Cooperation between the Hippo and MAPK pathway activation drives acquired resistance to TEAD inhibition
Bibliographic record
Abstract
TEAD (transcriptional enhanced associate domain) transcription factors (TEAD1-4) serve as the primary effectors of the Hippo signaling pathway in various cancers. Targeted therapy leads to the emergence of resistance and the underlying mechanism of resistance to TEAD inhibition in cancers is less characterized. We uncover that upregulation of the AP-1 (activator protein-1) transcription factors, along with restored YAP (yes-associated protein) and TEAD activity, drives resistance to GNE-7883, a pan-TEAD inhibitor. Acute GNE-7883 treatment abrogates YAP-TEAD binding and attenuates FOSL1 (FOS like 1) activity. TEAD inhibitor resistant cells restore YAP and TEAD chromatin occupancy, acquire additional FOSL1 binding and exhibit increased MAPK (mitogen-activated protein kinase) pathway activity. FOSL1 is required for the chromatin binding of YAP and TEAD. This study describes a clinically relevant interplay between the Hippo and MAPK pathway and highlights the key role of MAPK pathway inhibitors in mitigating resistance to TEAD inhibition in Hippo pathway dependent cancers. The underlying mechanism of acquired resistance to targeted therapy in cancer remains to be explored. Here, the authors show that upregulation of the FOSL1 transcription factor restores YAP/TEAD occupancy on chromatin to drive resistance to GNE-7883, an allosteric TEAD inhibitor.
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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 | 0.000 |
| 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".