Gene Expression-Based Survival Prediction in Lung Adenocarcinoma: A Multi-Site, Blinded Validation Study

By Kerby Shedden, Jeremy M. G. Taylor, Steven Enkemann, Ming-Sound Tsao, Timothy Yeatman, W. Gerald, S. Eschrich, Igor Jurisica, T. Giordano, David E. Misek and 30 more. Nature Medicine, 2008

1,144 citations recorded by the source corpus.

Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training–testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.

Subfields: Molecular Biology, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3bec87a01916d25f8393a8815fc75495fcef1e97

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

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].”
Identification of a Novel Biomarker, SEMA5A, for Non–Small Cell Lung Carcinoma in Nonsmoking Women (2010, methodology): “Secondly, because most of the patients who provided samples for our microarray experiments are still alive, we chose two other independent cohorts to further investigate the association between SEMA5A expression and survival rate (34, 35).”
Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction (2010, methodology): “Nonetheless, our signatures performed well compared to those previously reported [20,21,22,24,25,26] when tested using the Erasmus MC and Duke University cohorts.”
Radiomics: the facts and the challenges of image analysis (2018, methodology): “…in the first cluster (high homogeneity of the yellow blocks) machine-learning approaches, such as random forests [14, 20], neural networks [21], linear regression [21], logistic regression [15], least absolute shrinkage and selection operator [22], and Cox proportional hazards regression [23].”
WNT/TCF signaling through LEF1 and HOXB9 mediates lung adenocarcinoma metastasis (2009, methodology): “To determine the association of LWS status with overall patient survival, we tested four independent stage I lung adenocarcinoma cohorts from a multi-institutional study (Shedden et al., 2008).”
Activation of the PD-1 pathway contributes to immune escape in EGFR-driven lung tumors (2013, result): “We also observed a similar result in an analysis of previously reported microarray data from patients with lung adenocarcinoma (24), in which there was a significant correlation among expression of EGFR and its ligands and PD-L1 expression (p<10 −15 ; data not shown).”
Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes (2012, background): “lung cancer [6–8], lymphomas [9–13], leukemia [14,15], and”