Loss of PPARγ activity characterizes early protumorigenic stromal reprogramming and dictates the therapeutic window of opportunity.

Although robustly expressed in the disease-free (DF) breast stroma, CD36 is consistently absent from the stroma surrounding invasive breast cancers (IBCs). In this study, we primarily observed CD36 expression in adipocytes and intralobular capillaries within the DF breast. Larger vessels concentrated in interlobular regions lacked CD36 and were instead marked by the expression of CD31. When evaluated in perilesional capillaries surrounding ductal carcinoma in situ, a nonobligate IBC precursor, CD36 loss was more commonly observed in lesions associated with subsequent IBC. Peroxisome proliferator-activated receptor γ (PPARγ) governs the expression of CD36 and genes involved in differentiation, metabolism, angiogenesis, and inflammation. Coincident with CD36 loss, we observed a dramatic suppression of PPARγ and its target genes in capillary endothelial cells (ECs) and pericytes, which typically surround and support the stability of the capillary endothelium. Factors present in conditioned media from malignant cells repressed PPARγ and its target genes not only in cultured ECs and pericytes but also in adipocytes, which require PPARγ for proper differentiation. In addition, we identified a role for PPARγ in opposing the transition of pericytes toward a tumor-supportive myofibroblast phenotype. In mouse xenograft models, early intervention with rosiglitazone, a PPARγ agonist, demonstrated significant antitumor effects; however, following the development of a palpable tumor, the antitumor effects of rosiglitazone were negated by the repression of PPARγ in the mouse stroma. In summary, PPARγ activity in healthy tissues places several stromal cell types in an antitumorigenic state, directly inhibiting EC proliferation, maintaining adipocyte differentiation, and suppressing the transition of pericytes into tumor-supportive myofibroblasts.

The effects of contemporary treatment of DCIS on the risk of developing an ipsilateral invasive Breast cancer (iIBC) in the Dutch population.

PURPOSE: To assess the effects of contemporary treatment of ductal carcinoma in situ (DCIS) on the risk of developing an ipsilateral invasive breast cancer (iIBC) in the Dutch female population. METHODS: Clinical data was obtained from the Netherlands Cancer Registry (NCR), a nationwide registry of all primary malignancies in the Netherlands integrated with the data from PALGA, the Dutch nationwide network and registry of histo- and cytopathology in the Netherlands, on all women in the Netherlands treated for primary DCIS from 2005 to 2015, resulting in a population-based cohort of 14.419 women. Cumulative iIBC incidence was assessed and associations of DCIS treatment type with subsequent iIBC risk were evaluated by multivariable Cox regression analyses. RESULTS: Ten years after DCIS diagnosis, the cumulative incidence of iIBC was 3.1% (95% CI: 2.6-3.5%) in patients treated by breast conserving surgery (BCS) plus radiotherapy (RT), 7.1% (95% CI: 5.5-9.1) in patients treated by BCS alone, and 1.6% (95% CI: 1.3-2.1) in patients treated by mastectomy. BCS was associated with a significantly higher risk for iIBC compared to BCS + RT during the first 5 years after treatment (HR 2.80, 95% CI: 1.91-4.10%). After 5 years of follow-up, the iIBC risk declined in the BCS alone group but remained higher than the iIBC risk in the BCS + RT group (HR 1.73, 95% CI: 1.15-2.61). CONCLUSIONS: Although absolute risks of iIBC were low in patients treated for DCIS with either BCS or BCS + RT, risks remained higher in the BCS alone group compared to patients treated with BCS + RT for at least 10 years after DCIS diagnosis.

Current status and challenges in HER2 IHC assessment: scoring survey results in Japan.

PURPOSE: This study aimed to assess the concordance of human epidermal growth factor receptor 2 (HER2) expression scoring by immunohistochemistry (IHC) among practicing pathologists in Japan, given the challenging nature of scoring and the critical role of HER2 status in breast cancer management. METHODS: Whole slide images (WSI) from 20 invasive breast cancer cases (1 representative WSI per case) selected to represent a diverse IHC scores and staining patterns were used in an online survey involving seven reference pathologists who established consensus HER2 IHC scores (0 to 3 +) decided by majority interpretation. Participating pathologists nationwide scored the same 20 WSI cases online using the American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) 2018 guidelines. Deidentified case metadata were registered in the uPath system. RESULTS: A total of 144 participating pathologists responded. The scoring results of the participating pathologists most commonly agreed with the consensus IHC score, followed by a ± 1 point deviation and no survey responses with > 1 point deviation. The mean percentage of agreement with the consensus score for all 20 cases was 63.4%. In cases where the reference pathologists' scores were discordant, the participating pathologists also showed a lower concordance rate. CONCLUSION: This study highlighted the current status of HER2 expression scoring by IHC for breast cancer among pathologists in Japan. These findings underscore the challenges in HER2 IHC scoring cases and emphasize the need for improved standardization and training, especially in the evolving landscape of HER2-targeted therapies.

Diagnostic value of applying preoperative breast ultrasound and clinicopathologic features to predict axillary lymph node burden in early invasive breast cancer: a study of 1247 patients.

BACKGROUND: Since the Z0011 trial, the assessment of axillary lymph node status has been redirected from the previous assessment of the occurrence of lymph node metastasis alone to the assessment of the degree of lymph node loading. Our aim was to apply preoperative breast ultrasound and clinicopathological features to predict the diagnostic value of axillary lymph node load in early invasive breast cancer. METHODS: The 1247 lesions were divided into a high lymph node burden group and a limited lymph node burden group according to axillary lymph node status. Univariate and multifactorial analyses were used to predict the differences in clinicopathological characteristics and breast ultrasound characteristics between the two groups with high and limited lymph node burden. Pathological findings were used as the gold standard. RESULTS: Univariate analysis showed significant differences in ki-67, maximum diameter (MD), lesion distance from the nipple, lesion distance from the skin, MS, and some characteristic ultrasound features (P < 0.05). In multifactorial analysis, the ultrasound features of breast tumors that were associated with a high lymph node burden at the axilla included MD (odds ratio [OR], 1.043; P < 0.001), shape (OR, 2.422; P = 0.0018), hyperechoic halo (OR, 2.546; P < 0.001), shadowing in posterior features (OR, 2.155; P = 0.007), and suspicious lymph nodes on axillary ultrasound (OR, 1.418; P = 0.031). The five risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.702. CONCLUSION: Breast ultrasound features and clinicopathological features are better predictors of high lymph node burden in early invasive breast cancer, and this prediction helps to develop more effective treatment plans.

Association of clinicopathologic and sonographic features with stromal tumor-infiltrating lymphocytes in triple-negative breast cancer.

BACKGROUND: Increased level of stromal tumor-infiltrating lymphocytes (sTILs) are associated with therapeutic outcomes and prognosis in triple-negative breast cancer (TNBC). This study aimed to investigate the associations of clinicopathologic and sonographic features with sTILs level in TNBC. METHODS: This study included invasive TNBC patients with postoperative evaluation of sTILs after surgical resection. Tumor shape, margin, orientation, echo pattern, posterior features, calcification, and vascularity were retrospectively evaluated. The patients were categorized into high-sTILs (≥ 20%) and low-sTILs (< 20%) level groups. Chi-square or Fisher's exact tests were used to assess the association of clinicopathologic and sonographic features with sTILs level. RESULTS: The 171 patients (mean ± SD age, 54.7 ± 10.3 years [range, 22‒87 years]) included 58.5% (100/171) with low-sTILs level and 41.5% (71/171) with high-sTILs level. The TNBC tumors with high-sTILs level were more likely to be no special type invasive carcinoma (p = 0.008), higher histologic grade (p = 0.029), higher Ki-67 proliferation rate (all p < 0.05), and lower frequency of associated DCIS component (p = 0.026). In addition, the TNBC tumors with high-sTILs level were more likely to be an oval or round shape (p = 0.001), parallel orientation (p = 0.011), circumscribed or micro-lobulated margins (p < 0.001), complex cystic and solid echo patterns (p = 0.001), posterior enhancement (p = 0.002), and less likely to have a heterogeneous pattern (p = 0.001) and no posterior features (p = 0.002). CONCLUSIONS: This preliminary study showed that preoperative sonographic characteristics could be helpful in distinguishing high-sTILs from low-sTILs in TNBC patients.

Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.

BACKGROUND: The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics. METHODS: Two cohorts from the Cancer Imaging Archive project were used, one as the training cohort (TCGA-Breast, n = 88) and one as the validation cohort (Breast-MRI-NACT Pilot, n = 57). Radiomics and deep learning features were extracted from preoperative DCE-MRI. After dual selection by principal components analysis (PCA) and relief methods, radiomics and deep learning models for predicting LNM were constructed by the random forest (RF) method. A post-fusion strategy was used to construct the DLR nomograms (DLRNs) for predicting LNM. The performance of the models was evaluated using the receiver operating characteristic (ROC) curve and Delong test. In the training cohort, transcriptome data were downloaded from the UCSC Xena online database, and biological pathways related to the DLR phenotypes were identified. Finally, hub genes were identified to obtain DLR gene expression (RadDeepGene) scores. RESULTS: DLRNs were based on area under curve (AUC) evaluation (training cohort, AUC = 0.98; validation cohort, AUC = 0.87), which were higher than single radiomics models or GoogLeNet models. The Delong test (radiomics model, P = 0.04; GoogLeNet model, P = 0.01) also validated the above results in the training cohorts, but they were not statistically significant in the validation cohort. The GoogLeNet phenotypes were related to multiple classical tumor signaling pathways, characterizing the biological significance of immune response, signal transduction, and cell death. In all, 20 genes related to GoogLeNet phenotypes were identified, and the RadDeepGene score represented a high risk of LNM (odd ratio = 164.00, P < 0.001). CONCLUSIONS: DLRNs combining radiomics and deep learning features of DCE-MRI images improved the preoperative prediction of LNM in breast cancer, and the potential biological characteristics of DLRN were identified through genomics.

Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer.

BACKGROUND: The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC). METHODS: 50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality. RESULTS: Supervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype. CONCLUSION: The DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment.

Neutrophil-related Signature Characterizes Immune Landscape and Predicts Prognosis of Invasive Breast Cancer.

As a leading prevalent malignancy, breast cancer remains a significant worldwide health issue. Recent research indicates that neutrophils play a crucial role in breast cancer development. The prognostic significance of neutrophil-related genes (NRGs) or the immune landscape of the neutrophil-related signature in invasive breast cancer (IBC) is, nevertheless, unknown. To uncover innovative therapy alternatives, the significance of the neutrophil-related signatures in IBC was evaluated here. Briefly, a prediction model based on neutrophil-related core prognostic genes and The Cancer Genome Atlas data was created (TCGA). The model may assess IBC patients' prognosis. The IBC data from the Gene Expression Omnibus (GEO) confirmed the prognostic accuracy of the model. The overall survival (OS) of patients was worse in the group with a high NRGs score compared to the group with a low NRGs score. In addition, patients with low NRGs scores were considerably more sensitive to vinorelbine, cyclophosphamide, epirubicin, gemcitabine, paclitaxel, 5-fluorouracil, docetaxel, and cisplatin. Patients with low NRGs scores responded better to CTLA-4 and PD-1 treatments. Additionally, the immune microenvironment components were more abundant in patients with low NRGs scores. Moreover, qRT-PCR results confirmed that LEF1 had a higher expression level in tumor samples compared to normal samples, whereas NRG1 and STX11 exhibited lower expression levels in tumor samples than in normal samples. These results suggest that NRGs might be utilized as biomarkers to predict the prognosis of individuals with IBC, thereby paving the way for the creation of customized therapies for IBC.

Extracellular Microenvironment Alterations in Ductal Carcinoma In Situ and Invasive Breast Cancer Pathologies by Multiplexed Spatial Proteomics.

Ductal carcinoma in situ (DCIS) is a heterogeneous breast disease that remains challenging to treat due to its unpredictable progression to invasive breast cancer (IBC). Contemporary literature has become increasingly focused on extracellular matrix (ECM) alterations with breast cancer progression. However, the spatial regulation of the ECM proteome in DCIS has yet to be investigated in relation to IBC. We hypothesized that DCIS and IBC present distinct ECM proteomes that could discriminate between these pathologies. Tissue sections of pure DCIS, mixed DCIS-IBC, or pure IBC (n = 22) with detailed pathological annotations were investigated by multiplexed spatial proteomics. Across tissues, 1,005 ECM peptides were detected in pathologically annotated regions and their surrounding extracellular microenvironments. A comparison of DCIS to IBC pathologies demonstrated 43 significantly altered ECM peptides. Notably, eight fibrillar collagen peptides could distinguish with high specificity and sensitivity between DCIS and IBC. Lesion-targeted proteomic imaging revealed heterogeneity of the ECM proteome surrounding individual DCIS lesions. Multiplexed spatial proteomics reported an invasive cancer field effect, in which DCIS lesions in closer proximity to IBC shared a more similar ECM profile to IBC than distal counterparts. Defining the ECM proteomic microenvironment provides novel molecular insights relating to DCIS and IBC.

Identifying recurrences and metastasis after ductal carcinoma in situ (DCIS) of the breast.

Ductal carcinoma in situ (DCIS) of the breast is a non-invasive tumour that has the potential to progress to invasive ductal carcinoma (IDC). Thus, it represents a treatment dilemma: alone it does not present a risk to life, however, left untreated it may progress to a life-threatening condition. Current clinico-pathological features cannot accurately predict which patients with DCIS have invasive potential, and therefore clinicians are unable to quantify the risk of progression for an individual patient. This leads to many women being over-treated, while others may not receive sufficient treatment to prevent invasive recurrence. A better understanding of the molecular features of DCIS, both tumour-intrinsic and the microenvironment, could offer the ability to better predict which women need aggressive treatment, and which can avoid therapies carrying significant side-effects and such as radiotherapy. In this review, we summarise the current knowledge of DCIS, and consider future research directions.

Prognostic value of tumor necrosis based on the evaluation of frequency in invasive breast cancer.

BACKGROUND: Tumor necrosis (TN) was associated with poor prognosis. However, the traditional classification of TN ignored spatial intratumor heterogeneity, which may be associated with important prognosis. The purpose of this study was to propose a new method to reveal the hidden prognostic value of spatial heterogeneity of TN in invasive breast cancer (IBC). METHODS: Multiphoton microscopy (MPM) was used to obtain multiphoton images from 471 patients. According to the relative spatial positions of TN, tumor cells, collagen fibers and myoepithelium, four spatial heterogeneities of TN (TN1-4) were defined. Based on the frequency of individual TN, TN-score was obtained to investigate the prognostic value of TN. RESULTS: Patients with high-risk TN had worse 5-year disease-free survival (DFS) than patients with no necrosis (32.5% vs. 64.7%; P < 0.0001 in training set; 45.8% vs. 70.8%; P = 0.017 in validation set), while patients with low-risk TN had a 5-year DFS comparable to patients with no necrosis (60.0% vs. 64.7%; P = 0.497 in training set; 59.8% vs. 70.8%; P = 0.121 in validation set). Furthermore, high-risk TN "up-staged" the patients with IBC. Patients with high-risk TN and stage I tumors had a 5-year DFS comparable to patients with stage II tumors (55.6% vs. 62.0%; P = 0.565 in training set; 62.5% vs. 66.3%; P = 0.856 in validation set), as well as patients with high-risk TN and stage II tumors had a 5-year DFS comparable to patients with stage III tumors (33.3% vs. 24.6%; P = 0.271 in training set; 44.4% vs. 39.3%; P = 0.519 in validation set). CONCLUSIONS: TN-score was an independent prognostic factor for 5-year DFS. Only high-risk TN was associated with poor prognosis. High-risk TN "up-staged" the patients with IBC. Incorporating TN-score into staging category could improve its performance to stratify patients.

Prognostic value of the preoperative prognostic nutritional index and systemic immuno-inflammatory index in Chinese breast cancer patients: A clinical retrospective cohort study.

BACKGROUND: It has been shown that peripheral blood inflammatory factor ratios correlate with the prognosis of various malignancies. Although indicative of prognosis in some tumors, its value for prognosis in breast cancer patients is unclear. METHODS: The clinical data of breast cancer patients diagnosed with breast cancer in the Second Hospital of Jilin University from January 1, 2013, to December 31, 2017, were retrospectively analyzed. The prognostic nutritional index (PNI) optimal cutoff values of the subjects' operating characteristic curves divided the patients into a low PNI group (≤51.05) and a high PNI group (>51.05). Correlations between breast cancer and PNI clinicopathological variables were determined by the χ2 test or Fisher exact test. Kaplan-Meier plots and log-rank tests were used to assess clinical outcomes in terms of disease-free survival (DFS). The prognostic value of PNI was analyzed by univariate and multivariate Cox proportional risk regression models. RESULTS: The best cutoff value for predicting DFS by pretreatment PNI was 51.05 and the Youden index when was 0.416, with a sensitivity of 71.4% and specificity of 70.2%. Univariate analysis showed that PNI ≤ 51.05, human epidermal growth factor receptor-2 (HER-2) positivity, and the number of lymph node metastases >4 were risk factors affecting DFS in invasive breast cancer (p < 0.05). Cox multifactor analysis showed that PNI and lymph node status were the most important factors affecting the prognosis of invasive breast cancer. Neutrophils-to-lymphocytes ratio and platelets-to-lymphocytes ratio were not significantly correlated with patient prognosis (p > 0.05). CONCLUSION: Preoperative peripheral blood PNI in patients with invasive breast cancer are independent risk factors affecting patients' prognosis, they are positively correlated with prognosis and can be used as indicators to assess prognosis. PNI, HER-2, and lymph node status had the best predictive efficacy with the area under the curve = 0.816 (95% confidence interval: 0.680-0.951, p < 0.001).

High Inner Centromere Protein Expression Correlates with Aggressive Features and Predicts Poor Prognosis in Patients with Invasive Breast Cancer.

INTRODUCTION: Inner centromere protein (INCENP) is a member of the chromosomal passenger complex and plays a key role in mitosis and cell proliferation. This study aimed to evaluate the clinical and prognostic significance of INCENP in invasive breast cancer (BC). METHODS: INCENP expression was evaluated on a tissue microarray of a large BC cohort (n = 1,295) using immunohistochemistry. At the mRNA level, INCENP expression was assessed using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (n = 1,980) and The Cancer Genome Atlas (TCGA) BC cohorts (n = 854). The correlations between INCENP expression, clinicopathological parameters, and patient outcome were investigated. RESULTS: INCENP expression was detected in the nucleus and cytoplasm of the tumour cells. Its expression was significantly associated with features characteristic of aggressive BC behaviour including high tumour grade, larger tumour size, and high Nottingham prognostic index scores. High INCENP nuclear expression was a predictor of shorter BC-specific survival in the whole cohort, as well as in the luminal subtype (p < 0.001). High INCENP nuclear expression was predictive of poor prognosis in BC patients who received hormone treatment or chemotherapy. CONCLUSION: High INCENP expression is a poor prognostic biomarker in BC with potential therapeutic benefits.

Impact of cavity shaving on residual tumor rates in patients with primary invasive carcinoma and carcinoma in situ in breast conserving surgery.

BACKGROUND: Several international studies reported relatively high re-excision rates due to residual tumor in breast conserving surgery (BCS). Cavity shaving (CS) is a surgical strategy to reduce re-excision rates. This study aimed to investigate the effect of circumferential cavity shaving during BCS to reduce residual tumor. MATERIAL AND METHODS: A total of 591 patients with early invasive carcinoma or carcinoma in situ of the breast (ICD-10, C50 or D05) who were diagnosed between 01/01/2017 and 31/12/2019 and underwent BCS in a certified breast cancer center of the University Regensburg were analyzed regarding surgical excision methods. Patients with CS during BCS and patients with targeted re-excision in a specific direction depending on the result of intraoperative mammography or sonography during BCS were compared. The risk of pathologic residual tumor (R1) was compared between both groups by means of a multivariable binary logistic regression model to determine if there is a benefit of a certain surgical method to avoid a second intervention for re-excision. We adjusted for age, tumor size, nodal status, histologic type, surgeon, breast side, and neoadjuvant chemotherapy. RESULTS: 80 (n = 13.54%) patients had CS and 511 (n = 86.46%) had a targeted re-excision in a specific direction during BCS according to intraoperative mammography or sonography. After comparing both techniques in a multivariable regression model, there was no significant difference regarding risk of residual tumor (p = 0.738) in the total cohort. However, CS showed a tendency to be favorable regarding rates of residual tumor in patients with invasive breast cancer between 60 and 70 years (p = 0.072) and smaller T1-tumors (p = 0.057) compared to targeted intraoperative re-excision following mammographic or sonographic assessment. CONCLUSION: CS showed a tendency to reduce residual tumor compared to the standard technique of intraoperative re-excision in specific subgroups, although no statistical significance was reached. Further studies are needed to overcome potential limitations like surgeon-based bias and missing standardized definitions of CS to reduce residual tumor rates.

A sonogram radiomics model for differentiating granulomatous lobular mastitis from invasive breast cancer: a multicenter study.

PURPOSE: To construct a nomogram based on sonogram features and radiomics features to differentiate granulomatous lobular mastitis (GLM) from invasive breast cancer (IBC). MATERIALS AND METHODS: A retrospective collection of 213 GLMs and 472 IBCs from three centers was divided into a training set, an internal validation set, and an external validation set. A radiomics model was built based on radiomics features, and the RAD score of the lesion was calculated. The sonogram radiomics model was constructed using ultrasound features and RAD scores. Finally, the diagnostic efficacy of the three sonographers with different levels of experience before and after combining the RAD score was assessed in the external validation set. RESULTS: The RAD score, lesion diameter, orientation, echogenicity, and tubular extension showed significant differences in GLM and IBC (p < 0.05). The sonogram radiomics model based on these factors achieved optimal performance, and its area under the curve (AUC) was 0.907, 0.872, and 0.888 in the training, internal, and external validation sets, respectively. The AUCs before and after combining the RAD scores were 0.714, 0.750, and 0.830 and 0.834, 0.853, and 0.878, respectively, for sonographers with different levels of experience. The diagnostic efficacy was comparable for all sonographers when combined with the RAD score (p > 0.05). CONCLUSION: Radiomics features effectively enhance the ability of sonographers to discriminate between GLM and IBC and reduce interobserver variation. The nomogram combining ultrasound features and radiomics features show promising diagnostic efficacy and can be used to identify GLM and IBC in a noninvasive approach.

Identification of MicroRNAs Associated with Histological Grade in Early-Stage Invasive Breast Cancer.

This study aimed to identify microRNAs associated with histological grade using comprehensive microRNA analysis data obtained by next-generation sequencing from early-stage invasive breast cancer. RNA-seq data from normal breast and breast cancer samples were compared to identify candidate microRNAs with differential expression using bioinformatics. A total of 108 microRNAs were significantly differentially expressed in normal breast and breast cancer tissues. Using clinicopathological information and microRNA sequencing data of 430 patients with breast cancer from The Cancer Genome Atlas (TCGA), the differences in candidate microRNAs between low- and high-grade tumors were identified. Comparing the expression of the 108 microRNAs between low- and high-grade cases, 25 and 18 microRNAs were significantly upregulated and downregulated, respectively, in high-grade cases. Clustering analysis of the TCGA cohort using these 43 microRNAs identified two groups strongly predictive of histological grade. miR-3677 is a microRNA upregulated in high-grade breast cancer. The outcome analysis revealed that patients with high miR-3677 expression had significantly worse prognosis than those with low miR-3677 expression. This study shows that microRNAs are associated with histological grade in early-stage invasive breast cancer. These findings contribute to the elucidation of a new mechanism of breast cancer growth regulated by specific microRNAs.

Co-Delivery of Ylang Ylang Oil of Cananga odorata and Oxaliplatin Using Intelligent pH-Sensitive Lipid-Based Nanovesicles for the Effective Treatment of Triple-Negative Breast Cancer.

Smart pH-responsive niosomes loaded with either Oxaliplatin (Ox), Ylang ylang essential oil (Y-oil), or co-loaded with both compounds (Ox-Y) (Ox@NSs, Y@NSs, and Ox-Y@NSs, respectively) were formulated utilizing the thin film method. The developed nanocontainers had a spherical morphology with mean particle sizes lower than 170 nm and showed negative surface charges, high entrapment efficiencies, and a pH-dependent release over 24 h. The prepared pH-responsive niosomes' cytotoxicity was tested against the invasive triple-negative breast cancer (MDA-MB-231) cells, compared to free OX and Y-oil. All niosomal formulations loaded with Ox and/or Y-oil significantly improved cytotoxic activity relative to their free counterparts. The Ox-Y@NSs demonstrated the lowest IC50 (0.0002 µg/mL) when compared to Ox@NSs (0.006 µg/mL) and Y@NSs (18.39 µg/mL) or unloaded Ox (0.05 µg/mL) and Y-oil (29.01 µg/mL). In addition, the percentages of the MDA-MB-231 cell population in the late apoptotic and necrotic quartiles were profoundly higher in cells treated with the smart Ox-Y@NSs (8.38% and 5.06%) than those exposed to free Ox (7.33% and 1.93%) or Y-oil (2.3% and 2.13%) treatments. Gene expression analysis and protein assays were performed to provide extra elucidation regarding the molecular mechanism by which the prepared pH-sensitive niosomes induce apoptosis. Ox-Y@NSs significantly induced the gene expression of the apoptotic markers Tp53, Bax, and Caspase-7, while downregulating the antiapoptotic Bcl2. As such, Ox-Y@NSs are shown to activate the intrinsic pathway of apoptosis. Moreover, the protein assay ascertained the apoptotic effects of Ox-Y@NSs, generating a 4-fold increase in the relative protein quantity of the late apoptotic marker Caspase-7. Our findings suggest that combining natural essential oil with synthetic platinum-based drugs in pH-responsive nanovesicles is a promising approach to breast cancer therapy.

High platelet count as a predicting factor of histopathological grading among invasive breast cancer individuals: a single centre experience from Indonesia.

Background: The interplay between platelet and breast cancer (BC) pathology may have the potential to represent the malignancy status itself, evidently through predicting the histopathological results of each individual. This study aims to elaborate on the diagnostic value of a higher platelet count or thrombocytosis and the histopathological status of invasive BC in our center, explaining its correlation from the diagnostic perspective. Materials and methods: A retrospective cohort study was conducted using breast cancer patients' medical records from January to March 2022 at the Haji Adam Malik General Hospital, Medan, Indonesia. The patients' histopathological records and complete blood counts were collected from the hospital's medical records. We analyzed the risk analysis model in receiver operator characteristics analysis and diagnostic parameters, e.g., sensitivity and specificity, which we analyzed further using the correlation test to fulfill our objective. Result: The mean age of the 69 subjects we included, in the final analysis, was 49.0 ± 11.1 years old, of whom 35 (50.7%) individuals were histologically confirmed to be high-grade BC. By applying the cut-off value of 299 × 103 cells/µL, the diagnostic value of a platelet count was 60.0 % in sensitivity, 61.8% in specificity, and an area under the curve (AUC) value of 0.597 (0.462-0.732) in 95% confidence interval (CI) as presented by receiver operating characteristic (ROC). We also found that a higher platelet count may also predict the diagnosis of invasive BC by 2.423 times as shown in the odd ratio (OR) analysis. Conclusion: Platelet counts investigation is an applicable yet potential hematological biomarker to predict invasive BC histopathological grading.

Alcohol consumption, smoking, and invasive breast cancer risk after ductal carcinoma in situ.

PURPOSE: Alcohol is an established risk factor for invasive breast cancer, and women with a prior ductal carcinoma in situ diagnosis are at higher risk of invasive breast cancer than the general population. However, for women with a prior ductal carcinoma in situ diagnosis, few studies have evaluated the association between alcohol and smoking and risk of subsequent invasive breast cancer. METHODS: Utilizing a population-based case-control design nested among women diagnosed with a ductal carcinoma in situ between 1995 and 2013, we compared 243 cases diagnosed with a subsequent invasive breast cancer and 423 individually matched controls never diagnosed with a subsequent breast cancer. RESULTS: Compared with never to occasional drinkers, drinkers consuming at least 7 alcoholic drinks per week on average at ductal carcinoma in situ diagnosis had a higher risk of invasive breast cancer that was borderline significant (OR 1.79, 95% CI 1.01-3.17, P value = 0.04). Smoking was not significantly associated with risk of developing an invasive breast cancer after adjustment for alcohol consumption. CONCLUSIONS: These findings suggest that consuming at least one alcoholic drink per day on average is positively associated with invasive breast cancer for women with a prior ductal carcinoma in situ diagnosis. If confirmed, modulating alcohol consumption could be one strategy for women with a history of ductal carcinoma in situ to impact their risk of invasive breast cancer.

Mammographic breast density and survival in women with invasive breast cancer.

PURPOSE: We explored the under-debate association between mammographic breast density (MBD) and survival. METHODS: From the Piedmont Cancer Registry, we identified 693 invasive breast cancer (BC) cases. We analyzed the overall survival in strata of MBD through the Kaplan-Meier method. Using the Cox proportional hazards model, we estimated the hazard ratios (HRs) of death; using the cause-specific hazards regression model, we estimated the HRs of BC-related and other causes of death. Models included term for Breast Imaging-Reporting and Data System (BI-RADS) MBD (categorized as BI-RADS 1 and BI-RADS 2-4) and were adjusted for selected patient and tumour characteristics. RESULTS: There were 102 deaths, of which 49 were from BC. After 5 years, the overall survival was 69% in BI-RADS 1 and 88% in BI-RADS 2-4 (p < 0.01). Compared to BI-RADS 2-4, the HRs of death for BI-RADS 1 were 1.65 (95% CI 1.06-2.58) in the crude model and 1.35 (95% CI 0.84-2.16) in the fully adjusted model. Compared to BI-RADS 2-4, the fully adjusted HRs for BI-RADS 1 were 1.52 (95% CI 0.74-3.13) for BC-related death and 1.83 (95% CI 0.84-4.00) for the other causes of death. CONCLUSION: Higher MBD is one of the strongest independent risk factors for BC, but it seems not to have an unfavorable impact on survival.