APM2 is a novel mediator of cisplatin resistance in a variety of cancer cell types regardless of p53 or MMR status
Bibliographic record
Abstract
Cisplatin is one of the most widely used chemotherapeutics in the world today. Unfortunately, chemoresistance often develops hindering the effectiveness of the drug. Mismatch-repair (MMR) and p53 have previously been shown to be important determinants of cisplatin resistance and can contribute to cisplatin resistance clinically. Here, we have used cDNA microarray to identify several genes as up or downregulated in a previously described, cisplatin resistant, clone of the HCT116 cell line (HCT116-K). On follow-up, one gene, APM2, was found to promote cisplatin resistance when overexpressed in sensitive HCT116 clones. Furthermore, silencing APM2 in a panel of cell lines encompassing all combinations of p53 status and MMR proficiency (HCT116-K, HCT116, SW620, MCF7, PC-3 and OV2008) resulted in sensitization regardless of these 2 factors. In addition, silencing APM2 stably using shRNA also resulted in the sensitization of cells to cisplatin. More importantly, cisplatin inhibited the growth of APM2 silenced tumor xenografts (HCT116-K or OV2008 cells) significantly better than it inhibited the growth of xenografts carrying nontargeting control shRNAs. These findings represent a novel strategy that could be exploited to overcome cisplatin resistance in patients regardless of p53 status or ability to perform MMR.
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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".