Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction
By J. Hou, J. Aerts, Bianca den Hamer, W. V. van Ijcken, M. D. den Bakker, P. Riegman, C. van der Leest, P. J. van der Spek, John A. Foekens, H. Hoogsteden and 2 more. PLoS ONE, 2010
759 citations recorded by the source corpus.
Background Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. Methodology and Principal Findings A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. Conclusions The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.
Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/45e9b38c5c6acef9344f5e3462d7d05a3e9b3c47
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In Pulmonary and Respiratory Medicine
Citations in the corpus
- NCBI GEO: archive for functional genomics data sets—10 years on (2010, methodology): “Other authors incorporate GEO data into their own analyses, such as using GEO data as controls or training sets within their own study (10).”
- SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis (2013, methodology): “We used a special lung meta-base build in our research group, which is composed of more than 1,000 samples obtained from six authors (Bild [19], Raponi [20], Zhu [21], Hou [22], NCI [23], Okayama [24]), equivalent Affymetrix gene expression platform, and containing all biomarker genes.”
- Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer (2013, background): “A pre-requisite for the reproducibility of such genomic signatures is the availability of raw data, which was only ensured by publications of the last six years [9,10,11,12,13,14,15,16,17,18].”