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.
Wang Yang, Ming Zhang, Tian-Xing Zhang, Jia-Hui Liu, Man-Wei Hao, Xu Yan, Haicheng Gao, Qun-Ying Lei, Jiuwei Cui, Xin Zhou
Usage data is cumulative from December 2024 through December 2025.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 4,208 | 1,209 |
| 940 | 255 | |
| Figure | 1,125 | 8 |
| Supplemental data | 683 | 72 |
| Citation downloads | 124 | 0 |
| Totals | 7,080 | 1,544 |
| Total Views | 8,624 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.