Topic
Gene signature
About: Gene signature is a research topic. Over the lifetime, 2957 publications have been published within this topic receiving 103471 citations. The topic is also known as: gene expression signature & gene signatures.
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
Champalimaud Foundation1, University of California, San Francisco2, Université libre de Bruxelles3, Paris Descartes University4, Swiss Institute of Bioinformatics5, University of Paris6, University of Lausanne7, Institute of Oncology Ljubljana8, University of Texas Health Science Center at San Antonio9, Autonomous University of Barcelona10, University of Paris-Sud11, Bosch12, Imperial College London13, University of Texas MD Anderson Cancer Center14, Université catholique de Louvain15, Netherlands Cancer Institute16
TL;DR: Among women with early-stage breast cancer who were at high clinical risk and low genomic risk for recurrence, the receipt of no chemotherapy on the basis of the 70-gene signature led to a 5-year rate of survival without distant metastasis that was 1.5 percentage points lower than the rate with chemotherapy.
Abstract: BackgroundThe 70-gene signature test (MammaPrint) has been shown to improve prediction of clinical outcome in women with early-stage breast cancer. We sought to provide prospective evidence of the clinical utility of the addition of the 70-gene signature to standard clinical–pathological criteria in selecting patients for adjuvant chemotherapy. MethodsIn this randomized, phase 3 study, we enrolled 6693 women with early-stage breast cancer and determined their genomic risk (using the 70-gene signature) and their clinical risk (using a modified version of Adjuvant! Online). Women at low clinical and genomic risk did not receive chemotherapy, whereas those at high clinical and genomic risk did receive such therapy. In patients with discordant risk results, either the genomic risk or the clinical risk was used to determine the use of chemotherapy. The primary goal was to assess whether, among patients with high-risk clinical features and a low-risk gene-expression profile who did not receive chemotherapy, the...
1,291 citations
TL;DR: It is shown that normal dermal fibroblasts can be "educated" by carcinoma cells to express proinflammatory genes, and this ability to "educate" them is shown to be related to tumor-enhancing inflammation.
1,270 citations
Université libre de Bruxelles1, Swiss Institute of Bioinformatics2, Institut Gustave Roussy3, Karolinska Institutet4, French Institute of Health and Medical Research5, King's College London6, John Radcliffe Hospital7, European Organisation for Research and Treatment of Cancer8, Netherlands Cancer Institute9
TL;DR: The 70-gene signature adds independent prognostic information to clinicopathologic risk assessment for patients with early breast cancer and outperformed the clinicopathological risk assessment in predicting all endpoints.
Abstract: Background: A 70-gene signature was previously shown to have prognostic value in patients with node-negative breast cancer. Our goal was to validate the signature in an independent group of patients. Methods: Patients (n = 307, with 137 events after a median follow-up of 13.6 years) from fi ve European centers were divided into high- and low-risk groups based on the gene signature classifi cation and on clinical risk classifi cations. Patients were assigned to the gene signature low-risk group if their 5-year distant metastasis – free survival probability as estimated by the gene signature was greater than 90%. Patients were assigned to the clinicopathologic low-risk group if their 10-year survival probability, as estimated by Adjuvant! software, was greater than 88% (for estrogen receptor [ER] – positive patients) or 92% (for ERnegative patients). Hazard ratios (HRs) were estimated to compare time to distant metastases, disease-free survival, and overall survival in high- versus low-risk groups. Results: The 70-gene signature outperformed the clinicopathologic risk assessment in predicting all endpoints. For time to distant metastases, the gene signature yielded HR = 2.32 (95% confi dence interval [CI] = 1.35 to 4.00) without adjustment for clinical risk and hazard ratios ranging from 2.13 to 2.15 after adjustment for various estimates of clinical risk; clinicopathologic risk using Adjuvant! software yielded an unadjusted HR = 1.68 (95% CI = 0.92 to 3.07). For overall survival, the gene signature yielded an unadjusted HR = 2.79 (95% CI = 1.60 to 4.87) and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic risk yielded an unadjusted HR = 1.67 (95% CI = 0.93 to 2.98). For patients in the gene signature high-risk group, 10-year overall survival was 0.69 for patients in both the low – and high – clinical risk groups; for patients in the gene signature low-risk group, the 10-year survival rates were 0.88 and 0.89, respectively. Conclusions: The 70-gene signature adds independent prognostic information to clinicopathologic risk assessment for patients with early breast cancer. [J Natl Cancer Inst 2006;98: 1183 – 92 ]
1,189 citations
TL;DR: Gene signature-based tumor microenvironment inference revealed a decrease in invading monocytes and a subtype-dependent increase in macrophages/microglia cells upon disease recurrence within weeks of diagnosis and at recurrence.
1,182 citations