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Selivanova, G.

Publications and source records attributed to Selivanova, G..

2 recordsLinked to original sources

Novel allosteric mechanism of p53 activation by small molecules for targeted anticancer therapy

Given the immense significance of p53 restoration for anti-cancer therapy, elucidation of the mechanisms of action of p53-activating molecules is of the utmost importance. Here we report a discovery of novel allosteric modulation of p53 by small molecules, which is an unexpected turn in the p53 story. We identified a structural element involved in p53 regulation, whose targeting by RITA, PpIX and licofelone block the binding of p53 inhibitors, MDM2 and MDMX. Deletion and mutation analysis followed by molecular modeling, identified the key p53 residues S33 and S37 targeted by RITA and PpIX. We propose that the binding of small molecules to the identified site induces a conformational trap preventing p53 from the interaction with MDM2 and MDMX. These results point to a high potential of allosteric activators. Our study provides the basis for the development of therapeutics with a novel mechanism of action, thus extending the p53 pharmacological potential.

cancer biology

Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data

In order to tackle heterogeneity of cancer samples and high data space dimensionality, we propose a method NEAmarker for finding sensitive and robust biomarkers at the pathway level. In this method, scores from network enrichment analysis transform the original space of altered genes into a lower-dimensional space of pathways, which is then correlated with phenotype variables. The analysis was first done on in vitro anti-cancer drug screen datasets and then on clinical data. In parallel, we tested a panel of state-of-the-art enrichment methods. In this comparison, our method proved superior in terms of 1) universal applicability to different data types with a possibility of cross-platform integration, 2) consistency of the discovered correlates between independent drug screens, and 3) ability to explain differential survival of treated patients. Our new in vitro screen validated performance of the discovered multivariate models. Finally, NEAmarker was the only method to discover predictors of both in vitro response and patient survival given administration of the same drug.

cancer biology