Glioblastoma Cells Containing Mutations in the Cohesin Component <i>STAG2</i> Are Sensitive to PARP Inhibition
Bibliographic record
Abstract
Recent data have identified STAG2, a core subunit of the multifunctional cohesin complex, as a highly recurrently mutated gene in several types of cancer. We sought to identify a therapeutic strategy to selectively target cancer cells harboring inactivating mutations of STAG2 using two independent pairs of isogenic glioblastoma cell lines containing either an endogenous mutant STAG2 allele or a wild-type STAG2 allele restored by homologous recombination. We find that mutations in STAG2 are associated with significantly increased sensitivity to inhibitors of the DNA repair enzyme PARP. STAG2-mutated, PARP-inhibited cells accumulated in G2 phase and had a higher percentage of micronuclei, fragmented nuclei, and chromatin bridges compared with wild-type STAG2 cells. We also observed more 53BP1 foci in STAG2-mutated glioblastoma cells, suggesting that these cells have defects in DNA repair. Furthermore, cells with mutations in STAG2 were more sensitive than cells with wild-type STAG2 when PARP inhibitors were used in combination with DNA-damaging agents. These data suggest that PARP is a potential target for tumors harboring inactivating mutations in STAG2, and strongly recommend that STAG2 status be determined and correlated with therapeutic response to PARP inhibitors, both prospectively and retrospectively, in clinical trials.
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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) | 0.001 | 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".