YAP/TAZ mediates resistance to KRAS inhibitors through inhibiting proapoptosis and activating the SLC7A5/mTOR axis
Bibliographic record
Abstract
KRAS mutations are frequent in various human cancers. The development of selective inhibitors targeting KRAS mutations has opened a new era for targeted therapy. However, intrinsic and acquired resistance to these inhibitors remains a major challenge. Here, we found that cancer cells resistant to KRAS G12C inhibitors also display cross-resistance to other targeted therapies, such as inhibitors of RTKs or SHP2. Transcriptomic analyses revealed that the Hippo-YAP/TAZ pathway is activated in intrinsically resistant and acquired-resistance cells. Constitutive activation of YAP/TAZ conferred resistance to KRAS G12C inhibitors, while knockdown of YAP/TAZ or TEADs sensitized resistant cells to these inhibitors. This scenario was also observed in KRAS G12D-mutant cancer cells. Mechanistically, YAP/TAZ protects cells from KRAS inhibitor-induced apoptosis by downregulating the expression of proapoptotic genes such as BMF, BCL2L11, and PUMA, and YAP/TAZ reverses KRAS inhibitor-induced proliferation retardation by activating the SLC7A5/mTORC1 axis. We further demonstrated that dasatinib and MYF-03-176 notably enhance the efficacy of KRAS inhibitors by reducing SRC kinase activity and TEAD activity. Overall, targeting the Hippo-YAP/TAZ pathway has the potential to overcome resistance to KRAS inhibitors.
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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".