Publications

Publications

Small Molecule Inhibitors of ERCC1-XPF Protein-Protein Interaction Synergize Alkylating Agents in Cancer Cells

By:
Contributors: Jack Tuszynski Research Group
Mol Pharmacol. 2013 Jul;84(1):12-24. doi: 10.1124/mol.112.082347. Epub 2013 Apr 11.

Abstract

The benefit of cancer chemotherapy based on alkylating agents is limited because of the action of DNA repair enzymes, which mitigate the damage induced by these agents. The interaction between the proteins ERCC1 and XPF involves two major components of the nucleotide excision repair pathway. Here, novel inhibitors of this interaction were identified by virtual screening based on available structures with use of the National Cancer Institute diversity set and a panel of DrugBank small molecules. Subsequently, experimental validation of the in silico screening was undertaken. Top hits were evaluated on A549 and HCT116 cancer cells. In particular, the compound labeled NSC 130813 [4-[(6-chloro-2-methoxy-9-acridinyl)amino]-2-[(4-methyl-1-piperazinyl)methyl]] was shown to act synergistically with cisplatin and mitomycin C; to increase UVC-mediated cytotoxicity; to modify DNA repair as indicated by the staining of phosphorylated H2AX; and to disrupt interaction between ERCC1 and XPF in cells. In addition, using the Biacore technique, we showed that this compound interacts with the domain of XPF responsible for interaction with ERCC1. This study shows that small molecules targeting the protein-protein interaction of ERCC1 and XPF can be developed to enhance the effects of alkylating agents on cancer cells.

 

PubMed

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Russ Greiner’s Team “PC LEARN”, tied for 1st in the Prostate Cancer DREAM Challenge

Competing with 50 teams from around the world in the Prostate Cancer DREAM Challenge, University of Alberta’s PC LEARN team tied for 1st in one of the 3 sub-challenges
to predict the survival and toxicity of Docetaxel treatment in patients with metastatic castrate resistant prostate cancer!

“The DREAM Challenge was an exciting opportunity for us to apply machine learning to real medical data and possibly to contribute to medical research.” said lead PI and APCaRI member Russ Greiner.

The primary benefit of this Challenge will be to establish new quantitative benchmarks for prognostic modeling in mCRPC, with a potential impact for clinical decision making and ultimately understanding the mechanism of disease progression. https://www.synapse.org/#!Synapse:syn2813558/wiki/70844

- Russ Greiner