Dataset Information

Hybrid models identified a 12-gene signature for lung cancer prognosis and chemoresponse prediction.

ABSTRACT:

Background

Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35-50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment.

Methodology/principal findings

From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naïve Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7) and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4) and DFCI (n = 82; log-rank P = 2.57e-4), using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P<0.04). The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: [2.08, 8.46]) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P<0.017). The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples.

Conclusions/significance

The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of chemotherapeutic drugs.

SUBMITTER: Wan YW

PROVIDER: S-EPMC2923187 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Publications

Hybrid models identified a 12-gene signature for lung cancer prognosis and chemoresponse prediction.

Wan Ying-Wooi YW Sabbagh Ebrahim E Raese Rebecca R Qian Yong Y Luo Dajie D Denvir James J Vallyathan Val V Castranova Vincent V Guo Nancy Lan NL

PloS one 20100817 8

<h4>Background</h4>Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35-50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment.<h4>Methodology/principal findings</h4>From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection me ...[more]

PMID: 20808922

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Dataset Information

Hybrid models identified a 12-gene signature for lung cancer prognosis and chemoresponse prediction.

Background

Methodology/principal findings

Conclusions/significance

Publications

Hybrid models identified a 12-gene signature for lung cancer prognosis and chemoresponse prediction.

Similar Datasets

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