly increased . This li disease . This antibility . AssociaPTEN, TP53, STK11 and CDH1 confer a lifetime risk of breast cancer greater than 40% [Highly penetrant pathogenic variants in than 40% ,141,142.PTEN is well known as one of the most frequently somatically mutated tumour suppressors in human cancer. Somatic driver events in PTEN have been identified in an array of malignancies, including brain, prostate, and breast cancers [PTEN is a multi-functional protein which exerts its tumour suppressor capabilities in numerous processes, including the maintenance of genomic stability, cell survival, proliferation, migration, invasion, and metabolism [PTEN predispose an individual to several types of benign and malignant neoplasia, conferring a high risk of benign breast disease and breast cancer [BRCA1/BRCA2. The tumour phenotype of PHTS also includes benign and malignant neoplasia of the thyroid, kidney, endometrium, skin and gastrointestinal tract [PTEN variants often present in childhood or adolescence with non-neoplastic features, including macrocephaly, developmental delay, or arteriovenous malformations [ cancers . Heteroz cancers , Bannaya cancers , and Pro cancers . PTEN istabolism . Germlint cancer . The estt cancer , similaral tract . The carrmations .PTEN variant carriers may be offered surgical prophylaxis [In addition to screening for breast and other related malignancies, phylaxis ,150.STK11 are associated with the autosomal dominant disorder Peutz-Jeghers syndrome (PJS). Affected patients develop benign intestinal hamartomas and are also at a highly increased risk of developing other malignancies [Heterozygous, pathogenic germline variants in the tumour suppressor gene gnancies ,151, incgnancies , breast gnancies , and repgnancies . For femgnancies . Currentgnancies .CDH1 reflects a loss of function of E-cadherin, with increased risks of diffuse subtypes of gastric cancer, and/or lobular subtypes of breast cancer. The cumulative risk of lobular breast cancer in the female carriers of pathogenic CDH1 variants by age 80 years is 39–55%, with the average age of diagnosis being 53 years, while the cumulative risk of diffuse gastric cancer in male and female carriers of such variants is approximately 33–42% [Epithelial cadherin (E-cadherin) or cadherin-1 (CDH1) is a member of the cadherin superfamily. CDH1 is critical for cell adhesion, with roles in the regulation of cell polarity, differentiation and migration. E-cadherin expression is essential in normal embryonic development. Loss of E-cadherin is associated with the epithelial-mesenchymal transition (EMT), during which, epithelial cells demonstrate loss of apical-basal polarity and cellular adhesion, thereby acquiring the ability to migrate that leads, eventually, to metastatic dissemination. Loss of E-cadherin expression has been reported in gastric, colorectal, breast and ovarian cancers, with demonstrable changes in epithelial cell adhesion and motility. Loss of CDH1 activity results in increased cell motility leading to the increased metastatic ability of a tumour ,156. They 33–42% , with a CDH1 variant with a personal or family history (first-/second-degree relative) of diffuse gastric cancer, the associated cancer predisposition syndrome is termed hereditary diffuse gastric cancer. At present, the only proven method to minimise the risk of diffuse gastric cancer in the carriers of such variants is prophylactic gastrectomy, and in classic HDGC families this is recommended between the ages of 20–30. However, pathogenic CDH1 variants are increasingly being identified among patients with early-onset lobular breast cancer, in the absence of a personal or family history of diffuse gastric cancer [CDH1 variants is 7% [In the carriers of a pathogenic c cancer ,159,160,c cancer . Howeverts is 7% . Lobularts is 7% .CDH1 pathogenic variants without a personal/family history of gastric cancer may not be appropriate, given the significant morbidity associated with this intervention. The optimum approach to manage a hypothetical risk of gastric cancer in such families has yet to be determined [Prophylactic gastrectomy in carriers of termined .CDH1 variants [Breast screening with MRI from age 25–30 is recommended for the female carriers of pathogenic or likely pathogenic variants ,163. MRIvariants .CDH1 variants, who have non-malignant features including cleft lip and palate. Germline variants in CDH1 have also been reported as a cause of non-syndromic cleft lip and palate, and of familial Blephorocheilodontic (BCD) Syndrome, with or without an associated cancer risk [Appendiceal signet ring cancers, as well as colorectal cancers, have also been reported in the carriers of cer risk ,166.Therapeutic options in affected individuals, as well as the approach to surveillance in unaffected carriers, are also determined by their underlying genotype . The knoNF1 are associated with the autosomal dominant disorder, neurofibromatosis type 1 (NF1), which occurs as commonly as 1 in 2700 [NF1 is approximately 20%, with a significant risk of developing breast cancer under the age of 50 [NF1 and breast cancer risk [NF1 is a large protein with several domains. Germline pathogenic variants in  in 2700 . The fea in 2700 . The lifge of 50 ,169, sucge of 50 . Howevercer risk . Risk esA clinical diagnosis of NF1 should be considered based on the clinical criteria as outlined in MRE11, NBN) have been postulated to be associated with an increased breast cancer risk, but their association has not, as of yet, been proven. Breast cancer has been reported as part of the phenotypic spectrum of the rare, recessive, NTHL1 tumour predisposition syndrome, with a median age of onset of 49 years (38–63) [Variants in a number of other genes (including  (38–63) . More coHundreds of single nucleotide polymorphisms (SNPs) at loci scattered throughout the genome have been identified that are associated with a very slightly increased risk of breast cancer ,174. MosWhile the individual risk conferred by these SNPs is small, they are at least additive; homozygous carriers are at an increased risk of disease over heterozygotes . Low-penA number of environmental and lifestyle factors have been identified as risk factors for breast cancer. For breast cancer, these environmental and lifestyle factors include an exposure to ionizing radiation, exogenous hormones and reproductive choices, diet and alcohol consumption, obesity, and physical inactivity ,181,182.Between 2017–2019, 14% of women aged 15–49 years in the United States were using oral contraceptive pills (OCP) . OCP useIn addition, the Danish study did not account for the confounding influence of factors such as alcohol consumption, physical inactivity, and breastfeeding. When considering the contraceptive and non-contraceptive benefits of CHC use, the overall benefit likely outweighs the small increased risk of breast cancer for most women. Patient factors such as a family history of disease, BMI, age, and intended duration of use should be considered when choosing a CHC method ,190.The age of someone’s first full-term pregnancy and their number of pregnancies can modulate breast cancer risk. The short-term risk of developing breast cancer increases for approximately 20 years, peaking after 5 years, following a pregnancy, and most significantly for those with a prior family history of disease ,192. Thi2). The World Health Organisation (WHO) defines pre-obesity (overweight) and obesity as a BMI ≥ 25 Kg/m2 and ≥30 Kg/m2, respectively [Body mass index (BMI) is used as a tool for indicating nutritional status and is calculated by dividing a person’s weight in kilograms by the square of their height in metres , excessive consumption could lead to an increase in oestrogen levels, and thus increase the risk of breast cancer . HoweverAlcohol consumption has been associated with several cancer types . AlcoholIt is well established that individuals who are exposed to ionising radiation during childhood, adolescence, or young adulthood are at an increased risk of developing a subsequent breast cancer. This radiation exposure may occur in diagnostic or therapeutic settings, such as in the case with Hodgkin lymphoma ,249, or The risk of breast cancer in non-binary, transgender or intersex individuals is different to that of cisgender individuals, being significantly modified by factors such as hormone therapy, gender-confirming surgery, and surveillance, as well as those factors associated with breast cancer risk in cis-gender individuals. Breast cancer risk among transwomen has been found to be lower than that of ciswomen, but significantly increased when compared to cisgender males—in the order of 46-fold higher .Identification of a high risk of breast cancer among such individuals is important, to direct screening and other risk-reducing strategies. Careful consideration should be given to such interventions, given the significant psychological distress that may be associated with them. An inclusive and sensitive approach to the risk estimation and management of individuals from sexual and gender minorities is critical .Many transmen may choose to undergo bilateral mastectomy (“top surgery”) as part of their transition. Cases of breast cancer occurring in residual breast tissue in transmen who undergo such surgery have been reported ,262,263.To provide an accurate estimate of risk in AYAs (and indeed in older patients), a holistic approach is required, bearing in mind lifestyle and reproductive factors as well as breast density, family history and heritable predisposition . The risSeveral models have been developed to assist clinicians in quantifying an individual’s lifetime risk of breast cancer, and since the discovery of the genes that are associated with monogenic predisposition, many have been developed to also include an estimation of the risk of carrying a high-risk variant in a breast cancer susceptibility gene .The Breast Cancer Risk Assessment Tool, traditionally known as the Gail model, was developed in 1989 , and theBRCA1 or BRCA2, or other breast cancer susceptibility genes.The Claus model, which is based on data from the Cancer and Steroid Hormone study, allows an estimation of cancer risk that is based on family history and hereditary factors only . The GaiBRCA1 and BRCA2, penetrance estimates, as well as personal and family history of cancer—including data related to unaffected as well as affected relatives—to generate estimates of breast cancer risk as well as the probability of identifying a high-risk germline variant [The BRCAPRO model is a Bayesian tool that incorporates information regarding population frequencies of pathogenic variants in  variant .BRCA1 and BRCA2, as well as family history, reproductive risk factors and ductal atypical hyperplasia [The Tyrer-Cuzick model includes data from the International Breast Intervention Study. Compared to other models, this tool allows the incorporation of genotypes related to other cancer susceptibility genes which include erplasia .BRCA1 and BRCA2 genotype, as well as polygenic risk and family history. BOADICEA has now been superseded by CanRisk [BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D, and BRIP1, and considers these modifiers of disease which may inform risk management. The tool does not permit genotype-specific information, so the user should bear this in mind when considering variants associated with reduced penetrance. The adage of “garbage in, garbage out” is particularly relevant when using these tools—care should be taken to verify family history and genotype where possible.The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model incorporates information regarding  CanRisk . CanRiskFor individuals that have not yet had germline genetic testing, this tool can also estimate the a priori probability of identifying a pathogenic germline variant in one of these genes. In the UK, this is used to direct testing, as current guidelines permit NHS-funded germline testing where this probability is at least 10%—AYAs with breast cancer will meet this threshold with relatively little or no family history depending on the age at diagnosis.CDH1, PTEN, TP53, or STK11. In order to identify the carriers of such variants, the clinician should be alert to the phenotypes associated with rare syndromic causes of breast cancer.The tool does not include information related to syndromic causes of breast cancer, precluding use of this tool to generate risk estimates in the known carriers of pathogenic variants in PTEN [Where PTEN hamartoma tumour syndrome is a consideration, the Cleveland Clinic PTEN calculator is a useful tool to estimate the likelihood of identifying a germline pathogenic variant in PTEN . ClinicaPTEN ,275,276.Current screening for breast cancer relies on the physical exam, mammography, and, in selected patients, MRI. Mammography largely relies on the 5th edition of the BI-RADS scoring system to score images 0 to 6, with 0 being incomplete or inconclusive imaging and 6 being known, biopsy-proven malignancy.Recently, advancements in artificial intelligence (AI) have led to the development of systems that are potentially capable of surpassing human experts in breast cancer prediction.Zhu et al. analysed mammograms that were obtained in 6369 women without breast cancer, 1609 of whom developed screening-detected breast cancer, and 351 of whom developed interval invasive disease. This case–case–control study found that AI or deep learning systems outperformed clinical risk factors, including breast density, in detecting the screening-detected cancer risk but underperformed for detecting interval cancer risk .Another recent article in the journal of Nature compared an AI system to human experts in the United Kingdom and USA in predicting breast cancer. To assess the AI’s performance in the clinical setting, the authors curated a large dataset from the UK and USA. They showed that the AI system resulted in an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives .Ming et al. investigated the classification of lifetime breast cancer risk based on three different machine learning (ML) algorithms and the BOADICEA model in 112,587 individuals . ML algoFurther studies are required to explore the translational applications of these approaches.BRCA1 and BRCA2 is a rare event, and the associated phenotype is a mix of those found in BRCA1 and BRCA2 heterozygous carriers [BRCA1 or BRCA2 is relatively high.In the UK and other countries with public health systems, germline genetic testing is often restricted to single genes or narrow gene panels and offered only to individuals who fulfill certain criteria. However, there is an increasing move towards broader gene panels, or whole exome/genome approaches. Broader testing optimises the cost- and time-efficiency and maximises the diagnostic yield. However, such testing also increases the potential for uncertainty and unexpected results. Some proponents of this approach argue that the identification of unexpected variants is an opportunistic advantage—facilitating risk-reducing strategies that may not otherwise have been offered based on the personal/family history. Broader approaches are particularly useful in individuals with atypical phenotypes, or where more than one heritable cancer predisposition syndrome is suspected. Finding more than one germline pathogenic variant, known as Multilocus Inherited Neoplasia Alleles Syndrome, is not all that rare; this is a particular risk in certain ethnic groups where the prevalence of founder mutations is high. “Double heterozygosity” or “transheterozygosity” of variants in both carriers . Double Much of the additional diagnostic yield of broad cancer gene panels relates to the identification of variants associated with moderate, or uncertain, risks of breast cancer. This creates significant challenges in the clinical management of the carriers of such variants—as well as carrier and non-carrier relatives. In an AYA with a strong personal and family history of breast cancer, a variant associated with a moderate or low penetrance is likely to account only for a proportion of the disease risk. The residual risk may be attributable to environmental, lifestyle, or unidentified risk factors; however, a proportion may also be attributed to other co-inherited germline genetic variants that have not been detected. Risk management in such carriers should therefore consider family history and other modifying risk factors as well as the genotype; in non-carriers in families where a moderate risk allele has been identified, continued surveillance and/or other risk-reducing strategies may still be warranted.Breast cancer in AYAs is relatively rare, and a significant proportion of risk is attributable to high-risk germline genetic variants, such that germline genetic testing should always be considered in affected individuals. A “negative” germline genetic test does not exclude the possibility of heritable risk factors, and enhanced cancer screening in the proband and close relatives is warranted and should be guided by the family history. For accurate risk estimation, a holistic approach is needed, that considers not only high-risk genotypes, but other modifiers of risk—including reproductive and lifestyle risk factors, and co-existing genetic modifiers, including the polygenic risk score, where available. Careful phenotyping in an AYA with breast cancer is crucial to avoid missing syndromic causes of breast cancer. In individuals for whom the lifetime breast cancer risk is estimated to be high, consideration should be given to surveillance, as well as chemo- and/or surgical prophylaxis. Furthermore, the opportunity to instigate changes to modify risk, such as lowering alcohol intake, reducing body weight, or adjusting contraception, should be explored, as summarised in


The identification that breast cancer is hereditary was first described in the nineteenth century. With the identification of the BRCA1 and BRCA 2 breast/ovarian cancer susceptibility genes in the mid-1990s and the introduction of genetic testing, significant advancements have been made in tailoring surveillance, guiding decisions on medical or surgical risk reduction and cancer treatments for genetic variant carriers. This review discusses various medical and surgical management options for hereditary breast cancers. BRCA1 and BRCA2, in 1994 and 1995, respectively, the association between family history and the presence of inherited genetic events that predispose individuals to breast cancer development was confirmed [The concept that breast cancer can be inherited and passed from one generation to the next was first described by Paul Broca in 1866. His wife suffered from early age onset of breast cancer, and after studying her family pedigree, he identified four previous generations with breast cancer . With onBRCA genes were discovered, there have been significant advances in identifying additional germline pathogenic variants in cancer-predisposition genes that have also been associated with an increased risk of breast cancer [ATM, BARD1, BRCA1, BRCA2, CDHS1, CHEKs, NF1, PALB2, PTEN, RAD51C and TP53) is closer to 5% among women with breast cancer [BRCA1/2, PALB2, CDH1, PTEN and TP53 but in moderate penetrance genes as well . High penetrance genes confer a 5- to 20-fold lifetime increased risk of breast cancer and moderate penetrance genes confer a 1.5- to 5-fold increased risk [In the last two decades, since the t cancer ,10,11,12t cancer ,14,15,16t cancer . A recent cancer . Improvesed risk ,20. SincBRCA1/2, PTEN and TP53 based on the risk they infer and will be discussed later in this article, patients with ATM, CDH1, CHEK2, PALB2 and NF1 should have enhanced screening as data are not sufficient to support a risk-reducing mastectomy in the absence of a strong family history [Genetic mutations and variants have become more widely known and studied due to increased genetic testing and the increased number of panels used in screening for breast cancer susceptibility. Mutations and variants require nuanced and patient-centric algorithms in terms of management and decision making. Testing allows for the identification of patients at the highest hereditary risk for breast cancer and allows for informed discussions around the prevention and early detection of malignancy . Signifi history . Various history ,26. The  history ,29,30,31 history ,33,34.BRCA variant, the National Comprehensive Cancer Network (NCCN) recommends an annual mammogram, annual MRI and routine clinical breast exams beginning at an early age (25–30) [Screening guidelines for genetic variant carriers differ according to the gene as lifetime risk varies significantly. For women with a  (25–30) ,35,36,37 (25–30) . Multipl (25–30) ,40. Supp (25–30) ,42.PALB2, CDH1 or NF1 mutations, the NCCN recommends an annual mammogram and annual MRI beginning at age 30, unless the age of cancer diagnosis within the family was earlier [Patients with Li–Fraumeni syndrome (TP53) have an elevated risk of breast cancer, but studies of this population are fraught with ascertainment bias, making precise risk estimation difficult ,12. Howe earlier . For theSeveral general strategies are used to reduce cancer risk, morbidity and mortality in women with an increased risk of hereditary breast cancer, including regular imaging screening to detect tumors at an earlier stage, prophylactic surgeries—risk-reducing mastectomy (RRM) and at times, risk-reducing salpingo-oophrectomy (RRSO)—and chemoprevention. In published estimates of cancer risks, there is a considerable range among carriers of different genetic variants; therefore, the role of prophylactic surgery varies from patient to patient. After the initial identification of hereditary breast and ovarian cancer syndrome in 1971, some surgeons began performing prophylactic removals of breast and ovarian tissue in women from families suspected of having this syndrome. This was often performed as a subcutaneous mastectomy, which by today’s standards, would not be considered an adequate removal of all the breast parenchyma ,47. HoweBRCA1 gene was linked for the first time to breast cancer using a large group of early onset breast cancer families and linkage analysis. Mapping of the BRCA1 gene to chromosome 17q21, cloning of the gene and various truncating mutations were identified in 1994 [BRCA2 gene in 1995 [BRCA1/2 mutations depends on accurate age-specific cancer risk estimates, which can then be used to estimate the absolute risk reduction in preventative strategies and inform decisions regarding the age at which to start surveillance [BRCA1 carriers to be about 80% and 70% for BRCA2 carriers [In 1990, the  in 1994 ,52. The  in 1995 ,56,57,58eillance ,59. Multcarriers ,61. As sFrom 1999 to 2004, four studies were published that compared breast cancer outcomes of women who underwent prophylactic mastectomies to outcomes of women at similar risk who did not undergo surgery ,65,66,67Earlier studies that investigated risk-reducing prophylactic mastectomies in genetic variant carriers discussed simple versus skin-sparing mastectomies, with simple mastectomies remaining the most effective risk-reducing treatment option . IntroduPALB2 is now considered high risk by many because it confers an RR of breast cancer by more than five-fold. For this high-risk mutation, along with others including CDH1 and TP53, the recommendations are now to consider prophylactic mastectomies for risk reduction [eduction ,81. Outceduction . In compeduction .The risk of ovarian cancer has been well established in the BRCA 1 and 2 patient population, with lifetime estimates ranging from 22% to 65% for BRCA1 and 10% to 35% for BRCA2 mutation carriers ,59. UnliIt is often discussed whether a hysterectomy should be performed with salpingo-oophorectomy. There does not seem to be any survival benefit associated with removing the uterus; however, it can simplify future hormonal therapy given to women to reduce the risk of breast cancer or for menopausal symptoms, since Tamoxifen and estrogen replacements are both associated with an increased risk of endometrial cancer . The risBRCA2 carriers [As described, risk-reducing strategies for genetic variant carriers often comprise structured intensified surveillance, chemoprevention at times, lifestyle factors and risk-reducing surgeries. Risk-reducing surgeries can help prevent diseases and thus preserve health and result in additional life years in good health. Recognizing that these aspects of RRS are the most important, there are also data to suggest that RRS in BRCA-mutation carriers is cost effective compared to potentially avoidable cancer treatment costs ,94. Schrcarriers . Howevercarriers . When thcarriers . There acarriers .Approximately 5–10% of breast cancers are associated with a pathogenic germline variant in one of several different genes ,100. OveWith conflicting data regarding survival rates, questions still remain regarding the best choice of local therapy for genetic variant carriers, more specifically BRCA1 and BRCA 2 carriers, with the understanding that the BRCA1 gene confers a slightly higher risk of cancer development than BRCA2. Age is also an important factor when deciding on local therapy in genetic variant carriers as older women have a smaller additional risk of in-breast tumor recurrence and contralateral breast cancer based on longevity of life alone . It has The associations between the reduction of IBTR and contralateral breast cancer events with tamoxifen use and oophorectomy in the study by Pierce et al. are modest in comparison to the 90% or greater reductions observed after bilateral prophylactic mastectomy. A randomized comparison between BCT and RRM is not feasible; however, a meta-analysis by Carbine et al. demonstrated mixed results when examining survival differences in women who had a contralateral prophylactic mastectomy (CRM) after a diagnosis of breast cancer. There were some studies that demonstrated survival differences but, when analyzing the data, the survival advantage was thought to be due to selection bias, with healthier, younger women selecting CRM ,119,120.CHEK2 mutation appear to have a relative risk of CBC of approximately 3.0, although follow-up is limited [ATM and NF1. Various models of germline genetic cancer predisposition would suggest an increased risk of CBC; however, the number of carriers is small and follow-up is limited even in large series from commercial laboratories [There are significant differences between the various penetrance genes in relation to the estimation of contralateral breast cancer risk. For women with BRCA1, the cumulative lifetime risk of CBC 20 years after the initial breast cancer diagnosis has been estimated to be approximately 40%. Kuchenbaecker et al. demonstrated that the hazard ratio for CBC declined with increasing age after the first diagnosis of breast cancer . For BRC limited ,123. Theratories . This maThe ongoing development of therapeutic treatments for early stage and metastatic breast cancers, including immunotherapies and poly (ADP-ribose) polymerase inhibitors (PARPi), highlights the importance of genetic testing to help identify patients with breast cancer who might benefit from these various treatments. PARP inhibitors in combination with platinum-based chemotherapies have demonstrated significant promise in the treatment of metastatic BRCA 1/2 mutation carriers . There aAs genetic testing becomes more prevalent amongst women and indications for panel testing evolve, more confident risk estimates are expected to become available over time, especially for moderately penetrant genes . Initial


Breast cancer remains the most common cancer in females, warranting the development of new approaches to prevention. One such approach is personalized prevention using genetic risk models. Here, we developed a risk model using both genetic and environmental risk factors. Results showed that a genetic risk score defined by the number of risk alleles for 14 breast cancer risk SNPs clearly stratified breast cancer risk. Moreover, the combination of this genetic risk score model with an environmental risk model which included established environmental risk factors showed significantly better C-statistics than the environmental risk model alone. This genetic risk score model in combination with the environmental model may be suitable for stratifying individual breast cancer risk, and may form the basis for a new personalized approach to breast cancer prevention.Personalized approaches to prevention based on genetic risk models have been anticipated, and many models for the prediction of individual breast cancer risk have been developed. However, few studies have evaluated personalized risk using both genetic and environmental factors. We developed a risk model using genetic and environmental risk factors using 1319 breast cancer cases and 2094 controls from three case–control studies in Japan. Risk groups were defined based on the number of risk alleles for 14 breast cancer susceptibility loci, namely low , moderate (11–16) and high (17+). Environmental risk factors were collected using a self-administered questionnaire and implemented with harmonization. Odds ratio (OR) and C-statistics, calculated using a logistic regression model, were used to evaluate breast cancer susceptibility and model performance. Respective breast cancer ORs in the moderate- and high-risk groups were 1.69  and 3.27 (2.46–4.34) compared with the low-risk group. The C-statistic for the environmental model of 0.616 (0.596–0.636) was significantly improved by combination with the genetic model, to 0.659 (0.640–0.678). This combined genetic and environmental risk model may be suitable for the stratification of individuals by breast cancer risk. New approaches to breast cancer prevention using the model are warranted.  Breast cancer is the most common cancer in females, with an estimated global incidence in 2018 of 2,088,849 . This inConventional strategies for breast cancer prevention include control of risk factors and early detection through mammography screening, targeting women in the general population. Given the increased burden of breast cancer, however, the development of new prevention strategies is essential. As a revolutionary approach, personalized prevention or precision prevention has recently been proposed ,5,6. A dSince the late 2000s, genome-wide association studies (GWAS) have identified hundreds of polymorphic loci associated with sporadic breast cancer risk ,27,28,29We and others have speculated that feedback on genetic and environmental risk to individuals at high risk might be meaningful for breast cancer prevention ,33. In pHere, we aimed to develop a genetic risk score and integrate it with established risk factors for personalized risk assessment for breast cancer in Japanese.Breast cancer cases and corresponding controls from three hospital-based case–control studies were included in the study. The Nagano study was a multicenter, hospital-based case–control study of breast cancer conducted from May 2001 to September 2005 at four hospitals in Nagano Prefecture. Details of the study have been described previously . BrieflyIn total, the present study included 1319 breast cancer cases and 2094 non-cancer controls. Information on known environmental risk factors for breast cancer was collected by self-administered questionnaire in each study. Data from three studies were harmonized according to common items and a categorization of variables was defined. The following variables were considered as environmental risk factors: age at enrollment, body mass index , ethanol drinking , cigarette smoking , physical activity , family history of breast cancer , age at menarche , parity , number of children , age at first birth , breastfeeding , hormone therapy  and menopausal status . BMI was calculated as the reported weight in kilograms divided by the reported height in meters squared. Ethanol consumption was estimated using the average number of alcohol beverages per day. Subjects reporting regular leisure time exercise at least once per month were classified as having physical activity. Family history was considered positive if a mother or a sister had ever had breast cancer. We previously identified 23 breast cancer-associated SNPs reported in previous GWAS or candidate-gene association studies ,28,29,40p-values less than 0.05 were selected for risk prediction modeling. Linkage disequilibriums (LD) of SNPs located within same genes were calculated. LD of the candidate loci clustered in the same region were assessed by Haploview 4.2 [p-value for breast cancer risk. Similarly, SNPs with summary p-values less than 0.10 and 0.30 were also used in sensitivity analysis for genetic risk modeling. Genomic DNA was extracted from the peripheral blood using a Qiagen FlexiGene DNA Kit  in the Nagano study, a QIAamp DNA Blood Maxi Kit (Qiagen) in the Kagoshima study and a DNA Blood mini kit  in the Aichi study. The 114 loci were genotyped in the study subjects using SNPtype assays . Among 114 loci, 11 monomorphic SNPs were excluded. Sixteen SNPs that were not accordant with the Hardy Weinberg Equilibrium (HWE) in at least one of the three populations were excluded. In total, 87 SNPs were included in further analysis. The impact of each SNP on breast cancer risk was evaluated by per allele odds ratio (OR) and 95% confidence interval (CI) using a logistic regression model adjusted for age at enrollment. The results of the three studies were combined using random effects meta-analysis. SNPs with summary view 4.2 . Strong p-value in the Hosmer–Lemeshow test indicates disagreement between the predicted and observed outcomes. The mean predicted probability was plotted against the mean observed probability for each decile in a calibration plot. A p-value < 0.05 was defined as the threshold of significance. Statistical analyses were conducted using Stata version 15.2 .The genetic risk group for breast cancer was defined according to the number of risk alleles in each control subject. Three risk groups  were defined by the distribution of risk allele numbers. Approximately 20%, 70% and 10% of controls were defined as the low-, moderate- and high-risk group, respectively. Breast cancer susceptibility in each risk group was evaluated by OR and its 95% CI using both crude and adjusted logistic regression models. Age at enrollment was adjusted in the crude model. In addition to the crude model, environmental risk factors were included in the adjusted model. ORs in total populations were calculated by crude and adjusted models with the addition of study site as a covariate. To assess the discriminatory ability of the risk prediction model, the area under the curve (AUC) in the Receiver Operating Characteristic (ROC) curve—also known as the concordance statistic (C-statistic)—was used. The C-statistic in the genetic model for each study population and the total population was calculated using logistic regression models which included the genetic risk score in the risk model. Similarly, C-statistic in the environmental model was calculated using logistic regression models which included the environmental risk factors. All variables in the genetic and environmental models were included in the inclusive model. In the ROC, the y axis shows sensitivity and the x axis shows the false positive rate, with AUC values ranging from 0.5 to 1. The straight line in the ROC shows a random classification of case and control subjects with an AUC of 0.5, while an AUC value of 1 corresponds to a perfect classification. An AUC value between 0.7 and 0.8 is acceptable while a value greater than 0.8 represents excellent model discrimination . In addiThe three case–control studies are characterized in p-values of less than 0.05. Five loci located in 10q26  and two in 16q12 (rs3803662 and rs4784227) were in strong LD. Four loci in 10q26  and one in 16q12 (rs3803662) were excluded from further analysis. The list of breast cancer susceptibility loci and their allelic ORs is shown in p-values of less than 0.10 and 42 SNPs with summary p-values of less than 0.30 were selected for additional genetic risk assessment. Among 114 genotyped breast cancer susceptibility loci identified by GWAS studies , 19 SNPsGenetic risk groups were defined according to the risk allele distribution of the 14 SNPs in controls , with thp for Hosmer–Lemeshow test = 0.506) (= 0.506) . The impact of risk groups stratified by menopausal status is shown in To check the validity of the SNP selection in the genetic risk model, genetic risk models with additional SNPs were assessed . The C-sTo assess the validity of genetic risk categorization, C-statistics of the three levels of the genetic risk model, genetic risk score model  and allelic risk model  were assessed . The C-sWe established a genetic risk model for breast cancer in subjects from three case–control studies in Japan using 14 risk loci identified in GWASs. The high-risk group, which accounted for 6.86% of total population, had a 3.27 times higher breast cancer risk than the low-risk group. While the discriminatory ability of the genetic risk model alone was not satisfactory, its combination with an environmental risk model produced significantly improved performance. Further, performance of the combined risk model was consistent between premenopausal and postmenopausal females. Many GWASs aimed at breast cancer risk seek to elucidate carcinogenic mechanisms. However, these studies have no direct impact on clinical practice . One reaThe use of genetic risk stratification for breast cancer prevention should be investigated. Several studies have assessed preventive approaches to genetic risk ,52. One Compared to the allelic risk model, the risk score model appears to have had an attenuated discriminatory ability. The risk score model was based on the assumption that all risk alleles confer the same magnitude of breast cancer risk. While the allelic ORs of risk loci ranged from 1.11 to 1.46 in the total population, the C-statistics of risk score models were not poorer than those of allelic risk models, indicating that the risk score models could be used in place of the allelic risk models. The number of risk alleles and the three corresponding risk grouping levels are simple to implement and comprehensive for females in general populations, the characteristics which would facilitate preventive interventions for breast cancer. The risk model would be available in other populations, although useful sets of alleles must be assessed in the populations. Randomized controlled studies to determine whether genetic risk feedback modifies individual behavior for breast cancer prevention are warranted.Hundreds of loci associated with breast cancer risk have been identified in GWAS studies. A previous polygenic risk model based on a large GWAS dataset reported AUCs of 0.603, 0.630 and 0.636 using 77, 313 and 3820 SNPs, respectively . These fThe major strength of this study was its study population. Because we established the risk model using three case–control studies conducted in geographically distant regions, the results are generalizable to the Japanese population. Nevertheless, the study was based on hospital-based case–control studies, meaning that several methodological limitations exist. First, the values for self-reported lifestyle factors considered as potential confounding factors might have some misclassification and recall bias. Second, selection bias in study subjects is inevitable in hospital-based case–control studies, and external validity should be interpreted carefully. Distributions of alcohol drinking and cigarette smoking in controls were highly consistent with those in a national survey , suggestThis genetic risk score model using 14 GWAS-identified loci in combination with environmental factors is able to stratify breast cancer risk. New breast cancer prevention strategies for genetically high-risk populations should be developed.


OCPRS was composed of cytoplasmic PLK1, phosphoMet, and SGK2 IHC staining. After controlling for the influence of clinicopathological features, high-risk patients were 2.33 times more likely to experience cancer progression than low-risk patients (p = 0.020). In the multivariate model, patients with extranodal extension  demonstrated a significantly increased risk for disease progression. Risk stratification with OCPRS provided distinct PFS groups for patients with oral cancer after surgical intervention. OCPRS appears suitable for routine clinical use for progression and prognosis estimation.The aim of this single-center case-control study is to investigate the feasibility and accuracy of oral cancer protein risk stratification (OCPRS) to analyze the risk of cancer progression. All patients diagnosed with oral cancer in Taiwan, between 2012 and 2014, and who underwent surgical intervention were selected for the study. The tissue was further processed for immunohistochemistry (IHC) for 21 target proteins. Analyses were performed using the results of IHC staining, clinicopathological characteristics, and survival outcomes. Novel stratifications with a hierarchical clustering approach and combinations were applied using the Cox proportional hazard regression model. Of the 163 participants recruited, 102 patients were analyzed, and OCPRS successfully identified patients with different progression-free survival (PFS) profiles in high-risk (53 subjects) versus low-risk (49 subjects) groups ( Oral squamous cell cancer (OSCC) is the sixth most common cancer worldwide, with 630,000 new cases and 350,000 deaths estimated annually . Its strReported and well-known risk factors for oral cancer include alcohol consumption, betel nut use, and cigarette smoking ,6. SeverEGFR, TP53, CCND1, and RB1 are associated with OSCC progression  = 2.71–17.82, p < 0.001) and multivariate  OS analyses. Collectively, stratification via the 8th protein staining cluster  demonstrated a novel predictor of disease progression in oral cancer.In OS, no significant difference was observed in the risk of death between high-risk and low-risk subjects in the univariate . Finally, we developed a unique and novel approach to adopt the prognostic usefulness of a scoring system, OCPRS, for the diagnosis of patients with oral cancer. The OCPRS was generated based on a hierarchical agglomerative algorithm, sensitive to background noise that allows a decrease in type 1 errors . Consistent with previous studies, we demonstrated that the agglomerative hierarchical clustering algorithm is advantageous for handling high-dimensional data with uncertain interactions between factors ,36,37. Op = 0.020). However, similar results were not observed in OS  in univariate analysis. Patients with ENE demonstrated an increased risk of progression in both univariate (p = 0.030) and multivariate models (p = 0.045). Patients with higher histological grade and ENE demonstrated an increased mortality risk in both the univariate (p = 0.006 and p < 0.001) and multivariate models . Patients with higher histological grade presented a higher risk of progression in the univariate model (p = 0.043), with no significant effects in the multivariate model (p = 0.079). Patients with PNI, larger tumor size, lymph node invasion, and pathological stage demonstrated an increased mortality risk in the univariate model, with no significant effects in the multivariate model. These results suggest that there exist interactions that interfere with these clinicopathological factors [Furthermore, we investigated and predicted disease progression using the Cox proportional hazards regression analysis, controlled for the influence of clinicopathological features, indicating that patients with high-risk cancer were 2.33 times more likely to experience cancer progression than those with low-risk cancer , which could identify 100% (36 of 36) progressed subjects and 96.2% (25 of 26) dead subjects in high-risk strata. However, the results were attributed to the extremely imbalanced dichotomous results between high-risk (101 subjects) and low-risk (1 subject) strata. The results indicate the EGFR and CDK6 might obtain a good sensitivity to detect high-risk patients, but the low specificity to survival outcome estimation leads to the omission of EGFR and CDK6 in current study. Thence, further research to further investigate the interaction between EGFR, CDK6, and others factors using machine learning approaches such as evolutionary and optimization algorithm is necessary to identify the high-risk subjects more accurately.Besides, our study raised an interesting issue of localization of IHC staining. In previous studies of ovarian cancer, different localizations of thyroid hormone receptors and αvβ3 integrin demonstrated different nuclear and non-nuclear signaling pathway for cancerogenesis ,41. The Here, we developed a new statistical approach using hierarchical clustering. By combining predictive IHC biomarkers, we newly defined OCPRS to predict PFS outcomes for oral cancer. OCPRS is clinically available and easily measurable for the staining of surgical specimens. High-risk OCPRS with evaluation of cytoplasmic PLK1, PhosphoMet, and SGK2 staining is useful for the stratification of clinical outcomes. Therefore, OCPRS may be used as a promising biomarker for predicting progression outcomes and stratifying risk groups for oral cancer.