bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract ES11-1: The biology and plasticity of residual disease after neoadjuvant treatment
Authors: Perou C 
Published: March 2020

Abstract
Breast cancer neoadjuvant treatment is becoming more prevalent, and has emerged as a valuable tool to downstage tumors, to identify predictors of responsiveness, and to potentially adapt therapeutic interventions to that individual’s residual disease if present. This has led to increasingly complex patient management and the development of many clinical trials specifically designed for those with residual disease. However, the basic biology of residual disease, how it relates to pre-treated disease, and how it could be used for prognostic and redictive benefits, are still relatively unknown.
In this session I will cover a number of studies that have specifically studied breast tumors before and after neoadjuvant treatment, with a focus on changes within the tumor cell population, including acquisition of epithelial-to-mesenchymal (EMT) and stem-like properties. In addition, alterations to immune cell dynamics in the tumor microenvironment, the plasticity of post-treatment phenotypes, and how these features impact the treatment paradigm based upon residual disease, will also be discussed.
Multiple factors likely contribute to the poor prognosis associated with residual disease. Human breast tumors that remain following either chemotherapy, or endocrine therapy, often show an enrichment of gene expression signatures associated with tumor-initiating cells and EMT, suggesting that intrinsic tumor characteristics may contribute to selection of resistant populations. Additionally, residual tumors may show a variety of genomic aberrations at higher frequency than found in primary tumors, suggesting that resistance pathways may be selected for during neoadjuvant treatment. Tumor extrinsic factors, particularly the interaction between tumor and immune cells, are also altered by breast cancer neoadjuvant therapies. Nevertheless, there is hope that these residual disease specific alterations may provide key susceptibilities to other treatments.
Lastly, defining predictive biomarkers from residual disease has been difficult. In addition to the variety and complexity of resistance mechanisms that lead to residual disease, questions regarding tumor plasticity and the duration of post-treatment changes complicate the analysis of residual breast cancers specimens. An improved understanding of the biology of residual breast cancers will be critical as we develop novel treatment strategies for those with residual disease.
Citation Format: C Perou. The biology and plasticity of residual disease after neoadjuvant treatment [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr ES11-1.
Abstract ES12-3: Clinically relevant biomarkers of PARP1 inhibitor resistance and indicators of targeted combinatorial treatments
Authors: Serra V 
Published: March 2020

Abstract
Tumors with defective DNA repair by the homologous recombination repair (HRR) pathway are exquisitely sensitive to DNA damaging agents and to novel agents that block parallel pathways, including PARP inhibitors (PARPi). PARPi have been approved for the treatment of metastatic ovarian cancer (OvC) or breast cancer (BC). Currently used selection biomarkers to enrich the population of patients (pt) most likely to respond, namely the platinum-sensitive or BRCA1/2-mutated patients (pts), have limited predictive capacity. There is a need for more specific biomarkers to guide personalized treatment. Genomic scars/signatures have been proposed as putative biomarkers associated with DNA repair deficiency. A major limitation of these assays is the lack of specificity in HRR-altered tumors once they have restored the HRR function as mechanism of drug resistance. Functional assays are therefore needed to assess the actual status of tumor HRR, both in BRCA1/2-mutated pts and in BRCA1/2-wt, to identify tumors with germline/somatic HRR deficiency that could benefit of PARPi treatment. In this sense, the RAD51 foci assay is a functional and dynamic biomarker of HRR that correlates with PARPi response. In this educational talk, we will review the current knowledge on PARPi sensitivity and resistance in breast cancer, response biomarkers and the potential of targeting the replication stress response to revert PARPi resistance, namely using WEE1 or ATR inhibitors (WEE1i, ATRi). Our preclinical work using patient-derived tumor xenoimplant models (PDX) from triple negative breast cancer (TNBC) identified selection biomarkers for WEE1i or ATRi as single agents or in combination with PARP1/2 inhibitors, and highlights the potential of DNA damage repair inhibitors for the treatment of TNBC, an approach currently being tested in the clinic.
Citation Format: V Serra. Clinically relevant biomarkers of PARP1 inhibitor resistance and indicators of targeted combinatorial treatments [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr ES12-3.
Abstract ES6-2: Alternative practical models for genetic services in the era of increased demand
Authors: Arun BAA 
Published: March 2020

Abstract
Despite known treatment and prevention benefits, studies have reported that less than 50% of individuals who would be appropriate candidates for consideration of BRCA1/2 genetic testing actually are referred and undergo testing. Many mutations remain undetected leading to suboptimal recommendations regarding screening for patients. Furthermore, as a consequence, at-risk family members do not undergo predictive (“cascade”) testing. There are several potential strategies to identify individuals for genetic referrals and increase testing. One important approach is the increase awareness amongst healthcare providers. The 2019 USPSTF recommends that primary care providers screen women who have family members with breast, ovarian, tubal, or peritoneal cancer with one of several screening tools designed to identify a family history that may be associated with an increased risk for potentially harmful mutations in breast cancer susceptibility genes (BRCA1 or BRCA2). Using different and/or simplified screening tools in breast imaging settings for example have shown to be feasible in identifying such individuals. Studies implementing this approach in other primary care settings are ongoing. The next step in increasing genetic testing include identification of new strategies for genetic counseling and testing. Traditionally, genetic counseling has been delivered in in-person visits with the genetic counselor. However, genetic testing has become more complex due to expanding testing indications and the addition of multiple genes in testing panels. To respond to this increasing demand, new delivery models are now being implemented; such as using technology for remote genetic counseling, such as telegentic counseling, web-based counseling, or pre test video counseling (without genetic counselor) followed by back-end genetic counseling with a counselor for mutation positive individuals. Further studies are needed to understand optimal process flow in different clinical settings.
Citation Format: BAA Arun. Alternative practical models for genetic services in the era of increased demand [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr ES6-2.
Abstract ES7-1: Progression of Disease and End of life: How to Break Bad News
Authors: Winer E 
Published: March 2020

Abstract
As an oncologist, one of the challenges that we all face is breaking bad news to patients and families. We all face difficult conversations on a daily basis which include discussions about disease recurrence, disease progression, and end of life issues. How we approach these conversations is of critical importance to the doctor-patient relationship and can affect the quality of life of our patients. We will discuss approaches that are more and less successful, as well as the need to individualize the approach to patients. In general, a direct conversation that involves listening, expression of empathy, and incorporates optimism is often the most successful approach.
Citation Format: E Winer. Progression of Disease and End of life: How to Break Bad News [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr ES7-1.
Abstract ES7-2: The toll on health care providers
Authors: Giordano SH 
Published: March 2020

Abstract
The Toll on Health Care Providers
Challenging conversation between health care provider and patients and their families can contribute to provider’s burnout. A challenging interaction for a medical professional already overstretched by the healthcare system may increase levels of stress, anxiety and anger, which in turn will affect performance and communication.
Burnout is a well-known work-related stress syndrome characterized by exhaustion, cynicism, and inefficiency. It is established that at least 50% of US physicians experience professional burnout with consequent negative impact on quality care, patient satisfaction, and overall institutional success. Cancer care professionals have experienced a drastically increase in the incidence of burnout over the past decade due to the emotionally demanding nature of their work and the ever-growing burden of administrative work. A recent Medscape report describes a burnout rate of 55% among female oncologist and 45% among male oncologist.
Both individual physician level factors and organizational level factors contribute to oncologist burnout. We will discuss common driver factors and strategies to prevent and mitigate this public health problem. Many gaps in our knowledge on physician burnout remain, but effective individual and organizational interventions are crucial for the prevention and improvement of clinician well-being and need to be incorporated into oncology care.
Citation Format: SH Giordano. The toll on health care providers [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr ES7-2.
Abstract GS3-02: Durvalumab compared to maintenance chemotherapy in patients with metastatic breast cancer: Results from phase II randomized trial SAFIR02-IMMUNO
Authors: Dalenc Florence , Garberis Ingrid , Filleron Thomas , Lusque Amélie , Bachelot Thomas , Arnedos Monica , Campone Mario , Sablin Marie-Paule , Bonnefoi Hervé , Jimenez Marta , Alexandra Jacquet , Andre Fabrice 
Published: March 2020

Abstract
Background : While treatment with anti-PDL1 improves outcome in combination with chemotherapy in patients with metastatic triple negative breast cancer, its efficacy is not explored yet in the context of maintenance therapy after chemotherapy, in a population of Her2-negative metastatic breast cancer. Methods : In SAFIR02 trial (NCT : 02299999), patients are selected to present a Her2-negative metastatic breast cancer eligible to 1st or 2nd line chemotherapy. All patients underwent a biopsy of metastatic disease, followed by targeted sequencing and Oncoscan when &gt;30% cancer cells are present on HES slides. After 6 to 8 cycles of induction chemotherapy, patients who present an actionable genomic alteration are randomized between targeted therapies and continuation of maintenance chemotherapy, and patients without actionable alterations are randomized between durvalumab (dose, schedule) for 2 years and maintenance chemotherapy. As a substudy of the SAFIR02-breast trial, the SAFIR02-IMMUNO trial has its primary endpoint, the PFS, hierarchized as the primary end-point of the core of the study. The plan was to include 190 patients in order to have 80% power using a two-sided logrank test at the 5 % level of significance to detect an improvement from 3 months to 4.76 months corresponding to an HR of 0.63. Results : 191 patients have been randomized between durvalumab and maintenance chemotherapy (190 women) until now, 45.5% TNBC and 53.4% RH+. The median age was 55 years. . 91.6% (175/191) patients were randomized on their first line of chemotherapy in metastatic setting. Median number of metastatic sites at the beginning of the chemotherapy line was 2. PDL1 staining was performed on 89 available tumor FFPE blocks using SP142 antibody. 30% (n=27/89) patients presented a PDL1 positive tumor.
PFS and OS will be presented at the conference.Conclusion : This is the first randomized trial comparing aPDL1 inhibitor to chemotherapy in the maintenance setting and to address the question of the efficacy of aPDL1i in HR+/Her2- and triple negative breast cancers.
Citation Format: Florence Dalenc, Ingrid Garberis, Thomas Filleron, Amélie Lusque, Thomas Bachelot, Monica Arnedos, Mario Campone, Marie-Paule Sablin, Hervé Bonnefoi, Marta Jimenez, Jacquet Alexandra, Fabrice Andre. Durvalumab compared to maintenance chemotherapy in patients with metastatic breast cancer: Results from phase II randomized trial SAFIR02-IMMUNO [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr GS3-02.
Abstract P1-02-10: Using deep residual networks for malignant and benign classification of two-dimensional Doppler breast ultrasound imaging
Authors: Shia Weichung , Chen Darren 
Published: March 2020

Abstract
Background: The most important issue of the overall performance of traditional CAD is effective feature extraction. However, extract a meaningful image feature is a complicated and time-consuming task. This makes fine tuning of the overall performance of traditional CAD more difficult. Currently, the deep learning was considered the most advanced technology in image classification. The main benefit of deep learning is decreasing the feature selection and classification burden of generating a transformation function set and feature of the image from the data directly. We bring up a modified deep learning algorithm in classifying the benign and malignant patients in breast ultrasound image.
Methods: There are 150 benign breast tumor patients and 120 malignant patients were enrolled from January 1, 2017 to December 31, 2018. The ultrasound images are captured at the full-view of lesion in US image by experienced physicians, and the tumor size measuring was according to the largest diameter of the tumor. The patients’ ages ranged was from 35 to 75 years, and the classification of benign or malignant were pathologically proven (either by fine needle cytology, core-needle biopsy or open biopsy). A modified deep residual network was developed and utilized to generate the US image feature for classification. The 5-fold cross-validation was used to test error percentage, mean, standard deviation and the 95% level confidence interval for the baseline algorithms. The accuracy of diagnosis was estimated using the area under the receiver operating characteristic (ROC) curve (AUC) and compared with DeLong’s non-parametric test.
Results: In this study, after using the 373 ultrasonic image dataset (all images were above BI-RADS C3, according to the report) for training and testing this modified model with 5-fold cross-validation, the AUC = 0.84 (SE = 0.81 to 0.87). The sensitivity is 77.08 and the specificity is 91.07 (p &lt; 1x10e-5). With the accuracy as an indicator, the evaluation for classifying breast tumors in this study presents the similar performance to the subjective categories determined by physicians.
Conclusions: Comparing to the previous studies, the significant improvement of this study is the input was the full-view of the image, and waive the preprocess in tumor region selection. It decreases the human effort and make the automated computer aids diagnosis possible. By using the deep residual network, the performance of classification was not inferior previous studies. It represents the deep learning have its merits in benign and malignant classification of breast ultrasound image.
Citation Format: Weichung Shia, Darren Chen. Using deep residual networks for malignant and benign classification of two-dimensional Doppler breast ultrasound imaging [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-02-10.
Abstract P1-10-15: BRCA1 alterations with additional defects in DNA damage repair genes may confer chemoresistance to BRCA-like breast cancers treated with neoadjuvant chemotherapy
Authors: Takada Mamoru , Kaneko Yasuhiko , Haruta Masayuki , Nagai Shigenori , Inoue Kenichi , Toduka Katsunori , Kurozumi Masafumi , Takei Hiroyuki , Ogawa Seishi , Nagashima Takeshi , Sangai Takafumi , Fujimoto Hiroshi , Sakakibara Junta , Teranaka Ryotaro 
Published: March 2020

Abstract
The BRCA-like phenotype is a feature that some sporadic breast cancers share with those occurring in germinal BRCA1- or BRCA2-mutation carriers. Since tumors with the phenotype have defects in the DNA damage repair pathway, which may increase sensitivity to drugs such as DNA cross-linking agents and PARP inhibitors, a method to identify this phenotype is important. The prediction of chemoresistance, which frequently develops in these tumors, is also crucial for improving therapy. We examined genomic aberrations and BRCA1 promoter methylation in tumors of 73 breast cancer (20 HR-/HER2- and 53 HR+/HER2-) patients, who underwent diagnostic core-needle biopsy and received neoadjuvant chemotherapy with anthracycline, cyclophosphamide, and taxane, using SNP array CGH and quantitative PCR. They had a tumor size of T1, T2, T3, or T4, a lymph node status of N0, N1, N2, or N3, and no distant metastasis (M0) at diagnosis, and were enrolled as subjects in this study. The methylation and/or loss or UPD of BRCA1 (termed BRCA1 alterations) and the loss or UPD of BRCA2 (termed BRCA2 alterations) were detected in 27 (37%) and 21 (29%), respectively, out of 73 tumors. Tumors with BRCA1 or BRCA2 alterations were associated with a higher number of genomic aberrations (P&lt;0.001 and P&lt;0.001) and higher percentage of TP53 alterations (the loss or UPD and/or mutations) than those without (P&lt;0.001 and P&lt;0.001). Overall survival (OS) rates were similar between patients with or without BRCA1 or BRCA2 alterations. However, when 27 patients with BRCA1-altered tumors were classified into those with or without the loss or UPD of PALB2, PAGR1, RAD51B, FANCM, MLL4 or ERCC1/2 in tumors, patients with additional defects in DNA damage repair genes had worse OS (P=0.037, 0.045, 0.038, 0.044, 0.041, or 0.019) than those without. These defects may confer chemoresistance and predict poor outcomes in patients with BRCA1-altered breast cancer.
Citation Format: Mamoru Takada, Yasuhiko Kaneko, Masayuki Haruta, Shigenori Nagai, Kenichi Inoue, Katsunori Toduka, Masafumi Kurozumi, Hiroyuki Takei, Seishi Ogawa, Takeshi Nagashima, Takafumi Sangai, Hiroshi Fujimoto, Junta Sakakibara, Ryotaro Teranaka. BRCA1 alterations with additional defects in DNA damage repair genes may confer chemoresistance to BRCA-like breast cancers treated with neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-10-15.
Abstract P1-10-29: Radiomics improved pre-therapeutic prediction of breast cancers insensitive to neoadjuvant chemotherapy
Authors: Zhou Xuezhi , Liu Zhenyu , Du Yang , Xiong Qianqian , Wang Kun , Tian Jie 
Published: March 2020

Abstract
Background: Approximately 10–35% of breast cancers were found to be insensitive to neoadjuvant chemotherapy (NAC), and approximately 5% of patients had larger tumors after NAC; in these patients, NAC failed to exhibit a therapeutic effect and instead delayed surgical treatment. Thus, it is critical to identify predictive biomarkers to enhance patient selection for NAC. Here we report a radiomic model for pre-therapeutic prediction of breast cancers insensitive to NAC.
Materials and Methods: We retrospectively enrolled 125 breast cancer patients (63 in the primary cohort and 62 in the validation cohort) who underwent magnetic resonance imaging (imaging sequence: diffusion-weighted, T2-weighted and contrast-enhanced T1-weighted) before receiving NAC. All patients received surgical resection, and Miller-Payne grading system were applied to assess the response to NAC. Grade 1-2 cases were classified as insensitive to NAC. We extracted 1941 radiomic features in the primary cohort. After feature selection, the optimal feature set was used to construct a radiomic signature using machine learning. We built a combined prediction model incorporating the radiomic signature and independent clinical risk factors using multivariable logistic regression. The performance of the combined model was assessed in the validation cohort.
Results: The radiomic signature consisting 4 features showed good performance for identifying Grade 1-2 group, yielding an area under the curve (AUC) of 0.83 (95% confidence interval: 0.647–1) in the validation cohort. A clinical model based on human epidermal growth factor receptor-2 (HER2) status and Ki67 index yielded an AUC of 0.792 (95% confidence interval: 0.668–0.916) in the validation cohort. Incorporating radiomic signature, HER2 status and Ki67 index, the combined prediction model reached a better discrimination power than the radiomic signature and clinical model, with an AUC of 0.935 (95% confidence interval, 0.848–1) in the validation cohort.
Conclusion: The combined model based on radiomics and clinical variables has a great potential in predicting drug insensitive breast cancers.
Citation Format: Xuezhi Zhou, Zhenyu Liu, Yang Du, Qianqian Xiong, Kun Wang, Jie Tian. Radiomics improved pre-therapeutic prediction of breast cancers insensitive to neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-10-29.