Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA138815 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The purpose of this study was to identify molecular markers of pathologic response to neoadjuvant dose-dense docetaxel treatment using gene expression profiling on pretreatment biopsies. Patients with high-risk, operable breast cancer were treated with 75 mg/m2 IV of docetaxel on day 1 of each cycle every 2 weeks x 4 cycles . Tumor tissue from pretreatment biopsies was obtained from 12 patients enrolled in the study. Gene expression profiling were done on serial sections of the biopsies from patients that achieved a pathologic complete response (pCR) and compared to those with residual disease, non-pCR (NR). Tumor tissues from pretreatment needle biopsies from patients enrolled in a dose-dense docetaxel clinical trial were laser capture microdissected for RNA extraction and hybridization to Affymetrix microarrays. We analyzed one array (sample A) from duplicate samples from each patient.
2011-04-22 | E-GEOD-28796 | biostudies-arrayexpress
Project description:The purpose of this study was to identify molecular markers of pathologic response to neoadjuvant dose-dense docetaxel treatment using gene expression profiling on pretreatment biopsies. Patients with high-risk, operable breast cancer were treated with 75 mg/m2 IV of docetaxel on day 1 of each cycle every 2 weeks x 4 cycles . Tumor tissue from pretreatment biopsies was obtained from 12 patients enrolled in the study. Gene expression profiling were done on serial sections of the biopsies from patients that achieved a pathologic complete response (pCR) and compared to those with residual disease, non-pCR (NR).
2011-04-22 | GSE28796 | GEO
Project description:Between 2004 and 2012, patients with pT1-3, pN0-3, M0 breast tumors were randomised between adjuvant dose-dense doxorubicin-cyclophosphamide (ddAC) versus docetaxel-doxorubicin-cyclophosphamide (TAC). Trial registration numbers: ISRCTN61893718 and BOOG 2004-04.
2021-12-30 | GSE167977 | GEO
Project description:In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. They collected diverse data types including clinical, genomic, transcriptomic, and digital pathology profiles from pre-treatment biopsies of breast tumors. Leveraging this comprehensive multi-omic dataset, the team developed ensemble machine learning models using different algorithms (Logistic Regression, SVM and Random Forest). These predictive models identifies patients likely to achieve a pathological complete response (pCR) to therapy, showcasing their potential to enhance treatment selection.
Please note that the authors also have an interactive dashboard to apply the fully-integrated NAT response model on new (or any desired) data. The user can find its link in their GitHub repository: https://github.com/micrisor/NAT-ML
For more information and clarification, please refer to the ReadMe_NAT-ML document in the files section.
2023-08-28 | BIOMD0000001075 | BioModels
Project description:Gene expression profiles of human breast cancer tissues from 100 different patients treated with primary systemic chemotherapy (Gemcitabine, Epirubicin and Docetaxel) Keywords: expression profiling
2006-01-21 | GSE4056 | GEO
Project description:In the present investigation, we have exploited the opportunity provided by neoadjuvant treatment of a group of postmenopausal women with large operable or locally advanced breast cancer (in which therapy is given with the primary tumour remaining within the breast) to take sequential biopsies of the same cancers before and after 10-14 days treatment with letrozole. RNA extracted from the biopsies has been subjected to Affymetrix microarray analysis and the data from paired biopsies interrogated to discover genes whose expression is most influenced by oestrogen deprivation. Experiment Overall Design: biopsies were taken from the same subjects both pretreatment and after 10-14 days Letrozol, 2.5 mg/day, oral
2008-06-13 | E-GEOD-5462 | biostudies-arrayexpress
Project description:<p>HER2 (ERBB2) gene amplification and overexpression are present in 15-30% of invasive breast cancers. While HER2-targeted agents such as trastuzumab are effective treatments, therapeutic resistance remains a concern in HER2-positive breast cancer with 40-50% of patients having residual disease after neoadjuvant treatment with chemotherapy and trastuzumab.</p> <p>To investigate features that may make it possible to predict at diagnosis which cancers will be responsive to trastuzumab and chemotherapy, 48 tumor/normal DNA pairs extracted from pretreatment tumor biopsies and blood of HER2-positive breast cancer cases treated with neoadjuvant chemotherapy and trastuzumab were sequenced. Whole genome and exome sequence from tumor (average depth 49x and 71x) and normal (average depth 33x and 69x) DNA are included here as well as RNAseq data for 42 of the tumors. The study cohort was equally divided between patients who experienced pathological complete response and those with residual disease.</p> <p>Samples were obtained from the American College of Surgeons Oncology Group Z1041 trial (NCT00513292) and a local single-institution study (NCT00353483).</p>
| phs001291 | dbGaP
Project description:Abstract Introduction Response rates to chemotherapy remain highly variable in breast cancer patients. We set out to identify genes associated with chemotherapy resistance. We analyzed what is currently the largest single-institute set of gene expression profiles derived from breast cancers prior to a single neoadjuvant chemotherapy regimen (dose-dense doxorubicine and cyclophophamide). Methods We collected, gene expression-profiled and analyzed 178 HER2-negative breast tumor biopsies (M-bM-^@M-^XNKI datasetM-bM-^@M-^Y). We employed a recently developed approach for detecting imbalanced differential signal (DIDS) in order to identify markers of resistance to treatment. In contrast to traditional methods, DIDS is able to identify markers that show aberrant expression in only a small subgroup of the non-responder samples. Results We found a number of markers of resistance to anthracycline-based chemotherapy. We validated our findings by the analysis of three external datasets, which contained 456 HER2-negative samples in total. Since these external sets included patients who received differing treatment regimens we could only validate markers of general chemotherapy resistance. There was a highly significant overlap in the markers identified in the NKI dataset and the other three datasets. Five resistance markers, SERPINA6, BEX1, AGTR1, SLC26A3, and LAPTM4B, were identified in three of the four datasets (p-value overlap <1e-6). These five genes identified resistant tumors that could not have been identified by merely taking ER-status or proliferation into account. Conclusion The identification of these genes might lead to a better understanding of the mechanisms involved in (clinically) observed chemotherapy resistance and could possibly assist in the recognition of breast cancers in which chemotherapy does not contribute to response or survival. We collected, gene expression profiled and analyzed 178 HER2-negative breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy.
2012-12-01 | E-GEOD-34138 | biostudies-arrayexpress
Project description:In the present investigation, we have exploited the opportunity provided by neoadjuvant treatment of a group of postmenopausal women with large operable or locally advanced breast cancer (in which therapy is given with the primary tumour remaining within the breast) to take sequential biopsies of the same cancers before and after 10-14 days or 90 days treatment with letrozole. RNA extracted from the biopsies has been subjected to Affymetrix microarray analysis and the data from paired biopsies interrogated to discover genes whose expression is most influenced by oestrogen deprivation. Keywords: Timecourse between subjects Biopsies were taken from the same subjects at three timepoints: pretreatment, after 10-14 days Letrozol (2.5 mg/day, oral), and after 90 days Letrozol (2.5 mg/day, oral).
2010-07-28 | E-GEOD-20181 | biostudies-arrayexpress