Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA121873 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Molecular Subtype Predicts Response to Neoadjuvant Chemotherapy in Breast Cancer
| PRJNA127177 | ENA
Project description:A gene expression signature characterizes expression data from breast cancer samples of patients with pathological complete response (pCR) or residual disease (RD) following the neoadjuvant trial. Several gene expression profiles have been reported to predict breast cancer response to neoadjuvant chemotherapy. These studies often consider breast cancer as a homogeneous entity, although higher rates of pathologic complete response (pCR) are known to occur within the basal-like subclass. We postulated that profiles with higher predictive accuracy could be derived from a subset analysis of basal-like tumors in isolation. Using a previously described ‘‘intrinsic’’ signature to differentiate breast tumor subclasses, we identified 50 basal-like tumors from two independent clinical trials associated with gene expression profile data. 24 tumor data sets (included in this GEO submission) were derived from a 119-patient neoadjuvant trial at our institution and an additional 26 tumor data sets were identified from a published data set (Hess et al. J Clin Oncol 24:4236–4244, 2006). The combined 50 basal-like tumors were partitioned to form a 37 sample training set with 13 sequestered for validation. Clinical surveillance occurred for a mean of 26 months. We identified a 23-gene profile which predicted pCR in basal-like breast cancers with 92% predictive accuracy in the sequestered validation data set. Furthermore, distinct cluster of patients with high rates of cancer recurrence was observed based on cluster analysis with the 23-gene signature. Disease-free survival analysis of these three clusters revealed significantly reduced survival in the patients of this high recurrence cluster. We identified a 23- gene signature which predicts response of basal-like breast cancer to neoadjuvant chemotherapy as well as disease-free survival. This signature is independent of tissue collection method and chemotherapeutic regimen. Keywords: Disease state analysis
2009-12-30 | GSE19697 | GEO
Project description:In this study, we have analyzed the transcriptional patterns of breast cancer cell lines and tumors of NAC resistant patients evaluated by GGI, and screened potential genes associated with chemoresistance. Furthermore, we have constructed a neoadjuvant chemotherapy response risk model and examined the evaluation accuracy of the risk score for NAC response. We conducted molecular bioinformatics analysis of the genes that constitute the chemotherapy resistance risk score, and explored potential drugs to reverse breast cancer chemotherapy resistance. Finally, we have examined the the risk score for predicting prognosis in breast cancer. In all, we have reported a novel signature to evaluate neoadjuvant chemotherapy response and predicts prognosis in breast cancer, and screened out potential drugs to reverse chemotherapy resistance in breast cancer.
2022-10-11 | GSE197931 | GEO
Project description:Purpose:The identification of biomarkers predictive of neoadjuvant chemotherapy response in breast cancer patients would be an important advancement in personalized cancer therapy. We hypothesized that due to similarities between radiation and chemotherapy induced cellular response mechanisms, radiation responsive genes may be useful in predicting response to neoadjuvant chemotherapy. Materials and Methods: Murine p53 null breast cancer cell lines representative of the luminal, basal-like and claudin-low human breast cancer subtypes were irradiated to identify radiation responsive genes. These murine radiation induced genes were then converted to their human orthologs. These genes were then used to develop a predictor of pathologic complete response (pCR) that was validated on two independent published neoadjuvant chemotherapy data sets of genomic data with response. Results: A radiation induced gene signature consisting of 30 genes was identified on a training set of 337 human primary breast cancer tumor samples that was prognostic for survival. Mean expression of this signature was calculated for individual samples in two independent published datasets and was found to be significantly predictive of pathologic complete response. Multivariate logistic regression analysis in both independent datasets showed that this 30 gene signature added significant predictive information independent of that provided by standard clinical predictors and other gene expression based predictors of pathologic complete response. Conclusion: This study provides new biologic information regarding response to neoadjuvant chemotherapy and a means of possibly improving the prediction of pathologic complete response. reference x sample
2015-01-21 | E-GEOD-48073 | biostudies-arrayexpress
Project description:Purpose: Identified the expression profile of lncRNAs associated to neoadjuvant chemotherapy response in 47 luminal B tumors of locally advanced breast cancer patients Methods: We implemented the transcriptomic analysis from 47 luminal B breast cancer samples by paired-end RNA-Seq, as a case-control study (responders vs nonresponders group). Differential expression analysis for lncRNA and mRNA were made to identify lncRNA as predictive biomarkers. Results: We identified a signature of lncRNAs associated with nonresponders group. Additionally, we identified the pathways were differentially expressed lncRNA and mRNA are associated in neoadjuvant chemotherapy response. Additionally, we proposed the clinical application of lncRNA GATA3-AS1 as a predictive biomarker to neoadjuvant chemotherapy response in luminal B breast cancer patients detected by RNA-ISH. Conclusion: we propose the clinical utility of lncRNA GATA3-AS1 detected by RNA-ISH to identify luminal B breast cancer patients that will not respond to neoadjuvant chemotherapy.
2024-08-27 | GSE270967 | GEO
Project description:GSTP1 expression predicts poor pathological complete response to neoadjuvant chemotherapy in ER-negative breast cancer
| PRJNA146953 | ENA
Project description:Changes in cellular lipid metabolism are a common feature in most solid tumors, which occur already in early stages of the tumor progression. However, it remains unclear if the tumor-specific lipid changes can be detected at the level of systemic lipid metabolism. The objective of this study was to perform comprehensive analysis of lipids in breast cancer patient serum samples. Lipidomic profiling using an established analytical platform was performed in two cohorts of breast cancer patients receiving neoadjuvant chemotherapy. The analyses were performed for 142 patients before and after neoadjuvant chemotherapy, and the results before chemotherapy were validated in an independent cohort of 194 patients. The analyses revealed that in general the tumor characteristics are not reflected in the serum samples. However, there was an association of specific triacylglycerols (TGs) in patients' response to chemotherapy. These TGs containing mainly oleic acid (C18:1) were found in lower levels in those patients showing pathologic complete response before receiving chemotherapy. Some of these TGs were also associated with estrogen receptor status and overall or disease-free survival of the patients. The results suggest that the altered serum levels of oleic acid in breast cancer patients are associated with their response to chemotherapy.
2014-06-17 | MTBLS92 | MetaboLights
Project description:This SuperSeries is composed of the following subset Series: GSE25055: Discovery cohort for genomic predictor of response and survival following neoadjuvant taxane-anthracycline chemotherapy in breast cancer GSE25065: Validation cohort for genomic predictor of response and survival following neoadjuvant taxane-anthracycline chemotherapy in breast cancer Refer to individual Series
2011-05-11 | E-GEOD-25066 | biostudies-arrayexpress
Project description:Purpose:The identification of biomarkers predictive of neoadjuvant chemotherapy response in breast cancer patients would be an important advancement in personalized cancer therapy. We hypothesized that due to similarities between radiation and chemotherapy induced cellular response mechanisms, radiation responsive genes may be useful in predicting response to neoadjuvant chemotherapy. Materials and Methods: Murine p53 null breast cancer cell lines representative of the luminal, basal-like and claudin-low human breast cancer subtypes were irradiated to identify radiation responsive genes. These murine radiation induced genes were then converted to their human orthologs. These genes were then used to develop a predictor of pathologic complete response (pCR) that was validated on two independent published neoadjuvant chemotherapy data sets of genomic data with response. Results: A radiation induced gene signature consisting of 30 genes was identified on a training set of 337 human primary breast cancer tumor samples that was prognostic for survival. Mean expression of this signature was calculated for individual samples in two independent published datasets and was found to be significantly predictive of pathologic complete response. Multivariate logistic regression analysis in both independent datasets showed that this 30 gene signature added significant predictive information independent of that provided by standard clinical predictors and other gene expression based predictors of pathologic complete response. Conclusion: This study provides new biologic information regarding response to neoadjuvant chemotherapy and a means of possibly improving the prediction of pathologic complete response.
2015-01-21 | GSE48073 | GEO
Project description:Breast cancer is the most frequently diagnosed female cancer accounting for 23 % of the total cases and the second leading cause of cancer mortality in the world, particularly in western countries. Since GEPARDUO trial reported the therapeutic benefit of combined doxorubicin and cyclophosphamide regimen in sequential administration with docetaxel, the combination regimen has become a standard therapeutic strategy in neoadjuvant systemic therapy for patients with operable breast cancers regardless of an intrinsic subtype. Although approximately 70% of entire patients are currently receiving the chemotherapy regimen, pathologic complete response (pCR) rate is still low, ranging from 23% to 32.7% due to the high heterogeneity of breast cancers. Therefore, the need for a marker predictive of response to a particular cytotoxic regimen, especially before neoadjuvant chemotherapy, is becoming all the more necessary to optimize therapeutic efficacy and to avoid unnecessary complications caused by systemic therapy. In the study, here we generated the first high-coverage proteomic data for needle biopsy FFPE sample being characterized with identical clinical conditions including chemotherapeutic regimens and the stage classification.
2021-09-08 | PXD013431 | Pride