Determining the Dependency of Spy1 based on Rb Status
Bibliographic record
Abstract
Determining the Dependency of Spy1 based on Rb Status Visconti, T., Philbin, N, Fifield, B, Porter L.A. University of Windsor, Windsor, Ontario. N9B 3P4 Porter Lab, Department of Integrative Biology/Biomedical Sciences Breast cancer is the second most common cancer worldwide and the most common cancer among women. Triple Negative Breast Cancer (TNBC) is a particularly aggressive form of breast cancer with many subtypes based on gene expression profiles. There are currently no targeted treatments for TNBC due to its molecular characteristics, urging the discovery of new therapeutic targets. Potential therapeutic avenues are the cell cycle and its mediators which play an important role in cancer formation and progression. Spy1, a cyclin-like protein, promotes cell proliferation through the G1/S and G2/M checkpoints. Spy1 promotes proliferation even in the presence of DNA damage, overriding checkpoints and increasing cancer susceptibility. While Spy1 has been found to be elevated in breast cancer, its unique binding structure makes for an ideal candidate for cell cycle inhibition therapy. The retinoblastoma tumor suppressor protein (Rb) is known to regulate the DNA damage response system and is key in regulating the cell cycle. However, studies have shown that Rb is often mutated in TNBC inducing deregulated cell cycle progression potentially leading to tumor development. For some breast cancer subtypes the presence or absence of Rb (Rb status) can dictate response to treatment by cell cycle inhibitor drugs. Using in vitroTNBC models (MDA-MB-231 & Bt549 cell lines), this study aims to determine if Spy1 can override checkpoints independently of Rb status, and if elevated levels of Spy1 alter this response. These results could provide further guidance in developing cell cycle inhibition targeted therapies and potentially better TNBC patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".