EGAS00001002806-sc-2019-08-14T08:20:38Z - samples
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PROVIDER: EGAD00001005255 | EGA |
REPOSITORIES: EGA
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Project description:In this study a collection of core biopsies from breast cancer patients receiving neoadjuvant chemotherapy as part of the ChemoNEAR trial will be investigated. The study is intended to detect early acquired resistance in women with diagnosed breast carcinoma. Samples will undergo whole genome sequencing and analysis, including use of HR Predict.
| EGAS00001002806 | EGA
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:Abstract Motivation Breast cancer is a heterogeneous disease with distinct subtypes. Even within these subtypes differences at the molecular level are present which are reflected in variable responses to chemotherapy. We set out to identify genes associated with chemotherapy resistance by analyzing a set of HER2-positive breast cancers. Methods We collected, gene expression profiled and analyzed 60 HER2-positive breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy. In addition to conventional supervised approaches for the detection of reporters of resistance, we report on a novel approach specifically tailored to the detection of small groups of resistant samples that show aberrant gene expression patterns. Results We propose a novel analytical approach that takes heterogeneity in response into account. We show that this approach is more powerful than classical approaches for detecting small subgroups of samples showing aberrant expression in a controlled setting. We applied this approach to our 60 breast cancer samples prior to neoadjuvant chemotherapy, and generated candidate response reporter lists for each subtype. Discussion Using a novel analytical approach we report on the mRNA gene expression analysis of a cohort of breast cancers prior to neoadjuvant chemotherapy. An important characteristic of this approach is that it takes heterogeneity in neoadjuvant treatment response into account. Such approaches are needed to identify biomarkers for predicting treatment response. We collected, gene expression profiled and analyzed 60 breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy.
2013-10-22 | E-GEOD-41656 | biostudies-arrayexpress
Project description:Background: Gene expression profiling has been used extensively within breast cancer research. Patient matched transcriptomic studies of tumour samples before and after treatment offer great potential and have been initiated, but tend not to include a control group. Here we examine gene expression changes between patient-matched core biopsies and surgical resection samples in the absence of treatment, to consider sampling methods and tumour heterogeneity. Patients and Methods: Illumina BeadArray technology was used to measure dynamic changes in gene expression from thirty-seven paired baseline and surgically excised breast tumour samples obtained from women receiving no treatment prior to surgery. Results: Patient-matched sample pairs had significantly higher correlations than samples between different individuals, demonstrating that tumour heterogeneity and intra-tumour differences are less prominent than inter-tumour/patient differences. Perhaps surprisingly, consistent changes in gene expression were identified during the diagnosis-surgery interval, despite a lack of treatment. 50 genes were significantly differentially expressed (48 up, 2 down; FDR 0.05) in a manner that appears independent of both subtype and the sampling-interval length. Gene set enrichment analysis using four independent treated datasets has implicated the tumour sampling method as the likely cause of these expression changes which include increases in early growth response genes such as EGR1, 2 and 3 along with DUSP1 and FOS. Our data does not support the idea that there is a significant wounding or immune response. Conclusion: This is the largest cohort of patient-matched transcriptome profiling of tumours from patients receiving no treatment between diagnosis and surgery to date. It has revealed that consistent changes in gene expression do exist between diagnostic core biopsy and the surgical excision sample. We have confirmed these findings in a number of published breast cancer datasets. Ultimately, researchers should be aware of the potential for the tumour sampling method to introduce a confounding factor in future neoadjuvant studies. 37 paired patient-matched whole-transcriptome profiled primary breast tumours from patients receiving no treatment between diagnosis and surgery. Superseries is a product of two integrated individual batches.
2016-08-08 | E-GEOD-76726 | biostudies-arrayexpress
Project description:Background: Gene expression profiling has been used extensively within breast cancer research. Patient matched transcriptomic studies of tumour samples before and after treatment offer great potential and have been initiated, but tend not to include a control group. Here we examine gene expression changes between patient-matched core biopsies and surgical resection samples in the absence of treatment, to consider sampling methods and tumour heterogeneity. Patients and Methods: Illumina BeadArray technology was used to measure dynamic changes in gene expression from thirty-seven paired baseline and surgically excised breast tumour samples obtained from women receiving no treatment prior to surgery. Results: Patient-matched sample pairs had significantly higher correlations than samples between different individuals, demonstrating that tumour heterogeneity and intra-tumour differences are less prominent than inter-tumour/patient differences. Perhaps surprisingly, consistent changes in gene expression were identified during the diagnosis-surgery interval, despite a lack of treatment. 50 genes were significantly differentially expressed (48 up, 2 down; FDR 0.05) in a manner that appears independent of both subtype and the sampling-interval length. Gene set enrichment analysis using four independent treated datasets has implicated the tumour sampling method as the likely cause of these expression changes which include increases in early growth response genes such as EGR1, 2 and 3 along with DUSP1 and FOS. Our data does not support the idea that there is a significant wounding or immune response. Conclusion: This is the largest cohort of patient-matched transcriptome profiling of tumours from patients receiving no treatment between diagnosis and surgery to date. It has revealed that consistent changes in gene expression do exist between diagnostic core biopsy and the surgical excision sample. We have confirmed these findings in a number of published breast cancer datasets. Ultimately, researchers should be aware of the potential for the tumour sampling method to introduce a confounding factor in future neoadjuvant studies. 37 paired patient-matched whole-transcriptome profiled primary breast tumours from patients receiving no treatment between diagnosis and surgery. Superseries is a product of two integrated individual batches.
2016-08-08 | E-GEOD-76727 | biostudies-arrayexpress
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
Project description:Breast cancer, categorised into hormone receptor-positive (HR+), HER2-positive (HER2+), and triple-negative (TNBC) subtypes, exhibits varied outcomes based on the number of tumour-infiltrating lymphocytes (TILs). Increased TIL levels are associated with better outcomes in HER2+ and TNBC, while HR+ individuals with high TIL levels show shorter survival but greater neoadjuvant chemotherapy response. To explore the divergent roles of TIL levels across various subtypes and their effect on immune cell composition, we employed single-cell RNA sequencing on 31 patients with breast cancer. HR+ breast cancer with high TIL levels (TIL-high) revealed increased SPP1+ macrophages, increased SPP1 expression in other monocytes/macrophages (mono/macro) subgroups, and enriched pathways associated with extracellular matrix (ECM) remodelling in mono/macro. Moreover, cell–cell interaction analyses revealed enhanced SPP1, MIF, and FN1 signalling in the interaction between SPP1+ macrophages and T-cells in TIL-high HR+ breast cancer. Spatial transcriptomics data highlighted the close proximity of SPP1+ macrophages, CD8+ T-cells, and CD4+ T-cells in TIL-high HR+ breast cancer. Our findings unveil the novel influence of SPP1+ macrophages on T-cells in TIL-high HR+ breast cancer, potentially explaining the poor prognosis and offering insights for targeted interventions.
2024-10-09 | GSE261774 | GEO
Project description:Assessment of tumor pathological and transcriptional biomarkers in pre- and on-treatment core biopsies predictive of response and outcome after neoadjuvant chemotherapy plus Bevacizumab in patients with HER2-negative breast cancer: Results from a multi-center, single-arm, phase 2 study (the PROMIX trial) Global gene expression profiling was performed on samples from 3 time points (baseline, after 2 cycles and surgery) from women with breast cancer receivcing neoadjuvant chemotherapy with bevacizumab in a phase 2 trial
2017-09-01 | GSE87455 | GEO
Project description:Trastuzumab, a humanized monoclonal antibody directed to the HER2 protein, is the standard-of-care treatment for patients with HER2 positive breast cancer, reducing the risk of relapse and death in patients. Nonetheless, some patients do not benefit from this treatment, underscoring the need to identify patients for whom chemotherapy + trastuzumab is adequate versus patients requiring additional drugs. The series comprised 24 incisional biopsies of breast carcinomas derived from patients that received neoadjuvant trastuzumab based therapy. Gene expression profiling was performed using RNA from frozen core biopsies from 24 patients with primary HER2-positive (HER2+) tumors treated with neoadjuvant chemotherapy and trastuzumab.
2016-03-31 | E-GEOD-62327 | biostudies-arrayexpress
Project description:Eribulin prolongs overall survival in patients with pre-treated advanced breast cancer. However, no biomarker exists to prospectively select patients who will benefit the most from this drug. SOLTI-1007-NeoEribulin is a phase II, open-label, two cohort, exploratory pharmacogenomic study in patients with clinical stage I-II HER2-negative breast cancer receiving neoadjuvant eribulin monotherapy treatment. Primary objective was to explore the association of baseline tumor gene expression with pathological complete response in the breast (pCRB) at surgery. Key secondary objectives were pCRB rates in all patients and according to HR status, gene expression changes during treatment and safety. 101 hormonal receptor-positive (HR+) and 73 triple negative breast cancer (TNBC) patients were recruited. The pCRB rates were 6.4% in all patients, 4.9% in HR+ disease and 8.2% in TNBC. The TNBC cohort was interrupted due to a progression disease rate of 30.1%. The pCRB rates differed according to intrinsic subtypes: 28.6% in HER2-E, 11.1% in Normal-like, 7.9% in Luminal B, 5.9% in Basal-like and 0% in Luminal A (HER2-E vs. others odds ratio=7.05, 95% CI 1.80-42.14; p-value = 0.032). Intrinsic subtype changes at surgery occurred in 33.3% of cases, mostly (49.0%) Luminal B converting to Luminal A or Basal-likes converting to Normal-like. Baseline tumor infiltrating lymphocytes (TILs) were significantly associated with pCR. Eribulin showed a good safety profile with a low response and pCRB rates. Patients with HER2-negative disease with a HER2-enriched profile may benefit the most from eribulin. In addition, significant biological activity of eribulin is observed in Luminal B and Basal-like subtypes.
2021-12-10 | GSE186102 | GEO