Heterogeneity of breast cancer: The importance of interaction between different tumor cell populations

Exploration of 1-(2,4-difluorophenyl)-5-oxopyrrolidine-3-carboxylic acid derivatives effect on triple-negative breast, prostate cancer and melanoma cell 2D and 3D cultures

1-Substituted 5-oxopyrrolidine-3-carboxylic acid and its derivatives play an important role as components of many biologically active molecules. This study describes the synthesis of 1-(2,4-difluorophenyl)-5-oxopyrrolidine-3-carboxylic acid derivatives and their anticancer properties. The target compounds were prepared using 2,4-difluoroniline as a starting material; in this way, derivatives of benzimidazoles, hydrazones and azoles were formed. Investigation of the anticancer activity of all synthesized compounds showed that the hydrazones had the strongest effect on cancer cell lines. Compounds were tested for their cytotoxic effect by the MTT assay in human triple-negative breast cancer MDA-MB-231, prostate adenocarcinoma PPC1, melanoma A375 and human foreskin fibroblasts CRL-4001 after 72 hours of incubation. The impact of the compounds on cancer cell migration was assessed using a 'wound healing assay'. Activity in 3D cultures was determined by evaluating changes in spheroid size and assessing cell viability. Overall, the selected compounds 7b, 9c, 9e, 9f and 10 exhibited greater activity in the A375 cell line and were less active against the MDA-MB-231 cell line. Compounds 9c, 9e and 10 showed relatively higher selectivity for cancer cells over fibroblasts. Hydrazone 9f, bearing N'-(4-methylbenzylidene) moiety, was identifiedasthe most cytotoxic compound in both prostate adenocarcinoma PPC-1 and melanoma A375 cells in monolayer and 3D culture models. Compound 9e, with N'-(4-bromobenzylidene) moiety, exhibited the most pronounced inhibitory effect on cell migration as determined by the 'wound healing' assay.

Characterization and targeting of chemoresistant triple-negative breast cancer subtypes using amino-pyridine compounds

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with limited treatment options and high relapse rates due to chemoresistance and the presence of cancer stem cells (CSCs). This study explores the molecular profile and invasive properties of two TNBC cell lines, MDA-MB-231 (Basal-Like 1; BL1 subtype) and HCC1806 (BL2 subtype), as well as their chemotherapy-resistant derivatives (doxorubicin and paclitaxel). Both cell lines exhibited CD44+ and CD24-/low profiles with significant differences in epithelial-mesenchymal transition (EMT) markers. Chemoresistant variants exhibited significant changes in CSC markers, EMT genes, and ALDH activity, particularly the upregulation of CD133, suggesting its role in chemoresistance. Analysis of embryonic pathways revealed a prominent role of Sonic Hedgehog signaling, particularly in the BL2 subtype. Resistant models also exhibited increased Notch receptor expression. This study also examined novel polyamine compounds with an amino-pyridine structure. These compounds showed significant cytotoxicity against both sensitive and resistant TNBC cells, enhancing the efficacy of standard chemotherapeutics (paclitaxel and doxorubicin). Additionally, they reduced stem-like properties and self-renewal capacity of CSCs. This comprehensive characterization of TNBC cell lines and their chemoresistant variants underscores the molecular heterogeneity of TNBC and highlights potential therapeutic targets and strategies to enhance treatment efficacy and overcome resistance.

Proximity labeling and SILAC based proteomic approach identifies proteins at the interface of homotypic and heterotypic cancer cell interactions

Cell-cell interactions are critical for the growth of organisms and maintaining homeostasis. In the tumor microenvironment, these interactions promote cancer progression. Given their importance in healthy and diseased conditions, we have developed a method to analyze the cell-to-cell interactome. Our approach uses enzyme-catalyzed proximity labeling and SILAC-based proteomics to identify the proteins involved in cancer cell interactions. By targeting HRP to the outer leaflet of the plasma membrane in bait cells, we were able to label the neighboring prey cells and distinguish between the proteomes of bait and prey cells using SILAC labeling in a co-culture system. We mapped both the homotypic and heterotypic interactomes of epithelial and mesenchymal breast cancer cells. The enrichment of cell surface and extracellular proteins confirms the specificity of our methodology. We further verified selected hits from different cell-cell interactomes in co-cultures using microscopy. This method revealed prominent signaling pathways orchestrating homotypic and heterotypic interactions of epithelial and mesenchymal cells. It also highlights the importance of exosomes in these interactions. Our methodology can be applied to any type of cell-cell interaction in 2D co-culture or 3D tumor models.

Study of heterogeneity in immune responses to exposure in breast cancer: A protocol for a systematic review

BACKGROUND

Radiation is a treatment modality of interest in both palliative and curative settings for patients with breast cancer (BC). Despite many advances in radiation oncology, anti-radiation resistance remains a problem in mechanistic aspects. Studies show that immune cells respond to radiation in a heterogeneous manner and suggest the determinant role of these responses in the ultimate clinical classification of patients with BC as anti-radiation resistant/sensitive.

OBJECTIVE

A systematic review of in vivo and in vitro studies will be defined here to assess the radiation response of immune cells in BC comprehensively.

METHODS/DESIGN

A systematic search for in vivo and in vitro studies will collect studies that compare the radiation alone-treated and control (untreated or sham) subjects, as well as subjects treated with radiation in combination and subjects treated with radiation alone. The primary outcome of interest will be measures of the abundance of immune cell subsets. The secondary outcomes will include measures derived from co-culture assays leading to immunoregulatory roles or mechanisms proposed for the potential cross-talks between immune-BC cells that will be influenced by radiation. The review results will be primarily synthesized in a narrative and qualitative manner. Meta-analysis will be considered if there are three or more observations with data available for a specific outcome. We will use the SYRCLE "Risk of Bias" assessment tool for appraising the risk of bias in preclinical in vivo studies, and given the lack of standardized instruments for in vitro studies, we will design a checklist suitable for our specific in vitro research questions and publish it along with our systematic review.

Global trends and research hotspots in nanodrug delivery systems for breast cancer therapy: a bibliometric analysis (2013-2023)

OBJECTIVE

Nanomedicine offers fresh approaches for breast cancer treatment, countering traditional limitations. The nanodrug delivery system's precision and biocompatibility hold promise, yet integration hurdles remain. This study reviews nano delivery systems in breast cancer therapy from 2013 to 2023, guiding future research directions.

METHODS

In this study, we conducted a comprehensive search on Web of Science database (Guilin Medical University purchase edition) and downloaded literature related to the field published between 2013 and 2023. We analyzed these publications using R software, VOSviewer, and CiteSpace software.

RESULTS

This study reviewed 2632 documents, showing a steady publication increase from 2013 to 2023, peaking at 408 in 2022. China, USA, India, and Iran were prominent in publishing. The Chinese Academy of Sciences and Tabriz University of Medical Science were key collaboration centers. Notably, the Journal of Controlled Release and Biomaterials ranked among the top 10 journals for publications and citations, establishing their field representation. Key terms like "breast cancer," "nanoparticles," "drug delivery," "in-vitro," and "delivery" were widely used. Research focused on optimizing drug targeting, utilizing the tumor microenvironment for drug delivery, and improving delivery efficiency.

CONCLUSION

The nanodrug delivery system, as an innovative drug delivery approach, offers numerous advantages and has garnered global attention from researchers. This study provides an analysis of the status and hotspots in nano delivery systems within the realm of breast cancer therapy, offering valuable insights for future research in this domain.

Breast Cancer and Tumor Microenvironment: The Crucial Role of Immune Cells

Breast cancer is the most common type of cancer in women and the second leading cause of death by cancer. Despite recent advances, the mortality rate remains high, underlining the need to develop new therapeutic approaches. The complex interaction between cancer cells and the tumor microenvironment (TME) is crucial in determining tumor progression, therapy response, and patient prognosis. Understanding the role of immune cells in carcinogenesis and tumor progression can help improve targeted therapeutic options, increasing the likelihood of a favorable prognosis. Therefore, this review aims to critically analyze the complex interaction between tumor cells and immune cells, emphasizing the clinical and therapeutic implications. Additionally, we explore advances in immunotherapies, with a focus on immune checkpoint inhibitors.

Intratumoral heterogeneity of Ki67 proliferation index outperforms conventional immunohistochemistry prognostic factors in estrogen receptor-positive HER2-negative breast cancer

In breast cancer (BC), pathologists visually score ER, PR, HER2, and Ki67 biomarkers to assess tumor properties and predict patient outcomes. This does not systematically account for intratumoral heterogeneity (ITH) which has been reported to provide prognostic value. This study utilized digital image analysis (DIA) and computational pathology methods to investigate the prognostic value of ITH indicators in ER-positive (ER+) HER2-negative (HER2-) BC patients. Whole slide images (WSIs) of surgically excised specimens stained for ER, PR, Ki67, and HER2 from 254 patients were used. DIA with tumor tissue segmentation and detection of biomarker-positive cells was performed. The DIA-generated data were subsampled by a hexagonal grid to compute Haralick's texture indicators for ER, PR, and Ki67. Cox regression analyses were performed to assess the prognostic significance of the immunohistochemistry (IHC) and ITH indicators in the context of clinicopathologic variables. In multivariable analysis, the ITH of Ki67-positive cells, measured by Haralick's texture entropy, emerged as an independent predictor of worse BC-specific survival (BCSS) (hazard ratio (HR) = 2.64, p-value = 0.0049), along with lymph node involvement (HR = 2.26, p-value = 0.0195). Remarkably, the entropy representing the spatial disarrangement of tumor proliferation outperformed the proliferation rate per se established either by pathology reports or DIA. We conclude that the Ki67 entropy indicator enables a more comprehensive risk assessment with regard to BCSS, especially in cases with borderline Ki67 proliferation rates. The study further demonstrates the benefits of high-capacity DIA-generated data for quantifying the essentially subvisual ITH properties.

A Cancer Subpopulation Competition Model Reveals Optimal Levels of Immune Response that Minimize Tumor Size

Breast cancer is a complex disease with significant phenotypic heterogeneity of cells, even within a single breast tumor. Emerging evidence underscores the significance of intratumoral competition, which can serve as a key contributor to cancer drug resistance, imparting substantial clinical implications. Understanding the competitive dynamics is paramount as it can significantly influence disease progression and treatment outcomes. In the present work, a mathematical model was developed using a system of differential equations to describe the dynamic interactions between two cancer subtypes (each further classified into cancer stem cells and tumor cells) and innate immune cells. The purpose of the model is to comprehensively understand the competitive interactions between the heterogeneous subpopulations. The equilibrium points and stability analysis for each equilibrium point were established. Model simulations showed that the competition between two cancer subtypes directly affects the number of both species. When competition between two cancer subtypes is strong, increasing the immune response rate specific to the more competitive species effectively reduces the tumor size. However, if the competition is relatively weak, an optimal immune response rate is required to minimize the total number of tumor cells. Rates below the optimal level fail to reduce the population of the stronger species, whereas rates above the optimal level can lead to the recurrence of the weaker species. Overall, this model provides insights into breast cancer dynamics and guides the development of effective treatment strategies.

A method for in silico exploration of potential glioblastoma multiforme attractors using single-cell RNA sequencing

We presented a method to find potential cancer attractors using single-cell RNA sequencing (scRNA-seq) data. We tested our method in a Glioblastoma Multiforme (GBM) dataset, an aggressive brain tumor presenting high heterogeneity. Using the cancer attractor concept, we argued that the GBM's underlying dynamics could partially explain the observed heterogeneity, with the dataset covering a representative region around the attractor. Exploratory data analysis revealed promising GBM's cellular clusters within a 3-dimensional marker space. We approximated the clusters' centroid as stable states and each cluster covariance matrix as defining confidence regions. To investigate the presence of attractors inside the confidence regions, we constructed a GBM gene regulatory network, defined a model for the dynamics, and prepared a framework for parameter estimation. An exploration of hyperparameter space allowed us to sample time series intending to simulate myriad variations of the tumor microenvironment. We obtained different densities of stable states across gene expression space and parameters displaying multistability across different clusters. Although we used our methodological approach in studying GBM, we would like to highlight its generality to other types of cancer. Therefore, this report contributes to an advance in the simulation of cancer dynamics and opens avenues to investigate potential therapeutic targets.

Prognostic Value and Immune Signatures of Anoikis-related Genes in Breast Cancer

From databases of the Cancer Genome Atlas (TCGA) and GSE42568, transcriptome data of breast cancer patients was obtained. Then, anoikis-related genes (ANRGs) were identified and constructed a risk score system. As a threshold value, the median risk score was used to stratify patients into low-risk and high-risk groups. Kaplan-Meier analysis was then conducted to evaluate the prognostic ability of the risk score system, which was validated using GSE7390. Furthermore, we identified potential enrichment of function and tumor immune infiltration in the model. Finally, the biological functions of a risk gene (EPB41L4B) in breast cancer were investigated through in vitro experiments. We constructed a risk score system via 9 prognosis ANRGs (CXCL2, EPB41L4B, SLC7A5, SFRP1, SDC1, BHLHE41, SPINT1, KRT15, and CD24). The Kaplan-Meier analysis showed that both TCGA-BRCA (training set) and GSE7390 (testing set) patients with high-risk status had significantly worse survival outcomes. In addition, the calibration plots were in good agreement with the prognosis prediction. Breast cancer patients with immunosuppressive microenvironment could be screened using risk groups since risk scores were correlated negatively with ESTIMATE score, tumor-infiltration lymphocytes, immune checkpoints, and chemotactic factors. Furthermore, cellular viability and cell migration of cancerous breast cells were inhibited and apoptosis was promoted by down-regulation of EPB41L4B gene expression. Based on ANRGs, a 9-gene prognostic model could be developed to predict breast cancer prognosis; moreover, patients of the high-risk group were in an immunosuppressed tumor microenvironment.

Application and challenge of HER2DX genomic assay in HER2+ breast cancer treatment

HER2-positive breast cancer is highly aggressive, with a significant risk of recurrence and metastasis, leading to a poor prognosis. While most early-stage HER2-positive breast cancer patients benefit from combining trastuzumab monoclonal antibody with chemotherapy, the therapeutic response to various drug combinations varies across the HER2+ patient population. Therefore, predicting the prognosis and treatment response of HER2+ breast cancer patients to specific regimens is crucial for selecting appropriate precision individualized therapies. HER2DX is the first genomic tool designed to guide the treatment of HER2+ breast cancer patients. The three scores provided by HER2DX inform the entire treatment process, including predicting survival outcomes, recurrence, metastasis, and treatment responses like Pathological Complete Response Rate (pCR). It offers recommendations on follow-up intervals, treatment plans, and the duration of drug therapy. This review examines the literature and analyzes studies applying HER2DX to guide the comprehensive treatment and predict prognosis in HER2+ breast cancer patients, aiming to promote the widespread use of HER2DX in individualized treatment.

Advances in immunotherapy for breast cancer and feline mammary carcinoma: From molecular basis to novel therapeutic targets

The role of inflammation in cancer is a topic that has been investigated for many years. As established, inflammation emerges as a defining characteristic of cancer, presenting itself as a compelling target for therapeutic interventions in the realm of oncology. Controlling the tumor microenvironment (TME) has gained paramount significance, modifying not only the effectiveness of immunotherapy but also modulating the outcomes and prognoses of standard chemotherapy and other anticancer treatments. Immunotherapy has surfaced as a central focus within the domain of tumor treatments, using immune checkpoint inhibitors as cancer therapy. Immune checkpoints and their influence on the tumor microenvironment dynamic are presently under investigation, aiming to ascertain their viability as therapeutic interventions across several cancer types. Cancer presents a significant challenge in humans and cats, where female breast cancer ranks as the most prevalent malignancy and feline mammary carcinoma stands as the third most frequent. This review seeks to summarize the data about the immune checkpoints cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4), lymphocyte activation gene-3 (LAG-3), programmed cell death protein-1 (PD-1), V-domain Ig suppressor of T cell activation (VISTA), and T-cell immunoglobulin and mucin domain 3 (TIM-3) respective ongoing investigations as prospective targets for therapy for human breast cancer, while also outlining findings from studies reported on feline mammary carcinoma (FMC), strengthening the rationale for employing FMC as a representative model in the exploration of human breast cancer.

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.

Comparative profiling of whole-cell and exosome samples reveals protein signatures that stratify breast cancer subtypes

Identifying novel breast cancer biomarkers will improve patient stratification, enhance therapeutic outcomes, and help develop non-invasive diagnostics. We compared the proteomic profiles of whole-cell and exosomal samples of representative breast cancer cell subtypes to evaluate the potential of extracellular vesicles as non-invasive disease biomarkers in liquid biopsies. Overall, differentially-expressed proteins in whole-cell and exosome samples (which included markers for invasion, metastasis, angiogenesis, and drug resistance) effectively discriminated subtypes; furthermore, our results confirmed that the proteomic profile of exosomes reflects breast cancer cell-of-origin, which underscores their potential as disease biomarkers. Our study will contribute to identifying biomarkers that support breast cancer patient stratification and developing novel therapeutic strategies. We include an open, interactive web tool to explore the data as a molecular resource that can explain the role of these protein signatures in breast cancer classification.

A nomogram based on inflammation and nutritional biomarkers for predicting the survival of breast cancer patients

Background

We aim to develop a new prognostic model that incorporates inflammation, nutritional parameters and clinical-pathological features to predict overall survival (OS) and disease free survival (DFS) of breast cancer (BC) patients.

Methods

The study included clinicopathological and follow-up data from a total of 2857 BC patients between 2013 and 2021. Data were randomly divided into two cohorts: training (n=2001) and validation (n=856) cohorts. A nomogram was established based on the results of a multivariate Cox regression analysis from the training cohorts. The predictive accuracy and discriminative ability of the nomogram were evaluated by the concordance index (C-index) and calibration curve. Furthermore, decision curve analysis (DCA) was performed to assess the clinical value of the nomogram.

Results

A nomogram was developed for BC, incorporating lymphocyte, platelet count, hemoglobin levels, albumin-to-globulin ratio, prealbumin level and other key variables: subtype and TNM staging. In the prediction of OS and DFS, the concordance index (C-index) of the nomogram is statistically greater than the C-index values obtained using TNM staging alone. Moreover, the time-dependent AUC, exceeding the threshold of 0.7, demonstrated the nomogram's satisfactory discriminative performance over different periods. DCA revealed that the nomogram offered a greater overall net benefit than the TNM staging system.

Conclusion

The nomogram incorporating inflammation, nutritional and clinicopathological variables exhibited excellent discrimination. This nomogram is a promising instrument for predicting outcomes and defining personalized treatment strategies for patients with BC.

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

Navigating the Intricacies of Tumor Heterogeneity: An Insight into Potential Prognostic Breast Cancer Biomarkers

Breast cancer is a heterogeneous disease with diverse histological and molecular subtypes. Luminal breast tumors are the most diagnosed subtype. In luminal breast cancer, hormone receptors (including ER, PR, HER2) play a diagnostic and prognostic role. Despite the effectiveness of endocrine therapy in luminal breast tumors, tumor recurrence and resistance occur, and this may highlight evolutionary strategies for survival driven by stemness. In this review we thus consider the association between estrogen signaling and stemness in mediating tumor processes. Many studies report stemness as one of the factors promoting tumor progression. Its association with estrogen signaling warrants further investigation and provides an opportunity for the identification of novel biomarkers which may be used for diagnostic, prognostic, and therapeutic purposes. Breast cancer stem cells have been characterized (CD44+ CD24-) and their role in promoting treatment resistance and tumor recurrence widely studied; however, the complexity of tumor progression which also involve microenvironmental factors suggests the existence of more varied cell phenotypes which mediate stemness and its role in tumor progression.

Deep learning modeling using mammography images for predicting estrogen receptor status in breast cancer

BACKGROUND

The estrogen receptor (ER) serves as a pivotal indicator for assessing endocrine therapy efficacy and breast cancer prognosis. Invasive biopsy is a conventional approach for appraising ER expression levels, but it bears disadvantages due to tumor heterogeneity. To address the issue, a deep learning model leveraging mammography images was developed in this study for accurate evaluation of ER status in patients with breast cancer.

OBJECTIVES

To predict the ER status in breast cancer patients with a newly developed deep learning model leveraging mammography images.

MATERIALS AND METHODS

Datasets comprising preoperative mammography images, ER expression levels, and clinical data spanning from October 2016 to October 2021 were retrospectively collected from 358 patients diagnosed with invasive ductal carcinoma. Following collection, these datasets were divided into a training dataset (n = 257) and a testing dataset (n = 101). Subsequently, a deep learning prediction model, referred to as IP-SE-DResNet model, was developed utilizing two deep residual networks along with the Squeeze-and-Excitation attention mechanism. This model was tailored to forecast the ER status in breast cancer patients utilizing mammography images from both craniocaudal view and mediolateral oblique view. Performance measurements including prediction accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves (AUCs) were employed to assess the effectiveness of the model.

RESULTS

In the training dataset, the AUCs for the IP-SE-DResNet model utilizing mammography images from the craniocaudal view, mediolateral oblique view, and the combined images from both views, were 0.849 (95% CIs: 0.809-0.868), 0.858 (95% CIs: 0.813-0.872), and 0.895 (95% CIs: 0.866-0.913), respectively. Correspondingly, the AUCs for these three image categories in the testing dataset were 0.835 (95% CIs: 0.790-0.887), 0.746 (95% CIs: 0.793-0.889), and 0.886 (95% CIs: 0.809-0.934), respectively. A comprehensive comparison between performance measurements underscored a substantial enhancement achieved by the proposed IP-SE-DResNet model in contrast to a traditional radiomics model employing the naive Bayesian classifier. For the latter, the AUCs stood at only 0.614 (95% CIs: 0.594-0.638) in the training dataset and 0.613 (95% CIs: 0.587-0.654) in the testing dataset, both utilizing a combination of mammography images from the craniocaudal and mediolateral oblique views.

CONCLUSIONS

The proposed IP-SE-DResNet model presents a potent and non-invasive approach for predicting ER status in breast cancer patients, potentially enhancing the efficiency and diagnostic precision of radiologists.

Incidence of thyroid dysfunction caused by immune checkpoint inhibitors combined with chemotherapy: A systematic review and meta-analysis

BACKGROUND

The combination of immune checkpoint inhibitors (ICIs) and chemotherapy as a first-line treatment for triple-negative breast cancer (TNBC) has been associated with many adverse reactions. Thyroid dysfunction, the most common adverse reaction of the endocrine system, has also attracted significant attention. This study aimed to analyse the effect of ICIs combined with chemotherapy on thyroid function in patients with TNBC.

METHODS

As of November 4, 2023, we searched the PubMed, Web of Science, and Cochrane Library databases for clinical trials of ICIs combined with chemotherapy for the treatment of TNBC. The incidence of hypothyroidism and hyperthyroidism was calculated using a random-effects model.

RESULTS

In the final analysis, 3,226 patients from 19 studies were included. The total incidence of all-grade hypothyroidism induced by the combination of ICIs and chemotherapy in treating TNBC (12% (95% confidence intervals(CI): 0.10-0.15)) was higher than that of hyperthyroidism (5% (95% CI: 0.04-0.06)). Pembrolizumab combined with chemotherapy caused the highest incidence of all grades of hypothyroidism for 13% (95% CI: 0.05-0.06). Durvalumab combined with chemotherapy caused the highest incidence of all grades of hyperthyroidism, at 7% (95% CI: 0.03-0.11). ICIs combined with chemotherapy caused a higher incidence of all grades of hypothyroidism in advanced TNBC (15% (95% CI: 0.13-0.17)) than in early stage TNBC (10% (95% CI: 0.07-0.13)).

CONCLUSION

In TNBC, the incidence of hypothyroidism caused by the combination of ICIs and chemotherapy was significantly higher than that caused by hyperthyroidism. Pembrolizumab combined with chemotherapy resulted in the highest incidence of hypothyroidism. The incidence of hypothyroidism in patients with advanced TNBC was significantly higher than that in patients with early stage TNBC. In addition, ICIs combined with chemotherapy resulted in 16 out of 3,226 patients experiencing grade ≥ 3 thyroid dysfunction. Although the incidence of severe thyroid dysfunction is low, it requires attention.

PROSPERO

CRD42023477933.

Exploring neutrophil functionality in breast cancer progression: A review

Breast cancer remains a pressing global health concern, with a myriad of intricate factors contributing to its development, progression, and heterogeneity. Among these multifaceted elements, the role of immune cells within the tumor microenvironment is gaining increasing attention. In this context, neutrophils, traditionally regarded as the first responders to infections, are emerging as noteworthy participants in the complex landscape of breast cancer. This paper seeks to unravel the intricate and multifaceted role of neutrophils in breast cancer. Neutrophils, classically known for their phagocytic and pro-inflammatory functions, are now recognized for their involvement in promoting or restraining tumor growth. While their presence within the tumor microenvironment may exert antitumor effects through immune surveillance and cytotoxic activities, these innate immune cells can also facilitate tumor progression by fostering an immunosuppressive milieu, promoting angiogenesis, and aiding metastatic dissemination. The intricacies of neutrophil-tumor cell interactions, signaling pathways, and mechanisms governing their recruitment to the tumor site are explored in detail. Challenges and gaps in current knowledge are acknowledged, and future directions for research are outlined. This review underscores the dynamic and context-dependent role of neutrophils in breast cancer and emphasizes the significance of unraveling their multifaceted contributions. As we delve into the complexities of the immune landscape in breast cancer, a deeper understanding of the warriors within, the neutrophils, presents exciting prospects for the development of novel therapeutic strategies and a more comprehensive approach to breast cancer management.

The immune regulation and therapeutic potential of the SMAD gene family in breast cancer

Breast cancer is a serious threat to human health. The transforming growth factor-β signaling pathway is an important pathway involved in the occurrence and development of cancer. The SMAD family genes are responsible for the TGF-β signaling pathway. However, the mechanism by which genes of the SMAD family are involved in breast cancer is still unclear. Therefore, it is necessary to investigate the biological roles of the SMAD family genes in breast cancer. We downloaded the gene expression data, gene mutation data, and clinical pathological data of breast cancer patients from the UCSC Xena database. We used the Wilcox test to estimate the expression of genes of the SMAD family in cancers. And the biological functions of SMAD family genes using the DAVID website. The Pearson correlation method was used to explore the immune cell infiltration and drug response of SMAD family genes. We conducted in biological experiments vitro and vivo. In this study, we integrated the multi-omics data from TCGA breast cancer patients for analysis. The expression of genes of SMAD family was significantly dysregulated in patients with breast cancer. Except for SMAD6, the expression of other SMAD family genes was positively correlated. We also found that genes of the SMAD family were significantly enriched in the TGF-β signaling pathway, Hippo signaling pathway, cell cycle, and cancer-related pathways. In addition, SMAD3, SMAD6, and SMAD7 were lowly expressed in stage II breast cancer, while SMAD4 and SMAD2 were lowly expressed in stage III cancer. Furthermore, the expression of genes of the SMAD family was significantly correlated with immune cell infiltration scores. Constructing a xenograft tumor mouse model, we found that SMAD3 knockdown significantly inhibited tumorigenesis. Finally, we analyzed the association between these genes and the IC50 value of drugs. Interestingly, patients with high expression of SMAD3 exhibited significant resistance to dasatinib and staurosporine, while high sensitivity to tamoxifen and auranofin. In addition, SMAD3 knockdown promoted the apoptosis of BT-549 cells and decreased cell activity, and BAY-1161909 and XK-469 increased drug efficacy. In conclusion, genes of the SMAD family play a crucial role in the development of breast cancer.

Analyses of hypoxia-related risk factors and clinical relevance in breast cancer

Introduction

Hypoxia plays an important role in the heterogeneity, relapse, metastasis, and drug resistance of breast cancer. In this study, we explored the hypoxia-related biological signatures in different subtypes of breast cancer and identified the key prognostic factors by bioinformatics methods.

Methods

Based on The Cancer Genome Atlas (TCGA) Breast Cancer datasets, we divided the samples into immune-activated/suppressed populations by single-sample gene set enrichment analysis (ssGSEA) and then used hierarchical clustering to further identify hypoxic/non-hypoxic populations from the immune-suppressed samples. A hypoxia related risk model of breast cancer was constructed.

Results

Nuclear factor interleukin-3 regulated (NFIL3), serpin family E member 1 (SERPINE1), FOS, biglycan (BGN), epidermal growth factor receptor (EGFR), and sushi-repeat-containing protein, X-linked (SRPX) were identified as key hypoxia-related genes. Margin status, American Joint Committee on Cancer (AJCC) stage, hypoxia status, estrogen receptor/progesterone receptor (ER/PR) status, NFIL3, SERPINE1, EGFR, and risk score were identified as independent prognostic indicators for breast cancer patients. The 3- and 5-year survival curves of the model and immunohistochemical staining on the breast cancer microarray verified the statistical significance and feasibility of our model. Among the different molecular types of breast cancer, ER/PR+ and HER2+ patients might have higher hypoxia-related risk scores. ER/PR-negative samples demonstrated more activated immune-related pathways and better response to most anticancer agents.

Discussion

Our study revealed a novel risk model and potential feasible prognostic factors for breast cancer and might provide new perspectives for individual breast cancer treatment.

Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer

BACKGROUND

Breast Cancer (BC) is a highly heterogeneous and complex disease. Personalized treatment options require the integration of multi-omic data and consideration of phenotypic variability. Radiogenomics aims to merge medical images with genomic measurements but encounter challenges due to unpaired data consisting of imaging, genomic, or clinical outcome data. In this study, we propose the utilization of a well-trained conditional generative adversarial network (cGAN) to address the unpaired data issue in radiogenomic analysis of BC. The generated images will then be used to predict the mutations status of key driver genes and BC subtypes.

METHODS

We integrated the paired MRI and multi-omic (mRNA gene expression, DNA methylation, and copy number variation) profiles of 61 BC patients from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To facilitate this integration, we employed a Bayesian Tensor Factorization approach to factorize the multi-omic data into 17 latent features. Subsequently, a cGAN model was trained based on the matched side-view patient MRIs and their corresponding latent features to predict MRIs for BC patients who lack MRIs. Model performance was evaluated by calculating the distance between real and generated images using the Fréchet Inception Distance (FID) metric. BC subtype and mutation status of driver genes were obtained from the cBioPortal platform, where 3 genes were selected based on the number of mutated patients. A convolutional neural network (CNN) was constructed and trained using the generated MRIs for mutation status prediction. Receiver operating characteristic area under curve (ROC-AUC) and precision-recall area under curve (PR-AUC) were used to evaluate the performance of the CNN models for mutation status prediction. Precision, recall and F1 score were used to evaluate the performance of the CNN model in subtype classification.

RESULTS

The FID of the images from the well-trained cGAN model based on the test set is 1.31. The CNN for TP53, PIK3CA, and CDH1 mutation prediction yielded ROC-AUC values 0.9508, 0.7515, and 0.8136 and PR-AUC are 0.9009, 0.7184, and 0.5007, respectively for the three genes. Multi-class subtype prediction achieved precision, recall and F1 scores of 0.8444, 0.8435 and 0.8336 respectively. The source code and related data implemented the algorithms can be found in the project GitHub at https://github.com/mattthuang/BC_RadiogenomicGAN .

CONCLUSION

Our study establishes cGAN as a viable tool for generating synthetic BC MRIs for mutation status prediction and subtype classification to better characterize the heterogeneity of BC in patients. The synthetic images also have the potential to significantly augment existing MRI data and circumvent issues surrounding data sharing and patient privacy for future BC machine learning studies.

Breast cancer and its therapeutic targets: A comprehensive review

Breast cancer is a common and deadly disease, so there is a constant need for research to find efficient targets and therapeutic approaches. Breast cancer can be classified on a molecular and histological base. Breast cancer can be divided into ER (estrogen receptor)-positive and ER-negative, HER2 (human epidermal growth factor receptor2)-positive and HER2-negative subtypes based on the presence of specific biomarkers. Targeting hormone receptors, such as the HER2, progesterone receptor (PR), and ER, is very significant and plays a vital role in the onset and progression of breast cancer. Endocrine treatments and HER2-targeted drugs are examples of targeted therapies now being used against these receptors. Emerging immune-based medicines with promising outcomes in the treatment of breast cancer include immune checkpoint inhibitors, cancer vaccines, and adoptive T-cell therapy. It is also explored how immune cells and the tumor microenvironment affect breast cancer development and treatment response. The major biochemical pathways, signaling cascades, and DNA repair mechanisms that are involved in the development and progression of breast cancer, include the PI3K/AKT/mTOR system, the MAPK pathway, and others. These pathways are intended to be inhibited by a variety of targeted drugs, which are then delivered with the goal of restoring normal cellular function. This review aims to shed light on types of breast cancer with the summarization of different therapeutic approaches which can target different pathways for tailored medicines and better patient outcomes.

The Role of Tissue Factor In Signaling Pathways of Pathological Conditions and Angiogenesis

Tissue factor (TF) is an integral transmembrane protein associated with the extrinsic coagulation pathway. TF gene expression is regulated in response to inflammatory cytokines, bacterial lipopolysaccharides, and mechanical injuries. TF activity may be affected by phosphorylation of its cytoplasmic domain and alternative splicing. TF acts as the primary initiator of physiological hemostasis, which prevents local bleeding at the injury site. However, aberrant expression of TF, accompanied by the severity of diseases and infections under various pathological conditions, triggers multiple signaling pathways that support thrombosis, angiogenesis, inflammation, and metastasis. Protease-activated receptors (PARs) are central in the downstream signaling pathways of TF. In this study, we have reviewed the TF signaling pathways in different pathological conditions, such as wound injury, asthma, cardiovascular diseases (CVDs), viral infections, cancer and pathological angiogenesis. Angiogenic activities of TF are critical in the repair of wound injuries and aggressive behavior of tumors, which are mainly performed by the actions of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1 (HIF1-α). Pro-inflammatory effects of TF have been reported in asthma, CVDs and viral infections, including COVID-19, which result in tissue hypertrophy, inflammation, and thrombosis. TF-FVII induces angiogenesis via clotting-dependent and -independent mechanisms. Clottingdependent angiogenesis is induced via the generation of thrombin and cross-linked fibrin network, which facilitate vessel infiltration and also act as a reservoir for endothelial cells (ECs) growth factors. Expression of TF in tumor cells and ECs triggers clotting-independent angiogenesis through induction of VEGF, urokinase-type plasminogen activator (uPAR), early growth response 1 (EGR1), IL8, and cysteine-rich angiogenic inducer 61 (Cyr61).

Feasibility of Quantitative MRI Using 3D-QALAS for Discriminating Immunohistochemical Status in Invasive Ductal Carcinoma of the Breast

BACKGROUND

Two-dimensional synthetic MRI of the breast has limited spatial coverage. Three-dimensional (3D) synthetic MRI could provide volumetric quantitative parameters that may reflect the immunohistochemical (IHC) status in invasive ductal carcinoma (IDC) of the breast.

PURPOSE

To evaluate the feasibility of 3D synthetic MRI using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (QALAS) for discriminating the IHC status, including hormone receptor (HR), human epidermal growth factor receptor 2 (HER 2), and Ki-67 expression in IDC.

STUDY TYPE

Prospective observational study.

POPULATION

A total of 33 females with IDC of the breast (mean, 52.3 years).

FIELD STRENGTH/SEQUENCE

A 3-T, 3D-QALAS gradient-echo and fat-suppressed T1-weighted 3D fast spoiled gradient-echo sequences.

ASSESSMENT

Two radiologists semiautomatically delineated 3D regions of interest (ROIs) of the whole tumors on the dynamic MRI that was registered to the synthetic T1-weighted images acquired from 3D-QALAS. The mean T1 and T2 were measured for each IDC.

STATISTICAL TESTS

Intraclass correlation coefficient for assessing interobserver agreement. Mann-Whitney U test to determine the relationship between the mean T1 or T2 and the IHC status. Multivariate logistic regression analysis followed by receiver operating characteristics (ROC) analysis for discriminating IHC status. A P value <0.05 was considered statistically significant.

RESULTS

The interobserver agreement was good to excellent. There was a significant difference in the mean T1 between HR-positive and HR-negative lesions, while the mean T2 value differed between HR-positive and HR-negative lesions, between the triple-negative and HR-positive or HER2-positive lesions, and between the Ki-67 level > 14% and ≤ 14%. Multivariate analysis showed that the mean T2 was higher in HR-negative IDC than in HR-positive IDC. ROC analysis revealed that the mean T2 was predictive for discriminating HR status, triple-negative status, and Ki-67 level.

DATA CONCLUSION

3D synthetic MRI using QALAS may be useful for discriminating IHC status in IDC of the breast.

EVIDENCE LEVEL

1.

TECHNICAL EFFICACY

Stage 2.

A New Medium (HistoCold) for Surgical Specimens Preserving to Improve the Preanalytic Issues in Histopathological Samples Handling: Morphologic and Antigenic Analysis

Introduction: The onset of precision medicine has led to the integration of traditional morphologic tissues evaluation with biochemical and molecular data for a more appropriate pathological diagnosis. The preanalytic phase and, particularly, timing of cold ischemia are crucial to guarantee high-quality biorepositories of formalin-fixed paraffin-embedded (FFPE) tissues for patients' needs and scientific research. However, delayed fixation using the gold-standard and carcinogenic fixative neutral-buffered formalin (NBF) can be a significant limitation to diagnosis and biopathological characterization. HistoCold (patented; Bio-Optica Milano S.p.A., Milano, Italy) is a nontoxic, stable, and refrigerated preservative solution for tissue handling. This study examined HistoCold's potential role in improving the preanalytic phase of the pathological diagnostic process. Materials and Methods: Breast, lung, or colorectal cancers (20, 25, and 10 cases, respectively) that were to be surgically resected were recruited between 2019 and 2021. Once specimens were surgically removed, three residual samples for each patient were first promptly immersed into HistoCold for 24, 48, and 72 hours and then FFPE. These were compared with routine specimens regarding morphologic features (hematoxylin and eosin) and tissue antigenicity (immunohistochemical stains). Results: Good concordance regarding both the morphologic characteristics of the neoplasms and their proteins expression between the routine and HistoCold handled tissues were found. The tissue handling with the solution never affected the histopathological diagnosis. Conclusions: The use of HistoCold for samples transporting is easy, allows for improving the management of cold ischemia time, and monitoring the fixation times in NBF, resulting in good quality tissue blocks for biobanking. Moreover, it could be a candidate to eliminate formalin from operating theaters. HistoCold looks very promising for the preanalytic phase of human tissues handling in the era of precision medicine, to provide the best service to patients, and to scientific research.

MLLT11 siRNA Inhibits the Migration and Promotes the Apoptosis of MDA-MB-231 Breast Cancer Cells

Breast cancer is considered the most prevalent malignancy due to its high incidence rate, recurrence, and metastasis in women that makes it one of the deadliest cancers. The current study aimed to predict the genes associated with the recurrence and metastasis of breast cancer and to validate their effect on MDA-MB-231 cells. Through the bioinformatics analysis, the transcription factor 7 cofactor (MLLT11) as the target gene was obtained. MLLT11-specific siRNA was synthesized and transfected into MDA-MB-231 cells. The results demonstrated that the siRNA significantly reduced the MLLT11 mRNA levels. Moreover, cell migration and invasion, as well as the protein levels of phosphatidylinositol 3-kinase (PI3K), AKT, matrix metalloproteinase (MMP) 2, and MMP9, were significantly lower in the groups treated with siRNA while the apoptosis was augmented. Collectively, MLLT11 siRNA elicited ameliorative properties on breast cancer cells, possibly via the inhibition of the PI3K/AKT signaling pathway.

microRNA-944 inhibits breast cancer cell proliferation and promotes cell apoptosis by reducing SPP1 through inactivating the PI3K/Akt pathway

Breast cancer is a common malignancy in women with poor prognosis. This study aimed to investigate the molecular mechanism of microRNA-944 (miR-944) mediated secreted phosphoprotein-1 (SPP1) in breast cancer progression and its regulatory effect on the phosphoinositide 3-kinase (PI3K)/Akt signaling pathway. Differential gene analysis was performed to identify key genes associated with breast cancer development by screening breast cancer-related microarray data. The expression of miR-944 and SPP1 and their relationship were determined in clinical samples and cells. sh-SPP1, oe-SPP1, LY294002 or miR-944 mimic were transfected into MCF-7 cells to investigate the role of miR-944 mediated SPP1 in breast cancer development and its regulatory effect on the PI3K/Akt pathway. Finally, the tumorigenicity of breast cancer cells was observed in nude mice. Through bioinformatics analysis, we identified SPP1 as a key gene in breast cancer, and miR-944 as an upstream miRNA of SPP1. In breast cancer tissues and cells, the expression of miR-944 was decreased while that of SPP1 was increased. miR-944 negatively regulated the expression of SPP1. In breast cancer cells, SPP1 activated the PI3K/Akt pathway to promote cell proliferation and inhibit apoptosis. In vitro cell experiments showed that the downregulation of miR-944 promoted the high expression of SPP1, which then activated the PI3K/Akt signaling pathway, promoting breast cancer cell proliferation. In vivo experiments further confirmed the anti-cancer role of miR-944 mediated SPP1 in breast cancer. Our study highlights the role of miR-944 mediated SPP1 in inhibiting breast cancer progression by blocking the PI3K/Akt pathway.

Artificial intelligence in breast imaging: potentials and challenges

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

Simulating Interclonal Interactions in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of cancers, accounting for 37% of B-cell tumor cases globally. DLBCL is known to be a heterogeneous disease, resulting in variable clinical presentations and the development of drug resistance. One underexplored aspect of drug resistance is the evolving dynamics between parental and drug-resistant clones within the same microenvironment. In this work, the effects of interclonal interactions between two cell populations-one sensitive to treatment and the other resistant to treatment-on tumor growth behaviors were explored through a mathematical model. In vitro cultures of mixed DLBCL populations demonstrated cooperative interactions and revealed the need for modifying the model to account for complex interactions. Multiple best-fit models derived from in vitro data indicated a difference in steady-state behaviors based on therapy administrations in simulations. The model and methods may serve as a tool for understanding the behaviors of heterogeneous tumors and identifying the optimal therapeutic regimen to eliminate cancer cell populations using computer-guided simulations.

Breast tumor-on-chip: from the tumor microenvironment to medical applications

With the development of microfluidic technology, tumor-on-chip models have gradually become a new tool for the study of breast cancer because they can simulate more key factors of the tumor microenvironment compared with traditional models in vitro. Here, we review up-to-date advancements in breast tumor-on-chip models. We summarize and analyze the breast tumor microenvironment (TME), preclinical breast cancer models for TME simulation, fabrication methods of tumor-on-chip models, tumor-on-chip models for TME reconstruction, and applications of breast tumor-on-chip models and provide a perspective on breast tumor-on-chip models. This review will contribute to the construction and design of microenvironments for breast tumor-on-chip models, even the development of the pharmaceutical field, personalized/precision therapy, and clinical medicine.

Phenotypic heterogeneity drives differential disease outcome in a mouse model of triple negative breast cancer

The triple negative breast cancer (TNBC) subtype is one of the most aggressive forms of breast cancer that has poor clinical outcome and is an unmet clinical challenge. Accumulating evidence suggests that intratumoral heterogeneity or the presence of phenotypically distinct cell populations within a tumor play a crucial role in chemoresistance, tumor progression and metastasis. An increased understanding of the molecular regulators of intratumoral heterogeneity is crucial to the development of effective therapeutic strategies in TNBC. To this end, we used an unbiased approach to identify a molecular mediator of intratumoral heterogeneity in breast cancer by isolating two tumor cell populations (T1 and T2) from the 4T1 TNBC model. Phenotypic characterization revealed that the cells are different in terms of their morphology, proliferation and self-renewal ability in vitro as well as primary tumor formation and metastatic potential in vivo. Bioinformatic analysis followed by Kaplan Meier survival analysis in TNBC patients identified Metastasis associated colon cancer 1 (Macc1) as one of the top candidate genes mediating the aggressive phenotype in the T1 tumor cells. The role of Macc1 in regulating the proliferative phenotype was validated and taken forward in a therapeutic context with Lovastatin, a small molecule transcriptional inhibitor of Macc1 to target the T1 cell population. This study increases our understanding of the molecular underpinnings of intratumoral heterogeneity in breast cancer that is critical to improve the treatment of women currently living with the highly aggressive TNBC subtype.

Controlled tumor heterogeneity in a co-culture system by 3D bio-printed tumor-on-chip model

Cancer treatment resistance is a caused by presence of various types of cells and heterogeneity within the tumor. Tumor cell-cell and cell-microenvironment interactions play a significant role in the tumor progression and invasion, which have important implications for diagnosis, and resistance to chemotherapy. In this study, we develop 3D bioprinted in vitro models of the breast cancer tumor microenvironment made of co-cultured cells distributed in a hydrogel matrix with controlled architecture to model tumor heterogeneity. We hypothesize that the tumor could be represented by a cancer cell-laden co-culture hydrogel construct, whereas its microenvironment can be modeled in a microfluidic chip capable of producing a chemical gradient. Breast cancer cells (MCF7 and MDA-MB-231) and non-tumorigenic mammary epithelial cells (MCF10A) were embedded in the alginate-gelatine hydrogels and printed using a multi-cartridge extrusion bioprinter. Our approach allows for precise control over position and arrangements of cells in a co-culture system, enabling the design of various tumor architectures. We created samples with two different types of cells at specific initial locations, where the density of each cell type was carefully controlled. The cells were either randomly mixed or positioned in sequential layers to create cellular heterogeneity. To study cell migration toward chemoattractant, we developed a chemical microenvironment in a chamber with a gradual chemical gradient. As a proof of concept, we studied different migration patterns of MDA-MB-231 cells toward the epithelial growth factor gradient in presence of MCF10A cells in different ratios using this device. Our approach involves the integration of 3D bioprinting and microfluidic devices to create diverse tumor architectures that are representative of those found in various patients. This provides an excellent tool for studying the behavior of cancer cells with high spatial and temporal resolution.

YY1 regulates the proliferation and invasion of triple-negative breast cancer via activating PLAUR

It is well-established that breast cancer is a highly prevalent malignancy among women, emphasizing the need to investigate mechanisms underlying its pathogenesis and metastasis. In this study, the Gene Expression Omnibus (GEO) database was utilized to conduct differential expression analysis in breast cancer and adjacent tissues. Upregulated genes were selected for prognostic analysis of breast cancer. The expression of urokinase plasminogen activator receptor (uPAR), also known as PLAUR, was assessed using RT-qPCR and western blot. Immunofluorescence staining was employed to determine PLAUR localization. Various cellular processes were analyzed, including proliferation, migration, invasion, apoptosis, and cell cycle. Bioinformatics analysis was used to predict transcription factors of PLAUR, which were subsequently validated in a double luciferase reporter gene experiment. Rescue experiments confirmed the impact of PLAUR on the proliferation, apoptosis, and migration of MDA-MB-231 cells. Furthermore, the effects of PLAUR were evaluated in an orthotopic tumor transplantation and lung metastasis nude mouse model. Our findings substantiated the critical involvement of PLAUR in the progression of triple-negative breast cancer (TNBC) in vitro and among TNBC patients with a poor prognosis. Additionally, we demonstrated Yin Yang-1 (YY1) as a notable transcriptional regulator of PLAUR, whose activation could transcriptionally enhance the proliferation and invasion capabilities of TNBC cells. We also identified the downstream mechanism of PLAUR associated with PLAU, focal adhesion kinase (FAK), and AKT. Overall, these findings offer a novel perspective on PLAUR as a potential therapeutic target for TNBC.

Assessment of the safety and efficacy of combination chemotherapy and PD-1/PD-L1 inhibitor treatment of breast cancer: A meta-analysis

BACKGROUND

As the efficacy of programmed cell death-1/programmed death-ligand 1 (PD-1/PD-L1) inhibitors combined with chemotherapy in curing breast cancer is still controversial, this meta-analysis compares the efficacy and safety of PD-1/PD-L1 inhibitors combined with chemotherapy and chemotherapy alone in the treatment of breast cancer, which provides guidance for the clinical treatment.

METHODS

Relevant studies published as of April 2022 in the various databases including EMBASE, PubMed, and Cochrane Library were selected. Randomized controlled trials (RCTs) in which control patients underwent chemotherapy alone and experimental group patients underwent combination chemotherapy and PD-1/PD-L1 inhibitor treatment were included in this investigation. Investigations without complete information, researches from which information could not be extracted, duplicate articles, animal studies, review articles, and systematic reviews were excluded. STATA 15.1 was employed for all statistical analyses.

RESULTS

In total, eight eligible studies were identified, revealing that combination chemotherapy and PD-1/PD-L1 inhibitor treatment was linked to significant increases in progression-free survival (PFS) relative to chemotherapy alone (hazard ratio [HR] = 0.83, 95% confidence interval [CI]: 0.70-0.99, P = 0.032) but not overall survival (HR = 0.92, 95% CI: 0.80-1.06, P = 0.273). Pooled adverse event rates were also increased within the group of combination treatment relative to the chemotherapy group (risk ratio [RR] = 1.08, 95% CI: 1.03-1.14, P = 0.002). Specifically, nausea rates were lesser within the group of combination treatment relative to the group of chemotherapy (RR = 0.48, 95% CI: 0.25-0.92, P = 0.026). Subgroup analyses indicated that the PFS of patients who underwent combination atezolizumab or pembrolizumab and chemotherapy treatment were substantially longer than those of patients who underwent chemotherapy alone (HR = 0.79, 95% CI: 0.69-0.89, P ≤0.001; HR = 0.79, 95% CI: 0.67-0.92, P = 0.002).

CONCLUSIONS

The pooled results suggest that combination chemotherapy and PD-1/PD-L1 inhibitor treatment approaches help prolong PFS in breast cancer patients, but have no statistically significant effect on overall survival (OS). Additionally, combination therapy can significantly improve complete response rate (CRR) compared with chemotherapy alone. However, combination therapy was associated with greater rates of adverse events.

Organotypic Models for Functional Drug Testing of Human Cancers

In the era of personalized oncology, there have been accelerated efforts to develop clinically relevant platforms to test drug sensitivities of individual cancers. An ideal assay will serve as a diagnostic companion to inform the oncologist of the various treatments that are sensitive and insensitive, thus improving outcome while minimizing unnecessary toxicities and costs. To date, no such platform exists for clinical use, but promising approaches are on the horizon that take advantage of improved techniques in creating human cancer models that encompass the entire tumor microenvironment, alongside technologies for assessing and analyzing tumor response. This review summarizes a number of current strategies that make use of intact human cancer tissues as organotypic cultures in drug sensitivity testing.

Role of the Dietary Phytochemical Curcumin in Targeting Cancer Cell Signalling Pathways

The diarylheptanoid curcumin [(1E,6E)-1,7-bis(4-hydroxy-3-methoxyphenyl)hepta-1,6-diene-3,5-dione] is one of the phenolic pigments responsible for the yellow colour of turmeric (Curcuma longa L.). This phytochemical has gained much attention in recent years due to its therapeutic potential in cancer. A range of drug delivery approaches have been developed to optimise the pharmacokinetic profile of curcumin and ensure that it reaches its target sites. Curcumin exhibits numerous biological effects, including anti-inflammatory, cardioprotective, antidiabetic, and anti-aging activities. It has also been extensively studied for its role as a cancer chemopreventive and anticancer agent. This review focusses on the role of curcumin in targeting the cell signalling pathways involved in cancer, particularly via modulation of growth factors, transcription factors, kinases and other enzymes, pro-inflammatory cytokines, and pro-apoptotic and anti-apoptotic proteins. It is hoped that this study will help future work on the potential of curcumin to fight cancer.

Comprehensive research into prognostic and immune signatures of transcription factor family in breast cancer

BACKGROUND

Breast cancer (BRCA) is the most common malignancy with high morbidity and mortality in women, and transcription factor (TF) is closely related to the occurrence and development of BRCA. This study was designed to identify a prognostic gene signature based on TF family to reveal immune characteristics and prognostic survival of BRCA.

METHODS

In this study, RNA-sequence with corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and GSE42568. Prognostic differentially expressed transcription factor family genes (TFDEGs) were screened to construct a risk score model, after which BRCA patients were stratified into low-risk and high-risk groups based on their corresponding risk scores. Kaplan-Meier (KM) analysis was applied to evaluate the prognostic implication of risk score model, and a nomogram model was developed and validated with the TCGA and GSE20685. Furthermore, the GSEA revealed pathological processes and signaling pathways enriched in the low-risk and high-risk groups. Finally, analyses regarding levels of immune infiltration, immune checkpoints and chemotactic factors were all completed to investigate the correlation between the risk score and tumor immune microenvironment (TIME).

RESULTS

A prognostic 9-gene signature based on TFDEGs was selected to establish a risk score model. According to KM analyses, high-risk group witnessed a significantly worse overall survival (OS) than low-risk group in both TCGA-BRCA and GSE20685. Furthermore, the nomogram model proved great possibility in predicting the OS of BRCA patients. As indicted in GSEA analysis, tumor-associated pathological processes and pathways were relatively enriched in high-risk group, and the risk score was negatively correlated with ESTIMATE score, infiltration levels of CD4+ and CD8+T cells, as well as expression levels of immune checkpoints and chemotactic factors.

CONCLUSIONS

The prognostic model based on TFDEGs could distinguish as a novel biomarker for predicting prognosis of BRCA patients; in addition, it may also be utilized to identify potential benefit population from immunotherapy in different TIME and predict potential drug targets.

Single-Cell Transcriptome Analysis Revealed Heterogeneity and Identified Novel Therapeutic Targets for Breast Cancer Subtypes

Breast cancer (BC) is a heterogeneous disease, which is primarily classified according to hormone receptors and HER2 expression. Despite the many advances in BC diagnosis and management, the identification of novel actionable therapeutic targets expressed by cancerous cells has always been a daunting task due to the large heterogeneity of the disease and the presence of non-cancerous cells (i.e., immune cells and stromal cells) within the tumor microenvironment. In the current study, we employed computational algorithms to decipher the cellular composition of estrogen receptor-positive (ER⁺), HER2⁺, ER⁺HER2⁺, and triple-negative BC (TNBC) subtypes from a total of 49,899 single cells' publicly available transcriptomic data derived from 26 BC patients. Restricting the analysis to EPCAM⁺Lin^- tumor epithelial cells, we identified the enriched gene sets in each BC molecular subtype. Integration of single-cell transcriptomic with CRISPR-Cas9 functional screen data identified 13 potential therapeutic targets for ER⁺, 44 potential therapeutic targets for HER2⁺, and 29 potential therapeutic targets for TNBC. Interestingly, several of the identified therapeutic targets outperformed the current standard of care for each BC subtype. Given the aggressive nature and lack of targeted therapies for TNBC, elevated expression of ENO1, FDPS, CCT6A, TUBB2A, and PGK1 predicted worse relapse-free survival (RFS) in basal BC (n = 442), while elevated expression of ENO1, FDPS, CCT6A, and PGK1 was observed in the most aggressive BLIS TNBC subtype. Mechanistically, targeted depletion of ENO1 and FDPS halted TNBC cell proliferation, colony formation, and organoid tumor growth under 3-dimensional conditions and increased cell death, suggesting their potential use as novel therapeutic targets for TNBC. Differential expression and gene set enrichment analysis in TNBC revealed enrichment in the cycle and mitosis functional categories in FDPS^high, while ENO1^high was associated with numerous functional categories, including cell cycle, glycolysis, and ATP metabolic processes. Taken together, our data are the first to unravel the unique gene signatures and to identify novel dependencies and therapeutic vulnerabilities for each BC molecular subtype, thus setting the foundation for the future development of more effective targeted therapies for BC.

An inflammatory-related genes signature based model for prognosis prediction in breast cancer

Background

Breast cancer has become the most common malignant tumor in the world. It is vital to discover novel prognostic biomarkers despite the fact that the majority of breast cancer patients have a good prognosis because of the high heterogeneity of breast cancer, which causes the disparity in prognosis. Recently, inflammatory-related genes have been proven to play an important role in the development and progression of breast cancer, so we set out to investigate the predictive usefulness of inflammatory-related genes in breast malignancies.

Methods

We assessed the connection between Inflammatory-Related Genes (IRGs) and breast cancer by studying the TCGA database. Following differential and univariate Cox regression analysis, prognosis-related differentially expressed inflammatory genes were estimated. The prognostic model was constructed through the Least Absolute Shrinkage and Selector Operation (LASSO) regression based on the IRGs. The accuracy of the prognostic model was then evaluated using the Kaplan-Meier and Receiver Operating Characteristic (ROC) curves. The nomogram model was established to predict the survival rate of breast cancer patients clinically. Based on the prognostic expression, we also looked at immune cell infiltration and the function of immune-related pathways. The CellMiner database was used to research drug sensitivity.

Results

In this study, 7 IRGs were selected to construct a prognostic risk model. Further research revealed a negative relationship between the risk score and the prognosis of breast cancer patients. The ROC curve proved the accuracy of the prognostic model, and the nomogram accurately predicted survival rate. The scores of tumor-infiltrating immune cells and immune-related pathways were utilized to calculate the differences between the low- and high-risk groups, and then explored the relationship between drug susceptibility and the genes that were included in the model.

Conclusion

These findings contributed to a better understanding of the function of inflammatory-related genes in breast cancer, and the prognostic risk model provides a potentially promising prognostic strategy for breast cancer.

Liquid Biopsy in the Management of Breast Cancer Patients: Where Are We Now and Where Are We Going

Liquid biopsy (LB) is an emerging diagnostic tool that analyzes biomarkers in the blood (and possibly in other body fluids) to provide information about tumor genetics and response to therapy. This review article provides an overview of LB applications in human cancer with a focus on breast cancer patients. LB methods include circulating tumor cells and cell-free tumor products, such as circulating tumor DNA. LB has shown potential in detecting cancer at an early stage, monitoring tumor progression and recurrence, and predicting patient response to therapy. Several studies have demonstrated its clinical utility in breast cancer patients. However, there are limitations to LB, including the lack of standardized assays and the need for further validation. Future potential applications of LB include identifying the minimal residual disease, early detection of recurrence, and monitoring treatment response in various cancer types. LB represents a promising non-invasive diagnostic tool with potential applications in breast cancer diagnosis, treatment, and management. Further research is necessary to fully understand its clinical utility and overcome its current limitations.

NAD+ biosynthesis metabolism predicts prognosis and indicates immune microenvironment for breast cancer

The growing evidence implies that tumor cells need to increase NAD+ levels by upregulating NAD+ biosynthesis to satisfy their growth demand. NAD+ biosynthesis metabolism is implicated in tumor progression. Breast cancer (BC) is the most common malignant malignancy in the world. Nevertheless, the prognostic significance of NAD+ biosynthesis and its relationship with the tumor immune microenvironment in breast cancer still need further investigation. In this study, we obtained the mRNA expression data and clinical information of BC samples from public databases and calculated the level of NAD+ biosynthesis activity by single-sample gene set enrichment analysis (ssGSEA). We then explored the relationship between the NAD+ biosynthesis score, infiltrating immune cells, prognosis significance, immunogenicity and immune checkpoint molecules. The results demonstrated that patients with high NAD+ biosynthetic score displayed poor prognosis, high immune infiltration, high immunogenicity, elevated PD-L1 expression, and might more benefit from immunotherapy. Taken together, our studies not only deepened the understanding of NAD+ biosynthesis metabolism of breast cancer but also provided new insights into personalized treatment strategies and immunological therapies to improve the outcomes of breast cancer patients.

Ultrasound features for prediction of long-term outcomes of women with primary breast cancer <20 mm

Background

Some women die despite the favorable prognosis of small breast cancers. Breast ultrasound features may reflect pathological and biological characteristics of a breast tumor. This study aimed to explore whether ultrasound features could identify small breast cancers with poor outcomes.

Methods

This retrospective study examined confirmed breast cancers with a size of &lt;20 mm diagnosed in our hospital between 02/2008 and 08/2019. Clinicopathological and ultrasound features were compared between alive and deceased breast cancer patients. Survival was analyzed using the Kaplan-Meier curves. Multivariable Cox proportional hazards models were used to examine the factors associated with breast cancer-specific survival (BCSS) and disease-free survival (DFS).

Results

Among the 790 patients, the median follow-up was 3.5 years. The deceased group showed higher frequencies of spiculated (36.7% vs. 11.2%, P&lt;0.001), anti-parallel orientation (43.3% vs. 15.4%, P&lt;0.001), and spiculated morphology combined with anti-parallel orientation (30.0% vs. 2.4%, P&lt;0.001). Among 27 patients with spiculated morphology and anti-parallel orientation, nine cancer-specific deaths and 11 recurrences occurred, for a 5-year BCSS of 77.8% and DFS of 66.7%, while 21 breast-cancer deaths and 41 recurrences occurred among the remaining patients with higher 5-year BCSS (97.8%, P&lt;0.001) and DFS (95.4%, P&lt;0.001). Spiculated and anti-parallel orientation (HR=7.45, 95%CI: 3.26-17.00; HR=6.42, 95%CI: 3.19-12.93), age ≥55 years (HR=5.94, 95%CI: 2.24-15.72; HR=1.98, 95%CI: 1.11-3.54), and lymph nodes metastasis (HR=3.99, 95%CI: 1.89-8.43; HR=2.99, 95%CI: 1.71-5.23) were independently associated with poor BCSS and DFS.

Conclusions

Spiculated and anti-parallel orientation at ultrasound are associated with poor BCSS and DFS in patients with primary breast cancer &lt;20 mm.

Circular RNAs: New layer of complexity evading breast cancer heterogeneity

Advances in high-throughput sequencing techniques and bioinformatic analysis have refuted the "junk" RNA hypothesis that was claimed against non-coding RNAs (ncRNAs). Circular RNAs (circRNAs); a class of single-stranded covalently closed loop RNA molecules have recently emerged as stable epigenetic regulators. Although the exact regulatory role of circRNAs is still to be clarified, it has been proven that circRNAs could exert their functions by interacting with other ncRNAs or proteins in their own physiologically authentic environment, regulating multiple cellular signaling pathways and other classes of ncRNAs. CircRNAs have also been reported to exhibit a tissue-specific expression and have been associated with the malignant transformation process of several hematological and solid malignancies. Along this line of reasoning, this review aims to highlight the importance of circRNAs in Breast Cancer (BC), which is ranked as the most prevalent malignancy among females. Notwithstanding the substantial efforts to develop a suitable anticancer therapeutic regimen against the heterogenous BC, inter- and intra-tumoral heterogeneity have resulted in an arduous challenge for drug development research, which in turn necessitates the investigation of other markers to be therapeutically targeted. Herein, the potential of circRNAs as possible diagnostic and prognostic biomarkers have been highlighted together with their possible application as novel therapeutic targets.

Abdallah M, M. Mukhtar R, Moutasim F, Rafie Ahmed A, Edris A, Ibraheem W, Makki AA, M. Elshamly E, Elhag R, Osman W, A. Mothana R, Alzain AA. Identification of Novel Natural Dual HDAC and Hsp90 Inhibitors for Metastatic TNBC Using e-Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Studies

Breast cancer (BC) is one of the main types of cancer that endangers women's lives. The characteristics of triple-negative breast cancer (TNBC) include a high rate of recurrence and the capacity for metastasis; therefore, new therapies are urgently needed to combat TNBC. Dual targeting HDAC6 and Hsp90 has shown good synergistic effects in treating metastatic TNBC. The goal of this study was to find potential HDAC6 and Hsp90 dual inhibitors. Therefore, several in silico approaches have been used. An e-pharmacophore model generation based on the HDAC6-ligand complex and subsequently a pharmacophore-based virtual screening on 270,450 natural compounds from the ZINC were performed, which resulted in 12,663 compounds that corresponded to the obtained pharmacophoric hypothesis. These compounds were docked into HDAC6 and Hsp90. This resulted in the identification of three compounds with good docking scores and favorable free binding energy against the two targets. The top three compounds, namely ZINC000096116556, ZINC000020761262, and ZINC000217668954, were further subjected to ADME prediction and molecular dynamic simulations, which showed promising results in terms of pharmacokinetic properties and stability. As a result, these three compounds can be considered potential HDAC6 and Hsp90 dual inhibitors and are recommended for experimental evaluation.

Roles of TGF‑β signalling pathway‑related lncRNAs in cancer (Review)

Long non-coding RNAs (lncRNAs) are a class of RNAs that are >200 nucleotides in length that do not have the ability to be translated into protein but are associated with numerous diseases, including cancer. The involvement of lncRNAs in the signalling of certain signalling pathways can promote tumour progression; these pathways include the transforming growth factor (TGF)-β signalling pathway, which is related to tumour development. The expression of lncRNAs in various tumour tissues is specific, and their interaction with the TGF-β signalling pathway indicates that they may serve as new tumour markers and therapeutic targets. The present review summarized the role of TGF-β pathway-associated lncRNAs in regulating tumorigenesis in different types of cancer and their effects on the TGF-β signalling pathway.

Overexpression of PKMYT1 associated with poor prognosis and immune infiltration may serve as a target in triple-negative breast cancer

Breast cancer (BC) is one of the most common malignancies among women worldwide. It is necessary to search for improvement in diagnosis and treatment methods to improve the prognosis. Protein kinase, membrane associated tyrosine/threonine 1 (PKMYT1), a member of the Wee family of protein kinases, has been studied in some tumors except BC. This study has explored that PKMYT1 functional role by bioinformatics methods combined with local clinical samples and experiments. Comprehensive analysis showed that PKMYT1 expression was higher in BC tissues, especially in advanced patients than that in normal breast tissues. The expression of PKMYT1 was an independent determinant for BC patients' prognosis when combined with the clinical features. In addition, based on multi-omics analysis, we found that the PKMYT1 expression was closely relevant to several oncogenic or tumor suppressor gene variants. The analysis of single-cell sequencing indicated that PKMYT1 expression was upregulated in triple-negative breast cancer (TNBC), consistent with the results of bulk RNA-sequencing. High PKMYT1 expression was correlated with a poor prognosis. Functional enrichment analysis revealed that PKMYT1 expression was associated with cell cycle-related, DNA replication-related, and cancer-related pathways. Further research revealed that PKMYT1 expression was linked to immune cell infiltration in the tumor microenvironment. Additionally, loss-of-function experiments in vitro were performed to investigate the role of PKMYT1. TNBC cell lines' proliferation, migration, and invasion were inhibited when PKMYT1 expression was knock-down. Besides, the down-regulation of PKMYT1 induced apoptosis in vitro. As a result, PKMYT1 might be a biomarker for prognosis and a therapeutic target for TNBC.

Privileged Scaffolds in Drug Discovery against Human Epidermal Growth Factor Receptor 2 for Cancer Treatment

HER2 is the membrane receptor tyrosine kinase showing overexpression in several human malignancies, particularly breast cancer. HER2 overexpression causes the activation of Ras- MAPK and PI3K/Akt/ NF-κB cellular signal transduction pathways that lead to cancer development and progression. HER2 is, therefore, presumed as one of the key targets for the development of tumor-specific therapies. Several preclinical have been developed that function by inhibiting the HER2 tyrosine kinase activity through the prevention of the dimerization process. Most HER2 inhibitors act as ATP competitors and prevent the process of phosphorylation, and abort the cell cycle progression and proliferation. In this review, the clinical drug candidates and potent pre-clinical newly developed molecules are described, and the core chemical scaffolds typically responsible for anti-HER2 activity are deciphered. In addition, the monoclonal antibodies that are either used in monotherapy or in combination therapy against HER2-positive cancer are briefly described. The identified key moieties in this study could result in the discovery of more effective HER2-targeted anticancer drug molecules and circumvent the development of resistance by HER2-specific chemotherapeutics in the future.