1,4-dihydroxy quininib modulates the secretome of uveal melanoma tumour explants and a marker of oxidative phosphorylation in a metastatic xenograft model

Front Med (Lausanne). 2023 Jan 9:9:1036322. doi: 10.3389/fmed.2022.1036322. eCollection 2022.


Uveal melanoma (UM) is an intraocular cancer with propensity for liver metastases. The median overall survival (OS) for metastatic UM (MUM) is 1.07 years, with a reported range of 0.84-1.34. In primary UM, high cysteinyl leukotriene receptor 1 (CysLT1) expression associates with poor outcomes. CysLT1 antagonists, quininib and 1,4-dihydroxy quininib, alter cancer hallmarks of primary and metastatic UM cell lines in vitro. Here, the clinical relevance of CysLT receptors and therapeutic potential of quininib analogs is elaborated in UM using preclinical in vivo orthotopic xenograft models and ex vivo patient samples. Immunohistochemical staining of an independent cohort (n = 64) of primary UM patients confirmed high CysLT1 expression significantly associates with death from metastatic disease (p = 0.02; HR 2.28; 95% CI 1.08-4.78), solidifying the disease relevance of CysLT1 in UM. In primary UM samples (n = 11) cultured as ex vivo explants, 1,4-dihydroxy quininib significantly alters the secretion of IL-13, IL-2, and TNF-α. In an orthotopic, cell line-derived xenograft model of MUM, 1,4-dihydroxy quininib administered intraperitoneally at 25 mg/kg significantly decreases ATP5B expression (p = 0.03), a marker of oxidative phosphorylation. In UM, high ATP5F1B is a poor prognostic indicator, whereas low ATP5F1B, in combination with disomy 3, correlates with an absence of metastatic disease in the TCGA-UM dataset. These preclinical data highlight the diagnostic potential of CysLT1 and ATP5F1B in UM, and the therapeutic potential of 1,4-dihydroxy quininib with ATP5F1B as a companion diagnostic to treat MUM.

Keywords: ATP5B ATP synthase; cysteinyl leukotriene; immunohistochemistry; inflammation; tumour metabolism; tumour microenvironment; uveal melanoma (UM); xenograft model.

Grants and funding

This work was supported by an Irish Research Council Employment Based Postgraduate Scholarship (EBP/2017/473) (KS) and funding from Breakthrough Cancer Research (BCR-2019-01-UCD and BCR-2021-01-BPG) (KS and BK). This project received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 734907 (RISE/3D-NEONET project). Automated image analysis work was supported by Science Foundation Ireland (SFI) under the Investigator Programme OPTi-PREDICT (grant number: 15/IA/3104) and the Strategic Research Programme Precision Oncology Ireland (grant number: 18/SPP/3522), the latter also co-supported by the Irish Cancer Society.