Gene expression-based identification of prognostic markers in lung adenocarcinoma

Abstract

Introduction: Many studies have aimed at identifying additional prognostic tools to guide treatment choices and patient surveillance in lung cancer by assessing the expression of individual proteins through immunohistochemistry (IHC) or, more recently, through gene expression-based signatures. As a proof-of-concept, we used a multi-cohort, gene expression-based discovery and validation strategy to identify genes with prognostic potential in lung adenocarcinoma. The clinical applicability of this strategy was further assessed by evaluating a selection of the markers by IHC.

Materials and methods: Publicly available gene expression data sets from six microarray-based studies were divided into four discovery and two validation data sets. First, genes associated with overall survival (OS) in all four discovery data sets were identified. The prognostic potential of each identified gene was then assessed in the two validation data sets, and genes associated with OS in both data sets were considered as potential prognostic markers. Finally, IHC for selected potential prognostic markers was performed in two independent and clinically well-characterized lung cancer cohorts.

Results and conclusions: The gene expression-based strategy identified 19 genes with correlation to OS in all six data sets. Out of these genes, we selected Ki67, MCM4 and TYMS for further assessment with IHC. Although an independent prognostic ability of the selected markers could not be confirmed by IHC, this proof-of-concept study demonstrates that by employing a gene expression-based discovery and validation strategy, potential prognostic markers can be identified and further assessed by a technique universally applicable in the clinical practice. The concept of studying potential prognostic markers through gene expression-based strategies, with a subsequent evaluation of the clinical utility, warrants further exploration.

Fig 1. Schematic image of the gene expression-based strategy for identification of prognostic markers and subsequent IHC evaluation.

For each probe (matching to a gene) in the four discovery data sets, the median gene expression value was used to divide the samples into two groups (high/low). The log-rank test was employed to identify probes significantly associated with OS (P-value < 0.05). Results from the four discovery data sets were then compared and probes that were significantly associated with OS in all four data sets were tested in the same manner in two validation data sets. The genes significantly associated with OS in both data sets were classified as potential prognostic markers. Out of these genes, three were selected for IHC evaluation in two patient cohorts. One of the cohorts was used as an IHC discovery cohort were optimal cut-offs for each markers were selected. These cut-offs were then applied to the cases in the IHC validation cohort. * Only the U133 2plus array subset (n = 102) from Chitale et al. was included. Abbreviations: OS = overall survival, IHC = immunohistochemistry.

Fig 2. Spearman correlation of gene expression levels of the 19 candidate genes in the two validation data sets.

(A) Tomida et al. [15], (B) Tang et al [14]. If multiple probes were available for a gene then the probes with largest standard deviation was chosen to represent the gene. Area of the circles show the absolute value of corresponding correlation coefficients.

Fig 3. Prognostic value of Ki67 (A), MCM4 (B), TYMS (C), and combined score (D), on overall survival in the IHC discovery cohort.

Fig 4. Prognostic value of Ki67 (A), MCM4 (B), TYMS (C), and combined score (D), on overall survival in the IHC validation cohort.