Integrative High-Throughput RNAi Screening Identifies BRSK1, STK32C and STK40 as Novel Activators of YAP/TAZ
Bibliographic record
Abstract
Disruption of the Hippo pathway leads to activation of the YAP/TAZ transcriptional program which promotes tumor initiation, progression and metastasis in diverse cancers. Aggressive triple-negative breast cancers (TNBC) lack an effective therapy; thus, inactivating YAP and TAZ has emerged as an attractive approach and a new treatment modality. Thus, we performed two complementary high-throughput RNAi-based kinome screens to uncover cancer-associated activators of YAP/TAZ in two TNBC cell lines, MDA-MB231 and MDA-MB468. Integrated analysis that combined a YAP/TAZ localization screen with a TEAD-luciferase reporter screen, identified novel regulators including BRSK1, STK32C and STK40. The AMPK family members NUAKs, MARKs and SIKs are known to inhibit the Hippo kinase cassette; here, we uncover BRSK1, another AMPK family member as a regulator of YAP/TAZ. We also reveal that two poorly studied kinases, STK32C, a member of the AGC family, and STK40, a pseudokinase, can also inhibit the activity of YAP/TAZ. Thus, our studies expand the repertoire of known AMPK family members and reveal two new kinases that modulate the Hippo pathway and may play a role in YAP/TAZ driven breast cancers. Further analysis of other screen hits may similarly uncover new regulators that could be targeted for therapeutic interventions.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".