The Efficacy of Sunitinib Treatment of Renal Cancer Cells Is Associated with the Protein PHAX In Vitro

Abstract

Anti-angiogenic agents, such as the multi-tyrosine kinase inhibitor sunitinib, are key first line therapies for metastatic clear cell renal cell carcinoma (ccRCC), but their mechanism of action is not fully understood. Here, we take steps towards validating a computational prediction based on differential transcriptome network analysis that phosphorylated adapter RNA export protein (PHAX) is associated with sunitinib drug treatment. The regulatory impact factor differential network algorithm run on patient tissue samples suggests PHAX is likely an important regulator through changes in genome-wide network connectivity. Immunofluorescence staining of patient tumours showed strong localisation of PHAX to the microvasculature consistent with the anti-angiogenic effect of sunitinib. In normal kidney tissue, PHAX protein abundance was low but increased with tumour grade (G1 vs. G3/4; p < 0.01), consistent with a possible role in cancer progression. In organ culture, ccRCC cells had higher levels of PHAX protein expression than normal kidney cells, and sunitinib increased PHAX protein expression in a dose dependent manner (untreated vs. 100 µM; p < 0.05). PHAX knockdown in a ccRCC organ culture model impacted the ability of sunitinib to cause cancer cell death (p < 0.0001 untreated vs. treated), suggesting a role for PHAX in mediating the efficacy of sunitinib.

Dynamics of Gene Co-expression Networks in Time-Series Data: A Case Study in Drosophila melanogaster Embryogenesis

“…To identify key regulators of Drosophila embryogenesis among the 791 pre-defined regulators mentioned before, we applied the regulatory impact factor metrics (RIF; Reverter et al, 2010) considering all clusters and regulators as targets (4,133 genes). RIF has been applied in a range of biological circumstances reviewed in Hudson et al (2012) and Ehsani and Drabløs (2020), including a very recent example relating to how sunitinib drug treatment influences kidney cancer (Al-Lamki et al, 2020). In brief, RIF combines the correlation between a regulator and its potential targets with the degree of differential expression of the targets between the tested conditions.…”