Targeting of Lung Cancer Mutational Hotspots by Polycyclic Aromatic Hydrocarbons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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) | 0.000 | 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 it