Single-nucleotide-resolution genomic maps of <i>O</i> <sup>6</sup> -methylguanine from the glioblastoma drug temozolomide
Bibliographic record
Abstract
ABSTRACT Temozolomide kills cancer cells by forming O 6 -methylguanine ( O 6 -MeG), which leads to apoptosis due to mismatch-repair overload. However, O 6 -MeG repair by O 6 -methylguanine-DNA methyltransferase (MGMT) contributes to drug resistance. Characterizing genomic profiles of O 6 -MeG could elucidate how O 6 -MeG accumulation is influenced by repair, but there are no methods to map genomic locations of O 6 -MeG. Here, we developed an immunoprecipitation- and polymerase-stalling-based method, termed O 6 -MeG-seq, to locate O 6 -MeG across the whole genome at single-nucleotide resolution. We analyzed O 6 -MeG formation and repair with regards to sequence contexts and functional genomic regions in glioblastoma-derived cell lines and evaluated the impact of MGMT. O 6 -MeG signatures were highly similar to mutational signatures from patients previously treated with temozolomide. Furthermore, MGMT did not preferentially repair O 6 -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 O 6 -MeG accumulation in highly expressed genes, suggesting an additional transcription-associated contribution to its repair. These data provide high resolution insight on how O 6 -MeG formation and repair is impacted by genome structure and regulation. Further, O 6 -MeG-seq is expected to enable future studies of DNA modification signatures as diagnostic markers for addressing drug resistance and preventing secondary cancers. GRAPHICAL ABSTRACT
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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 | 0.000 |
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".