Back

Effects of breast fibroepithelial tumor associated retinoic acid receptor alpha ligand binding domain mutations on receptor function and retinoid signaling

Huang, X. X.; Ng, L. M.; Lee, P.-H.; Guan, P.; Chow, M. J.; Binte Mohamed Bashir, A.; Lau, M.; Tan, K.; Li, Z.; Chan, J.; Hong, J.; Ng, S. R.; Teo, H. L.; Rhodes, D.; Tan, P.; Tan, P. H.; McDonnell, D. P.; Teh, B. T.

2022-12-03 cancer biology
10.1101/2022.12.01.518133 bioRxiv
Show abstract

Point mutations in the ligand binding domain of retinoic acid receptor alpha (RAR) have been implicated in breast fibroepithelial tumors development. However, their role in the tumorigenesis of solid tumors is currently unknown. In this study, using a combination of biochemical and cellular assays, we evaluated the functional consequences of known tumor associated RAR mutations on retinoic acid signaling. All of the clinically associated mutants tested showed diminished transcriptional activities compared to wild type RAR. These mutants also exhibited a dominant negative effect, an activity which has previously been linked to developmental defects and tumor formation in mice. X-ray crystallography showed that mutants remain relatively intact structurally and the loss of transcriptional activity is due to altered co-activator recruitment. In agreement with our biochemical analyses, transcriptomics and cell growth analysis showed that the mutant RAR proteins confer resistance to growth inhibition in the presence of its ligand in phyllodes tumor cells. Although the mutations impair the receptor responses to retinoic acid, certain mutant RAR are partially reactivatable with alternative synthetic agonists. Our data provide insights into the mechanisms by which RAR mutations impact tumorigenesis.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.