Molecular and cellular heterogeneity in breast cancer: challenges for personalized medicine

Intratumoral Heterogeneity of Primary Breast Cancer on 18F-FES PET/CT and dbPET Anticipated a Heterogeneous Response to Chemotherapy

ABSTRACT

A 59-year-old woman with cT3N3M1 invasive breast cancer (ER low positive, PgR positive, HER2 negative) underwent PET/CT and dedicated breast PET scans using 18F-FDG and 18F-fluoroestradiol (18F-FES). While most primary tumor regions displayed low FES uptake, regions of high FES uptake were also identified. Following chemotherapy with the paclitaxel and bevacizumab, 18F-FDG PET/CT demonstrated a favorable response, but residual disease was noted in areas with high FES uptake on the pretreatment images. This case illustrated that 18F-FES PET can depict intratumoral heterogeneity within the primary tumor, which was strongly associated with a heterogeneous response to subsequent chemotherapy.

Efficacy outcomes of CDK4/6 inhibitors in combination with endocrine therapy treatment in hormone receptor-positive/HER2-negative advanced breast cancer according to PAM50 intrinsic subtype: Primary results of SOLTI-1801 CDK-PREDICT study

INTRODUCTION

The prognostic value of PAM50 intrinsic subtypes (IS), cell cycle, and immune-related gene expression in HR+ /HER2- advanced breast cancer (BC) treated with CDK4/6 inhibitors (CDK4/6i) and endocrine therapy (ET) in a first-line metastatic setting is unclear. This study evaluates these biomarkers in metastatic biopsies from patients diagnosed with HR+ /HER2- advanced BC.

METHODS

CDK-PREDICT study is a multicentric, ambispective observational cohort study conducted in six Spanish hospitals. It included patients diagnosed with HR+ /HER2- advanced BC treated in the first-line setting with CDK4/6i and ET. Baseline biopsies were obtained prior to treatment to determine research-based PAM50 IS, cell cycle and immune-related gene expression. The primary objective was to evaluate progression-free survival (PFS) differences among PAM50 IS using uni- and multivariable Cox regression models. Secondary objectives included overall survival (OS), overall response rate (ORR), and correlating cell cycle and immune response gene expression with PFS.

RESULTS

A total of 185 patients were included, with a median follow-up of 38.5 months. PAM50 luminal subtypes were predominant (82.7 %). Non-luminal subtypes showed significantly shorter median PFS (10.2 vs. 25.7 months; HR, 2.50; p < 0.001) and OS (32.3 vs. 58.1 months; HR, 2.54; p < 0.001) than luminal subtypes. Higher cell cycle and immune-related genes expression, such as CCNE1 and PDCD1, as well as tumor infiltrating lymphocytes were associated with poorer outcomes.

CONCLUSIONS

This study confirms the independent prognostic value of PAM50 IS in HR+ /HER2- advanced BC treated with CDK4/6i and ET. Non-luminal subtypes exhibited the worst prognosis, underscoring the need for novel therapeutic strategies in this population.

MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells

Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, such as the classification of cell phenotypes, the existing machine learning models often suffer from low adaptability and transferability. In addition, SCMS studies of rare cells can be restricted by limited number of cell samples. To overcome these limitations, we performed SCMS analyses of melanoma cancer cell lines with two phenotypes (i.e., primary and metastatic cells). We then developed a meta-learning-based model, MetaPhenotype, that can be trained using a small amount of SCMS data to accurately classify cells into primary or metastatic phenotypes. Our results show that compared with standard transfer learning models, MetaPhenotype can rapidly predict and achieve a high accuracy of over 90% with fewer new training samples. Overall, our work opens the possibility of accurate cell phenotype classification based on fewer SCMS samples, thus lowering the demand for sample acquisition.

A regularized Cox hierarchical model for incorporating annotation information in predictive omic studies

BACKGROUND

Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes.

METHODS

A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent.

RESULTS

In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level.

CONCLUSIONS

The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

Breast cancer epigenetics: current and evolving treatment

BACKGROUND

Breast cancer (BC) presents persistent challenges due to subtype-specific limited efficacy and potential resistance to standard therapy, influenced by the dynamic reversible nature of epigenetic plasticity. This study aims to comprehensively explore the evolving BC epigenetic landscape, analyzing trends and evaluating the therapeutic potential of epigenetic drugs (epi-drugs) for BC treatment.

METHODS

We conducted a cross-sectional study of BC epigenetic trials using ClinicalTrials.gov until July 18, 2023. Additionally, results from randomized controlled trials were retrieved from the registry or PubMed using trial registration numbers.

RESULTS

In total, 22 epi-drugs were investigated in 100 trials, with 11 currently being studied in 38 ongoing trials for BC. Over the years, epigenetic clinical trials for BC have notably increased, with histone deacetylase inhibitors constituting 45.45% of the candidate agents in the development pipeline. All ongoing trials are enrolling human epidermal growth factor receptor2 (HER2)-negative BC patients. Epi-drugs are commonly explored in combination with multiple anti-cancer therapies, such as aromatase or microtubule inhibitors, using an intermittent sequential administration approach. Emerging strategies include new-generation epi-drugs and combination involving immunotherapy or targeted therapy. Among candidate drugs, tucidinostat and entinostat, in combination with exemestane, demonstrated significant improvements in progression-free survival in phase III trials for hormone receptor-positive, HER2-negative BC patients.

CONCLUSION

This study highlights the growing interest in BC epigenetics, suggesting a potential shift from a one-size-fits-all approach to precision medicine, and emphasizes the necessity for robust evidence on their efficacy and safety to support continuous development and approval, addressing the unmet needs in BC treatment.

Applicability of Quantum Dots in Breast Cancer Diagnostic and Therapeutic Modalities-A State-of-the-Art Review

The increasing incidence of breast cancers (BCs) in the world population and their complexity and high metastatic ability are serious concerns for healthcare systems. Despite the significant progress in medicine made in recent decades, the efficient treatment of invasive cancers still remains challenging. Chemotherapy, a fundamental systemic treatment method, is burdened with severe adverse effects, with efficacy limited by resistance development and risk of disease recurrence. Also, current diagnostic methods have certain drawbacks, attracting attention to the idea of developing novel, more sensitive detection and therapeutic modalities. It seems the solution for these issues can be provided by nanotechnology. Particularly, quantum dots (QDs) have been extensively evaluated as potential targeted drug delivery vehicles and, simultaneously, sensing and bioimaging probes. These fluorescent nanoparticles offer unlimited possibilities of surface modifications, allowing for the attachment of biomolecules, such as antibodies or proteins, and drug molecules, among others. In this work, we discuss the potential applicability of QDs in breast cancer diagnostics and treatment in light of the current knowledge. We begin with introducing the molecular and histopathological features of BCs, standard therapeutic regimens, and current diagnostic methods. Further, the features of QDs, along with their uptake, biodistribution patterns, and cytotoxicity, are described. Based on the reports published in recent years, we present the progress in research on possible QD use in improving BC diagnostics and treatment efficacy as chemotherapeutic delivery vehicles and photosensitizing agents, along with the stages of their development. We also address limitations and open questions regarding this topic.

Treatment outcomes and prognostic factors in nonmetastatic metaplastic breast cancer patients: a multicenter retrospective cohort study

BACKGROUND AND PURPOSE

Metaplastic breast carcinoma (BC-Mp) is an uncommon subtype that poses unique challenges. The limited information on patient prognosis and therapeutic strategies motivated our research initiative. We aimed to assess disease-free survival (DFS), overall survival (OS), and influential factors in patients with nonmetastatic BC-Mp.

MATERIALS AND METHODS

In this multicenter retrospective cohort study, clinicopathological data for nonmetastatic BC-Mp patients treated at four oncology units in Poland (2012-2022) were gathered.

RESULTS

Among 115 women (median age 61, range: 28-91), the median tumor size was 40 mm (range 20-130); 30% of patients exhibited positive local lymph nodes. The majority of patients presented with stage II (46%) and triple-negative breast cancer (TNBC) (84%). Radiotherapy was administered to 61% of patients. Surgical procedures included breast-conserving surgery in 31% of patients and mastectomy in 68%. Eighty-three per cent of patients received chemotherapy. The median estimated DFS and OS were 59 and 68 months, respectively. Multivariable analysis revealed that tumor size influenced DFS and OS (Hazard ratios [HR] = 1.02, 95%CI 0.01-0.03 for both endpoints) and taxanes application improved DFS (HR = 0.47, 95%CI 0.24-0.93), but other factors did not. For patients receiving neoadjuvant systemic therapy (N = 51), taxanes improved DFS and OS according to univariable analysis.

INTERPRETATION

Our findings highlight poor DFS and OS regardless of receiving optimal treatment, emphasizing the need for tailored therapeutic strategies for BC-Mp patients. Taxanes appear promising in a neoadjuvant setting, particularly within the current standard of care for the TNBC subtype.

Identification of Time-Series Pattern Marker in Its Application to Mortality Analysis of Pneumonia Patients in Intensive Care Unit

Electronic Health Records (EHRs) are a significant source of big data used to track health variables over time. The analysis of EHR data can uncover medical markers or risk factors, aiding in the diagnosis and monitoring of diseases. We introduce a novel method for identifying markers with various temporal trend patterns, including monotonic and fluctuating trends, using machine learning models such as Long Short-Term Memory (LSTM). By applying our method to pneumonia patients in the intensive care unit using the MIMIC-III dataset, we identified markers exhibiting both monotonic and fluctuating trends. Specifically, monotonic markers such as red cell distribution width, urea nitrogen, creatinine, calcium, morphine sulfate, bicarbonate, sodium, troponin T, albumin, and prothrombin time were more frequently observed in the mortality group compared to the recovery group throughout the 10-day period before discharge. Conversely, fluctuating trend markers such as dextrose in sterile water, polystyrene sulfonate, free calcium, and glucose were more frequently observed in the mortality group as the discharge date approached. Our study presents a method for detecting time-series pattern markers in EHR data that respond differently according to disease progression. These markers can contribute to monitoring disease progression and enable stage-specific treatment, thereby advancing precision medicine.

A Regularized Cox Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies

Background

Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes.

Methods

A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent.

Results

In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level.

Conclusions

The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

Luteinizing Hormone-Releasing Hormone (LHRH)-Conjugated Cancer Drug Delivery from Magnetite Nanoparticle-Modified Microporous Poly-Di-Methyl-Siloxane (PDMS) Systems for the Targeted Treatment of Triple Negative Breast Cancer Cells

This study presents LHRH conjugated drug delivery via a magnetite nanoparticle-modified microporous Poly-Di-Methyl-Siloxane (PDMS) system for the targeted suppression of triple-negative breast cancer cells. First, the MNP-modified PDMS devices are fabricated before loading with targeted and untargeted cancer drugs. The release kinetics from the devices are then studied before fitting the results to the Korsmeyer-Peppas model. Cell viability and cytotoxicity assessments are then presented using results from the Alamar blue assay. Apoptosis induction is then elucidated using flow cytometry. The in vitro drug release studies demonstrated a sustained and controlled release of unconjugated drugs (Prodigiosin and paclitaxel) and conjugated drugs [LHRH conjugated paclitaxel (PTX+LHRH) and LHRH-conjugated prodigiosin (PG+LHRH)] from the magnetite nanoparticle modified microporous PDMS devices for 30 days at 37 °C, 41 °C, and 44 °C. At 24, 48, 72, and 96 h, the groups loaded with conjugated drugs (PG+LHRH and PTX+LHRH) had a significantly higher (p < 0.05) percentage cell growth inhibition than the groups loaded with unconjugated drugs (PG and PTX). Additionally, throughout the study, the MNP+PDMS (without drug) group exhibited a steady rise in the percentage of cell growth inhibition. The flow cytometry results revealed a high incidence of early and late-stage apoptosis. The implications of the results are discussed for the development of biomedical devices for the localized and targeted release of cancer drugs that can prevent cancer recurrence following tumor resection.

Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study

BACKGROUND

Providing appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients.

METHOD

A systematic review was conducted using the PubMed, Embase, Scopus, and Web of Science databases in September 2023. We performed the search strategy with keywords, namely: Breast Cancer, Artificial intelligence, and precision Oncology along with their synonyms in the article titles. Descriptive, qualitative, review, and non-English studies were excluded. The quality assessment of the articles and evaluation of bias were determined based on the SJR journal and JBI indices, as well as the PRISMA2020 guideline.

RESULTS

Forty-six studies were selected that focused on personalized breast cancer management using artificial intelligence models. Seventeen studies using various deep learning methods achieved a satisfactory outcome in predicting treatment response and prognosis, contributing to personalized breast cancer management. Two studies utilizing neural networks and clustering provided acceptable indicators for predicting patient survival and categorizing breast tumors. One study employed transfer learning to predict treatment response. Twenty-six studies utilizing machine-learning methods demonstrated that these techniques can improve breast cancer classification, screening, diagnosis, and prognosis. The most frequent modeling techniques used were NB, SVM, RF, XGBoost, and Reinforcement Learning. The average area under the curve (AUC) for the models was 0.91. Moreover, the average values for accuracy, sensitivity, specificity, and precision were reported to be in the range of 90-96% for the models.

CONCLUSION

Artificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data. Intelligent processing of omics data through protein and gene pattern classification and the utilization of deep neural patterns has the potential to significantly transform the field of complex disease management.

Predicting the Risk of Breast Cancer Recurrence and Metastasis based on miRNA Expression

Background:

Even after surgery, breast cancer patients still suffer from recurrence and metastasis. Thus, it is critical to predict accurately the risk of recurrence and metastasis for individual patients, which can help determine the appropriate adjuvant therapy.

Methods:

The purpose of this study is to investigate and compare the performance of several categories of molecular biomarkers, i.e., microRNA (miRNA), long non-coding RNA (lncRNA), messenger RNA (mRNA), and copy number variation (CNV), in predicting the risk of breast cancer recurrence and metastasis. First, the molecular data (miRNA, lncRNA, mRNA, and CNV) of 483 breast cancer patients were downloaded from the Cancer Genome Atlas, which were then randomly divided into the training and test sets with a ratio of 7:3. Second, the feature selection process was applied by univariate Cox and multivariate Cox variance analysis on the training set (e.g., 15 miRNAs). According to the selected features (e.g., 15 miRNAs), a random forest classifier and several other classification methods were established according to the label of recurrence and metastasis. Finally, the performances of the classification models were compared and evaluated on the test set.

Results:

The area under the ROC curve was 0.70 for miRNA, better than those using other biomarkers.

Conclusion:

These results indicated that miRNA has important guiding significance in predicting recurrence and metastasis of breast cancer.

Advancements and challenges in triple-negative breast cancer: a comprehensive review of therapeutic and diagnostic strategies

Triple-negative breast cancer (TNBC) poses significant challenges in oncology due to its aggressive nature, limited treatment options, and poorer prognosis compared to other breast cancer subtypes. This comprehensive review examines the therapeutic and diagnostic landscape of TNBC, highlighting current strategies, emerging therapies, and future directions. Targeted therapies, including PARP inhibitors, immune checkpoint inhibitors, and EGFR inhibitors, hold promise for personalized treatment approaches. Challenges in identifying novel targets, exploring combination therapies, and developing predictive biomarkers must be addressed to optimize targeted therapy in TNBC. Immunotherapy represents a transformative approach in TNBC treatment, yet challenges in biomarker identification, combination strategies, and overcoming resistance persist. Precision medicine approaches offer opportunities for tailored treatment based on tumor biology, but integration of multi-omics data and clinical implementation present challenges requiring innovative solutions. Despite these challenges, ongoing research efforts and collaborative initiatives offer hope for improving outcomes and advancing treatment strategies in TNBC. By addressing the complexities of TNBC biology and developing effective therapeutic approaches, personalized treatments can be realized, ultimately enhancing the lives of TNBC patients. Continued research, clinical trials, and interdisciplinary collaborations are essential for realizing this vision and making meaningful progress in TNBC management.

Bridging the Divide: A Review on the Implementation of Personalized Cancer Medicine

The shift towards personalized cancer medicine (PCM) represents a significant transformation in cancer care, emphasizing tailored treatments based on the genetic understanding of cancer at the cellular level. This review draws on recent literature to explore key factors influencing PCM implementation, highlighting the role of innovative leadership, interdisciplinary collaboration, and coordinated funding and regulatory strategies. Success in PCM relies on overcoming challenges such as integrating diverse medical disciplines, securing sustainable investment for shared infrastructures, and navigating complex regulatory landscapes. Effective leadership is crucial for fostering a culture of innovation and teamwork, essential for translating complex biological insights into personalized treatment strategies. The transition to PCM necessitates not only organizational adaptation but also the development of new professional roles and training programs, underscoring the need for a multidisciplinary approach and the importance of team science in overcoming the limitations of traditional medical paradigms. The conclusion underscores that PCM's success hinges on creating collaborative environments that support innovation, adaptability, and shared vision among all stakeholders involved in cancer care.

Survival, treatment patterns, and costs of HER2+ metastatic breast cancer patients in Ontario between 2005 to 2020

BACKGROUND

To enable the integration of novel therapies, it is critical to understand current long-term outcomes in HER2-positive metastatic breast cancer (mBC), including survival, treatment patterns, and costs. We sought to define these outcomes among patients with mBC in Ontario.

METHODS

We conducted a retrospective population-level study in Ontario women diagnosed with breast cancer of any stage between January 1, 2005 and December 31, 2019, with follow-up until December 31, 2020. HER2-positivity was based on receipt of a HER2-targeted therapy (HER2-TT) in the first line (1L) metastatic setting. Administrative databases at ICES were used to assess outcomes.

RESULTS

In Ontario, 2557 patients were diagnosed with mBC and received a HER2-TT, and of these 1606 were diagnosed with early-stage (stage I-III) that became metastatic (recurrent), while 951 were diagnosed with late stage/de novo mBC (stage IV). The average age of all patients was 54.8 years ± 12.7 years. Treatment regimens that included pertuzumab and trastuzumab (cohort name: pert_tras) were the most frequently used HER2-TT for 1L mBC (51.4%), while T-DM1 was the most frequent therapy (87.5%) in second line (2L). The median overall survival (mOS) from initiation of 1L pert_tras was not reached, whereas mOS from initiation of T-DM1 in 2L was 18.7 months. The overall mean cost per patient on pert_tras during 1L was $267,282. The main cost drivers were the cost of systemic therapy, followed by cancer clinic visits, with a mean cost per patient at $158,961 and $73,882, respectively.

CONCLUSION

The baseline characteristics and treatment patterns for patients who received HER2-TT in our study align with previously reported results. However, the mOS observed for 2L T-DM1 was shorter than that found in pivotal, clinical trial literature. As expected, anti-cancer systemic therapy costs were the main contributor to the over quarter-million dollar mean cost per patient on pert_tras in 1L.

Modelling-based joint embedding of histology and genomics using canonical correlation analysis for breast cancer survival prediction

Traditional approaches to predicting breast cancer patients' survival outcomes were based on clinical subgroups, the PAM50 genes, or the histological tissue's evaluation. With the growth of multi-modality datasets capturing diverse information (such as genomics, histology, radiology and clinical data) about the same cancer, information can be integrated using advanced tools and have improved survival prediction. These methods implicitly exploit the key observation that different modalities originate from the same cancer source and jointly provide a complete picture of the cancer. In this work, we investigate the benefits of explicitly modelling multi-modality data as originating from the same cancer under a probabilistic framework. Specifically, we consider histology and genomics as two modalities originating from the same breast cancer under a probabilistic graphical model (PGM). We construct maximum likelihood estimates of the PGM parameters based on canonical correlation analysis (CCA) and then infer the underlying properties of the cancer patient, such as survival. Equivalently, we construct CCA-based joint embeddings of the two modalities and input them to a learnable predictor. Real-world properties of sparsity and graph-structures are captured in the penalized variants of CCA (pCCA) and are better suited for cancer applications. For generating richer multi-dimensional embeddings with pCCA, we introduce two novel embedding schemes that encourage orthogonality to generate more informative embeddings. The efficacy of our proposed prediction pipeline is first demonstrated via low prediction errors of the hidden variable and the generation of informative embeddings on simulated data. When applied to breast cancer histology and RNA-sequencing expression data from The Cancer Genome Atlas (TCGA), our model can provide survival predictions with average concordance-indices of up to 68.32% along with interpretability. We also illustrate how the pCCA embeddings can be used for survival analysis through Kaplan-Meier curves.

Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis

Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep-learning model for the identification and categorization of breast tumors. The system's performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study's findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1-score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1-score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1-score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.

Single-cell analysis reveals one cancer-associated fibroblasts subtype linked to metastasis in breast cancer: MXRA5 as a potential novel marker for prognosis

Cancer-associated fibroblasts (CAFs) are prevalent in the tumor microenvironment of breast cancer, comprising a group of cell subpopulations with spatial, phenotypic, and functional heterogeneity. Due to the lack of specific markers for CAF subpopulations, their specific mechanisms in breast cancer remain unclear. We identified eight distinct CAF phenotypes in breast cancer using multiple single-cell RNA sequencing datasets and determined distinct transcription factors (TFs) of CAFs through SCENIC analysis. Our study highlights one CAF subtype in breast cancer, FN1+CAF2, associated with metastasis and macrophage polarization. We observed elevated FN1 expression in the stromal tissue of breast cancer patients. Furthermore, FN1 knockdown in CAFs reduced the migration ability of breast cancer cells. We identified a regulatory gene, MXRA5, in CAF2, which may play crucial roles in breast cancer. Our results indicated upregulated MXRA5 expression in breast cancer tissues and CAFs from patients with lymph node metastasis in the following experiment. Overall, our study reveals that the FN1+CAF2 subtype is associated with metastasis and suggests that MXRA5 may be a novel marker mediating the effects of CAF2 on breast cancer metastasis. This study enriches our understanding of CAF heterogeneity and offers new insights for treating breast cancer metastasis.

MicroRNA signatures differentiate types, grades, and stages of breast invasive ductal carcinoma (IDC): miRNA-target interacting signaling pathways

BACKGROUND

Invasive ductal carcinoma (IDC) is the most common form of breast cancer which accounts for 85% of all breast cancer diagnoses. Non-invasive and early stages have a better prognosis than late-stage invasive cancer that has spread to lymph nodes. The involvement of microRNAs (miRNAs) in the initiation and progression of breast cancer holds great promise for the development of molecular tools for early diagnosis and prognosis. Therefore, developing a cost effective, quick and robust early detection protocol using miRNAs for breast cancer diagnosis is an imminent need that could strengthen the health care system to tackle this disease around the world.

METHODS

We have analyzed putative miRNAs signatures in 100 breast cancer samples using two independent high fidelity array systems. Unique and common miRNA signatures from both array systems were validated using stringent double-blind individual TaqMan assays and their expression pattern was confirmed with tissue microarrays and northern analysis. In silico analysis were carried out to find miRNA targets and were validated with q-PCR and immunoblotting. In addition, functional validation using antibody arrays was also carried out to confirm the oncotargets and their networking in different pathways. Similar profiling was carried out in Brca2/p53 double knock out mice models using rodent miRNA microarrays that revealed common signatures with human arrays which could be used for future in vivo functional validation.

RESULTS

Expression profile revealed 85% downregulated and 15% upregulated microRNAs in the patient samples of IDC. Among them, 439 miRNAs were associated with breast cancer, out of which 107 miRNAs qualified to be potential biomarkers for the stratification of different types, grades and stages of IDC after stringent validation. Functional validation of their putative targets revealed extensive miRNA network in different oncogenic pathways thus contributing to epithelial-mesenchymal transition (EMT) and cellular plasticity.

CONCLUSION

This study revealed potential biomarkers for the robust classification as well as rapid, cost effective and early detection of IDC of breast cancer. It not only confirmed the role of these miRNAs in cancer development but also revealed the oncogenic pathways involved in different progressive grades and stages thus suggesting a role in EMT and cellular plasticity during breast tumorigenesis per se and IDC in particular. Thus, our findings have provided newer insights into the miRNA signatures for the classification and early detection of IDC.

Molecular Characterization and Subtyping of Breast Cancer Cell Lines Provide Novel Insights into Cancer Relevant Genes

Continuous cell lines are important and commonly used in vitro models in breast cancer (BC) research. Selection of the appropriate model cell line is crucial and requires consideration of their molecular characteristics. To characterize BC cell line models in depth, we profiled a panel of 29 authenticated and publicly available BC cell lines by mRNA-sequencing, mutation analysis, and immunoblotting. Gene expression profiles separated BC cell lines in two major clusters that represent basal-like (mainly triple-negative BC) and luminal BC subtypes, respectively. HER2-positive cell lines were located within the luminal cluster. Mutation calling highlighted the frequent aberration of TP53 and BRCA2 in BC cell lines, which, therefore, share relevant characteristics with primary BC. Furthermore, we showed that the data can be used to find novel, potential oncogenic fusion transcripts, e.g., FGFR2::CRYBG1 and RTN4IP1::CRYBG1 in cell line MFM-223, and to elucidate the regulatory circuit of IRX genes and KLF15 as novel candidate tumor suppressor genes in BC. Our data indicated that KLF15 was activated by IRX1 and inhibited by IRX3. Moreover, KLF15 inhibited IRX1 in cell line HCC-1599. Each BC cell line carries unique molecular features. Therefore, the molecular characteristics of BC cell lines described here might serve as a valuable resource to improve the selection of appropriate models for BC research.

A novel approach for transforming breast cancer stem cells into endothelial cells

Tumor vascular endothelial cells play a pivotal in the tumor microenvironment, influencing the proliferation, invasion, and metastasis of tumor progression. The present study investigated a novel method for inducing the transformation of breast cancer stem cells into endothelial cells, providing a cellular model investigating anti-angiogenic mechanisms in vitro. The breast cancer cell line MCF-7 was used, and the expression of CD133 was initially detected using flow cytometry. CD133+ breast cancer cells were purified using immunomagnetic bead sorting technology, yielding an MCF-7CD133+ subpopulation. The proliferation ability of these cells was assessed using an MTT assay, while their microsphere formation ability was evaluated using a microsphere formation assay. Post-transformation in an optimized endothelial cell culture medium, expression of endothelial cell markers CD31 and CD105 were detected using flow cytometry. Endothelial cell tube formation assays and DiI-labeled acetylated low-density lipoprotein (DiI-Ac-LDL) assays were employed to analyze the endothelial cell function of the MCF-7CD133+ cells. MDM2/CEN12 gene amplification was detected through fluorescence in situ hybridization (FISH). The MCF-7 breast cancer cell line exhibited 1.7±0.3% trace cells expressing the stem cell surface marker CD133. After anti-CD133 immunomagnetic bead sorting, MCF-7CD133+ and MCF-7CD133- subpopulation cells were obtained, with CD133 expression rates of 85.6±2.8 and 0.18±0.08%, respectively. MTT assay results demonstrated that, after 7 days, the proliferation rate of MCF-7CD133+ cells was significantly higher compared with MCF-7CD133- cells. MCF-7CD133+ subpopulation cells displayed strong stem cell characteristics, growing in suspension in serum-free media and forming tumor cell spheres. In contrast, MCF-7CD133- cells failed to form microspheres. After culturing cells in endothelial cell differentiation and maintenance media, the percentage of MCF-7CD133+ cells before and after endothelial cell culture was 0.3±0.16 and 81.4±8.37% for CD31+ cells and 0.2±0.08 and 83.8±7.24% for CD105+ cells, respectively. Vascular-like structure formation and Ac-LDL phagocytosis with red fluorescence in the tube formation assays confirmed endothelial cell function in the MCF-7CD133+ cells. FISH was used to verify MDM2/CEN12 gene amplification in the induced MCF-7CD133+ cells, indicating tumor cell characteristics. The modified endothelial cell transformation medium effectively induced differentiated tumor stem cells to express vascular endothelial cell markers and exhibit endothelial functions, ideal for in vitro anti-angiogenesis research.

Bioinformatic analysis reveals an association between Metadherin with breast cancer prognosis and tumor immune infiltration

Breast cancer metastasis and invasion are both promoted by the oncoprotein Metadherin (MTDH). However, the the role of Metadherin in breast cancer progression and its role in the immune microenvironment. Are not clear. A bioinformatic analysis was performed to demonstrate the prognostic value of Metadherin in BC. In the present study, we found that Metadherin is overexpressed in BC and is significantly correlated with individual cancer stage, age, subclasses, menopause and nodal metastasis status. Metadherin overexpression was associated with a significant decrease in OS and DSS. Cox multivariate analysis indicated that Metadherin was an independent negative prognostic indicator for OS and DSS. Moreover, Metadherin hypomethylation status was associated with poor prognosis. A negative correlation was also noted between Metadherin overexpression and the number of plasmacytoid dendritic cells, cluster of differentiation 8+ T cells, and natural killer cells. Association patterns varied with different subtypes. Various associations between Metadherin levels and immune cell surface markers were revealed. A total of 40 groups of BC and adjacent normal breast tissue samples were collected. Metadherin mRNA was detected by PCR, and its expression levels in BC tissues were significantly increased compared with those noted in normal tissues. The expression levels of Metadherin were also measured in normal and BC cell lines, respectively, and similar conclusions were obtained. The Metadherin mRNA levels were knocked down in SK-BR3 and MDA-MB-231 cell lines and the cell proliferative and migratory activities were determined using Cell Counting Kit-8 and scratch assays, respectively. The results indicated that the cell proliferative and migratory abilities were reduced following knockdown of Metadherin expression. Therefore, Metadherin may be considered as a novel prognostic biomarker in BC.

In-vivo studies of targeted and localized cancer drug release from microporous poly-di-methyl-siloxane (PDMS) devices for the treatment of triple negative breast cancer

Triple-negative breast cancer (TNBC) treatment is challenging and frequently characterized by an aggressive phenotype and low prognosis in comparison to other subtypes. This paper presents fabricated implantable drug-loaded microporous poly-di-methyl-siloxane (PDMS) devices for the delivery of targeted therapeutic agents [Luteinizing Hormone-Releasing Hormone conjugated paclitaxel (PTX-LHRH) and Luteinizing Hormone-Releasing Hormone conjugated prodigiosin (PG-LHRH)] for the treatment and possible prevention of triple-negative cancer recurrence. In vitro assessment using the Alamar blue assay demonstrated a significant reduction (p < 0.05) in percentage of cell growth in a time-dependent manner in the groups treated with PG, PG-LHRH, PTX, and PTX-LHRH. Subcutaneous triple-negative xenograft breast tumors were then induced in athymic female nude mice that were four weeks old. Two weeks later, the tumors were surgically but partially removed, and the device implanted. Mice were observed for tumor regrowth and organ toxicity. The animal study revealed that there was no tumor regrowth, six weeks post-treatment, when the LHRH targeted drugs (LHRH-PTX and LHRH-PGS) were used for the treatment. The possible cytotoxic effects of the released drugs on the liver, kidney, and lung are assessed using quantitative biochemical assay from blood samples of the treatment groups. Ex vivo histopathological results from organ tissues showed that the targeted cancer drugs released from the implantable drug-loaded device did not induce any adverse effect on the liver, kidneys, or lungs, based on the results of qualitative toxicity studies. The implications of the results are discussed for the targeted and localized treatment of triple negative breast cancer.

Multi-modal fusion network with intra- and inter-modality attention for prognosis prediction in breast cancer

Accurate breast cancer prognosis prediction can help clinicians to develop appropriate treatment plans and improve life quality for patients. Recent prognostic prediction studies suggest that fusing multi-modal data, e.g., genomic data and pathological images, plays a crucial role in improving predictive performance. Despite promising results of existing approaches, there remain challenges in effective multi-modal fusion. First, albeit a powerful fusion technique, Kronecker product produces high-dimensional quadratic expansion of features that may result in high computational cost and overfitting risk, thereby limiting its performance and applicability in cancer prognosis prediction. Second, most existing methods put more attention on learning cross-modality relations between different modalities, ignoring modality-specific relations that are complementary to cross-modality relations and beneficial for cancer prognosis prediction. To address these challenges, in this study we propose a novel attention-based multi-modal network to accurately predict breast cancer prognosis, which efficiently models both modality-specific and cross-modality relations without bringing in high-dimensional features. Specifically, two intra-modality self-attentional modules and an inter-modality cross-attentional module, accompanied by latent space transformation of channel affinity matrix, are developed to successfully capture modality-specific and cross-modality relations for efficient integration of genomic data and pathological images, respectively. Moreover, we design an adaptive fusion block to take full advantage of both modality-specific and cross-modality relations. Comprehensive experiment demonstrates that our method can effectively boost prognosis prediction performance of breast cancer and compare favorably with the state-of-the-art methods.

Identification of hub genes to determine drug-disease correlation in breast carcinomas

The exact molecular mechanism underlying the heterogeneous drug response against breast carcinoma remains to be fully understood. It is urgently required to identify key genes that are intricately associated with varied clinical response of standard anti-cancer drugs, clinically used to treat breast cancer patients. In the present study, the utility of transcriptomic data of breast cancer patients in discerning the clinical drug response using machine learning-based approaches were evaluated. Here, a computational framework has been developed which can be used to identify key genes that can be linked with clinical drug response and progression of cancer, offering an immense opportunity to predict potential prognostic biomarkers and therapeutic targets. The framework concerned utilizes DeSeq2, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape, and machine learning techniques to find these crucial genes. Total RNA extraction and qRT-PCR were performed to quantify relative expression of few hub genes selected from the networks. In our study, we have experimentally checked the expression of few key hub genes like APOA2, DLX5, APOC3, CAMK2B, and PAK6 that were predicted to play an immense role in breast cancer tumorigenesis and progression in response to anti-cancer drug Paclitaxel. However, further experimental validations will be required to get mechanistic insights of these genes in regulating the drug response and cancer progression which will likely to play pivotal role in cancer treatment and precision oncology.

Simple droplet microfluidics platform for drug screening on cancer spheroids

3D in vitro biological systems are progressively replacing 2D systems to increase the physiological relevance of cellular studies. Microfluidics-based approaches can be powerful tools towards such biomimetic systems, but often require high-end complicated and expensive processes and equipment for microfabrication. Herein, a drug screening platform is proposed, minimizing technicality and manufacturing steps. It provides an alternate way of spheroid generation in droplets in tubes. Droplet microfluidics then elicit multiple droplets merging events at programmable times, to submit sequentially the spheroids to chemotherapy and to reagents for cytotoxicity screening. After a comprehensive study of tumorogenesis within the droplets, the system is validated for drug screening (IC50) with chemotherapies in cancer cell lines as well as cells from a patient-derived-xenografts (PDX). As compared to microtiter plates methods, our system reduces the initial number of cells up to 10 times and opens new avenues towards primary tumors drug screening approaches.

miRNAs in the prognosis of triple-negative breast cancer: A review

Triple-Negative Breast Cancer (TNBC) is a highly aggressive and invasive type of breast cancer (BC) with high mortality rate wherein effective target medicaments are lacking. It is a very heterogeneous group with several subtypes that account for 10-20% of cancer among women globally, being negative for three most important receptors (estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)), with an early and high recurrence resulting in poor survival rate. Therefore, a more thorough knowledge on carcinogenesis of TNBC is required for the development of personalized treatment options. miRNAs can either promote or suppress tumorigenesis and have been linked to a number of features of cancer progression, including proliferation, metastasis, apoptosis, and epithelial-mesenchymal transition (EMT). Recent miRNA research shows that there is great potential for the development of novel biomarkers as they have emerged as drivers of tumorigenesis and provide opportunities to target various components involved in TNBC, thus helping to solve this difficult-to-treat disease. In this review, we summarize the most relevant miRNAs that play an essential role in TNBC biology. Their role with regard to molecular mechanisms underlying TNBC progression has been discussed, and their potential use as therapeutic or prognostic markers to unravel the intricacy of TNBC based on the pieces of evidence obtained from various works of literature has been briefly addressed.

Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care

Breast cancer remains a significant public health issue, being a leading cause of cancer-related mortality among women globally. Timely diagnosis and efficient treatment are crucial for enhancing patient outcomes, reducing healthcare burdens and advancing community health. This systematic review, following the PRISMA guidelines, aims to comprehensively synthesize the recent advancements in computer-aided diagnosis and treatment for breast cancer. The study covers the latest developments in image analysis and processing, machine learning and deep learning algorithms, multimodal fusion techniques and radiation therapy planning and simulation. The results of the review suggest that machine learning, augmented and virtual reality and data mining are the three major research hotspots in breast cancer management. Moreover, this paper discusses the challenges and opportunities for future research in this field. The conclusion highlights the importance of computer-aided techniques in the management of breast cancer and summarizes the key findings of the review.

The Role of MicroRNAs in Breast Cancer and the Challenges of Their Clinical Application

MicroRNAs (miRNAs) constitute a subclass of non-coding RNAs that exert substantial influence on gene-expression regulation. Their tightly controlled expression plays a pivotal role in various cellular processes, while their dysregulation has been implicated in numerous pathological conditions, including cancer. Among cancers affecting women, breast cancer (BC) is the most prevalent malignant tumor. Extensive investigations have demonstrated distinct expression patterns of miRNAs in normal and malignant breast cells. Consequently, these findings have prompted research efforts towards leveraging miRNAs as diagnostic tools and the development of therapeutic strategies. The aim of this review is to describe the role of miRNAs in BC. We discuss the identification of oncogenic, tumor suppressor and metastatic miRNAs among BC cells, and their impact on tumor progression. We describe the potential of miRNAs as diagnostic and prognostic biomarkers for BC, as well as their role as promising therapeutic targets. Finally, we evaluate the current use of artificial intelligence tools for miRNA analysis and the challenges faced by these new biomedical approaches in its clinical application. The insights presented in this review underscore the promising prospects of utilizing miRNAs as innovative diagnostic, prognostic, and therapeutic tools for the management of BC.

Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype

Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations and clinical characteristics. The molecular subtypes of breast cancer are closely tied to prognosis and therapeutic treatment options. We investigate using deep graph learning on a collection of patient factors from multiple diagnostic disciplines to better represent breast cancer patient information and predict molecular subtype. Our method models breast cancer patient data into a multi-relational directed graph with extracted feature embeddings to directly represent patient information and diagnostic test results. We develop a radiographic image feature extraction pipeline to produce vector representation of breast cancer tumors in DCE-MRI and an autoencoder-based genomic variant embedding method to map variant assay results to a low-dimensional latent space. We leverage related-domain transfer learning to train and evaluate a Relational Graph Convolutional Network to predict the probabilities of molecular subtypes for individual breast cancer patient graphs. Our work found that utilizing information from multiple multimodal diagnostic disciplines improved the model's prediction results and produced more distinct learned feature representations for breast cancer patients. This research demonstrates the capabilities of graph neural networks and deep learning feature representation to perform multimodal data fusion and representation in the breast cancer domain.

Exploring Single-Cell Exposomics by Mass Spectrometry

Single-cell exposomics, a revolutionary approach that investigates cell-environment interactions at cellular and subcellular levels, stands distinct from conventional bulk exposomics. Leveraging advancements in mass spectrometry, it provides a detailed perspective on cellular dynamics, interactions, and responses to environmental stimuli and their impacts on human health. This work delves into this innovative realm, highlighting the nuanced interplay between environmental stressors and biological responses at cellular and subcellular levels. The application of spatial mass spectrometry in single-cell exposomics is discussed, revealing the intricate spatial organization and molecular composition within individual cells. Cell-type-specific exposomics, shedding light on distinct susceptibilities and adaptive strategies of various cell types to environmental exposures, is also examined. The Perspective further emphasizes the integration with molecular and cellular biology approaches to validate hypotheses derived from single-cell exposomics in a comprehensive biological context. Looking toward the future, we anticipate continued technological advancements and convergence with other -omics approaches and discuss implications for environmental health research, disease progression studies, and precision medicine. The final emphasis is on the need for robust computational tools and interdisciplinary collaboration to fully leverage the potential of single-cell exposomics, acknowledging the complexities inherent to this paradigm.

Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels

Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.

Evaluating the role of MEN1 gene expression and its clinical significance in breast cancer patients

BACKGROUND

Breast cancer is a multifactorial disease which involves number of molecular factors that are critically involved in proliferation of breast cancer cells. MEN1 gene that is traditionally known for its germline mutations in neuroendocrine tumors is associated with high risk of developing breast cancer in females with MEN1 syndrome. However, the paradoxical role of MEN1 is reported in sporadic breast cancer cases. The previous studies indicate the functional significance of MEN1 in regulating breast cells proliferation but its relevance in development and progression of breast cancer is still not known. Our study targets to find the role of MEN1 gene aberration and its clinical significance in breast cancer.

METHODS

Breast tumor and adjacent normal tissue of 142 sporadic breast cancer patients were collected at the time of surgery. The expression analysis of MEN1 mRNA and protein was done through RT-PCR, immunohistochemistry and western blotting. Further to find the genetic and epigenetic alterations, automated sequencing and MS-PCR was performed respectively. Correlation between our findings and clinical parameters was determined using appropriate statistical tests.

RESULTS

MEN1 expression was found to be significantly increased in the breast tumor tissue with its predominant nuclear localization. The elevated expression of MEN1 mRNA (63.38% cases) and protein (60.56% cases) exhibited a significant association with ER status of the patients. Most of the cases had unmethylated (53.52%) MEN1 promoter region, which can be a key factor responsible for dysregulated expression of MEN1 in breast cancer cases. Our findings also revealed the significant association of MEN1 mRNA overexpression with Age and lymph node status of the patients.

CONCLUSION

Our results indicate upregulated expression of MEN1 in sporadic breast cancer patients and it could be critically associated with development and advancement of the disease.

A Comparison of Spectroscopy and Imaging Techniques Utilizing Spectrally Resolved Diffusely Reflected Light for Intraoperative Margin Assessment in Breast-Conserving Surgery: A Systematic Review and Meta-Analysis

Up to 19% of patients require re-excision surgery due to positive margins in breast-conserving surgery (BCS). Intraoperative margin assessment tools (IMAs) that incorporate tissue optical measurements could help reduce re-excision rates. This review focuses on methods that use and assess spectrally resolved diffusely reflected light for breast cancer detection in the intraoperative setting. Following PROSPERO registration (CRD42022356216), an electronic search was performed. The modalities searched for were diffuse reflectance spectroscopy (DRS), multispectral imaging (MSI), hyperspectral imaging (HSI), and spatial frequency domain imaging (SFDI). The inclusion criteria encompassed studies of human in vivo or ex vivo breast tissues, which presented data on accuracy. The exclusion criteria were contrast use, frozen samples, and other imaging adjuncts. 19 studies were selected following PRISMA guidelines. Studies were divided into point-based (spectroscopy) or whole field-of-view (imaging) techniques. A fixed-or random-effects model analysis generated pooled sensitivity/specificity for the different modalities, following heterogeneity calculations using the Q statistic. Overall, imaging-based techniques had better pooled sensitivity/specificity (0.90 (CI 0.76-1.03)/0.92 (CI 0.78-1.06)) compared with probe-based techniques (0.84 (CI 0.78-0.89)/0.85 (CI 0.79-0.91)). The use of spectrally resolved diffusely reflected light is a rapid, non-contact technique that confers accuracy in discriminating between normal and malignant breast tissue, and it constitutes a potential IMA tool.

A novel exosome-derived prognostic signature and risk stratification for breast cancer based on multi-omics and systematic biological heterogeneity

Tumor heterogeneity remains a major challenge for disease subtyping, risk stratification, and accurate clinical management. Exosome-based liquid biopsy can effectively overcome the limitations of tissue biopsy, achieving minimal invasion, multi-point dynamic monitoring, and good prognosis assessment, and has broad clinical prospects. However, there is still lacking comprehensive analysis of tumor-derived exosome (TDE)-based stratification of risk patients and prognostic assessment for breast cancer with systematic dissection of biological heterogeneity. In this study, the robust corroborative analysis for biomarker discovery (RCABD) strategy was used for the identification of exosome molecules, differential expression verification, risk prediction modeling, heterogenous dissection with multi-ome (6101 molecules), our ExoBCD database (306 molecules), and 53 independent studies (481 molecules). Our results showed that a 10-molecule exosome-derived signature (exoSIG) could successfully fulfill breast cancer risk stratification, making it a novel and accurate exosome prognostic indicator (Cox P = 9.9E-04, HR = 3.3, 95% CI 1.6-6.8). Interestingly, HLA-DQB2 and COL17A1, closely related to tumor metastasis, achieved high performance in prognosis prediction (86.35% contribution) and accuracy (Log-rank P = 0.028, AUC = 85.42%). With the combined information of patient age and tumor stage, they formed a bimolecular risk signature (Clinmin-exoSIG) and a convenient nomogram as operable tools for clinical applications. In conclusion, as an extension of ExoBCD, this study conducted systematic analyses to identify prognostic multi-molecular panel and risk signature, stratify patients and dissect biological heterogeneity based on breast cancer exosomes from a multi-omics perspective. Our results provide an important reference for in-depth exploration of the "biological heterogeneity - risk stratification - prognosis prediction".

Ebastine impairs metastatic spread in triple-negative breast cancer by targeting focal adhesion kinase

We sought to investigate the utility of ebastine (EBA), a second-generation antihistamine with potent anti-metastatic properties, in the context of breast cancer stem cell (BCSC)-suppression in triple-negative breast cancer (TNBC). EBA binds to the tyrosine kinase domain of focal adhesion kinase (FAK), blocking phosphorylation at the Y397 and Y576/577 residues. FAK-mediated JAK2/STAT3 and MEK/ERK signaling was attenuated after EBA challenge in vitro and in vivo. EBA treatment induced apoptosis and a sharp decline in the expression of the BCSC markers ALDH1, CD44 and CD49f, suggesting that EBA targets BCSC-like cell populations while reducing tumor bulk. EBA administration significantly impeded BCSC-enriched tumor burden, angiogenesis and distant metastasis while reducing MMP-2/-9 levels in circulating blood in vivo. Our findings suggest that EBA may represent an effective therapeutic for the simultaneous targeting of JAK2/STAT3 and MEK/ERK for the treatment of molecularly heterogeneous TNBC with divergent profiles. Further investigation of EBA as an anti-metastatic agent for the treatment of TNBC is warranted.

Estrogen receptor status heterogeneity in breast cancer tumor: role in response to endocrine treatment

Tumor heterogeneity affects diagnosis, prognosis and response to therapy. Heterogeneity is found in both normal and neoplastic human mammary gland. Indeed, luminal ER-negative cells can give rise to various phenotypes, including ER-negative and ER-positive mammary tumors. As a result, the tumor phenotype does not necessarily reflects the cell of origin of cancer. With regard to the ER status, heterogeneity can challenge endocrine therapies, where the elimination of responsive clones could lead to reduced treatment efficacy and tumor relapse through the expansion of the resistant clones. The aim of this study was to investigate breast tumor heterogeneity and its role in endocrine resistance onset. For this purpose, we used ER⁺ (T47D, CAMA1) and triple-negative breast cancer cell lines (TNBC; MDA-MB-231, HCC70), co-cultures using 2D and 3D models. Our results showed that ER status is modulated when ER⁺ cells are cultured in the presence of TNBC cells, leading to a different response to endocrine therapy, demonstrating that the response to treatment can be affected by the influence that different breast cancer cell types exert on each other. In addition, ER⁺ positive cells doubling time was modified after exposure to TNBC cell co-culturing. Further experiments are required to fully elucidate the molecular mechanism of these observations.

Multimodal adversarial representation learning for breast cancer prognosis prediction

With the increasing incidence of breast cancer, accurate prognosis prediction of breast cancer patients is a key issue in current cancer research, and it is also of great significance for patients' psychological rehabilitation and assisting clinical decision-making. Many studies that integrate data from different heterogeneous modalities such as gene expression profile, clinical data, and copy number alteration, have achieved greater success than those with only one modality in prognostic prediction. However, many of these approaches that exist fail to dramatically reduce the modality gap by aligning multimodal distributions. Therefore, it is crucial to develop a method that fully considers a modality-invariant embedding space to effectively integrate multimodal data. In this study, to reduce the modality gap, we propose a multimodal data adversarial representation framework (MDAR) to reduce the modal heterogeneity by translating source modalities into distributions for the target modality. Additionally, we apply reconstruction and classification losses to embedding space to further constrain it. Then, we design a multi-scale bilinear convolutional neural network (MS-B-CNN) for uni-modality to improve the feature expression ability. In addition, the embedding space generates predictions as stacked feature inputs to the extremely randomized trees classifier. With 10-fold cross-validation, our results show that the proposed adversarial representation learning improves prognostic performance. A comparative study of this method and other existing methods on the METABRIC (1980 patients) dataset showed that Matthews correlation coefficient (Mcc) was significantly enhanced by 7.4% in the prognosis prediction of breast cancer patients.

A positive feedback loop driven by fibronectin and IL-1β sustains the inflammatory microenvironment in breast cancer

Inflammatory alterations of the extracellular matrix shape the tumor microenvironment and promote all stages of carcinogenesis. This study aims to determine the impact of cellular fibronectin on inflammatory facets of tumor-associated macrophages (TAMs) in breast cancer. Cellular fibronectin (FN) harboring the alternatively spliced extra domain A (FN-EDA) was determined to be a matrix component produced by the triple-negative breast cancer (TNBC) cells. High levels of FN-EDA correlated with poor survival in breast cancer patients. The proinflammatory cytokine IL-1β enhanced the expression of cellular fibronectin including FN-EDA. TAMs were frequently observed in the tumor areas rich in FN-EDA. Conditioned media from TNBC cells induced the differentiation of CD206⁺CD163⁺ macrophages and stimulated the STAT3 pathway, ex vivo. In the macrophages, the STAT3 pathway enhanced FN-EDA-induced IL-1β secretion and NF-κB signaling. In conclusion, our data indicate a self-reinforcing mechanism sustained by FN-EDA and IL-1β through NF-κB and STAT3 signaling in TAMs which fosters an inflammatory environment in TNBC.

A Lamb1Dendra2 mouse model identifies basement-membrane-producing origins and dynamics in PyMT breast tumors

The basement membrane (BM) around tumor lobes forms a barrier to prevent cancer cells from invading the surrounding tissue. Although myoepithelial cells are key producers of the healthy mammary epithelium BM, they are nearly absent in mammary tumors. To study the origin and dynamics of the BM, we developed and imaged a laminin beta1-Dendra2 mouse model. We show that the turnover of laminin beta1 is faster in the BMs that surround the tumor lobes than in the BMs that surround the healthy epithelium. Moreover, we find that epithelial cancer cells and tumor-infiltrating endothelial cells synthesize laminin beta1 and that this production is temporarily and locally heterogeneous, leading to local discontinuity of the BM laminin beta1. Collectively, our data draw a new paradigm for tumor BM turnover in which the disassembly happens at a constant rate, and a local misbalance of compensating production leads to reduction or even complete disappearance of the BM.

Digital image analysis and assisted reading of the HER2 score display reduced concordance: pitfalls in the categorisation of HER2-low breast cancer

AIMS

Digital image analysis (DIA) is used increasingly as an assisting tool to evaluate biomarkers, including human epidermal growth factor receptor 2 (HER2) in invasive breast cancer (BC). DIA can assist pathologists in HER2 evaluation by presenting quantitative information about the HER2 staining in APP assisted reading (AR). Concurrently, the HER2-low category (HER2-1+/2+ without HER2 gene amplification) has gained prominence due to newly developed antibody-drug conjugates. However, major inter- and intraobserver variability have been observed for the entity. The present quality assurance study investigated the concordance between DIA and AR in clinical use, especially concerning the HER2-low category.

METHODS AND RESULTS

HER2 immunohistochemistry (IHC) in 761 tumours from 727 patients was evaluated in tissue microarray (TMA) cores by DIA (Visiopharm HER2-CONNECT) and AR. Overall concordance between HER2-scores were 73% (n = 552, weighted-κ: 0.66), and 88% (n = 669, weighted-κ: 0.70), when combining HER2-0/1+. A total of 205 scores were discordant by one category, while four were discordant by two categories. A heterogeneous HER2 pattern was relatively common in the discordant cases and a pitfall in the categorisation of HER2-low BC. AR more commonly reassigned a lower HER2 score (from HER2-1+ to HER2-0) within the HER2-low subgroup (n = 624) compared with DIA.

CONCLUSION

DIA and AR display moderate agreement with heterogeneous and aberrant staining, representing a source of discordance and a pitfall in the evaluation of HER2.

Aberrant promoter hypermethylation of miR-335 and miR-145 is involved in breast cancer PD-L1 overexpression

PD-L1 is one of the most important immune checkpoint molecules in breast cancer that plays an important role in suppressing the immune system when confronted with tumor cells and is regulated by various microRNAs. Among them, microRNA-335-3p and microRNA-145-5p, regulated by DNA methylation, have tumor suppressor activities. We studied the role of miR-335 and -145 on PD-L1 suppression in breast cancer. The expression of miR-355 and miR-145 was significantly downregulated in BC tissues and cell lines compared to their controls, and their downregulation was negatively correlated with PD-L1 overexpression. In-silico and luciferase reporter systems confirmed that miR-335 and -145 target PD-L1. In BC tissues and cell lines, cancer-specific methylation was found in CpG-rich areas upstream of miR-335 and-145, and up-regulation of PD-L1 expression was connected with hypermethylation (r = 0.4089, P = 0.0147, and r = 0.3373, P = 0.0475, respectively). The higher levels of miR-355 and -145 in BC cells induced apoptosis, arrested the cell cycle, and reduced proliferation significantly. In summary, we found that miR-335 and -145 are novel tumor suppressors inactivated in BC, and these miRs may serve as potential therapeutic targets for breast cancer treatment.

Breast carcinogenesis induced by organophosphorous pesticides

Breast cancer is a major health threat to women worldwide and the leading cause of cancer-related death. The use of organophosphorous pesticides has increased in agricultural environments and urban settings, and there is evidence that estrogen may increase breast cancer risk in women. The mammary gland is an excellent model for examining its susceptibility to different carcinogenic agents due to its high cell proliferation capabilities associated with the topography of the mammary parenchyma and specific stages of gland development. Several experimental cellular models are presented here, in which the animals were exposed to chemical compounds such as pesticides, and endogenous substances such as estrogens that exert a significant effect on normal breast cell processes at different levels. Such models were developed by the effect of malathion, parathion, and eserine, influenced by estrogen demonstrating features of cancer initiation in vivo as tumor formation in rodents; and in vitro in the immortalized normal breast cell line MCF-10F, that when transformed showed signs of carcinogenesis such as increased cell proliferation, anchorage independence, invasive capabilities, modulation of receptors and genomic instability. The role of acetylcholine was also demonstrated in the MCF-10F, suggesting a role not only as a neurotransmitter but also with other functions, such as induction of cell proliferation, playing an important role in cancer. Of note, this is a unique experimental approach that identifies mechanistic signs that link organophosphorous pesticides with breast carcinogenesis.

Molecular imaging of HER2 receptor: Targeting HER2 for imaging and therapy in nuclear medicine

Targeting HER 2 for imaging and therapy in nuclear medicine has been used with a special emphasis on developing more powerful radiopharmaceuticals. Zirconium-89 plays an essential role in immune PET imaging so was used labeled with anti-HER2 antibody (Trastuzumab and Pertuzumab). Also there were attempts with other PET tracers as Cuprum-64 and Galium-68, as well as SPECT radiopharmaceuticals Indium-111 and Technetium- 99m. Regarding antibody pharmacokinetic that is not quite appropriate for imaging acquisition, several smaller molecules with shorter residence times have been developed. These molecules called nanobody, affibody, minibody do not compromize HER2 receptor affinity and specificity. Excess of Trastuzumab do not block the affinity of labeled affibodies. Silica nanoparticles have been conjugated to anti-HER2 antibodies to enable targeting of HER2 expressing cells with potential of drug delivery carry for antitumor agents and b(beta) or a(alfa) emitting radioisotopes commonly used for radionuclide therapy, as Iodine-131, Lutetium-177, Yttrium-90, Rhenium-188 and Thorium-277.

THOR is a targetable epigenetic biomarker with clinical implications in breast cancer

BACKGROUND

Breast cancer (BC) is the most frequently diagnosed cancer and a leading cause of death among women worldwide. Early BC is potentially curable, but the mortality rates still observed among BC patients demonstrate the urgent need of novel and more effective diagnostic and therapeutic options. Limitless self-renewal is a hallmark of cancer, governed by telomere maintenance. In around 95% of BC cases, this process is achieved by telomerase reactivation through upregulation of the human telomerase reverse transcriptase (hTERT). The hypermethylation of a specific region within the hTERT promoter, termed TERT hypermethylated oncological region (THOR) has been associated with increased hTERT expression in cancer. However, its biological role and clinical potential in BC have never been studied to the best of our knowledge. Therefore, we aimed to investigate the role of THOR as a biomarker and explore the functional impact of THOR methylation status in hTERT upregulation in BC.

RESULTS

THOR methylation status in BC was assessed by pyrosequencing on discovery and validation cohorts. We found that THOR is significantly hypermethylated in malignant breast tissue when compared to benign tissue (40.23% vs. 12.81%, P < 0.0001), differentiating malignant tumor from normal tissue from the earliest stage of disease. Using a reporter assay, the addition of unmethylated THOR significantly reduced luciferase activity by an average 1.8-fold when compared to the hTERT core promoter alone (P < 0.01). To further investigate its biological impact on hTERT transcription, targeted THOR demethylation was performed using novel technology based on CRISPR-dCas9 system and significant THOR demethylation was achieved. Cells previously demethylated on THOR region did not develop a histologic cancer phenotype in in vivo assays. Additional studies are required to validate these observations and to unravel the causality between THOR hypermethylation and hTERT upregulation in BC.

CONCLUSIONS

THOR hypermethylation is an important epigenetic mark in breast tumorigenesis, representing a promising biomarker and therapeutic target in BC. We revealed that THOR acts as a repressive regulatory element of hTERT and that its hypermethylation is a relevant mechanism for hTERT upregulation in BC.

Case report: MicroRNA-10b as a therapeutic target in feline metastatic mammary carcinoma and its implications for human clinical trials

Ninety percent of deaths from cancer are caused by metastasis. miRNAs are critical players in biological processes such as proliferation, metastasis, apoptosis, and self-renewal. We and others have previously demonstrated that miRNA-10b promotes metastatic cell migration and invasion. Importantly, we also showed that miR-10b is a critical driver of metastatic cell viability and proliferation. To treat established metastases by inhibiting miR-10b, we utilized a therapeutic, termed MN-anti-miR10b, composed of anti-miR-10b antagomirs, conjugated to iron oxide nanoparticles, that serve as delivery vehicles to tumor cells in vivo and a magnetic resonance imaging (MRI) reporter. In our previous studies using murine models of metastatic breast cancer, we demonstrated the effectiveness of MN-anti-miR10b in preventing and eliminating existing metastases. With an outlook toward clinical translation of our therapeutic, here we report studies in large animals (companion cats) with spontaneous feline mammary carcinoma (FMC). We first investigated the expression and tissue localization of miR-10b in feline tumors and metastases and showed remarkable similarity to these features in humans. Next, in the first case study involving this therapeutic we intravenously dosed an FMC patient with MN-anti-miR10b and demonstrated its delivery to the metastatic lesions using MRI. We also showed the initial safety profile of the therapeutic and demonstrated significant change in miR-10b expression and its target HOXD10 after dosing. Our results provide support for using companion animals for further MN-anti-miR10b development as a therapy and serve as a guide for future clinical trials in human patients.

Chromatin Variants Reveal the Genetic Determinants of Oncogenesis in Breast Cancer

Breast cancer presents as multiple distinct disease entities. Each tumor harbors diverse cell populations defining a phenotypic heterogeneity that impinges on our ability to treat patients. To date, efforts mainly focused on genetic variants to find drivers of inter- and intratumor phenotypic heterogeneity. However, these efforts have failed to fully capture the genetic basis of breast cancer. Through recent technological and analytical approaches, the genetic basis of phenotypes can now be decoded by characterizing chromatin variants. These variants correspond to polymorphisms in chromatin states at DNA sequences that serve a distinct role across cell populations. Here, we review the function and causes of chromatin variants as they relate to breast cancer inter- and intratumor heterogeneity and how they can guide the development of treatment alternatives to fulfill the goal of precision cancer medicine.

Breast Cancer-Delivered Exosomal miRNA as Liquid Biopsy Biomarkers for Metastasis Prediction: A Focus on Translational Research with Clinical Applicability

Metastasis represents the most important cause of breast cancer-associated mortality. Even for early diagnosed stages, the risk of metastasis is significantly high and predicts a grim outcome for the patient. Nowadays, efforts are made for identifying blood-based biomarkers that could reliably distinguish patients with highly metastatic cancers in order to ensure a closer follow-up and a more personalized therapeutic method. Exosomes are nano vesicles secreted by cancer cells that can transport miRNAs, proteins, and other molecules and deliver them to recipient cells all over the body. Through this transfer, cancer cells modulate their microenvironment and facilitate the formation of the pre-metastatic niche, leading to sustained progression. Exosomal miRNAs have been extensively studied due to their promising potential as prognosis biomarkers for metastatic breast cancer. In this review, we tried to depict an overview of the existing literature regarding exosomal miRNAs that are already validated as potential biomarkers, and which could be immediately available for the clinic. Moreover, in the last section, we highlighted several miRNAs that have proven their function in preclinical studies and could be considered for clinical validation. Considering the lack of standard methods for evaluating exosomal miRNA, we also discussed the challenges and the technical aspects underlying this issue.

Image quality and whole-lesion histogram and texture analysis of diffusion-weighted imaging of breast MRI based on advanced ZOOMit and simultaneous multislice readout-segmented echo-planar imaging

Objectives

To investigate the image quality and diagnostic capability a of whole-lesion histogram and texture analysis of advanced ZOOMit (A-ZOOMit) and simultaneous multislice readout-segmented echo-planar imaging (SMS-RS-EPI) to differentiate benign from malignant breast lesions.

Study design

From February 2020 to October 2020, diffusion-weighted imaging (DWI) using SMS-RS-EPI and A-ZOOMit were performed on 167 patients. Three breast radiologists independently ranked the image datasets. The inter-/intracorrelation coefficients (ICCs) of mean image quality scores and lesion conspicuity scores were calculated between these three readers. Histogram and texture features were extracted from the apparent diffusion coefficient (ADC) maps, respectively, based on a WL analysis. Student’s t-tests, one-way ANOVAs, Mann–Whitney U tests, and receiver operating characteristic curves were used for statistical analysis.

Results

The overall image quality scores and lesion conspicuity scores for A-ZOOMit and SMS-RS-EPI showed statistically significant differences (4.92 ± 0.27 vs. 3.92 ± 0.42 and 4.93 ± 0.29 vs. 3.87 ± 0.47, p < 0.0001). The ICCs for the image quality and lesion conspicuity scores had good agreements among the three readers (all ICCs >0.75). To differentiate benign and malignant breast lesions, the entropy of ADC_A-Zoomit had the highest area (0.78) under the ROC curve.

Conclusions

A-ZOOMit achieved higher image quality and lesion conspicuity than SMS-RS-EPI. Entropy based on A-ZOOMit is recommended for differentiating benign from malignant breast lesions.