Single-nucleotide-resolution genomic maps of <i>O</i> 6-methylguanine from the glioblastoma drug temozolomide
Bibliographic record
Abstract
Temozolomide kills cancer cells by forming O6-methylguanine (O6-MeG), which leads to cell cycle arrest and apoptosis. However, O6-MeG repair by O6-methylguanine-DNA methyltransferase (MGMT) contributes to drug resistance. Characterizing genomic profiles of O6-MeG could elucidate how O6-MeG accumulation is influenced by repair, but there are no methods to map genomic locations of O6-MeG. Here, we developed an immunoprecipitation- and polymerase-stalling-based method, termed O6-MeG-seq, to locate O6-MeG across the whole genome at single-nucleotide resolution. We analyzed O6-MeG formation and repair across sequence contexts and functional genomic regions in relation to MGMT expression in a glioblastoma-derived cell line. O6-MeG signatures were highly similar to mutational signatures from patients previously treated with temozolomide. Furthermore, MGMT did not preferentially repair O6-MeG with respect to sequence context, chromatin state or gene expression level, however, may protect oncogenes from mutations. Finally, we found an MGMT-independent strand bias in O6-MeG accumulation in highly expressed genes. These data provide high resolution insight on how O6-MeG formation and repair are impacted by genome structure and nucleotide sequence. Further, O6-MeG-seq is expected to enable future studies of DNA modification signatures as diagnostic markers for addressing drug resistance and preventing secondary cancers.
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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) | 0.001 | 0.001 |
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".