Abstract P5-08-14: Deregulated histone and cell cycle pathways are associated with anthracycline resistance in breast cancer
Bibliographic record
Abstract
Abstract Drug resistance in breast cancer is the major obstacle to a successful outcome following chemotherapy treatment. While a well-recognized mechanism of resistance involves upregulation of multidrug resistance (MDR) genes, the complexity and hierarchy of other non-MDR driven pathways are still largely unknown. The aim of this study was to identify pathways contributing to anthracycline resistance using isogenic drug resistant breast cancer cell lines. We generated MDA-MB-231, MCF7, SKBR3 and ZR-75-1 epirubicin-resistant breast cancer cell lines, all of which were cross-resistant to doxorubicin and SN-38; only SKBR3 cell line was also resistant to taxanes. Epirubicin-resistant cells were morphologically different from native cells, and had alterations in apoptosis and cell cycle profile. Using gene expression and small inhibitor analyses we identified deregulation of histone H2A and H2B genes in all four cell lines. These genes contribute to several biological pathways, which include cell cycle, chromosomal maintenance, epigenetics, RNA polymerase and mitochondrial transcription, as well as post-translational protein modifications. Importantly, histone deacetylase and cell cycle/DNA damage small molecule inhibitors reversed resistance and were cytotoxic for all four epirubicin-resistant cell lines confirming that histone and cell cycle pathways are associated with epirubicin resistance. Our study has established a model system for investigating drug resistance in all four breast cancer subtypes and revealed key pathways that contribute to the molecular mechanisms of anthracycline resistance. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P5-08-14.
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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) | 0.003 | 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".