Test on existence of histology subtype-specific prognostic signatures among early stage lung adenocarcinoma and squamous cell carcinoma patients using a Cox-model based filter.
Suyan Tian, Chi Wang, Ming-Wen An
Author Information
Suyan Tian: Division of Clinical Epidemiology, First Hospital of Jilin University, 71Xinmin Street, Changchun, Jilin, 130021, China. stian@rockefeller.edu.
Chi Wang: Department of Biostatistics and Markey Cancer Center, University of Kentucky, 800 Rose St., Lexington, KY, 40536, USA. chi.wang@uky.edu.
Ming-Wen An: Department of Mathematics, Vassar College, Poughkeepsie, NY, 12604, USA. mian@vassar.edu.
BACKGROUND: Non-small cell lung cancer (NSCLC) is the predominant histological type of lung cancer, accounting for up to 85% of cases. Disease stage is commonly used to determine adjuvant treatment eligibility of NSCLC patients, however, it is an imprecise predictor of the prognosis of an individual patient. Currently, many researchers resort to microarray technology for identifying relevant genetic prognostic markers, with particular attention on trimming or extending a Cox regression model. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two major histology subtypes of NSCLC. It has been demonstrated that fundamental differences exist in their underlying mechanisms, which motivated us to postulate the existence of specific genes related to the prognosis of each histology subtype. RESULTS: In this article, we propose a simple filter feature selection algorithm with a Cox regression model as the base. Applying this method to real-world microarray data identifies a histology-specific prognostic gene signature. Furthermore, the resulting 32-gene (32/12 for AC/SCC) prognostic signature for early-stage AC and SCC samples has superior predictive ability relative to two relevant prognostic signatures, and has comparable performance with signatures obtained by applying two state-of-the art algorithms separately to AC and SCC samples. CONCLUSIONS: Our proposal is conceptually simple, and straightforward to implement. Furthermore, it can be easily adapted and applied to a range of other research settings.
References
J Clin Oncol. 2010 Oct 10;28(29):4417-24
[PMID: 20823422]
