The Influence of Administrative Timing in Triple-Negative Breast Cancer Treatments
Bibliographic record
Abstract
Breast cancer accounts for 25% of all cancers in Canadian women, and 15-20% of these are triple-negative breast cancers (TNBC), which have a poorer prognosis than other breast cancer subtypes. TNBC lacks expression of the estrogen receptor, progesterone receptor, and the human epidermal growth factor receptor 2 (HER2), which are common therapeutic targets in breast cancer. Due to the lack of target therapy, generalized chemotherapy treatments are used instead. The standard of care for treatment of TNBC instead consists of doxorubicin (A), cyclophosphamide (C) paclitaxel (T), and carboplatin (Carbo), that target various aspects of the cell cycle to induce cell cycle arrest. Pre-clinical models may be tested to determine how the administrative timing of ACT+Carbo may affect efficacy of treatments. The purpose of this study was to determine how the addition and timing of TNBC treatments influence cell cycle progression and how pre-clinical models can be used to optimize current ACT+Carbo treatments. MDA-MB-231 and MDA-MB-468 TNBC cells were treated with AC, T, TCarbo, or Carbo at various time points in vitro. Flow cytometry, trypan blue exclusion assay, and MTT were used to determine cell cycle progression, proliferation rate, and synergy. Casper zebrafish were used as an in vivo model. It was found that the combination pattern of T/TCarbo resulted in increased efficacy comparable to all other combinations via pre-clinical models. This information may be appliable to current TNBC treatments to improve efficacy, lower toxicity, and increase the 5-year survival rate of TNBC patients.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".