Computational drugs repositioning identifies inhibitors of oncogenic PI3K/AKT/P70S6K-dependent pathways among FDA-approved compounds.

Diego Carrella,Margherita Mutarelli,Fabrizio Loreni,Federica Papaccio,Valentina Aria,Roberta Bosotti,Francesco Sirci,Antonella Isacchi,Laura Ciolli,Carla A Amoreo,Diego Di Bernardo,Rosanna Dattilo,Isabella Manni,Vittorio E Avvedimento,Manuela Iezzi,Luca Cardone,Marcella Mottolese,Barbara Tumaini,Alberto Bardelli

doi:10.18632/oncotarget.11318

Abstract

The discovery of inhibitors for oncogenic signalling pathways remains a key focus in modern oncology, based on personalized and targeted therapeutics. Computational drug repurposing via the analysis of FDA-approved drug network is becoming a very effective approach to identify therapeutic opportunities in cancer and other human diseases. Given that gene expression signatures can be associated with specific oncogenic mutations, we tested whether a “reverse” oncogene-specific signature might assist in the computational repositioning of inhibitors of oncogenic pathways. As a proof of principle, we focused on oncogenic PI3K-dependent signalling, a molecular pathway frequently driving cancer progression as well as raising resistance to anticancer-targeted therapies. We show that implementation of “reverse” oncogenic PI3K-dependent transcriptional signatures combined with interrogation of drug networks identified inhibitors of PI3K-dependent signalling among FDA-approved compounds. This led to repositioning of Niclosamide (Niclo) and Pyrvinium Pamoate (PP), two anthelmintic drugs, as inhibitors of oncogenic PI3K-dependent signalling. Niclo inhibited phosphorylation of P70S6K, while PP inhibited phosphorylation of AKT and P70S6K, which are downstream targets of PI3K. Anthelmintics inhibited oncogenic PI3K-dependent gene expression and showed a cytostatic effect in vitro and in mouse mammary gland. Lastly, PP inhibited the growth of breast cancer cells harbouring PI3K mutations. Our data indicate that drug repositioning by network analysis of oncogene-specific transcriptional signatures is an efficient strategy for identifying oncogenic pathway inhibitors among FDA-approved compounds. We propose that PP and Niclo should be further investigated as potential therapeutics for the treatment of tumors or diseases carrying the constitutive activation of the PI3K/P70S6K signalling axis.

Highlights

Targeted inhibition of oncogenic signalling pathways represents the main goal of modern oncology: selective inhibitors of oncogenes such as mutated kinases have been identified mainly through traditional small molecule drug screening, aimed at identifying inhibitors of their catalytic activity or cellular localization
We have previously developed a computational approach to predict drug Mode of Action (MoA) and drug repurposing by using the analysis of the Connectivity Map (Cmap) [22], a compendium of gene expression profiles (GEPs) following drug treatment of human cell lines with 1,309 bioactive small molecules
Among selected drugs (Table 1), we focused our attention on Niclosamide (Niclo) and Pyrvinium Pamoate (PP), two FDA-approved anthelmintic drugs, as a result of following considerations: i) recent preclinical data have outlined anticancer effects of both compounds in several in vitro and in vivo cancer models [29,30,31,32,33]; ii) the possibility that these compounds can inhibit the oncogenic activation of PI3K-dependent pathways was never directly tested

Summary

Introduction

Targeted inhibition of oncogenic signalling pathways represents the main goal of modern oncology: selective inhibitors of oncogenes such as mutated kinases have been identified mainly through traditional small molecule drug screening, aimed at identifying inhibitors of their catalytic activity or cellular localization. One opportunity to implement these molecular data into drug discovery is offered by the inspection of drug networks These networks can be derived by computing the similarity between gene signatures generated by drug treatments in specific cell lines, with the assumption that each drug treatment generates a drug-specific signature [6]. In this drug-network, drugs can be grouped into communities composed of drugs that produce similar transcriptional profiles and may act through a similar Mode of Action (MoA) [7]. The inspection of drug networks with disease- or oncogene-associated signatures might help repositioning drugs able to “revert” a disease signature and, the disease phenotype [9]

Methods

Results

Conclusion