Clinical Research Papers:

Generating a robust prediction model for stage I lung adenocarcinoma recurrence after surgical resection

Yu-Chung Wu _, Nien-Chih Wei, Jung-Jyh Hung, Yi-Chen Yeh, Li-Jen Su, Wen-Hu Hsu and Teh-Ying Chou

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Oncotarget. 2017; 8:79712-79721. https://doi.org/10.18632/oncotarget.19161

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Yu-Chung Wu1,2,*, Nien-Chih Wei3,*, Jung-Jyh Hung1,2, Yi-Chen Yeh4,5, Li-Jen Su6, Wen-Hu Hsu1,2 and Teh-Ying Chou4,5,*

1Division of Thoracic Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan

2Department of Surgery, School of Medicine, National Yang-Ming University, Taipei, Taiwan

3Auspex Diagnostics, Taipei, Taiwan

4Division of Molecular Pathology, Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan

5Institute of Clinical Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan

6Core Facilities for High Throughput Experimental Analysis, Institute of Systems Biology and Bioinformatics, National Central University, Jhong-Li, Taiwan

*These authors contributed equally to this work

Correspondence to:

Yu-Chung Wu, email: [email protected]

Keywords: lung adenocarcinoma, recurrence, prediction model, data aggregation, adjuvant therapy

Received: February 06, 2017     Accepted: June 28, 2017     Published: July 11, 2017


Lung cancer mortality remains high even after successful resection. Adjuvant treatment benefits stage II and III patients, but not stage I patients, and most studies fail to predict recurrence in stage I patients. Our study included 211 lung adenocarcinoma patients (stages I–IIIA; 81% stage I) who received curative resections at Taipei Veterans General Hospital between January 2001 and December 2012. We generated a prediction model using 153 samples, with validation using an additional 58 clinical outcome-blinded samples. Gene expression profiles were generated using formalin-fixed, paraffin-embedded tissue samples and microarrays. Data analysis was performed using a supervised clustering method. The prediction model generated from mixed stage samples successfully separated patients at high vs. low risk for recurrence. The validation tests hazard ratio (HR = 4.38) was similar to that of the training tests (HR = 4.53), indicating a robust training process. Our prediction model successfully distinguished high- from low-risk stage IA and IB patients, with a difference in 5-year disease-free survival between high- and low-risk patients of 42% for stage IA and 45% for stage IB (p < 0.05). We present a novel and effective model for identifying lung adenocarcinoma patients at high risk for recurrence who may benefit from adjuvant therapy. Our prediction performance of the difference in disease free survival between high risk and low risk groups demonstrates more than two fold improvement over earlier published results.

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