Abstract
Background/Aims Intrahepatic cholangiocarcinoma (iCCA) represents an unmet clinical need due to its increasing incidence, aggressive biology, and limited treatment options. The extremely low-response rates to current systemic regimens and the emergence of adaptive resistance to targeted therapies underscore the urgent need for alternative therapeutic strategies. Given that the lineage-defining transcription factors SOX9 and YAP1 are central regulators of cholangiocyte and iCCA identity, we investigated their functional roles as potential therapeutic vulnerabilities across multiple preclinical models.
Methods
Patient tissue-microarray (TMA) analysis, Sleeping-Beauty hydrodynamic tail vein injection–based iCCA models, and Cre-mediated inducible gene deletion systems were used to investigate the roles of Sox9 and Yap1. Deep-learning–based prediction, RNA-seq, ChIP-seq and immunohistochemistry analyses were performed to delineate transcriptional networks and downstream effectors associated with SOX9/YAP1 signaling.
Results
Dual deletion of Sox9 and Yap1 effectively eradicated advanced iCCA while preserving intrahepatic bile ducts, regardless of oncogenic drivers. Mechanistically, SOX9 and YAP1 transcriptionally compensated for each other when one was absent, and ILF2, MGAT5, and WWTR1 were identified as key downstream effectors mediating this compensatory mechanism. Loss of Ilf2, Mgat5, or Taz suppressed iCCA, whereas overexpression of Ilf2 or Taz following Sox9/Yap1 co-deletion restored tumor development, indicating that ILF2 or TAZ can functionally substitute for YAP1 and SOX9 in sustaining iCCA.
Conclusions
Co-targeting SOX9 and YAP1 offers a promising and safe broad-spectrum preventive/therapeutic approach for iCCA, potentially overcoming resistance to YAP1 inhibition. The adaptive resistance mechanism identified may extend to other malignancies, providing insights for addressing the advanced resistant to YAP1-TEAD-directed therapies.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Funding: This work was supported by NIH grants R01CA258449, R01CA300059 and by PLRC Pilot & Feasibility grant PF 2019-05 to S.K., and Innovation in Cancer Informatics Discover grant (https://www.the-ici-fund.org) to S.K and S.L., and NIH grant 1P30DK120531-01 to Pittsburgh Liver Research Center (PLRC), and in part by R35GM154967 and R00CA248944 to Y.C. This work was supported in part by the University of Pittsburgh Center for Research Computing through the resources provided. Specifically, this work used the HTC cluster, which is supported by NIH award number S10OD028483. This project was also partly supported by NIH grant R35GM159862, the Competitive Medical Research Fund (CMRF) of the UPMC Health System to S.L.
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