Associations of Estrogen Receptor, Progesterone Receptor, Human Epidemic Growth Factor Receptor-2 and Ki-67 with Ultrasound Signs and Prognosis of Breast Cancer Patients

Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer

Background

Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. Neoadjuvant therapy (NAT), administered prior to surgery, is integral to breast cancer treatment strategies. It aims to downsize tumors, optimize surgical outcomes, and evaluate tumor responsiveness to treatment. However, accurately predicting NAT efficacy remains challenging due to the disease's complexity and the diverse responses across different molecular subtypes.

Methods

In this study, we harnessed multimodal data, including proteomic, genomic, MRI imaging, and clinical information, sourced from multiple cohorts such as I-SPY2, TCGA-BRCA, GSE161529, and METABRIC. Post data preprocessing, Lasso regression was utilized for feature extraction and selection. Five machine learning algorithms were employed to construct diagnostic models, with pathological complete response (pCR) as the predictive endpoint.

Results

Our results revealed that the multi-omics Ridge regression model achieved the optimal performance in predicting pCR, with an AUC of 0.917. Through unsupervised clustering using the R package MOVICS and nine clustering algorithms, we identified four distinct multimodal breast cancer subtypes associated with NAT. These subtypes exhibited significant differences in proteomic profiles, hallmark cancer gene sets, pathway activities, tumor immune microenvironments, transcription factor activities, and clinical characteristics. For instance, CS1 subtype, predominantly ER-positive, had a low pCR rate and poor response to chemotherapy drugs, while CS4 subtype, characterized by high immune infiltration, showed a better response to immunotherapy. At the single-cell level, we detected significant heterogeneity in the tumor microenvironment among the four subtypes. Malignant cells in different subtypes displayed distinct copy number variations, differentiation levels, and evolutionary trajectories. Cell-cell communication analysis further highlighted differential interaction patterns among the subtypes, with implications for tumor progression and treatment response.

Conclusion

Our multimodal diagnostic model and subtype analysis provide novel insights into predicting NAT efficacy in breast cancer. These findings hold promise for guiding personalized treatment strategies. Future research should focus on experimental validation, in-depth exploration of the underlying mechanisms, and extension of these methods to other cancers and treatment modalities.

Analysis of Ultrasound Features for Breast Cancers With Different Risk Categories and Evaluation of a New Predicting Method

OBJECTIVES

To explore pathology and ultrasound features of breast cancers with different risk categories. To establish and validate a nomogram primarily based on grayscale ultrasound features for non-invasive preoperative prediction of high-risk breast cancers and for rapid individual risk assessment and clinical decision making.

METHODS

A total of 685 breast malignant lesions were enrolled in this study. All lesions were classified according to the St. Gallen risk categories criteria. The pathology and ultrasound features were compared among different risk groups. A multifactorial Logistic model and a nomogram primarily based on grayscale ultrasound were established. Then prediction ability was evaluated.

RESULTS

In training cohort, the ultrasound features with significant differences were selected again through Lasso regression. Then, age, maximum diameter in ultrasound, posterior echo attenuation, spiculate margin and suspicious axillary lymph nodes were selected to establish the prediction model and nomogram. The areas under the curve in training cohort and internal test cohort were 0.833 and 0.827. Diagnostic sensitivity, specificity, accuracy, positive likelihood ratio and negative likelihood ratio were 75.6%, 76.6%, 76.4%, 41.4% and 93.5%, respectively.

CONCLUSIONS

Breast cancers with different risk categories exhibit distinct pathology and ultrasound features. The prediction model and nomogram have good and stable diagnostic efficiency.

Predicting breast cancer prognosis using PR and PIK3CA biomarkers: a comparative analysis of diagnostic groups

PURPOSE

To evaluate the prognostic significance of progesterone receptor (PR) expression and the PIK3CA mutation status in HR+/HER2 - breast cancer patients, with the goal of screening patients who may derive the greatest benefit from PI3K-targeted therapy.

METHODS

A retrospective analysis was conducted on 152 HR+/HER2 - breast cancer patients stratified by PR expression levels and PIK3CA mutation status. The study population was divided into groups on the basis of a median PR threshold of 50% and further subdivided by PIK3CA mutation status. To evaluate the variability of clinicopathologic features among these groups, t tests and ANOVA were employed. The influence of these variables on survival was analyzed via Cox regression. Additionally, a risk prediction model was developed using the PR expression level and PIK3CA mutation status. The prognostic utility of this model was examined via both Kaplan‒Meier (KM) survival curves and receiver operating characteristic (ROC) analyses. These methods have also been utilized to explore the associations between clinicopathologic parameters and clinical outcomes with respect to survival prediction and prognosis.

RESULTS

Significant differences in age, ER expression, and Ki67, HER2, and PIK3CA mutation status were detected between the groups (P < 0.05). Specifically, elevated PR expression was correlated with lower levels of Ki67 and low HER2 expression. The presence of a PIK3CA mutation was significantly linked to survival outcomes according to both univariate and multivariate Cox regression analyses. Moreover, ROC analysis revealed that models incorporating both PR expression and PIK3CA mutation status achieved the highest level of diagnostic precision (AUC = 0.82).

CONCLUSION

PR expression and PIK3CA mutation status are significant prognostic markers in HR+/HER2 - breast cancer patients. Assessing these biomarkers in combination can enhance prognostic stratification, potentially guiding more informed clinical decision-making.

Prediction of Disease-Free Survival in Breast Cancer using Deep Learning with Ultrasound and Mammography: A Multicenter Study

BACKGROUND

Breast cancer is a leading cause of cancer morbility and mortality in women. The possibility of overtreatment or inappropriate treatment exists, and methods for evaluating prognosis need to be improved.

MATERIALS AND METHODS

Patients (from January 2013 to December 2018) were recruited and divided into a training group and a testing group. All patients were followed for more than 3 years. Patients were divided into a disease-free group and a recurrence group based on follow up results at 3 years. Ultrasound (US) and mammography (MG) images were collected to establish deep learning models (DLMs) using ResNet50. Clinical data, MG, and US characteristics were collected to select independent prognostic factors using a cox proportional hazards model to establish a clinical model. DLM and independent prognostic factors were combined to establish a combined model.

RESULTS

In total, 1242 patients were included. Independent prognostic factors included age, neoadjuvant chemotherapy, HER2, orientation, blood flow, dubious calcification, and size. We established 5 models: the US DLM, MG DLM, US + MG DLM, clinical and combined model. The combined model using US images, MG images, and pathological, clinical, and radiographic characteristics had the highest predictive performance (AUC = 0.882 in the training group, AUC = 0.739 in the testing group).

CONCLUSION

DLMs based on the combination of US, MG, and clinical data have potential as predictive tools for breast cancer prognosis.

Biomarker-specific biosensors revolutionise breast cancer diagnosis

Breast cancer is the most common cancer among women across the globe. In order to treat breast cancer successfully, it is crucial to conduct a comprehensive assessment of the condition during its initial stages. Although mammogram screening has long been a common method of breast cancer screening, high rates of type I error and type II error results as well as radiation exposure have always been of concern. The outgrowth cancer mortality rate is primarily due to delayed diagnosis, which occurs most frequently in a metastatic III or IV stage, resulting in a poor prognosis after therapy. Traditional detection techniques require identifying carcinogenic properties of cells, such as DNA or RNA alterations, conformational changes and overexpression of certain proteins, and cell shape, which are referred to as biomarkers or analytes. These procedures are complex, long-drawn-out, and expensive. Biosensors have recently acquired appeal as low-cost, simple, and super sensitive detection methods for analysis. The biosensor approach requires the existence of biomarkers in the sample. Thus, the development of novel molecular markers for diverse forms of cancer is a rising complementary affair. These biosensor devices offer two major advantages: (1) a tiny amount of blood collected from the patient is sufficient for analysis, and (2) it could help clinicians swiftly select and decide on the best therapy routine for the individual. This review will include updates on prospective cancer markers and biosensors in cancer diagnosis, as well as the associated detection limitations, with a focus on biosensor development for marker detection.

Naples score: a novel prognostic biomarker for breast cancer patients undergoing neoadjuvant chemotherapy

BACKGROUND AND PURPOSE

The Naples Score (NPS) is a novel prognostic indicator that has been used in various cancers, but its potential in breast malignant tumor patients receiving neoadjuvant chemotherapy (NAC) has not been discovered. This study aimed to investigate the relationship between NPS and overall survival (OS) and disease-free survival (DFS) in breast cancer patients.

METHODS

A total of 217 breast cancer patients undergoing NAC were incorporated into this retrospectively research. K-M survival curves and log-rank tests are used to determine OS and DFS. Cox regression model was used to evaluate the relationship between NPS and OS and DFS. Nomogram was developed based on the results of multivariate Cox regression analysis. Prognostic models were internally validated using bootstrapping and the consistency index (C-index).

RESULTS

Age group was correlated with NPS (p < 0.05). Low and moderate Naples risk patients had higher 5-year OS and DFS rates than high risk Naples patients (93.8% vs. 75.4% vs. 60.0%; X2 = 9.2, P = 0.01; 82.4% vs 64.5% vs 43.7%; X2 = 7.4, P = 0.024; respectively). The nomogram based on demonstrated good performance in predicting OS and DFS (AUC = 0.728, 0.630; respectively).

CONCLUSIONS

In breast cancer patients who have undergone NAC, NPS is a novel prognostic indicator. NPS combined with clinicopathological features showed good predictive ability, and its performance was better than that of traditional pathological TNM staging.

Identifying clinicopathological risk factors for regional lymph node metastasis in Chinese patients with T1 breast cancer: a population-based study

Objectives

To analyze clinicopathological risk factors and regular pattern of regional lymph node metastasis (LNM) in Chinese patients with T1 breast cancer and the effect on overall survival (OS) and disease-free survival (DFS).

Materials and methods

Between 1999 and 2020, breast cancer patients meeting inclusion criteria of unilateral, no distant metastatic site, and T1 invasive ductal carcinoma were reviewed. Clinical pathology characteristics were retrieved from medical records. Survival analysis was performed using Kaplan-Meier methods and an adjusted Cox proportional hazards model.

Results

We enrolled 11,407 eligible patients as a discovery cohort to explore risk factors for LNM and 3484 patients with stage T1N0 as a survival analysis cohort to identify the effect of those risk factors on OS and DFS. Compared with patients with N- status, patients with N+ status had a younger age, larger tumor size, higher Ki67 level, higher grade, higher HR+ and HER2+ percentages, and higher luminal B and HER2-positive subtype percentages. Logistic regression indicated that age was a protective factor and tumor size/higher grade/HR+ and HER2+ risk factors for LNM. Compared with limited LNM (N1) patients, extensive LNM (N2/3) patients had larger tumor sizes, higher Ki67 levels, higher grades, higher HR- and HER2+ percentages, and lower luminal A subtype percentages. Logistic regression indicated that HR+ was a protective factor and tumor size/higher grade/HER2+ risk factors for extensive LNM. Kaplan-Meier analysis indicated that grade was a predictor of both OS and DFS; HR was a predictor of OS but not DFS. Multivariate survival analysis using the Cox regression model demonstrated age and Ki67 level to be predictors of OS and grade and HER2 status of DFS in stage T1N0 patients.

Conclusion

In T1 breast cancer patients, there were several differences between N- and N+ patients, limited LNM and extensive LNM patients. Besides, HR+ plays a dual role in regional LNM. In patients without LNM, age and Ki67 level are predictors of OS, and grade and HER2 are predictors of DFS.

The Prognostic Significance of P16 Immunohistochemical Expression Pattern in Women with Invasive Ductal Breast Carcinoma

Background

Breast cancer is the most common malignancy in women worldwide. The p16 protein is a cell cycle regulator and tumor suppressor implicated in several types of cancers. However, its relationship to breast cancer is still unknown. The present study aimed to assess the association of p16 protein expression with clinicopathological features in breast cancer.This study aimed to investigate the anti-cancer effects of different gum extracts on metabolic changes and their impact on gene expression in HT-29 cell.

Methods

The study enrolled 100 patients with invasive ductal carcinoma. The samples were collected before any adjuvant chemotherapy, and p16 protein expression was determined using immunohistochemistry. Clinicopathological features were obtained from the patient's medical records.

Results

Our findings demonstrated that p16 protein expression increased in estrogen receptor-positive tumor tissues (P< 0.01). However, no significant correlation was found between the p16 protein expression and the other clinicopathological features.

Conclusions

Our study demonstrated that p16 protein expression increased in ER-positive tumor tissue from patients with invasive ductal breast carcinoma. However, no correlation was found between the p16 protein expression and the other clinicopathological features.

Correlation of androgen receptor with ultrasound, clinicopathological features and clinical outcomes in breast cancer

BACKGROUND

This study aimed to explore whether there is an association between androgen receptor (AR) expression and ultrasound, clinicopathological features and prognosis of breast cancer.

METHODS

A total of 141 breast cancer patients were included in this retrospective study. AR expression was analyzed by immunohistochemistry. The images of B-mode, color Doppler and strain elastography from 104 patients were collected continuously, and the corresponding ultrasound characteristics were obtained. The differences in ultrasound and clinicopathological features in different AR status were analyzed. Progression-free survival (PFS) of patients was obtained through up to 90 months of follow-up; then, the effect of AR on PFS was analyzed. Subsequently, a nomogram was constructed to predict the AR status. The predictive accuracy was calculated using C-index.

RESULTS

The positive expression of AR (AR +) was associated with lower histological grade (p = 0.034) and lower Ki-67 level (p = 0.029). Triple-negative breast cancer (TNBC) had the lowest probability of AR + (p < 0.001). The AR + group mostly showed unsmooth margin (p < 0.001), posterior acoustic shadowing (p = 0.002) and higher elasticity score (p = 0.022) on ultrasound. The echo pattern of most tumors with AR + was heterogeneous (p = 0.024) in Luminal A subtype. AR + could be a sign of a better prognosis in overall breast cancer (p < 0.001), as well as in human epidermal growth factor receptor 2 (HER2) overexpression and Luminal B subtypes (p = 0.001 and 0.025). The nomogram showed relatively reliable performance with a C-index of 0.799.

CONCLUSION

Our research demonstrated that AR expression was closely related to ultrasound, clinicopathological features and prognosis of breast cancer.

Association study of Bif-1 gene expression with histopathological characteristics and hormone receptors in breast cancer

BACKGROUND

Breast cancer is a heterogeneous disease that has various clinical outcomes. Bax-interacting factor-1 (Bif-1) is a member of the endophilin B family that generates the pro-apoptotic BCL2-Associated X (BAX) protein in response to apoptotic signals. Lack of Bif-1 inhibits the intrinsic pathway of apoptosis and enhancements the risk of tumor genesis. The present study aimed to investigate the relationship between hormone receptors (ER, PR, and HER2) status and different levels of Bif-1 gene expression in breast cancer patients.

METHODS

Bif-1 gene expression was evaluated in 50 breast cancer tumors and 50 normal breast mammary tissues using the SYBR Green real-time RT-PCR technique. Multivariate and univariate analyses were used to appraise the relationship between the prognostic significance of the Bif-1 gene using SPSS software. In this study, the Bif-1 was selected as a candidate for a molecular biomarker and its expression status in breast cancer patients with hormone receptors (ER, RR, and HER2) compared to patients without these hormone receptors.

RESULTS

The study showed that the relative expression of the Bif-1 gene in tissues of patients with hormone receptors in breast cancer compared to those without hormone receptors was not statistically significant. The expression levels of the Bif-1 gene in different groups were evaluated for hormone receptor status. No significant relationship was found between the Bif-1 gene expression and hormone receptors (ER, PR, and HER2) (p > 0.05).

CONCLUSION

Bif-1 gene expression may be a useful prognostic marker in breast cancer.

Role of Ultrasound Imaging in the Prediction of TRIM67 in Brain Metastases From Breast Cancer

Objectives

Ultrasound (US) imaging is a relatively novel strategy to monitor the activity of the blood–brain barrier, which can facilitate the diagnosis and treatment of neurovascular-related metastatic tumors. The purpose of this study was to investigate the clinical significance of applying a combination of US imaging outcomes and the associated genes. This was performed to construct line drawings to facilitate the prediction of brain metastases arising from breast cancer.

Methods

The RNA transcript data from The Cancer Genome Atlas (TCGA) database was obtained for breast cancer, and the differentially expressed genes (DEGs) associated with tumor and brain tumor metastases were identified. Subsequently, key genes associated with survival prognosis were subsequently identified from the DEGs.

Results

Tripartite motif-containing protein 67 (TRIM67) was identified and the differential; in addition, the survival analyses of the TCGA database revealed that it was associated with brain tumor metastases and overall survival prognosis. Applying independent clinical cohort data, US-related features (microcalcification and lymph node metastasis) were associated with breast cancer tumor metastasis. Furthermore, ultrasonographic findings of microcalcifications showed correlations with TRIM67 expression. The study results revealed that six variables [stage, TRIM67, tumor size, regional lymph node staging (N), age, and HER2 status] were suitable predictors of tumor metastasis by applying support vector machine–recursive feature elimination. Among these, US-predicted tumor size correlated with tumor size classification, whereas US-predicted lymph node metastasis correlated with tumor N classification. The TRIM67 upregulation was accompanied by upregulation of the integrated breast cancer pathway; however, it leads to the downregulation of the miRNA targets in ECM and membrane receptors and the miRNAs involved in DNA damage response pathways.

Conclusions

The TRIM67 is a risk factor associated with brain metastases from breast cancer and it is considered a prognostic survival factor. The nomogram constructed from six variables—stage, TRIM67, tumor size, N, age, HER2 status—is an appropriate predictor to estimate the occurrence of breast cancer metastasis.