Breast Cancer Risk Stratification Research Articles

Women’s experiences of risk-stratified breast cancer screening in the MyPeBS trial: a qualitative comparative study across two European countries

Objective Risk-stratification should improve the benefits-to-harms ratio for breast screening, whereby higher-risk women receive additional screening and low-risk women are screened less. This study investigated the effects of healthcare context by comparing how women in England and France experienced risk-based breast screening. Methods and measures Fifty-two women were purposively sampled from participants who underwent risk-based screening in the MyPeBS trial. Women received objectively-derived 5-year breast cancer risk estimates (low = < 1%, average = 1–1.66%, high = ≥ 1.67 to <6%, very-high-risk = ≥ 6%). This determined future trial-related screening schedules and prevention options. Semi-structured interviews were transcribed for thematic framework analysis. Results Two overarching themes were produced: the importance of supported risk communication and accessibility of risk management. Overall, risk-based breast screening was viewed positively. However, trial procedures, especially in risk estimate provision, differed across sites. Women at increased risk were more reassured when appointments were with specialist healthcare professionals (HCP). When absent, this resulted in reduced satisfaction with risk communication and greater uncertainty about its personal relevance. Low-risk women’s views on extended mammogram schedules seemed linked to how health services are organised differently. Conclusions Context is an important consideration regarding acceptability of healthcare innovations such as risk-stratified screening: it should not be assumed that findings from one country apply universally.

Polygenic Risk Score (PRS) Combined with NGS Panel Testing Increases Accuracy in Hereditary Breast Cancer Risk Estimation.

Breast cancer (BC) is the most prominent tumor type among women, accounting for 32% of newly diagnosed cancer cases. BC risk factors include inherited germline pathogenic gene variants and family history of disease. However, the etiology of the disease remains occult in most cases. Therefore, in the absence of high-risk factors, a polygenic basis has been suggested to contribute to susceptibility. This information is utilized to calculate the Polygenic Risk Score (PRS) which is indicative of BC risk. This study aimed to evaluate retrospectively the clinical usefulness of PRS integration in BC risk calculation, utilizing a group of patients who have already been diagnosed with BC. The study comprised 105 breast cancer patients with hereditary genetic analysis results obtained by NGS. The selection included all testing results: high-risk gene-positive, intermediate/low-risk gene-positive, and negative. PRS results were obtained from an external laboratory (Allelica). PRS-based BC risk was computed both with and without considering additional risk factors, including gene status and family history. A significantly different PRS percentile distribution consistent with higher BC risk was observed in our cohort compared to the general population. Higher PRS-based BC risks were detected in younger patients and in those with FH of cancers. Among patients with a pathogenic germline variant detected, reduced PRS values were observed, while the BC risk was mainly determined by a monogenic etiology. Upon comprehensive analysis encompassing FH, gene status, and PRS, it was determined that 41.90% (44/105) of the patients demonstrated an elevated susceptibility for BC. Moreover, 63.63% of the patients with FH of BC and without an inherited pathogenic genetic variant detected showed increased BC risk by incorporating the PRS result. Our results indicate a major utility of PRS calculation in women with FH in the absence of a monogenic etiology detected by NGS. By combining high-risk strategies, such as inherited disease analysis, with low-risk screening strategies, such as FH and PRS, breast cancer risk stratification can be improved. This would facilitate the development of more effective preventive measures and optimize the allocation of healthcare resources.

Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images

BackgroundStratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts.MethodsThis retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint.ResultsIn the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63–4.66, p-value < 0.001) after adjusting for established risk factors. In the ER+/HER2- Nottingham histological grade (NHG) 2 subgroup, the HR was 2.20 (95% CI: 1.22–3.98, p-value = 0.009) between low- and high-risk groups.ConclusionThe results indicate an independent prognostic value of Stratipath Breast among all breast cancer patients, as well as in the clinically relevant ER+/HER2- subgroup and the NHG2/ER+/HER2- subgroup. Improved risk stratification of intermediate-risk ER+/HER2- breast cancers provides information relevant for treatment decisions of adjuvant chemotherapy and has the potential to reduce both under- and overtreatment. Image-based risk stratification provides the added benefit of short lead times and substantially lower cost compared to molecular diagnostics and therefore has the potential to reach broader patient groups.

Validation of a clinical breast cancer risk assessment tool combining a polygenic score for all ancestries with traditional risk factors

PurposeWe previously described a combined risk score (CRS) that integrates a multiple-ancestry polygenic risk score (MA-PRS) with the Tyrer-Cuzick (TC) model to assess breast cancer (BC) risk. Here, we present a longitudinal validation of CRS in a real-world cohort. MethodsThis study included 130,058 patients referred for hereditary cancer genetic testing and negative for germline pathogenic variants in BC-associated genes. Data were obtained by linking genetic test results to medical claims (median follow-up 12.1 months). CRS calibration was evaluated by the ratio of observed to expected BCs. ResultsThree hundred forty BCs were observed over 148,349 patient-years. CRS was well-calibrated and demonstrated superior calibration compared with TC in high-risk deciles. MA-PRS alone had greater discriminatory accuracy than TC, and CRS had approximately 2-fold greater discriminatory accuracy than MA-PRS or TC. Among those classified as high risk by TC, 32.6% were low risk by CRS, and of those classified as low risk by TC, 4.3% were high risk by CRS. In cases where CRS and TC classifications disagreed, CRS was more accurate in predicting incident BC. ConclusionCRS was well-calibrated and significantly improved BC risk stratification. Short-term follow-up suggests that clinical implementation of CRS should improve outcomes for patients of all ancestries through personalized risk-based screening and prevention.

Women's Knowledge, Attitudes, and Perception on Personalized Risk-Stratified Breast Cancer Screening: A Cross-Sectional Study in Malaysia.

Breast cancer is commonest cancer among Malaysian women and screening is essential for the early detection. Therefore our study aimed at measuring the levels of knowledge, attitude and perception towards personalized risk stratified breast cancer screening in Malaysia. A cross-sectional study was carried out in Malaysia to assess the knowledge, perception and attitudes of the women in Malaysia. The study was conducted using an online questionnaire, and samples were obtained using convenience sampling. The questionnaire was distributed trilingual in English, Bahasa Malaysia and Chinese. The data was collected with content validated questionnaire. Data was analyzed with descriptive statistics and General Linear Model analysis in SPSS (Version 27). A total of 201 respondents' data were analyzed. From our study we were able to summarize that the women in Malaysia have a suboptimal knowledge towards personalized risk-stratified breast cancer screening as only 48.9% aware of the term for personalized risk-stratified breast cancer screening. Meanwhile, the majority of the respondents (96.7%) showed positive attitudes towards the importance of risk assessment and screening. Experience of participating in health education programmes about breast cancer and personalized risk-stratified screening was found to be significantly associated with knowledge, attitude and perception towards personalized risk-stratified breast cancer screening. General population's awareness of individualized risk-stratified breast cancer screening was insufficient despite their favourable attitude towards the disease. A multimodal strategy may be used to improve women's knowledge, attitude, and perception of individualized risk-stratified breast cancer screening.

Abstract 3510: A radiogenomic approach for triple-negative breast cancer risk stratification

Abstract Background: Triple-negative breast cancer (TNBC) is an aggressive disease that accounts for 15-20% of all breast cancers. Expressions of ER, PR and HER2 receptors are lacking in this disease, and thus targeted therapies are not effective. TNBC has a shorter relapse-free survival, higher metastasis rate and decreased overall survival compared with other breast cancers. However, when undergoing standard treatment, some patients respond well, while others have poor outcome, suggesting TNBC heterogeneity. Early stratification of patients with long versus short survival could identify the subgroup of patients who would not benefit from exposure to toxicity of chemotherapy treatment. Here, we developed a non-invasive radiogenomic approach for TNBC risk stratification. Methods: A transcriptomic-based prognostic gene signature was previously developed using the TCGA-BRCA cohort (n=860). Briefly, LASSO Cox regression model analysis with the ‘glmnet’ R package was used to identify the transcriptomic signature gene-set consisting of 50 genes. We tested this signature to prognosticate overall survival in a Stanford cohort (n=63) and a previously published SCANB cohort (n=604). The patients were stratified into high- and low-risk groups based on the median risk-score. Next, we developed a machine learning model that identified a radiomic feature set to predict the prognostic transcriptomic risk-groups. Radiomic features were extracted from pre-treatment breast MRI. Radiomics features were extracted using PyRadiomics. The model utilized Decision Tree Classifier and LeaveOneOut method was used for cross-validation. Results: The transcriptomic signature low-risk group was significantly associated with improved overall survival in the two TNBC cohorts, with hazard ratios of 0.11 [95% CI: 0.01-0.88] for the Stanford cohort and 0.71 [95% CI: 0.52-0.97] for the SCANB cohort (log-rank p-values p=0.012 and p=0.032, respectively). Including this transcriptomic signature in a multivariate analysis, which adjusted for clinical features (patient age, grade, stage and Ki67%), the transcriptomic prognostic signature remained a significant prognostic factor (p&amp;lt;0.05). The radiomic feature set (consisting of 20 features) predicted the high- and low-risk transcriptomic groups with a mean accuracy of 72.2% and a mean AUROC of 71%. The precision, F1 and recall scores were 67%, 74% and 82%, respectively. In an independent dataset consisting of 116 Stanford TNBC patients, we used this model to predict risk groups based on the MRI radiomics features, and evaluated the prognostic effects of predicted risk groups. The overall survival of the predicted high-risk group was significantly poorer than the predicted low-risk group (p=0.013). Conclusions: We present a prognostic model that can non-invasively stratify TNBC patients for low versus high mortality risk using radiomic features derived from pre-treatment patient MRI data. Citation Format: Humaira Noor, Yuanning Zheng, Adam Mantz, Ryle Zhou, Andrew Kozlov, Wendy B. DeMartini, Shu-tian Chen, Satoko Okamoto, Debra Ikeda, Sarah Mattonen, Sandy Napel, Melinda L. Telli, George Sledge, Allison Kurian, Mina Satoyoshi, Olivier Gevaert, Haruka Itakura. A radiogenomic approach for triple-negative breast cancer risk stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3510.

Attitudes towards risk-stratified breast cancer screening: a population-based survey among 5,001 Danish women

BackgroundThe individual woman’s risk of being diagnosed with breast cancer can now be estimated more precisely, and screening can be stratified accordingly. The risk assessment requires that women are willing to provide a blood test, additional personal information, to know their risk, and alter screening intervals. This study aimed to investigate Danish women’s attitudes towards risk-stratified breast cancer screening.MethodsAn online, cross-sectional survey was conducted among Danish women aged 52–67 years. We used logistic regression analyses to assess how personal characteristics were associated with the women’s attitudes.Results5,001 women completed the survey (response rate 44%) of which 74% approved of risk estimation to potentially alter their screening intervals. However, only 42% would accept an extended screening interval if found to have low breast cancer risk, while 89% would accept a reduced interval if at high risk. The main determinants of these attitudes were age, education, screening participation, history of breast cancer, perceived breast cancer risk and to some extent breast cancer worry.ConclusionThis study indicates that women are positive towards risk-stratified breast cancer screening. However, reservations and knowledge among subgroups of women must be carefully considered and addressed before wider implementation of risk-stratified breast cancer screening in a national program.

Association of preoperative and postoperative circulating tumour DNA (ctDNA) with PIK3CA gene mutation with risk of recurrence in patients with non-metastatic breast cancer

BackgroundCirculating tumour DNA (ctDNA), contains tumour-specific gene mutation in blood circulation and could aid in postoperative risk stratification of non-metastatic breast cancer. In this study, we investigated the feasibility of detecting PIK3CA gene mutations in ctDNA in the preoperative (preop) and postoperative period (postop), and its prognostic significance in patients with breast cancer. MethodsA cohort of patients with breast cancer undergoing curative surgery with available blood samples preoperatively and postoperatively (Post op) at either Post op time period; week 1–2, week 3–4 or weeks 5–12 were enrolled. PIK3CA gene mutations at exons 9 and 20 were detected in ctDNA with High resolution melting (HRM) PCR and Allele specific fluorescence probe-based PCR. ResultsA total of 62 patients (age, median (IQR), 51.50 (45.0–65.0) years), with a median follow-up of 90 months (interquartile range (IQR),60–120 months) were enrolled. In total, 25 (40.3%) and 22 (35%) patients with breast cancer had detectable PIK3CA gene mutations in ctDNA in preoperative and postoperative period, respectively. PIK3CA gene mutations in ctDNA in postoperative period (hazard ratio (H.R: 18.05, p = 0.001) were a negative prognostic factor for recurrencefree survival (RFS) and overall survival (OS) (H.R: 11.9, p = 0.01) in patients with breast cancer. Subgroup analysis of ctDNA indicate that positive ctDNA in both preoperative/postoperative period and post op period only were found to have prognostic effect on RFS and OS (RFS; p < 0.0001, O·S; p = 0.0007). Moreover, ctDNA-based detection preceded clinical detection of recurrence in patients with an average lead time of 12 months (IQR:20–28.5 months) across all the breast cancer subtypes. ConclusionWe highlighted the prognostic ability of ctDNA in patients with breast cancer in perioperative period. However, future prospective studies are needed to assess the utility of ctDNA in clinical practice

An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population: A prospective cohort and modeling study.

Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors. The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years old from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020. The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy. We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

Association of Fibroids, Endometriosis, and Gynecologic Surgeries with Breast Cancer Incidence and Hormone Receptor Subtypes.

Fibroids and endometriosis are sex hormone-mediated and exhibit cancer-like behavior. Breast cancer may be more common in women who have had these conditions, but the literature is conflicting and does not always address factors like hysterectomy/oophorectomy status, race/ethnicity, menopause, and hormone receptor subtypes. Data are from the Sister Study, a cohort of 50,884 U.S. women enrolled in 2003 to 2009 and followed through 2020. Cox proportional hazards models with time-varying exposures and covariates assessed the relationship of fibroids or endometriosis with breast cancer. Logistic regression examined the association with estrogen receptor (ER) status among cases. Fibroids (19,932 cases) were positively associated with breast cancer [fully adjusted HR: 1.07; 95% confidence interval (CI): 1.01-1.14], notably among Black participants (HR: 1.34; 95% CI: 1.07-1.69) and women who had a hysterectomy (HR: 1.18; 95% CI: 1.05-1.31). Endometriosis (3,970 cases) was not associated with breast cancer (HR: 0.99; 95% CI: 0.91-1.08). Among 4,419 breast cancer cases, fibroids were positively associated with ER+ subtypes (OR: 1.34; 95% CI: 1.10-1.65), while endometriosis was negatively associated with ER+ subtypes (OR: 0.78; 95% CI: 0.61-1.01). We observed a modest positive association between fibroids and breast cancer, particularly ER+ breast cancer. No relationship with endometriosis and breast cancer incidence was found. Fibroids, even in those with a family history of breast cancer, might modify breast cancer risk stratification tools. Future studies should further assess this link and interrogate shared risk factors.

Combining rare and common genetic variants improves population risk stratification for breast cancer

PurposeThis study aimed to evaluate the performance of different genetic screening approaches to identify women at high risk of breast cancer in the general population. MethodsWe retrospectively studied 25,591 women with available electronic health records and genetic data, participants in the Healthy Nevada Project. ResultsFamily history of breast cancer was ascertained on or after the record of breast cancer for 78% of women with both, indicating that this risk assessment method is not being properly utilized for early screening. Genetics offered an alternative method for risk assessment. 11.4% of women were identified as high risk based on possessing a predicted loss-of-function (pLOF) variant in BRCA1, BRCA2, or PALB2 (hazard ratio = 10.4, 95% confidence interval: 8.1-13.5) or a pLOF variant in ATM or CHEK2 (hazard ratio = 3.4, CI: 2.4-4.8) or being in the top 10% of the polygenic risk score (PRS) distribution (hazard ratio = 2.4, CI: 2.0-2.8). Moreover, women with a pLOF in ATM or CHEK2 and ranking in the top 50% of the PRS displayed a high risk (39.2% probability of breast cancer at age 70), whereas their counterparts in the bottom 50% of the PRS were not at high risk (14.4% probability at age 70). ConclusionOur findings suggest that a combined monogenic and polygenic approach allowed a better identification of participants with high risk while minimizing false positives.

Association between allostatic load and breast cancer risk: a cohort study

BackgroundAllostatic load (AL) reflects the collective load of chronic stress during lifetime. Previous studies have shown that higher AL is associated with poor clinical outcomes among breast cancer patients. However, the relationship between AL and breast cancer risk is still unclear.MethodsTo fill the gap, we analyzed the association between AL and the development of breast cancer in 181,455 women identified from the UK Biobank.ResultsDuring the follow-up from 2006 to 2020, 5,701 women were diagnosed with incident breast cancer. Significantly higher AL was observed among incident breast cancer cases than all study participants (mean: 2.77 vs. 2.63, P < 0.01). Univariate Cox regression analysis indicated the risk of breast cancer was increased by 5% per one AL unit increase (hazard ratio (HR) = 1.05, 95% confidence interval (CI) 1.04, 1.07). In multivariate analyses, after adjusting demographics, family history of breast cancer, reproductive factors, socioeconomic status, lifestyle factors, and breast cancer polygenic risk score (PRS), the significant association remained (HR = 1.05, 95%CI 1.03, 1.07). The significant relationship was further confirmed in the categorical analysis. Compared with women in the low AL group (AL: 0 ~ 2), those in the high AL group (AL: 3 ~ 11) had a 1.17-fold increased risk of breast cancer (HR = 1.17, 95%CI 1.11, 1.24). Finally, in the stratified analysis, joint effects on the risk of breast cancer were observed between the AL and selected known breast cancer risk factors, including age, family history of breast cancer, PRS, income, physical activity, and alcohol consumption.ConclusionIn summary, those findings have demonstrated that higher AL was associated with an increased breast cancer risk in women. This association is likely independent of known breast cancer risk factors. Thus, the AL could be a valuable biomarker to help breast cancer risk prediction and stratification.

Benign Breast Disease and Breast Cancer Risk in the Percutaneous Biopsy Era

Benign breast disease (BBD) comprises approximately 75% of breast biopsy diagnoses. Surgical biopsy specimens diagnosed as nonproliferative (NP), proliferative disease without atypia (PDWA), or atypical hyperplasia (AH) are associated with increasing breast cancer (BC) risk; however, knowledge is limited on risk associated with percutaneously diagnosed BBD. To estimate BC risk associated with BBD in the percutaneous biopsy era irrespective of surgical biopsy. In this retrospective cohort study, BBD biopsy specimens collected from January 1, 2002, to December 31, 2013, from patients with BBD at Mayo Clinic in Rochester, Minnesota, were reviewed by 2 pathologists masked to outcomes. Women were followed up from 6 months after biopsy until censoring, BC diagnosis, or December 31, 2021. Benign breast disease classification and multiplicity by pathology panel review. The main outcome was diagnosis of BC overall and stratified as ductal carcinoma in situ (DCIS) or invasive BC. Risk for presence vs absence of BBD lesions was assessed by Cox proportional hazards regression. Risk in patients with BBD compared with female breast cancer incidence rates from the Iowa Surveillance, Epidemiology, and End Results (SEER) program were estimated. Among 4819 female participants, median age was 51 years (IQR, 43-62 years). Median follow-up was 10.9 years (IQR, 7.7-14.2 years) for control individuals without BC vs 6.6 years (IQR, 3.7-10.1 years) for patients with BC. Risk was higher in the cohort with BBD than in SEER data: BC overall (standard incidence ratio [SIR], 1.95; 95% CI, 1.76-2.17), invasive BC (SIR, 1.56; 95% CI, 1.37-1.78), and DCIS (SIR, 3.10; 95% CI, 2.54-3.77). The SIRs increased with increasing BBD severity (1.42 [95% CI, 1.19-1.71] for NP, 2.19 [95% CI, 1.88-2.54] for PDWA, and 3.91 [95% CI, 2.97-5.14] for AH), comparable to surgical cohorts with BBD. Risk also increased with increasing lesion multiplicity (SIR: 2.40 [95% CI, 2.06-2.79] for ≥3 foci of NP, 3.72 [95% CI, 2.31-5.99] for ≥3 foci of PDWA, and 5.29 [95% CI, 3.37-8.29] for ≥3 foci of AH). Ten-year BC cumulative incidence was 4.3% for NP, 6.6% for PDWA, and 14.6% for AH vs an expected population cumulative incidence of 2.9%. In this contemporary cohort study of women diagnosed with BBD in the percutaneous biopsy era, overall risk of BC was increased vs the general population (DCIS and invasive cancer combined), similar to that in historical BBD cohorts. Development and validation of pathologic classifications including both BBD severity and multiplicity may enable improved BC risk stratification.

3MO Multimodal data fusion for improved risk stratification of breast cancer with multi-task 3D deep learning model: A multicenter study

3MO Multimodal data fusion for improved risk stratification of breast cancer with multi-task 3D deep learning model: A multicenter study

The cost-effectiveness of risk-stratified breast cancer screening in the UK

BackgroundThere has been growing interest in the UK and internationally of risk-stratified breast screening whereby individualised risk assessment may inform screening frequency, starting age, screening instrument used, or even decisions not to screen. This study evaluates the cost-effectiveness of eight proposals for risk-stratified screening regimens compared to both the current UK screening programme and no national screening.MethodsA person-level microsimulation model was developed to estimate health-related quality of life, cancer survival and NHS costs over the lifetime of the female population eligible for screening in the UK.ResultsCompared with both the current screening programme and no screening, risk-stratified regimens generated additional costs and QALYs, and had a larger net health benefit. The likelihood of the current screening programme being the optimal scenario was less than 1%. No screening amongst the lowest risk group, and triannual, biennial and annual screening amongst the three higher risk groups was the optimal screening strategy from those evaluated.ConclusionsWe found that risk-stratified breast cancer screening has the potential to be beneficial for women at the population level, but the net health benefit will depend on the particular risk-based strategy.

Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence

GoalsTo determine whether an 18 single nucleotide polymorphisms (SNPs) polygenic risk score (PRS18) improves breast cancer (BC) risk prediction for women at above-average risk of BC, aged 40–49, in a Central European population with BC incidence below EU average. Methods502 women aged 40–49 years at the time of BC diagnosis completed a questionnaire on BC risk factors (as per Tyrer-Cuzick algorithm) with data known at age 40 and before BC diagnosis. Blood samples were collected for DNA isolation. 250 DNA samples from healthy women aged 50 served as a control cohort. 18 BC-associated SNPs were genotyped in both groups and PRS18 was calculated. The predictive power of PRS18 to detect BC was evaluated using a ROC curve. 10-year BC risk was calculated using the Tyrer-Cuzick algorithm adapted to the Slovenian incidence rate (S-IBIS): first based on questionnaire-based risk factors and, second, including PRS18. ResultsThe AUC for PRS18 was 0.613 (95 % CI 0.570–0.657). 83.3 % of women were classified at above-average risk for BC with S-IBIS without PRS18 and 80.7 % when PRS18 was included. ConclusionBC risk prediction models and SNPs panels should not be automatically used in clinical practice in different populations without prior population-based validation. In our population the addition of an 18SNPs PRS to questionnaire-based risk factors in the Tyrer-Cuzick algorithm in general did not improve BC risk stratification, however, some improvements were observed at higher BC risk scores and could be valuable in distinguishing women at intermediate and high risk of BC.

Robust cross-vendor mammographic texture models using augmentation-based domain adaptation for long-term breast cancer risk.

Risk-stratified breast cancer screening might improve early detection and efficiency without comprising quality. However, modern mammography-based risk models do not ensure adaptation across vendor-domains and rely on cancer precursors, associated with short-term risk, which might limit long-term risk assessment. We report a cross-vendor mammographic texture model for long-term risk. The texture model was robustly trained using two systematically designed case-control datasets. Textural features, indicative of future breast cancer, were learned by excluding samples with diagnosed/potential malignancies from training. An augmentation-based domain adaption technique, based on flavorization of mammographic views, ensured generalization across vendor-domains. The model was validated in 66,607 consecutively screened Danish women with flavorized Siemens views and 25,706 Dutch women with Hologic-processed views. Performances were evaluated for interval cancers (IC) within 2 years from screening and long-term cancers (LTC) from 2 years after screening. The texture model was combined with established risk factors to flag 10% of women with the highest risk. In Danish women, the texture model achieved an area under the receiver operating characteristic curve (AUC) of 0.71 and 0.65 for ICs and LTCs, respectively. In Dutch women with Hologic-processed views, the AUCs were not different from AUCs in Danish women with flavorized views. The AUC for texture combined with established risk factors increased to 0.68 for LTCs. The 10% of women flagged as high-risk accounted for 25.5% of ICs and 24.8% of LTCs. The texture model robustly estimated long-term breast cancer risk while adapting to an unseen processed vendor-domain and identified a clinically relevant high-risk subgroup.

Clinical Significance of Background Parenchymal Enhancement in Breast Cancer Risk Stratification.

Background parenchymal enhancement (BPE) is an established breast cancer risk factor. However, the relationship between BPE levels and breast cancer risk stratification remains unclear. To evaluate the clinical relationship between BPE levels and breast cancer risk with covariate adjustments for age, ethnicity, and hormonal status. Retrospective. 954 screening breast MRI datasets representing 721 women divided into four cohorts: women with pathogenic germline breast cancer (BRCA) mutations (Group 1, N = 211), women with non-BRCA germline mutations (Group 2, N = 60), women without high-risk germline mutations but with a lifetime breast cancer risk of ≥20% using the Tyrer-Cuzick model (Group 3, N = 362), and women with <20% lifetime risk (Group 4, N = 88). 3 T/axial non-fat-saturated T1, short tau inversion recovery, fat-saturated pre-contrast, and post-contrast T1-weighted images. Data on age, body mass index, ethnicity, menopausal status, genetic predisposition, and hormonal therapy use were collected. BPE levels were evaluated by two breast fellowship-trained radiologists independently in accordance with BI-RADS, with a third breast fellowship-trained radiologist resolving any discordance. Propensity score matching (PSM) was utilized to adjust covariates, including age, ethnicity, menopausal status, hormonal treatments, and prior bilateral oophorectomy. The Mann-Whitney U test, chi-squared test, and univariate and multiple logistic regression analysis were performed, with an odds ratio (OR) and corresponding 95% confidence interval. Weighted Kappa statistic was used to assess inter-reader variation. A P value <0.05 indicated a significant result. In the assessment of BPE, there was substantial agreement between the two interpreting radiologists (κ = 0.74). Patient demographics were not significantly different between patient groups after PSM. The BPE of Group 1 was significantly lower than that of Group 4 and Group 3 among premenopausal women. In estimating the BPE level, the OR of gene mutations was 0.35. Adjusting for potential confounders, the BPE level of premenopausal women with BRCA mutations was significantly lower than that of non-high-risk women. 3 TECHNICAL EFFICACY: Stage 3.

Comprehensive analysis of ICD-related lncRNAs in predicting risk stratification, clinical prognosis and immune response for breast cancer.

Breast cancer (BRCA) represents a significant threat with high mortality rates due to relapse, metastasis, and chemotherapy resistance. As a regulated cell death process characterized by the induction of immunogenic signals, immunogenic cell death (ICD) has been identified as an effective anti-tumorigenesis approach. However, the comprehensive study and its clinical value of ICD-related lncRNAs in BRCA is still missing. The transcriptome matrix and corresponding clinical information of BRCA patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was performed to identify ICD-related lncRNAs (ICDRLs). To determine the prognostic value of the identified ICDRLs, univariate Cox regression analysis, LASSO algorithm, and multivariate Cox regression analysis were employed to construct a risk model. The prognostic risk model was subsequently evaluated using univariate and multivariate Cox regression analysis, as well as Nomogram analysis. In vitro experiments were also conducted to validate the bioinformatics findings using quantitative real-time PCR (qRT-PCR). We established a prognostic risk signature consisting of five ICDRLs. The prognostic value of this model was subsequently confirmed in guiding BRCA prognostic stratification. Furthermore, we explored the correlation of the risk score with various clinical characteristics and chemotherapy response. qRT-PCR result confirmed the abnormal expression of ICDRLs, which was consistent with the bioinformatics data. Our findings provide evidence of the critical role of ICDRLs in BRCA and offer a novel perspective for exploring precise treatment options for BRCA patients.

Risk Stratification of Early Breast Cancer (HR +/HER 2–) by CanAssist Breast and Its Corelation with Other Online Prognostic Tools: Experience from a Single Center

Abstract Introduction Risk assessment by various methods for HR +/HER2– early-stage breast cancer (EBC) patients help clinicians stratify risk and tailor individual treatment. Multiple prognostic tests are available, both free and expensive. Free prognostic tools, the Nottingham Prognostic Index (NPI), and modified Adjuvant Online (mAOL) rely on clinical parameters. CanAssist Breast (CAB) considers both clinical parameters and tumor biology for assessing the risk of recurrence. Objectives The objective is to assess risk by CAB, NPI, and mAOL and discern the differences in the risk stratification in the EBC cohort of Bhagwan Mahaveer Cancer Hospital and Research Centre, Jaipur, Rajasthan, India. Methods Study cohort comprises 100 patients. Risk concordance was assessed by the kappa correlation coefficient and restratification analysis between risk groups of CAB, NPI, and mAOL was assessed using a two-sided p-value. Results Cohort was predominated by patients aged above 50, with T2/N0/G2 tumors. Low-risk (LR) and high-risk (HR) proportions by CAB, NPI, and mAOL were 67:33, 19:81, and 14:86, respectively. Across both age groups, CAB stratified more patients as LR compared with NPI and mAOL. In subgroups of patients with N0, G2, and T2 tumors, CAB identified significantly (p &lt; 0.0001) higher (3–8 times) patients as LR than NPI and mAOL. In patients with T1/G1 tumors, risk proportions were similar by all three tools. Interestingly, CAB LR (57%) was four times that of NPI (14%) in the N1 subgroup. In G3 tumors CAB LR was 13%. mAOL failed to identify LR in the N1 and G3 subgroups and NPI in the G3 subgroup. There was poor agreement between CAB and NPI/mAOL (k 0.14 [95% confidence interval: 0.03–0.24]/0.11 [0.02–0.20]). Up to 11% of mAOL/NPI LR were detected as HR by CAB and up to 63% of mAOL and NPI HR as LR by CAB. Conclusion Prognostication by tools that use clinical parameters alone might be inadequate. Prognostication using CAB that integrates critical biomarkers indicative of tumor biology along with clinical parameters could be significant. The earlier published data on CAB across various ethnic cohorts and its comparable performance with Oncotype DX makes CAB a relevant prognostic test in HR +/HER2– EBC to make decisions on chemotherapy use.