journal article·2026-06-29Open access

Targeting the ATX‐LPA Axis Overcomes TKI Resistance and Immunosuppression in Renal Cell Carcinoma via Dual Inhibition of AKT/mTOR and TBK1/IRF3 Pathways

Jinchen Luo, Hansen Lin, Haoqian Feng, Lei Tan, Xi Liu, Yong Huang, Junjie Cen, Jiajie Chen, Xinwei Zhou, Mingjie Lin, Wuyuan Liao, Zheyu Ai, Minyu Chen, Yinghan Wang, Wei Chen, Junhang Luo, Yanping Liang

Abstract

ABSTRACT Background Therapeutic resistance limits durable survival in advanced/metastatic renal cell carcinoma (RCC) treated with first‐line tyrosine kinase inhibitor (TKI) plus immune checkpoint inhibitor (ICI). We sought to define key resistance drivers and actionable targets. Methods Integrated RNA sequencing of cabozantinib‐resistant RCC cells, lipid metabolomics, and PD‐L1 correlation analyses identified ENPP2 as a candidate driver. Its role in TKI resistance and survival signaling was validated by apoptosis, CCK‐8, and colony formation assays in vitro and by nude‐mouse xenograft models in vivo. ELISA, flow cytometry and tumor cell–T‐cell co‐culture assays were used to dissect ENPP2‐dependent CD8+ T‐cell dysfunction. The therapeutic benefit of pharmacologic ATX inhibition combined with standard TKI–ICI regimens was tested in RCC patient‐derived xenograft models. Results The ATX–LPA axis conferred TKI resistance via constitutive AKT/mTOR activation and promoted immune evasion by upregulating PD‐L1 through TBK1/IRF3 signaling, thereby impairing intratumoral CD8+ T‐cell function. ENPP2 enhanced PD‐L1 transcription by facilitating IRF3 nuclear translocation and its direct recruitment to the CD274 promoter. ATX inhibition improved the antitumor efficacy of TKI–ICI therapy in preclinical models. Conclusions Targeting the ATX–LPA axis represents a promising strategy to overcome resistance to current TKI–ICI combinations.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Renal cell carcinoma treatment · Cancer Immunotherapy and Biomarkers · Ferroptosis and cancer prognosis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Urology The First Affiliated Hospital of Sun Yat‐Sen University Guangzhou China

Urology Department The Fourth Affiliated Hospital of Guangzhou Medical University Guangzhou China

Huadu District People's Hospital of Guangzhou Guangzhou China

Huadu Institute of Medical Sciences Guangzhou China

Guangxi Hospital Division of The First Affiliated Hospital Sun Yat‐sen University Nanning China

Department of Pediatrics The First Affiliated Hospital of Sun Yat‐Sen University Guangzhou China

Department of Urology Cancer Hospital Chinese Academy of Medical Sciences Shenzhen Center Shenzhen China

Department of Laboratory Medicine The First Affiliated Hospital of Sun Yat‐Sen University Guangzhou China

Funding

National Natural Science Foundation of China

References (54)

Show source-provided references
1. CA: A Cancer Journal for Clinicians
2. Molecular Aspects of Renal Cell Carcinoma: a Review
3. 10.6004/jnccn.2022.000110.6004/jnccn.2022.0001 ↗
4. 10.2217/imt‐2020‐032310.2217/imt‐2020‐0323 ↗
5. First and Second‐line Treatments in Metastatic Renal Cell Carcinoma10.1016/j.eururo.2024.10.019 ↗
6. 10.1038/s41585‐022‐00676‐010.1038/s41585‐022‐00676‐0 ↗
7. 10.1016/j.cell.2017.04.01610.1016/j.cell.2017.04.016 ↗
8. 10.1038/nrc324510.1038/nrc3245 ↗
9. 10.1038/s41581‐020‐0316‐310.1038/s41581‐020‐0316‐3 ↗
10. 10.1158/2326‐6066.CIR‐14‐024410.1158/2326‐6066.CIR‐14‐0244 ↗
11. 10.1158/1078‐0432.CCR‐19‐073310.1158/1078‐0432.CCR‐19‐0733 ↗
12. 10.1186/s13578‐022‐00790‐x10.1186/s13578‐022‐00790‐x ↗
13. Lipid Metabolism and Cancer10.1084/jem.20201606 ↗
14. 10.1172/JCI12720110.1172/JCI127201 ↗
15. 10.34133/research.053910.34133/research.0539 ↗
16. 10.1016/j.jare.2024.04.03210.1016/j.jare.2024.04.032 ↗
17. 10.1038/s41420‐025‐02479‐910.1038/s41420‐025‐02479‐9 ↗
18. 10.1016/j.plipres.2007.02.00110.1016/j.plipres.2007.02.001 ↗
19. 10.1016/j.bbalip.2020.15871610.1016/j.bbalip.2020.158716 ↗
20. 10.1007/s13402‐025‐01038‐910.1007/s13402‐025‐01038‐9 ↗
21. 10.1016/j.jbior.2024.10102910.1016/j.jbior.2024.101029 ↗
22. 10.1038/ncomms1065610.1038/ncomms10656 ↗
23. 10.3390/cells1008205910.3390/cells10082059 ↗
24. 10.1158/0008‐5472.CAN‐17‐379710.1158/0008‐5472.CAN‐17‐3797 ↗
25. 10.1158/1078‐0432.CCR‐13‐128410.1158/1078‐0432.CCR‐13‐1284 ↗
26. 10.1111/imr.1320810.1111/imr.13208 ↗
27. 10.1158/2326‐6066.CIR‐13‐0043‐T10.1158/2326‐6066.CIR‐13‐0043‐T ↗
28. 10.1172/JCI16312810.1172/JCI163128 ↗
29. 10.1038/s41392‐020‐0117‐y10.1038/s41392‐020‐0117‐y ↗
30. 10.1002/advs.20250636710.1002/advs.202506367 ↗
31. 10.1158/0008‐5472.CAN‐22‐222410.1158/0008‐5472.CAN‐22‐2224 ↗
32. 10.1016/j.cell.2021.04.03810.1016/j.cell.2021.04.038 ↗
33. 10.1016/j.scib.2025.02.04310.1016/j.scib.2025.02.043 ↗
34. 10.1126/science.abe647410.1126/science.abe6474 ↗
35. 10.1038/s41577‐019‐0223‐710.1038/s41577‐019‐0223‐7 ↗
36. 10.1038/s43018‐022‐00433‐710.1038/s43018‐022‐00433‐7 ↗
37. 10.1016/j.cell.2018.05.06110.1016/j.cell.2018.05.061 ↗
38. 10.1038/s41392‐025‐02133‐x10.1038/s41392‐025‐02133‐x ↗
39. 10.1016/j.bbcan.2025.18934610.1016/j.bbcan.2025.189346 ↗
40. 10.1038/s41392‐024‐01963‐510.1038/s41392‐024‐01963‐5 ↗
41. 10.3390/cancers1710161310.3390/cancers17101613 ↗
42. 10.1016/j.trecan.2017.09.00410.1016/j.trecan.2017.09.004 ↗
43. TCellSI: A Novel Method for T Cell State Assessment and Its Applications in Immune Environment Prediction10.1002/imt2.231 ↗
44. 10.1056/NEJMoa202698210.1056/NEJMoa2026982 ↗
45. 10.1200/JCO.2016.70.739810.1200/JCO.2016.70.7398 ↗
46. 10.1158/1535‐7163.MCT‐11‐026410.1158/1535‐7163.MCT‐11‐0264 ↗
47. 10.3389/fonc.2014.0006410.3389/fonc.2014.00064 ↗
48. 10.1016/j.hoc.2011.04.00810.1016/j.hoc.2011.04.008 ↗
49. 10.1038/nature1230610.1038/nature12306 ↗
50. 10.1186/s13045‐020‐00916‐z10.1186/s13045‐020‐00916‐z ↗
51. 10.1016/j.celrep.2017.04.03110.1016/j.celrep.2017.04.031 ↗
52. 10.1016/j.chembiol.2023.05.00510.1016/j.chembiol.2023.05.005 ↗
53. 10.1136/jitc‐2023‐00871810.1136/jitc‐2023‐008718 ↗
54. 10.1038/s41467‐023‐38933‐410.1038/s41467‐023‐38933‐4 ↗