Targeting of Lung Cancer Mutational Hotspots by Polycyclic Aromatic Hydrocarbons
Bibliographic record
Abstract
BACKGROUND: Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in combustion products of organic matter, including cigarette smoke. Metabolically activated diol epoxides of these compounds, including benzo[a]pyrene diol epoxide (B[a]PDE), have been suggested as causative agents in the development of lung cancer. We previously mapped the distribution of B[a]PDE adducts within the p53 tumor suppressor gene (also known as TP53), which is mutated in 60% of human lung cancers, and found that B[a]PDE adducts preferentially form at lung cancer mutational hotspots (codons 154, 157, 158, 245, 248, and 273). Other PAHs may be important in lung cancer as well. METHODS: Here we have mapped the distribution of adducts induced by diol epoxides of additional PAHs: chrysene (CDE), 5-methylchrysene (5-MCDE), 6-methylchrysene (6-MCDE), benzo[c]phenanthrene (B[c]PDE), and benzo[g]chrysene (B[g]CDE) within exons 5, 7, and 8 of the p53 gene in human bronchial epithelial cells. RESULTS: CDE exposure produced only low levels of adducts. Exposure of cells to the other activated PAHs resulted in DNA damage patterns similar to those previously observed with B[a]PDE but with some distinct differences. 5-MCDE, 6-MCDE, B[g]CDE, and B[c]PDE efficiently induced adducts at guanines within codons 154, 156, 157, 158, and 159 of exon 5, codons 237, 245 and 248 of exon 7, and codon 273 of exon 8, but the relative levels of adducts at each site varied for each compound. B[g]CDE, B[c]PDE, and 5-MCDE induced damage at codon 158 more selectively than 6-MCDE or B[a]PDE. The sites most strongly involved in PAH adduct formation were also the sites of highest mutation frequency (codons 157, 158, 245, 248, and 273). CONCLUSION: The data suggest that PAHs contribute to the mutational spectrum in human lung cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".