A study released on Wednesday has revealed that scientists can now identify which cigarette smokers run the highest risk of developing lung cancer using a new genetic-based approach.
Researchers from Boston University School of Medicine and the University of Utah said they used "a genomic approach to prevent lung cancer in these individuals and to personalize cancer chemoprophylaxis and therapy."
While 10 to 20 percent of smokers develop lung cancer in their lifetime, there have been no tools available to identify which of the approximately 90 million current and former smokers in the United States are at the highest risk, the researchers said.
Usually the diagnosis is made at a very advanced stage where treatment is largely ineffective.
Study lead author Avrum Spira said the new method relies on a gene expression-based biomarker that distinguishes smokers with and without lung cancer.
The scientists located a "cancer-related pathway" called PI3K, activated in the cells that line the airway of smokers who have lung cancer.
"This finding is significant as these cells can be obtained in a relatively non-invasive fashion from the airway of smokers at risk for lung cancer, and does not require invasive sampling of lung tissue where lung tumors normally arise," said Spira, who is an associate professor of medicine and pathology at Boston University.
The data also suggested that by measuring the gene expression activity, doctors can help determine which specific cancer pathways have been deregulated within an individual smoker, allowing one to tailor a specific drug to reduce that individual's risk of lung cancer.
"This represents a critical advance in the field of lung cancer prevention as there are currently no effective strategies for lung cancer prevention among high risk smokers," Spira said.
The researchers said the work could "help address the enormous and growing public health burden associated with lung cancer, the leading cause of cancer-related death among men and women in the US and the world."
The findings appear in the April 7 issue of Science Translational Medicine.