Comprendre la mutagenèse somatique grâce à la cartographie des dommages à l’ADN
Bibliographic record
Abstract
Somatic mutation theory explains how DNA damage can lead to the malignant transformation of cells. It therefore elucidates the connection between genotoxic agents and cancers. Mutational spectra, which tend to be characteristic of a cancer type, are available for certain genes like p53 which is frequently mutated in tumors. A mutational spectrum could therefore be the signature of the genotoxic agent(s) at the origin of the malignant transformation. Ligation-mediated PCR (LMPCR) is a genomic sequencing method that can be used for the mapping of DNA damage at nucleotide resolution. Such a mapping can then be compared to a mutational spectrum to test the hypothesis that implies one agent can cause mutations into one cancer type. LMPCR has been used this way to map DNA damage generated by different UV wavelengths. The frequently damaged sites following UVB irradiation correlate with the mutational spectrum of p53 in skin cancer. Similarly, BPDE, the activated form of the benzo[a]pyrene present in tobacco smoke, generates frequent adducts at sites corresponding to mutation hotspots of p53 in lung cancers. Still, the correlation between BPDE damage sites and p53 mutations is not perfect and this suggests a role of other genotoxic substances that are also present in tobacco smoke, such as the nitrosamine NNK. Finally, and beyond this objective of better understanding somatic mutagenesis, LMPCR is commonly used whenever DNA damage frequency and/or repair is to be investigated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".