HER2-negative Breast Cancer Research Articles

Impact of low HER2 expression on response to CDK4/6 inhibitor treatment in advanced HR + /HER2- breast cancer: a multicenter real-world data analysis.

CDK4/6 inhibitors (CDK4/6i) represent the first-line therapy approach of choice for patients with hormone receptor-positive, HER2-negative advanced breast cancer (HR + /HER-ABC). Approximately 50% of HR + /HER2-ABC displays low HER2 expression (HER2 low). Recent data emerging from the DESTINY-Breast04 trial demonstrated practice-changing efficacy of the antibody-drug conjugate trastuzumab deruxtecan (T-DXd) in patients with low HER2 expression. Here, we aimed to analyze the impact of low HER2 expression on CDK4/6i therapy response in a well-characterized multicenter HR + /HER-ABC cohort. Patients diagnosed with HR + /HER2-ABC who were treated with CDK4/6i in clinical routine between November 2016 and December 2020 at four certified German Breast Cancer Centers were retrospectively identified. The cohort was stratified according to graduation of positivity in HER2 immunohistochemistry (IHC; HER2 zero = IHC score 0 and HER2 low = IHC score 1 + , 2 + /fluorescence in situ hybridization negative). Subgroups were analyzed with regard to progression-free survival (PFS) following CDK4/6i initiation. The study cohort comprised n = 448 patients. For n = 311 patients, HER2 status from the metastatic site was available. n = 91 (29.3%) cases were HER2 zero and n = 220 cases (70.7%) were HER2 low. There was no significant difference in PFS between the two groups (PFS: 17months versus 18months, log-rank p = 0.42). Further, we examined the influence of HER2 expression changes between primary and metastatic tissue (n = 171; HER2 gain/HER2 loss/HER2 stable expression) on CDK4/6i treatment response. Again, there was no significant difference between these three groups, respectively (PFS: 16months versus 13months versus 17months, log-rank p = 0.86). In our analysis, HER2 status did not have a significant impact on treatment response to CDK4/6i.

A Hierarchically Discriminative Loss with Group Regularization for Fine-Grained Image Classification

Fine-grained visual classification targets the discrimination of subordinate categories within broader classes, such as avian species, aerial crafts, and notably, in medical diagnostics like breast cancer. In the domain of breast cancer classification, there has been an emergence of numerous fine-grained models leveraging pathological images. Elevating the interpretability of the model’s discriminative processes among these nuanced categories holds promise in enhancing the transparency of AI decision-making. However, the effective utilization of hierarchical label structures within medical data remains a crucial consideration. In this work, we explore how the label hierarchy can be used to better learn subtle feature embeddings. We observe that the semantic relationships between fine-grained categories can help to analyze the misclassified samples. To this end, with the label hierarchy, we introduce two novel losses to cultivate subtle feature representations, coordinate with feature learning, and align with the principles of Explainable AI (XAI). First, we propose hierarchically discriminative loss to enhance the interactions between fine- and coarse-level features, which can help reinforce the discriminability of fine-grained features for reducing false predictions belonging to the out-group relation. Second, we introduce the in-group regularization loss to establish the interactions between the target class and those confusing classes belonging to the in-group relation. Treating another confusing class as a distraction can regularize feature learning of the target class. Thus, it allows the network to discover more discriminative features and reduces in-group false predictions. Five commonly used datasets for fine-grained classification are extensively evaluated. Our experimental results validate the effectiveness of our proposed novel losses when compared to state-of-the-art methods that utilize hierarchical multi-granularity labels.

Advanced deep learning strategies for breast cancer image analysis

One of the leading causes of cancer-related deaths in women is breast cancer (BC). Patients' chances of survival are improved when this ailment is diagnosed in a timely manner and suitable treatments are prescribed. Thus, the chance of survival increases with the early detection of BC. Deep learning neural networks have garnered significant attention in recent years for use in BC screening, identification, and classification. Even though some encouraging results have surfaced, more improvement and confirmation are necessary. In this regard, the creation and comparison of many deep learning approaches for the early identification and classification of BC from mammography pictures is the main focus of our research. In this study, Innovative deep learning methods are developed by freezing the first 40 layers an dropping the 43 layers of MobileNetV2, InceptionV3 and DenseNet121 pretrained models, this approach was reinforced by using Data augmentation techniques. Our suggested methodologies are tested through simulations on MIAS dataset and SA private dataset. The three modified models achieved very high classification accuracies, specifically the modified DenseNet121 model, which reached 99,1% on MIAS dataset and 98,8% on SA dataset.

Irish national real-world analysis of the clinical and economic impact of 21-gene oncotype DX® testing in early-stage, 1-3 lymph node-positive, oestrogen receptor-positive, HER2-negative, breast cancer.

The treatment landscape of Oestrogen receptor-positive (ER-positive) breast cancer is evolving, with declining chemotherapy use as a result of Oncotype DX Breast Recurrence Score® testing. Results from the SWOG S1007 RxPONDER trial suggest that adjuvant chemotherapy may benefit some premenopausal women with ER-positive, HER2-negative disease with 1-3 positive lymph nodes (N1), and a Recurrence Score® (RS) of ≤ 25. Postmenopausal women with similar characteristics did not benefit from adjuvant chemotherapy. We examine the clinical and economic impact of Oncotype DX® testing on treatment decisions in patients with N1 disease in Ireland using real world data. From March 2011 to October 2022, a retrospective, cross-sectional observational study was performed of patients with ER-positive, HER2-negative N1 breast cancer, who had Oncotype DX testing across 5 of Ireland's largest cancer centres. Patients were classified into low risk (RS 0-13), intermediate risk (RS 14-25) and high risk (RS > 25). Data were collected via electronic patient records. Information regarding costing was provided primarily by pre-published sources. A total of 828 N1 patients were included in this study. Post Oncotype DX testing, 480 patients (58%) were spared chemotherapy. Of the patients who had a change in chemotherapy recommendation based on Oncotype DX testing, 271 (56%), 205 (43%), 4 (1%) had a RS result of 0-13, 14-25 and > 25 respectively. Use of Oncotype DX testing was associated with a 58% reduction in chemotherapy administration overall. This resulted in estimated savings of over €6 million in treatment costs. Deducting the assay cost, estimated net savings of over €3.3 million were achieved. Changes in the ordering demographics of Oncotype DX tests were identified after RxPONDER data were presented, with increased testing in women ≥ 50years and a reduction in proportion of tests ordered for women < 50years. Between 2011 and 2022, assay use resulted in a 58% reduction in chemotherapy administration and net savings of over €3.3 million.

Quality of life in women with early-stage and metastatic hormone receptor-positive, HER2-negative breast cancer receiving endocrine therapy.

Early discontinuation of endocrine therapy (ET) is higher among patients with early breast cancer (EBC) compared to patients with metastatic hormone receptor-positive (HR+) breast cancer (MBC). In our clinical experience the reasons for this may include a significant burden of ET side effects impacting quality of life (QOL) in patients with EBC. We hypothesized that QOL is lower in patients with HR + EBC compared to patients with HR + MBC on ET. We conducted a cross-sectional observational study to assess QOL utilizing FACT-ES & EORTC QLQ C30 tools among patients with EBC and MBC receiving ET across 5 Irish hospitals. A total of 417 patients were enrolled-EBC (79% n = 331) and MBC 21% (n = 86). Using the FACT-ES, we found no difference in overall QOL by stage (139.2 vs 141, P = .33). Patients with HR + MBC had a lower symptom burden from ET compared to HR + EBC (61.4 vs 54, P < .01). In adjusted multivariate linear regression models, there was no difference in QOL for patients with EBC and MBC receiving ET. There was no significant difference in overall QOL for patients with EBC and MBC. However, patients with EBC experienced more endocrine symptoms. In adjusted multivariate linear regression models, the stage did not predict QOL. Our results suggest that endocrine symptoms are significant contributors to impaired QOL for patients with EBC but the role of other determinants of QOL (eg, stage) is less clear. Future work could include the development of stage-specific QOL tools and utilization of electronic patient-reported outcomes (ePROs) to identify and manage emergent toxicities.

Real world evidence study to assess incidence, treatment patterns, clinical outcomes, and health care resource utilization in early-stage, high-risk HER2-negative breast cancer in Alberta, Canada

BackgroundData are needed to improve the current understanding of the epidemiology of patients with high-risk, HER2-negative, early breast cancer (eBC) (hormone receptor positive [HR+]/HER2-negative BC and triple-negative BC [TNBC]). Patients and MethodsThis retrospective longitudinal cohort study used real-world, population-level data that included all individuals newly diagnosed with high-risk, HER2-negative eBC in Alberta, Canada, between 2010-2019. Data on treatment, laboratory results and pathology findings were collected through electronic health records and administrative databases. ResultsThe annual cumulative incidence of high-risk, HER2-negative eBC ranged from 6-9% of all incident BC cases. Individuals with TNBC were more likely to be younger, had stage II disease, grade 3 histology and received systemic therapy at a community centre (p < 0.05) compared to individuals with HR+/HER2-negative eBC. Only 14% of individuals diagnosed in 2010-2017 underwent germline BRCA testing post-diagnosis. Neoadjuvant systemic therapy was given to 37% of individuals. Adjuvant systemic therapy use increased from 77% (2012-2015) to 84% (2019). The 5-year overall survival (OS) from initiation of adjuvant systemic therapy or date of surgery (for individuals who did not receive adjuvant systemic therapy) was 77% (95% CI: 75 to 79). OS was significantly worse among individuals who were older, had grade 3 histology, had stage III disease, or had nodal involvement (p < 0.05). OS among individuals with TNBC between 2016-2019 who initiated adjuvant capecitabine was markedly worse compared to the overall cohort (2-year OS: 70% vs. 89%). ConclusionOutcomes analyses in this high-risk, HER2-negative eBC population suggest a continued unmet clinical need. Micro abstractWe characterized treatment patterns and clinical outcomes in early-stage, high-risk HER2-negative breast cancer (BC) (HR+/HER2-negative BC and triple-negative BC). Adjuvant systemic therapy use increased from 77% (2012-2015) to 84% (2019). The 5-year overall survival from initiation of adjuvant systemic therapy or date of surgery (for individuals who did not receive adjuvant systemic therapy) was 77% (95% CI: 75 to 79).

Pregnancy and Breast Cancer: A Challenge for the Multidisciplinary Team. A Single Center Experience and Narrative Review.

The diagnosis of breast cancer during pregnancy is a rare event, but it is more frequent in our daily clinical practice due to the progressing aging of pregnant women. The management of a woman affected by pregnancy-associated breast cancer (PABC) remains a challenge for the clinician as it is related to ethical and psychological decisions. Here, we retrospectively described 10 cases of PABC in women treated at our Institution. All cases were discussed in the multidisciplinary team. We reviewed available literature data on the topic. Nine out 10 patients were diagnosed with localized breast cancer. The remaining patients were presented with metastatic de novo disease. Median age was 37.5 years (range 26-42). Seven patients presented with grade 3 tumor and 9 patients had Ki-67 value higher than 30%. All but 2 patients received neoadjuvant chemotherapy consisting of sequential anthracyclines and cyclophosphamide followed by weekly paclitaxel during pregnancy. No safety concerns or complications during delivery for both the mothers and the babies were reported. Breast cancer during pregnancy is a challenging clinical situation and all the decisions need to consider both the patients and the fetus safety. Data from our series and from literature confirm the safety of standard chemotherapy approach starting from the second trimester of gestation. More research and effort are needed to offer these patients excellent outcomes and it is mandatory that cases should be closely followed up by a multidisciplinary team.

Improving the OncotypeDX ordering process in patients with ER+ HER2- early-stage breast cancer: A longitudinal QI project.

292 Background: Use of the 21-gene recurrence score assay, OncotypeDX, is an NCCN category 1 recommendation to define adjuvant treatment planning for patients with hormone receptor positive (HR+), HER2 negative (HER2-) early-stage breast cancer. The assay is a key part of the decision-making process to define who may benefit from adjuvant chemotherapy with delays in testing that may impact patient care and loss of trust between oncologists and patients. Methods: We first created a comprehensive process map of the system in place to order OncotypeDX and met with an interdisciplinary team to determine the best area of intervention. A nurse coordinator from medical oncology took over OncotypeDX ordering in September 2023. To determine impact, we obtained data on OncotypeDX orders (N=473) sent by medical oncologists at Washington University in St. Louis between 9/2022 and 4/2024. We determined the delay between specimen collection and OncotypeDX order and the time from order to delivery of the report in the electronic medical record system. We then assessed our outcome measure, the date of the first office visit after surgery and whether patients had an OncotypeDX test result at that visit for 270 consecutive orders spanning pre- and post-intervention (6/2023 – 4/2024). Results: We used a p-chart to assess our outcome measure of OncotypeDX availability at the time of office visit with medical oncology, a key metric for both treatment planning and patient trust. We observed a positive trend of seven data points in the most recent data when mapping the 270 patients by consecutive groups of 10. In our most recent data pull from April 2024, 85% (22/26) of patients who required an OncotypeDX had one completed at the time of office visit, in comparison to 50% (20/40) in August 2023, the month prior to the intervention. We used XmR and S charts to determine impact on delay from specimen collection to OncotypeDX order, which showed increased precision and a trend of decreased delay after implementing the nurse coordinator. The overall delay decreased from an average of 28 days pre-intervention (9/2022-9/2023) to 24 days in the most recent post-intervention data (3/2024). Average time between order and delivery, our balancing measure, decreased during these time periods from 16 days to 13 days. Conclusions: Implementing a nurse coordinator improved delays in specimen collection to order and order to receipt. This intervention increased the percentage of patients who had an OncotypeDX result at the time of office visit with medical oncology after surgery.

An approach for classification of breast cancer using lightweight deep convolution neural network

The rapid advancement of deep learning has generated considerable enthusiasm regarding its utilization in addressing medical imaging issues. Machine learning (ML) methods can help radiologists to diagnose breast cancer (BCs) barring invasive measures. Informative hand-crafted features are essential prerequisites for traditional machine learning classifiers to achieve accurate results, which are time-consuming to extract. In this paper, our deep learning algorithm is created to precisely identify breast cancers on screening mammograms, employing a training method that effectively utilizes training datasets with either full clinical annotation or solely the cancer status of the entire image. The proposed approach utilizes Lightweight Convolutional Neural Network (LWCNN) that allows automatic extraction features in an end-to-end manner. We have tested LWCNN model in two experiments. In the first experiment, the model was tested with two cases' original and enhancement datasets 1. It achieved 95 %, 93 %, 99 % and 98 % for training and testing accuracy respectively. In the second experiment, the model has been tested with two cases' original and enhancement datasets 2. It achieved 95 %, 91 %, 99 % and 92 % for training and testing accuracy respectively. Our proposed method, which uses various convolutional network to classify screening mammograms achieved exceptional performance when compared to other methods. The findings from these experiments clearly indicate that automatic deep learning techniques can be trained effectively to attain remarkable accuracy across a wide range of mammography datasets. This holds significant promise for improving clinical tools and reducing both false positive and false negative outcomes in screening mammography.

A genome-wide CRISPR/Cas9 knockout screen identifies SEMA3F gene for resistanceto cyclin-dependent kinase 4 and 6 inhibitors in breast cancer.

Palbociclib is a cell-cycle targeted small molecule agent used as one of the standards of care in combination with endocrine therapy for patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer. Although several gene alterations such as loss of Rb gene and amplification of p16 gene are known to be conventional resistance mechanisms to cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors, the comprehensive landscape of resistance is not yet fully elucidated. The purpose of this study is to identify the novel resistant genes to the CDK4/6 inhibitors in HR-positive HER2-negative breast cancer. The wholegenome knockout screen using CRISPR/Cas9 genome editing was conducted in MCF7 to identify resistant genes to palbociclib. The candidate genes for resistance were selected by NGS analysis and GSEA analysis and validated by cell viability assay and mouse xenograft models. We identified eight genes including RET, TIRAP, GNRH1, SEMA3F, SEMA5A, GATA4, NOD1, SSTR1 as candidate genes from the wholegenome knockout screen. Among those, knockdown of SEMA3F by siRNA significantly and consistently increased the cell viability in the presence of CDK4/6 inhibitors in vitro and in vivo. Furthermore, the level of p-Rb was maintained in the palbociclibtreated SEMA3F-downregulated cells, indicating that the resistance is driven by increased activity of cyclin kinases. Our observation provided the first evidence of SEMA3F as a regulator of sensitivity to CDK4/6 inhibitors in breast cancer. The detailed mechanisms of resistance deserve further functional studies to develop the better strategy to overcome resistance in CDK4/6 inhibitors.

Eribulin plus carboplatin combination for HER2-negative metastatic breast cancer: a multicenter, real-world cohort study

BackgroundPre-clinical data suggests a potential synergistic effect of eribulin and platinum. However, clinical data on the combination for metastatic breast cancer (mBC) is lacking. We evaluated the efficacy and safety of eribulin plus carboplatin (ErCb) in patients with mBC.Patients and methodsThis multicenter, real-world cohort study included patients with pre-treated metastatic triple negative breast cancer (TNBC) or endocrine-refractory hormone receptor (HR) positive, HER2-negative mBC who received ErCb. Eribulin (1.4 mg/m2) and carboplatin (target AUC = 2) were administered intravenously on day 1 and 8 of 21-day cycle. Objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and adverse events (AEs) were evaluated.ResultsFrom March 2022 to December 2023, a cohort of 37 patients were recruited to the study. Among them, 22 patients have TNBC and 15 have HR + HER2 − mBC. Of the 22 patients with TNBC, 8 had an initial diagnosis of the HR + HER2 − subtype. The median treatment was 6 cycles (range, 2 − 8 cycles). In the full cohort, TNBC, and HR + HER2 − subgroup, the ORR were 51.4%, 54.5% and 46.7%, the DCR were 81.1%, 81.8% and 80%, and the median PFS were 5 months, 5 months, and 5.2 months, respectively. The median OS was 12.7 months in the entire cohort and 12.8 months in TNBC subgroup. The most common grade 3/4 hematological AEs were neutropenia (37.8%), leukopenia (35.1%), febrile neutropenia (10.8%), thrombocytopenia (5.4%), and anemia (2.7%). No grade 3/4 non-hematological AEs were observed.ConclusionErCb demonstrated favorable efficacy and tolerability in patients with heavily pre-treated mBC, especially TNBC. The findings of the current study warrant further investigation of the application of this combination in earlier lines of mBC treatment.

Comparison of clinicopathological characteristics, efficacy of neoadjuvant therapy, and prognosis in HER2-low and HER2-ultralow breast cancer

BackgroundThis study aims to analyze potential differences in clinicopathology, efficacy of neoadjuvant therapy (NAT), and clinical outcome among HER2-null, HER2-ultralow and HER2-low breast cancers.MethodsConsecutive cases of HER2-negative breast cancer that received NAT were included. They were classified as HER2-null (no staining), HER2-ultralow (incomplete faint staining in ≤ 10% of tumour cells) and HER2-low (HER2-1 + or HER2-2+, in situ hybridisation negative). Subgroup analysis was performed based on the HER2 expression level.ResultsOut of 302 patients, 215 (71.19%) were HER2-low, 59 (19.54%) were HER2-ultralow, and 28 (9.27%) were HER2-null. In comparison to the HER2-ultralow group, the HER2-low group exhibited higher expression frequencies of ER (p < 0.001), PR (p < 0.001), and AR (p = 0.004), along with a greater prevalence of the luminal subtype (p < 0.001). The HER2-ultralow group also demonstrated a higher prevalence of lymph node metastasis compared to the HER2-null group (p = 0.026). Varied rates of pathologic complete response (pCR) were observed among the three subgroups: HER2-null, HER2-ultralow, and HER2-low, with rates of 35.71%, 22.03%, and 12.56%, respectively. Only the HER2-low subgroup exhibited a significant difference compared to HER2-null (p = 0.001). Despite variations in pCR rates, the three subgroups exhibited comparable disease-free survival (DFS) (p = 0.571). Importantly, we found HER2-low patients with better treatment response (RCB-0/I) exhibited significantly better DFS than those with significant residual disease (RCB-II/III) (P = 0.036). The overall rate of HER2 immunohistochemical score discordance was 45.24%, mostly driven by the conversion between HER2-0 and HER2-low phenotype. Notably, 32.19% of cases initially classified as HER2-0 phenotype on baseline biopsy were later reclassified as HER2-low after neoadjuvant therapy, and it is noteworthy that 22 out of these cases (78.57%) originally had an HER2-ultralow status in the pretreatment biopsy sample.ConclusionsOur results demonstrate the distinct clinicopathological features of HER2-low and HER2-ultralow breast tumors and confirm that RCB is an effective predictor of prognosis in HER2-low populations for the first time. Notably, our findings demonstrate high instability in both HER2-low and HER2-ultralow expression from the primary baseline biopsy to residual disease after NAT. Furthermore, this study is the first to investigate the clinicopathological feature and the effectiveness of NAT for HER2-ultralow breast cancer.

Eligibility for Adjuvant Cyclin-Dependent Kinase 4/6 Inhibitors in Endocrine Receptor-Positive and HER2-Negative Early Breast Cancer by Age and Type of Surgery

Background: Despite early diagnosis, approximately 20% of patients with ER-positive and HER2-negative breast cancer (BC) will experience disease recurrence. Improved survival has been reported with adjuvant treatment combining cyclin-dependent kinase 4/6 inhibitors with endocrine therapy, in high-risk patients with ER-positive and HER2-negative BC, regardless of age. Older patients have higher rates of ER-positive/HER2-negative BC than younger patients. Methods: In this real-world data analysis, MonarchE and NataLEE high-risk patients accounted for 9.5% and 33% of patients undergoing upfront surgery, respectively. Significantly higher eligibility rates were observed in patients who underwent a mastectomy, &gt;70 years and ≤40 years for adjuvant abemaciclib and ribociclib, and in patients &gt;80 years for ribociclib. Results: Eligibility rates in patients ≤40 years and &gt;80 years who underwent mastectomy were 27.8% and 24.7% for abemaciclib, respectively, and 56.6% and 65.2% for ribociclib, respectively. A higher discontinuation rate for abemaciclib was reported in patients aged ≥65 years and it can be assumed that discontinuation rates may increase in even older patients. Conclusions: If the results of the NataLEE trial translate into clinical practice, the number of patients potentially eligible for adjuvant CDK4/6 inhibitors may increase, especially in the elderly population.

Feature-based detection of breast cancer using convolutional neural network and feature engineering.

Breast cancer (BC) is a prominent cause of female mortality on a global scale. Recently, there has been growing interest in utilizing blood and tissue-based biomarkers to detect and diagnose BC, as this method offers a non-invasive approach. To improve the classification and prediction of BC using large biomarker datasets, several machine-learning techniques have been proposed. In this paper, we present a multi-stage approach that consists of computing new features and then sorting them into an input image for the ResNet50 neural network. The method involves transforming the original values into normalized values based on their membership in the Gaussian distribution of healthy and BC samples of each feature. To test the effectiveness of our proposed approach, we employed the Coimbra and Wisconsin datasets. The results demonstrate efficient performance improvement, with an accuracy of 100% and 100% using the Coimbra and Wisconsin datasets, respectively. Furthermore, the comparison with existing literature validates the reliability and effectiveness of our methodology, where the normalized value can reduce the misclassified samples of ML techniques because of its generality.

Efficacy and prognosis of neoadjuvant chemotherapy in HER2 low-expressing breast cancer: a retrospective single-center study.

Human epidermal growth factor receptor 2 (HER2) is vital for breast cancer prognosis. The aim of this study was to analyze the clinicopathological data of HER2-negative breast cancer patients receiving neoadjuvant chemotherapy and the associated factors affecting the pathological complete response rate (pCR) and prognosis. Clinical data of 173 patients with primary HER2-negative breast cancer, who initially received neoadjuvant chemotherapy followed by surgical treatment at the Breast Surgery Department of Bethune Hospital in Shanxi Province from January 2012 to December 2022, were collected. Compared to HER2-0 patients, HER2-low patients had higher T staging (p = 0.008), higher Ki67 proliferation index (p < 0.001), lower N staging (p = 0.001), and lower pCR rate (p < 0.001). Univariate analysis revealed that T stage, TNM stage, HR status, HER2 status, and Ki67 are risk factors that affect the pCR rate in HER-2 negative. Multivariate analysis identified HR status as an independent predictor of pCR rate. Kaplan-Meier survival curves showed that menstrual status, N staging, T staging, TNM staging, and pCR status affected the prognosis of HER2-low breast cancer patients (p < 0.05). HER2-low breast cancer exhibits distinct biological behaviors, suggesting personalized treatment approaches.

Monitoring the Response of Cyclin-Dependent Kinase 4/6 Inhibitors with Mean Corpuscular Volume.

Currently, the combination of cyclin-dependent kinase 4/6 (CDK4/6) inhibitors and endocrine therapy is a first-line treatment for hormone-receptor-positive and HER2-negative metastatic breast cancer. This study aimed to assess the impact of changes in Mean Corpuscular Volume (MCV) on predicting responses to treatment and survival in patients with hormone-receptor-positive, HER2-negative metastatic breast cancer receiving CDK4/6 inhibitors and endocrine therapy. Retrospectively, data on hemoglobin levels, MCV, B12, folate levels, and survival times were collected from 275 patients. Patients were categorized into two groups based on the degree of MCV change (delta MCV ≤ 10 vs. >10). Kaplan-Meier survival analysis was performed, with significance set at p < 0.05. The average age of the patients was 56.1 ± 12.1 years. In total, 72.7% received CDK4/6 inhibitors as first-line treatment, while 27.3% received them as second-line treatment. Before CDK4/6 inhibitor use, the median MCV level was 87.7 fL (IQR: 83-91), which increased to 98 fL (IQR: 92-103) after treatment (p < 0.001). ECOG performance score, CDK4/6 inhibitor treatment line, type of endocrine therapy, and MCV change were identified as independent predictors of progression-free survival in the Cox regression model. The median progression-free survival for the entire group was 28 months. Patients with MCV delta > 10 had a median progression-free survival of 33 months, compared to 23 months for those with MCV delta ≤ 10 (p = 0.029). There was no significant difference in median overall survival times between the two groups (p = 0.158). This study highlights that patients with MCV delta > 10 had longer median progression-free survival compared to those with MCV delta ≤ 10.

Clinical use of Abemaciclib a cyclin-dependent kinase (CDK) 4/6 inhibitor in patients with breast cancer — literature review

Breast cancer is the most common neoplasma affecting women. Over, the past few years, the incidence of breast cancer has significantly increased, including among young women. Hormone receptor-positive (HR+) Her2 negative (HER2-) early stage breast cancer can be successfully treated using the currently available treatment methods based on endocrine theraphy (ET). However, if we consider early stage breast cancer with high risk of recurrence or metastatic disease, endocrine therapy alone may be insufficient. Unfortunately, resistance to drugs is observed in both adjuvant and palliative endocrine therapy, therefore there is a need for new treatments that are both effective and less toxic than conventional chemotherapy. One of the most successful applications of this strategy has been the use of cyclin-dependent kinase (CDK) 4 and 6 inhibitors alongside endocrine therapy significantly enhance its effectiveness. This combination has been shown to substantially increase progression-free survival while maintaining relatively low levels of toxicity. One of them is abemaciclib, whose efficacy will be shown in this research work.

Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications

Being the most common type of cancer worldwide, and affecting over 2.3 million women, breast cancer poses a significant health threat. Although survival rates have improved around the world due to advances in screening, diagnosis, and treatment, early detection remains crucial for effective management. This study seeks to introduce a novel hybrid model that makes use of image-preprocessing techniques and deep-learning algorithms on mammograms to enhance the detection and classification accuracy of breast cancer lesions. The model was tested on a dataset comprising 20,000 mammograms. First, image-processing techniques, such as Contrast-Limited Adaptive Histogram Equalization, Gaussian Blur, and sharpening methods were used to optimize the images for enhanced feature extraction. In addition, the Ensemble Deep Random Vector-Functional Link Neural Network algorithm, YOLOv5, and MedSAM segmentation models were utilized for robust deep learning-based extraction, classification, and visualization of lesions. Finally, the model was clinically validated on 800 patients. The study found a notable enhancement in both accuracy and processing time for benign and malignant diagnoses using the hybrid model. The model achieves an impressive accuracy of 99.7 % and demonstrates a remarkable processing time of 0.75 s. In clinical applications, the hybrid model exhibits high proficiency, reporting 97.2 % accuracy for benign cases and 98.6 % for malignant scenarios. These results highlight the effectiveness of the hybrid model in improving diagnostic accuracy, offering a promising tool for early breast cancer detection.

Fulvestrant en la práctica clínica: análisis de efectividad en pacientes uruguayas con cáncer de mama HR+/HER2.

Fulvestrant demonstrated benefits in overall survival and progression-free survival in patients with advanced breast cancer, who are hormone receptor-positive and human epidermal growth factor receptor 2 negative. The characteristics, evolution, and survival of patients with hormone receptor-positive, HER2-negative breast cancer treated with fulvestrant were evaluated according to the national treatment coverage protocols of the National Resources Fund, with the aim of understanding the efficacy of fulvestrant in patients treated in usual clinical practice and comparing our results with those from pivotal studies. A database from the National Resources Fund covering the period from 2009 to 2022 was used. Survival curves were assessed using the Kaplan-Meier method, and differences were analyzed using the Log-Rank test. A total of 1085 patients with an average age of 63,66 years were included. Following a follow-up of 14 months, the median overall survival was 16 months, and the median progression-free survival was 6 months. The presence of liver and bone metastases was associated with a shorter overall survival. Patients from the public sector and those with a better performance status experienced longer overall survival. Our findings provide a valuable perspective for treatment management in a context of limited resources. Overall survival and progression-free survival were somewhat lower than those reported in pivotal clinical trials. The presence of liver and bone metastases was associated with worse prognosis and survival; additionally, patients with worse performance status had shorter overall survival. These findings underscore the need for personalized therapies, opening new lines of future research.

Comparison of Risk Stratification by CanAssist Breast Test Performed on Core Needle Biopsies Versus Surgical Specimens in Hormone Receptor-Positive, Her2-Negative Early Breast Cancer.

Introduction Core needle biopsies (CNB) are being increasingly utilized for biomarker, prognostic, and predictive testing in breast cancer (BC).CanAssist Breast (CAB) is a prognostic test performed to assess the 'risk of breast cancer recurrence' in early-stage hormone receptor-positive, Her2-negative BC patients. CAB segregates tumors as 'low risk' or 'high risk' for distant recurrence.Risk assessment done by CAB aids in planning and making adjuvant chemotherapy or hormone therapy decisions.CAB is typically performed on surgical specimens (SS).However, performing it on CNB does offer additional insights into tumor biology leading to different strategies for treatment planning; hence, we aimed to compare the risk stratification performance of CAB using CNB versus SS. Method We analyzed 103 paired formalin-fixed paraffin-embedded CNB and SS samples from hormone receptor-positive, Her2-negative early BC tissue samples submitted for performing CAB at OncoStem Diagnostics between November 2021 and September 2023. Concordance on 'risk categories' of CAB performed on CNB versus SS was reported using overall percentage agreement and Pearson correlation coefficient. Results We found excellent overall concordance of 92.2% for CAB risk stratification between paired CNB and SS tumor samples with a strong Pearson correlation coefficient of r= 0.8351 (p< 0.0001) when either SS or CNB was used as the gold standard.In prognostic testing patients with a 'low risk' of recurrence may avoid chemotherapy and hence it is crucial to assess the accuracy of CAB in the low-risk category. Additionally, in a real-world scenario, it is more likely that CAB will be performed on CNB first. Conclusion CAB when performed on CNB samples showed high concordance with SS thus demonstrating that CNB was a suitable sample for the CanAssist Breast test.The accuracy in the low-risk category is 97.5%, which ensures that physicians can reliably use prognostic information by testing CNB to guide adjuvant therapy decisions.