This study identified several personal characteristics that were more strongly linked to breast cancer occurrence, highlighting the value of integrating clinical and self-reported information to better understand individual risk and support more targeted prevention and screening efforts.
Abstract
Simple Summary Early detection of breast cancer greatly improves outcomes, with the 5-year survival rate reaching 99% when the disease is found at an early stage. One promising approach to improve early detection is risk-based breast screening, which tailors screening recommendations according to a woman’s individual risk factors, including medical history, personal characteristics, and lifestyle habits. The Prevention, Imaging, Network, Knowledge (P.I.N.K.) study was created to collect real-world data from a large group of women across Italy and to support the development of more personalized breast cancer screening strategies. By combining information gathered during routine breast clinic visits with answers provided by women through a structured questionnaire, we examined factors associated with the development of breast cancer. Our findings identified several personal characteristics that were more strongly linked to breast cancer occurrence, highlighting the value of integrating clinical and self-reported information to better understand individual risk and support more targeted prevention and screening efforts. By providing additional evidence on the contribution of potentially modifiable risk factors, this study helps identify priorities for future research aimed at improving breast cancer risk stratification, informing the development of more targeted screening strategies, and facilitating discussions with women about the impact of lifestyle on breast cancer risk.
Background Population-based mammographic screening is primarily age-based. However, breast cancer risk is multifactorial, and women may benefit from personalized risk-based screening. This pilot study aimed to explore the use of polygenic risk score (PRS) as a tool for risk stratification in personalized screening. Methods We included 80 women aged 40–49 years referred for clinical mammography. Exclusion criteria were prior breast cancer or premalignant breast disease, and previous genetic testing. After DNA collection, PRS was calculated from 2805 Single Nucleotide Polymorphisms (SNPs). Screening recommendations were based on each participant’s relative 10-year breast cancer risk estimated from PRS and compared with the 10-year risk of an average woman of the same age. Women with a self-reported family history of cancer meeting standard criteria were referred for gene panel testing for pathogenic variants in high-risk genes. A follow up questionnaire regarding participants’ experiences was distributed 6–9 months after PRS testing. Results Mean age was 45.2 years (SD 2.8). Mean relative 10-year breast cancer risk was 1.18 (SD 0.57). Based on PRS, 40 participants were recommended standard biennial screening 50–69 years, while 40 were advised to begin biennial screening before age 50. Among these, 7 were recommended annual mammography from when their 10-year risk reached twice that of an average 50-year-old. Twenty-one women underwent gene panel testing; no pathogenic variants in breast cancer genes were identified. Five women were advised annual mammography from 40–60 years due to family history of breast cancer, regardless of PRS. Most respondents viewed breast cancer risk assessment positively and did not report increased anxiety after testing. Conclusions In this pilot cohort, PRS-based risk stratification led to earlier or more intensive screening recommendations in half of the participants, while family history also influenced management.
T. Hovda, S. Sõber, P. Padrik et al.· PLoS ONE· 0 citations
The rising incidence of breast cancer represents a serious medical problem. Women diagnosed with breast cancer before the age of 45 - that is, those who were not included in mammography screening programs - constitute a specific patient group. Identification of risk factors for the pathogenesis of breast cancer has led to the development of triage models based on both medical history data and genetic testing, as well as imaging methods; their updated versions are based on a combination of these modalities. The oldest empirical Gail model relies solely on basic medical history. The IBIS model, on the other hand, incorporates an expanded family history, as well as mammographic breast tissue density. The BOADICEA model enables the calculation of lifetime risk and risk of being a carrier of BRCA1/2 and other major mutations (BRCA1/2, TP53, PTEN, CHEK2, ATM). The Pecný model represents a Czech contribution. With new possibilities in genetic testing, the latest versions of these models now include the polygenic risk score (PRS), which increases their predictive value. With the growing integration of artificial intelligence (AI) and deep learning into clinical medicine, we can expect the emergence of new, AI-dependent triage models. A major limitation of the aforementioned models is their restriction to the Caucasian population. Calibration based on statistical data for other populations often does not work. This article will present current statistical data for the GAIL model, IBIS and its variants, and the BOADICEA model. The aim of this article is to present predictive models to the professional community and to highlight their potential applications in routine gynecological outpatient practice.
Introduction: Breast cancer remains the most prevalent malignancy among women worldwide, with prognostic outcomes and therapeutic responses differing considerably across subtypes. The conventional staging framework developed by the Union for International Cancer Control (UICC), based on the Tumor, Node, Metastasis classification, reflects anatomical stage (AS) but overlooks biological characteristics. To address this limitation, the American Joint Committee on Cancer (AJCC) introduced the Prognostic Stage (PS) in its 8th edition, which integrates biomarkers including estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and histological grade (HG). This study aimed to assess and compare the prognostic utility of UICC AS and AJCC PS in patients with high-risk luminal breast cancer. Methods: This retrospective study included patients who underwent surgery for ER-positive, HER2-negative breast cancer at Tokyo Metropolitan Komagome Hospital from 2011 to 2018. High-risk classification was defined based on MonarchE trial criteria: axillary lymph node metastasis, tumor diameter ≥5 cm, HG3, or Ki-67 ≥20%. Staging was performed using both UICC AS and AJCC PS, and prognostic values were evaluated via Kaplan-Meier survival analysis. Results: Among 105 eligible cases, 43 (41%) were downstaged when reclassified from UICC AS to AJCC PS. While UICC AS failed to yield significant stratification in terms of invasive disease-free survival (IDFS) and distant recurrence-free survival (DRFS), AJCC PS demonstrated significant prognostic discrimination (IDFS p = 0.014; DRFS p = 0.004). Conclusions: American Joint Committee on Cancer PS offers superior prognostic stratification compared to UICC AS in high-risk luminal breast cancer. Its incorporation into clinical practice may enable more personalized treatment approaches, particularly when identifying candidates for adjuvant abemaciclib therapy. Further validation through larger, prospective studies is warranted.
Evaluating the occurrence of new-onset CVD in women with breast cancer undergoing chemotherapy and identifying factors associated with increased risk of cardiovascular disease highlights the need for sustained cardiovascular surveillance in breast cancer survivors.
V. Dvorovy, L. Kováčová, M. Selvek et al.· European Heart Journal, Supp...· 0 citations
Interval breast cancers (IBC), diagnosed between routine screening rounds, tend to have a worse prognosis than screen-detected breast cancers (SDBC). Identifying risk factors for IBCs is critical for improving early detection and developing risk-stratified screening strategies to reduce their incidence. We evaluated associations of breast density, reproductive, hormonal, lifestyle and medical factors with IBC compared to SDBC in a large UK cohort.
Analyses included 1,940 women diagnosed with breast cancer between 2004 and 2018 after enrolment in the Breast Cancer Now Generations Study, a prospective UK cohort linked to the National Health Service Breast Screening Programme. Pre-diagnostic risk factors were collected at enrolment, and breast density was estimated from pre-diagnostic mammograms using Cumulus in a subset of 1,191 cases. Risk factor associations with density were estimated using linear regression models. We identified 1,185 SDBCs and 755 IBCs included in logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (CI) for associations between risk factors and IBC, adjusting for age at diagnosis, time since recruitment and year of diagnosis, breast density and tumour characteristics.
Higher breast density was associated with later age at menarche, nulliparity, breastfeeding, history of benign breast disease (BBD), alcohol consumption, lower body mass index (BMI) at recruitment and at age 20, and current use of menopausal hormone therapy (MHT). In a fully-adjusted model, IBC risk was lower for overweight women (OR (95% CI ) = 0.74 (0.58–0.93) vs. normal weight), and higher for high breast density (2.13 (1.44–3.17) for Q4 vs. Q1), later age at menopause (1.60 (1.02–2.50) 55 + vs. < 50), current MHT use (1.41 (1.04–1.91) vs. never users), history of BBD (1.36 (1.11–1.68)), being underweight at age 20 (1.65 (1.14–2.38) vs. normal weight), family history of breast cancer (1.26 (1.00–1.58)) and ever using oral contraceptives (1.25 (0.93–1.69) vs. never). These risk factor associations were independent of tumour characteristics.
Breast density and several risk factors independently increase the likelihood of IBC, highlighting opportunities for tailored screening strategies to enhance early detection and reduce IBCs incidence.
L. Johns, Martina Brayley, R. Frost et al.· Breast Cancer Research· 0 citations
This pooled analysis of 15,731 cases showed that nulliparity, age at menopause, MHT, and BMI have independent, dose-response associations with ER, PR, and grade, clarifying patterns of etiologic heterogeneity.
Daniel Adams, Amber N. Hurson, T. Ahearn et al.· Journal of the National Canc...· 0 citations
