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Using gene chips to predict relapse in lung cancer
  

 

A new study reports that lung cancer patients who relapsed after surgery could be distinguished from patients who did not based on patterns of gene expression. The risk of relapse correlated with the expression levels of certain genes in patients with non-small cell lung cancer. Though based on a small sample, the findings demonstrate that gene chips may soon have a role to play in guiding treatment strategies.


Detail of cluster diagram from study. View full

As larger lung cancer studies are completed, researchers will likely have gene expression profiles of individuals most likely to relapse. These patients could receive more aggressive therapy soon after surgery. The cancer reoccurs in more than half of all lung cancer patients treated by surgery.

The research, led by Ming Tsao of Princess Margaret Hospital and the University of Toronto, Canada, is the latest evidence that gene expression profiling can reveal clinically relevant information about disease. The expression profiling of childhood leukemias, for instance, has revealed new categories of disease, some of which are associated with distinct disease outcomes. In two studies on lung cancer published last fall, researchers concluded that gene expression analysis could help refine the classification of lung cancers and aid in their diagnosis.

In the new study, Tsao and colleagues profiled 39 patients using 19,000 gene microarrays. They analyzed a subset of nearly 3,000 genes to generate distinct expression patterns for two groups: 24 patients who relapsed and 15 who remained free of disease during the course of the study. The findings appear in Cancer Research.

"Some patients will relapse and some will not," says Tsao. "If you can demonstrate who is at high risk for recurrence they could be targeted to receive more aggressive treatment after their surgery, while the ones with a good prognosis should continue on without receiving therapy at that time."

The challenge now is to identify the expression profile of patients in the early stages of disease who have a poor prognosis. The researchers plan to develop a custom gene chip containing genes most affected by the disease.

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Wigle, D.A. et al. Molecular pofiling of non-small cell lung cancer and correlation with disease-free survival. Cancer Res 62, 3005-3008 (June 1, 2002).
 
Bhattacharjee, A. et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA 98, 13790-13795 (November 20, 2001).
 
Garber, M.E. et al. Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci USA 98, 13784-13789 (November 20, 2001).
 

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