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15,247 results for “Breast Cancer”
Effects of Tamoxifen on the Reproductive System of Females with Breast Cancer – an Ultrasound-based Cohort study
<p>This data represent an ultrasound-based cohort study conducted in three oncology centers. The studied groups included a total of ‎‎255 patients, 140 premenopausal (PreM) and 115 postmenopausal (PostM) female patients with ER-positive BC using TMX adjuvant hormonal treatment in ‎a dose of 20 mg/day for at least three months after surgery and adjuvant ‎chemo/radiotherapy.‎ The study conducted at the three main oncology centers in Baghdad. The collected data includes: age of the patient, menopausal status, co-morbid chronic illness such as hypertension, diabetes mellitus, etc, and used medications.an ultrasound-based cohort study conducted in three oncology centers. The studied groups included a total of ‎‎255 patients, 140 premenopausal (PreM) and 115 postmenopausal (PostM) female patients with ER-positive BC using TMX adjuvant hormonal treatment in ‎a dose of 20 mg/day for at least three months after surgery and adjuvant ‎chemo/radiotherapy.‎ The study conducted at the three main oncology centers in Baghdad. The collected data includes: age of the patient, menopausal status, co-morbid chronic illness such as hypertension, diabetes mellitus, etc, and used medications.</p>
Evaluation of the impact of imprinted polymer particles on morphology and motility of breast cancer cells by using digital holographic cytometry
<p>Supplemented Videos used in "Evaluation of the impact of imprinted polymer particles on morphology and motility of breast cancer cells by using digital holographic cytometry"</p>
Artificial Intelligence (AI), in the Breast Cancer Screening Programme Questionnaire and Data
<p>The goal of this study is to gain insight into the level of trust of women in the Netherlands, in the decisions made by radiologists with the support of different applications of Artificial Intelligence (AI), in the Breast Cancer Screening Programme. Gaining insight into your level of trust in the decisions made by radiologists and AI regarding whether you have breast cancer or not, is of high importance, as this will help to anticipate which steps can be taken by hospitals and software developers, in the near future. The survey consists of 10 introductory questions and 42 statements and takes approximately 10 minutes to complete.</p> <p> </p>
Metrics of diabetes risk are only minimally improved by exercise training in postmenopausal breast cancer survivors
<p>Context</p> <p>Insulin resistance is a risk factor for breast cancer recurrence. How exercise training changes fasting and post-glucose insulin resistance in breast cancer survivors is unknown.</p> <p>Objective</p> <p>To evaluate exercise-induced changes in post-glucose ingestion insulin concentrations, insulin resistance, and their associations with cancer-relevant biomarkers in breast cancer survivors.</p> <p>Setting</p> <p>The University of Massachusetts Kinesiology Department.</p> <p>Participants</p> <p>Fifteen postmenopausal breast cancer survivors not meeting the physical activity guidelines (150 minutes/week of exercise).</p> <p>Intervention</p> <p>a supervised 12-week aerobic exercise program (60 min/day, 3-4 days/week).</p> <p>Main outcome measures</p> <p>Post-glucose ingestion insulin was determined by peak insulin and area under the insulin curve (iAUC) during a five-sample oral glucose tolerance test. Insulin sensitivity was estimated from the Matsuda composite insulin sensitivity index (C-ISI). Changes in fitness and body composition were determined from submaximal VO<sub>2peak</sub> and dual energy X-ray absorptiometry (DEXA).</p> <p>Results</p> <p>Participants averaged 156.8±16.6 minutes/week of supervised exercise. Estimated VO<sub>2peak</sub> significantly increased (+2.8±1.4 ml/kg/min, p<0.05) and body weight significantly decreased (-1.1±0.8 kg, p<0.05) following the intervention. There were no differences in fasting insulin, iAUC, C-ISI or peak insulin following the intervention. Insulin was only significantly lower 120 minutes following glucose consumption (68.8 ± 34.5 vs. 56.2 ± 31.9 uU/ml, p<0.05), and there was a significant interaction with past/present aromatase inhibitor (AI) use for peak insulin (-11.99 (non-AI) vs +13.91 (AI) uU/mL) and iAUC (-24.03 (non-AI) vs +32.73 (AI) uU/mL).</p> <p>Conclusions</p> <p>Exercise training had limited overall benefits on insulin concentrations following glucose ingestion in breast cancer survivors but was strongly influenced by AI use.</p>
Breast Cancer TGCA Microarray and RNA-seq Data for RNA-seq Titration Project
<p>Breast cancer from The Cancer Genome Atlas (TCGA; Cancer Genome Atlas Network, 2012) gene expression data from two platforms, microarray and RNA-seq, in PCL format and accompanying clinical data. Used as a test case for cross-platform normalization for machine learning applications because these data contain "matched" samples that were run on both platforms.</p>
Example Analysis Output of Xenium Breast Cancer Data
<p>This is an example CartoStore dataset from Xenium Breast Cancer Analysis.</p> <p>Outputs generated from XeniumRanger and FICTURE are combined in this repository. </p> <p>Please refer to https://github.com/seqscope/cartloader for more details. </p>
Effect of Digoxin on clusters of circulating tumor cells in patients with metastatic breast cancer: a phase 1 trial
<p>This repository contains processed transcriptomics data, large data sets and additional files required to reproduce the code available at the repository https://github.com/TheAcetoLab/dicct-trial</p>
Feasibility and metabolic outcomes of a well-formulated ketogenic diet as an adjuvant therapeutic intervention for women with stage IV metastatic breast cancer: The Keto-CARE trial
<p><em>Purpose:</em></p> <p>Ketogenic diets may positively influence cancer through pleiotropic mechanisms, but only a few small and short-term studies have addressed feasibility and efficacy in cancer patients. The primary goals of this study were to evaluate the feasibility and the sustained metabolic effects of a personalized well-formulated ketogenic diet (WFKD) designed to achieve consistent blood beta-hydroxybutyrate (<span>β</span>HB) >0.5 mM in women diagnosed with stage IV metastatic breast cancer (MBC) undergoing chemotherapy.</p> <p><em>Methods: </em></p> <p>Women (n = 20) were enrolled in a six-month two-phase, single-arm WFKD intervention (NCT03535701). Phase I was a highly-supervised, ad libitum, personalized WFKD, where women were provided with ketogenic-appropriate food daily for three-months. Phase II transitioned women to a self-administered WFKD with ongoing coaching for an additional three-months. Fasting capillary <span>β</span>HB and glucose were collected daily; weight, body composition, plasma insulin, and insulin resistance were collected at baseline, three- and six-months. </p> <p><em>Results:</em></p> <p>Capillary <span>β</span>HB indicated women achieved nutritional ketosis (Phase I mean: 0.8 mM (n = 15); Phase II mean: 0.7 mM (n = 9)). Body weight decreased 10% after three-months, primarily from body fat. Fasting plasma glucose, plasma insulin, and insulin resistance also decreased significantly after three-months (p < 0.01), an effect that persisted at six-months.</p> <p><em>Conclusions</em>:</p> <p>Women diagnosed with MBC undergoing chemotherapy can safely achieve and maintain nutritional ketosis, while improving body composition and insulin resistance, out to six-months.</p>
Data from: Digital PCR quantification of ultrahigh ERBB2 copy number identifies poor breast cancer survival after trastuzumab
<p>HER2/ERBB2 evaluation is necessary for treatment decision-making in breast cancer (BC), however current methods have limitations and considerable variability exists. DNA copy number (CN) evaluation by droplet digital PCR (ddPCR) has complementary advantages for HER2/ERBB2 diagnostics. In this study, we developed a single-reaction multiplex ddPCR assay for determination of ERBB2 CN in reference to two control regions, CEP17 and a copy-number-stable region of chr. 2p13.1, validated CN estimations to clinical in situ hybridization (ISH) HER2 status, and investigated the association of ERBB2 CN with clinical outcomes. 909 primary BC tissues were evaluated and the area under the curve for concordance to HER2 status was 0.93 and 0.96 for ERBB2 CN using either CEP17 or 2p13.1 as reference, respectively. The accuracy of ddPCR ERBB2 CN was 93.7% and 94.1% in the training and validation groups, respectively. Positive and negative predictive value for the classic HER2 amplification and non-amplification groups was 97.2% and 94.8%, respectively. An identified biological "ultrahigh" ERBB2 ddPCR CN group had significantly worse survival within patients treated with adjuvant trastuzumab for both recurrence-free survival (hazard ratio, HR: 3.3; 95% CI 1.1–9.6; <em>p</em> = 0.031, multivariable Cox regression) and overall survival (HR: 3.6; 95% CI 1.1–12.6; <em>p</em> = 0.041). For validation using RNA-seq data as a surrogate, in a population-based SCAN-B cohort (NCT02306096) of 682 consecutive patients receiving adjuvant trastuzumab, the ultrahigh-ERBB2 mRNA group had significantly worse survival. Multiplex ddPCR is useful for ERBB2 CN estimation and ultrahigh ERBB2 may be a predictive factor for decreased long-term survival after trastuzumab treatment.</p>
"iDCNNPred: An interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against triple-negative breast cancer"
<p>In this study, we proposed a novel interpretable deep convolutional neural network prediction (iDCNNPred) system for classifying molecular bioactivity and identifying predictive potential inhibitors for the PI3Ka isoform protein. This system utilizes 2D molecular image representation as input features, instead of traditional molecular fingerprints or descriptors.</p> <p><strong>The datasets used for model construction, prediction and screening of chemical library are provided in this uploaded data in <a href="../api/records/10947610/draft/files/Molecular_image_Custom_DCNN_datasets.zip/content" target="_blank" rel="noopener noreferrer">Molecular_image_Custom_DCNN_datasets.zip</a> file for Custom-DCNN models and <a href="../api/records/10947610/draft/files/Molecular_image_pre_trained_datasets.zip/content" target="_blank" rel="noopener noreferrer">Molecular_image_pre_trained_datasets.zip</a> file for Pre-trained fine-tuned models. </strong><strong>The final run of models results given in file <a href="../api/records/10947610/draft/files/Custom_DCNN_Pre_trained_models.zip/content" target="_blank" rel="noopener noreferrer">Custom_DCNN_Pre_trained_models.zip</a></strong></p>
The Role of Genomic Data in Stratifying Patients within Predictive Models for Breast Cancer Survival Outcome
<p>Data associated with my PhD thesis titled "The Role of Genomic Data in Stratifying Patients within Predictive Models for Breast Cancer Survival Outcome".</p>
Intermediate files used for generating co-register result on the Xenium Breast Cancer Dataset with Giotto Suite
Open the record for dataset details and reuse information.
Human breast cancer PDTX models bulk and single cell RNA sequencing
<p>This dataset includes information relevant to the following manuscript from the labs of Prof. Carlos Caldas (University of Cambridge), and Dr. Long V. Nguyen (Princess Margaret Cancer Centre, University Health Network):</p> <p>Nguyen LV et al. Dynamics and plasticity of human breast cancer single cell-derived clones. Under consideration for publication.</p> <p>Bulk RNA sequencing raw count matrices are provided (RawCounts.csv) along with the normalized count matrices (LogCPMNormCounts.csv).</p> <p>Single cell RNA sequencing count matrix processed from R package metacell is provided (mat.pdx_LN_v2_filt.Rda), along with the mc and mc2d files with information on metacell partitions (mc.pdx_LN_v2_filt.Rda and mc2d.pdx_LN_v2_filt.Rda).</p> <p>Single cell RNA sequencing count matrices processed using Seurat are also provided separately for each PDTX model analysed (STG139.rds, STG201.rds, AB040.rds and IC07.rds).</p> <p>Code and information on data analysis is provided for reviewers in our unpublished manuscript and on Github (https://github.com/cclab-brca/clone-dynamics).</p>
Loss of multi-level 3D genome organization during breast cancer progression - FISH dataset
<p>This entry contains the raw and processed FISH images produced by the following study:<br><strong>Loss of multi-level 3D genome organization during breast cancer progression</strong></p> <p>The raw images contained in file 2022-mcf10a-cancer-progression-fish-db.tar.gz were processed using fish data analysis workflow (<a href="https://github.com/paulsengroup/2022-mcf10a-cancer-progression/blob/main/run_fish.sh" target="_blank" rel="noopener">link</a>) hosted at <a href="https://github.com/paulsengroup/2022-mcf10a-cancer-progression" target="_blank" rel="noopener">paulsengroup/2022-mcf10a-cancer-progression</a>.<br>The resulting files have been archived in file 2022-mcf10a-cancer-progression-fish-processed-data.tar.gz.</p>
Mutation and methylation data for study: Assessment of the molecular heterogeneity of E-cadherin expression in invasive lobular breast cancer
<p>Processed mutation data and DNA methylation beta values published with this study.</p>
Tracking breast cancer cells migrating collectively and imaged in fluorescence with TrackMate-Cellpose
<p>Breast cancer cells migrating collectively.</p> <p>This dataset is used in a tutorial on using TrackMate and its cellpose integration to track such cells.</p> <p>See here for details: <a href="https://imagej.net/plugins/trackmate/trackmate-cellpose">https://imagej.net/plugins/trackmate/trackmate-cellpose</a> </p>
Recurrence and metastasis detection of breast cancer
<p>This dataset includes 88 H&E stained whole slide images (WSI) of breast cancer downloaded from TCGA (<a href="https://portal.gdc.cancer.gov/repository/">https://portal.gdc.cancer.gov/repository/</a>) with the type of Formalin-Fixed Paraffin-Embedded (FFPE), of which 5 cases have recurrence or metastasis.</p>
Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified
<p>Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis and stratified treatment but does not explain each subtype's mechanism. Nowadays, deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. Ours is the first study on a deep-learning approach to reveal the mechanisms embedded in the PAM50-classified subtypes. We developed an explainable deep learning model called a point-wise linear model, which uses a meta-learning approach to generate a custom-made logistic regression model for each sample. Logistic regression is familiar to physicians and medical informatics researchers, and we can use it to analyze which genes are important for subtype prediction. The custom-made logistic regression models generated by the point-wise linear model for each subtype used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. And analyzing the point-wise linear model's inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways. The results of this study suggest the potential of our explainable deep learning to play a vital role in cancer treatment.</p>
Response to immune checkpoint blockade improved in pre-clinical model of breast cancer after bariatric surgery
<p>Bariatric surgery is becoming more prevalent as a sustainable weight loss approach, with vertical sleeve gastrectomy (VSG) being the first line of surgical intervention. We and others have shown that obesity exacerbates tumor growth while diet-induced weight loss impairs obesity-driven progression. It remains unknown how bariatric surgery-induced weight loss impacts cancer progression or alters responses to therapy. Using a pre-clinical model of diet induced obesity followed by VSG or diet-induced weight loss, breast cancer progression and immune checkpoint blockade therapy was investigated. Weight loss by bariatric surgery or weight matched dietary intervention before tumor engraftment protected against obesity-exacerbated tumor progression. However, VSG was not as effective as dietary intervention in reducing tumor burden despite achieving similar extent of weight and adiposity loss. Circulating leptin did not associate with changes in tumor burden. Uniquely, tumors in mice that received VSG displayed elevated inflammation and checkpoint ligand PD-L1. Further, mice that received VSG had reduced tumor infiltrating T lymphocytes suggesting an ineffective anti-tumor microenvironment. VSG-associated elevation of PD-L1 prompted us to next investigate the efficacy of immune checkpoint inhibitors in lean, obese, and formerly obese mice that lost weight by VSG or weight matched controls. While obese mice were resistant to immunotherapy, anti-PD-L1 potently impaired tumor progression after VSG through improved anti-tumor immunity. Thus, in formerly obese mice, surgical weight loss followed by immunotherapy reduced breast cancer burden. Further studies are necessary to determine how bariatric surgery sensitizes tumors to immune checkpoint inhibition.</p>
Factors affecting delay in the presentation of breast cancer symptoms among women in Gaza, occupied Palestinian territory: A cross-sectional survey
<p>Objective: To identify factors related to women's delay in presenting with breast cancer symptoms to improve earlier diagnosis in the occupied Palestinian territory (oPt).</p> <p>Design: Cross-sectional</p> <p>Setting: Two government cancer hospitals.</p> <p>Participants: A consecutive sample of 130 Palestinian women living in Gaza with newly diagnosed breast cancer were approached in the waiting rooms of cancer hospitals in Gaza between 1 January and 31 December 2017. 120 women took part and returned the completed questionnaire.</p> <p>Primary and secondary outcome measures: Clinical information about breast cancer were collected from hospital cancer records. An interval of three months or more between women's self-discovery of symptoms and their first presentation to a medical provider was considered as a delay.</p> <p>Results: 94% (122/130) of women attending cancer hospitals in Gaza agreed to take part in the study. Their mean age was 51 years (range: 23-72), 33.6% (31/122) had a family history of breast cancer, and 74.5% (41/55) of those whose cancer stage was known had been diagnosed at stage III or IV. Around one half (62/122) said they had not recognised the seriousness of their breast changes but only 20% (24/122) of women delayed seeking healthcare by three months and more. Considering that the symptom was not serious (X2= 13.94, p> 0.05), and lack of pain (X2= 6.63, p> 0.05) were the only two factors statistically associated with later presentation. Lower socio-economic status, older age, lower education, and negative family history of breast cancer were not statistically associated with women's delay.</p> <p>Conclusions: Women's awareness about the seriousness of breast changes and the critical importance of seeking prompt diagnosis needs to be improved using context-relevant and evidence-based awareness campaigns. This should be accompanied with training of female nurses on promoting early detection and improvement in diagnostic facilities to ensure timely diagnosis of cancer in the oPt.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.