Breast Cancer In Clinical Practice Research Articles

Impact of clinical targeted sequencing on endocrine responsiveness in estrogen receptor-positive, HER2-negative metastatic breast cancer

Clinical targeted sequencing allows for the selection of patients expected to have a better treatment response, and reveals mechanisms of resistance to molecular targeted therapies based on actionable gene mutations. We underwent comprehensive genomic testing with either our original in-house CLHURC system or with OncoPrime. Samples from 24 patients with estrogen receptor-positive, human epidermal growth factor receptor 2-negative metastatic breast cancer underwent targeted sequencing between 2016 and 2018. Germline and somatic gene alterations and patients’ prognosis were retrospectively analyzed according to the response to endocrine therapy. All of the patients had one or more germline and/or somatic gene alterations. Four patients with primary or secondary endocrine-resistant breast cancer harbored germline pathogenic variants of BRCA1, BRCA2, or PTEN. Among somatic gene alterations, TP53, PIK3CA, AKT1, ESR1, and MYC were the most frequently mutated genes. TP53 gene mutation was more frequently observed in patients with primary endocrine resistance compared to those with secondary endocrine resistance or endocrine-responsive breast cancer. Recurrent breast cancer patients carrying TP53-mutant tumors had significantly worse overall survival compared to those with TP53-wild type tumors. Our 160-gene cancer panel will be useful to identify clinically actionable gene alterations in breast cancer in clinical practice.

ACOSOG Z-0011 criteria impact on axillary surgery for early breast cancer in clinical practice: Evaluation in a retrospective cohort of 1900 patients

BackgroundThere is a trend towards de-escalation in early breast cancer axillary surgery. In the American College of Surgeons Oncology Group (ACOSOG) Z-0011 trial, observation was shown to be non-inferior in terms of overall survival to complementary axillary lymph node dissection (cALND) in patients with up to two sentinel lymph node (SLN) metastases. The study included patients with T1-T2 invasive breast cancer, clinically node negative, undergoing breast-conserving surgery with SLN biopsy, followed by systemic therapy and radiotherapy. The aim of our study was to evaluate the impact of applying these ACOSOG Z-0011 inclusion criteria in routine practice. Patients and methodsThis retrospective observational study was conducted in a French comprehensive cancer center where patients treated for breast cancer with primary surgery were prospectively included between 2010 and 2016. Patients meeting ACOSOG Z-0011 inclusion criteria were analyzed. ResultsAmong the 1900 included patients, 1497 (79 %) met the ACOSOG Z-0011 criteria before surgery. Of these, 390 (20 %) had one or two metastatic SLN and could have avoided cALND. Out of these patients, 319 (81 %) presented cT1 tumors. During the study period, cALND was performed in 320 (82 %) patients and was free of metastases in 80 % of cases, having an impact on eligibility for adjuvant chemotherapy in only 3 (0.8 %) patients. ConclusionsIn situations of primary breast cancer surgery, use of ACOSOG Z-0011 criteria could reduce the rate of cALND by 20 %. Further studies are needed to help select patients for whom abstention from any axillary surgery would be reasonable.

The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review.

Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.

Comparison Between Accelerated Partial Breast Irradiation and Whole Breast Irradiation: APBI IMRT Florence Retrospective Analysis of Subjective, Objective and Patient-reported Cosmetic Outcomes

Accelerated partial breast irradiation (APBI) represents a valid alternative to whole breast irradiation (WBI) in selected low risk patients with early stage breast cancer. The long-term analysis of the APBI-IMRT Florence randomized phase 3 trial showed no significant difference in terms of both local and distant disease control and survival outcomes. Acute/late toxicity and cosmesis evaluations were significantly in favor of the APBI arm. The aim of this analysis is to compare the agreement between objective and subjective patient-reported outcome measures (PROMs) of cosmesis assessment. From June 2019 to January 2020, 95 women previously enrolled in the APBI-IMRT Florence trial accepted to undergo photographic assessment. Late skin toxicity was assessed following the RTOG/EORTC late Radiation Morbidity Scoring questionnaire. Digital photographs were assessed (objective method) using the BCCT.core software v.31 (Breast Cancer Conservative Treatment. Cosmetic Results). Breast cosmetic outcomes scored both by physician and patient (subjective methods) were recorded using the Harvard Breast Cosmesis Grading Scale. At median follow up of 11 years (IQR 10.5-11.6 years), we evaluated 95 patients (51 APBI, 44 WBI). Late skin toxicity rates (any grade) were significantly in favor of the APBI group of patients (36.4% vs 6%; p = 0.0003). Also, cosmetic assessment was significantly in favor of the APBI group both evaluated by physician (excellent-good [EG] 98% vs 90%, p<0.0001; fair-poor [FP] 2% vs 10%, p = 0.0455) and patient (EG 86% vs 70%, p<0.0001; FP 14% vs 30%, p<0.0001). The BCCT.core cosmetic assessment showed significantly improved outcomes in the APBI group (EG 65% vs FP 35%) as compared to WBI group (EG 52% vs FP 48%; p = 0.0001). We observed a good agreement between BCCT.core objective evaluation and both patients (p = 0.19) and physicians PROMs (p = 0.17). Patients treated with APBI showed significant improved late toxicity and cosmetic outcomes. There is a significative agreement between patients/physician PROMs and BCCT.core. This score has proven to be a feasible tool in breast cancer clinical practice.

The use of moderately hypofractionated post-operative radiation therapy for breast cancer in clinical practice: A critical review.

Post-operative radiation therapy (RT) reduces loco-regional recurrence rates and mortality in most patients with non-metastatic breast cancer. The aim of this critical review is to provide an overview of the applicability of moderately hypofractionated RT for breast cancer patients, focusing on factors influencing clinical decision-making. An international group of radiation oncologists agreed to assess, integrate, and interpret the existing evidence into a practical report to guide clinicians in their daily management of breast cancer patients. We conclude that moderately hypofractionated RT to the breast, chest wall (with/without breast reconstruction), and regional lymph nodes is at least as safe and effective as conventionally fractionated regimens and could be considered as the treatment option for the vast majority of the patients.For those who are still concerned about its generalised application, we recommend participating in ongoing trials comparing moderately hypofractionated RT to conventionally fractionated RT for breast cancer patients in some clinical circumstances.

High Parity Predicts Poor Outcomes in Patients With Luminal B-Like (HER2 Negative) Early Breast Cancer: A Prospective Finnish Single-Center Study.

While breast cancer prognoses are generally good, different molecular subtypes are known to have varying outcomes. Previous studies using breast cancer registries have suggested that high parity may be an adverse prognostic factor in luminal breast cancer, but breast cancer subtype definitions have varied and there have been few prospective studies. We therefore collected prospective data from patients diagnosed with early breast cancer at a single institution and followed them for a median of 8.5 years. All patients (N = 594) were treated according to Finnish national guidelines using modern treatment modalities in a Finnish university hospital. Clinicopathological surrogates of the intrinsic breast cancer subtypes were updated to match European Society for Medical Oncology 2015 Early Breast Cancer Clinical Practice Guidelines. The overall 10-year breast cancer–specific survival (BCSS) was 91.4%, with the longest 10-year BCSS observed in luminal A-like cancers (97.9%) and the worst in luminal B-like (HER2 positive) cancers (80.6%). Parity of ≥ 5 deliveries was also associated with poor BCSS (univariate P = 0.0020). However, when the subtypes were assessed separately in a multivariate analysis that included tumor size and nodal status, high parity remained significant only in luminal B-like (HER2 negative) cancers (HR = 2.63; 95% confidence interval = 1.04–6.62; P = 0.040). Our results suggest excellent overall 10-year BCSS but indicate that high parity is an adverse prognostic factor in luminal B-like (HER2 negative) breast cancers.

Implementation of an ontological reasoning to support the guideline-based management of primary breast cancer patients in the DESIREE project.

The DESIREE project has developed a platform offering several complementary therapeutic decision support modules to improve the quality of care for breast cancer patients. All modules are operating consistently with a common breast cancer knowledge model (BCKM) following the generic entity-attribute-value model. The BCKM is formalized as an ontology including both the data model to represent clinical patient information and the termino-ontological model to represent the application domain concepts. This ontological model is used to describe data semantics and to allow for reasoning at different levels of abstraction. We present the guideline-based decision support module (GL-DSS). Three breast cancer clinical practice guidelines have been formalized as decision rules including evidence levels, conformance levels, and two types of dependency, "refinement" and "complement", used to build complete care plans from the reconciliation of atomic recommendations. The system has been assessed on 138 decisions previously made without the system and re-played with the system after a washout period on simulated tumor boards (TBs) in three pilot sites. When TB clinicians changed their decision after using the GL-DSS, it was for a better decision than the decision made without the system in 75 % of the cases.

Quality of life during chemotherapy, hormonotherapy or antiHER2 therapy of patients with advanced, metastatic breast cancer in clinical practice

IntroductionBreast cancer is one of the most important health problems in the world. In recent years, this cancer has achieved a reduction in mortality, which is attributed to the introduction of mass screening and greater efficacy of post-operative treatment. Many patients with breast cancer have indications only for palliative therapy, but the impact of these methods on the quality of life of patients remains a subject of controversy. It remains unknown whether the progress in improving the quality of life in clinical trials also applies to patients treated as part of daily clinical practice. Data on the results of the impact of conducted therapies on the quality of life outside of clinical trials are scarce.MethodsThe results of palliative chemotherapy and first-line hormonotherapy in 351 patients with advanced, metastatic breast cancer treated in the period from January 2010 to December 2016 in two centres were analysed.ResultsThe average age of patients was 62 ± 9.8 years; 139 patients received chemotherapy, 91 - therapy containing trastuzumab, and 121 - hormone therapy. A partial response was obtained in 111 patients (32%), stabilization in 150 (43%), and in 90 patients (26%) progression. Median survival time in the whole group of patients was 36 months. Chemotherapy compared to trastuzumab and hormonotherapy was associated with greater total toxicity (p = 0.03). There was a significant relationship between the type of therapy (hormonotherapy, chemotherapy, targeted therapy) and the general average quality of women’s life measured with the EORC-QLQ-C30 questionnaire. In addition, a statistically significant difference was found in some somatic complaints (the scale of QLQ-BR23 symptoms) depending on the type of therapy performed. The lowest intensity of complaints was reported by patients during hormonotherapy, then during targeted therapy, and the largest during chemotherapy.ConclusionsThere is no effect of chemotherapy on the overall quality of life. Hormone therapy and trastuzumab therapy improved the quality of life of the treated patients in clinical practice.

360 Health Analysis (H360) - A Proposal for an Integrated Vision of Breast Cancer in Portugal.

H360 aims to provide a comprehensive picture of breast cancer management in Portugal by retrieving real-world data from 10 Portuguese hospitals and deriving a snapshot from the medical interpretation of evidence-based data to patient perspective on the quality and effectiveness of medical care provided. This article reviews evidence on breast cancer clinical practice and quality of care and disease management in Portugal. A review of evidence on breast cancer clinical practice and quality of care over the last 10 years was performed in PubMed using the query "Organization and Administration"[Mesh] AND "breast cancer"[All Fields] NOT "Review" [ptyp]. National cancer initiatives relevant for quality of care and national and international guidelines and consensus were analyzed. Retrieved results showed that breast cancer incidence is still increasing, including in Portugal. Studies investigating disease outcomes seek to derive improvements to clinical practice and better financial resource allocation. Setting performance measures (KPIs) in institutions treating cancer is not a reality in Portugal yet, but has potential to leverage the quality of clinical performance. A multidisciplinary approach within one health structure is also desirable. More investment in clinical (including academic) research is key to optimize the quality of care. Implementation of clinical practice guidelines (largely based on ESMO guidelines in Portugal) is crucial to improve patient outcomes. Not less importantly, quality of life is a treatment goal on its own in breast cancer care. Breast cancer remains a health challenge and a multidimensional, 360-degree appraisal, beyond the exclusively clinical perspective, may provide new insights towards an optimal patient-centered approach.

Breast Cancer Image Classification via Multi-Network Features and Dual-Network Orthogonal Low-Rank Learning

Histopathological image analysis is an important technique for early diagnosis and detection of breast cancer in clinical practice. However, it has limited efficiency and thus the detection of breast cancer is still an open issue in medical image analysis. To improve the early diagnostic accuracy of breast cancer and reduce the workload of doctors, we devise a classification framework based on histology images by combining deep learning with machine learning methodologies in this paper. Specifically, we devise a multi-network feature extraction model by using pre-trained deep convolution neural networks (DCNNs), develop an effective feature dimension reduction method and train an ensemble support vector machine (E-SVM). First, we preprocess the histological images via scale transformation and color enhancement methods. Second, the multi-network features are extracted by using four pre-trained DCNNs (e.g., DenseNet-121, ResNet-50, multi-level InceptionV3, and multi-level VGG-16). Third, a feature selection method via dual-network orthogonal low-rank learning (DOLL) is further developed for performance boosting and overfitting alleviation. Finally, an E-SVM is trained via fused features and voting strategy to perform the classification task, which classifies the images into four classes (i.e., benign, in situ carcinomas, invasive carcinomas, and normal). We evaluate the proposed method on the public ICIAR 2018 Challenge dataset of histology images of breast cancer and achieve a high classification accuracy of 97.70%. Experimental results show that our method can achieve quite promising performance and outperform state-of-the-art methods.

Computer-aided diagnosis system for breast ultrasound images using deep learning

The purpose of this study was to develop a computer-aided diagnosis (CAD) system for the classification of malignant and benign masses in the breast using ultrasonography based on a convolutional neural network (CNN), a state-of-the-art deep learning technique. We explored the regions for the correct classification by generating a heat map that presented the important regions used by the CNN for human malignancy/benign classification. Clinical data was obtained from a large-scale clinical trial previously conducted by the Japan Association of Breast and Thyroid Sonology. Images of 1536 breast masses (897 malignant and 639 benign) confirmed by pathological examinations were collected, with each breast mass captured from various angles using an ultrasound (US) imaging probe. We constructed an ensemble network by combining two CNN models (VGG19 and ResNet152) fine-tuned on balanced training data with augmentation and used the mass-level classification method to enable the CNN to classify a given mass using all views. For an independent test set consisting of 154 masses (77 malignant and 77 benign), our network showed outstanding classification performance with a sensitivity of 90.9% (95% confidence interval 84.5–97.3), a specificity of 87.0% (79.5–94.5), and area under the curve (AUC) of 0.951 (0.916–0.987) compared to that of the two CNN models. In addition, our study indicated that the breast masses themselves were not detected by the CNN as important regions for correct mass classification. Collectively, this CNN-based CAD system is expected to assist doctors by improving the diagnosis of breast cancer in clinical practice.

The Japanese Breast Cancer Society Clinical Practice Guidelines, 2018 edition: the tool for shared decision making between doctor and patient

The Breast Cancer Clinical Practice Guidelines, 2018 edition, written in Japanese and organized by the Japanese Breast Cancer Society, were published in Japanese in May 2018. The process of making these guidelines, as well as the content, was largely changed and compared with previous editions. The concept of these guidelines is to act as a support tool for shared decision making between doctor and patient. The procedure of creating the guidelines referred to Minds Handbook for Clinical Practice Guideline Development 2014. This guideline, written in Japanese, consists of two booklets: (1) the epidemiology and diagnosis booklet covering screening, radiological, and pathological diagnosis and (2) the treatment booklet covering surgical therapy, radiation therapy, and systemic therapy. This review article consists of five parts, including the history of the Breast Cancer Clinical Practice Guidelines, the concept, process, content, and recommendation grade. I believe this brief summary concerning the Breast Cancer Clinical Practice Guidelines 2018 edition in English will be helpful for both Japanese and foreign investigators.

The Japanese Breast Cancer Society Clinical Practice Guideline for radiation treatment of breast cancer, 2018 edition

PurposeThe Japanese Breast Cancer Society (JBCS) Clinical Practice Guideline was revised in 2018. This article describes the revise points in the section on radiation therapy (RT).Methods and materialsThe JBCS formed task force to update the JBCS Clinical Practice Guideline 2015 edition. Background questions (BQs) deal with standard treatments of breast cancer in clinical practice. Clinical questions (CQs) highlight the important treatments in which controversy remains. The task force for RT section addressed the 10 BQs, the 10 CQs, and the 4 Future reseach questions (FQs). For each CQ, systematic literature reviews and meta-analyses were conducted, and recommendations, strength of recommendation and strength of evidence were determined according to the protocol in Morizane et al. (Minds Handbook for Clinical Practice Guideline Development, 2014).ResultsThe recommendations, the strength of recommendation and the strength of evidence were determined based on the systematic literature reviews and the meta-analyses for each CQ.ConclusionThe JBCS updated the Clinical Practice Guideline. RT represents a significant portion of the breast cancer treatment, and these recommendations regarding RT will be useful in individualized, shared decision making between physicians and patients.

233P - Number of deliveries as a prognostic factor in different breast cancer subtypes

BackgroundSome registry-based and population-based studies have suggested that high parity could be an adverse prognostic factor in luminal breast cancer, although the definition of breast cancer subtypes has been varied and prospective studies are lacking. MethodsWe report long-term follow-up (median 8.5 years) from prospectively collected single-institution material of early breast cancers. The patients (n=612) were treated with modern treatment modalities in a Finnish university hospital clinic and clinicopathological surrogates of intrinsic subtypes were updated to match with the ESMO 2015 Early Breast Cancer Clinical Practice Guidelines. Long-term outcomes were recorded and special emphasis was given to exact reproductive factor anamnesis as a potential prognostic factor. ResultsTen-year breast cancer-specific survival (BCSS) in this real-life prospective population was 91.4% in the whole cohort. The longest ten-year BCSS was observed in luminal A-like cancers (97.6%) and the worst in luminal B-like (HER2-positive) subgroup (80.6%). Having five or more deliveries associated with dismal BCSS (univariate p=0.0015). When subtypes were assessed separately in multivariate analysis, this association remained significant only in luminal B-like (HER2-negative) cancers (HR 2.64; 95% CI 1.05-6.65; p=0.04) when tumor size and nodal status were also included to the analysis. Having 5 or more deliveries also associated with node positivity in the whole cohort (p=0.0016), but not with different subtypes. ConclusionsThis is the first prospectively collected study with the modern definition of breast cancer subtypes and contemporary treatments to assess parity as a breast cancer prognostic factor. Our results suggest that high parity is an adverse prognostic factor, but only in luminal B-like (HER2-negative) subtype. The biological effects of parity seem extend to later breast cancer and its metastasis in estrogen dependent, rapidly proliferating breast cancers. Legal entity responsible for the studyThe authors. FundingHas not received any funding. DisclosureAll authors have declared no conflicts of interest.

Mechanisms of resistance to estrogen receptor modulators in ER+/HER2- advanced breast cancer.

Endocrine therapy represents a mainstay adjuvant treatment of estrogen receptor-positive (ER+) breast cancer in clinical practice with an overall survival (OS) benefit. However, the emergence of resistance is inevitable over time and is present in one-third of the ER+ breast tumors. Several mechanisms of endocrine resistance in ER+/HER2- advanced breast cancers, through ERα itself, receptor tyrosine signaling, or cell cycle pathway, have been identified to be pivotal in endocrine therapy. The epigenetic alterations also contribute to ensuring tumor cells' escape from endocrine therapies. The strategy of combined hormone therapy with targeted pharmaceutical compounds has shown an improvement of progression-free survival or OS in clinical practice, including three different classes of drugs: CDK4/6 inhibitors, selective inhibitor of PI3Kα and mTOR inhibitors. Many therapeutic targets of cell cycle pathway and cell signaling and their combination strategies have recently entered clinical trials. This review focuses on Cyclin D-CDK4/6-RB axis, PI3K pathway and HDACs. Additionally, genomic evolution is complex in tumors exposed to hormonal therapy. We highlight the genomic alterations present in ESR1 and PIK3CA genes to elucidate adaptive mechanisms of endocrine resistance, and discuss how these mutations may inform novel combinations to improve clinical outcomes in the future.

What is the added value of digital image analysis of HER2 immunohistochemistry in breast cancer in clinical practice? A study with multiple platforms.

AimsWe aimed to compare digital image analysis (DIA) of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) in breast cancer by two platforms: (i) to validate DIA against standard diagnostics; and (ii) to evaluate the added value of DIA in clinical practice.Methods and results HER2 IHC and in‐situ hybridisation (ISH) were performed on 152 consecutive invasive breast carcinomas. IHC scores were determined with DIA using two independent platforms. Manual scoring was performed by two independent observers. HER2 status was considered positive in 3+ and ISH‐positive 2+ cases. HER2 status using DIA was compared to HER2 status with standard diagnostics (manual scoring with ISH in 2+ cases). Interplatform agreement of IHC scores was ‘moderate’ (linear weighted κ = 0.58), agreement between manual scoring and platform A was ‘moderate’ (κ = 0.60) and between manual scoring and platform B ‘almost perfect’ (κ = 0.85). Compared to manual scoring, DIA resulted in a reduction of 2+ cases from 17.1 to 1.3% with platform A and from 17.1 to 15.8% with platform B. However, compared to standard diagnostics, there were three false‐negative cases with DIA using platform A [81.3% sensitivity, 100% specificity, 100% positive predictive value (PPV), 97.8% negative predictive value (NPV)]. Sensitivity, specificity, PPV and NPV were 100% with DIA using platform B.Conclusions DIA of HER2 IHC is a valid tool in determining HER2 status in breast carcinoma. Algorithms in different platforms can behave differently, and optimal calibration is essential. In clinical practice, DIA offers an objective alternative to manual scoring, but a reduction in 2+ cases could result in loss of sensitivity.

Luminal breast cancer in clinical practice. How different are LUMINAL A from LUMINAL B? Differences, similarities and aggressiveness

Background: Today the molecular classification of breast cancer is considered as an important decision making tool. Immunohistochemistry is successfully used to classify the tumours in luminal, subtypes which are hormone sensitive, and non luminal subtypes. There is less clear subclassification of the luminal tumors in luminal A and B. This is based on the HER2 status and the magnitude of the Ki67 positivity. The aim of this study was to assess the similarities, the differences and the aggressiveness of the 2 luminal subtypes based on their characteristics.

Computer-aided diagnosis system for breast ultrasound images using deep learning

Introduction Ultrasonography has been recommended as an adjunctive modality to mammography, which is insufficient for accurate diagnosis of breast cancer in women with dense breast tissue. However, ultrasonography has the disadvantage of being operator dependent, requiring proficiency in reading ultrasound (US) images and increasing the false positive rate. To overcome these problems, we aimed to develop a computer-aided diagnosis (CAD) system for classification of breast malignant and benign masses on ultrasonography based on the convolutional neural network (CNN), a state-of-the-art deep learning technique. Methods From a large ultrasound (US) image database, managed by Japan Association of Breast and Thyroid Sonology (JABTS), we collected images of 1536 breast masses (897 malignant and 639 benign) confirmed by pathological examinations, with each breast mass captured from various angles using the US imaging probe. We constructed an ensemble network by combining two CNN models (VGG192 and ResNe1523) fine-tuned on balanced training data with the data augmentation which is a popular technique for synthetically generating new samples from the original and used the mass level classification method enabling the CNN to classify a given mass using all views. We explored the grounds of classification by generating a heatmap capable of presenting important regions used by the CNN for classification in humans. Results On an independent 154 test masses (77 malignant and 77 benign), our network showed outstanding classification performance with a sensitivity of 90.9%, a specificity of 87.0%, and an AUC of 0.951 compared with the two CNN models. In addition, our study indicated that not only breast masses but also surrounding tissues are important regions for correct classification. Conclusion This CNN-based CAD system is expected to assist doctors from the viewpoint of second opinion and improve the diagnosis of breast cancer in clinical practice.

Rate of Breast-Conserving Surgery vs Mastectomy in Breast Cancer: a Tertiary Care Centre Experience from South India.

Worldwide, breast conservation has become increasingly accepted as the surgical management of breast cancer in clinical practice. Cancer care in India is also evolving tremendously with many cancer treatment centres following evidence-based practice hence the rates of breast conservation are expected to increase. Here, we are reporting the rate of breast-conserving surgery (BCS) at our centre. A retrospective study of 401 patients who underwent breast cancer surgery at a tertiary care centre in South India from January 2015 to August 2017 were analysed to study the rate of BCS. All early breast cancers (EBC) were offered BCS. For large operable breast cancer (LOBC) and locally advanced breast cancer (LABC), neoadjuvant chemotherapy (NACT) followed by BCS was offered to these patients who wish to conserve their breast. The mean age was 45years. A total of 163 patients underwent BCS. Yearly, BCS rates were 38.8% in 2015, 36.7% in 2016 and 46.5% in 2017. Majority had EBC 310 (77.3%) of which 62.7% of T1 lesions (n = 51) had BCS, and 45.7% of T2 lesions (n = 258) had BCS of which 5 patients had to undergo NACT to preserve their breast whereas 100% Tis patient (n = 1) had mastectomy. Fifty patients had LOBC and only 2 (4%) patients had upfront BCS whereas 9 of them had to undergo NACT (18%). cT4 lesions had NACT followed by BCS in 2 patients. The rates of BCS have been increasing in India over the past few years. The majority of the women presented with EBC which makes them suitable for BCS.

Sensitive and Selective Immunofluorescence Assay for CA15-3 Detection Using Fluorescein Derivative A10254.

Carbohydrate antigen 15-3 (CA15-3), a specific breast cancer-related biomarker in serum, can provide direct information to monitor a patient's postoperative status and can predict recurrence and metastasis of breast cancer. The conventional Enzyme-Linked Immunosorbent Assay (ELISA) is an effective approach for CA15-3 detection, but there is a high limit of detection which is more than 1 U mL-1. Therefore, a convenient, highly sensitive and specific CA15-3 detection method is needed for earlier prognoses of breast cancer. In this work, we aim to develop a novel sensitive and selective immunofluorescence assay for the highly effective detection of CA15-3. We pre-coat the antibody I onto the solid phase to capture antigen CA15-3. The A10254- labeled antibody II identified by electrophoresis and fluorescence spectra recognized the antigen CA15-3 and formed the sandwich format. The fluorescence signal of A10254 directly relating to the amount of antigen could be detected and quantified by the microplate reader excited at 440nm. The obtained method for CA15-3 detection showed lower limits of detection (0.11 and 0.18 U mL-1) in a linear range of 0.25-14 U mL-1 in the buffer and 2% serum, respectively. This assay exhibited a good specificity and reproducibility in 2% serum with lower relative standard deviations in different tested concentrations. The developed immunofluorescence assay enhanced the performance of the conventional CA15-3 ELISA kit. Moreover, the acceptable accuracy and good reproducibility in diluted serum indicated the assay has great potential for the diagnosis of breast cancer in clinical practice.