The Validation Set for the initial development of the 95GC in 2011.
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PROVIDER: PRJNA975564 | ENA |
REPOSITORIES: ENA
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Project description:Our aim to develop the 95GC (gene classifier) was to make an accurate diagnostic system using gene expression analysis by means of DNA microarray for prognosis of node-negative and estrogen receptor (ER)-positive breast cancer patients in order to identify a subset of patients who can be safely spared adjuvant chemotherapy. A diagnostic system comprising a 95-gene classifier was developed for predicting the prognosis of node-negative and ER-positive breast cancer patients by using DNA microarray (gene expression) data (n = 549) as the training set and the DNA microarray data (n = 105) as the validation set (= this data set). With the 95-gene classifier we could classify the 105 patients receiving only endocrine therapy without chemotherapy in the validation set into a high-risk (n = 44) and a low-risk (n = 61) group with 10-year recurrence-free survival rates of 93 and 53%, respectively (P = 8.6e-7). Multivariate analysis demonstrated that the 95-gene classifier was the most important and significant predictor of recurrence (P = 9.6e-4) independently of tumor size, histological grade, progesterone receptor, HER2, Ki67, or GGI. The 95-gene classifier developed by us can predict the prognosis of node-negative and ER-positive breast cancer patients with high accuracy. *Note: This old data has been updated multiple times by others. Then, there are some differences from the original 2011 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. DOI: 10.1007/s10549-010-1145-z
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Project description:The second Validation set of the 95GC in 2013.
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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 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