Bibliographic record
Abstract
Triple negative breast cancer (TNBC) refers to an invasive subset of breast cancer that lacks oestrogen receptors (ER), progesterone receptors (PR) and lacks amplification of HER2 [1].Thus, these patients cannot be treated with a targeted therapy and have poorer outcomes compared to patients with other subforms of breast cancer.p53 it is the most frequently mutated gene in human cancer.Approximately 80% of patients with TNBC carry a p53 mutation.Recently, arsenic trioxide (ATO) was found to reactivate mutant p53 and convert it back to its normal wild-type form [2]. The aim of this research was to test if ATO might be a new treatment for TNBC.The ability of ATO to inhibit cell proliferation was determined using MTT assays while induction of apoptosis was measured using flow cytometry.IC 50 values for growth inhibition across 10 breast cancer cell lines ranged from 0.297-2.99μM.Inhibition of proliferation was found to be independent of the cell line molecular subtype.No significant differences were found between IC 50 values for TN vs non-TN cell lines (p=0.597) or between contact vs structural p53 mutants (p=0.481).For all cell lines investigated, ATO induced significant levels of apoptosis at a concentration of 5 μM.Although our data are preliminary, we conclude that ATO is a potential new therapy for the treatment of p53 mutated cancer, including triple negative breast cancer.Since ATO is already approved for the treatment of acute promyelocytic leukaemia (APL), it should be straightforward to repurpose it for TNBC.I would like to express my gratitude to my supervisor Professor Joe Duffy and all in the breast cancer research group in St. Vincent's University Hospital.Thank you for all your encouragement and for giving me the opportunity to work with and learn from you.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.917 | 0.819 |
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".