Low ATM protein expression and depletion of p53 correlates with olaparib sensitivity in gastric cancer cell lines
Bibliographic record
Abstract
Small-molecule inhibitors of poly (ADP-ribose) polymerase (PARP) have shown considerable promise in the treatment of homologous recombination (HR)-defective tumors, such as BRCA1- and BRCA2-deficient breast and ovarian cancers. We previously reported that mantle cell lymphoma cells with deficiency in ataxia telangiectasia mutated (ATM) are sensitive to PARP-1 inhibitors in vitro and in vivo. Here, we report that PARP inhibitors can potentially target ATM deficiency arising in a solid malignancy. We show that ATM protein expression varies between gastric cancer cell lines, with NUGC4 having significantly reduced protein levels. Significant correlation was found between ATM protein expression and sensitivity to the PARP inhibitor olaparib, with NUGC4 being the most sensitive. Moreover, reducing ATM kinase activity using a small-molecule inhibitor (KU55933) or shRNA-mediated depletion of ATM protein enhanced olaparib sensitivity in gastric cancer cell lines with depletion or inactivation of p53. Our results demonstrate that ATM is a potential predictive biomarker for PARP-1 inhibitor activity in gastric cancer harboring disruption of p53, and that combined inhibition of ATM and PARP-1 is a rational strategy for expanding the utility of PARP-1 inhibitors to gastric cancer with p53 disruption.
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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 | 0.001 | 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 | 0.001 |
| 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".