Effects of gemcitabine on APE/ref-1 endonuclease activity in pancreatic cancer cells, and the therapeutic potential of antisense oligonucleotides
Bibliographic record
Abstract
Apurinic/apyrimidinic endonuclease (APE) is a key enzyme involved in DNA base excision repair (BER) that is often expressed at elevated levels in human cancers. Pancreatic cancer cells treated with the nucleoside analogue gemcitabine (2', 2'-difluoro-2'deoxycytidine) showed increases in APE/redox effector factor (ref-1) protein levels (approximately two-fold for Panc-1 and six-fold for MiaPaCa-2), with corresponding increases in endonuclease activity. These results suggested that the activation of APE/ref-1 might be an adaptive response that contributes to gemcitabine resistance by facilitating BER. To test this hypothesis, we examined the effects of disrupting APE/ref-1 using antisense on gemcitabine toxicity. Antisense oligonucleotides decreased protein levels three-fold in MiaPaCa-2 and five-fold in Panc-1 in comparison to controls, associated with reduced endonuclease activity. Combination treatments with antisense oligonucleotides and gemcitabine partially suppressed the increase in APE/ref-1 activity seen in cells exposed to gemcitabine alone. While clonogenic assays showed only slight decreases in colony formation in cells treated with either antisense oligonucleotides or gemcitabine alone, the combination with APE/ref-1 antisense resulted in a 2-log enhancement of gemcitabine toxicity in Panc-1 cells. Overall these findings suggest that APE/ref-1 plays a significant role in gemcitabine resistance in some pancreatic cancer cells, and support the further investigation of novel treatments that target this protein.
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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 | 0.000 |
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".