Development of a New HiBiT Biosensor Monitoring Stability of YAP/TAZ Proteins in Cells
Bibliographic record
Abstract
The Hippo signaling cascade is frequently dysregulated in a variety of cancers, such as breast cancer (BC), which is one of the most commonly diagnosed malignancies in women. Among BC subtypes, triple-negative BC (TNBC) stands out due to its poor prognosis and high metastatic potential. Despite extensive research aimed at establishing treatment options, existing therapies demonstrate limited efficacy for TNBC. Recently, it has been recognized that targeting the core components of the Hippo pathway (YAP and its paralog TAZ) is a promising strategy for developing anti-cancer treatment. However, no YAP/TAZ inhibitors have been approved by the FDA as anti-TNBC treatments, and only a few compounds have been identified that directly affect YAP and TAZ activity and stability to enhance the prospect of innovative HiBiT biosensors for monitoring of YAP and TAZ in cells. Employing these biosensors, we conducted a small-scale drug screen involving 279 compounds, leading to the identification of several small molecule inhibitors (SMIs) capable of inducing YAP/TAZ degradation in diverse TNBC cell lines. It is worth noting that some drugs may indirectly affect the protein stability following prolonged treatment, and a shorter exposure can be included in the future to identify drug candidates with more direct effects. Nevertheless, our study introduces a novel approach for assessing YAP and TAZ levels, which can have significant implications for developing anti-TNBC targeted therapies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".