The second Validation set of the 95GC in 2013.
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ABSTRACT:
PROVIDER: PRJNA978565 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The aim of this study was to validate the efficacy of 95(GC) and compare it with that of 21(GC) (Oncotype DX) as well as to evaluate the combination of 95(GC) and 21(GC). DNA microarray data (gene expression) of ER-positive and node-negative breast cancer patients (n = 459) treated with adjuvant hormone therapy alone were classified with 95(GC) and 21(GC) (Recurrence Online at http://www.recurrenceonline.com/ ). 95(GC) classified the 459 patients into low-risk (n = 285; 10 year relapse-free survival: 88.8 %) and high-risk groups (n = 174; 70.6 %) (P = 5.5e-10), and 21(GC) into low-risk group (n = 286; 89.3 %), intermediate-risk (n = 81; 75.7 %), and high-risk (n = 92; 64.7 %) groups (P = 2.9e-10). The combination of 95(GC) and 21(GC) classified them into low-risk (n = 324; 88.9 %) and high-risk (n = 135; 65.0 %) groups (P = 5.9e-14). This DATA set: J02 is 56 of the above 459 cases from Japan who were assayed after the J01 DATA set. *Note: This old data has been updated multiple times by others. Then, there are some differences from the original 2013 paper and unclear points still remain. Therefore, do not use it for formal analysis aimed at public insurance coverage etc. This is for research purposes only. Please cite this paper when writing a new paper. PMID: 23884597 DOI: 10.1007/s10549-013-2640-9
2023-06-03 | GSE233909 | GEO
Project description:The aim of this study was to construct a prediction model for axillary lymph node metastasis (ALNM) using a DNA microarray assay for gene expression in breast tumor tissues. Luminal A breast cancers, diagnosed by PAM50 testing, were analyzed, and a prediction model (genomic nodal index (GNI)) consisting of 292 probe sets for ALNM was constructed in a training set of patients (n=388), and was validated in the first (n=59) and the second (n=103) validation sets. AUCs of ROC were 0.820, 0.717, and 0.749 in the training, first, and second validation sets, respectively. GNI was most significantly associated with ALNM, independently of the other conventional clinicopathological parameters in all cohorts. It is suggested that GNI can be used to identify the patients with a low risk for ALNM so that sentinel lymph node biopsy can be spared safely. This DATA set: J03 contains 120 (n+ 60, n- 60) samples of the above first and second validation sets from Japan (OUH_1,2). A long time passed, and now it is unclear how these 120 cases were distributed among the first and second validation sets. *Note: This old data has been updated multiple times by others. Then, there are some differences from the original 2014 paper and unclear points still remain. Therefore, do not use it for formal analysis aimed at public insurance coverage etc. This is for research purposes only. Please cite this paper when writing a new paper. PMID: 25016059 DOI: 10.1016/j.canlet.2014.07.003
2023-06-09 | GSE234114 | GEO
Project description:Normalized expression data; validation set
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Project description:Background: Meningiomas account for about 27% of primary brain tumors, making them one of the most common brain tumor. They are most common in people between the ages of 40 and 70 and are more common in women than in men. Most meningiomas (90%) are categorized as benign tumors, with the remaining 10% being atypical or malignant. Multiple classifications exist today, but the most commonly used is the World Health Organization’s (WHO) which classifies meningiomas into three histological grades: grade I (benign), grade II (atypical), and grade III (anaplastic) in accordance with the clinical prognosis. Most of these subtypes behave similarly, however anaplastics are the most aggressive. The ability to distinguish benign from atypical and anaplastic tumors is important because of its impact on treatment decisions. A molecular based classification system has the likelihood of being a better prognostic indicator and is useful for identifying alterations in pathways and networks that drive tumor progression and growth. The information obtained can potentially be translated into more effective and less toxic targeted therapies. We tested a method for genome wide expression profiling of formalin-fixed, paraffin-embedded tissues. We applied the method to the analysis of the clinical outcome of meningioma tumor. Materials and Methods: The training set consisted of tissue samples from 63 patients who were consecutively treated with surgery for meningioma between 1990 and 2005. For each patient data on clinical outcomes and formalin-fixed, paraffin-embedded blocks of tumor were available. The validation set included tissue samples from 189 patients with meningioma who consecutively underwent surgery between 1992 and 2006. We used a custom 60-mer amino modified oligo- array, containing 912 probes, a lot of which specific for genes commonly altered in cancer. Functional annotation was performed by means of gene set enrichment analysis (GSEA, www. broad.mit.edu/gsea/). Survival analyses were performed with the use of the log-rank test and Cox regression modeling. All analyses were performed with the use of GenePattern. Results: We investigated whether gene-expression profiles of meningioma tumors were associated with the clinical outcome. Using a standard leaveone- out cross-validation procedure, we found the meningioma signature to be significantly correlated with survival (P = 0.0001). The survival correlated signature contained 219 genes and was tested in the validation set. Conclusion: These results support the validity of the survival signature and highlight the potential role of tumoral meningioma tissue in predicting the outcome for patients with meningioma tumors.
2011-05-11 | GSE16156 | GEO
Project description:The Validation set of the genomic nodal index (GNI) in 2014
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Project description:The Validation Set for the initial development of the 95GC in 2011.
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Project description:DNA methylation-based classification of human central nervous system tumors [validation set]
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