HER2-negative Breast Cancer Research Articles

Abstract C062: Racial and ethnic disparities in risk of second primary non-breast cancer in breast cancer survivors

Abstract Purpose: Disparities in cancer outcomes are multifactorial and are attributed to differences in access to care, socioeconomic status, tumor characteristics, treatment, family history, and comorbidity burden. Studies evaluating disparities in breast cancer outcomes have mostly been restricted to Black and White patients. Additionally, whether racial and ethnic disparities affect the risk of second primary cancer in breast cancer survivors, remains under-studied. We evaluated disparities in risk of second primary non-breast cancer in a racially and ethnically diverse cohort of breast cancer survivors treated at an integrated healthcare system. Methods: This retrospective cohort study included 37,222 breast cancer patients (aged ≥18 years) diagnosed between 2008-2020 at Kaiser Permanente Southern California (KPSC). Data on tumor characteristics, cancer treatments and sociodemographic variables were extracted from KPSC’s cancer registry and electronic health records. Patients were followed electronically from breast cancer diagnosis until they developed second primary non-breast cancer, died, disenrolled from the health plan, or reached study’s end (12/31/2021), whichever occurred first. Cox proportional hazards regression was used to report the association [Hazard Ratio (95% Confidence Interval)] between race and ethnicity and second primary non-breast cancer. Results: More than half of the study cohort consisted of non-Hispanic White survivors (51.2%), followed by 20.9% Hispanic survivors, 13.9% Asian/Pacific Islander (API) survivors, 12.7% non-Hispanic Black survivors, and 1.3% survivors of other/mixed races. Overall, 55.7% and 23.9% were diagnosed with local and regional breast cancer, respectively. During a median follow-up of 5.2 years, 4.6% survivors developed second primary non-breast cancers. In the multivariable model adjusted for sociodemographic characteristics, breast tumor characteristics, and breast cancer treatments, Hispanic and API survivors were 25% and 28% less likely to develop second primary cancer compared to White survivors [adjusted HR (95%CI): 0.75 (0.65-0.86)] and 0.72 (0.61-0.86), respectively]. We did not find an increased risk in Black survivors compared to White survivors [adjusted HR (95%CI): 0.91 (0.78-1.04)]. However, factors related to higher risk of second primary non-breast cancers were older age at breast cancer diagnosis, smoking, and higher comorbidity burden. Conclusion: Interestingly, risk of second primary non-breast cancer was lower in Hispanic and API survivors compared to White survivors in this insured cohort. Next steps will include examining which lifestyle factors (e.g. less smoking), or if pharmacological management of modifiable comorbidities might have played a role in the decreased risks seen in these two racial and ethnic groups. Citation Format: Amrita Mukherjee, Zheng Gu, Lie H. Chen, Reina Haque. Racial and ethnic disparities in risk of second primary non-breast cancer in breast cancer survivors [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C062.

Abstract C101: Shared gene expression pattern among breast cancer subtypes and its association with prognostic variables in Colombian patients

Abstract Introduction: The current molecular classification of breast cancer has significantly improved treatment targeting and disease prognosis. However, the heterogeneity among subtypes also poses a limitation when proposing new targets that integrate common biological processes, hindering their applicability across the entire spectrum of the disease. In that sense, it is essential to gain a deeper understanding of the similarities and common mechanisms involved in the molecular subtypes of breast cancer. Therefore, in this study, we aimed to identify shared differentially expressed genes (DEGs) and biological processes among the different breast cancer subtypes that could potentially impact the presentation of clinically significant variables in Colombian patients diagnosed at the National Cancer Institute (NCI). Methods: We included 97 women diagnosed with breast cancer at the NCI in Colombia. From each patient, we collected tumor biopsy and adjacent non-tumoral tissue for RNA extraction and sequencing (RNA-seq). A comparative analysis of DEGs and gene set enrichment (GSE) was performed between tumor tissue and its corresponding adjacent non-tumor tissue for each subtype independently to identify common DEGs and enriched biological processes. Shared over- and under-expressed genes in at least two subtypes were tested for their association with clinically significant prognostic variables. Results: Among breast cancer subtypes, ER-/HER2+ and ER-/HER2- tumors showed the highest number of shared DEGs (22 genes), of which 19 were over-expressed and 3 under-expressed. In contrast, GSE analysis revealed a high proportion of shared biological processes (5), molecular functions (3), and cellular component classification (11) between ER+/HER2+ and ER-/HER2+ tumors, corresponding to mechanisms involved in tissue homeostasis and extracellular matrix structural constituents. Furthermore, the association with clinically significant prognostic variables was assessed for 22 over-expressed genes and 9 under-expressed genes shared in at least two subtypes. Of this set of 31 genes, 9 were significantly associated with the degree of tumor differentiation measured by the Bloom-Richardson (BR) score. Interestingly, tumor suppressor genes like ADAMTS18 showed a decline in expression as the BR score increased (fold-change (FC) in BR I: 14 vs BR II: 11; BR III: 5, p-value: 0.028). Conversely, oncogenes like EFNA4 showed the opposite trend, with an increase in expression as the BR score also increased (FC in BR I: 9 vs. BR II: 11 vs. BR III: 17, p-value: 0.019). Another identified shared over-expressed gene, JPT1, which is involved in β-catenin signaling, was found to be associated not only with higher BR scores but also with a higher proliferation index Ki67 (FC in <20%: 16.5 vs. ≥20%: 23; p-value: 0.05). Conclusions: Despite the high degree of molecular heterogeneity among subtypes, it is still possible to identify shared over and under-expressed genes in at least 2 subtypes with significant prognostic implications in breast cancer subtypes. Citation Format: Laura Rey-Vargas, Lina Maria Bejarano, Diego Felipe Ballen-Lozano, Silvia J. Serrano-Gomez. Shared gene expression pattern among breast cancer subtypes and its association with prognostic variables in Colombian patients [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C101.

Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques.

Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20-25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on HER2 gene amplification. Literature from 1997 to 2023 is analyzed, emphasizing silver-enhanced in situ hybridization (SISH) and its integration with image processing and machine learning techniques. Both conventional machine learning approaches and recent advances in deep learning are compared. The review reveals that automated ISH analysis in combination with bright-field microscopy provides a cost-effective and scalable solution for routine pathology. The integration of deep learning techniques shows promise in improving accuracy over conventional methods, although there are limitations related to data variability and computational demands. Automated ISH analysis can reduce manual labor and increase diagnostic accuracy. Future research should focus on refining these computational methods, particularly in handling the complex nature of HER2 status evaluation, and integrate best practices to further enhance clinical adoption of these techniques.

Effectiveness of combined targeted and hormonal therapies for post-menopausal women with hormone receptor-positive and HER2-negative advanced breast cancer: A systematic review and meta-analysis of RCTs.

we aim to synthesize available evidence on the effectiveness of hormonal plus targeted therapies for post-menopausal women with hormone receptor-positive and HER2-negative advanced breast cancer. We searched the following databases: Medline, PubMed, Cochrane Library, CINAHL, Web of Science, Scopus, and African Journal. Only studies that investigated the effectiveness of hormonal therapy combined with targeted therapy for HR+/HER2- advanced breast cancer treatment were included. The outcomes were progression-free survival (PFS), overall survival (OS) and objective response rate (ORR). A random-effect meta-analysis model was employed. Statistical analysis was performed using Comprehensive Meta-analysis version 3. 24 studies were included in the meta-analysis with an overall sample size of 7635. Median PFS, OS and ORR were found to be significantly increased in the combination group compared to hormonal monotherapy [SMD = 6.072 (95% CI = 3.785-8.360), p < 0.001], [SMD = 1.614 (95% CI = 0.139-3.089), p = 0.032] and [OR = 1.584 (CI 1.134-2.213), p = 0.007] respectively. Subgroup analysis showed a significant difference in PFS and ORR between patients who received "hormonal therapy + CDK4/6 inhibitors" vs hormonal therapy only [SMD = 6.015 (CI 3.069-8.960), p < 0.001], (OR = 1.828 (CI 1.030-3.243), p = 0.039] respectively. Compared with hormonal monotherapy, targeted plus hormonal therapy significantly improves PFS, OS and ORR in postmenopausal women with HR+/HER2- advanced breast cancer.

Alginate-functionalized and 4T1 cell membrane-coated multi-tasking nanoparticulate system for near-infrared-triggered photodynamic therapy on breast cancer: In vitro cellular and in vivo mice models

Recently, combination treatment of chemo-dynamic therapy (CDT) and photodynamic therapies (PDT) is well-known and prominent therapeutic strategy for breast tumor. Importantly, tailored nanomedicines with unique personalized properties including nanocarrier suitability and effective targeting ability that requires the exhaustive understanding of tumor microenvironment. Recently, researchers have designed a smart upconversion nanoparticles (UCNPs) that have unique characteristics to achieve targeting for respond tumor microenvironment. In the present study, we first time fabricated a novel combination of oxidized sodium alginate-enveloped and 4T1 cancer cell membrane-coated up-conversion nanoparticles (4TUCNP@SMZ) were engineered for breast cancer-targeted therapy. The nanoparticles could be formed via electrostatic interaction, which showed excellent biocompatibility, increased cellular uptake with normal and cancer cells. The fabricated Alginate and 4T1-membrane functionalized up-conversion nanoparticles significantly benefitted to targeted delivery system for local breast cancer and prominently inhibit tumor development via chemo-dynamic therapy (CDT) and PDT. The greater susceptibility of tumor cells to oxidative stress may also result from the depletion of intracellular GSH by the 4TUCNP@SMZ nanoparticles. The in vitro cell models (4T1 and MCF-7), in vivo tumor model and histological observation demonstrated that 4TUCNP@SMZ nanoparticles effectively targeted to breast tumor microenvironment and inhibit breast cancer cells. This report suggested that multifunctional Alginate-functionalized nanoparticles are a versatile agent for phototherapy for breast cancer treatment.

Computer-aided diagnosis of breast cancer from mammogram images using deep learning algorithms

Even though accurate detection of dangerous malignancies from mammogram images is mostly dependent on radiologists' experience, specialists occasionally differ in their assessments. Computer-aided diagnosis provides a better solution for image diagnosis that can help experts make more reliable decisions. In medical applications for diagnosing cancerous growths from mammogram images, computerized and accurate classification of breast cancer mammogram images is critical. The deep learning approach has been widely applied in medical image processing and has had considerable success in biological image classification. The Convolutional Neural Network (CNN), Inception, and EfficientNet are proposed in this paper. The proposed models attain better performance compared to the conventional CNN. The models are used to automatically classify breast cancer mammogram images from Kaggle into benign and malignant. Simulation results demonstrated that EfficientNet, with an accuracy between 97.13 and 99.27%, and overall accuracy of 98.29%, perform better than the other models in this paper.

Absolute lymphocyte count predicts efficacy of palbociclib in patients with metastatic luminal breast cancer

BackgroundAbsolute lymphocyte count (ALC) is a predictive and prognostic factor for various tumor types, including breast cancer. Palbociclib is a CDK4/6 inhibitor widely used for the treatment of metastatic estrogen receptor (ER)-positive, HER2-negative breast cancer. However, predictive biomarkers of the efficacy of palbociclib remain unelucidated. We conducted a retrospective study to examine the predictive value of the baseline ALC in patients treated with palbociclib.MethodsThe medical records of patients with ER-positive, HER2-negative breast cancer treated with palbociclib plus hormonal therapy between December 2017 and December 2021 were analyzed retrospectively. The cutoff value of ALC was set at 1800 cells/μL at the initiation of palbociclib treatment. The clinical benefit rate (CBR) was defined as the rate of complete or partial response or stable disease for at least 6 months. Progression-free survival (PFS) rates were estimated using the Kaplan–Meier method and compared using the log-rank test. Univariate and multivariate analyses were performed using Cox proportional hazards regression.ResultsAll of the 202 patients were women, with a median age of 59 years and a performance status (PS) of ≤ 2. The median numbers of lines of chemotherapy and endocrine therapy before palbociclib treatment were 0 (range, 0–9) and 1 (range, 0–7), respectively. Fifty-one patients had liver metastases. Forty-six patients tested negative for progesterone receptor (PgR) expression. The median follow-up time was 9.1 months. The CBR was significantly higher in the ALC-high group than in the ALC-low group (79% vs. 60%; P = 0.018). The median PFS was significantly longer in the ALC-high group than in the ALC-low group (26.8 months vs. 8.4 moths, respectively; P = 0.000013). ALC, age, PS, PgR status, prior chemotherapy, prior endocrine therapy, and liver metastasis were entered into the multivariate analysis. ALC was identified as an independent factor for PFS (P = 0.00085), along with liver metastasis (P = 0.0020), PS (P = 0.026), and prior endocrine therapy (P = 0.019).ConclusionALC can serve as a predictor of palbociclib efficacy in patients with metastatic ER-positive, HER2-negative breast cancer.

High mechanical conditioning by tumor extracellular matrix stiffness is a predictive biomarker for anti-fibrotic therapy in HER2-negative breast cancer.

Tumor progression has been linked to stiffening of the extracellular matrix (ECM) caused by fibrosis. Cancer cells can be mechanically conditioned by stiff ECM, exhibiting a 1004-gene signature (MeCo score). Nintedanib has demonstrated anti-fibrotic activity in idiopathic pulmonary fibrosis. This study explores nintedanib's anti-fibrotic effect on breast cancer outcomes. We present long-term follow-up and analysis of a neoadjuvant randomized Phase 2 trial in early HER2-negative breast cancer. Patients (N = 130) underwent a baseline biopsy and received 12 paclitaxel courses alone (control arm) or in combination with nintedanib (experimental arm). Tumor MeCo score was determined by RNAseq. The primary aim was to assess nintedanib's impact on event-free survival (EFS) based on MeCo scores. Follow-up data were retrieved from 111 patients; 75 baseline and 24 post-run-in phase samples were sequenced. After median follow-up of 9.67 years, median EFS was not statistically different between arms (P = 0.37). However, in the control arm, High versus Low MeCo patients had a statistically higher relapse risk: hazard ratio (HR) = 0.21; P = 0.0075. This risk was corrected by nintedanib in the experimental arm: HR = 0.37; P = 0.16. Nintedanib demonstrated pharmacodynamic engagement, reducing the MeCo score by 25% during the run-in phase (P<0.01). Patients with Low MeCo after run-in had the best long-term prognosis (HR = 0.087; P = 0.03). High MeCo is predictive of poor outcomes in HER2-negative early breast cancer, although this risk can be mitigated by nintedanib, which is able to specifically reduce mechanical conditioning.

An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.

Current imaging methods for diagnosing breast cancer (BC) are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve BC diagnosis and subtype differentiation. In this case, novel quantitative computational methods, such as radiomics, have been developed to enhance the sensitivity and specificity of early BC diagnosis and classification. The potential of radiomics in improving the diagnostic efficacy of imaging studies has been shown in several studies. In this review article, we discuss the radiomics workflow and current handcrafted radiomics methods in the diagnosis and classification of BC based on the most recent studies on different imaging modalities, e.g., MRI, mammography, contrast-enhanced spectral mammography (CESM), ultrasound imaging, and digital breast tumosynthesis (DBT). We also discuss current challenges and potential strategies to improve the specificity and sensitivity of radiomics in breast cancer to help achieve a higher level of BC classification and diagnosis in the clinical setting. The growing field of AI incorporation with imaging information has opened a great opportunity to provide a higher level of care for BC patients.

A plain language summary of the CAPItello-291 study: Capivasertib in hormone receptor-positive advanced breast cancer.

This is a summary of the article discussing the results of the CAPItello-291 study. In the study, participants had advanced breast cancer that could not be completely removed with surgery, and that was diagnosed as a type of breast cancer where tumor cells had hormone receptors (HR-positive) but did not have HER2 receptors (HER2-negative). All participants were also required to have previously received treatment with a type of therapy called an aromatase inhibitor (with or without a CDK4/6 inhibitor), but over time their cancer cells had still grown or spread. The CAPItello-291 study researchers wanted to find out if a treatment combination of the medications capivasertib plus fulvestrant worked better than placebo plus fulvestrant. Capivasertib is a drug that blocks the activity of a protein called AKT, which is found inside breast cancer cells. The main finding was that participants who took capivasertib plus fulvestrant lived longer without their disease getting worse (progressing) compared with those treated with placebo plus fulvestrant. This is called progression-free survival. This result was seen across all participants (median progression-free survival of 7.2months with capivasertib plus fulvestrant vs 3.6months with placebo plus fulvestrant). It was also seen in participants whose tumors had detectable genetic alterations in genes called PIK3CA, AKT1, and/ or PTEN (median progression-free survival of 7.3months with capivasertib plus fulvestrant vs 3.1months with placebo plus fulvestrant). The most common side effects experienced by participants included diarrhea and different types of rash. These were as expected (given how capivasertib works). The CAPItello-291 study is still ongoing, and more results are expected to be released in the future. Results from the CAPItello-291 study showed that capivasertib plus fulvestrant compared with placebo plus fulvestrant improved progression-free survival in participants with HR-positive/ HER2-negative advanced breast cancer whose cancer had grown or spread despite hormone therapy (with/without a CDK4/6 inhibitor).Clinical Trial Registration: NCT04305496 (CAPItello-291) (ClinicalTrials.gov).

Neutrophil-to-lymphocyte ratio at the end of treatment with CDK4/6 inhibitors is an independent prognostic factor for ER-positive HER2-negative advanced breast cancer.

The aim of this study was to elucidate the clinical significance of peripheral blood biomarkers, including absolute lymphocyte count (ALC) and neutrophil-to-lymphocyte ratio (NLR), at the end of treatment (EOT) with CDK4/6 inhibitors abemaciclib and palbociclib in patients with estrogen receptor-positive human epidermal growth factor receptor 2-negative advanced breast cancer. We included 67 patients treated with fulvestrant plus abemaciclib or palbociclib. Overall survival (OS) since the EOT with CDK/4/6 inhibitors was compared in relation to the levels of ALC and NLR. The cut-off values of ALC and NLR were set at 1000/μL and 3, respectively. Patients with a high ALC at EOT showed significantly longer OS than those with a low ALC (p = 0.0358). Moreover, patients with a low NLR at EOT showed significantly longer OS than those with a high NLR at EOT (p = 0.0044). Looking at the changes of ALC and NLR between baseline and the EOT, patients with a high ALC both at baseline and at the EOT showed significantly longer OS than others (p = 0.0201). Similarly, patients with a low NLR both at baseline and at the EOT showed significantly longer OS after EOT than others (p = 0.0136). Multivariable analysis revealed that the NLR at EOT (low vs. high) and changes in NLR (low at baseline to low at EOT vs. others) were significant and independent prognostic factors for OS after EOT (p = 0.0337, p = 0.0039, respectively). NLR at EOT with CDK4/6 inhibitors is a significant and independent prognostic marker for patients with ER-positive HER2-negative advanced breast cancer.

Statin use and risks of breast cancer recurrence and mortality.

Preclinical evidence suggests improved breast cancer survival associated with statin use, but findings from observational studies are conflicting and remain inconclusive. The objective of this study was to assess the association between statin use after cancer diagnosis and cancer outcomes among breast cancer patients. In this retrospective cohort study, 38,858 women aged ≥66 years who were diagnosed with localized and regional stage breast cancer from 2008 through 2017 were identified from the linked Surveillance, Epidemiology, and End Results Medicare database. Statin use was ascertained from Medicare Part D pharmacy claims data. Multivariable Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between post-diagnosis statin use and risks of breast cancer recurrence and breast cancer-specific mortality. Over a median follow-up of 2.9 years for recurrence and 3.7 years for mortality, 1446 women experienced a recurrence, and 2215 died from breast cancer. The mean duration of post-diagnosis statin use was 2.2 years. Statin use post-diagnosis was not associated with recurrence risk (HR, 1.05; 95% CI, 0.91-1.21), but was associated with a reduced risk of cancer-specific mortality (HR, 0.85; 95% CI, 0.75-0.96). The reduction was more pronounced in women with hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer (HR, 0.71; 95% CI, 0.57-0.88). These findings suggest that post-diagnosis statin use is associated with improved cancer-specific survival in women with breast cancer and should be confirmed in randomized trials of statin therapy in breast cancer patients.

Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer

BackgroundBreast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors.ResultsOur study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues.ConclusionsConsequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings.

Unraveling the causal links and novel molecular classification of Crohn’s disease in breast Cancer: a two-sample mendelian randomization and transcriptome analysis with prognostic modeling

BackgroundCrohn’s disease (CD), a prominent manifestation of chronic gastrointestinal inflammation, and breast cancer (BC), seemingly disparate in the medical domain, exhibit a shared characteristic. This convergence arises from their involvement in chronic inflammation and immune responses, an aspect that has progressively captivated the attention of investigators but remain controversial.MethodsWe used two-sample Mendelian Randomization (MR) and transcriptomics to explore the relationship between CD and BC. MR assessed causality of CD on different BC subtypes and reverse causality of BC on CD. We identified CD-related differentially expressed genes and their prognostic impact on BC, and developed a new molecular BC classification based on these key genes.ResultsMR revealed a causal link between CD and increased BC risk, especially in estrogen receptor-positive (ER+) patients, but not in ER-negative (ER-) cases. BC showed no causal effect on CD. Transcriptomics pinpointed genes like B4GALNT2 and FGF19 that affected BC prognosis in CD patients. A nomogram based on these genes predicted BC outcomes with high accuracy. Using these genes, a new molecular classification of BC patients was proposed.ConclusionsCD is a risk factor for ER + BC but not for ER- BC. BC does not causally affect CD. Our prognostic model and new BC molecular classifications offer insights for personalized treatment strategies.

Neoadjuvant Chemotherapy with Concurrent Letrozole for Estrogen Receptor-Positive and HER2-Negative Breast Cancer: An Open-Label, Single-Center, Nonrandomized Phase II Study (NeoCHAI).

The role of combining neoadjuvant endocrine therapy with conventional chemotherapy remains unclear; therefore, we conducted an open-label, single-center, nonrandomized phase II trial to assess the effect of this combination. Patients with previously untreated stage II or III HR-positive, HER2-negative breast cancer received concurrent letrozole 2.5 mg with standard neoadjuvant chemotherapy. The primary endpoint was pathologic complete response (pCR) at the time of surgery. We used Simon's minimax two-stage design; a pCR rate > 6% was necessary at the first stage to continue. Between November 2017 and November 2020, 53 women were enrolled in the first stage of the trial. Their median age was 49 years (range, 33-63), and 60% of them were premenopausal. Subsequently, 66% and 34% of patients with clinical stages II and III, respectively, were included; 93% had clinically node-positive disease. Two patients (4%) achieved pCR after neoadjuvant chemo-endocrine treatment, which did not satisfy the criteria for continuing to the second stage. The overall response rate was 83%. During the median follow-up of 53.7 months, the 3-year disease-free survival and overall survival rates were 87% and 98%, respectively. Neutropenia was the most common grade 3/4 adverse event (40%), but rarely led to febrile neutropenic episodes (4%). Myalgia (32%), nausea (19%), constipation (17%), heartburn (11%), oral mucositis (9%), and sensory neuropathy (9%) were frequently observed, but classified as grade 1 or 2. No deaths occurred during preoperative treatment. The addition of letrozole to standard neoadjuvant chemotherapy was safe and beneficial in terms of overall response rate, but did not provide a higher pCR rate in locally advanced HR-positive, HER2-negative breast cancer. Further research is needed to enhance neoadjuvant treatment strategies for this cancer subtype.

Multi-omics-based Machine Learning for the Subtype Classification of Breast Cancer

AbstractCancer is a complicated disease that produces deregulatory changes in cellular activities (such as proteins). Data from these levels must be integrated into multi-omics analyses to better understand cancer and its progression. Deep learning approaches have recently helped with multi-omics analysis of cancer data. Breast cancer is a prevalent form of cancer among women, resulting from a multitude of clinical, lifestyle, social, and economic factors. The goal of this study was to predict breast cancer using several machine learning methods. We applied the architecture for mono-omics data analysis of the Cancer Genome Atlas Breast Cancer datasets in our analytical investigation. The following classifiers were used: random forest, partial least squares, Naive Bayes, decision trees, neural networks, and Lasso regularization. They were used and evaluated using the area under the curve metric. The random forest classifier and the Lasso regularization classifier achieved the highest area under the curve values of 0.99 each. These areas under the curve values were obtained using the mono-omics data employed in this investigation. The random forest and Lasso regularization classifiers achieved the maximum prediction accuracy, showing that they are appropriate for this problem. For all mono-omics classification models used in this paper, random forest and Lasso regression offer the best results for all metrics (precision, recall, and F1 score). The integration of various risk factors in breast cancer prediction modeling can aid in early diagnosis and treatment, utilizing data collection, storage, and intelligent systems for disease management. The integration of diverse risk factors in breast cancer prediction modeling holds promise for early diagnosis and treatment. Leveraging data collection, storage, and intelligent systems can further enhance disease management strategies, ultimately contributing to improved patient outcomes.

High-dose chemotherapy for patients with stage III breast cancer with homologous recombination deficiency: a discrete choice experiment among healthcare providers.

High-dose chemotherapy with autologous stem cell rescue (HDCT) is currently under investigation as a potential therapy for patients with stage III HER2-negative breast cancer with homologous recombination deficiency (HRD). In addition to survival, the impact on short- and long-term side effects might influence the uptake of HDCT by healthcare professionals. As part of the SUBITO trial, we investigated healthcare professionals' treatment (outcome) preferences for patients with HRD stage III HER2-negative breast cancer and established how healthcare professionals make trade-offs between these treatment outcomes. We conducted a discrete choice experiment in which healthcare professionals were asked to choose repeatedly between scenarios with two treatment options (HDCT or standard of care (SOC)) that varied in outcome with respect to 10-year overall survival (OS), short-term toxicity, long-term cognitive impairment, and premature menopause. We analysed treatment preferences, relative importance, and trade-offs using a multinomial logistic model. Thirty-five of the 151 dedicated breast cancer professionals with extensive experience in treating breast cancer patients completed the survey. The 10-year OS and long-term cognitive impairment were the most important attributes. The results indicate a requirement of 10.4% and 25.1% absolute additional improvement in the 10-year survival rate to justify accepting moderate or severe long-term cognitive impairment as a trade-off, respectively. Therefore, we found in our dataset that healthcare professionals expected a large improvement in 10-year OS to accept moderate to severe cognitive impairment. This information calls for further research into chemotherapy-related cognitive impairment, shared decision-making, and treatment preferences for patients with stage III breast cancer.

Prosigna Assay for Treatment Decisions in Early Breast Cancer: A Decision Impact Study.

Introduction: Therapeutic decisions in early breast cancer are based on clinico-pathological features which are subject to intra- and inter-observer variability. This single-center decision impact study aimed to evaluate the effects of the Prosigna assay on physicians' adjuvant treatment choices. Methods: Between 09/2017 and 02/2018, formalin-fixed tumor samples from 52 newly diagnosed, postmenopausal, hormone receptor-positive, HER2-negative breast cancer (T1-T2; pN0-N1a) patients were analyzed. Pre-test clinical judgements and Prosigna test results were compared. Results: The mean age was 59 (42-77). Invasive ductal carcinoma (79.2%), grade 2 (52.8%) and T1c-N0 tumors (43.4%) were represented. There was 40.4% discordance between the pre- and post-test risk of recurrences. No significant change was observed in the clinical intermediate risk category, while there was a net reclassification of low-risk patients into a high Prosigna recurrence risk group. In addition, clinically determined intrinsic subtypes were 34.6% discordant with the Prosigna results, which is largely driven by the reclassification of the luminal A tumors into the Prosigna-assessed luminal B group. Before the Prosigna test, endocrine treatment was the primary choice in 20 patients (39.2%), and chemotherapy was recommended to 31 patients (60.8%). Overall, the Prosigna assay led to a change in treatment choice for one patient. Conclusions: Although conventional risk assessment methods are relatively inexpensive with shorter turnaround times, their accuracy and value for risk reduction are suboptimal. According to our results, the Prosigna assay was found to be a relevant tool for the clinical decision making process. Long-term follow-up of these patients will elucidate the potential benefits of using multigene molecular tests as biomarkers for treatment.

EP283 - Sentinel Lymph Node Biopsy Should Not Be Withheld In Patients With Early Breast Cancer Over 70 Years Of Age

Abstract Introduction North American Choosing Wisely guidelines currently state patients with hormone receptor (HR) positive, HER2 negative early breast cancer over 70 [EBC] should avoid sentinel lymph node biopsy (SLNB). Methods Data was collected over a 2-year period in patients over 70 years with EBC. Results 22 patients, mean age 77.0, (70-89). Dual tracer was attempted in all cases. There was no significant difference in age, tumour size or BMI between dual lymphatic mapping vs single tracer success (unpaired t test, p = 0.80, 0.61 and 0.31). SLNB failed in one patient (EBC, breast cancer surgery with axillary dissection 15 years before). SNLB success rate 97.5% Positive nodes at SLNB were found in 5 EBC patients (22.7%), mean age 79.2 years (74-86), mean tumour size 27.8mm (11-78mm); mean number of sentinel nodes removed was 5.8 (range 4-9), mean number of positive nodes 1.8 (range 1-4). In the 5 patients with lymph node disease, 3 had metastasis, two micrometastasis), 4 nodes were retrieved at SLNB in all 3 cases, positive node range 1-4. All 5 patients with positive nodes were HR positive, HER negative. All had adjuvant treatment, 4 out of the 5 had tumours less than 1.5cm in size. Mean survival after surgery 63.5 months (40-87 months), mean follow up 71.4 months (50-90 months). Conclusion Our study indicates contrary to current Choosing Wisely guidelines, there is benefit for SLNB in over 70s, even with small HR positive, tumours, usually thought to be at low risk of metastasis. We found SLNB safe and effective. To accurately identify positive nodes, up to 4 sentinel nodes should be retrieved.

New treatment strategy for early hormone receptor-positive HER2-negative breast cancer: updated results of adjuvant abemaciclib trial in operable and locally advanced breast cancer

Abemaciclib is an oral inhibitor 4 and 6 (CDK4/6). Abemaciclib differs from other drugs in this group in suppression spectrum of cyclin-dependent kinases and is proven to improve survival rates in different treatment lines of metastatic breast cancer. In randomized clinical trials 3rd phase in patients with early hormone-dependent HR+ HER2 negative breast cancer high risk of progression abemaciclib in conjunction with hormone therapy significantly improves invasive disease-free survival (IDFS) and distant relapse-free survival (DRFS). Long- term outcome studies monarchE with 5 – year follow – up of patients showed that abemaciclib adding to ET increases 5-year IDFS from 76 to 83.6% (HR0.680; 95% CI 0.599 to 0.772; p &lt;0.001) and 5-year DRFS from 79.2% to 86.0% (HR0.675; 95% CI 0.588 to 0.774; p &lt;0.001). Adverse events of 3rd degree and higher are registrated in 45.5% of patients in abemaciclib group and in 12,7% in control group and mainly presented by neutropenia (18.6 and 0.7%) and diarrhea (7,6 and 0.1%). Toxicity profile was expected and controlled. The reasonable dose reduction of abemaciclib did not lead to deterioration of long-term treatment result.