The second Validation set of the 95GC in 2013.
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE233909 | GEO | 2023/06/03
REPOSITORIES: GEO
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Project description:Molecular prognostic assays, such as Oncotype DX, are increasingly incorporated into the management of patients with invasive breast carcinoma. BreastPRS is a new molecular assay developed and validated from a meta-analysis of publically available genomic datasets. We applied the assay to matched fresh-frozen (FF) and formalin-fixed paraffin embedded (FFPE) tumor samples to translate the assay to FFPE. A linear relationship of the BreastPRS prognostic score was observed between tissue preservation formats. BreastPRS recurrence scores were compared with Oncotype DX recurrence scores from 246 patients with invasive breast carcinoma and known Oncotype DX results. Using this series, a 120-gene linear discriminant algorithm (LDA) was trained to predict Oncotype DX risk groups and then applied to series of untreated, node-negative, estrogen receptor (ER) – positive patients from previously published studies with known clinical outcomes. Correlation of recurrence score and risk group between Oncotype DX and BreastPRS was statistically significant (P<0.0001). 59 of 260 (23%) patients from four previously published studies were classified as intermediate-risk when the 120-gene LDA was applied. BreastPRS reclassified the 59 patients into binary risk groups (high vs. low-risk). 23 (39%) patients were classified as low-risk 36 (61%) as high-risk [P=0.029, HR: 3.64, 95% CI: 1.40 to 9.50]. At 10 years from diagnosis, the low-risk group had a 90% recurrence-free survival (RFS) rate, compared to 60% for the high-risk group. BreastPRS recurrence score is comparable to Oncotype DX and can reclassify Oncotype DX intermediate-risk patients into two groups with significant differences in RFS. Further studies are needed to validate these findings. Expression profiles of 246 invasive breast carcinomas.
2013-06-23 | E-GEOD-47109 | biostudies-arrayexpress
Project description:Molecular prognostic assays, such as Oncotype DX, are increasingly incorporated into the management of patients with invasive breast carcinoma. BreastPRS is a new molecular assay developed and validated from a meta-analysis of publically available genomic datasets. We applied the assay to matched fresh-frozen (FF) and formalin-fixed paraffin embedded (FFPE) tumor samples to translate the assay to FFPE. A linear relationship of the BreastPRS prognostic score was observed between tissue preservation formats. BreastPRS recurrence scores were compared with Oncotype DX recurrence scores from 246 patients with invasive breast carcinoma and known Oncotype DX results. Using this series, a 120-gene linear discriminant algorithm (LDA) was trained to predict Oncotype DX risk groups and then applied to series of untreated, node-negative, estrogen receptor (ER) – positive patients from previously published studies with known clinical outcomes. Correlation of recurrence score and risk group between Oncotype DX and BreastPRS was statistically significant (P<0.0001). 59 of 260 (23%) patients from four previously published studies were classified as intermediate-risk when the 120-gene LDA was applied. BreastPRS reclassified the 59 patients into binary risk groups (high vs. low-risk). 23 (39%) patients were classified as low-risk 36 (61%) as high-risk [P=0.029, HR: 3.64, 95% CI: 1.40 to 9.50]. At 10 years from diagnosis, the low-risk group had a 90% recurrence-free survival (RFS) rate, compared to 60% for the high-risk group. BreastPRS recurrence score is comparable to Oncotype DX and can reclassify Oncotype DX intermediate-risk patients into two groups with significant differences in RFS. Further studies are needed to validate these findings.
2013-06-23 | GSE47109 | GEO
Project description:The study aimed to develop the 72GC (the 72-gene classifier) for recurrence-risk prediction for patients with estrogen receptor positive and node-negative breast cancer. 72-GC could differentiate the high-risk from the low-risk patients with a high statistical significance, and is considered to be applicable to formalin-fixed, paraffin embedded (FFPE) tumor tissues because the results of 72-GC on FF (fresh-frozen) tissues and FFPE tissues showed a high concordance. J06 31 pairs of FF (fresh‑frozen) and FFPE data are included in the concordance analysis of 72GC high/low results between FF and FFPE (Table 3). A long time passed, and now it is unclear how these 31 cases were distributed among the analysis. *Note: This old data has been updated multiple times by the other members. Then, there are some differences from the original 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: 24461457 DOI: 10.1016/j.clbc.2013.11.006
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Project description:To identify miRNAs involved in drug resistance of human breast cancer, a miRNA microarray was performed on 10 cases of drug resistant tissues and 10 cases of drug sensitive tissues. There were 29 differentially expressed miRNAs between drug resistant and drug sensitive tissues, including 22 significantly up-regulated and 7 down-regulated RNAs in drug resistant tissues compared to drug sensitive tissues. A signature of five miRNAs, including miR-23a-3p, miR-200c-3p, miR-214-3p, miR-638, and miR-451a was established. A risk score from the miRNA signature was calculated and patients were classified into high-risk and low-risk groups. Compared with the patients with low-risk scores, the patients with high risk scores showed resistant response to chemotherapy in the training, internal testing and independent sets.
2017-12-31 | GSE73736 | GEO
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
2023-05-28 | GSE233251 | GEO
Project description:Purpose: The JBR.10 trial demonstrated benefit from adjuvant cisplatin/vinorelbine (ACT) in early-stage non-small-cell lung cancer (NSCLC). We hypothesized that expression profiling may identify stage-independent subgroups who might benefit from ACT. Patients and Methods: Gene expression profiling was conducted on mRNA from 133 frozen JBR.10 tumor samples (62 observation [OBS], 71 ACT). The minimum gene set that was selected for the greatest separation of good and poor prognosis patient subgroups in OBS patients was identified. The prognostic value of this gene signature was tested in four independent published microarray data sets and by quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR). Results: A 15-gene signature separated OBS patients into high-risk and low-risk subgroups with significantly different survival (hazard ratio [HR], 15.02; 95% CI, 5.12 to 44.04; P .001; stage I HR, 13.31; P .001; stage II HR, 13.47; P .001). The prognostic effect was verified in the same 62 OBS patients where gene expression was assessed by qPCR. Furthermore, it was validated consistently in four separate microarray data sets (total 356 stage IB to II patients without adjuvant treatment) and additional JBR.10 OBS patients by qPCR (n 19). The signature was also predictive of improved survival after ACT in JBR.10 high-risk patients (HR, 0.33; 95% CI, 0.17 to 0.63; P .0005), but not in low-risk patients (HR, 3.67; 95% CI, 1.22 to 11.06; P = .0133; interaction P .001). Significant interaction between risk groups and ACT was verified by qPCR. Conclusion: This 15-gene expression signature is an independent prognostic marker in early-stage, completely resected NSCLC, and to our knowledge, is the first signature that has demonstrated the potential to select patients with stage IB to II NSCLC most likely to benefit from adjuvant chemotherapy with cisplatin/vinorelbine.
2010-09-09 | GSE14814 | GEO
Project description:Classification of tamixifen-treated breast cancer patients into high and low risk groups using the 76-gene signature Experiment Overall Design: 136 breast cancer samples that were treated with tamoxifen were classified using the 76-gene signature
2008-10-26 | E-GEOD-12093 | biostudies-arrayexpress
Project description:Liposarcoma is the most common soft tissue sarcoma, accounting for about 20% of cases. Liposarcoma is classified into 5 histologic subtypes that fall into 3 biological groups characterized by specific genetic alterations. To identify genes that contribute to liposarcomagenesis and to better predict outcome for patients with the disease, we undertook expression profiling of liposarcoma. U133A expression profiling was performed on 140 primary liposarcoma samples, which were randomly split into training set (n=95) and test set (n=45). A multi-gene predictor for distant recurrence-free survival (DRFS) was developed using the supervised principal component method. Expression levels of the 588 genes in the predictor were used to calculate a risk score for each patient. In validation of the predictor in the test set, patients with low risk score had a 3-year DRFS of 83% vs. 45% for high risk score patients (P=0.001). The hazard ratio for high vs. low score, adjusted for histologic subtype, was 4.42 (95% confidence interval 1.26-15.55; P=0.021). The concordance probability for risk score was 0.732. Genes related to adipogenesis, DNA replication, mitosis, and spindle assembly checkpoint control were all highly represented in the multi-gene predictor. Three genes from the predictor, TOP2A, PTK7, and CHEK1, were found to be overexpressed in liposarcoma samples of all five subtypes and in liposarcoma cell lines. Knockdown of these genes in liposarcoma cell lines reduced proliferation and invasiveness and increased apoptosis. Thus, genes identified from this predictor appear to have roles in liposarcomagenesis and have promise as therapeutic targets. In addition, the multi-gene predictor will improve risk stratification for individual patients with liposarcoma. 140 human liposarcoma specimens were profiled on Affymetrix U133A arrays per manufacturer's instructions.
2011-09-01 | E-GEOD-30929 | biostudies-arrayexpress
Project description:This study aimed to identify the genetic signatures associated with disease prognosis in bladder cancer. We used 165 primary bladder cancer samples, 23 recurrent non-muscle invasive tumor tissues, 58 normal looking bladder mucosae surrounding cancer and 10 normal bladder mucosae for microarray analysis. Hierarchical clustering was used to stratify the prognosis-related gene classifiers. For validation, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) of top-ranked 14 genes was performed. On unsupervised hierarchical clustering using prognosis related gene-classifier, tumors were divided into 2 groups. The high risk gene signatures had significantly poor prognosis compared to low risk gene signatures (P<0.001 by the log-rank test, respectively). The prognosis-related gene classifiers correlated significantly with recurrence of non-muscle invasive bladder cancer (hazard ratio, 4.09; 95% confidence interval [CI], 1.94 to 8.64; P<0.001), and progression (hazard ratio, 23.68; 95% confidence interval [CI], 4.91 to 114.30; P<0.001), cancer-specific survival (hazard ratio, 29.25; 95% confidence interval [CI], 3.47 to 246.98; P=0.002) and overall survival (hazard ratio, 23.33; 95% confidence interval [CI], 4.97 to 109.50; P<0.001) of muscle invasive bladder cancer (p < 0.001, respectively). No patient with non-muscle invasive bladder cancer experienced cancer progression in low risk gene signature group. Prognosis-related gene classifiers validated by RT- PCR showed identical results. Prognosis related gene-classifiers provided strong predictive value for disease outcome. These gene classifiers could assist in selecting patients who might benefit from more aggressive therapeutic intervention or surveillance. Keywords: Gene expression, Bladder cancer, Prognosis
2010-05-06 | GSE13507 | GEO
Project description:Liposarcoma is the most common soft tissue sarcoma, accounting for about 20% of cases. Liposarcoma is classified into 5 histologic subtypes that fall into 3 biological groups characterized by specific genetic alterations. To identify genes that contribute to liposarcomagenesis and to better predict outcome for patients with the disease, we undertook expression profiling of liposarcoma. U133A expression profiling was performed on 140 primary liposarcoma samples, which were randomly split into training set (n=95) and test set (n=45). A multi-gene predictor for distant recurrence-free survival (DRFS) was developed using the supervised principal component method. Expression levels of the 588 genes in the predictor were used to calculate a risk score for each patient. In validation of the predictor in the test set, patients with low risk score had a 3-year DRFS of 83% vs. 45% for high risk score patients (P=0.001). The hazard ratio for high vs. low score, adjusted for histologic subtype, was 4.42 (95% confidence interval 1.26-15.55; P=0.021). The concordance probability for risk score was 0.732. Genes related to adipogenesis, DNA replication, mitosis, and spindle assembly checkpoint control were all highly represented in the multi-gene predictor. Three genes from the predictor, TOP2A, PTK7, and CHEK1, were found to be overexpressed in liposarcoma samples of all five subtypes and in liposarcoma cell lines. Knockdown of these genes in liposarcoma cell lines reduced proliferation and invasiveness and increased apoptosis. Thus, genes identified from this predictor appear to have roles in liposarcomagenesis and have promise as therapeutic targets. In addition, the multi-gene predictor will improve risk stratification for individual patients with liposarcoma.
2011-09-02 | GSE30929 | GEO