Docetaxel‐induced alterations in cellular RNA in ovarian cancer cells
Bibliographic record
Abstract
A major obstacle to the ablation of tumors using chemotherapy is resistance to anti‐cancer agents. Recently, the relationship between tumor characteristics and treatment response was assessed in a phase I/II clinical trial (Breast Cancer Res Treat, 119: 347) for advanced breast cancer patients treated with epirubicin and docetaxel. This study reported significant, dose‐dependent reductions in tumor RNA integrity (RIN) values which correlated with response to treatment. Results from this study suggest the possible utility of RIN as a measure of clinical response to chemotherapy. The purpose of the present study is to assess chemotherapy‐dependent alterations in tumor RNA quantity and integrity in vitro. Cells were plated and treated with docetaxel concentrations from 0.001–40 μM for 24 hr, to determine the effect on RNA content and integrity, measured using an Agilent Bioanalyzer 2100. RNA content increased per cell (p<0.05) while RIN did not change significantly in this range. Interestingly, discrete bands consistently appeared in the rRNA banding pattern at 0.005 μM, peaking at 0.2 μM DXL, just below the 28s and 18s rRNA bands. In contrast, docetaxel‐resistant A2780 cells did not display similar changes upon treatment, indicating that changes in tumor cell RNA content and integrity could be used to monitor response to docetaxel. Research funded by Northern Ontario School of Medicine and RNA Diagnostics Inc.
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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 | 0.000 |
| 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".