This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Lee, E.-S. Articles by Kim, J. Search for Related Content PubMed PubMed Citation Articles by Lee, E.-S. Articles by Kim, J.

Prediction of Recurrence-Free Survival in Postoperative Non–Small Cell Lung Cancer Patients by Using an Integrated Model of Clinical Information and Gene Expression

Authors' Affiliations: ¹ Cancer Research Center, Center for Clinical Research, Samsung Biomedical Research Institute and ² Department of Pathology and ³ Department of Thoracic Surgery, Samsung Medical Center, College of Medicine, Sungkyunkwan University; ⁴ Department of Statistics, Korea University, Seoul, South Korea and ⁵ Bio & Health Lab and ⁶ Emerging Center, Samsung Advanced Institute of Technology; and ⁷ Department of Thoracic Surgery, Hando General Hospital, Gyunggi-do, South Korea

Requests for reprints: Jhingook Kim, Department of Thoracic Surgery, Samsung Medical Center, College of Medicine, Sungkyunkwan University, Seoul 135-230, South Korea. Phone: 82-2-3410-3489; Fax: 82-2-3410-0089; E-mail: jkimsmc{at}skku.edu.

Purpose: One of the main challenges of lung cancer research is identifying patients at high risk for recurrence after surgical resection. Simple, accurate, and reproducible methods of evaluating individual risks of recurrence are needed.

Experimental Design: Based on a combined analysis of time-to-recurrence data, censoring information, and microarray data from a set of 138 patients, we selected statistically significant genes thought to be predictive of disease recurrence. The number of genes was further reduced by eliminating those whose expression levels were not reproducible by real-time quantitative PCR. Within these variables, a recurrence prediction model was constructed using Cox proportional hazard regression and validated via two independent cohorts (n = 56 and n = 59).

Results: After performing a log-rank test of the microarray data and successively selecting genes based on real-time quantitative PCR analysis, the most significant 18 genes had P values of <0.05. After subsequent stepwise variable selection based on gene expression information and clinical variables, the recurrence prediction model consisted of six genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, and IFI44). Two pathologic variables, pStage and cellular differentiation, were developed. Validation by two independent cohorts confirmed that the proposed model is significantly accurate (P = 0.0314 and 0.0305, respectively). The predicted median recurrence-free survival times for each patient correlated well with the actual data.

Conclusions: We have developed an accurate, technically simple, and reproducible method for predicting individual recurrence risks. This model would potentially be useful in developing customized strategies for managing lung cancer.

This article has been cited by other articles:

				S. Dubey and C. A. Powell Update in Lung Cancer 2008 Am. J. Respir. Crit. Care Med., May 15, 2009; 179(10): 860 - 868. [Full Text] [PDF]