RAD51C-Deficient Cancer Cells Are Highly Sensitive to the PARP Inhibitor Olaparib
Bibliographic record
Abstract
A PARP inhibitor is a rationally designed targeted therapy for cancers with impaired DNA repair abilities. RAD51C is a paralog of RAD51 that has an important role in the DNA damage response. We found that cell lines sensitive to a novel oral PARP inhibitor, olaparib, had low levels of RAD51C expression using microarray analysis, and we therefore hypothesized that low expression of RAD51C may hamper the DNA repair process, resulting in increased sensitivity to olaparib. Compared with the cells with normal RAD51C expression levels, RAD51C-deficient cancer cells were more sensitive to olaparib, and a higher proportion underwent cell death by inducing G2-M cell-cycle arrest and apoptosis. The restoration of RAD51C in a sensitive cell line caused attenuation of olaparib sensitivity. In contrast, silencing of RAD51C in a resistant cell line enhanced the sensitivity to olaparib, and the number of RAD51 foci decreased with ablated RAD51C expression. We also found the expression of RAD51C was downregulated in cancer cells due to epigenetic changes and RAD51C expression was low in some gastric cancer tissues. Furthermore, olaparib significantly suppressed RAD51C-deficient tumor growth in a xenograft model. In summary, RAD51C-deficient cancer cells are highly sensitive to olaparib and offer preclinical proof-of-principle that RAD51C deficiency may be considered a biomarker for predicting the antitumor effects of olaparib.
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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 | 0.001 |
| 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".