Kinase activity ranking using phosphoproteomics data (KARP) quantifies the contribution of protein kinases to the regulation of cell viability

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Comprehensive Profiling of Rapamycin Interacting Proteins with Multiple Mass Spectrometry-Based Omics Techniques

Profiling drug-protein interactions is critical for understanding a drug's mechanism of action and predicting the possible adverse side effects. However, to comprehensively profile drug-protein interactions remains a challenge. To address this issue, we proposed a strategy that integrates multiple mass spectrometry-based omics analysis to provided global drug-protein interactions, including physical interactions and functional interactions, with rapamycin (Rap) as a model. Chemoproteomics profiling reveals 47 Rap binding proteins including the known target protein FKBP12 with high confidence. Gen Ontology enrichment analysis suggested that the Rap binding proteins are implicated in several important cellular processes, such as DNA replication, immunity, autophagy, programmed cell death, aging, transcription modulation, vesicle-mediated transport, membrane organization, and carbohydrate and nucleobase metabolic processes. The phosphoproteomics profiling revealed 255 down-regulated and 150 up-regulated phosphoproteins responding to Rap stimulation; they mainly involve the PI3K-Akt-mTORC1 signaling axis. Untargeted metabolomic profiling revealed 22 down-regulated metabolites and 75 up-regulated metabolites responding to Rap stimulation; they are mainly associated with the synthesis processes of pyrimidine and purine. The integrative multiomics data analysis provides deep insight into the drug-protein interactions and reveals Rap's complicated mechanism of action.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data

Kinase inhibitors as targeted therapies have played an important role in improving cancer outcomes. However, there are still considerable challenges, such as resistance, non-response, patient stratification, polypharmacology, and identifying combination therapy where understanding a tumor kinase activity profile could be transformative. Here, we develop a graph- and statistics-based algorithm, called KSTAR, to convert phosphoproteomic measurements of cells and tissues into a kinase activity score that is generalizable and useful for clinical pipelines, requiring no quantification of the phosphorylation sites. In this work, we demonstrate that KSTAR reliably captures expected kinase activity differences across different tissues and stimulation contexts, allows for the direct comparison of samples from independent experiments, and is robust across a wide range of dataset sizes. Finally, we apply KSTAR to clinical breast cancer phosphoproteomic data and find that there is potential for kinase activity inference from KSTAR to complement the current clinical diagnosis of HER2 status in breast cancer patients.

eEF2K Activity Determines Synergy to Cotreatment of Cancer Cells With PI3K and MEK Inhibitors

PI3K-mammalian target of rapamycin and MAPK/ERK kinase (MEK)/mitogen-activated protein kinase (MAPK) are the most frequently dysregulated signaling pathways in cancer. A problem that limits the success of therapies that target individual PI3K-MAPK members is that these pathways converge to regulate downstream functions and often compensate each other, leading to drug resistance and transient responses to therapy. In order to overcome resistance, therapies based on cotreatments with PI3K/AKT and MEK/MAPK inhibitors are now being investigated in clinical trials, but the mechanisms of sensitivity to cotreatment are not fully understood. Using LC-MS/MS-based phosphoproteomics, we found that eukaryotic elongation factor 2 kinase (eEF2K), a key convergence point downstream of MAPK and PI3K pathways, mediates synergism to cotreatment with trametinib plus pictilisib (which target MEK1/2 and PI3Kα/δ, respectively). Inhibition of eEF2K by siRNA or with a small molecule inhibitor reversed the antiproliferative effects of the cotreatment with PI3K plus MEK inhibitors in a cell model-specific manner. Systematic analysis in 12 acute myeloid leukemia cell lines revealed that eEF2K activity was increased in cells for which PI3K plus MEKi cotreatment is synergistic, while PKC potentially mediated resistance to such cotreatment. Together, our study uncovers eEF2K activity as a key mediator of responses to PI3Ki plus MEKi and as a potential biomarker to predict synergy to cotreatment in cancer cells.

Inference of kinase-signaling networks in human myeloid cell line models by Phosphoproteomics using kinase activity enrichment analysis (KAEA)

BACKGROUND

Despite the introduction of targeted therapies, most patients with myeloid malignancies will not be cured and progress. Genomics is useful to elucidate the mutational landscape but remains limited in the prediction of therapeutic outcome and identification of targets for resistance. Dysregulation of phosphorylation-based signaling pathways is a hallmark of cancer, and therefore, kinase-inhibitors are playing an increasingly important role as targeted treatments. Untargeted phosphoproteomics analysis pipelines have been published but show limitations in inferring kinase-activities and identifying potential biomarkers of response and resistance.

METHODS

We developed a phosphoproteomics workflow based on titanium dioxide phosphopeptide enrichment with subsequent analysis by liquid chromatography tandem mass spectrometry (LC-MS). We applied a novel Kinase-Activity Enrichment Analysis (KAEA) pipeline on differential phosphoproteomics profiles, which is based on the recently published SetRank enrichment algorithm  with reduced false positive rates. Kinase activities were inferred by this algorithm using an extensive reference database comprising five experimentally validated kinase-substrate meta-databases complemented with the NetworKIN in-silico prediction tool. For the proof of concept, we used human myeloid cell lines (K562, NB4, THP1, OCI-AML3, MOLM13 and MV4-11) with known oncogenic drivers and exposed them to clinically established kinase-inhibitors.

RESULTS

Biologically meaningful over- and under-active kinases were identified by KAEA in the unperturbed human myeloid cell lines (K562, NB4, THP1, OCI-AML3 and MOLM13). To increase the inhibition signal of the driving oncogenic kinases, we exposed the K562 (BCR-ABL1) and MOLM13/MV4-11 (FLT3-ITD) cell lines to either Nilotinib or Midostaurin kinase inhibitors, respectively. We observed correct detection of expected direct (ABL, KIT, SRC) and indirect (MAPK) targets of Nilotinib in K562 as well as indirect (PRKC, MAPK, AKT, RPS6K) targets of Midostaurin in MOLM13/MV4-11, respectively. Moreover, our pipeline was able to characterize unexplored kinase-activities within the corresponding signaling networks.

CONCLUSIONS

We developed and validated a novel KAEA pipeline for the analysis of differential phosphoproteomics MS profiling data. We provide translational researchers with an improved instrument to characterize the biological behavior of kinases in response or resistance to targeted treatment. Further investigations are warranted to determine the utility of KAEA to characterize mechanisms of disease progression and treatment failure using primary patient samples.

Mouse models of growth hormone deficiency

Nearly one century of research using growth hormone deficient (GHD) mouse lines has contributed greatly toward our knowledge of growth hormone (GH), a pituitary-derived hormone that binds and signals through the GH receptor and affects many metabolic processes throughout life. Although delayed sexual maturation, decreased fertility, reduced muscle mass, increased adiposity, small body size, and glucose intolerance appear to be among the negative characteristics of these GHD mouse lines, these mice still consistently outlive their normal sized littermates. Furthermore, the absence of GH action in these mouse lines leads to enhanced insulin sensitivity (likely due to the lack of GH's diabetogenic actions), delayed onset for a number of age-associated physiological declines (including cognition, cancer, and neuromusculoskeletal frailty), reduced cellular senescence, and ultimately, extended lifespan. In this review, we provide details about history, availability, growth, physiology, and aging of five commonly used GHD mouse lines.

Robust inference of kinase activity using functional networks

Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer’s disease and Parkinson’s disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io.

Kinases drive fundamental changes in cell state, but predicting kinase activity based on substrate-level changes can be challenging. Here the authors introduce a computational framework that utilizes similarities between substrates to robustly infer kinase activity.

Cancer Burden Is Controlled by Mural Cell-β3-Integrin Regulated Crosstalk with Tumor Cells

Enhanced blood vessel (BV) formation is thought to drive tumor growth through elevated nutrient delivery. However, this observation has overlooked potential roles for mural cells in directly affecting tumor growth independent of BV function. Here we provide clinical data correlating high percentages of mural-β3-integrin-negative tumor BVs with increased tumor sizes but no effect on BV numbers. Mural-β3-integrin loss also enhances tumor growth in implanted and autochthonous mouse tumor models with no detectable effects on BV numbers or function. At a molecular level, mural-cell β3-integrin loss enhances signaling via FAK-p-HGFR-p-Akt-p-p65, driving CXCL1, CCL2, and TIMP-1 production. In particular, mural-cell-derived CCL2 stimulates tumor cell MEK1-ERK1/2-ROCK2-dependent signaling and enhances tumor cell survival and tumor growth. Overall, our data indicate that mural cells can control tumor growth via paracrine signals regulated by β3-integrin, providing a previously unrecognized mechanism of cancer growth control.

Kinase Inhibitor Treatment of Patients with Advanced Cancer Results in High Tumor Drug Concentrations and in Specific Alterations of the Tumor Phosphoproteome

Identification of predictive biomarkers for targeted therapies requires information on drug exposure at the target site as well as its effect on the signaling context of a tumor. To obtain more insight in the clinical mechanism of action of protein kinase inhibitors (PKIs), we studied tumor drug concentrations of protein kinase inhibitors (PKIs) and their effect on the tyrosine-(pTyr)-phosphoproteome in patients with advanced cancer. Tumor biopsies were obtained from 31 patients with advanced cancer before and after 2 weeks of treatment with sorafenib (SOR), erlotinib (ERL), dasatinib (DAS), vemurafenib (VEM), sunitinib (SUN) or everolimus (EVE). Tumor concentrations were determined by LC-MS/MS. pTyr-phosphoproteomics was performed by pTyr-immunoprecipitation followed by LC-MS/MS. Median tumor concentrations were 2–10 µM for SOR, ERL, DAS, SUN, EVE and >1 mM for VEM. These were 2–178 × higher than median plasma concentrations. Unsupervised hierarchical clustering of pTyr-phosphopeptide intensities revealed patient-specific clustering of pre- and on-treatment profiles. Drug-specific alterations of peptide phosphorylation was demonstrated by marginal overlap of robustly up- and downregulated phosphopeptides. These findings demonstrate that tumor drug concentrations are higher than anticipated and result in drug specific alterations of the phosphoproteome. Further development of phosphoproteomics-based personalized medicine is warranted.

Proteomics and phosphoproteomics in precision medicine: applications and challenges

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases

Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label-free kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild-type versus mutant, +/- drug), (iii) pre- and on-treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient-derived tumor xenografts with INKA-guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate-based single-sample tool KARP, and underscore target potential of high-ranking kinases, encouraging further exploration of INKA's functional and clinical value.

The temporal profile of activity-dependent presynaptic phospho-signalling reveals long-lasting patterns of poststimulus regulation

Depolarization of presynaptic terminals stimulates calcium influx, which evokes neurotransmitter release and activates phosphorylation-based signalling. Here, we present the first global temporal profile of presynaptic activity-dependent phospho-signalling, which includes two KCl stimulation levels and analysis of the poststimulus period. We profiled 1,917 regulated phosphopeptides and bioinformatically identified six temporal patterns of co-regulated proteins. The presynaptic proteins with large changes in phospho-status were again prominently regulated in the analysis of 7,070 activity-dependent phosphopeptides from KCl-stimulated cultured hippocampal neurons. Active zone scaffold proteins showed a high level of activity-dependent phospho-regulation that far exceeded the response from postsynaptic density scaffold proteins. Accordingly, bassoon was identified as the major target of neuronal phospho-signalling. We developed a probabilistic computational method, KinSwing, which matched protein kinase substrate motifs to regulated phosphorylation sites to reveal underlying protein kinase activity. This approach allowed us to link protein kinases to profiles of co-regulated presynaptic protein networks. Ca²⁺- and calmodulin-dependent protein kinase IIα (CaMKIIα) responded rapidly, scaled with stimulus strength, and had long-lasting activity. Mitogen-activated protein kinase (MAPK)/extracellular signal–regulated kinase (ERK) was the main protein kinase predicted to control a distinct and significant pattern of poststimulus up-regulation of phosphorylation. This work provides a unique resource of activity-dependent phosphorylation sites of synaptosomes and neurons, the vast majority of which have not been investigated with regard to their functional impact. This resource will enable detailed characterization of the phospho-regulated mechanisms impacting the plasticity of neurotransmitter release.

Analysis of activity-dependent phosphorylation-based signalling in synaptosomes revealed six patterns of long-lasting presynaptic regulation from 1,917 phosphopeptides. The authors identified patterns most likely to be regulated by CamKII and MAPK/ERK and showed the active zone scaffold protein bassoon to be a major signalling target.

Neurobiological processes are altered by linking neuronal activity to regulated changes in protein phosphorylation levels that influence protein function. Although some of the major targets of activity-dependent phospho-signalling have been identified, a large number of substrates remain unknown. Here, we have screened systematically for these substrates and extended the list from hundreds to thousands of phosphorylation sites, thereby providing a new depth of understanding. We monitored phospho-signalling for 15 min after the stimulation, which to our knowledge had not been attempted at a large scale. We focused on presynaptic protein substrates of phospho-signalling by isolating the presynaptic terminal. We also stimulated hippocampal neurons but did not monitor the poststimulus. Although the phospho-signalling is immensely complex, the findings could be simplified through data exploration. We identified distinct patterns of presynaptic phospho-regulation across the time course that may constitute co-regulated protein networks. In addition, we found a subset of proteins that had many more phosphorylation sites than the average and high-magnitude responses, implying major signalling or functional roles for these proteins. We also determined the likely protein kinases with the strongest responses to the stimulus at different times using KinSwing, a computational tool that we developed. This resource reveals a new depth of activity-dependent phospho-signalling and identifies major signalling targets, major protein kinases, and co-regulated phosphoprotein networks.