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. 2010 Jun;2(2):89-99.

The role of gene expression profiling in early-stage non-small cell lung cancer

Wenlong Shao  1 , Daoyuan Wang, Jianxing He
Affiliations

The role of gene expression profiling in early-stage non-small cell lung cancer

Wenlong Shao et al. J Thorac Dis. 2010 Jun.

Abstract

For patients with identical clinical-pathological characteristics or the same stage of lung cancer, great uncertainties remain regarding how some patients will be cured while other patients will have cancer recurrence, metastasis, or death after surgical resection. Identification of patients at high risk of recurrence, those who are unlikely to respond to specific chemotherapeutic agents, is the rationale for measuring specific biochemical markers. Thus, main investigational studies nowadays are focused in identifying molecular markers of recurrence, beyond pathologic stage, after surgical treatment and factors that can predict a benefit from adjuvant chemotherapy in poor prognosis subgroups, to individualize treatments. Advances in genomics and proteomics have generated many candidate markers with potential clinical value. Gene expression profiling (GEP) by microarray or real-time quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) can be useful in the classification or prognosis of various types of cancer, including lung cancer. A number of prognostic gene expression signatures have been reported to predict survival in non-small cell lung cancer (NSCLC). In this review, we focus on the role of GEP in early-stage NSCLC as predictive and prognostic biomarker and its potential use for a 'personalized' medicine in the years to come.

Keywords: gene expression profiling; non-small cell lung cancer; prognostic biomarker.

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Conflict of interest statement

This paper received no research funding. No conflict of interest.

Figures

Fig 1.
Fig 1.. Kaplan-Meier survival analysis of three clusters of patients. Average survival time of patients in cluster 1, 66.9 months; average survival time of patients in cluster 2, 22.4 months; average survival time of patients in cluster 3, 27.6 months (P<0.0005, log--rank test) (ref 63).
Fig 2.
Fig 2.. Kaplan–Meier survival estimates for a group of patients with stage IA disease from the Duke, ACOSOG, and CALGB cohorts and the subgroups predicted to have either a high probability (>0.5) or a low probability (≤0.5) of recurrence. P values were obtained with the use of a log-rank test. Tick marks indicate patients whose data were censored by the time of last follow-up or owing to death (ref 52).
Fig 3.
Fig 3.. A, Kaplan-Meier curves comparing OS among patients with low- and high-risk (all stages) using the four-gene prognostic model (all stages); B, comparing disease-free survival (DFS) among patients with low- and high-risk scores (all stages); C, comparing OS among stage I patients with low- and high-risk scores using the four-gene prognostic model; D, comparing DFS among stage I patients with low- and high-risk scores; E, comparing OS among stage I patients with low- and high-risk scores using the 5-gene model published by Chen and colleagues (ref 13).
Fig 4.
Fig 4.. Gene overlap between NSCLC prognostic signatures. Overlap in genes of recent NSCLC survival signatures is limited to 5of a total of 327 genes used. Likely, all identified signatures are subsets from a larger NSCLC prognostic space (ref 62).
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