Breast最新文献

Background: An increased risk of breast cancer has been reported in women with neurofibromatosis type 1 (NF1), especially at younger ages, and NF1-related breast cancer has been associated with poor survival. We performed a large population-based cohort study to estimate the age-related breast cancer risk and survival in Danish and Swedish women with NF1.

Patients and methods: We used national registers to identify all women with a diagnosis of NF1 in Denmark and Sweden born between 1930 and 1990 (Denmark) or 1987 (Sweden). Age- and sex-matched comparisons were randomly selected from population registers. Cox proportional hazards models were used to study the association between NF1, breast cancer risk, and overall 5-year mortality after a breast cancer diagnosis.

Results: We included 2164 women with NF1 and 71 586 comparisons. A two-fold increased risk of breast cancer was observed in women with NF1 (hazard ratio (HR) 1.94, 95% confidence interval (CI) 1.59-2.36). The strongest association was observed in women between 30 and 39 years of age (HR = 3.98, 95% CI 2.16-7.32). Five-year mortality after a breast cancer diagnosis was higher in women with NF1 (HR = 1.98, 95% CI 1.31-2.99).

Conclusions: Our results suggest that although increased, the risk of breast cancer in women with NF1 is not as high as previously reported, particularly among young women. These findings add to previous data and will contribute to the gathered knowledge needed to accurately address the risk of breast cancer in women with NF1.

Background: In HR+/HER2- metastatic breast cancer (MBC), CDK4/6 inhibitors combined with endocrine therapy (ET) significantly improve progression-free survival (PFS). Machine learning (ML) approaches may improve individualized progression risk estimation.

Methods: We retrospectively analysed HR+/HER2- MBC patients treated with first-line CDK4/6i plus ET to develop CoxNet regression and Gradient Boosting Machine (GBM) models from baseline clinicopathological features. The primary endpoint was PFS prediction. The dataset was split into a 70/30 train/validation set. Performance was assessed by Harrell's C-index (1000 bootstrap replicates). Risk stratification was performed using Gaussian Mixture Modeling (GMM) to define high- and low-risk groups. Cox regression estimated the corresponding hazard ratios (HR). Early progression at 6 months (EP) prediction was evaluated using the area under the receiver operating characteristic curve (AUROC).

Results: 459 patients were included, with a median follow-up of 43.7 months (95% CI 39.6-48.3). Median PFS was 29.3 months (95% CI 24.0-33.7). Both ML models achieved strong predictive performance, with a Harrell's C-index of 0.74 (95% CI 0.67-0.80) in the validation set. The main predictors were liver metastases, Ki67 expression, and primary endocrine resistance. Stratification defined two risk groups with significantly different PFS in the validation set (HR 2.58, 95% CI 1.65-4.03, p = 3.3 × 10^-5). Median PFS was 34.8 (95%CI 24.0-52.4) in the low-risk and 10.6 (95%CI 7.7-14.6) in the high-risk group. For EP prediction, the model achieved an AUROC of 0.77 (95% CI 0.61-0.89).

Conclusions: This study supports the clinical applicability ML models using baseline clinicopathological variables for individualized risk stratification in HR+/HER2- MBC.

Background: An adjusted Breast Cancer Index (BCI) model with an additional cutpoint identified postmenopausal women with hormone-receptor-positive node-negative disease at minimal (<5%) risk of distant recurrence (DR) within 10 years.

Methods: 2025 premenopausal patients with hormone-receptor-positive node-negative breast cancer, randomized to adjuvant endocrine therapy in SOFT and TEXT (35.6% and 40.4% received adjuvant chemotherapy, respectively), previously had BCI assessed. The additional BCI cutpoint re-classified a subset of the low-risk group into minimal-risk; those in intermediate- or high-risk groups were unchanged. The 10-year DR was estimated by Kaplan-Meier method.

Results: The adjusted BCI model re-classified 17.8 % and 19.6 % of node-negative disease in SOFT and TEXT into BCI minimal-risk groups; 43.2 % and 38.3 % remained classified in low-risk groups, respectively. In SOFT, the estimated 10-year DR was 2.3 % (95 %CI 0.9-6.0 %) and 4.1 % (95 %CI 2.6-6.5 %) in the minimal-risk and revised low-risk groups, respectively. In TEXT, the estimated 10-year DR was 2.0 % (95 %CI 0.7-6.2 %) and 4.6 % (95 %CI 2.8-7.7 %) in the minimal- and low-risk groups, respectively.

Conclusions: This study confirmed prognostic ability of the minimal-risk BCI cutpoint to classify patients estimated to have minimal-risk of distant recurrence within 10 years among premenopausal patients treated for hormone-receptor-positive node-negative breast cancer, providing relevant information for personalizing adjuvant endocrine therapy. SOFT: (clinicaltrials.gov NCT00066690) TEXT: (clinicaltrials.gov NCT00066703).

Background: CDK4/6 inhibitors (CDK4/6i) in combination with endocrine therapy (ET) represent the standard of care for hormone receptor-positive, HER2-negative (HR+/HER2-) metastatic breast cancer (MBC) patients. However, no head-to-head randomized trials have directly compared palbociclib, ribociclib, and abemaciclib. Moreover, predictive biomarkers of resistance to CDK4/6i remain largely undefined. This study aimed to evaluate circulating tumor DNA (ctDNA)-based epigenetic and fragmentomic biomarkers as potential predictors of response and resistance in patients receiving CDK4/6i.

Methods: We conducted a biomarker-driven analysis within the prospective, multicenter MAGNETIC.1 study, enrolling 149 patients with HR+/HER2- MBC treated with first-line endocrine therapy and a CDK4/6 inhibitor. Plasma samples were collected at baseline and during treatment (3 and 6 months). Droplet digital PCR was used to assess ESR1 promoter methylation and ACTB fragmentomic profiles. Progression-free survival (PFS) and overall survival (OS) were evaluated, and molecular dynamics were compared between treatment groups.

Results: After a median follow-up of 34.8 months, no statistically significant differences in PFS or OS were observed between ribociclib and palbociclib treated patients, although ribociclib was associated with numerically longer PFS and higher survival rates. At the molecular level, palbociclib treatment was characterized by transient increases in ESR1 promoter methylation at the first evaluation and a rebound in ACTB_short fragment levels at six months relative to baseline. These dynamic patterns were not observed among patients receiving ribociclib.

Conclusions: ctDNA-based methylation and fragmentomic profiling revealed exploratory, treatment specific molecular dynamics, highlighting biological differences between CDK4/6 inhibitors. These findings support the feasibility of liquid biopsy-based biomarker studies in this setting, although their potential clinical relevance remains preliminary and requires validation in larger cohorts with earlier and more granular on-treatment timepoints.

Background: Higher body mass index (BMI) is a risk factor for breast cancer (BC) development, but the relationship with BC subtypes in pre- and post-menopausal women remains unclear.

Methods: We performed a systematic search from PubMed, Embase and Cochrane databases until 09/24 (CRD42020206108) for cohort and case-control studies assessing the association between BMI and BC subtypes and/or menopausal status. BC risk in overweight (BMI 25-29.9 kg/m²) or obese (BMI≥30 kg/m²) subjects was compared to risk in under/normal weight (BMI<25 kg/m²). BC subtypes were classified as i) ER-positive (regardless HER2-status) ii) ER-negative (regardless HER2-status) iii) HER2-positive (regardless ER-status); iv) triple-negative BC (TNBC).

Results: Out of 2841 records screened, 33 studies (9 cohort and 24 case-control) including 2,103,181 women were eligible. Obesity was associated with a modestly increased risk of ER-positive BC (pOR 1.13; 95 % CI 1.03-1.24). In postmenopausal women, obesity was associated with a moderate higher risk of ER-positive BC (pOR 1.29; 95 % CI 1.18-1.41), while overweight was associated with an increased risk of ER-positive (pOR 1.14; 95 % CI 1.06-1.22) and HER2-positive BC (pOR 1.13; 95 % CI 1.05-1.22). In premenopausal women, overweight was linked with reduced risk of ER-positive (pOR 0.80 95 % CI 0.71-0.91) but increased risk of ER-negative BC (pOR 1.15 95 % CI 1.05-1.26) and TNBC (pOR 1.30; 95 % CI 1.15-1.47).

Conclusions: Obesity was associated with a modestly higher risk of ER-positive BC, driven by postmenopausal status. Considering potential confounders, in premenopausal women, higher BMI was associated with lower risk of ER-positive BC, and an increased risk of ER-negative BC.

Breast cancer is the leading cause of cancer-related deaths among women worldwide. It is standard practice for patients to undergo a sentinel lymph node biopsy (SLNB) with breast surgery for staging. However, more than 60% of patients with primary operable breast cancers do not have axillary lymph node metastases. Hence, most patients risk complications from SLNB, but do not benefit. There has been considerable research into non-invasive methods for axillary lymph node staging, but few are externally validated. We hypothesized that a new method using large-scale DNA organization (LDO) analysis on a biopsy of the breast primary could predict axillary metastases. LDO, which measures various nuclear characteristics such as the size, shape, and chromatin texture, has been correlated with a variety of clinical outcomes in different cancers, including survival in breast cancer. In this study, we determined that Random Forest models performed best for LDO analysis. On an external test set, our models using LDO features alone achieved an area under the curve (AUC) = 0.711 and clinicopathological features alone achieved AUC = 0.741. Our best and final model using LDO and clinicopathological features together achieved AUC = 0.775. We identified several LDO features that were important for predicting axillary lymph node metastases, including nuclear radius and staining intensity. We present the first histologically-based, externally-validated, and explainable machine learning model capable of predicting axillary lymph node metastases preoperatively.