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ABSTRACT:
PROVIDER: PRJNA208896 | ENA |
REPOSITORIES: ENA
Similar Datasets
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: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:We identified a 17-gene Her2-enriched tumor initiating cell (HTIC) signature in MMTV-Her2/Neu mouse mammary TICs. Here, we show that patients with HTICS+ HER2+:ERα− tumors are more likely to achieve a pathologic complete response to trastuzumab-based neoadjuvant chemotherapy compared with HER2+:ER+ tumors. Neoadjuvant study of 50 HER2-positive breast cancer cases treated with trastuzumab-based chemotherapy pre-operatively. Pre-treatment FNA from primary tumors were obtained and RNA extracted and hybridized to Affymetrix microarrays according to manufacturer protocol. Pathologic response was assessed at the end of neoadjuvant treatment.
2012-05-11 | E-GEOD-37946 | biostudies-arrayexpress
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: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:This study focused on patients with estrogen receptor positive/human epidermal growth factor receptor 2 positive (ER+/HER2+) breast cancer treated with neoadjuvant chemotherapy and HER2-targeted therapy (NAC+H), and was designed to identify novel biomarkers by correlating gene expression, histology and immunohistochemistry with pathologic response. We performed gene expression profiling on 11 pre-treatment tumors samples: 5 who had no or minimal residual disease residual cancer burden (RCB) score of 0 or 1 and 6 who had significant residual disease (RCB score of 2 or 3). ER2/HER2 postive breast tumors biopsied before neoadjuvant chemotherapy were selected to identify potential biomarkers of pathological complete response (pCR)
2015-09-08 | E-GEOD-67982 | biostudies-arrayexpress
Project description:We identified a 17-gene Her2-enriched tumor initiating cell (HTIC) signature in MMTV-Her2/Neu mouse mammary TICs. Here, we show that patients with HTICS+ HER2+:ERα− tumors are more likely to achieve a pathologic complete response to trastuzumab-based neoadjuvant chemotherapy compared with HER2+:ER+ tumors.
2012-05-12 | GSE37946 | GEO
Project description:PURPOSE: To develop a predictive test for response and survival following neoadjuvant taxane-anthracycline chemotherapy for HER2-negative invasive breast cancer. METHODS: We developed a microarray-based gene expression test from pre-treatment tumor biopsies (310 patients) to predict favorable outcome based on estrogen receptor (ER) status,pathologic response to chemotherapy, 3-year disease outcomes, and sensitivity to endocrine therapy. Tumors were classified as treatment-sensitive if predicted to have pathologic response (and not resistance) to chemotherapy, or sensitive to endocrine therapy. We tested predictive accuracy, with 95% confidence interval (CI), for pathologic response (PPV, positive predictive value), distant relapse-free survival (DRFS), and absolute risk reduction at median follow-up in 198 other patients. Independence from clinical-pathologic factors was assessed in a multivariate Cox regression analysis based on the likelihood ratio test. Other evaluable, published response predictors (genomic grade index (GGI), intrinsic subtype (PAM50), pCR predictor (DLDA30)) were compared. Neoadjuvant validation cohort of 198 HER2-negative breast cancer cases treated with taxane-anthracycline chemotherapy pre-operatively and endocrine therapy if ER-positive. Response was assessed at the end of neoadjuvant treatment and distant-relapse-free survival was followed for at least 3 years post-surgery.
2011-05-11 | E-GEOD-25065 | biostudies-arrayexpress
Project description:PURPOSE: To develop a predictive test for response and survival following neoadjuvant taxane-anthracycline chemotherapy for HER2-negative invasive breast cancer. METHODS: We developed a microarray-based gene expression test from pre-treatment tumor biopsies (310 patients) to predict favorable outcome based on estrogen receptor (ER) status,pathologic response to chemotherapy, 3-year disease outcomes, and sensitivity to endocrine therapy. Tumors were classified as treatment-sensitive if predicted to have pathologic response (and not resistance) to chemotherapy, or sensitive to endocrine therapy. We tested predictive accuracy, with 95% confidence interval (CI), for pathologic response (PPV, positive predictive value), distant relapse-free survival (DRFS), and absolute risk reduction at median follow-up in 198 other patients. Independence from clinical-pathologic factors was assessed in a multivariate Cox regression analysis based on the likelihood ratio test. Other evaluable, published response predictors (genomic grade index (GGI), intrinsic subtype (PAM50), pCR predictor (DLDA30)) were compared. Neoadjuvant study of 310 HER2-negative breast cancer cases treated with taxane-anthracycline chemotherapy pre-operatively and endocrine therapy if ER-positive. Response was assessed at the end of neoadjuvant treatment and distant-relapse-free survival was followed for at least 3 years post-surgery.
2011-05-11 | E-GEOD-25055 | biostudies-arrayexpress
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