Breast Cancer Research最新文献

Background: Tissue stiffness, dictated by organisation of interstitial fibrillar collagens, increases breast cancer risk and contributes to cancer progression. Tamoxifen is a standard treatment for receptor-positive breast cancer and is also aproved for primary prevention. We investigated the effect of tamoxifen and its main metabolites on the breast tissue collagen organisation as a proxy for stiffness and explored the relationship between mammographic density (MD) and collagen organisation.

Material and methods: This sub-study of the double-blinded dose-determination trial, KARISMA, included 83 healthy women randomised to 6 months of 20, 10, 5, 2.5, and 1 mg of tamoxifen or placebo. Ultrasound-guided core-needle breast biopsies collected before and after treatment were evaluated for collagen organisation by polarised light microscopy.

Results: Tamoxifen reduced the amount of organised collagen and overall organisation, reflected by a shift from heavily crosslinked thick fibres to thinner, less crosslinked fibres. Collagen remodelling correlated with plasma concentrations of tamoxifen metabolites. MD change was not associated with changes in amount of organised collagen but was correlated with less crosslinking in premenopausal women.

Conclusions: In this study of healthy women, tamoxifen decreased the overall organisation of fibrillar collagens, and consequently, the breast tissue stiffness. These stromal alterations may play a role in the well-established preventive and therapeutic effects of tamoxifen. Trial registration ClinicalTrials.gov ID: NCT03346200. Registered November 1st, 2017. Retrospectively registered.

Background: The clinically high comorbidity between polycystic ovary syndrome (PCOS) and breast cancer (BC) has been extensively reported. However, limited knowledge exists regarding their shared genetic basis and underlying mechanisms.

Method: Leveraging summary statistics from the largest genome-wide association studies (GWASs) to date, we conducted a comprehensive genome-wide cross-trait analysis of PCOS and BC. A variety of genetic statistical methods were employed to uncover potential shared genetic causes.

Results: Our analysis revealed genetic overlap between the three trait pairs. After partitioning the genome into 2,495 independent regions, we identified two loci, chr8: 75,011,700-76,295,483 and chr17: 6,305,079-7,264,458, with significant localized genetic correlations. Pleiotropic analysis under a composite null hypothesis identified 1,183 significant pleiotropic single nucleotide polymorphisms (SNPs) across three trait pairs. FUMA mapped 26 pleiotropic loci, with regions 16q12.2 and 6q25.1 duplicated across all three trait pairs, while COLOC detected three loci with colocalization evidence. Gene-based analysis identified 23 unique candidate pleiotropic genes, including the FTO shared by all trait pairs, as well as SER1, RALB, and others in two trait pairs. Pathway enrichment analysis further highlighted key biological pathways, primarily involving the significant biological pathways were the metabolism of regulation of autophagy, regulation of cellular catabolic process, and positive regulation of catabolic process. Latent Heritable Confounder Mendelian randomization (LHC-MR) supported a positive causal relationship between PCOS and both BCALL and ERPBC but not with ERNBC.

Conclusion: In conclusion, our genome-wide cross-trait analysis identified a shared genetic basis between PCOS and BC, specific identical genetic mechanisms and causality between PCOS and various BC subtypes, which could better explains the genetics of the co-morbidity of PCOS and ERPBC rather than PCOS and ERNBC. These findings provide new insights into the biological mechanisms underlying the co-morbidity of these two complex diseases, which have important implications for clinical disease intervention, treatment, and improved prognosis.

Background: The clinical management of breast cancer (BC) is mainly based on the assessment of receptor expression by tumour cells. However, there is still an unmet need for novel biomarkers important for prognosis and therapy. The tumour immune microenvironment (TIME) is thought to play a key role in prognosis and therapy selection, therefore this study aimed to describe the TIME in Ethiopian BC patients.

Methods: RNA was isolated from formalin-fixed paraffin-embedded (FFPE) tissue from 82 women with BC. Expression of PAM50 and 54 immune genes was analysed using the Nanostring platform and differentially expressed genes (DEGs) were determined using ROSALIND^®. The abundance of different cell populations was estimated using Nanostring's cell type profiling module, while tumour infiltrating lymphocytes (TILs) were analysed using haematoxylin and eosin (H&E) staining. In addition, the PIK3CA gene was genotyped for three hotspot mutations using qPCR. Kaplan-Meier survival analysis and log-rank test were performed to compare the prognostic relevance of immune subgroups.

Results: Four discrete immune phenotypes (IP1-4) were identified through hierarchical clustering of immune gene expression data. These IPs were characterized by DEGs associated with both immune activation and inhibition as well as variations in the extent of immune infiltration. However, there were no significant differences regarding PIK3CA mutations between the IPs. A downregulation of immune suppressive and activating genes and the lowest number of infiltrating immune cells were found in IP2, which was associated with luminal tumours. In contrast, IP4 displayed an active TME chracterized by an upregulation of cytotoxic genes and the highest density of immune cell infiltrations, independent of the specific intrinsic subtype. IP1 and IP3 exhibited intermediate characteristics. The IPs had a prognostic relevance and patients with an active TME had improved overall survival compared to IPs with a significant downregulation of the majority of immune genes.

Conclusion: Immune gene expression profiling identified four distinct immune contextures of the TME with unique gene expression patterns and immune infiltration. The classification into distinct immune subgroups may provide important information regarding prognosis and the selection of patients undergoing conventional treatments or immunotherapies.

Background: Human epidermal growth factor receptor 2-targeted (HER2) therapy with antibody-drug conjugates has proven effective for patients with HER2-low breast cancer. However, intratumoral heterogeneity (ITH) poses a great challenge in identifying HER2-low tumors. ITH signatures were developed by quantifying ITH to differentiate HER2-positive, -low and -zero breast cancers.

Methods: This retrospective study included 614 patients from two institutions. The study was structured into two primary tasks: task 1 was to differentiate between HER2-positive and -negative tumors, followed by task 2 to differentiate HER2-low and -zero tumors. Whole-tumor radiomics features and habitat radiomics features were extracted from MRI to construct the radiomics and ITH signatures. Multivariable logistic regression analysis was used to determine significant independent predictors. A combined model integrating significant clinicopathologic variables, radiomics signature, and ITH signature was developed for task (1) Subsequently, the better-performing model was established using the same approach for task (2) The area under the receiver operating characteristic curve (AUC) was used to assess the performance of each model.

Results: Task 1 comprised 614 patients (training, n = 348; validation, n = 149; and test cohorts, n = 117). Task 2 encompassed 501 patients (training, n = 283; validation, n = 122; and test cohorts, n = 96). For task1, the ITH signature showed outstanding performance, achieving AUCs of 0.81, 0.81, and 0.81 in the training, validation and test cohorts, respectively. The combined model achieved improved performance, with AUCs of 0.83, 0.84 and 0.83 across the three cohorts, respectively. For task2, the ITH signature maintained superior performance, with AUCs of 0.94, 0.93 and 0.84 across the training, validation and test cohorts, respectively. Multivariable logistic regression analysis indicated that none of the clinicopathologic characteristics were retained as predictors associated with odds of HER2-low tumors.

Conclusions: Our study developed ITH signatures that quantified ITH using habitat-based MRI radiomics, achieving outstanding performance in differentiating HER2-postive and -negative tumors, and further differentiating HER2-low and -zero breast cancers.

Background: Growing epidemiological evidence suggests an association between exposure to air pollutants and breast cancer. Yet, the underlying mechanisms remain poorly understood. This study explored the mediating role of thirteen metabolic health biomarkers in the relationship between exposure to three air pollutants, i.e. nitrogen dioxide (NO₂), polychlorinated biphenyls 153 (PCB153), and benzo[a]pyrene (BaP), and breast cancer risk.

Methods: We used data from a nested case-control study within the French national prospective E3N-Generations cohort, involving 523 breast cancer cases and 523 matched controls. The four-way decomposition mediation of total effects for thirteen biomarkers was applied to estimate interaction and mediation effects (controlled direct, reference interaction, mediated interaction, and pure indirect effects).

Results: The analyses indicated a significant increase in breast cancer risk associated with BaP exposure (odds ratio (OR)_{Q4 vs Q1} = 2.32, 95% confidence intervals (CI): 1.00-5.37). PCB153 exposure showed a positive association only in the third quartile (OR_{Q3 vs Q1} = 2.25, CI 1.13-4.57), but it appeared to be non-significant in the highest quartile (OR_{Q4 vs Q1} = 2.07, CI 0.93-4.61). No association was observed between NO₂ exposure and breast cancer risk. Estradiol was associated with an increased risk of breast cancer (OR per one standard deviation (SD) increment = 1.22, CI 1.05-1.42), while thyroid-stimulating hormone was inversely related to breast cancer risk (OR per 1SD increase = 0.87, CI 0.75-1.00). We observed a suggestive mediated effect of the association between the three pollutants and breast cancer risk, through albumin, high-density lipoproteins cholesterol, low-density lipoprotein cholesterol, parathormone, and estradiol.

Conclusion: Although limited by a lack of statistical power, this study provides relevant insights into the potential mediating role of certain biomarkers in the association between air pollutant exposure and breast cancer risk, highlighting the need for further in-depth studies in large populations.

Background: It has been suggested that the association between body mass index and breast cancer risk differs between Asian women and Western women. We aimed to assess the associations between body mass index and breast cancer incidence in East Asian women.

Methods: Pooled analyses were performed using individual participant data of 319,189 women from 13 cohort studies in Japan, Korea, and China. Participants' height and weight were obtained by measurement or self-reports at cohort baseline. Breast cancer was defined as code C50.0-C50.9 according to the International Classification. Using a Cox proportional hazards model, hazard ratios of breast cancer were estimated for each body mass index category, with the reference group set as the group with a body mass index of 21 to < 23 kg/m². The hazard ratio for a 5 kg/m² increase in body mass index was also calculated.

Results: During a mean 16.6 years of follow-up, 4819 women developed breast cancer. Similar to Westerners, a steady increase in breast cancer risk with increasing body mass index was observed in postmenopausal women, but the slope of the risk increase appeared to slow at a body mass index of 26-28 kg/m². In premenopausal women, the inverse association seen in Westerners was not observed. The risk of developing breast cancer after 50 years of age increased slightly with increasing body mass index, which was more pronounced in the older birth cohort. There was no significant association between body mass index and the risk of developing breast cancer before 50 years of age, but the risk estimates changed from positive to negative as the birth cohort got younger.

Conclusions: In East Asia, the role of body mass index in breast cancer in premenopausal women may be changing along with the increase in obesity and breast cancer. The increased risk of postmenopausal breast cancer with a higher body mass index was as robust as that of Western women.

Background: Several studies have confirmed the potential value of applying radiomics to predict prognosis of breast cancer. However, the tumor segmentation in these studies depended on delineation or annotation of breast cancer by radiologist, which is often laborious, tedious, and vulnerable to inter- and intra-observer variability. Automatic segmentation is expected to overcome this difficulty. Herein, we aim to investigate the value of automatic segmentation-based multi-modal radiomics signature and magnetic resonance imaging (MRI) features in predicting disease-free survival (DFS) of patients diagnosed with invasive breast cancer.

Methods: This retrospective multicenter study included a total of 643 female patients with invasive breast cancer who underwent preoperative ultrasound (US) and MRI for prognostic analysis. Data (n = 480) from center 1 were divided into training and internal testing sets, while data (n = 163) from centers 2 and 3 were analyzed as the external testing set. We developed automatic segmentation frameworks for tumor segmentation by deep learning. Then, Least absolute shrinkage and selection operator Cox regression was used to select features to construct radiomics signature, and corresponding radiomics score (Rad-score) was calculated. Finally, six models for predicting DFS were constructed by using Cox regression and assessed in terms of discrimination, calibration, and clinical usefulness.

Results: The multi-modal radiomics signature combining intra- and peri-tumoral radiomics signatures of US and MRI achieved a higher C-index in the internal (0.734) and external (0.708) testing sets than most other radiomics signatures in predicting DFS, and successfully stratified patients into low- and high-risk groups. The multi-modal clinical imaging model combining the multi-modal Rad-score and clinical traditional MRI model-score resulted in a higher C-index (0.795) than other models in the external testing set, and it had a better calibration and higher clinical benefit.

Conclusions: This study demonstrates that the multi-modal radiomics signature derived from automatic segmentations of US and MRI is a promising risk stratification biomarker for breast cancer, and highlights that the appropriate combination of multi-modal radiomics signature, clinical characteristics, and MRI feature can improve performance of individualized DFS prediction, which might assist in guiding decision-making related to breast cancer.

Background: Fibroepithelial lesions (FELs) of the breast encompass a broad spectrum of lesions, ranging from commonly encountered fibroadenomas (FAs) to rare phyllodes tumors (PTs). Accurately diagnosing and grading these lesions is crucial for making management decisions, but it can be challenging due to their overlapping features and the subjective nature of histological assessment. Here, we evaluated the role of digital nuclear morphometric analysis in FEL diagnosis and prognosis.

Methods: A digital nuclear morphometric analysis was conducted on 241 PTs and 59 FAs. Immunohistochemical staining for cytokeratin and Leukocyte common antigen (LCA) was used to exclude non-stromal components, and nuclear area, perimeters, calipers, circularity, and eccentricity in the stromal cells were quantified with QuPath software. The correlations of these features with FEL diagnosis and prognosis was assessed.

Results: All nuclear features, including area, perimeter, circularity, maximum caliper, minimum caliper and eccentricity, showed significant differences between FAs and benign PTs (p ≤ 0.002). Only nuclear area, perimeter, minimum caliper and eccentricity correlated significantly with PT grading (p ≤ 0.022). For differentiation of FAs from benign PTs, the model integrating all differential nuclear features demonstrated a specificity of 90% and sensitivity of 70%. For PT grading, the nuclear morphometric score showed a specificity of 78% and sensitivity of 96% for distinguishing benign/borderline from malignant PTs. In addition, a relationship of nuclear circularity was found with PT recurrence. The Kaplan-meier analysis, using the best cutoff determined by ROC curve, showed shorter event free survival in benign PTs with high circularity (chi-square = 4.650, p = 0.031).

Conclusions: Our data suggested the digital nuclear morphometric analysis could have potentials to objectively differentiate different FELs and predict PT outcome. These findings could provide the evidence-based data to support the development of deep-learning based algorithm on nuclear morphometrics in FEL diagnosis.

Background: Breast cancer, one of the most common forms of cancer, is associated with the highest cancer-related mortality among women worldwide. In comparison to other types of breast cancer, patients diagnosed with the triple-negative breast cancer (TNBC) subtype have the worst outcome because current therapies do not produce long-lasting responses. Hence, innovative therapies that produce persisting responses are a critical need. We previously discovered that hyperactivating purinergic receptors (P2RXs) by increasing extracellular adenosine triphosphate (eATP) concentrations enhances TNBC cell lines' response to chemotherapy. Heparan sulfate inhibits multiple extracellular ATPases, so it is a molecule of interest in this regard. In turn, heparanase degrades polysulfated polysaccharide heparan sulfate. Importantly, previous work suggests that breast cancer and other cancers express heparanase at high levels. Hence, as heparan sulfate can inhibit extracellular ATPases to facilitate eATP accumulation, it may intensify responses to chemotherapy. We postulated that heparanase inhibitors would exacerbate chemotherapy-induced decreases in TNBC cell viability by increasing heparan sulfate in the cellular microenvironment and hence, augmenting eATP.

Methods: We treated TNBC cell lines MDA-MB 231, Hs 578t, and MDA-MB 468 and non-tumorigenic immortal mammary epithelial MCF-10A cells with paclitaxel (cytotoxic chemotherapeutic) with or without the heparanase inhibitor OGT 2115 and/or supplemental heparan sulfate. We evaluated cell viability and the release of eATP. Also, we compared the expression of heparanase protein in cell lines and tissues by immunoblot and immunohistochemistry, respectively. In addition, we examined breast-cancer-initiating cell populations using tumorsphere formation efficiency assays on treated cells.

Results: We found that combining heparanase inhibitor OGT 2115 with chemotherapy decreased TNBC cell viability and tumorsphere formation through increases in eATP and activation of purinergic receptors as compared to TNBC cells treated with single-agent paclitaxel.

Conclusion: Our data shows that by preventing heparan sulfate breakdown, heparanase inhibitors make TNBC cells more susceptible to chemotherapy by enhancing eATP concentrations.

Background: HER2-positivity is an essential marker for therapeutic decisions, while HER2 expression is heterogenous. In recent years, there has been increasing recognition of a subgroup of breast cancer patients who have low levels of HER2 expression, also known as HER2-low because trastuzumab deruxtecan offers clinical benefit for patients with HER2-low metastatic breast cancer. Despite the growing interest in HER2-low breast cancer, there is limited research on how multigene assays can help differentiate between HER2-low and HER2-negative breast cancer. Among HR + HER2- breast cancer, we compared genomic characteristics between HER2-low and HER2-zero using the 21-gene assay.

Methods: A retrospective review of clinical records was performed in 2,295 patients who underwent Oncotype DX^® test in two hospitals between 2013 and 2020. Patients were classified into two groups as the HER2-zero and HER2-low based on HER2 immunohistochemistry. In cases with HER2 2+, no amplification of HER2 gene was confirmed by silver in situ hybridization. High genomic risk was defined as cases with 21-gene recurrence score (RS) > 25. Multivariable binary logistic-regression analysis was performed.

Results: Of these, 944 (41.1%) patients were assigned to the HER2-zero group, while 1351 (58.9%) patients were assigned to the HER2-low group. The average Recurrence Score (RS) was found to be 17.802 in the HER2-zero breast cancer group and 18.503 in the HER2-low group, respectively (p-value < 0.005). When comparing the proportion of high RS between the two groups, the HER2-zero group had a high RS rate of 12.4% (117 out of 944), while the HER2-low group had a high RS rate of 17.0% (230 out of 1351) (p = 0.002). The HER2 score identified by qRT-PCR was 8.912 in the HER2-zero group and 9.337 in the HER2-low group (p < 0.005). In multivariable analysis, HER2-low status was found to be an independent factor for high RS, with an odds ratio of 1.517 (1.172-1.964), independent of ER, PR, and Ki67. Within the subgroup of patients with invasive ductal carcinoma, the high RS rates were 19% in the HER2-low group and 14% in the HER2-zero group. However, when considering all patients, there were no significant differences observed in recurrence-free survival and overall survival between the HER2-low and HER2-zero groups.

Conclusion: Within HR + HER2- breast cancer, HER2-low tumors are associated with high RS, especially for histologically invasive ductal carcinoma. A prognostic influence of HER2-low expression among HR + HER2- breast cancer remains as an area that requires further study.