Novel Lung Cancer Biomarkers Identified

By now, if you aren’t aware of the association between smoking and lung cancer, then it is possible you may have just awoken from some Van Winkle-esque spell, as tobacco smoking is the most well-known environmental risk factor associated with lung cancer.

Now, new evidence from investigators at Dartmouth College has cemented the interactions between genes and smoking, underscoring the fundamental role smoking plays in the etiology of lung cancer. Findings from the new study were published recently in Carcinogenesis in an article entitled “Genome-Wide Interaction Study of Smoking Behavior and Non-Small Cell Lung Cancer Risk in Caucasian Population.”

In the current study, three novel single-nucleotide polymorphisms (SNPs), or variations in the DNA that underlie our susceptibility to developing disease, were identified in the interaction analysis, including two SNPs for non-small-cell lung cancer (NSCLC) risk and one SNP for squamous cell lung cancer risk. The three identified novel SNPs provide potential candidate biomarkers for lung cancer risk screening and intervention.

“Genome-wide interaction scanning remains a challenge, as most genome-wide association studies are designed for main-effect association analysis and have limited power for interaction analysis,” explained lead study investigator Yafang Li, Ph.D., instructor in biomedical data science at Dartmouth. “This study is by far the largest genome-wide SNP–smoking interaction analysis reported for lung cancer. We also adopted a two-step strategy in the analysis to reduce the power loss from ordinary gene-environment interaction analysis.”

The three SNPs, identified in the team’s study, stratify lung cancer risk by smoking behavior. These three SNPs can be potential biomarkers used to improve the precision to which researchers can categorize an individual’s risk of lung cancer disease by smoking behavior, which are helpful for individualized prognosis and prediction of treatment plan.

“We adopted a two-step analysis strategy in the discovery stage: we first conducted a case-only interaction analysis to assess the relationship between SNPs and smoking behavior using 13,336 NSCLC cases,” the authors wrote. “Candidate SNPs with p-value less than 0.001 were further analyzed using a standard case-control interaction analysis including 13970 controls.”

The authors continued, stating that “the significant SNPs with p-value less than 3.5×10^–5 (correcting for multiple tests) from the case-control analysis in the discovery stage were further validated using an independent replication dataset comprising 5377 controls and 3054 NSCLC cases. We further stratified the analysis by histological subtypes. Two novel SNPs, rs6441286 and rs17723637, were identified for overall lung cancer risk. The interaction odds ratio and meta-analysis p-value for these two SNPs were 1.24 with 6.96×10^–7 and 1.37 with 3.49×10^–7, respectively. Additionally, the interaction of smoking with rs4751674 was identified in squamous cell lung carcinoma with an odds ratio of 0.58 and p-value of 8.12×10^–7.”

While this reported study was restricted to a Caucasian population, and the results may not be generalized to other ethnicities because of the different genetic backgrounds, the team aims to further test the identified interaction effect using genotypes from other populations.

“The limited overlap between discovery genotype and replication genotype may have reduced the power in our validation study,” concluded Dr. Li. “We believe as more genotype data becomes available in the future we can discover more important gene–smoking interactions in lung cancer disease.”

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