Novel Association of Genetic Markers Affecting CYP2A6 Activity and Lung Cancer Risk
Bibliographic record
Abstract
Abstract Metabolism of nicotine by cytochrome P450 2A6 (CYP2A6) is a suspected determinant of smoking dose and, consequently, lung cancer risk. We conducted a genome-wide association study (GWAS) of CYP2A6 activity, as measured by the urinary ratio of trans-3′-hydroxycotinine and its glucuronide conjugate over cotinine (total 3HCOT/COT), among 2,239 smokers in the Multiethnic Cohort (MEC) study. We identified 248 CYP2A6 variants associated with CYP2A6 activity (P < 5 × 10−8). CYP2A6 activity was correlated (r = 0.32; P < 0.0001) with total nicotine equivalents (a measure of nicotine uptake). When we examined the effect of these variants on lung cancer risk in the Transdisciplinary Research in Cancer of the Lung (TRICL) consortium GWAS dataset (13,479 cases and 43,218 controls), we found that the vast majority of these individual effects were directionally consistent and associated with an increased lung cancer risk. Two hundred and twenty-six of the 248 variants associated with CYP2A6 activity in the MEC were available in TRICL. Of them, 81% had directionally consistent risk estimates, and six were globally significantly associated with lung cancer. When conditioning on nine known functional variants and two deletions, the top two SNPs (rs56113850 in MEC and rs35755165 in TRICL) remained significantly associated with CYP2A6 activity in MEC and lung cancer in TRICL. The present data support the hypothesis that a greater CYP2A6 activity causes smokers to smoke more extensively and be exposed to higher levels of carcinogens, resulting in an increased risk for lung cancer. Although the variants identified in these studies may be used as risk prediction markers, the exact causal variants remain to be identified. Cancer Res; 76(19); 5768–76. ©2016 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".