Clinical impact of high-throughput gene expression studies in lung cancer

Abstract

Lung cancer is the leading cause of cancer death in the United States and the world. The high mortality rate results, in part, from the lack of effective tools for early detection and the inability to identify subsets of patients who would benefit from adjuvant chemotherapy or targeted therapies. The development of high-throughput genome-wide technologies for measuring gene expression, such as microarrays, have the potential to impact the mortality rate of lung cancer patients by improving diagnosis, prognosis, and treatment. This review will highlight recent studies using high-throughput gene expression technologies that have led to clinically relevant insights into lung cancer. The hope is that diagnostic and prognostic biomarkers that have been developed as part of this work will soon be ready for wide-spread clinical application and will have a dramatic impact on the evaluation of patients with suspect lung cancer, leading to effective personalized treatment regimens.

Figure 1

Overview of multi-gene biomarkers. A. While individual genes may show significantly different expression levels between patients in two disease states (e.g. healthy patients and patients with lung cancer), the distribution of expression levels for any single gene may overlap sufficiently that no single gene can function as an accurate marker of disease state. The histograms plot the number of individuals (y-axis) with a given expression level of each gene (x-axis) as a function of disease state. For simplicity, this example uses two disease states, but the principle is the same for multiple disease states or a continuous outcome such as survival. B. Hierarchical clustering of gene expression levels across genes that vary between disease states shows that in aggregate these genes are able to distinguish between the two disease states. The dendrogram at the top of the panel shows the degree of relatedness between samples. C. A critical step in creating a biomarker from the expression levels of multiple genes is combining the expression levels into a single metric that associates with disease status. In this example, we show how following Principal Component Analysis, the values of the first component (x-axis) distinguish between the two disease states. While here we illustrate the use of Principal Component Analysis, there are many methods for performing this sort of dimensionality reduction. D. The distribution of multi-gene biomarker scores as a function of disease state shows that compared with the distribution of expression levels in individual genes, the multi-gene biomarker can distinguish between the two disease states with a high degree of accuracy. E. When the expression levels of the genes in the biomarker are measured in a clinical sample, a prediction can be made as to the disease state of the patient from which the sample was obtained based on the biomarker score.