HER2-negative Breast Cancer Research Articles

A Canadian real-world, multi-center, prospective, observational study assessing the treatment duration, the treatment sequence, and the overall survival for patients treated with endocrine therapy ± targeted therapy in HR + HER2-negative advanced breast cancer.

Understanding real-world treatment patterns and their effectiveness in HR + HER2- advanced breast cancer (aBC) in Canadian patients. This was a multi-center, observational, prospective cohort study including men and pre-/peri-/postmenopausal women with HR + HER2- aBC receiving endocrine therapy (ET) or ET + targeted therapy (ET + TT). The primary objective was duration of treatment (DOT) with ET and ET + TT. Sequence of therapies, treatment patterns, and Overall Survival (OS) were also evaluated. DOT was prolonged in patients receiving ET + TT compared to ET (median DOT: ET + TT 397days vs ET 192days; Log-Rank test p value < .0001; HR = 0.66; 95% CI; 0.52, 0.85). An extended DOT was observed in ET + CDK4/6i subgroup when compared to ET (median DOT: ET + CDK4/6i 601days vs ET 192days; Log-Rank test p value < .0001). This increase was statistically significant irrespective of line of therapy at baseline (1L: median DOT: ET + CDK4/6i: 649days vs ET: 217days, p value = < .0001; 2L: median DOT: ET + CDK4/6i: 487days vs ET: 203days, p value = 0.0013; 3L: median DOT: ET + CDK4/6i: 597days vs ET: 143days therapy: p value = 0.0006). ET alone and ET + CDK4/6i were the most frequently administered therapies in both 1st (ET alone: 43.5% and ET + CDK4/6i: 43.3%) and 2nd lines (ET alone: 36.3% and ET + CDK4/6i: 24.6%). Among patients who received at least one CDK4/6i in 1st, 2nd, or 3rd line, CDK4/6i were mostly administered in 1st line (61.9%) and 2nd line (38.5%). gov ID: NCT02753686; Registration Date:20-04-2016. Results support current treatment recommendations of early introduction of CDK4/6i in HR + /HER2- aBC.

Classification of Molecular Subtypes of Breast Cancer Using Radiomic Features of Preoperative Ultrasound Images.

Radiomics has been used as a non-invasive medical image analysis technique for diagnosis and prognosis prediction of breast cancer. This study intended to use radiomics based on preoperative Doppler ultrasound images to classify four molecular subtypes of breast cancer. A total of 565 female breast cancer patients diagnosed by postoperative pathology in a hospital between 2014 and 2022 were included in this study. Radiomic features extracted from preoperative ultrasound images and clinical features were used to construct models for the classification of molecular subtypes of breast cancer. The least absolute shrinkage and selection operator (LASSO) regression was applied for the final screening of radiomic features and clinical features. Three classifiers including Logistic regression, support vector machine (SVM), and XGBoost were utilized to construct model. Model performance was assessed primarily by the area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI). The mean age of these patients was 54.58 (± 11.27) years. Of these 565 patients, 130 (23.01%) were Luminal A subtype, 329 (58.23%) were Luminal B subtype, 65 (11.50%) were human epidermal growth factor receptor-2 (HER-2) subtype, and 41 (7.26%) were triple negative (TN) subtype. A total of 12 clinical features and 8 radiomic features were selected for model construction. The AUC of the SVM model [0.826 (95%CI 0.808-0.845)] was higher than that of the Logistic regression model [0.776 (95%CI 0.756-0.796)] and the XGB model [0.800 (95%CI 0.779-0.821)] in the multiple classification of breast cancer. For the single classification of breast cancer, the AUC of the SVM model was 0.710 (95%CI 0.660-0.760) for Luminal A subtype, 0.639 (95%CI 0.592-0.685) for Luminal B subtype, 0.754 (95%CI 0.695-0.813) for HER-2 subtype, and 0.832 (95%CI 0.771-0.892) for TN subtype. The SVM model with radiomic features combined with clinical features shows good performance in classifying four molecular subtypes of breast cancer.

GradeDiff-IM: an ensembles model-based grade classification of breast cancer

Cancer grade classification is a challenging task identified from the cell structure of healthy and abnormal tissues. The practitioners learns about the malignant cell through the grading and plans the treatment strategy accordingly. A major portion of researchers used DL models for grade classification. However, the behavior of DL models is hidden type, it is unknown which features contribute to the accuracy and how the features are chosen for grading. To address the issue the study proposes a Grade Differentiation Integrated Model (GradeDiff-IM) to classify the grades G1, G2, and G3. In GradeDiff-IM, different ML models, are used for grade classification from clinical and pathological reports. The biological-significant features with ranking technique prioritize influential features are used to identify grades G. Subsequently, histopathological images are used by DL models for grade classification and compared with ML models. Instead of employing a single ML model, the GradeDiff-IM model uses the stack-ensembled approach to improve the grade G classification performance. The maximum accuracy is attained by stacking G1-98.2, G2-97.6, and G3-97.5. The proposed study shows that the ML ensemble model is more accurate than the DL models. As a result, the proposed model achieved higher accuracy for G by implementing the stacking technique than the other state-of-the-art models.

Real-World Experience with CDK4/6 Inhibitors in the First-Line Palliative Setting for HR+/HER2- Advanced Breast Cancer.

CDK4/6 inhibitors in combination with aromatase inhibitors (AIs) are the standard first-line treatment for hormone receptor-positive (HR+), HER2-negative (HER2-) metastatic breast cancer. Landmark trials have demonstrated a comparable progression-free survival (PFS) across CDK4/6 inhibitors, but the overall survival (OS) outcomes have varied. This study aimed to evaluate the real-world PFS and OS for palbociclib and ribociclib when combined with AIs in patients with HR+/HER2- advanced breast cancer. This was a retrospective chart review of adult patients with HR+/HER2- metastatic breast cancer treated at a single academic center between 1 January 2015 and 1 December 2022. The baseline demographics, clinical characteristics, and treatment details were extracted. A Kaplan-Meier analysis was used to estimate the PFS and OS, and differences between the treatment groups were assessed using the log-rank test. Cox proportional hazards models were constructed to adjust for confounding factors. Seventy-five patients were included in the final analysis. The cohort was predominantly female (98.7%) and postmenopausal (77.3%), with 52.0% having de novo stage IV disease. Palbociclib was prescribed to 74.7% of the patients, and ribociclib to 25.3%. The patients receiving ribociclib were significantly younger (57.6 vs. 67.5 years, p = 0.013) and more likely to be premenopausal (42.1% vs. 5.4%, p < 0.001). The real-world median PFS and OS for palbociclib were 20.3 months (95% CI: 14.8-46) and 37.2 months (95% CI: 20.3-not reached [NR]), respectively. For ribociclib, the median PFS and OS were not reached. The Cox proportional hazards models adjusting for age and menopausal status found no significant differences between ribociclib and palbociclib for the PFS (HR = 0.92, p = 0.86) or OS (HR = 0.95, p = 0.92). In this real-world analysis, palbociclib demonstrated a median PFS consistent with the results from landmark trials, although the observed OS was shorter. The ribociclib-treated patients had a numerically longer PFS and OS compared with those treated with palbociclib, but the differences were not statistically significant. The discontinuation rates were similar between the two groups.

An improved soft voting-based machine learning technique to detect breast cancer utilizing effective feature selection and SMOTE-ENN class balancing

Breast cancer is the primary cause of death among women globally, and it is becoming more prevalent. Early detection and precise diagnosis of breast cancer can reduce the disease’s mortality rate. Recent advances in machine learning have benefited in this regard. However, if the dataset contains duplicate or irrelevant features, machine learning-based algorithms are unable to give the intended results. To address this issue, a series of effective strategies such as the Robust Scaler is used for data scaling, Synthetic Minority Over-sampling Technique-Edited Nearest Neighbor (SMOTE-ENN) is utilized for class balancing, and Boruta and Coefficient-Based Feature Selection (CBFS) methods are employed for feature selection. For more accurate and reliable breast cancer classification, this study proposes a soft voting-based ensemble model that harnesses the capabilities of three diverse classifiers: Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). To show the efficiency of the proposed ensemble model, it is compared with its base classifiers using the Wisconsin Diagnosis Breast Cancer Dataset (WDBC). The results of the experiment revealed that the soft voting classifier achieved high scores with an accuracy of 99.42%, precision of 100.0%, recall of 98.41%, F1 score of 99.20%, and AUC of 1.0 when it is trained on optimal features obtained from the CBFS method. However, with tenfold cross-validation (10-FCV), it shows a mean accuracy score of 99.34%. A comprehensive analysis of the results revealed that the suggested technique outperformed the existing state-of-the-art methods due to the efficient data preprocessing, feature selection, and ensemble methods.

The impact of adverse childhood experiences on pain and subjective cognitive decline in patients treated for localized breast cancer: The mediating role of sense of coherence, sense of danger and psychosocial distress and danger.

Pain and subjective cognitive decline (SCD) are common sequala of breast cancer (BC) treatment. Adverse childhood experiences (ACEs) are associated with pain and adverse health outcomes in noncancer population. Sense of coherence (SOC) reflects the disposition that life is manageable and predictable. Sense of danger (SOD) is the extent of perceived danger to oneself and family from a specific stressor. We aimed to assess if ACEs are associated with pain and SCD in patients treated for localized BC, and whether decreased SOC, increased SOD from BC, and increased psychological distress mediate these links. This study is a primary analysis of an on-going prospective trial, recruiting patients with localized BC before (neo) adjuvant oncological therapy. Patients completed validated questionnaires on ACEs, pain, SCD, SOC, SOD, and psychosocial distress. Demographic and clinical data were also collected. We performed an analysis of baseline assessments in 127 patients. After controlling for demographic and clinical factors that correlated with study variables, serial mediation analyses confirmed that ACEs were associated with increased pain and SCD. These links were mediated by decreased SOC, followed by increased SOD, followed by increased psychosocial distress (all p's<0.001). The models explained 50.14% of the variance in pain and 43.37% of the variance in SCD. Our study suggests that ACEs increase the risk of pain and SCD in patients with localized BC, mediated by SOC, SOD, and psychosocial distress. These factors should be addressed when aiming to reduce symptom burden in BC patients.

Efficacy and Safety of CDK4/6 Inhibitors: A Focus on HR+/HER2- Early Breast Cancer.

Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) have revolutionized the treatment of hormone-receptor positive (HR+), HER2 negative (HER2-) metastatic breast cancer, and are now also established agents in the treatment of high-risk and intermediate-risk HR+ early breast cancer. Several strategies regarding CDK4/6i combinations or continuation beyond progression have been successfully evaluated in the metastatic setting, and are considered a standard of care. Mechanism of action of and resistance mechanisms against CDK4/6i in addition to endocrine resistance represent an important research topic, important for the treatment of HR+ breast cancer. Clinically, CDK4/6i are efficient substances that are usually well tolerated. However, side effects differing between the substances have been reported, and might lead to treatment discontinuation, including in the early disease setting. In the adjuvant setting, the addition of palbociclib to standard endocrine treatment has not improved outcomes, whereas large randomized phase III trials have demonstrated significant disease-free survival benefit for the addition of ribociclib (NATALEE trial) and abemaciclib (monarchE trial). Patient selection, treatment duration, endocrine backbone therapy, and other study details differ between these pivotal trials. This review focuses on both the scientific background as well as all available clinical data of CDK4/6i, with particular emphasis on their use in early breast cancer.

Elacestrant in women with estrogen receptor-positive and HER2-negative early breast cancer: results from the preoperative window-of-opportunity ELIPSE trial.

Elacestrant has shown significantly prolonged progression-free survival compared to standard-of-care endocrine therapy in estrogen receptor-positive (ER-positive), HER2-negative metastatic breast cancer (BC), while potential benefit in early-stage disease requires further exploration. The SOLTI-ELIPSE window-of-opportunity trial investigated the biological changes induced by a short course of preoperative elacestrant in postmenopausal women with early BC. Eligible patients with untreated T1c (≥1.5 cm)-T3, N0, ER-positive/HER2-negative BC with locally assessed Ki67≥10% received elacestrant at a daily dose of 345mg for 4 weeks. The primary efficacy endpoint was complete cell cycle arrest (CCCA), defined as Ki67≤2.7%, at day 28. Overall, 22 patients were evaluable for the primary endpoint. Elacestrant was associated with a CCCA rate of 27.3% and a statistically significant geometric mean change of -52.9% (p=0.007; 95%CI, -67.4 to -32.1). The treatment with elacestrant led to a shift toward a more endocrine-sensitive and less proliferative tumor phenotype based on PAM50-based gene signatures. Elacestrant increased the expression of immune-response genes (GZMB, CD4, CD8A) and suppressed proliferation and estrogen-signaling (MKI67, ESR1, and AR). These biological changes were independent of the levels of Ki67 suppression at day 28. Most common adverse events were grade 1: anemia (21.7%), hot flushes, constipation and abdominal pain (8.7%, each). One patient experienced a grade 3 cutaneous rash leading to treatment discontinuation. No other serious adverse events were reported. Preoperative treatment with elacestrant in early BC demonstrated relevant biological and molecular responses and exhibited a manageable safety profile. These findings support further investigation of elacestrant in the early setting.

LightweightUNet: Multimodal Deep Learning with GAN-Augmented Imaging Data for Efficient Breast Cancer Detection.

Breast cancer ranks as the second most prevalent cancer globally and is the most frequently diagnosed cancer among women; therefore, early, automated, and precise detection is essential. Most AI-based techniques for breast cancer detection are complex and have high computational costs. Hence, to overcome this challenge, we have presented the innovative LightweightUNet hybrid deep learning (DL) classifier for the accurate classification of breast cancer. The proposed model boasts a low computational cost due to its smaller number of layers in its architecture, and its adaptive nature stems from its use of depth-wise separable convolution. We have employed a multimodal approach to validate the model's performance, using 13,000 images from two distinct modalities: mammogram imaging (MGI) and ultrasound imaging (USI). We collected the multimodal imaging datasets from seven different sources, including the benchmark datasets DDSM, MIAS, INbreast, BrEaST, BUSI, Thammasat, and HMSS. Since the datasets are from various sources, we have resized them to the uniform size of 256 × 256 pixels and normalized them using the Box-Cox transformation technique. Since the USI dataset is smaller, we have applied the StyleGAN3 model to generate 10,000 synthetic ultrasound images. In this work, we have performed two separate experiments: the first on a real dataset without augmentation and the second on a real + GAN-augmented dataset using our proposed method. During the experiments, we used a 5-fold cross-validation method, and our proposed model obtained good results on the real dataset (87.16% precision, 86.87% recall, 86.84% F1-score, and 86.87% accuracy) without adding any extra data. Similarly, the second experiment provides better performance on the real + GAN-augmented dataset (96.36% precision, 96.35% recall, 96.35% F1-score, and 96.35% accuracy). This multimodal approach, which utilizes LightweightUNet, enhances the performance by 9.20% in precision, 9.48% in recall, 9.51% in F1-score, and a 9.48% increase in accuracy on the combined dataset. The LightweightUNet model we proposed works very well thanks to a creative network design, adding fake images to the data, and a multimodal training method. These results show that the model has a lot of potential for use in clinical settings.

Predictive and prognostic value of 18F-FES PET/CT for patients with recurrent or metastatic breast cancer treated with endocrine therapy plus cyclin-dependent kinase 4/6 inhibitors.

Estrogen receptor (ER) expression and heterogeneity affect endocrine therapy efficacy. 18F-fluoroestradiol (18F-FES) PET/CT is an effective non-invasive method to analyze systemic ER expression. This study aimed to examine the predictive/prognostic value of 18F-FES PET/CT for patients treated with endocrine therapy plus cyclin-dependent kinase 4/6 (CDK4/6) inhibitors. Patients were identified from a prospective cohort for post-marketing surveillance of 18F-FES enrolled between April 2021 and April 2023 at Asan Medical Center. In this retrospective analysis, patients with ER-positive, HER2-negative recurrent/metastatic breast cancer who underwent CDK4/6 inhibitor-based endocrine therapy and pre-treatment 18F-FES PET/CT were included. A total of 127 women were included. The endocrine therapy used was aromatase inhibitors in 96 patients (76%) and fulvestrant in 31 patients (24%). There were 25 (20%) and 102 (80%) patients in the "with FES-negative" (3 completely negative, 22 mixed) and "FES-positive" groups, respectively. 18F-FES status correlated with progression-free survival (PFS) following endocrine therapy with CDK4/6 inhibitors and overall survival (OS) ("with FES-negative" group: hazard ratio for PFS, 3.9, p < 0.001; for OS, 3.7, p = 0.008). Reduced benefit from endocrine treatment in the "with FES-negative" group was consistent across subgroups including menopausal status, endocrine sensitivity, and treatment regimen. ER expression determined by 18F-FES PET/CT predicted the efficacy of CDK4/6 inhibitor-based endocrine therapy and was prognostic for survival in recurrent/metastatic ER-positive, HER2-negative breast cancer.

Exploration of Novel Therapeutic Targets for Breast Carcinoma and Molecular Docking Studies of Anticancer Compound Libraries with Cyclin-dependent Kinase 4/6 (CDK4/6): A Comprehensive Study of Signalling Pathways for Drug Repurposing.

This study aims to identify and evaluate promising therapeutic proteins and compounds for breast cancer treatment through a comprehensive database search and molecular docking analysis. Breast cancer (BC), primarily originating from the terminal ductal-lobular unit of the breast, is the most prevalent form of cancer globally. In 2020, an estimated 2.3 million new cases were reported, resulting in approximately 685,000 deaths. Mutations in the BRCA1 and BRCA2 genes are well-established in hereditary breast cancer. The identification of effective therapeutic proteins for BC remains a complex and evolving area of research. This study aims to identify and evaluate promising therapeutic proteins and compounds specific to breast cancer through a comprehensive database search and molecular docking analysis. A rigorous search was conducted within the National Cancer Institute (NCI), NCI Metathesaurus, SIGnaling Network Open Resource (SIGNOR), Human Protein Atlas (HPA), and the Human Phenotype Ontology (HPO) to shortlist proteins linked to BC (CUI C0678222). Recent studies were reviewed to understand the administration of CDK4/6 inhibitors (palbociclib, ribociclib, abemaciclib) combined with endocrine therapy for HR-positive and HER2-negative breast cancer. Anticancer compound libraries available at ZINC and PubChem were analyzed. Compounds were evaluated based on their binding energies with CDK4 protein, a rationally selected druggable target. Key proteins linked to breast cancer were identified through database searches. Proliferation, apoptosis, and G1/S transition pathways were frequently found dysregulated in breast cancer. ZINC13152284 exhibited the strongest binding energy at -10.9 Kcal/mol, followed by ZINC05492794 with a binding energy of -10.4 Kcal/mol. Preexisting drugs showed lower binding energies with the CDK4 protein. The study highlights the importance of drug repurposing as a strategy for the safe and effective treatment of breast cancer. Synthetic inhibitors often cause severe side effects, emphasizing the need for novel targets and compounds with better therapeutic profiles. Molecular docking identified promising compounds from the ZINC database, suggesting potential new avenues for breast cancer therapy.

Physics-informed hybrid models for enhanced precision in breast cancer classification

Breast cancer poses a significant health and safety risk for women globally, making early detection vital for effective treatment. Artificial intelligence (AI) can enhance early detection by differentiating between cancerous and non-cancerous breast tissues. Existing AI approaches for breast cancer detection face limitations, including overfitting, the need for fine-tuning, and loss of fine details. This research introduces an adaptive hybrid model infused with physics insights framework to address these challenges. The transformed dynamic and adaptive filter is proposed for data preprocessing and achieving a balance between noise reduction and edge preservation, thereby retaining critical image structures. Then, robotic physics informed model is proposed which contains diffusion convolution, batch normalization layers, activation layers, pooling layers, optimization layer and ends with a classification layer. The proposed approach compared with the baseline AOADL-HBCC, DTLRO-HCBC, Inception v3, Inception v3 Long Short Term Memory, Inception v3 Bi-directional Long Short Term Memory, VGG-16, and Residual Network such as 96.77%, 93.52%, 81.67%, 91.46%, 92.05%, 80.15%, and 82.18%, respectively. The accuracy of the proposed approach is 99.56%. This demonstrates our model’s superior performance and effectiveness in breast cancer detection.

Neutropenia and Palbociclib in Advanced Luminal HER2-Negative Breast Cancer: Experience of the Mohammed VI Oncology Center at CHU IBN ROCHD, Casablanca, Morocco

Background: Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors, including palbociclib, are approved for treating advanced hormone receptor (HR)-positive/HER2-negative breast cancer. Although these agents are generally safe and effective, their use is associated with high rates of hematological adverse events, particularly neutropenia, compared to hormone therapy alone. Dose reductions and adjustments are recommended for managing these toxicities. We evaluated the neutropenia profiles, dose management of palbociclib, and clinical responses after dose reductions. Methodology: This retrospective study included 93 patients with advanced HR-positive/HER2-negative breast cancer treated with palbociclib and hormone therapy at the Mohammed VI Cancer Center, CHU IBN ROCHD, Casablanca, over three years (January 2021 to December 2023). Results: Ninety-three patients received at least one dose of palbociclib with hormone therapy. The mean patient age was 56 years, and 13 had visceral disease. The frequencies of neutropenia of any grade/grade 3/grade 4 were 81.72%/60.22%/7.53%. Twenty-nine patients required a dose reduction from 125 to 100 mg of palbociclib, and three patients required further reduction to 75 mg. The incidence of neutropenia of any grade and grade 3/4 decreased after dose reduction. No cases of febrile neutropenia or permanent treatment discontinuation were reported. Conclusion: Palbociclib combined with hormone therapy was well tolerated. Neutropenia, the most frequently reported adverse event in women with advanced HR-positive/HER2-negative breast cancer, was primarily transient and manageable through dose adjustments for patients experiencing grade ≥3 neutropenia, without compromising efficacy. Keywords: Advanced breast cancer; Cyclin-dependent kinase; Palbociclib; Neutropenia; Dose reduction.

The tRF-33/IGF1 Axis Dysregulates Mitochondrial Homeostasis in HER2-Negative Breast Cancer.

Transfer RNA-derived small RNAs (tsRNAs), a recently identified non-coding RNA subset, are mainly classified into tRNA-derived small RNA fragments (tRFs) and tRNA-derived stress-induced RNAs (tiRNAs). tsRNAs dysregulation is frequently observed in numerous cancer types, suggesting involvement in tumorigenesis. However, their functions in breast cancer (BC) remain to be fully understood. Here, it was discovered that tRF-33-MEF91SS2PMFI0Q (tRF-33), derived from mature tRNA-LysTTT, was markedly upregulated in HER2-negative BC cells and tissue samples. tRF-33 stimulated the proliferation, migration, and invasiveness of BC cells in vitro and facilitated tumor progression in vivo. Mechanistically, tRF-33 was found for the first time to bind directly to the 3'-UTR of IGF1, resulting in downregulation of both its mRNA and protein and thus affecting mitochondrial homeostasis and progression of BC. These results demonstrate a novel tsRNA modulatory mechanism and a potential direction for treating HER2-negative BC.

XGBoost Enhances the Performance of SAFE: A Novel Microwave Imaging System for Early Detection of Malignant Breast Cancer.

Breast cancer is a significant global health concern, and early detection is crucial for improving patient outcomes. Mammography is widely used but has limitations, particularly for younger women with denser breasts. These include reduced sensitivity, false positives, and radiation risks. This highlights the need for alternative screening methods. In this study, we assess the performance of SAFE (Scan and Find Early), a novel microwave imaging device, in detecting breast cancer in a larger patient cohort. Unlike previous studies that predominantly relied on cross-validation, this study employs a more reliable, independent evaluation methodology to enhance generalizability. We developed an XGBoost model to classify breast cancer cases into positive (malignant) and negative (benign or healthy) groups. The model was analyzed with respect to key factors such as breast size, density, age, tumor size, and histopathological findings. This approach provides a better understanding of how these factors influence the model's performance, using an independent evaluation methodology for increased reliability. Our results demonstrate that SAFE exhibits high sensitivity, particularly in dense breasts (91%) and younger patients (83%), suggesting its potential as a supplemental screening tool. Additionally, the system shows high detection accuracy for both small (<2 cm) and larger lesions, proving effective in early cancer detection. This study reinforces the potential of SAFE to complement existing screening methods, particularly for patients with dense breasts, where mammography's sensitivity is reduced. The promising results warrant further research to solidify SAFE's clinical application as an alternative screening tool for breast cancer detection.

Hormone Receptor-Positive HER2-Negative/MammaPrint High-2 Breast Cancers Closely Resemble Triple-Negative Breast Cancers.

The MammaPrint (MP) prognostic assay categorizes breast cancers into high- and low-risk subgroups, and the high-risk group can be further subdivided into high-1 (MP-H1), and very high-risk high-2 (MP-H2). The aim of this analysis was to assess clinical and molecular differences between the hormone receptor-positive (HR+)/HER2-negative MP-H1, -H2, and triple-negative (TN) MP-H1 and -H2 cancers. Pretreatment gene expression data from 742 HER2-negative breast cancers enrolled in the I-SPY2 neoadjuvant trial were used. Prognostic risk categories were assigned using the MP assay. Transcriptional similarities across the four receptor and prognostic groups were assessed using principal component analyses and by identifying differentially expressed genes. We also examined pathologic complete response rates and event-free survivals by risk group. Principal component analysis showed that HR+/MP-H2 tumors clustered with TN/MP-H2 cancers. Only 125 genes showed differential expression between the HR+/MP-H2 and TN/MP-H2 cancers, whereas 1,465 genes were differentially expressed between HR+/MP-H2 and -H1. Gene set analysis revealed similarly high expression of cell cycle, DNA repair, and immune infiltration-related pathways in HR+/MP-H2 and TN/MP-H2 cancers. HR+/MP-H2 cancers also showed low estrogen receptor-related gene expression. Pathologic complete response rates were similarly high in TN/MP-H2 and HR+/MP-H2 cancers (42% vs. 30.5%; P = 0.11), and MP-H2 cancers with residual cancer had similarly poor event-free survival regardless of estrogen receptor status. In conclusion, HR+/MP-H2 cancers closely resemble TN breast cancers in transcriptional and clinical features and benefit from similar treatment strategies.

Classification of Breast Cancer Through the Perspective of Cell Identity Models.

The mammary epithelium has an inner luminal layer that contains estrogen receptor (ER)-positive hormone-sensing cells and ER-negative alveolar/secretory cells, and an outer basal layer that contains myoepithelial/stem cells. Most human tumours resemble either hormone-sensing cells or alveolar/secretory cells. The most widely used molecular classification, the Intrinsic classification, assigns hormone-sensing tumours to Luminal A/B and human epidermal growth factor 2-enriched (HER2E)/molecular apocrine(MA)/luminal androgen receptor (LAR)-positive classes, and alveolar/secretory tumours to the Basal-like class. Molecular classification is most useful when tumours have classic invasive carcinoma of no special type (NST) histology. It is less useful for special histological types of breast cancer, such as metaplastic breast cancer and adenoid cystic cancer, which are better described with standard pathology terms. Compared to mice, humans show a strong bias towards making tumours that resemble mammary hormone-sensing cells. This could be caused by the formation in adolescence of der(1;16), a translocation through the centromeres of chromosomes 1 and 16, which only occurs in humans and could trap the cells in the hormone-sensing state.

Tinagl1 restores tamoxifen sensitivity and blocks fibronectin-induced EMT by simultaneously blocking the EGFR and β1-integrin/FAK signaling pathways in tamoxifen-resistant breast cancer cells.

Tamoxifen (TAM) is employed to treat premenopausal ER-positive breast cancer patients, but TAM resistance is the main reason affecting its efficacy. Thus, addressing TAM resistance is crucial for improving therapeutic outcomes. This study explored the potential role of Tinagl1, a secreted extracellular matrix protein, whose expression is compromised in TAM-resistant MCF-7 breast cancer cells (MCF-7R). We discovered that Tinagl1 plays a pivotal role in countering TAM resistance by inhibiting the EGFR and β1-integrin/focal adhesion kinase (FAK) signaling pathways, both of which are abnormally activated in MCF-7R cells and contribute to the resistance mechanism. Our data showed that the expression level of Tinagl1 in MCF-7R cells was lower compared to their wild-type counterparts, and TAM could further reduce Tinagl1 expression in MCF-7R cells, which was consistent with our microarray results. Moreover, Tinagl1 could restore the sensitivity of MCF-7R cells to TAM and inhibit the motility of MCF-7R cells by regulating epithelial-mesenchymal transition (EMT) in vitro and in vivo experiments. In addition, the level of Tinagl1 in TAM-resistant breast cancer samples was significantly lower than that in their matched primary tumors. Analysis of an online database further indicated that high Tinagl1 expression correlates with better recurrence-free survival (RFS), particularly in patients with ER-positive, HER2-negative breast cancer. Overall, this study positions Tinagl1 not only as a potential prognostic marker but also as a promising therapeutic target.

CDK4/6 Inhibitor Resistance in ER+ Breast Cancer.

The cyclin-dependent kinases 4 and 6 inhibitors are the mainstay of treatment for patients with hormone receptor-positive and HER2-negative breast cancer. The ability of these drugs to improve the outcome of patients both in the metastatic and the early setting has been largely demonstrated. However, resistance, either de novo or acquired, represents a major clinical challenge. In the past years, efforts have been made to identify biomarkers that might help in a better selection of patients or to unravel the mechanisms leading to resistance in order to develop new therapeutic strategies to overcome it. Alterations of cell cycle-related genes and proteins are among the best characterized markers of resistance, and pathways impacting the cell cycle, including nuclear and growth factor receptors signaling, have been thoroughly investigated. Despite this, to date, cyclin-dependent kinases 4 and 6 inhibitors are administered based only on the hormone receptor and HER2 status of the tumor, and patients progressing on therapy are managed with currently available treatments. Here we summarize present knowledge on the cyclin-dependent kinases 4 and 6 inhibitors' mechanisms of action, efficacy data, and mechanisms of resistance.

Recent advances of antibody-drug conjugates in treating breast cancer with different HER2 status.

Despite the availability of multiple treatment options for breast cancer, challenges such as adverse events, drug resistance, and disease progression persist for patients. The identification of human epidermal growth factor receptor 2 (HER2) as an oncogenic driver in a subset of breast cancers, alongside the development of HER2-targeted therapies, has significantly improved the prognosis of HER2-amplified breast cancers. However, therapeutic options remain limited for HER2-overexpressing or HER2-negative breast cancers. In response to this gap, antibody-drug conjugates (ADCs) have emerged as a promising approach. ADCs combine the specificity of monoclonal antibodies with the cytotoxic effects of chemotherapy, which allows for the targeted delivery of a cytotoxic payload to cancer cells. ADCs have been used as adjuvant chemotherapeutic treatments and salvage therapies across various breast cancer subtypes, which have greatly improved the prognosis of breast cancer patients. Numerous ongoing clinical trials seek to optimize dosing strategies and identify patient populations that would benefit most from ADCs. This review presents an updated and comprehensive overview of emerging investigational ADCs for treating breast cancer patients with various HER2 subtypes. These ADCs are spearheading a new era in targeted cancer therapy, promising to innovate treatment paradigms for both HER2-positive and HER2-low breast cancers.