Molecular epidemiology of EGFR and KRAS mutations in 3,026 lung adenocarcinomas: higher susceptibility of women to smoking-related KRAS-mutant cancers

Abstract

Purpose: The molecular epidemiology of most EGFR and KRAS mutations in lung cancer remains unclear.

Experimental design: We genotyped 3,026 lung adenocarcinomas for the major EGFR (exon 19 deletions and L858R) and KRAS (G12, G13) mutations and examined correlations with demographic, clinical, and smoking history data.

Results: EGFR mutations were found in 43% of never smokers and in 11% of smokers. KRAS mutations occurred in 34% of smokers and in 6% of never smokers. In patients with smoking histories up to 10 pack-years, EGFR predominated over KRAS. Among former smokers with lung cancer, multivariate analysis showed that, independent of pack-years, increasing smoking-free years raise the likelihood of EGFR mutation. Never smokers were more likely than smokers to have KRAS G > A transition mutation (mostly G12D; 58% vs. 20%, P = 0.0001). KRAS G12C, the most common G > T transversion mutation in smokers, was more frequent in women (P = 0.007) and these women were younger than men with the same mutation (median 65 vs. 69, P = 0.0008) and had smoked less.

Conclusions: The distinct types of KRAS mutations in smokers versus never smokers suggest that most KRAS-mutant lung cancers in never smokers are not due to second-hand smoke exposure. The higher frequency of KRAS G12C in women, their younger age, and lesser smoking history together support a heightened susceptibility to tobacco carcinogens.

Figure 1

Age distribution of EGFR exon 19 del and EGFR L858R. Patients with EGFR L858R mutant tumors presented at older age than those harboring EGFR exon 19 del (median age 68 vs. 64; p=8.1×10⁻⁵). Fisher exact test, P value <0.01 is considered significant.

Figure 2

(A) Frequency of EGFR and KRAS mutations by smoking history. (B) Frequency of EGFR and KRAS mutations by pack-years of smoking. In the range of up to 10 pack-years, tumors with EGFR mutations are still more common than KRAS mutations.

Figure 2

(A) Frequency of EGFR and KRAS mutations by smoking history. (B) Frequency of EGFR and KRAS mutations by pack-years of smoking. In the range of up to 10 pack-years, tumors with EGFR mutations are still more common than KRAS mutations.

Figure 3

KRAS mutation type as a function of smoking history. (A) KRAS G12D is the most common mutation in never smokers and KRAS G12C is the most frequent mutation among former and current smokers. (B) Never smokers are significantly more likely to have G>A transition mutation (p<0.0001). G>T transversion is the most common nucleotide change in former and current smokers (p<0.0001). (C) KRAS G12C was relatively more frequent in women than in men (p=0.007). Fisher exact test, P value <0.01 is considered significant.

Figure 3

KRAS mutation type as a function of smoking history. (A) KRAS G12D is the most common mutation in never smokers and KRAS G12C is the most frequent mutation among former and current smokers. (B) Never smokers are significantly more likely to have G>A transition mutation (p<0.0001). G>T transversion is the most common nucleotide change in former and current smokers (p<0.0001). (C) KRAS G12C was relatively more frequent in women than in men (p=0.007). Fisher exact test, P value <0.01 is considered significant.

Figure 3

KRAS mutation type as a function of smoking history. (A) KRAS G12D is the most common mutation in never smokers and KRAS G12C is the most frequent mutation among former and current smokers. (B) Never smokers are significantly more likely to have G>A transition mutation (p<0.0001). G>T transversion is the most common nucleotide change in former and current smokers (p<0.0001). (C) KRAS G12C was relatively more frequent in women than in men (p=0.007). Fisher exact test, P value <0.01 is considered significant.

Figure 4

Development of a nomogram including clinical variables and smoking history data for prediction of EGFR mutant status among Caucasian smokers (current or former). Mark the Smoking-free years on the axis and draw vertical line up to the Points axis to determine the number of points. Repeat the same for Pack-years, Gender and Age and sum the total points for all four variables. Plot the given number on the Total Points axis and draw a vertical line down to the Probability of EGFR mutation.