Kinome-wide decoding of network-attacking mutations rewiring cancer signaling

Neomorphic mutations create therapeutic challenges in cancer

Oncogenesis is a pathologic process driven by genomic aberrations, including changes in nucleotide sequences. The majority of these mutational events fall into two broad categories: inactivation of tumor suppressor genes (hypomorph, antimorph or amorph) or activation of oncogenes (hypermorph). The recent surge in genome sequence data and functional genomics research has ushered in the discovery of aberrations in a third category: gain-of-novel-function mutation (neomorph). These neomorphic mutations, which can be found in both tumor suppressor genes and oncogenes, produce proteins with entirely different functions from their respective wild-type (WT) proteins and the other morphs. The unanticipated phenotypic outcomes elicited by neomorphic mutations imply that tumors with the neomorphic mutations may not respond to therapies designed to target the WT protein. Therefore, understanding the functional activities of each genomic aberration to be targeted is crucial in devising effective treatment strategies that will benefit specific cancer patients.

Computational approaches for the identification of cancer genes and pathways

High‐throughput DNA sequencing techniques enable large‐scale measurement of somatic mutations in tumors. Cancer genomics research aims at identifying all cancer‐related genes and solid interpretation of their contribution to cancer initiation and development. However, this venture is characterized by various challenges, such as the high number of neutral passenger mutations and the complexity of the biological networks affected by driver mutations. Based on biological pathway and network information, sophisticated computational methods have been developed to facilitate the detection of cancer driver mutations and pathways. They can be categorized into (1) methods using known pathways from public databases, (2) network‐based methods, and (3) methods learning cancer pathways de novo. Methods in the first two categories use and integrate different types of data, such as biological pathways, protein interaction networks, and gene expression measurements. The third category consists of de novo methods that detect combinatorial patterns of somatic mutations across tumor samples, such as mutual exclusivity and co‐occurrence. In this review, we discuss recent advances, current limitations, and future challenges of these approaches for detecting cancer genes and pathways. We also discuss the most important current resources of cancer‐related genes. WIREs Syst Biol Med 2017, 9:e1364. doi: 10.1002/wsbm.1364

For further resources related to this article, please visit the WIREs website.

Network biology concepts in complex disease comorbidities

The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.

Theory and practice of clinical pharmacodynamics in oncology drug development

The clinical development of molecularly targeted cancer therapies is enhanced by proof of mechanism of action as well as proof of concept, which relate molecular pharmacodynamics to efficacy via changes in cancer cell biology and physiology resulting from drug action on its intended target. Here, we present an introduction to the field of clinical pharmacodynamics, its medical and laboratory aspects, and its practical incorporation into clinical trials. We also describe key success factors that are useful for judging the quality of clinical pharmacodynamic studies, including biopsy quality and suitability, specimen handling, assay fitness-for-purpose, and reagent quality control. This introduction provides not only context for the following articles in this issue, but also an appreciation of the role of well-conducted clinical pharmacodynamic studies in oncology drug development.

Keeping Signals Straight: How Cells Process Information and Make Decisions

As we become increasingly dependent on electronic information-processing systems at home and work, it’s easy to lose sight of the fact that our very survival depends on highly complex biological information-processing systems. Each of the trillions of cells that form the human body has the ability to detect and respond to a wide range of stimuli and inputs, using an extraordinary set of signaling proteins to process this information and make decisions accordingly. Indeed, cells in all organisms rely on these signaling proteins to survive and proliferate in unpredictable and sometimes rapidly changing environments. But how exactly do these proteins relay information within cells, and how do they keep a multitude of incoming signals straight? Here, I describe recent efforts to understand the fidelity of information flow inside cells. This work is providing fundamental insight into how cells function. Additionally, it may lead to the design of novel antibiotics that disrupt the signaling of pathogenic bacteria or it could help to guide the treatment of cancer, which often involves information-processing gone awry inside human cells.

PLOS Biology's first ever Research Matters explains the importance of understanding the transmission and interpretation of complex biological signals by our own cells and by the bacterial cells that live in or on us.

KinMutRF: a random forest classifier of sequence variants in the human protein kinase superfamily

Background

The association between aberrant signal processing by protein kinases and human diseases such as cancer was established long time ago. However, understanding the link between sequence variants in the protein kinase superfamily and the mechanistic complex traits at the molecular level remains challenging: cells tolerate most genomic alterations and only a minor fraction disrupt molecular function sufficiently and drive disease.

Results

KinMutRF is a novel random-forest method to automatically identify pathogenic variants in human kinases. Twenty six decision trees implemented as a random forest ponder a battery of features that characterize the variants: a) at the gene level, including membership to a Kinbase group and Gene Ontology terms; b) at the PFAM domain level; and c) at the residue level, the types of amino acids involved, changes in biochemical properties, functional annotations from UniProt, Phospho.ELM and FireDB. KinMutRF identifies disease-associated variants satisfactorily (Acc: 0.88, Prec:0.82, Rec:0.75, F-score:0.78, MCC:0.68) when trained and cross-validated with the 3689 human kinase variants from UniProt that have been annotated as neutral or pathogenic. All unclassified variants were excluded from the training set. Furthermore, KinMutRF is discussed with respect to two independent kinase-specific sets of mutations no included in the training and testing, Kin-Driver (643 variants) and Pon-BTK (1495 variants). Moreover, we provide predictions for the 848 protein kinase variants in UniProt that remained unclassified.

A public implementation of KinMutRF, including documentation and examples, is available online (http://kinmut2.bioinfo.cnio.es). The source code for local installation is released under a GPL version 3 license, and can be downloaded from https://github.com/Rbbt-Workflows/KinMut2.

Conclusions

KinMutRF is capable of classifying kinase variation with good performance. Predictions by KinMutRF compare favorably in a benchmark with other state-of-the-art methods (i.e. SIFT, Polyphen-2, MutationAssesor, MutationTaster, LRT, CADD, FATHMM, and VEST). Kinase-specific features rank as the most elucidatory in terms of information gain and are likely the improvement in prediction performance. This advocates for the development of family-specific classifiers able to exploit the discriminatory power of features unique to individual protein families.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2723-1) contains supplementary material, which is available to authorized users.

Systems medicine approaches for peptide array-based protein kinase profiling: progress and prospects

INTRODUCTION

Pharmacological manipulation of signalling pathways is becoming an increasingly important avenue for the rational clinical management of disease but is hampered by a lack of technologies that allow the generation of comprehensive descriptions of cellular signalling.

AREAS COVERED

Herein, the authors discuss the potential of peptide array-based kinome profiling for evaluating cellular signalling in the context of drug discovery. Expert commentary: Genomic and proteomic approaches have been of significant value to our elucidation of the molecular mechanisms that govern physiology. However, an equally, if not more important goal, is to define those proteins that participate in signalling pathways that ultimately control cell fate, especially kinases. Traditional genetic and biochemical approaches can certainly provide answers here, but for technical and practical reasons, are typically pursued one gene or pathway at a time. A more comprehensive approach is one in which peptide arrays of kinase-specific substrates are incubated with cell lysates and (33)P-ATP generating comprehensive descriptions, or where arrays are interrogated with phosphospecific antibodies. Both approaches allow analysis of cellular signalling without a priori assumptions to possibly influenced pathways.

An organelle-specific protein landscape identifies novel diseases and molecular mechanisms

Cellular organelles provide opportunities to relate biological mechanisms to disease. Here we use affinity proteomics, genetics and cell biology to interrogate cilia: poorly understood organelles, where defects cause genetic diseases. Two hundred and seventeen tagged human ciliary proteins create a final landscape of 1,319 proteins, 4,905 interactions and 52 complexes. Reverse tagging, repetition of purifications and statistical analyses, produce a high-resolution network that reveals organelle-specific interactions and complexes not apparent in larger studies, and links vesicle transport, the cytoskeleton, signalling and ubiquitination to ciliary signalling and proteostasis. We observe sub-complexes in exocyst and intraflagellar transport complexes, which we validate biochemically, and by probing structurally predicted, disruptive, genetic variants from ciliary disease patients. The landscape suggests other genetic diseases could be ciliary including 3M syndrome. We show that 3M genes are involved in ciliogenesis, and that patient fibroblasts lack cilia. Overall, this organelle-specific targeting strategy shows considerable promise for Systems Medicine.

Mutations in proteins that localize to primary cilia cause devastating diseases, yet the primary cilium is a poorly understood organelle. Here the authors use interaction proteomics to identify a network of human ciliary proteins that provides new insights into several biological processes and diseases.

Gain-of-function mutations in protein kinase Cα (PKCα) may promote synaptic defects in Alzheimer's disease

Alzheimer's disease (AD) is a progressive dementia disorder characterized by synaptic degeneration and amyloid-β (Aβ) accumulation in the brain. Through whole-genome sequencing of 1345 individuals from 410 families with late-onset AD (LOAD), we identified three highly penetrant variants in PRKCA, the gene that encodes protein kinase Cα (PKCα), in five of the families. All three variants linked with LOAD displayed increased catalytic activity relative to wild-type PKCα as assessed in live-cell imaging experiments using a genetically encoded PKC activity reporter. Deleting PRKCA in mice or adding PKC antagonists to mouse hippocampal slices infected with a virus expressing the Aβ precursor CT100 revealed that PKCα was required for the reduced synaptic activity caused by Aβ. In PRKCA(-/-) neurons expressing CT100, introduction of PKCα, but not PKCα lacking a PDZ interaction moiety, rescued synaptic depression, suggesting that a scaffolding interaction bringing PKCα to the synapse is required for its mediation of the effects of Aβ. Thus, enhanced PKCα activity may contribute to AD, possibly by mediating the actions of Aβ on synapses. In contrast, reduced PKCα activity is implicated in cancer. Hence, these findings reinforce the importance of maintaining a careful balance in the activity of this enzyme.

Computational approaches in target identification and drug discovery

In the big data era, voluminous datasets are routinely acquired, stored and analyzed with the aim to inform biomedical discoveries and validate hypotheses. No doubt, data volume and diversity have dramatically increased by the advent of new technologies and open data initiatives. Big data are used across the whole drug discovery pipeline from target identification and mechanism of action to identification of novel leads and drug candidates. Such methods are depicted and discussed, with the aim to provide a general view of computational tools and databases available. We feel that big data leveraging needs to be cost-effective and focus on personalized medicine. For this, we propose the interplay of information technologies and (chemo)informatic tools on the basis of their synergy.

Large-Scale Profiling of Kinase Dependencies in Cancer Cell Lines

One approach to identifying cancer-specific vulnerabilities and therapeutic targets is to profile genetic dependencies in cancer cell lines. Here, we describe data from a series of siRNA screens that identify the kinase genetic dependencies in 117 cancer cell lines from ten cancer types. By integrating the siRNA screen data with molecular profiling data, including exome sequencing data, we show how vulnerabilities/genetic dependencies that are associated with mutations in specific cancer driver genes can be identified. By integrating additional data sets into this analysis, including protein-protein interaction data, we also demonstrate that the genetic dependencies associated with many cancer driver genes form dense connections on functional interaction networks. We demonstrate the utility of this resource by using it to predict the drug sensitivity of genetically or histologically defined subsets of tumor cell lines, including an increased sensitivity of osteosarcoma cell lines to FGFR inhibitors and SMAD4 mutant tumor cells to mitotic inhibitors.

Pharmacometabolomics-aided Pharmacogenomics in Autoimmune Disease

Inter-individual variability has been a major hurdle to optimize disease management. Precision medicine holds promise for improving health and healthcare via tailor-made therapeutic strategies. Herein, we outline the paradigm of "pharmacometabolomics-aided pharmacogenomics" in autoimmune diseases. We envisage merging pharmacometabolomic and pharmacogenomic data (to address the interplay of genomic and environmental influences) with information technologies to facilitate data analysis as well as sense- and decision-making on the basis of synergy between artificial and human intelligence. Humans can detect patterns, which computer algorithms may fail to do so, whereas data-intensive and cognitively complex settings and processes limit human ability. We propose that better-informed, rapid and cost-effective omics studies need the implementation of holistic and multidisciplinary approaches.

The kinome 'at large' in cancer

Over the past decade, rapid advances in genomics, proteomics and functional genomics technologies that enable in-depth interrogation of cancer genomes and proteomes and high-throughput analysis of gene function have enabled characterization of the kinome 'at large' in human cancers, providing crucial insights into how members of the protein kinase superfamily are dysregulated in malignancy, the context-dependent functional role of specific kinases in cancer and how kinome remodelling modulates sensitivity to anticancer drugs. The power of these complementary approaches, and the insights gained from them, form the basis of this Analysis article.

Positive cross talk between protein kinase D and β-catenin in intestinal epithelial cells: impact on β-catenin nuclear localization and phosphorylation at Ser552

Given the fundamental role of β-catenin signaling in intestinal epithelial cell proliferation and the growth-promoting function of protein kinase D1 (PKD1) in these cells, we hypothesized that PKDs mediate cross talk with β-catenin signaling. The results presented here provide several lines of evidence supporting this hypothesis. We found that stimulation of intestinal epithelial IEC-18 cells with the G protein-coupled receptor (GPCR) agonist angiotensin II (ANG II), a potent inducer of PKD activation, promoted endogenous β-catenin nuclear localization in a time-dependent manner. A significant increase was evident within 1 h of ANG II stimulation (P< 0.01), peaked at 4 h (P< 0.001), and declined afterwards. GPCR stimulation also induced a marked increase in β-catenin-regulated genes and phosphorylation at Ser(552) in intestinal epithelial cells. Exposure to preferential inhibitors of the PKD family (CRT006610 or kb NB 142-70) or knockdown of the isoforms of the PKD family prevented the increase in β-catenin nuclear localization and phosphorylation at Ser(552) in response to ANG II. GPCR stimulation also induced the formation of a complex between PKD1 and β-catenin, as shown by coimmunoprecipitation that depended on PKD1 catalytic activation, as it was abrogated by cell treatment with PKD family inhibitors. Using transgenic mice that express elevated PKD1 protein in the intestinal epithelium, we detected a marked increase in the localization of β-catenin in the nucleus of crypt epithelial cells in the ileum of PKD1 transgenic mice, compared with nontransgenic littermates. Collectively, our results identify a novel cross talk between PKD and β-catenin in intestinal epithelial cells, both in vitro and in vivo.

Studying Autism in Context

Studying autism genes in the context of the protein complexes to which they belong illustrates the potential of network-centric approaches for understanding complex genetic disease.

Unmasking determinants of specificity in the human kinome

Protein kinases control cellular responses to environmental cues by swift and accurate signal processing. Breakdowns in this high-fidelity capability are a driving force in cancer and other diseases. Thus, our limited understanding of which amino acids in the kinase domain encode substrate specificity, the so-called determinants of specificity (DoS), constitutes a major obstacle in cancer signaling. Here, we systematically discover several DoS and experimentally validate three of them, named the αC1, αC3, and APE-7 residues. We demonstrate that DoS form sparse networks of non-conserved residues spanning distant regions. Our results reveal a likely role for inter-residue allostery in specificity and an evolutionary decoupling of kinase activity and specificity, which appear loaded on independent groups of residues. Finally, we uncover similar properties driving SH2 domain specificity and demonstrate how the identification of DoS can be utilized to elucidate a greater understanding of the role of signaling networks in cancer (Creixell et al., 2015 [this issue of Cell]).

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Residues driving specificity in the kinase and SH2 domains are globally identified

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Three new such residues, termed αC1, αC3, and APE-7, are experimentally validated

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Specificity and catalytic activity appear to be encoded in distinct sets of residues

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The global identification of determinants allows the modeling of rewiring mutations