Number Of Breast Cancer Patients Research Articles

Detection of breast cancer of various clinical stages based on serum FT-IR spectroscopy combined with multiple algorithms.

Breast cancer screening is time consuming, requires expensive equipment, and has demanding requirements for doctors. Hence, a large number of breast cancer patients may miss screening and early treatment, which greatly threatens their health around the world. Infrared spectroscopy may be able to be used as a screening tool for breast cancer detection. Fourier transform infrared (FT-IR) spectroscopy of serum was combined with traditional machine learning algorithms to achieve an auxiliary diagnosis that could quickly and accurately distinguish patients with different stages of breast cancer, including stage 1 disease, from control subjects without breast cancer. FT-IR spectroscopy were performed on the serum of 114 non-cancer control subjects, 35 patients with stage I, 43 patients with stage II, and 29 patients with stage III & IV breast cancer. Due to the experimental sample imbalance, we used the oversampling to process the four classes of sample. The oversampling selected Synthetic Minority Oversampling Technique (SMOTE). Subsequently, we used the random discarding method in undersampling to do experiments as well. The average FT-IR spectroscopy results for the four groups showed differences in phospholipids, nucleic acids, lipids, and proteins between non-cancer control subjects and breast cancer patients at different stages. Based on these differences, four classification models were used to classify stage I, II, III & IV breast cancer patients and non-cancer control subjects. First, standard normal variate transformation (SNV) was used to preprocess the original data, and then partial least squares (PLS) was used for feature extraction. Finally, the five models were established including extreme learning machine (ELM), k-nearest neighbor (KNN), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM) and grid search-support vector machine (GS-SVM). In oversampling experiment, the GS-SVM classifier obtained the highest average classification accuracy of 95.45 %; the diagnostic accuracy of non-cancer control subjects was 100 %; breast cancer stage I was 90 %; breast cancer stage II was 84.62 %; and breast cancer stage III & IV was 100 %. In undersampling experiment, the GA-SVM model obtained the highest average classification accuracy of 100 %; the diagnostic accuracy of non-cancer control subjects was 100 %; breast cancer stage I was 100 %; breast cancer stage II was 100 %; and breast cancer stage III & IV was 100 %. The results show that FT-IR spectroscopy combined with powerful classification algorithms has great potential in distinguishing patients with different stages of breast cancer from non-cancer control subjects. In addition, this research provides a reference for future multiclassification studies of cervical cancer, ovarian cancer and other female high-incidence cancers through serum FT-IR spectroscopy.

Immunotherapy in Breast Cancer Patients: A Focus on the Use of the CurrentlyAvailable Biomarkers in Oncology

Immune checkpoint inhibitors (ICIs) have remarkably modified the way solid tumors are managed, including breast cancer. Unfortunately, only a relatively small number of breast cancer patients significantly respond to these treatments. To maximize the immunotherapy benefit in breast cancer, several efforts are currently being put forward for the identification of i) the best therapeutic strategy (i.e. ICI monotherapy or in association with chemotherapy, radiotherapy, or other drugs); ii) the optimal timing for administration (e.g. early/advanced stage of disease; adjuvant/neoadjuvant setting); iii) the most effective and reliable predictive biomarkers of response (e.g. tumor-infiltrating lymphocytes, programmed death-ligand 1, microsatellite instability associated with mismatch repair deficiency, and tumor mutational burden). In this article, we review the impacts and gaps in the characterization of immune-related biomarkers raised by clinical and translational research studies with immunotherapy treatments. Particular emphasis has been put on the documented evidence of significant clinical benefits of ICI in different randomized clinical trials, along with preanalytical and analytical issues in predictive biomarkers pathological assessment.

Functional and Structural Connectome Features for Machine Learning Chemo-Brain Prediction in Women Treated for Breast Cancer with Chemotherapy.

Breast cancer is the leading cancer among women worldwide, and a high number of breast cancer patients are struggling with psychological and cognitive disorders. In this study, we aim to use machine learning models to discriminate between chemo-brain participants and healthy controls (HCs) using connectomes (connectivity matrices) and topological coefficients. Nineteen female post-chemotherapy breast cancer (BC) survivors and 20 female HCs were recruited for this study. Participants in both groups received resting-state functional magnetic resonance imaging (rs-fMRI) and generalized q-sampling imaging (GQI). Logistic regression (LR), decision tree classifier (CART), and xgboost (XGB) were the models we adopted for classification. In connectome analysis, LR achieved an accuracy of 79.49% with the functional connectomes and an accuracy of 71.05% with the structural connectomes. In the topological coefficient analysis, accuracies of 87.18%, 82.05%, and 83.78% were obtained by the functional global efficiency with CART, the functional global efficiency with XGB, and the structural transitivity with CART, respectively. The areas under the curves (AUCs) were 0.93, 0.94, 0.87, 0.88, and 0.84, respectively. Our study showed the discriminating ability of functional connectomes, structural connectomes, and global efficiency. We hope our findings can contribute to an understanding of the chemo brain and the establishment of a clinical system for tracking chemo brain.

Size Preferences Uptake of Glycosilica Nanoparticles to MDA-MB-231 Cell.

Recently, studies on the development and investigation of carbohydrate-functionalized silica nanoparticles (NPs) and their biomedicine applications such as cell-specific targeting and bioimaging has been carried out extensively. Since the number of breast cancer patients has been growing in recent years, potential NPs were being studied in this project for targeting breast cancer cells. Mannose receptors can be found on the surface of MDA-MB-231, which is a kind of human breast cancer cell line. Therefore, we decorated a cyanine 3 fluorescent dye (Cy3) and mannosides on the surface of silica NPs for the purpose of imaging and targeting. Galactoside was also introduced onto the surface of silica NPs acting as a control sample. Various sizes of silica NPs were synthesized by using different amounts of ammonium to investigate the effect of the size of NPs on the cellular uptake rate. The physical properties of these NPs were characterized by scanning electron microscope, dynamic light scattering, and their zeta potential. Cellular experiments demonstrated that mannoside-modified NPs can be uptaken by MDA-MB-231. From the experiment, we found out that the best cellular uptake rate of nanoparticle size is about 250 nm. The MTT assay showed that Man@Cy3SiO2NPs are not cytotoxic, indicating they may have the potential for biomedical applications.

Information Needs of Breast Cancer Patients: Theory-Generating Meta-Synthesis.

BackgroundBreast cancer has become one of the most frequently diagnosed carcinomas and the leading cause of cancer deaths. The substantial growth in the number of breast cancer patients has put great pressure on health services. Meanwhile, the information patients need has increased and become more complicated. Therefore, a comprehensive and in-depth understanding of their information needs is urgently needed to improve the quality of health care. However, previous studies related to the information needs of breast cancer patients have focused on different perspectives and have only contributed to individual results. A systematic review and synthesis of breast cancer patients’ information needs is critical.ObjectiveThis paper aims to systematically identify, evaluate, and synthesize existing primary qualitative research on the information needs of breast cancer patients.MethodsWeb of Science, EBSCO, Scopus, ProQuest, PubMed, PsycINFO, The Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature were searched on February 12 and July 9, 2019, to collect relevant studies. A Google Scholar search, interpersonal network recommendations, and reference chaining were also conducted. Eligible studies included qualitative or mixed-methods studies focusing on the information needs (across the cancer continuum) of breast cancer patients or their social networks. Subsequently, a Critical Appraisals Skills Programme checklist was used to assess the quality of included research. The results, findings, and discussions were extracted. Data analysis was guided by the theory-generating meta-synthesis and grounded theory approach.ResultsThree themes, 19 categories, and 55 concepts emerged: (1) incentives (physical abnormality, inquiry from others, subjective norm, and problems during appointments); (2) types of information needs (prevention, etiology, diagnosis, clinical manifestation, treatment, prognosis, impact and resumption of normal life, scientific research, and social assistance); (3) moderating variables (attitudes, health literacy, demographic characteristics, disease status, as well as political and cultural environment). The studies revealed that the information needs of breast cancer patients were triggered by different incentives. Subsequently, the patients sought a variety of information among different stages of the cancer journey. Five types of variables were also found to moderate the formation of information needs.ConclusionsThis study contributes to a thorough model of information needs among breast cancer patients and provides practical suggestions for health and information professionals.

Contralateral Risk-Reducing Mastectomy in Breast Cancer Patients Who Undergo Multigene Panel Testing.

An increasing number of breast cancer patients are undergoing expanded genetic testing and are being identified as germline mutation carriers. We sought to determine rates of contralateral risk-reducing mastectomy (CRRM) in patients with various germline mutations. All women ≥ 18years of age with unilateral breast cancer who underwent multigene panel testing between January 1, 2014 and August 1, 2019 at our academic institution were identified. Demographic, tumor, and treatment variables were identified from the medical record. Multivariable analyses were performed to compare factors associated with performance of CRRM. We identified 1613 patients, of whom 28.1% had a pathogenic variant and 40.1% had variants of uncertain significance (VUS). Overall, 420 patients (26.0%) underwent a CRRM. On multivariable analysis, factors associated with CRRM included age < 50years (OR 3.8, 95% CI 3.0, 5.0), race (OR 0.5, 95% CI 0.3, 0.7 and OR 0.4, 95% CI 0.2, 0.7 for Black and Asian women, respectively, versus White women), and the presence of any germline mutation or VUS (OR 13.2, 95% CI 8.7, 20.2 for BRCA1/2; OR 3.9, 95% CI 2.7, 5.8 for non-BRCA germline mutation; and OR 1.8, 95% CI 1.3, 2.6 for VUS). In breast cancer patients who undergo multigene panel testing, a sizeable number of women with pathogenic non-BRCA germline findings are opting for CRRM. Given that the risk of contralateral breast cancer in women with most pathogenic mutations other than BRCA1/2 remains poorly characterized, these data have implications for risk counseling and for ascertaining the true risks of contralateral breast cancer in this population.

Detection of Breast Region of Interest via Breast MR Scan on an Axial Slice

Breast cancer is one of the most common cancer types especially met in women. The number of breast cancer patients increases every year. Thus, to detect breast cancer at its early stages gains importance. Breast region detection is the pioneering step of breast cancer diagnosis researches performed via image processing techniques. The performance of computer-aided breast cancer diagnosis systems can be improved by exactly determining the breast region of interest. In this study, the goal is to determine a region of interest for breast MR images, in which one or more lesion can appear. The achieved region includes two breasts and lymph nodes. The proposed region of interest detection system is fully automatic and it utilizes several image processing techniques. At first, the local adaptive thresholding technique is applied to the noise-filtered grey level breast magnetic resonance images taken with ethical permissions from Sakarya Education and Research Hospital. After adaptive thresholding, connected component analysis is performed to exclude extra structures around the breast region as thorax area. This analysis selects the largest area in the binary image which corresponds to a gyrate region including breast area and lymph nodes over the backbone. Then, the integral of horizontal projection is calculated to determine an optimum horizontal line that allows setting the region of interest apart. In the following step, sternum midpoint is detected to separate the right breast from the left one. Finally, a masking operation is applied to get corresponding right and left breast regions in the original MR image. To evaluate the performance of the proposed study, the results of automatic region of interest detection system are compared with the manual region of interest selection performed by an expert radiologist. Dice similarity coefficient and Jaccard coefficient are used as performance criteria. According to the results, the proposed system can detect region of interest for computer-aided breast cancer diagnosis researches, exactly.

The Value of Repeated Breast Surgery as a Quality Indicator in Breast Cancer Care.

Breast-conserving surgery, a major achievement in surgical oncology, has allowed an increasing number of breast cancer patients to avoid the mutilation of mastectomy. However, mastectomy still is performed in certain circumstances although breast-conserving surgery would be equally safe. Many reasons, including patients' and surgeons' personal motivations, influence the decision-making process before the final choice between breast preservation and mastectomy. The importance of quality measurement and reporting in medicine is increasingly recognized, and breast surgery is no exception. The substantial variability of re-excision rates for positive surgical margins after a first attempt at breast-conserving surgery suggests that improvement is possible. Therefore, the re-excision rate has been proposed as a quality metric for assessing and comparing the performance of different institutions. Indeed, re-excision rates can be reduced by actionable factors such as accurate preoperative local staging, localization of occult lesions, and intraoperative assessment of the oriented specimen. However, equally important non-actionable risk factors pertaining the biology, detectability, and resectability of the tumor also should be taken into account. Therefore, if the re-excision rate has to be used as a performance indicator of breast surgical care, critical interpretation of results with accurate case-mix adjustment are mandatory, and reasonable targets must be appropriately set so that surgeons treating patients at higher risk of positive margins are not unduly penalized.

Determinan Kejadian Kanker Payudara Pada Wanita Di Rsud Arifin Achmad Provinsi Riau Tahun 2019

Breast cancer is among the most discussed cancers because of its malignancy which often ends in death. Based on the 2013 Basic Health Research Data (Riskesdas), the number of breast cancer patients reached 61,682 cases in Indonesia. As in Riau Province according to the medical records of Arifin Achmad Regional Hospital, breast cancer ranks first with 325 cases out of 580 cases in Irna Surgical Cendrawasih in 2016. Purpose of this study was to determine the determinants of breast cancer incidence in women in Arifin Achmad Regional Hospital in Riau Province in 2019. This research uses a quantitative research design with a case control approach. The research instrument used was a questionnaire. The sampling technique was Consecutive Sampling. This research was conducted in May to July 2019. Case samples in this study were women who were diagnosed positive for breast cancer and control samples were women who were diagnosed with negative breast cancer. Univariate and bivariate data analysis with chi square test and multivariate analysis using Simple Logistic Regression test. The results showed the most dominant variables related to cause and effect with the incidence of breast cancer were a history of abortion 3.7 times, a history of hormonal contraceptive use 5.4 times, the age of first child birth&gt; 35 years 3 times, and the number of nulliparous children 2.3 times. It is expected that health workers and related institutions will be able to provide health education regarding the positive and negative impacts of contraceptive use, the importance of planning the marriage age and promoting family planning programs that have been launched by the government.

Nanobody-targeted photodynamic therapy induces significant tumor regression of trastuzumab-resistant HER2-positive breast cancer, after a single treatment session

RationaleA substantial number of breast cancer patients with an overexpression of the human epidermal growth factor receptor 2 (HER2) have residual disease after neoadjuvant therapy or become resistant to trastuzumab. Photodynamic therapy (PDT) using nanobodies targeted to HER2 is a promising treatment option for these patients. Here we investigate the in vitro and in vivo antitumor efficacy of HER2-targeted nanobody-photosensitizer (PS) conjugate PDT. MethodsNanobodies targeting HER2 were obtained from phage display selections. Monovalent nanobodies were engineered into a biparatopic construct. The specificity of selected nanobodies was tested in immunofluorescence assays and their affinity was evaluated in binding studies, both performed in a panel of breast cancer cells varying in HER2 expression levels. The selected HER2-targeted nanobodies 1D5 and 1D5-18A12 were conjugated to the photosensitizer IRDye700DX and tested in in vitro PDT assays. Mice bearing orthotopic HCC1954 trastuzumab-resistant tumors with high HER2 expression or MCF-7 tumors with low HER2 expression were intravenously injected with nanobody-PS conjugates. Quantitative fluorescence spectroscopy was performed for the determination of the local pharmacokinetics of the fluorescence conjugates. After nanobody-PS administration, tumors were illuminated to a fluence of 100 J∙cm-2, with a fluence rate of 50 mW∙cm-2, and thereafter tumor growth was measured with a follow-up until 30 days. ResultsThe selected nanobodies remained functional after conjugation to the PS, binding specifically and with high affinity to HER2-positive cells. Both nanobody-PS conjugates potently and selectively induced cell death of HER2 overexpressing cells, either sensitive or resistant to trastuzumab, with low nanomolar LD50 values. In vivo, quantitative fluorescence spectroscopy showed specific accumulation of nanobody-PS conjugates in HCC1954 tumors and indicated 2 h post injection as the most suitable time point to apply light. Nanobody-targeted PDT with 1D5-PS and 1D5-18A12-PS induced significant tumor regression of trastuzumab-resistant high HER2 expressing tumors, whereas in low HER2 expressing tumors only a slight growth delay was observed. ConclusionNanobody-PS conjugates accumulated selectively in vivo and their fluorescence could be detected through optical imaging. Upon illumination, they selectively induced significant tumor regression of HER2 overexpressing tumors with a single treatment session. Nanobody-targeted PDT is therefore suggested as a new additional treatment for HER2-positive breast cancer, particularly of interest for trastuzumab-resistant HER2-positive breast cancer. Further studies are now needed to assess the value of this approach in clinical practice.

MED12 Mutations Play a Pivotal Role in Breast Cancer Oncogenesis

There is a critical need for superior breast cancer treatment since this disease remains the second leading cause of cancer deaths among American women. Though radiation and chemotherapy have been the frontline choice of treatment for cancer over the past few decades, due to the detrimental side effects, personalized treatment is rapidly rising as a superior treatment method. This is largely due to recent advances in genome‐wide DNA sequencing that have allowed for the identification of cancer‐related mutational landscapes. Through these technologies, MED12, a subunit of Mediator protein, has been found to be commonly mutated in a significant number of breast cancer patients. Therefore, uncovering the mechanistic basis behind MED12 mutant breast cancer is critical in order to find a personalized treatment regimen for this subclass of breast cancer. Previous work has shown that MED12 restricts GLI3‐dependent Sonic Hedgehog (SHH) signaling, a pathway that is often hyper activated in advanced breast cancer. We therefore hypothesize that mutated MED12 promotes GLI3‐dependent SHH signaling in breast cancer cells thereby promoting tumorigenesis and that this signaling pathway could be an effective therapeutic target in MED12 mutant breast cancer. To study the effect of mutant MED12, a lentivirus carrying an shRNA against MED12 was generated and infected into MCF‐7 cells. Since the vast majority of breast cancer‐associated MED12 mutations lead to loss of protein function, the knockdown strategy through lentiviral shRNA is assumed to mimic the MED12 mutant setting. Proliferation assays were utilized to confirm that downregulated MED12 plays a role in the increased proliferation of breast cancer cells. Next, quantitative PCR was performed to determine the effect of downregulated MED12 on the expression of genes that are known to be regulated by GLI3. Finally, a screening strategy was employed by using a natural compound library to find potential novel treatment strategies for MED12 mutant breast cancer. Our findings confirmed that downregulation of MED12 increases proliferation of breast cancer cells in vitro thereby providing a strong argument that mutant MED12 would have the same effect. Through quantitative PCR it was confirmed that GLI3 target genes are upregulated in cancer cells when MED12 expression is low. Importantly, our natural compound screen identified several novel therapeutic compounds that specifically target MED12 downregulated breast cancer cells through a mechanism that involves SHH signaling. We therefore uncovered that MED12 mutations promote GLI3‐dependent SHH signaling in breast cancer and we identified potential novel therapeutic strategies for MED12 mutant breast cancer patients.

Fully automated lesion segmentation and visualization in automated whole breast ultrasound (ABUS) images.

The number of breast cancer patients has increased each year, and the demand for breast cancer detection has become quite large. There are many common breast cancer diagnostic tools. The latest automated whole breast ultrasound (ABUS) technology can obtain a complete breast tissue structure, which improves breast cancer detection technology. However, due to the large amount of ABUS image data, manual interpretation is time-consuming and labor-intensive. If there are lesions in multiple images, there may be some omissions. In addition, if further volume information or the three-dimensional shape of the lesion is needed for therapy, it is necessary to manually segment each lesion, which is inefficient for diagnosis. Therefore, automatic lesion segmentation for ABUS is an important issue for guiding therapy. Due to the amount of speckle noise in an ultrasonic image and the low contrast of the lesion boundary, it is quite difficult to automatically segment the lesion. To address the above challenges, this study proposes an automated lesion segmentation algorithm. The architecture of the proposed algorithm can be divided into four parts: (I) volume of interest selection, (II) preprocessing, (III) segmentation, and (IV) visualization. A volume of interest (VOI) is automatically selected first via a three-dimensional level-set, and then the method uses anisotropic diffusion to address the speckled noise and intensity inhomogeneity correction to eliminate shadowing artifacts before the adaptive distance regularization level set method (DRLSE) conducts segmentation. Finally, the two-dimensional segmented images are reconstructed for visualization in the three-dimensional space. The ground truth is delineated by two radiologists with more than 10 years of experience in breast sonography. In this study, three performance assessments are carried out to evaluate the effectiveness of the proposed algorithm. The first assessment is the similarity measurement. The second assessment is the comparison of the results of the proposed algorithm and the Chan-Vese level set method. The third assessment is the volume estimation of phantom cases. In this study, in the 2D validation of the first assessment, the area Dice similarity coefficients of the real cases named cases A, real cases B and phantoms are 0.84±0.02, 0.86±0.03 and 0.92±0.02, respectively. The overlap fraction (OF) and overlap value (OV) of the real cases A are 0.84±0.06 and 0.78±0.04, real case B are 0.91±0.04 and 0.82±0.05, respectively. The overlap fraction (OF) and overlap value (OV) of the phantoms are 0.95±0.02 and 0.92±0.03, respectively. In the 3D validation, the volume Dice similarity coefficients of the real cases A, real cases B and phantoms are 0.85±0.02, 0.89±0.04 and 0.94±0.02, respectively. The overlap fraction (OF) and overlap value (OV) of the real cases A are 0.82±0.06 and 0.79±0.04, real cases B are 0.92±0.04 and 0.85±0.07, respectively. The overlap fraction (OF) and overlap value (OV) of the phantoms are 0.95±0.01 and 0.93±0.04, respectively. Therefore, the proposed algorithm is highly reliable in most cases. In the second assessment, compared with Chan-Vese level set method, the Dice of the proposed algorithm in real cases A, real cases B and phantoms are 0.84±0.02, 0.86±0.03 and 0.92±0.02, respectively. The Dice of Chan-Vese level set in real cases A, real cases B and phantoms are 0.65±0.23, 0.69±0.14 and 0.76±0.14, respectively. The Dice performance of different methods on segmentation shows a highly significant impact (P<0.01). The results show that the proposed algorithm is more accurate than Chan-Vese level set method. In the third assessment, the Spearman's correlation coefficient between the segmented volumes and the corresponding ground truth volumes is ρ=0.929 (P=0.01). In summary, the proposed method can batch process ABUS images, segment lesions, calculate their volumes and visualize lesions to facilitate observation by radiologists and physicians.

Abstract P4-12-06: Long-term follow-up of patients with invasive breast cancer treated with catheter-based accelerated partial breast irradiation following lumpectomy

Abstract Introduction: A large number of breast cancer patients who are eligible for breast-conserving therapy (radiation following complete resection via lumpectomy) elect to undergo mastectomy to avoid radiation - with treatment length being the most significant deterrent. The development of accelerated partial breast irradiation (APBI) tailored treatment to target tissue at greatest risk of recurrence, balancing treatment-associated benefits with radiation exposure risk while shortening therapy duration from 6 weeks to 5 days. This study analyzed rates of ipsilateral breast tumor recurrence (IBTR) in patients with invasive breast cancer treated with resection followed by APBI delivered via catheter-based brachytherapy. Methods: Retrospective data was collected on 406 patients with primary invasive breast cancer diagnosed from January 2007 to February 2014, treated with lumpectomy and adjuvant APBI catheter-based brachytherapy. Follow-up data was available for 390 patients (97.5%). Mean and median follow-up were 80.9 and 83.5 months respectively. 193 patients (49.4%) had at least 7 years of follow-up. Results: 21 patients (5.4%) were found to have an IBTR. Of these patients, 14 (66.7%) experienced an IBTR in the same quadrant as their index cancer. 7 patients (33.3%) experienced an IBTR in a different quadrant. Only one patient (4.8%) experienced an IBTR within the first two years after conclusion of their primary treatment. Of the entire cohort, 7 patients (1.8%) were found to have a metastatic recurrence (one which occurred within the first two years after primary treatment) and 7 patients (1.8%) experienced new primary cancers diagnosed in their contralateral breasts. Conclusion: The results of this investigation demonstrate catheter-based brachytherapy APBI delivered after breast-conserving surgery carries a relatively low risk for IBTR in patients with invasive breast cancer. We support its' use as an effective and valid alternative for selected early breast cancer patients desiring a significantly shorter, more convenient course of treatment in the adjuvant setting without increased risk of recurrence. Citation Format: Cynthia Chen, Chi Chi Do-Nguyen, Lisa Poole, Lina Sizer, William B Carter, Thomas G. Frazier. Long-term follow-up of patients with invasive breast cancer treated with catheter-based accelerated partial breast irradiation following lumpectomy [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 P4-12-06.

Abstract P2-16-06: In-breast pathologic complete response (pCR) following neoadjuvant chemotherapy predicts nodal pCR in early stage breast cancer

Abstract Background: With advances in neoadjuvant chemotherapy (NAC), an increasing number of breast cancer patients achieve a pathologic complete response (pCR). Efforts are underway to non-invasively identify pCR in the breast, such that characterization of the concomitant nodal response will be invaluable for locoregional treatment planning. Here, we sought to evaluate the correlation between in-breast response and residual nodal disease among early stage breast cancer patients. Materials/Methods: We identified 728 patients who received NAC at our institution from 2006 to 2016. Breast and nodal response rates were evaluated and further stratified by clinicopathologic features, and biologic subtype approximation (hormone receptor positive [HR+], HER2 negative [HER2-] and low or intermediate grade = luminal A; HR+/HER2- and high grade = luminal B; HER2/neu-amplified = HER2+; ER-/HER2- = triple negative). Descriptive statistics and univariate analyses were employed to evaluate the correlation between breast and nodal response. Results: Of 728 patients, 269 (37%) were clinically node negative (cN0) and 459 (63%) were clinically node positive (cN+). Median age was 51 and median tumor size was 4.0cm (IQR 2.7 - 5.5cm). The cohort comprised 13% luminal A patients, 20% luminal B, 38% HER2, and 29% triple negative. Following NAC, the overall pCR rate (breast and nodes) was 29.8%. Among all cN0 patients who had a breast pCR (ypT0; n = 96), none exhibited residual nodal disease (100% ypN0). Among cN+ patients who had a breast pCR (n = 128), only 5.4% (n = 7) had residual nodal disease (ypN+). Rates of residual nodal disease among cN+ patients who achieved breast pCR varied by biologic subtype: luminal A patients with pCR in the breast had a 50.0% probability of residual nodal disease (n = 1/2 were ypT0N+), luminal B 6.7% (1/15), HER2+ 2.5% (2/80) and triple-negative 9.6% (3/31) (p=0.03). Conversely, among cN+ patients who exhibited nodal pCR following NAC, the rate of residual in-breast disease varied by receptor subtype: 80.0% of clinically node-positive luminal A patients who became ypN0 had residual in-breast disease (ypT+N0; n = 4/5), 60.0% of luminal B (21/35), 38.1% HER2+ (48/126), and 50.9% triple negative (29/57) (p=0.03). Conclusions: Among early stage breast cancer patients receiving NAC, in-breast pCR is predictive of nodal pCR. Among 96 cN0 patients who had in-breast pCR, we found none with residual nodal disease, representing a population that may be suitable for further limiting nodal therapy. The ability to pre-operatively identify in-breast pCR in this population would be particularly valuable if nodal pCR can consequently be inferred, potentially paving the way for studies of therapeutic de-escalation. Citation Format: Achraf Shamseddine, Jessica Flynn, Zhigang Zhang, Monica Morrow, Boris Mueller, Erin Gillespie, Atif Khan, Beryl McCormick, Oren Cahlon, Simon Powell, Andrea Barrio, Lior Z Braunstein. In-breast pathologic complete response (pCR) following neoadjuvant chemotherapy predicts nodal pCR in early stage breast cancer [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 P2-16-06.

Abstract P2-14-14: Adjuvant chemotherapy treatment refusal not associated with increased mortality

Abstract Background: Chemotherapy treatment refusal is a constant over time in oncology practice. Methods: We reviewed patients in our registry cohort from 1990-2015 with first primary surgically treated invasive TNM 7 stage I-III breast cancer recommended for chemotherapy treatment (chemotx) (N= 5488 given, N= 217 refused (3.8%)). Presentation and diagnostic characteristics were analyzed for association with chemotherapy refusal using Pearson chi squares. Logistic regression analysis was used to evaluate patient characteristics association with chemotherapy treatment refusal as the outcome of interest (risk ratio = RR) and Cox proportional hazards modelling for association of chemotx refusal with breast cancer specific survival. Results: Adjuvant chemotherapy refusal did not decline over time and was constant at an average of 3.8% per year over 25 years from 1990 to 2015. Percentage of chemotx refusers increased with increasing age from 1.4% for age 20-39, age 40-49 (2.6%), age 50-59 (3.6%), age 60-69 (5.0%), age 70-79 (12.4%) and to 13.5% among patients age 80 and above (chi square = 98.67, p&amp;lt;.001). Chemotx refusal was highest among hormone receptor (HR) positive patients (4.2%) vs. HR- (2.6%) (p&amp;lt;.010). After 1998, chemotx refusal was highest among HR+/HER-2- patients (4.8%) and lowest among HER-2+ patients (1.6%). 20% of HR+ chemotx refusers also refused hormone treatment (p&amp;lt;.001). Chemotx refusers were more often stage I (5.5% vs. 3.7% stage II, 2.4% stage III) with refusal decreasing with higher stage (p&amp;lt;.001). After 2015 when recurrence score became available, 7% of HR+/LN- patients tested (N = 388) refused chemotx but only 2% refused hormone treatment. In binary conditional logistic regression modeling order of entry was 1) age, 2) TNM stage, 3) hormone receptor status and 4) diagnosis year. In Cox proportional hazards model adjusted for the regression identified factors, chemotherapy treatment refusal was not associated with increased hazard of breast cancer death. Conclusions: We observed a direct linear trend of increasing chemotherapy refusal with increasing age and age was the most significant variable in the regression model. Chemotherapy treatment refusal decreased with higher stage breast cancer at diagnosis, hormone receptor negative status and later diagnosis year. Even with a constant treatment refusal rate, mortality was unaffected perhaps related to better prognostic factors among treatment refusers. Better tolerated treatment for older patients and newly developed breast cancer recurrence scores which decrease number of breast cancer patients recommended chemotherapy treatment may help reduce refusals in the future. Table. Logistic regression model of adjuvant chemotherapy treatment refusal (n=5705)By order of entry into the model:RR (95% CI)p valueAge 20-49reference&amp;lt;.001Age 50-591.72 (1.19, 2.48)Age 60-692.57 (1.73, 3.81)Age 70-797.91 (5.09, 12.31)Age 80+10.09 (4.24, 24.03)TNM 7 stage I3.13 (2.04, 4.81)&amp;lt;.001TNM 7 stage II1.83 (1.22, 2.77)TNM 7 stage IIIreferencehormone receptor status negativereference.001hormone receptor status positive1.87 (1.30, 2.71)Diagnosis year 1990-19981.49 (1.06, 2.09).033Diagnosis year 1999-2004.92 (.64, 1.31)Diagnosis year 2005-2015reference Citation Format: Judith Malmgren, Robinette Struckel, Mary Atwood, Henry Kaplan. Adjuvant chemotherapy treatment refusal not associated with increased mortality [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 P2-14-14.

The Chemopreventive Effects of The Combination Between Tea Leaf and Mandarine Peel Extract on Breast Cancer Cell

The number of breast cancer patients is increasing high in the world. This study aims to determine the chemopreventive effect of a combination of ethanolic extracts of tea leaves and mandarin peel in silico and in vitro on T47D breast cancer cells. Extraction by the maceration method used ethanol solvent 70%. The research is an in silico molecular docking utilized software of Autodock Vina to determine the binding affinity of tangeretin compounds and Epigallocatechin gallate (EGCG) on HER-2 protein. The antioxidant test used the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method to determine the antioxidant activity of the combination of tea leaf and mandarine peel (CTM) extract. The in vitro test used the method of 3-(4,5-dimethyltiazol-2-il) -2,5-diphenyltetrazolium bromide (MTT) to determine the value of IC 50 fromCTM on T47D breast cancer cells. The result of this study showed that CTM had a vigorous antioxidant activity with an IC 50 value of 83 μg/ml. CTM had a weak cytotoxic activity with an IC 50 value of 1889 μg/ml. Molecular tethering results of tangeretin and EGCG compounds produced a docking score of -6.6 and -5.0 kcal/mol with docking score proportion consisting of -4.9 kcal/mol of original ligand, -6.1 kcal/mol of doxorubicin and -4.5 kcal/mol of 5-fluorouracil. CTM had potential as a chemopreventive agent based on the robust antioxidant activity data on T47D breast cancer cells and molecular docking on the HER-2 protein.

Vitamin D and selenium blood levels and acute skin toxicity during radiotherapy for breast cancer

ObjectivesVitamin D blood levels have been shown to influence acute chemotherapy toxicities. Therefore, it was investigated whether it is an intrinsic factor influencing acute skin toxicity in patients receiving radiotherapy for breast cancer. Design/settingIn a total of 107 patients receiving radiotherapy for resected breast cancer, vitamin D and selenium blood levels were determined. Correlations between these levels and skin toxicity due to radiotherapy (CTC scores, Skindex scores) were investigated as primary endpoints. Furthermore, the statistical relationship between skin toxicity, vitamin D and selenium blood levels with patient and disease characteristics such as tumor stage, breast size, skin thickness, blood cell counts as well as individual quality of life measured by SEIQoL-Q was analyzed. Main outcome measures/resultsIn our patient collective large deficiencies of vitamin D (mean level 20.9 ng/ml, normal range 36–60 ng/ml) and selenium (mean level 76.1 μg/l, normal range 74–139 μg/l) were found. No correlations between skin toxicities, vitamin D and selenium blood levels were found. Neither did these blood levels correlate with any tumor or patient characteristics nor with individual quality of life. As expected by clinical experience, skin toxicities correlated significantly with breast size and skin thickness. ConclusionsIn this study, radiotherapy skin toxicity was not influenced by vitamin D or selenium blood levels. On the basis of our data we cannot recommend vitamin D or selenium supplementation as a prophylaxis for skin toxicity. Nevertheless, large numbers of breast cancer patients have substantial deficiencies of both substances. Therefore, supplementation may be reasonable for other reasons.

Overuse of Neo-adjuvant Chemotherapy for Primary Breast Cancer.

In recent years, there has been a wider use of chemotherapy in the pre-operative setting for breast cancer (i.e., as neo-adjuvant chemotherapy). Most clinicians would agree that neo-adjuvant chemotherapy is justified for patients with inflammatory breast cancer, locally advanced breast cancer, or patients with large tumors and small breasts who are keen to undergo breast-conserving surgery. However, in the USA and many other western countries, neo-adjuvant chemotherapy is now used for greater numbers of breast cancer patients who do not fall within these categories. Yet, randomized trials have consistently shown that there are no differences in overall survival (OS) between breast cancer patients treated with neo-adjuvant chemotherapy versus adjuvant chemotherapy. However, neo-adjuvant chemotherapy may increase the risk of loco-regional recurrence after breast-conserving surgery, perhaps because of an increased risk of leaving behind residual tumor foci. Moreover, the effects of neo-adjuvant chemotherapy on the primary tumor does not appear to be a suitable way for assessing the potential overall benefits of systemic therapy regimens on distant micrometastases and risk of death. Yet, based on the results of the KATHERINE and CREATE-X trials, one might argue that neo-adjuvant chemotherapy should be recommended for patients with HER-2-positive and triple-negative tumors to identify the subsets of patients who do not achieve pathologic complete response (PCR). Patients with HER-2-positive tumors who do not achieve PCR may benefit from additional treatment with T-DM1, and those with triple-negative tumors who do not achieve PCR may benefit from additional treatment with capecitabine.

Are baseline ultrasound and mammographic features associated with rates of pathological completes response in patients receiving neoadjuvant chemotherapy for breast cancer?

BackgroundIncreasing numbers of breast cancer patients receive neoadjuvant chemotherapy (NACT). We seek to investigate whether baseline mammographic and ultrasound features are associated with complete pathological response (pCR) after NACT.MethodsA database of NACT patients was reviewed. Baseline imaging parameters assessed were ultrasound: posterior effect; echo pattern; margin and lesion diameter; mammography: spiculation and microcalcification. Core biopsy grade and immunophenotype were documented. Data were analysed for the whole study group and by immunophenotype.ResultsOf the 222 cancers, 83 (37%) were triple negative (TN), 61 (27%) ER positive/HER-2 negative and 78 (35%) HER-2 positive. A pCR occurred in 46 of 222 cancers (21%). For the whole group, response was associated with high core biopsy grade (grade 3 vs. grade 1 or 2) (26% vs. 9%, p = 0.0044), absence of posterior shadowing on ultrasound (26% vs. 10%, p < 0.001) and the absence of mammographic spiculation (26 vs. 6%, p < 0.001). Within the HER-2 positive group; the absence of shadowing and spiculation remained highly associated with pCR, in addition to small ultrasound size (AUC = 0.71, p < 0.001) and the absence of microcalcification (39% vs. 21%, p < 0.02). On multivariable analysis absence of spiculation and core grade remained significant for the whole cohort, size and absence of spiculation remained significant for HER-2 positive tumours. No feature predicted pCR in TN tumours.ConclusionA pCR is less likely when there is mammographic spiculation. Small ultrasound size is associated with pCR in HER-2 positive tumours. These findings may be helpful when discussing NACT and surgical options with patients.Trial registrationUK Clinical Trials Gateway: registration number 16712.

Importance of Feature Selection and Data Visualization Towards Prediction of Breast Cancer

Background: Breast cancer is one of the most common forms of cancers among women and the leading cause of death among them. Countries like United States, England and Canada have reported a high number of breast cancer patients every year and this number is continuously increasing due to detection at later stages. Hence, it is very important to create awareness among women and develop such algorithms which help to detect malignant cancer. Several research studies have been conducted to analyze the breast cancer data. Objective: This paper presents an effective method in predicting breast cancer and its stage and will also analyze the performance of different supervised learning algorithms such as Random Classifier, Chi2 Square test used in order to predict. The paper focuses on the three important aspects such as the feature selection, the corresponding data visualisation and finally making a prediction call on different machine learning models. Methods: The dataset used for this work is breast cancer Wisconsin data taken from UCI library. The dataset has been used to show the different 32 features which are all important and how it can be achieved using data visualisation. Secondly, after the feature selection, different machine learning models have been applied. Conclusion: The machine learning models involved are namely Support Vector Machine (SVM), KNearest Neighbour (KNN), Random Forest, Principal Component Analysis (PCA), Neural Network using Perceptron (NNP). This has been done to check which type of model is better under what conditions. At different stages several charts have been plotted and eliminated based on relative comparison. Results have shown that Random Tree classifier along with Chi2 Square proves to be an efficient one.