Imatinib Radiosensitizes Bladder Cancer by Targeting Homologous Recombination
Bibliographic record
Abstract
Radiotherapy is a major treatment modality used to treat muscle-invasive bladder cancer, with patient outcomes similar to surgery. However, radioresistance is a significant factor in treatment failure. Cell-free extracts of muscle-invasive bladder tumors are defective in nonhomologous end-joining (NHEJ), and this phenotype may be used clinically by combining radiotherapy with a radiosensitizing drug that targets homologous recombination, thereby sparing normal tissues with intact NHEJ. The response of the homologous recombination protein RAD51 to radiation is inhibited by the small-molecule tyrosine kinase inhibitor imatinib. Stable RT112 bladder cancer Ku knockdown (Ku80KD) cells were generated using short hairpin RNA technology to mimic the invasive tumor phenotype and also RAD51 knockdown (RAD51KD) cells to show imatinib's pathway selectivity. Ku80KD, RAD51KD, nonsilencing vector control, and parental RT112 cells were treated with radiation in combination with either imatinib or lapatinib, which inhibits NHEJ and cell survival assessed by clonogenic assay. Drug doses were chosen at approximately IC40 and IC10 (nontoxic) levels. Imatinib radiosensitized Ku80KD cells to a greater extent than RAD51KD or RT112 cells. In contrast, lapatinib radiosensitized RAD51KD and RT112 cells but not Ku80KD cells. Taken together, our findings suggest a new application for imatinib in concurrent use with radiotherapy to treat muscle-invasive bladder cancer. Cancer Res; 73(5); 1611-20. ©2012 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 | 0.000 | 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) | 0.002 | 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".