THE STABILIZATAION OF C-MYC BY THE NOVEL CELL CYCLE REGULATOR, SPY1, DECREASES EFFICACY OF BREAST CANCER TREATMENTS
Bibliographic record
Abstract
Abstract Purpose c-Myc is frequently upregulated in breast cancers, however, targeting c-Myc has proven to be a challenge. Targeting of downstream mediators of c-Myc, such as the ‘cyclin-like’ cell cycle regulator Spy1, may be a viable therapeutic option in a subset of breast cancer subtypes. Methods Mouse mammary tumour cells isolated from MMTV-Myc mice and human breast cancer cell lines were used to manipulate Spy1 levels followed by tamoxifen or chemotherapeutic treatment with a variety of endpoints. Patient samples from TNBC patients were obtained and constructed into a TMA and stained for c-Myc and Spy1 protein levels. Results Over time, MMTV-Myc cells show a decreased response to tamoxifen treatment with increasing levels of Spy1 in the tamoxifen-resistant cells. shRNA against Spy1 re-establishes tamoxifen sensitivity. Spy1 was found to be highly elevated in human TNBC cell and patient samples, correlating to c-Myc protein levels. c-Myc was found to be stabilized by Spy1 and knocking down Spy1 in TNBC cells shows a significant increase in response to chemotherapy treatments. Conclusions Understanding the interplay between protein expression level and response to treatment is a critical factor in developing novel treatment options for breast cancer patients. These data have shown a connection between Spy1 and c-Myc protein levels in more aggressive breast cancer cells and patient samples. Furthermore, targeting c-Myc has proven difficult, these data suggest targeting Spy1 even when c-Myc is elevated can confer an advantage to current chemotherapies.
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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) | 0.002 | 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".