Diagnosis Of Early-stage Breast Cancer Research Articles

Abstract PO1-28-06: Development of a plasma-based real-time qPCR gene expression assay for targeted screening and diagnosis of early-stage breast cancer

Abstract Background Over 12 million US women undergo follow-up breast imaging and exams annually due to breast cancer suspicions, incurring an $8 billion total cost, of which $2.18 billion is attributed to false-positive breast biopsies. Recent updates published to the Mammography Quality Standards Act (MQSA) now recommend supplemental imaging for women with heterogeneously dense and extremely dense breasts, regardless of suspicious findings. Given sensitivity limitations associated with mammography in dense-breasted women, as low as 30-47%, and non-specificity associated with ultrasound and MRI, these recent recommendations are likely to further strain the healthcare system and place substantial burden on patients and providers. With an estimated 25 million dense-breasted women who regularly screen for breast cancer now considered at-risk, there is a critical need for affordable and highly accurate testing, offering better specificity without sacrificing sensitivity. To address this gap, we developed an affordable and accurate plasma-based gene expression assay using real-time qPCR for targeted screening and diagnosis of early-stage breast cancer. Methods In previous studies, 26 cross-correlated mRNA gene targets were discovered and independently validated for non-invasive breast cancer detection across five independent patient cohorts using microarray gene expression profiling, comprised of peripheral blood mononuclear cells (n=337) and saliva (n=20), demonstrating strong efficacy, reproducibility, and concordance across bio-fluids and assay platforms in all studies. A sixth independent validation cohort comprised of 203 plasma samples was subsequently obtained for real-time qPCR clinical assay development and validation in our CLIA laboratory. The CLIA validation cohort was designed to be representative of different diagnosis status, stage, and ethnicity, and included Caucasian, Black/African American, Hispanic, and Asian women. After normalizing mRNA expression and clustering analysis, the signature was refined to 8 target genes and was assessed in a cohort of 87 plasma samples [Table 1] to verify the assay’s diagnostic performance for stage I breast cancer detection, under locked laboratory protocols. We leveraged machine learning methods derived from XGBoost classification, a supervised-learning algorithm that uses sequentially built shallow decision trees to provide accurate results and avoidance of overfitting. Results The XGBoost model, selecting from the 8 mRNA gene targets and patient age, achieved >99% sensitivity, 89% specificity for stage I breast cancer detection in the held-out test set, with an overall diagnostic accuracy of 94.5%. Normalized data showed significant differences in gene expression between healthy controls and stage I patients, and distinct clustering was observed for the 8 mRNA gene signature, including patient age. Conclusions Our plasma-based real-time qPCR gene expression clinical assay, with machine learning, is positioned as a highly accurate non-invasive tool for targeted screening and diagnosis of early-stage breast cancer. Furthermore, a clinically validated assay in this space addresses the need for more targeted diagnostics, while serving as a promising tool for clinicians as they make critical care decisions across the breast health continuum, accelerating time to earlier and more precise intervention and treatment, mitigating unnecessary healthcare expenditures, and reducing the mental, emotional, and financial burden on patients and their families. Citation Format: Martin Keiser, Elizabeth Cormier-May, Matthew Alderdice, Joy Kavanagh, William Guesdon, Heather Healy, Nathalie Jean-Charles, Jay Harness. Development of a plasma-based real-time qPCR gene expression assay for targeted screening and diagnosis of early-stage breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-28-06.

3D amide proton transfer-weighted imaging may be useful for diagnosing early-stage breast cancer: a prospective monocentric study

BackgroundWe investigated the value of three-dimensional amide proton transfer-weighted imaging (3D-APTWI) in the diagnosis of early-stage breast cancer (BC) and its correlation with the immunohistochemical characteristics of malignant lesions.MethodsSeventy-eight women underwent APTWI and dynamic contrast-enhanced (DCE)-MRI. Pathological results were categorized as either benign (n = 43) or malignant (n = 37) lesions. The parameters of APTWI and DCE-MRI were compared between the benign and malignant groups. The diagnostic value of 3D-APTWI was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) to establish a diagnostic threshold. Pearson’s correlation was used to analyze the correlation between the magnetization transfer asymmetry (MTRasym) and immunohistochemical characteristics.ResultsThe MTRasym and time-to-peak of malignancies were significantly lower than those of benign lesions (all p < 0.010). The volume transfer constant, rate constant, and wash-in and wash-out rates of malignancies were all significantly greater than those of benign lesions (all p < 0.010). ROC-AUCs of 3D-APTWI, DCE-MRI, and 3D-APTWI+DCE to differential diagnosis between early-stage BC and benign lesions were 0.816, 0.745, and 0.858, respectively. Only the difference between AUCAPT+DCE and AUCDCE was significant (p < 0.010). When a threshold of MTRasym for malignancy for 2.42%, the sensitivity and specificity of 3D-APTWI for BC diagnosis were 86.5% and 67.6%, respectively; MTRasym was modestly positively correlated with pathological grade (r = 0.476, p = 0.003) and Ki-67 (r = 0.419, p = 0.020).Conclusions3D-APTWI may be used as a supplementary method for patients with contraindications of DCE-MRI. MTRasym can imply the proliferation activities of early-stage BC.Relevance statement3D-APTWI can be an alternative diagnostic method for patients with early-stage BC who are not suitable for contrast injection.Key points• 3D-APTWI reflects the changes in the microenvironment of early-stage breast cancer.• Combined 3D-APTWI is superior to DCE-MRI alone for early-stage breast cancer diagnosis.• 3D-APTWI improves the diagnostic accuracy of early-stage breast cancer.Graphical

Implementing Indicators and Trajectories of Return to Work After Breast Cancer Diagnosis: A Mixed-Methods Study Using the French National Healthcare Insurance Database and Stakeholder Consultation

PurposeReturn to work (RTW) is important for quality of life after breast cancer but its analysis at the population-level remains limited in France. This study aimed to implement Electronic Healthcare Data (EHD)-based indicators and trajectories to measure RTW after breast cancer diagnosis, and to examine stakeholders’ perspectives regarding these indicators. MethodsWe followed a mixed-methods approach that consisted of (i) implementing RTW indicators and identifying clusters of trajectories using state sequence analysis with data from a representative sample of the French National Health Data System and (ii) exploring, through qualitative focus group and interviews, stakeholders’ perceptions on the interpretation, limitations, and utility of these indicators. ResultsWe extracted data from 317 women aged 25-55 years with a first diagnosis of early-stage breast cancer. The median number of sickness absence periods was 2 for a total of 434 days during the 3-year follow-up, and the median time to sustainable RTW was 240 days. Three clusters of RTW trajectories were identified: “early RTW” (49.5% of the population), “RTW after partial resumption” (37.5%) and “continuous compensation” (12.9%). Feedback from stakeholders highlighted the multi-factorial nature of RTW and underscored the added value of EHD for studying RTW, despite certain limitations. ConclusionsWe demonstrated the feasibility of calculating RTW indicators and identifying trajectories using the French National Health Data System. These indicators can serve as outcome measures in RTW promotion and provide a basis for designing targeted interventions for breast cancer survivors.

Evaluation of diagnostic value of Doppler ultrasound in the diagnosis of malignant breast masses.

Breast cancer is the leading cause of cancer mortality in women, and it is on the rise in Iran. Therefore, an early-stage diagnosis of breast cancer is of critical importance. Because ultrasound is one of the available, inexpensive, and minimally invasive techniques for distinguishing malignant from benign masses, a comparison of conventional ultrasound, color Doppler, and spectral Doppler findings can be useful. The purpose of this study was to determine the diagnostic value of sonographic indices, specifically Doppler parameters, in identifying the nature of breast masses. This is a cross-sectional study, with diagnostic value analysis. Before undergoing a biopsy, 80 patients with breast masses underwent B-mode and Doppler breast ultrasound. The ultrasound findings were then compared to pathologic results to determine which groups were malignant or benign. The resulting data were analyzed using statistical tests and diagnostic values with SPSS 22 software. B-mode grey-scale ultrasound indices such as mass shape, mass margin, mass orientation, and posterior features, as well as Doppler indices such as vascularity, RI (Resistive Index), PI (Pulsatility Index), and PSV (Peak Systolic Velocity), were found to be statistically significant with pathological findings. Color Doppler revealed vascularity in 65% of benign and 84% of malignant masses. The diagnostic value results revealed that mass shape, mass margin, mass orientation, and posterior features all play a significant role in predicting lesion malignancy, with a sensitivity of 92%, 58%, 64%, 56%, and specificity of 59%, 66%, 82%, and 84%, respectively. The RI, PI, and PSV indices were significantly higher in malignant masses, and all of them had remarkable diagnostic values in predicting malignancy, with a (Area Under The Curve) AUC of 0.863, 0.882, 0.702, a sensitivity of 84% and 84%, 68%, and a specificity of 83%, 86%, and 62%, respectively, at the optimal cut-off points (0.65, 1.32, 12.40) obtained from the Receiver Operating Characteristics (ROC) curves.

Retrospective descriptive analysis of the demographic and clinicopathological presentation of breast cancer patients in Kasr Al-Ainy Hospital over 5 years

Background Breast cancer is the most common type of cancer and the second leading cause of mortalities among women. Early-stage diagnosis of breast cancer increases the chances of survival, and therefore, reduces mortality rates. A national initiative was implemented in Egypt in 2019 when women above the age of 18 years were granted free screening at 3538 healthcare units, and 114 hospitals nationwide. Objective To describe the sociodemographic factors and clinicopathological presentation of breast cancer among the patients presented to Cairo University Hospital over 5 years. Patients and methods We conducted a descriptive historical study at Kasr Al-Ainy Teaching Hospital of Cairo University, we retrospectively assessed all women with primary breast cancer diagnosed over 5 years during the period from the July 1, 2017 to the July 1, 2022. Data was collected from medical records, operative notes, radiology reports, and pathology reports from the database system of the Oncology Department of Cairo University Hospitals (Kasr Al-Ainy). The patient data is put into a spreadsheet that focuses on the demographic and clinicopathological characteristics of the patients (age, size of the tumor, side of breast affected, histopathology findings, imaging characteristics, immunohistochemistry, and TNM staging of breast cancer). Results We enrolled a total of 509 female patients who were screened for early detection of breast cancer during the past 5 years in general surgery, radio diagnosis, and oncology departments, Kasr Al-Ainy Teaching Hospital; they showed a mean age of 53.7±11.7 years and ranged between 26 and 88 years. In the current study molecular subtyping showed that 24.4% were Luminal A, 34% were Luminal B1, 19.4% were Luminal B2, 9.2% were HER2-enriched and 13% were triple-negative breast cancer subtype. We found that neoadjuvant therapy was prescribed for 190 (37.3%) patients, 36 (19%) of those patients achieved pCR, while the remaining patients had residual disease in the postoperative specimen. Conclusion We concluded that in our center, Egyptian females are diagnosed with breast cancer earlier compared with developed countries; however, the current study reports approximately the same percentages of molecular subtypes, rates of pCR, and metastatic disease at the time of presentation compared with developed countries.

A Novel Visible and Near-Infrared Hyperspectral Imaging Platform for Automated Breast-Cancer Detection

Background: Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early and accurate detection is crucial for improving patient outcomes. Our study utilizes Visible and Near-Infrared Hyperspectral Imaging (VIS-NIR HSI), a promising non-invasive technique, to detect cancerous regions in ex-vivo breast specimens based on their hyperspectral response.Methods: In this paper, we present a novel HSI platform integrated with fuzzy c-means clustering for automated breast cancer detection. We acquire hyperspectral data from breast tissue samples, and preprocess it to reduce noise and enhance hyperspectral features. Fuzzy c-means clustering is then applied to segment cancerous regions based on their spectral characteristics.Results: Our approach demonstrates promising results. We evaluated the quality of the clustering using metrics like Silhouette Index (SI), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The clustering metrics results revealed an optimal number of 6 clusters for breast tissue classification, and the SI values ranged from 0.68 to 0.72, indicating well-separated clusters. Moreover, the CHI values showed that the clusters were well-defined, and the DBI values demonstrated low cluster dispersion. Additionally, the sensitivity, specificity, and accuracy of our system were evaluated on a dataset of breast tissue samples. We achieved an average sensitivity of 96.83%, specificity of 93.39%, and accuracy of 95.12%. These results indicate the effectiveness of our HSI-based approach in distinguishing cancerous and non-cancerous regions.Conclusions: The paper introduces a robust hyperspectral imaging platform coupled with fuzzy c-means clustering for automated breast cancer detection. The clustering metrics results support the reliability of our approach in effectively segmenting breast tissue samples. In addition, the system shows high sensitivity and specificity, making it a valuable tool for early-stage breast cancer diagnosis. This innovative approach holds great potential for improving breast cancer screening and, thereby, enhancing our understanding of the disease and its detection patterns.

Unmet Social Needs and Breast Cancer Screening Utilization and Stage at Presentation

Unmet social needs in local populations may hinder the development of targeted cancer control interventions aimed at improving screening utilization and early-stage breast cancer diagnosis to ultimately improve breast cancer survival disparities. To evaluate if (1) city-funded screening mammography is associated with utilization of screening mammography, (2) unmet social needs are associated with utilization of screening mammography, and (3) unmet social needs are associated with later-stage disease at diagnosis. This cohort study included patients with stages I-IV invasive ductal or lobular carcinoma treated at an academic medical center (including both an underserved safety-net hospital [SNH] and a National Cancer Institute-designated academic cancer center [ACC]) from 2020 to 2023. Eligible patients were aged 18 years or older and able to consent. Data were analyzed between July 2023 and September 2023. The Health Leads Social Needs Screening Toolkit, a screening tool that gathers information on the most common social need domains affecting patient health. Univariable and multivariable logistic regression was utilized to evaluate the following primary outcomes: (1) routine screening mammography and (2) American Joint Committee on Cancer 8th edition clinical stage at presentation. Of the 322 women who completed the Health Leads Social Needs Screening Toolkit, 201 (62%) self-identified as Hispanic, 63 (19%) as non-Hispanic Black, and 63 (19%) as non-Hispanic White. Two hundred fifty-five (76%) patients with access to city-funded screening mammography completed a screening mammogram. Patients who presented to the SNH were more likely to present with late-stage disease compared with early-stage disease (15 of 48 [31%] vs 50 of 274 [18%]; P = .04). On multivariable logistic regression, not completing a screening mammography was associated with having an increasing number of unmet social needs (OR, 0.74; 95% CI, 0.55-0.99; P = .047) and an increasing age at diagnosis (OR, 0.92; 95% CI, 0.89-0.96; P < .001). Moreover, increasing unmet social needs was significantly associated with late-stage diagnosis above and beyond screening mammography (OR, 1.38; 95% CI, 1.01-1.89; P = .04). In this cohort study, access to screening mammography did not translate to utilization of screening mammography, increasing unmet social needs were significantly associated with lower rates of screening mammography, and those with increasing unmet social needs were more likely to present with late-stage disease. This association transcended recruitment site (SNH vs ACC), indicating that patients in either hospital setting may benefit from unmet social needs screening to overcome access to care barriers associated with late-stage disease at diagnosis.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Adaptation and Implementation of Pictorial Conversation Aids for Early-Stage Breast Cancer Surgery and Reconstruction: A Quality Improvement Study.

After a diagnosis of early-stage breast cancer, women of lower socioeconomic position (SEP) report worse outcomes than women of higher SEP. A pictorial conversation aid was shown to improve decision outcomes in controlled contexts. No such intervention existed in France. In Phase 1, our aim was to adapt, for use in France, two pictorial conversation aids for breast cancer surgery and reconstruction. In Phase 2, our aim was to implement them in a regional cancer center serving a diverse population. In phase 1, we used iterative qualitative methods to adapt the conversation aids with a convenience sample of patients and health professionals. In phase 2, we tested their implementation using PDSA cycles with volunteer surgeons. In phase 1, we interviewed 10 health professionals and 5 patients to reach thematic data saturation. They found the conversation aids usable and very acceptable (especially patients) and suggested small changes to further simplify the layout and content (including a glossary). In phase 2, three surgeons started the first PDSA cycle, for 4 weeks. Only one additional surgeon agreed to take part in the second cycle. The third cycle was cancelled since no new surgeon agreed to take part. Time was a barrier for 2 out of 4 surgeons, potentially explaining the difficulty recruiting for the third cycle. The evaluation was otherwise positive. The surgeons found the conversation aids very useful during their consultations and all intended to continue using them in the future. It was possible to adapt, for use in France, pictorial conversation aids proven to be effective elsewhere. While the adapted conversation aids were deemed usable by health professionals and very acceptable to patients, their implementation using PDSA cycles proved slow.

Diagnostic Value of a Combined Serum α-Hydroxybutyrate Dehydrogenase, Carcinoembryonic Antigen and Glycoantigen 125 Test for Early-Stage Breast Cancer.

To investigate the diagnostic value of the combined detection of α-hydroxybutyrate dehydrogenase (α-HBDH), carcinoembryonic antigen (CEA) and cancer antigen 125 (CA125) in early-stage breast cancer (ESBC). This was a retrospective analysis of 169 patients with ESBC, 138 patients with benign breast disease (BBD) and 200 normal healthy controls (NHCs). The levels of serum α-HBDH, CEA and CA125 in the two groups were detected. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to analyse the diagnostic value of the above indicators alone and in combination for ESBC. The levels of α-HBDH, CEA and CA125 in the ESBC group were significantly higher than those in the BBD and NHC groups ([118.18 ± 11.19 vs 91.24 ± 9.17 vs 89.38 ± 9.01, F = 6.189, p = 0.004], [2.39 ± 1.12 vs 1.48 ± 0.76 vs 1.58 ± 0.58, F = 5.362, p = 0.017] and [14.44 ± 6.78 vs 11.19 ± 3.17 vs 7.18 ± 4.71, F = 8.912, p = 0.001], respectively). In the ESBC group, the positive rate of combined detection was higher than that of single detection (96.12% vs 72.64% vs 53.67% vs 42.41%, X2 = 27.174, p < 0.05). ROC curve analysis showed that serum α-HBDH, CEA, CA125 alone and combined detection in the diagnosis of ESBC. The sensitivity was 48.1%, 63.6%, 44.2% and 54.5%, the specificity was 75.4%, 75.4%, 86.0% and 91.2% and the AUC was 0.654, 0.715, 0.636 and 0.772, respectively. The diagnostic value of combined detection was the highest. The levels of serum α-HBDH, CEA and CA125 in ESBC are high, and the combined detection of the three has a high diagnostic value for ESBC.

Identification of four candidates circulating microRNAs in plasma as a potential biomarker for early-stage breast cancer diagnosis in Vietnamese women

Breast cancer is the most commonly occurring malignant neoplasm among the female population. This study aims to ascertain novel biomarkers that may prove advantageous in the early detection of breast cancer. The present investigation involved a comparative analysis of the plasma expression of four distinct microRNA (namely, miR-425-5p, miR-142-3p, miR-9-3p and miR-15b-5p) in a cohort of 80 Vietnamese patients who were newly diagnosed with breast cancer. The expression levels of these microRNA were compared with those of 80 healthy individuals who served as controls. The plasma total RNA was extracted and the miRNA expression levels were assessed using Stem-loop RT-qPCR followed by statistical analysis. According to the findings of our study, the plasma levels of four microRNAs namely miR-425-5p, miR-142-3p, miR-9-3p and miR-15b-5p, were significantly reduced in both breast cancer patients in stage 0-I and the general breast cancer patient population as compared to healthy controls. The diagnostic accuracy of both miR-9-3p levels and miR-15b-5p levels was satisfactory with 74% and 88% values respectively. The combined effect of miR-425-5p/miR-142-3p/miR-9-3p/miR-15b-5p revealed the highest level of diagnostic precision, attaining a rate of 98% and an area under the curve value of 0.98 (with a sensitivity of 87.5% and specificity of 100%). In summary, the microRNAs miR-425-5p, miR-142-3p, miR-9-3p and miR-15b-5p exhibit potential as non-invasive diagnostic biomarkers for breast cancer, particularly in the initial stages of the disease.

Breast ultrasound image classification with hard sample generation and semi-supervised learning

Ultrasound is a fast and non-invasive imaging technique widely used in the early-stage diagnosis of breast cancer. Ultrasound images are also commonly used in deep learning, but traditional computer algorithms and even ultrasound diagnosticians often struggle to accurately distinguish between benign and malignant cases, as some ultrasound images exhibit mixed characteristics of both. To address the above problems, we defined hard sample mining rules firstly. To address the above problems, we defined rules for hard sample mining and utilized shape descriptors (Concavity, Growth Direction, Compactness, Circle Variance, and Elliptic Variance) to describe the samples. For those samples with overlapped features of both categories, i.e., benign and malignant, we identify them as hard samples. In order to make the network pay more attention to hard samples, we propose a Phased GAN (PGAN) that can generate hard samples to increase the number of such samples in the dataset. However, since the generated hard samples lack labels, they cannot be used in supervised training. To overcome this limitation, we propose a new semi-supervised network (SSN) that trains them with labeled samples. We also add a hard sample loss function to the SSN to regularize the features of the hard samples, which helps the network learn these features better. In summary, we propose a breast ultrasound image classification using PGAN and SSN, which achieves a classification accuracy of 97.7% on a public breast dataset (445 cases of benign and 210 cases of malignant).

Adjuvant therapy for HR+/HER2– early-stage breast cancer (EBC): Changes in practice patterns among community oncologists in Brazil.

e18820 Background: The management of HR+/HER2– early breast cancer (EBC) has changed significantly, with more patients are being classified as low-risk and spared adjuvant chemotherapy (CT) and de-escalating endocrine therapy (ET); on the other hand, other patient groups have experienced treatment escalation, such as high-risk premenopausal women. This study aims to assess practice patterns and real-world data for patients with HR+/HER2- EBC treated in Brazil's largest network of community oncology practices. Methods: Retrospective real-world study from the Oncoclínicas with longitudinal EHR data merged in a platform where structured variables were integrated with unstructured elements from physician notes using technology-based abstraction and expert human curators that follow mCODE standards and predefined ontology. Index data was EBC diagnosis date, and we restricted the study to patients diagnosed between Jan 2017 and Jun 2022, with comparative analysis of adjuvant CT/ET practice trends in 2017-2019 versus 2020-2022. Results: From over 30k patients with breast cancer in Oncoclínicas Database, 19k had HR+/HER2– EBC, 12k were treated in high-volume clinics, and 2,988 were selected for the study due to data accessibility in the pre-specific observation period. Stage distribution: I in 45%, II in 37%, and III in 18%. Pre-menopausal status at diagnosis: 22%. Less than 5% of the study population had access to breast cancer prognostic risk scores. The disease-free survival rate in the entire cohort was 87% (CI95% 86-89) at 2 years and 74% (CI95% 70-78) at 5 years. Overall, we observed a decrease in adjuvant CT use from 51% during 2017-2019 to 41% during 2020-2022. In pre- menopausal patients, CT use reduced from 68% in stage I during 2017-2019 to 41% during 2020-2022; for stage II or III, the reduction was less noticeable (83% in 2017-2019 to 75% in 2020-2022). In post-menopausal patients, CT use reduced from 36% in stage I during 2017-2019 to 18% during 2020-2022; while it remained stable in stage II or III (55% in 2017-2019 versus 52% in 2020-2022). Concerning ET, there were no evident changes in treatment practice. In pre-menopausal, tamoxifen alone was the first option in 90% with stage I and in 64% with stage II or III, with the stability over the years. In post-menopausal, aromatase inhibitors were the first option in 61% with stage I, 70% with stage II and 77% with stage III, with the stability over the years. Conclusions: We perceived a reduction in chemotherapy use in HR+/HER2– stage I breast cancer over time. Reasons for treatment selection must be investigated further, including other prognostic factors, but might be related to greater physician confidence in avoiding CT and access to genomic signatures. There were no significant changes in patterns of adjuvant ET, with most pre- and postmenopausal patients receiving tamoxifen alone and an aromatase inhibitor respectively.

Cascaded microfluidic circuits for pulsatile filtration of extracellular vesicles from whole blood for early cancer diagnosis

Tumor-derived extracellular vesicles (EVs) hold the potential to substantially improve noninvasive early diagnosis of cancer. However, analysis of nanosized EVs in blood samples has been hampered by lack of effective, rapid, and standardized methods for isolating and detecting EVs. To address this difficulty, here we use the electric-hydraulic analogy to design cascaded microfluidic circuits for pulsatile filtration of EVs via integration of a cell-removal circuit and an EV-isolation circuit. The microfluidic device is solely driven by a pneumatic clock pulse generator, allowing for preprogrammed, clog-free, gentle, high-yield, and high-purity isolation of EVs directly from blood within 30 minutes. We demonstrate its clinical utility by detecting protein markers of isolated EVs from patient blood using a polyethylene glycol-enhanced thermophoretic aptasensor, with 91% accuracy for diagnosis of early-stage breast cancer. The cascaded microfluidic circuits can have broad applications in the field of EV research.

The dual-crosslinked prospective values of RAI14 for the diagnosis and chemosurveillance in triple negative breast cancer

Objective The exploration of non-invasive biomarkers for assessing tumor response is critical to optimize treatment decisions. In this study, we aimed at determining the potential role of RAI14 in the early diagnosis and evaluation of chemotherapy efficacy in triple-negative breast cancer (TNBC). Methods We recruited 116 patients newly diagnosed with breast cancer, 30 patients with benign breast disease and 30 healthy controls. In addition, 57 TNBC patients were collected in serum at different time points (C0, C2 and C4) for chemotherapy monitoring. The expression of serum RAI14 and CA15-3 were quantified by Elisa and electrochemiluminescence assay, respectively. Then we compared the performances of markers with the chemotherapy efficacy assessed by imaging. Results RAI14 is significantly overexpressed in TNBC and is linked to adverse clinicopathological features such as tumor burden, CA15-3 levels and the ER, PR, and HER2 status of the patients. ROC curve analysis showed that RAI14 improves the diagnostic performance for CA15-3(AUCRAI14 = 0.934 vs. AUCCA15-3 = 0.836), especially embodied in early-stage breast cancer diagnosis and patients with CA15-3 negativity. Furthermore, RAI14 behaves well in reproducing treatment response which was consistent with clinical Imaging assessment. Conclusions Recent studies showed that RAI14 has a complementary effect to CA15-3 and a test combining the two parameters can improve the detection rate of early triple-negative breast cancer. At the same time, RAI14 plays a more important role in chemotherapy monitoring than CA15-3 as the change in its concentration is in line with the tumor volume variation. Taken together, RAI14 is a reliable novel marker in the early diagnosis and chemotherapy monitoring of triple-negative breast cancer.

Abstract P2-02-05: Patterns of adjuvant bone modifying agent use in patients with early-stage breast cancer in the United States

Abstract Background: The bone modifying agents (BMAs), bisphosphonates, reduce risk of cancer recurrence after diagnosis of early-stage breast cancer (EBC), particularly in postmenopausal women, while another BMA, denosumab, has not been shown to improve EBC outcomes. The 2017 American Society of Clinical Oncology (ASCO) and Cancer Care Ontario (CCO) clinical guidelines recommended consideration of adjuvant bisphosphonates for postmenopausal women with EBC who are candidates for adjuvant systemic therapy. Despite this, small survey-based studies suggest that their prescribing is variable. The goal of this study was to evaluate prescribing of adjuvant BMAs in the United States before and after publication of the 2017 ASCO/CCO guidelines (ACGD). Methods: This retrospective cohort study used the nationwide Flatiron Health electronic health record (EHR)-derived de-identified database and included 11,740 patients diagnosed with stage I-III breast cancer from 2012 to 2019. We analyzed data on use of BMAs (bisphosphonates or denosumab) in the adjuvant setting, defined as receiving first dose of BMA within 24 months of breast cancer diagnosis and before diagnosis of metastatic disease. Any episodes of BMA use after a metastatic recurrence were excluded. We used Chi-squared test to compare the proportion of patients receiving adjuvant BMAs pre- and post-ACGD. We used univariable and multivariable logistic regression analyses with list-wise missing value deletions to identify predictors of adjuvant BMA prescribing. Results: Of 11,740 patients, 7,391 were diagnosed pre-ACGD and 4,349 post-ACGD. Of the pre-ACGD patients, 545 (7.4%) patients received adjuvant BMAs, and of the post-ACGD patients, 390 (9.0%) patients received adjuvant BMAs. Patients diagnosed post-ACGD were more likely to receive adjuvant BMAs compared with patients diagnosed pre-ACGD (OR 1.23; 95% confidence interval (CI) 1.08-1.42; p=0.002). Of patients age ≥ 50 (n=9,654), 522 of 6,027 (8.7%) and 360 of 3,627 (9.9%) received adjuvant BMAs pre- and post-ACGD, respectively (OR 1.146; 95% CI 0.996-1.319; p=0.0572). In multivariable analysis, age ≥ 50 years at diagnosis, post-menopausal status, having T2 or higher stage at diagnosis, adjuvant endocrine therapy, and coexisting bone loss diagnosis were significantly associated with receipt of adjuvant BMAs (Table 1). Of the 935 patients who received adjuvant BMAs, 615 (65.8%) had denosumab only, 305 (32.6%) had a bisphosphonate only, and 13 (1.4%) received both a bisphosphonate and denosumab during the course of adjuvant treatment. Two patients received an adjuvant BMA as part of a clinical trial. Conclusions: Adjuvant BMA prescribing in our cohort was overall low; there was a modest increase in uptake in patients with EBC diagnosed after the publication of the 2017 ACGD. Older age, higher T stage, endocrine therapy, and coexisting bone loss diagnoses were significantly associated with receipt of adjuvant BMAs. Despite the recommendations for bisphosphonates in the adjuvant setting in EBC, the majority of patients received denosumab only. Considering these findings, further research is required to determine barriers to BMA prescribing and factors that influence physician and patient decisions. There continues to be a need for improved implementation and dissemination of recent guidelines, including the updated 2021 ASCO/CCO recommendations which continue to endorse bisphosphonate use in the adjuvant setting. Table 1: Univariable and multivariable logistic regression analyses to identify predictors of receiving adjuvant BMAs sabs Citation Format: Nicole Odzer, Rachel Jaber Chehayeb, Maryam Lustberg, Cary P. Gross, Julia Foldi. Patterns of adjuvant bone modifying agent use in patients with early-stage breast cancer in the United States [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-02-05.

Abstract P1-05-11: Improving the Performance of Early Breast Cancer Diagnosis by a Model Combining Breast Ultrasound with Methylation Markers in Non-Invasive Circulating Tumor DNA

Abstract Background Breast cancer is one of the most common cancers worldwide with the highest incidence among females in 2020 [1]. The application of breast ultrasound benefits the diagnosis of breast cancer at the early stage, but also leads to over-diagnosis. Patients with breast nodules detected by ultrasound at BI-RADS 4a or higher are usually advised to have a fine needle biopsy or surgery, although the PPVs of BI-RADS 4a and 4b categories are low (6% and 25%, respectively) [2]. Methods In this study, AnchorIRIS™ assay was used for analyzing the methylated status of cfDNA. Target enrichment was performed using an AnchorDx breast cancer-specific methylation panel consisting of 129,794 methylated markers. One hundred and twelve pairs of breast tissue and plasma samples (Malignant: Benign= 56: 56) and 40 leukocyte samples (Malignant: Benign = 20: 20) were used to identify reliable breast cancer-specific methylation markers with low noise background. Finally, a methylation model trained on 307 plasma samples (train set: test set = 214: 93) was selected for differentiating benign from malignant nodules, which was validated by two independent sets (Validation-1: Malignant: Benign = 42:46; Validation-2: Malignant: Benign = 62: 46).Results This methylation model exhibited a powerful performance on differentiating benign from malignant nodules with an AUC of 0.837 (95% CI: 0.757-0.918) in the test set, and maintained a stable predictive power with AUCs of 0.820 and 0.801 in two independent validation sets, respectively. In addition, this methylation model can reflect the difference in methylation signals between metastatic and non-metastatic cancers three years in advance. In contrast to ultrasound, the prediction rate of breast cancer is more accurate across the different age groups using the methylation model, especially for younger women less than 40 years old. Under the ultrasound BI-RADS 4 category, the accuracy (ACC) of the methylation model (IndVal-1, 73.02%; IndVal-2, 78.31%) is on average 22% higher than ultrasound (IndVal-1, 55.56%; IndVal-2, 51.81%). In both of the independent validation sets, the overall accuracy (78.7%) and specificity (SP) (58.7%) at the sensitivities above 95% of the combined model is greater than applying either ultrasound (ACC: 65%; SP: 26.1%) or the methylation model (ACC: 70.6%; SP: 52.2%) alone. Conclusion This methylation model has great potential for the diagnosis of early-stage breast cancer. It improves the diagnostic accuracy of the indeterminate breast nodules, which may assist in decreasing the unnecessary biopsies or surgeries of patients with benign lesions. The methylation model also has the potential in predicting metastatic and non-metastatic breast cancers that is valuable for patient surveillance and risk prediction.

The Association Between Medication Use in Older Women with Early-Stage Operable Primary Breast Cancer and Decision Regarding Primary Treatment.

Polypharmacy is one factor contributing to increased mortality, hospitalization, and adverse drug reactions in older adults. The aim of this study was to measure the prevalence of polypharmacy in a cohort of older women with early-stage operable primary breast cancer and the relationship of polypharmacy to primary treatment decision and functional status. A total of 139 patients with a new diagnosis of early-stage operable primary breast cancer proven histologically were recruited as part of a prospective study. The average age was 77 years. Assessment using a cancer-specific Comprehensive Geriatric Assessment (CGA) tool was conducted within 6 weeks of diagnosis of breast cancer. Association was determined between number of medications and treatment decision and physical status as measured by the CGA outcomes. Additional analysis was performed to determine the associations above with polypharmacy defined by ≥5 daily medications, and if cardiovascular-related diseases have a role in the treatment decision. Polypharmacy was present in 48% of patients (n = 139). CGA determined that polypharmacy was associated with greater comorbidity (P < .001), reduced physical status rated by physicians (P = .009) and patients (P = .019), and reduced ability to perform activities of instrumental ADLs (P = .008). Similar findings were present in the analysis of cardiovascular-related diseases. This work suggests that patients with polypharmacy are more likely to be frail. The number of medications could help us screen patients who should go on to receive full CGA.

Development of a Novel NGS Methodology for Ultrasensitive Circulating Tumor DNA Detection as a Tool for Early-Stage Breast Cancer Diagnosis

Breast cancer (BC) is the most prevalent cancer in women. While usually detected when localized, invasive procedures are still required for diagnosis. Herein, we developed a novel ultrasensitive pipeline to detect circulating tumor DNA (ctDNA) in a series of 75 plasma samples from localized BC patients prior to any medical intervention. We first performed a tumor-informed analysis to correlate the mutations found in tumor tissue and plasma. Disregarding the tumor data next, we developed an approach to detect tumor mutations in plasma. We observed a mutation concordance between the tumor and plasma of 29.50% with a sensitivity down to 0.03% in mutant variant allele frequency (VAF). We detected mutations in 33.78% of the samples, identifying eight patients with plasma-only mutations. Altogether, we determined a specificity of 86.36% and a positive predictive value of 88.46% for BC detection. We demonstrated an association between higher ctDNA median VAF and higher tumor grade, multiple plasma mutations with a likelihood of relapse and more frequent TP53 plasma mutations in hormone receptor-negative tumors. Overall, we have developed a unique ultra-sensitive sequencing workflow with a technology not previously employed in early BC, paving the way for its application in BC screening.

Clinical Impact of ASCO Choosing Wisely Guidelines on Staging Imaging for Early-Stage Breast Cancers: A Time Series Analysis Using SEER-Medicare Data.

American Society for Clinical Oncology released the Choosing Wisely list in 2012, highlighting low-value procedures that lack evidence, advising against the use of positron emission tomography, computerized tomography, and radionuclide bone scans for the staging of early-stage breast cancer at low risk for metastasis. The objective of the study was to assess the impact of the American Society of Clinical Oncology Choosing Wisely guidelines on inappropriate staging imaging among early-stage breast cancers. The Surveillance, Epidemiology, and End Results Program-Medicare data set was used to identify 50,004 women age 66 years and older with new incident diagnosis of early-stage breast cancer (stage 0 through stage 2a; T < 4, N = 0, and M = 0). The primary outcome was the incidence of patients with inappropriate imaging following an early-stage breast cancer diagnosis. The primary outcome was identified within 6 months of the first diagnosis. An interrupted time series analysis using negative binomial regression was performed for outpatient claims for these diagnostic studies versus the two interruptions of guidelines release and guidelines reinforcement. Mean images per patient, percent change for the study period, and rate of change per year were calculated. Imaging rates fell by a modest 2.32% following guidelines release in April 2012 (point estimate = -2.32%; 95% CI, -6.34% to 1.88%). By contrast, imaging rates fell by a four-fold larger amount (point estimate = -9.36%; 95% CI, -13.20% to -5.35%) following guidelines published reminders in journals (or reinforcement) in October 2013. Mean imaging studies per patient (95% CI) declined from 1.80 (1.76 to 1.84) in January 2012 to 1.50 (1.48 to 1.53) by January 2015, representing a 16% decline in imaging overuse in 2015 compared with 3 years earlier. The rate of change (95% CI) in images per patient was initially small at -0.47% (-4.27% to 3.33%) per year between April 2012 and October 2013, but almost eight times faster at -3.70% (-5.81% to -1.60%) per year after October 2013. This analysis demonstrates a substantial decrease in the prevalence of imaging overuse in early-stage breast cancers correlating with the 2013 reinforcement of American Society of Clinical Oncology's 2012 Choosing Wisely guidelines. The creation and dissemination of such resources serves as a powerful tool to improve clinical practice, cost-effectiveness, and patient safety from secondary malignancies, anxiety, and overdiagnosis.