CRISPR Loss-of-Function Screen Identifies the Hippo Signaling Pathway as the Mediator of Regorafenib Efficacy in Hepatocellular Carcinoma
Bibliographic record
Abstract
Regorafenib is used for hepatocellular carcinoma (HCC), but its response does not last long, partly due to chemoresistance acquisition. We performed a clustered regularly interspaced short palindromic repeats (CRISPR)-based loss-of-function genetic screen and aimed to discover molecules involved in regorafenib resistance in HCC. Xenograft tumors established from Cas9-expressing HCC cells with pooled CRISPR kinome libraries were treated with regorafenib or a vehicle. Sequencing analysis identified 31 genes, with the abundance of multiple guide RNAs increased in regorafenib-treated tumors compared to that in vehicle-treated tumors, including 2 paralogues, LATS2 and LATS1, core components of the Hippo signaling pathway. Notably, all eight designed guide RNAs targeting LATS2 increased in regorafenib-treated tumors, suggesting that LATS2 deletion confers regorafenib resistance in HCC cells. LATS2 knockdown significantly mitigated the cytotoxic and proapoptotic effects of regorafenib on HCC cells. LATS2 inhibition stabilized the Hippo signaling mediator YAP, leading to the upregulation of antiapoptotic Bcl-xL and the multidrug resistance transporter ABCB1. Among 12 hepatoma cell lines, the half maximal inhibitory concentration (IC50) values of regorafenib were positively correlated with any of YAP, Bcl-xL and ABCB1 levels. The inhibition of YAP or Bcl-xL in regorafenib-insensitive HCC cells restored their susceptibility to regorafenib. In conclusion, our screen identified the Hippo signaling pathway as the mediator of regorafenib efficacy in HCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".