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Clin Cancer Res. 2010 Oct 15;16(20):5038-47. doi: 10.1158/1078-0432.CCR-10-0612. Epub 2010 Aug 25.

Prognostic gene expression signature for squamous cell carcinoma of lung.

Author information

1
Ontario Cancer Institute and Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. czhu@uhnres.utoronto.ca

Abstract

PURPOSE:

This study aimed to identify and validate a gene expression signature for squamous cell carcinoma of the lung (SQCC).

EXPERIMENTAL DESIGN:

A published microarray dataset from 129 SQCC patients was used as a training set to identify the minimal gene set prognostic signature. This was selected using the MAximizing R Square Algorithm (MARSA), a novel heuristic signature optimization procedure based on goodness-of-fit (R square). The signature was tested internally by leave-one-out-cross-validation (LOOCV), and then externally in three independent public lung cancer microarray datasets: two datasets of non-small cell lung cancer (NSCLC) and one of adenocarcinoma (ADC) only. Quantitative-PCR (qPCR) was used to validate the signature in a fourth independent SQCC cohort.

RESULTS:

A 12-gene signature that passed the internal LOOCV validation was identified. The signature was independently prognostic for SQCC in two NSCLC datasets (total n = 223) but not in ADC. The lack of prognostic significance in ADC was confirmed in the Director's Challenge ADC dataset (n = 442). The prognostic significance of the signature was validated further by qPCR in another independent cohort containing 62 SQCC samples (hazard ratio, 3.76; 95% confidence interval, 1.10-12.87; P = 0.035).

CONCLUSIONS:

We identified a novel 12-gene prognostic signature specific for SQCC and showed the effectiveness of MARSA to identify prognostic gene expression signatures.

PMID:
20739434
DOI:
10.1158/1078-0432.CCR-10-0612
[Indexed for MEDLINE]
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