Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression

Eung Sirk Lee, Dae Soon Son, Sung Hyun Kim, Jinseon Lee, Jisuk Jo, Joungho Han, Heesue Kim, Joo Lee Hyun, Young Choi Hye, Youngja Jung, Miyeon Park, Sung Lim Yu, Kwhanmien Kim, Mog Shim Young, Chul Kim Byung, Kyusang Lee, Nam Huh, Christopher Ko, Kyunghee Park, Won Lee JaeSoo Choi Yong, Jhingook Kim

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Abstract

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.

Original languageEnglish
Pages (from-to)7397-7404
Number of pages8
JournalClinical Cancer Research
Volume14
Issue number22
DOIs
StatePublished - 15 Nov 2008

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Non-Small Cell Lung Carcinoma
Gene Expression
Recurrence
Survival
Genes
Real-Time Polymerase Chain Reaction
Lung Neoplasms
Research Design
Research

Cite this

Lee, Eung Sirk ; Son, Dae Soon ; Kim, Sung Hyun ; Lee, Jinseon ; Jo, Jisuk ; Han, Joungho ; Kim, Heesue ; Hyun, Joo Lee ; Hye, Young Choi ; Jung, Youngja ; Park, Miyeon ; Yu, Sung Lim ; Kim, Kwhanmien ; Young, Mog Shim ; Byung, Chul Kim ; Lee, Kyusang ; Huh, Nam ; Ko, Christopher ; Park, Kyunghee ; Jae, Won Lee ; Yong, Soo Choi ; Kim, Jhingook. / Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression. In: Clinical Cancer Research. 2008 ; Vol. 14, No. 22. pp. 7397-7404.
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title = "Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression",
abstract = "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.",
author = "Lee, {Eung Sirk} and Son, {Dae Soon} and Kim, {Sung Hyun} and Jinseon Lee and Jisuk Jo and Joungho Han and Heesue Kim and Hyun, {Joo Lee} and Hye, {Young Choi} and Youngja Jung and Miyeon Park and Yu, {Sung Lim} and Kwhanmien Kim and Young, {Mog Shim} and Byung, {Chul Kim} and Kyusang Lee and Nam Huh and Christopher Ko and Kyunghee Park and Jae, {Won Lee} and Yong, {Soo Choi} and Jhingook Kim",
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Lee, ES, Son, DS, Kim, SH, Lee, J, Jo, J, Han, J, Kim, H, Hyun, JL, Hye, YC, Jung, Y, Park, M, Yu, SL, Kim, K, Young, MS, Byung, CK, Lee, K, Huh, N, Ko, C, Park, K, Jae, WL, Yong, SC & Kim, J 2008, 'Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression', Clinical Cancer Research, vol. 14, no. 22, pp. 7397-7404. https://doi.org/10.1158/1078-0432.CCR-07-4937

Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression. / Lee, Eung Sirk; Son, Dae Soon; Kim, Sung Hyun; Lee, Jinseon; Jo, Jisuk; Han, Joungho; Kim, Heesue; Hyun, Joo Lee; Hye, Young Choi; Jung, Youngja; Park, Miyeon; Yu, Sung Lim; Kim, Kwhanmien; Young, Mog Shim; Byung, Chul Kim; Lee, Kyusang; Huh, Nam; Ko, Christopher; Park, Kyunghee; Jae, Won Lee; Yong, Soo Choi; Kim, Jhingook.

In: Clinical Cancer Research, Vol. 14, No. 22, 15.11.2008, p. 7397-7404.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression

AU - Lee, Eung Sirk

AU - Son, Dae Soon

AU - Kim, Sung Hyun

AU - Lee, Jinseon

AU - Jo, Jisuk

AU - Han, Joungho

AU - Kim, Heesue

AU - Hyun, Joo Lee

AU - Hye, Young Choi

AU - Jung, Youngja

AU - Park, Miyeon

AU - Yu, Sung Lim

AU - Kim, Kwhanmien

AU - Young, Mog Shim

AU - Byung, Chul Kim

AU - Lee, Kyusang

AU - Huh, Nam

AU - Ko, Christopher

AU - Park, Kyunghee

AU - Jae, Won Lee

AU - Yong, Soo Choi

AU - Kim, Jhingook

PY - 2008/11/15

Y1 - 2008/11/15

N2 - 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.

AB - 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.

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JF - Clinical Cancer Research

SN - 1078-0432

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ER -