Development and Validation of a Quantitative Real-Time Polymerase Chain Reaction Classifier for Lung Cancer Prognosis

Abstract

Purpose This prospective study aimed to develop a robust and clinically-applicable method to identify high-risk early stage lung cancer patients and then to validate this method for use in future translational studies. Patients and Methods Three published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model and random survival forest (RSF) analysis. A final set of 91 genes was selected and tested as a predictor of survival using a qRT-PCR-based assay utilizing an independent cohort of 101 lung adenocarcinomas. Results The RSF model built from 91 genes in the training set predicted patient survival in an independent cohort of 101 lung adenocarcinomas, with a prediction error rate of 26.6%. The mortality risk index (MRI) was significantly related to survival (Cox model p < 0.00001) and separated all patients into low, medium, and high-risk groups (HR = 1.00, 2.82, 4.42). The MRI was also related to survival in stage 1 patients (Cox model p = 0.001), separating patients into low, medium, and high-risk groups (HR = 1.00, 3.29, 3.77). Conclusions The development and validation of this robust qRT-PCR platform allows prediction of patient survival with early stage lung cancer. Utilization will now allow investigators to evaluate it prospectively by incorporation into new clinical trials with the goal of personalized treatment of lung cancer patients and improving patient survival.

Incorporating auxiliary information for improved prediction using combination of kernel machines

“…With continual development of genomic technologies, one consequence is that different data with biomarkers measured by different technologies are available. As a motivating example, we consider data from a lung cancer study in Chen et al [8]. One of the main scientific goals in Chen et al [8] focuses on predicting survival time in patients with lung cancer.…”

“…As a motivating example, we consider data from a lung cancer study in Chen et al [8]. One of the main scientific goals in Chen et al [8] focuses on predicting survival time in patients with lung cancer. Affymetrix gene expression data were obtained on 439 tumor samples.…”

EXPERIMENTAL CARDIOVASCULAR AND LUNG RESEARCH From mutation to methylation – molecular markers in lung cancer

“…Selected genes play a rolein different biological processes, the main ones including: the communication of signals, regulation of transcription, cell cycle, adhesion and proliferation of cells. Some of these genes have previously been identified as predictive markers: DUSP6 and ERBB3 [18], SLC2A1 and MEF2C, AKAP12, CYP24A1, CSTL, SLC2A1, GAPD [15,19].…”

“…The published results of research on gene expression in primary lung tumors samples (adenocarcinoma and squamous cell carcinoma), performed using microarray platforms (Affymetrix), helped identify 91 genes which may serve as survival indicators [19]. Selected genes play a rolein different biological processes, the main ones including: the communication of signals, regulation of transcription, cell cycle, adhesion and proliferation of cells.…”

“…Taking into consideration the fact that mutations in exons 18,19,20, and 21 take place in about 10-13% of Caucasian NSCLC patients [5,6], their therapeutic usefulness is significant. Further lung cancer research helped identify the EML4-ALK fusion gene.…”