Data from <i>PTEN</i> Deletion in Prostate Cancer Cells Does Not Associate with Loss of RAD51 Function: Implications for Radiotherapy and Chemotherapy
Bibliographic record
Abstract
Abstract Purpose:PTEN deletions in prostate cancer are associated with tumor aggression and poor outcome. Recent studies have implicated PTEN as a determinant of homologous recombination (HR) through defective RAD51 function. Similar to BRCA1/2-defective tumor cells, PTEN-null prostate and other cancer cells have been reported to be sensitive to PARP inhibitors (PARPi). To date, no direct comparison between PTEN and RAD51 expression in primary prostate tumors has been reported. Experimental Design: Prostate cancer cell lines and xenografts with known PTEN status (22RV1-PTEN+/+, DU145-PTEN+/−, PC3-PTEN−/−) and H1299 and HCT116 cancer cells were used to evaluate how PTEN loss affects RAD51 expression and PARPi sensitivity. Primary prostate cancers with known PTEN status were analyzed for RAD51 expression. Results:PTEN status is not associated with reduced RAD51 mRNA or protein expression in primary prostate cancers. Decreased PTEN expression did not reduce RAD51 expression or clonogenic survival following PARPi among prostate cancer cells that vary in TP53 and PTEN. PARPi sensitivity instead associated with a defect in MRE11 expression. PTEN-deficient cells had only mild PARPi sensitivity and no loss of HR or RAD51 recruitment. Clonogenic cell survival following a series of DNA damaging agents was variable: PTEN-deficient cells were sensitive to ionizing radiation, mitomycin-C, UV, H2O2, and methyl methanesulfonate but not to cisplatin, camptothecin, or paclitaxel. Conclusions: These data suggest that the relationship between PTEN status and survival following DNA damage is indirect and complex. It is unlikely that PTEN status will be a direct biomarker for HR status or PARPi response in prostate cancer clinical trials. Clin Cancer Res; 18(4); 1015–27. ©2011 AACR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".