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Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy.

ABSTRACT: The human kinome is gaining importance through its promising cancer therapeutic targets, yet no general model to address the kinase inhibitor resistance has emerged. Here, we constructed a systems biology-based framework to catalogue the human kinome, including 538 kinase genes, in the broader context of the human interactome. Specifically, we constructed three networks: a kinase-substrate interaction network containing 7,346 pairs connecting 379 kinases to 36,576 phosphorylation sites in 1,961 substrates, a protein-protein interaction network (PPIN) containing 92,699 pairs, and an atomic resolution PPIN containing 4,278 pairs. We identified the conserved regulatory phosphorylation motifs (e.g., Ser/Thr-Pro) using a sequence logo analysis. We found the typical anticancer target selection strategy that uses network hubs as drug targets, might lead to a high adverse drug reaction risk. Furthermore, we found the distinct network centrality of kinases creates a high anticancer drug resistance risk by feedback or crosstalk mechanisms within cellular networks. This notion is supported by the systematic network and pathway analyses that anticancer drug resistance genes are significantly enriched as hubs and heavily participate in multiple signaling pathways. Collectively, this comprehensive human kinome interactome map sheds light on anticancer drug resistance mechanisms and provides an innovative resource for rational kinase inhibitor design.

SUBMITTER: Cheng F

PROVIDER: S-EPMC4116514 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy.

Cheng Feixiong F Jia Peilin P Wang Quan Q Zhao Zhongming Z

Oncotarget 20140601 11

The human kinome is gaining importance through its promising cancer therapeutic targets, yet no general model to address the kinase inhibitor resistance has emerged. Here, we constructed a systems biology-based framework to catalogue the human kinome, including 538 kinase genes, in the broader context of the human interactome. Specifically, we constructed three networks: a kinase-substrate interaction network containing 7,346 pairs connecting 379 kinases to 36,576 phosphorylation sites in 1,961 ...[more]

PMID: 25003367

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Project description:Many complex diseases such as cancer are associated with multiple pathological manifestations. Moreover, the therapeutics for their treatments often lead to serious side effects. Thus, it is needed to develop multi-indication therapeutics that can simultaneously target multiple clinical indications of interest and mitigate the side effects. However, conventional one-drug-one-gene drug discovery paradigm and emerging polypharmacology approach rarely tackle the challenge of multi-indication drug design. For the first time, we propose a one-drug-multi-target-multi-indication strategy. We develop a novel structural systems pharmacology platform 3D-REMAP that uses ligand binding site comparison and protein-ligand docking to augment sparse chemical genomics data for the machine learning model of genome-scale chemical-protein interaction prediction. Experimentally validated predictions systematically show that 3D-REMAP outperforms state-of-the-art ligand-based, receptor-based, and machine learning methods alone. As a proof-of-concept, we utilize the concept of drug repurposing that is enabled by 3D-REMAP to design dual-indication anti-cancer therapy. The repurposed drug can demonstrate anti-cancer activity for cancers that do not have effective treatment as well as reduce the risk of heart failure that is associated with all types of existing anti-cancer therapies. We predict that levosimendan, a PDE inhibitor for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments and systems biology analyses confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to predict cancer cell-line's and a patient's response to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe, effective, and precision multi-indication anti-cancer therapy. This study demonstrates the potential of structural systems pharmacology in designing polypharmacology for precision medicine. It may facilitate transforming the conventional one-drug-one-gene-one-disease drug discovery process and single-indication polypharmacology approach into a new one-drug-multi-target-multi-indication paradigm for complex diseases.

Project description:Protein kinases play key roles in oncogenic signaling and are a major focus in the development of targeted cancer therapies. Imatinib, a BCR-Abl tyrosine kinase inhibitor, is a successful front-line treatment for chronic myelogenous leukemia (CML). However, resistance to imatinib may be acquired by BCR-Abl mutations or hyperactivation of Src family kinases such as Lyn. We have used multiplexed kinase inhibitor beads (MIBs) and quantitative mass spectrometry (MS) to compare kinase expression and activity in an imatinib-resistant (MYL-R) and -sensitive (MYL) cell model of CML. Using MIB/MS, expression and activity changes of over 150 kinases were quantitatively measured from various protein kinase families. Statistical analysis of experimental replicates assigned significance to 35 of these kinases, referred to as the MYL-R kinome profile. MIB/MS and immunoblotting confirmed the over-expression and activation of Lyn in MYL-R cells and identified additional kinases with increased (MEK, ERK, IKK?, PKC?, NEK9) or decreased (Abl, Kit, JNK, ATM, Yes) abundance or activity. Inhibiting Lyn with dasatinib or by shRNA-mediated knockdown reduced the phosphorylation of MEK and IKK?. Because MYL-R cells showed elevated NF-?B signaling relative to MYL cells, as demonstrated by increased I?B? and IL-6 mRNA expression, we tested the effects of an IKK inhibitor (BAY 65-1942). MIB/MS and immunoblotting revealed that BAY 65-1942 increased MEK/ERK signaling and that this increase was prevented by co-treatment with a MEK inhibitor (AZD6244). Furthermore, the combined inhibition of MEK and IKK? resulted in reduced IL-6 mRNA expression, synergistic loss of cell viability and increased apoptosis. Thus, MIB/MS analysis identified MEK and IKK? as important downstream targets of Lyn, suggesting that co-targeting these kinases may provide a unique strategy to inhibit Lyn-dependent imatinib-resistant CML. These results demonstrate the utility of MIB/MS as a tool to identify dysregulated kinases and to interrogate kinome dynamics as cells respond to targeted kinase inhibition.

Dataset Information

Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy.

Publications

Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy.

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