Elsevier

Lung Cancer

Volume 60, Issue 3, June 2008, Pages 313-324
Lung Cancer

Review
Gene expression profiling of Non-small cell lung cancer

https://doi.org/10.1016/j.lungcan.2008.03.007Get rights and content

Summary

Functional genomics has emerged over the past 10 years as a novel technology to study genetic alterations. Gene expression arrays are one genomic technique employed to discover changes in the DNA expression that occur in neoplastic transformation. Microarrays have been applied to investigating lung cancer. Specific applications include discovering novel genetic changes that occur in lung tumors. Microarrays can also be applied to improve diagnosis, staging and discover prognostic markers. The eventual goal of this technology is to discover new markers for therapy and to customize therapy based on an individual tumor genetic composition. In this review, we present the current state of gene expression array technology in its application to lung cancer.

Introduction

Cancer is the second leading cause of death in the United States, second only to cardiovascular diseases. In 2007, one in four deaths was due to cancer. Approximately 1.4 million people were diagnosed with cancer, and over a half million people died from cancer last year [1]. The top five most common cancer-related deaths were due to lung, breast, prostate, colorectal and pancreatic cancer. Together, these five diseases accounted for over one-half of all cancer deaths in the United States in 2007 [1]. Lung cancer alone killed over 160,000 people, more than the other four diseases put together. The prognosis from lung cancer remains dismal at 15% 5-year survival across all stages.

Despite the epidemic amongst smokers, we still know very little about the disease and therapies remain dismal. We continue to have limited understanding of the pathophysiology of this disease and a lack of a diagnostic serum marker. Gene expression profiling promises to provide a more functional molecular understanding of this disease. This information will assist in both staging, understanding pathophysiology, prognostication and therapeutic decision trees. In this article, we review how gene expression arrays analyzing lung cancer is being used to advance our knowledge in all aspects of this disease.

Section snippets

Gene expression profiling

Genomics is the study of genomes and the complete collection of genes they contain [2]. Completed in 2003, the Human Genome Project (HGP) was a 13-year project coordinated by the U.S. Department of Energy and the National Institutes of Health to identify all the 40,000–50,000 genes in human DNA, and to determine the sequences of the 3 billion chemical base pairs that make up human DNA [3], [4]. The sequencing of the genome has given us the molecular blueprints for the genetic profile of human

Gene signatures that describe molecular alterations and cellular pathways

Overexpression of oncogenes, loss of tumor suppressor genes and amplification of chromosome copy number have all been associated with carcinogenesis. Historically, immunohistochemistry and blotting techniques have been used to examine gene expression in tumors. A large number of genes, pathways and chromosomal regions have already been associated with lung cancer [10]. Hundreds of studies have demonstrated increases in chromosomal copy numbers of 1p, 1q, 3q, 5p, 6p, 8q, 12, 17q, 19p, 19q, 20p,

Diagnostic biomarkers discovered by gene expression profiling

One arena which genomic technology has had some success is identifying gene expression patterns that differentiate and classify various types of cancers in the lung. When presented with unidentified tissue, our best method of diagnosis is histology and immunohistochemistry. Combined with clinical suspicion, a diagnosis is made. However, this approach has several limitations. One of the great strides of gene expression profiling is trying to solve diagnostic dilemmas.

One of the challenges facing

Gene signatures that improve pathological staging and molecular classification

Another application of gene array data is to improve classification of lung cancers. Lung cancer is classified into histological types such as small cell, squamous cell, adeno- or large cell carcinoma. Within these broad categories are some subdivisions, i.e. bronchioloalveolar carcinoma for adenocarcinoma. The World Health Organization classification of lung carcinomas represents our current differentiation of lung cancers. The therapy for different tumors and even subclasses of the same

Gene signatures that predict patient outcome

Another potential utility of classifying lung tumors is predicting which lung cancers have good versus poor clinical outcome. Clinically this could be used to determine which patients would benefit from aggressive therapies. Hundreds of studies have examined genetic changes using immunohistochemistry and RT-PCR to identify patients with tumors with a poor outcome [10]. However, no single study has found a gene that is clinically useful in lung cancer to predict prognosis. Gene expression

The future: genomics to treat lung cancer?

Surgery is the main therapeutic option for curative intent in lung cancer. However, less than 50% of patients with lung cancer present with resectable stages I–IIIA disease. In advanced disease, systemic chemotherapy prolongs survival. Combination of a platinum agent (carboplatin, cisplatin) with a cytotoxic agent (i.e. paclitaxel, docetaxel, gemcitabine) is currently the most accepted therapy. A full understanding of the molecular mechanisms in NSCLC could lead to a more effective therapy for

Conclusion

Genomics could serve as a powerful tool for the classification and analysis of lung cancer. It has the potential to provide new insights into this largely fatal disease. Though the prospects are exciting, functional genomics is still in its very early stages and has a number of potential limitations. Over time, it remains to be seen if gene expression profiling becomes a usable clinical tool. It clearly will have some application in the field of diagnosis and discovery of markers for

Conflict of interest statement

The authors wish to disclose they have no conflict of interests.

Acknowledgement

This work was supported by funding from the Emory Center for Respiratory Health and Emory-GT Center for Cancer Nanotechnology Excellence. The authors would like to thank Dr. Steven M. Albelda and Dr. Charles Powell for their valuable input into preparation of this manuscript

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