Acquired mutations and transcriptional remodeling in long-term estrogen-deprived locoregional breast cancer recurrences

Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles

BACKGROUND

Breast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades, drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However, with a growing number of potential small-molecule cancer inhibitors, in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However, these models heavily relied on a large number of drug chemical structural fingerprints as their main features, which made model interpretation a challenge.

RESULTS

This study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models, our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins, we gained insights into the patterns our model recognized as indicative of synergistic effects.

CONCLUSIONS

The framework employed in the present study lays the groundwork for future advancements, especially in model interpretation. By combining deep learning techniques and target-specific models, this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment.

Radioactive seed localization is a safe and effective tool for breast cancer surgery: an evaluation of over 25,000 cases

Radioactive seed localization (RSL) provides a precise and efficient method for removing non-palpable breast lesions. It has proven to be a valuable addition to breast surgery, improving perioperative logistics and patient satisfaction. This retrospective review examines the lessons learned from a high-volume cancer center's RSL program after 10 years of practice and over 25 000 cases. We provide an updated model for assessing the patient's radiation dose from RSL seed implantation and demonstrate the safety of RSL to staff members. Additionally, we emphasize the importance of various aspects of presurgical evaluation, surgical techniques, post-surgical management, and regulatory compliance for a successful RSL program. Notably, the program has reduced radiation exposure for patients and medical staff.

A Novel Mathematical Approach for Analysis of Integrated Cell-Patient Data Uncovers a 6-Gene Signature Linked to Endocrine Therapy Resistance

A significant number of breast cancers develop resistance to hormone therapy. This progression, while posing a major clinical challenge, is difficult to predict. Despite important contributions made by cell models and clinical studies to tackle this problem, both present limitations when taken individually. Experiments with cell models are highly reproducible but do not reflect the indubitable heterogenous landscape of breast cancer. On the other hand, clinical studies account for this complexity but introduce uncontrolled noise due to external factors. Here, we propose a new approach for biomarker discovery that is based on a combined analysis of sequencing data from controlled MCF7 cell experiments and heterogenous clinical samples that include clinical and sequencing information from The Cancer Genome Atlas. Using data from differential gene expression analysis and a Bayesian logistic regression model coupled with an original simulated annealing-type algorithm, we discovered a novel 6-gene signature for stratifying patient response to hormone therapy. The experimental observations and computational analysis built on independent cohorts indicated the superior predictive performance of this gene set over previously known signatures of similar scope. Together, these findings revealed a new gene signature to identify patients with breast cancer with an increased risk of developing resistance to endocrine therapy.

Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models

Objective

Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, such as the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face serious limitations in portability and privacy due to their need for circulating user data in remote servers for operation. Our objective was to overcome these limitations.

Materials and Methods

We refactored R-iCARE into a Python package (Py-iCARE) then compiled it to WebAssembly (Wasm-iCARE): a portable web module, which operates entirely within the privacy of the user's device.

Results

We showcase the portability and privacy of Wasm-iCARE through two applications: for researchers to statistically validate risk models, and to deliver them to end-users. Both applications run entirely on the client-side, requiring no downloads or installations, and keeps user data on-device during risk calculation.

Conclusions

Wasm-iCARE fosters accessible and privacy-preserving risk tools, accelerating their validation and delivery.

Giant Cell Tumors With HMGA2::NCOR2 Fusion : Clinicopathologic, Molecular, and Epigenetic Study of a Distinct Entity

Giant cell tumors (GCTs) with high mobility group AT-Hook 2 ( HMGA2 )::nuclear receptor corepressor 2 ( NCOR2 ) fusion are rare mesenchymal tumors of controversial nosology, which have been anecdotally reported to respond to CSFR1 inhibitors. Here, we performed a comprehensive study of 6 GCTs with HMGA2::NCOR2 fusion and explored their relationship with other giant cell-rich neoplasms. Tumors occurred in 4 females and 2 males ranging in age from 17 to 32 years old (median 24). Three lesions originated in subcutaneous soft tissue and 3 in bone. Tumor size ranged from 20 to 33 mm (median 27 mm). The lesions had a nodular/multinodular architecture and were composed of sheets of mononuclear "histiocytoid" cells with uniform nuclei intermingled with multinucleated giant cells. Mitotic activity was low and nuclear atypia and metaplastic bone were absent. Variable findings included necrosis, cystic degeneration, lymphocytic infiltrate (sometimes forming nodules), and xanthogranulomatous inflammation. On immunohistochemistry, all cases focally expressed pan-keratin and were negative with SATB2 and H3.3G34W. Whole RNA-sequencing was performed in all cases of GCT with HMGA2::NCOR2 fusion and a subset of giant cell-rich tumors (tenosynovial-GCT, n = 19 and "wild-type" GCT of soft tissue, n = 9). Hierarchical clustering of RNA-sequencing data showed that GCT with HMGA2::NCOR2 fusion formed a single cluster, independent of the other 2 entities. Methylome profiling showed similar results, but the distinction from "wild-type" GCT of soft tissue was less flagrant. Gene expression analysis showed similar levels of expression of the CSF1/CSFR1 axis between GCT with HMGA2::NCOR2 fusion and tenosynovial-GCT, supporting their potential sensitivity to CSFR1 inhibitors. Clinical follow-up was available for 5 patients (range: 10 to 64 mo; median 32 mo). Three patients (60%) experienced local recurrences, whereas none had distant metastases or died of disease. Overall, our study confirms and expands previous knowledge on GCT with HMGA2::NCOR2 fusion and supports its inclusion as an independent entity.

Molecular Classification, Treatment, and Genetic Biomarkers in Triple-Negative Breast Cancer: A Review

Breast cancer is the most common malignancy and the second most common cause of cancer-related mortality in women. Triple-negative breast cancers do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptor 2 and have a higher recurrence rate, greater metastatic potential, and lower overall survival rate than those of other breast cancers. Treatment of triple-negative breast cancer is challenging; molecular-targeted therapies are largely ineffective and there is no standard treatment. In this review, we evaluate current attempts to classify triple-negative breast cancers based on their molecular features. We also describe promising treatment methods with different advantages and discuss genetic biomarkers and other prediction tools. Accurate molecular classification of triple-negative breast cancers is critical for patient risk categorization, treatment decisions, and surveillance. This review offers new ideas for more effective treatment of triple-negative breast cancer and identifies novel targets for drug development.

How do women who are informed that they are at increased risk of breast cancer appraise their risk? A systematic review of qualitative research

This review aimed to synthesise qualitative research on how women notified that they are at increased risk of breast cancer view their risk. Five electronic databases were systematically reviewed for qualitative research investigating how women who have received an increased breast cancer risk estimate appraise their risk status. Fourteen records reporting 12 studies were included and critically appraised. Data were thematically synthesised. Four analytical themes were generated. Women appraise their risk of breast cancer through comparison with their risk of other familial diseases. Clinically derived risk estimates were understood in relation to pre-conceived risk appraisals, with incongruences met with surprise. Family history is relied upon strongly, with women exploring similarities and differences in attributes between themselves and affected relatives to gauge the likelihood of diagnosis. Women at increased risk reported living under a cloud of inevitability or uncertainty regarding diagnosis, resulting in concerns about risk management. Women hold stable appraisals of their breast cancer risk which appear to be mainly formed through their experiences of breast cancer in the family. Healthcare professionals should explore women's personal risk appraisals prior to providing clinically derived risk estimates in order to address misconceptions, reduce concerns about inevitability and increase perceived control over risk reduction.

Novel Therapies and Strategies to Overcome Resistance to Anti-HER2-Targeted Drugs

The prognosis and quality of life of HER2 breast cancer patients have significantly improved due to the crucial clinical benefit of various anti-HER2 targeted therapies. However, HER2 tumors can possess or develop several resistance mechanisms to these treatments, thus leaving patients with a limited set of additional therapeutic options. Fortunately, to overcome this problem, in recent years, multiple different and complementary approaches have been developed (such as antibody-drug conjugates (ADCs)) that are in clinical or preclinical stages. In this review, we focus on emerging strategies other than on ADCs that are either aimed at directly target the HER2 receptor (i.e., novel tyrosine kinase inhibitors) or subsequent intracellular signaling (e.g., PI3K/AKT/mTOR, CDK4/6 inhibitors, etc.), as well as on innovative approaches designed to attack other potential tumor weaknesses (such as immunotherapy, autophagy blockade, or targeting of other genes within the HER2 amplicon). Moreover, relevant technical advances such as anti-HER2 nanotherapies and immunotoxins are also discussed. In brief, this review summarizes the impact of novel therapeutic approaches on current and future clinical management of aggressive HER2 breast tumors.

Human epidermal growth factor receptor-2 and endocrine resistance in hormone-dependent breast cancer

Endocrine therapies are the main treatment strategies for the clinical management of hormone-dependent breast cancer. Despite prolonged time to recurrence in the adjuvant setting and the initial clinical responses in the metastatic setting, many patients eventually encounter tumour relapse due to acquired resistance to these agents. Other patients experience a lack of tumour regression at the beginning of treatment indicating de novo resistance that significantly limits its efficacy in the clinic. There is compelling evidence that human epidermal growth factor receptor-2 (HER2) overexpression contributes to resistance to endocrine therapies in oestrogen receptor-positive (ER+) breast cancer. ER+/HER2+ tumours comprise about 10% of all breast cancer cases and about 60% of the whole set of HER2+ tumours. Most patients with primary ER+/HER2+ disease will receive antibody-based HER2-targeted therapy, but this is generally for no more than one year while endocrine treatment is usually for at least 5 years. A number of HER2-kinase inhibitors are also now in clinical use or in clinical trials, and the interaction of these with endocrine treatment may differ from that of antibody treatment. In this review article, we aim to summarise knowledge on molecular mechanisms of breast cancer resistance to endocrine therapies attributable to the impact of HER2 signalling on endocrine sensitivity, to discuss data from clinical trials addressing the role of HER2 in the development of endocrine resistance in the metastatic, neoadjuvant and adjuvant settings and to explore rational new therapeutic strategies.

Targeting specificity protein 1 with miR-128-3p overcomes TGF-β1 mediated epithelial-mesenchymal transition in breast cancer: An in vitro study

BACKGROUND

Specificity protein 1 (SP1) was found to play a critical role in the regulation of TGF-β1 driven epithelial-mesenchymal transition (EMT). Recent clinical findings demonstrated a significant drop in the expression of miR-128-3p with the cancer progression in breast cancer patients. However, the impact of miR-128-3p on the SP1 expression in breast cancer remains unknown. Herein, we evaluated the role of miR-128-3p mimics in suppressing EMT of breast cancer cell lines by regulating the TGF-β1/SP1 axis.

METHODS

miR-128-3p interaction with SP1 was detected by in silico tools and dual-luciferase reporter assay. qPCR, western blot, and immunocytochemistry experiments were conducted for determining the expression levels of miR-128-3p and EMT markers with and without the treatment of miR-128-3p mimics. Further, to understand the effect of miR-128-3p mimics on cancer progression, experiments such as wound healing assay, transwell assay, adhesion assay, and cell cycle analysis were performed.

RESULTS

A significant inverse relation between SP1 and miR-128-3p levels was found in MCF-7 and MDA-MB-231 cell lines. miR-128-3p overexpression impeded the SP1 mediated EMT markers in TGF-β1 stimulated cells by inhibiting the SP1 nuclear function. Further, treatment with miR-128-3p mimics significantly reduced the migration, invasion and spreading capability of TGF-β1 stimulated cells. Flow cytometry results showed the impeding role of miR-128-3p on the cell cycle progression.

CONCLUSIONS

Upregulated miR-128-3p inhibited SP1, thereby limiting the TGF-β1 induced EMT in MCF-7 and MDA-MB-231 cell lines for the first time. This study may pave the path to explore novel miRNA therapeutics for eradicating advanced breast cancer cases.

Prominin 1 Significantly Correlated with Bone Metastasis of Breast Cancer and Influenced the Patient’s Prognosis

Background

This study is aimed at identifying the important biomarkers associated with bone metastasis (BM) in breast cancer (BRCA).

Methods

The GSE175692 dataset was used to detect significant differential expressed genes (DEGs) between BRCA samples with or without BM, and DEG-related pathways were then explored. Further, we constructed the protein-protein interaction (PPI) network on GEGs and filtered 5 vital nodes. We then performed the Cox regression, Kaplan-Meier analysis, nomogram, and ROC curve to filter the most significant prognosis genes. The GSE14020 and GSE124647 datasets were used to verify the expression and prognostic value of hub genes, respectively. Finally, the gene set enrichment analysis (GSEA) was performed to reveal the potential mechanism.

Results

Totally, 74 DEGs were detected, which mainly correlated with infectious disease, signaling molecules, and interaction. The 5 important DEGs were then filtered, and the Cox regression further showed that 2 genes, including prominin 1 (PROM1) and C-C motif chemokine ligand 2 (CCL2), were related to the prognosis of BRCA metastasis patients. Especially, PROM1 presented a better prognostic performance on the survival probability of patients than CCL2. Verification analysis further confirmed the abnormal expression and significant prognostic influence of PROM1. Finally, GSEA revealed that PROM1 was negatively related to IGF1 and mTOR pathways in BRCA metastasis.

Conclusion

PROM1 was an important biomarker associated with BRCA bone metastasis and affected the prognosis of metastatic BRCA patients. It may play a vital role in metastatic BRCA by negatively regulating IGF1 and mTOR pathways.

Therapy resistance: opportunities created by adaptive responses to targeted therapies in cancer

Normal cells explore multiple states to survive stresses encountered during development and self-renewal as well as environmental stresses such as starvation, DNA damage, toxins or infection. Cancer cells co-opt normal stress mitigation pathways to survive stresses that accompany tumour initiation, progression, metastasis and immune evasion. Cancer therapies accentuate cancer cell stresses and invoke rapid non-genomic stress mitigation processes that maintain cell viability and thus represent key targetable resistance mechanisms. In this Review, we describe mechanisms by which tumour ecosystems, including cancer cells, immune cells and stroma, adapt to therapeutic stresses and describe three different approaches to exploit stress mitigation processes: (1) interdict stress mitigation to induce cell death; (2) increase stress to induce cellular catastrophe; and (3) exploit emergent vulnerabilities in cancer cells and cells of the tumour microenvironment. We review challenges associated with tumour heterogeneity, prioritizing actionable adaptive responses for optimal therapeutic outcomes, and development of an integrative framework to identify and target vulnerabilities that arise from adaptive responses and engagement of stress mitigation pathways. Finally, we discuss the need to monitor adaptive responses across multiple scales and translation of combination therapies designed to take advantage of adaptive responses and stress mitigation pathways to the clinic.

Using population-based data to evaluate the impact of adherence to endocrine therapy on survival in breast cancer through the web-application BreCanSurvPred

We show how the use and interpretation of population-based cancer survival indicators can help oncologists talk with breast cancer (BC) patients about the relationship between their prognosis and their adherence to endocrine therapy (ET). The study population comprised a population-based cohort of estrogen receptor positive BC patients (N = 1268) diagnosed in Girona and Tarragona (Northeastern Spain) and classified according to HER2 status (+ / −), stage at diagnosis (I/II/III) and five-year cumulative adherence rate (adherent > 80%; non-adherent ≤ 80%). Cox regression analysis was performed to identify significant prognostic factors for overall survival, whereas relative survival (RS) was used to estimate the crude probability of death due to BC (P_BC). Stage and adherence to ET were the significant factors for predicting all-cause mortality. Compared to stage I, risk of death increased in stage II (hazard ratio [HR] 2.24, 95% confidence interval [CI]: 1.51–3.30) and stage III (HR 5.11, 95% CI 3.46–7.51), and it decreased with adherence to ET (HR 0.57, 95% CI 0.41–0.59). P_BC differences were higher in non-adherent patients compared to adherent ones and increased across stages: stage I: 6.61% (95% CI 0.05–13.20); stage II: 9.77% (95% CI 0.59–19.01), and stage III: 22.31% (95% CI 6.34–38.45). The age-adjusted survival curves derived from this modeling were implemented in the web application BreCanSurvPred (https://pdocomputation.snpstats.net/BreCanSurvPred). Web applications like BreCanSurvPred can help oncologists discuss the consequences of non-adherence to prescribed ET with patients.

ESR1 mutant breast cancers show elevated basal cytokeratins and immune activation

Estrogen receptor alpha (ER/ESR1) is frequently mutated in endocrine resistant ER-positive (ER+) breast cancer and linked to ligand-independent growth and metastasis. Despite the distinct clinical features of ESR1 mutations, their role in intrinsic subtype switching remains largely unknown. Here we find that ESR1 mutant cells and clinical samples show a significant enrichment of basal subtype markers, and six basal cytokeratins (BCKs) are the most enriched genes. Induction of BCKs is independent of ER binding and instead associated with chromatin reprogramming centered around a progesterone receptor-orchestrated insulated neighborhood. BCK-high ER+ primary breast tumors exhibit a number of enriched immune pathways, shared with ESR1 mutant tumors. S100A8 and S100A9 are among the most induced immune mediators and involve in tumor-stroma paracrine crosstalk inferred by single-cell RNA-seq from metastatic tumors. Collectively, these observations demonstrate that ESR1 mutant tumors gain basal features associated with increased immune activation, encouraging additional studies of immune therapeutic vulnerabilities.

Clinical and genomic analyses of neuroendocrine neoplasms of the breast

Breast neuroendocrine neoplasms (NENs) constitute a rare histologic subtype that includes both neuroendocrine tumors (NETs) and neuroendocrine carcinomas (NECs). In this study, we aimed to gain insight into the clinical and molecular characteristics of NENs of the breast. NEN and paired distant normal fresh tissues and clinicopathological data were obtained from 17 patients with NENs, and clinicopathological data were collected from 755 patients with invasive breast carcinomas of no special type (IBCs-NST). We compared the clinicopathological characteristics of NENs and IBCs-NST and performed whole-exome sequencing (WES) of both NEN and paired normal tissues. Compared with the IBC-NST patients, the NEN patients had a higher mean age, lower clinical stage, and lower pathological nodal (pN) stage (P < 0.001, P < 0.001, and P = 0.017, respectively). The most frequently mutated gene in NENs was KMT2C (3/17, 17.6%). NENs had copy number variations (CNVs) of 8q, 11q, and 17q amplification and 17q and 11q deletion and harbored the following specific genes related to tumorigenesis: (i) suppressor genes with loss of heterozygosity (LOH) such as ACE (2/17, 11.8%); (ii) tumor driver genes such as GATA3 (2/17, 11.8%); and (iii) susceptibility genes such as MAP3K4 (17/17, 100%) and PDE4DIP (17/17, 100%). The oncogenic/likely oncogenic mutations of NETs in PI3K pathway genes (50.0%, 18.2%; P < 0.001) and MAPK signaling pathway genes (83.3%, 18.2%; P = 0.035) affected higher proportions than those of NECs. In conclusion, this study provides certain clinical and molecular evidence supporting NENs as a distinct subtype of breast cancer and provides some potential molecular features for distinguishing NETs from NECs.

Targeting Ribosome Biogenesis to Combat Tamoxifen Resistance in ER+ve Breast Cancer

Simple Summary

Resistance to tamoxifen treatment is an obstacle for ER+ve breast cancer therapy. The overexpression of c-MYC is a known driver of cancer progression and is associated with tamoxifen resistance. Through mediating the up-regulation of ribosome biogenesis and alteration of the transcriptome, c-MYC modulates the translation profile to facilitate the development of tamoxifen resistance. c-MYC is, however, undruggable. Thus, targeting downstream mechanisms mediated by c-MYC might be a more feasible approach. Studies have demonstrated that inhibition of ribosome biogenesis can achieve tumour suppression. Targeting ribosome biogenesis may thus be a feasible strategy to reverse tamoxifen resistance. This article reviews the current evidence to support the feasibility of suppressing ribosome biogenesis to reverse tamoxifen resistance in ER+ve breast cancer.

Abstract

Breast cancer is a heterogeneous disease. Around 70% of breast cancers are estrogen receptor-positive (ER+ve), with tamoxifen being most commonly used as an adjuvant treatment to prevent recurrence and metastasis. However, half of the patients will eventually develop tamoxifen resistance. The overexpression of c-MYC can drive the development of ER+ve breast cancer and confer tamoxifen resistance through multiple pathways. One key mechanism is to enhance ribosome biogenesis, synthesising mature ribosomes. The over-production of ribosomes sustains the demand for proteins necessary to maintain a high cell proliferation rate and combat apoptosis induced by therapeutic agents. c-MYC overexpression can induce the expression of eIF4E that favours the translation of structured mRNA to produce oncogenic factors that promote cell proliferation and confer tamoxifen resistance. Either non-phosphorylated or phosphorylated eIF4E can mediate such an effect. Since ribosomes play an essential role in c-MYC-mediated cancer development, suppressing ribosome biogenesis may help reduce aggressiveness and reverse tamoxifen resistance in breast cancer. CX-5461, CX-3543 and haemanthamine have been shown to repress ribosome biogenesis. Using these chemicals might help reverse tamoxifen resistance in ER+ve breast cancer, provided that c-MYC-mediated ribosome biogenesis is the crucial factor for tamoxifen resistance. To employ these ribosome biogenesis inhibitors to combat tamoxifen resistance in the future, identification of predictive markers will be necessary.

Non-conventional and Investigational PET Radiotracers for Breast Cancer: A Systematic Review

Breast cancer is one of the most common malignancies in women, with high morbidity and mortality rates. In breast cancer, the use of novel radiopharmaceuticals in nuclear medicine can improve the accuracy of diagnosis and staging, refine surveillance strategies and accuracy in choosing personalized treatment approaches, including radioligand therapy. Nuclear medicine thus shows great promise for improving the quality of life of breast cancer patients by allowing non-invasive assessment of the diverse and complex biological processes underlying the development of breast cancer and its evolution under therapy. This review aims to describe molecular probes currently in clinical use as well as those under investigation holding great promise for personalized medicine and precision oncology in breast cancer.

Hybrid high-definition microvessel imaging/shear wave elastography improves breast lesion characterization

BACKGROUND

Low specificity in current breast imaging modalities leads to increased unnecessary follow-ups and biopsies. The purpose of this study is to evaluate the efficacy of combining the quantitative parameters of high-definition microvasculature imaging (HDMI) and 2D shear wave elastography (SWE) with clinical factors (lesion depth and age) for improving breast lesion differentiation.

METHODS

In this prospective study, from June 2016 through April 2021, patients with breast lesions identified on diagnostic ultrasound and recommended for core needle biopsy were recruited. HDMI and SWE were conducted prior to biopsies. Two new HDMI parameters, Murray's deviation and bifurcation angle, and a new SWE parameter, mass characteristic frequency, were included for quantitative analysis. Lesion malignancy prediction models based on HDMI only, SWE only, the combination of HDMI and SWE, and the combination of HDMI, SWE and clinical factors were trained via elastic net logistic regression with 70% (360/514) randomly selected data and validated with the remaining 30% (154/514) data. Prediction performances in the validation test set were compared across models with respect to area under the ROC curve as well as sensitivity and specificity based on optimized threshold selection.

RESULTS

A total of 508 participants (mean age, 54 years ± 15), including 507 female participants and 1 male participant, with 514 suspicious breast lesions (range, 4-72 mm, median size, 13 mm) were included. Of the lesions, 204 were malignant. The SWE-HDMI prediction model, combining quantitative parameters from SWE and HDMI, with AUC of 0.973 (95% CI 0.95-0.99), was significantly higher than the result predicted with the SWE model or HDMI model alone. With an optimal cutoff of 0.25 for the malignancy probability, the sensitivity and specificity were 95.5% and 89.7%, respectively. The specificity was further improved with the addition of clinical factors. The corresponding model defined as the SWE-HDMI-C prediction model had an AUC of 0.981 (95% CI 0.96-1.00).

CONCLUSIONS

The SWE-HDMI-C detection model, a combination of SWE estimates, HDMI quantitative biomarkers and clinical factors, greatly improved the accuracy in breast lesion characterization.

Targeted Therapy in HR+ HER2- Metastatic Breast Cancer: Current Clinical Trials and Their Implications for CDK4/6 Inhibitor Therapy and beyond Treatment Options

A metastatic state of breast cancer (MBC) affects hundreds of thousands of women worldwide. In hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) MBC, cyclin-dependent kinase (CDK)4/6 inhibitors can improve the progression-free survival (PFS), as well as the overall survival (OS), in selected patients and have been established as first- and second-line therapies. However, as MBC remains uncurable, resistance to CDK4/6 inhibitors occurs and requires alternative treatment approaches. Data on targeted therapy continue to mature, and the number of publications has been constantly rising. This review provides a summary and update on the clinical relevance, patient selection, ongoing trials of CDK4/6 inhibitors, and further targeted therapy options. It focuses on clinical aspects and practicability, as well as adverse events and patient-reported outcomes.

ARID1A Mutation in Metastatic Breast Cancer: A Potential Therapeutic Target

Distant metastasis is the principal cause of mortality for breast cancer patients. Targeting specific mutations that have been acquired during the evolution process of advanced breast cancer is a potential means of enhancing the clinical efficacy of treatment strategies. In metastatic breast cancer, ARID1A is the most prevalent mutation of the SWI/SNF complex, which regulates DNA repair, recombination, and gene transcription. The low expression of ARID1A is associated with poor disease-free survival and overall survival of patients with luminal A or HER2-rich breast cancer. In addition, ARID1A plays a prominent role in maintaining luminal characteristics and has an advantage for identifying responses to treatment, including endocrine therapies, HDAC inhibitors and CDK4/6 inhibitors. The therapeutic vulnerabilities initiated by ARID1A alterations encourage us to explore new approaches to cope with ARID1A mutant-related drug resistance or metastasis. In this review, we describe the mutation profiles of ARID1A in metastatic breast cancer and the structure and function of ARID1A and the SWI/SNF complex as well as discuss the potential mechanisms of ARID1A-mediated endocrine resistance and therapeutic potential.

Breast cancer immune microenvironment: from pre-clinical models to clinical therapies

The breast cancer tumour microenvironment (BC-TME) is characterized by significant cellular and spatial heterogeneity that has important clinical implications and can affect response to therapy. There is a growing need to develop methods that reliably quantify and characterize the BC-TME and model its composition and functions in experimental systems, in the hope of developing new treatments for patients. In this review, we examine the role of immune-activating cells (including tumour-infiltrating lymphocytes and natural killer cells) and immune inhibitory cells (including T regulatory cells, tumour-associated macrophages and myeloid-derived suppressor cells) in the BC-TME. We summarize methods being used to characterize the microenvironment, with specific attention to pre-clinical models including co-cultures, organoids, and genetically modified and humanized mouse models. Finally, we explore the implications and applications of existing preclinical data for drug development and highlight several drugs designed to alter the BC-TME in order to improve treatment outcomes for patients.

Occult Disseminated Metastatic Breast Carcinoma Presenting as Acquired Thrombotic Thrombocytopenic Purpura

Cancer-related microangiopathic hemolytic anemia (MAHA) is a rare and life-threatening condition. We present a patient who had been treated for invasive lobular breast carcinoma in clinical remission with fever and hemolytic anemia. The peripheral blood film showed MAHA and thrombocytopenia, and a functional deficiency of ADAMTS13 activity of 23% consistent with acquired thrombotic thrombocytopenic purpura. Bone marrow aspirate and trephine biopsy confirmed metastatic carcinoma. Further evaluation revealed the involvement of multiple bone sites without recurrence of the primary tumor. The patient received a daily plasma exchange with cryosupernatant and was pulsed with corticosteroids. MAHA related to breast cancer appears to be a rare occurrence.

Exocyst-mediated membrane trafficking of the lissencephaly-associated ECM receptor dystroglycan is required for proper brain compartmentalization

To assemble a brain, differentiating neurons must make proper connections and establish specialized brain compartments. Abnormal levels of cell adhesion molecules disrupt these processes. Dystroglycan (Dg) is a major non-integrin cell adhesion receptor, deregulation of which is associated with dramatic neuroanatomical defects such as lissencephaly type II or cobblestone brain. The previously established Drosophila model for cobblestone lissencephaly was used to understand how Dg is regulated in the brain. During development, Dg has a spatiotemporally dynamic expression pattern, fine-tuning of which is crucial for accurate brain assembly. In addition, mass spectrometry analyses identified numerous components associated with Dg in neurons, including several proteins of the exocyst complex. Data show that exocyst-based membrane trafficking of Dg allows its distinct expression pattern, essential for proper brain morphogenesis. Further studies of the Dg neuronal interactome will allow identification of new factors involved in the development of dystroglycanopathies and advance disease diagnostics in humans.

Expression Characteristics and Clinical Correlations of BRD1 in Colorectal Cancer Samples

The incidence of colorectal cancer (CRC), as well as subsequent patient mortality, has increased in the last decade; an unhealthy diet is considered to be the leading cause. Previous studies have shown the potential of the bromodomain containing 1 (BRD1) gene as a therapeutic target for CRC based on its specificity; however, the genetic mode of action and expression in CRC cells are yet to be investigated. In this study, target genes were screened from single-cell transcriptome sequencing data, and the collected clinical specimens were subjected to immunohistochemistry (IHC) to identify the protein expression of target genes; the results were verified in the GSE17536 array set. Receiver operating characteristic curves (ROC) and overall survival (OS) were used to test target genes as biomarkers and independent predictive markers for CRC. Based on these results, BRD1 was screened as a target gene, and IHC results showed that BRD1 protein expression in CRC was higher than that in normal tissues and was significantly upregulated in poorly differentiated (PD) CRC. ROC analysis showed that the area under the curve in the collected clinical specimens and GSE17536 were 0.6062 and 0.6094, respectively. OS analysis showed that higher BRD1 protein expression was associated with a significantly shorter survival time. In conclusion, BRD1 expression was positively correlated with PD CRC and negatively correlated with OS, indicating that BRD1 could predict the differentiation state of CRC and may be a novel predictive biomarker.