Analysis Of Phosphoproteomic Data Research Articles

Abstract B015: Wildtype RAS activity and PI3K signaling as new vulnerabilities in cells with acquired resistance to sotorasib

Abstract Purpose: Sotorasib (AMG510) has demonstrated remarkable response in lung cancer patients with tumors driven by oncogenic KRASG12C mutation. However, recently published clinical data identified acquired mutations in RAS and other genomic alterations as potential mechanisms of resistance. The cause of resistance in more than half of the patient cohort was undetermined with genomic sequencing. We hypothesize that rewiring of signaling networks could be the newly acquired vulnerabilities in cells progressing after sotorasib treatment. Experimental Design: We performed whole exome sequencing, transcriptomic profiling, and mass spectrometry-based proteomics/phosphoproteomics of the parental and sotorasib resistant isogenic line of LU65 (LU65-AMGR). The omics analyses were integrated with functional screens to prioritize a set of druggable targets/pathways. Results: We did not observe obvious acquired mutations that drive resistance to sotorasib in LU65-AMGR cells. To investigate compensatory signaling critically important for the survival/growth of LU65-AMGR cells, we studied signaling perturbations using mass spectrometry-based phosphoproteomics approach. We identified 430 and 1,574 phosphosites differentially expressed in resistant cells (± 1.5-fold & p < 0.05) with phosphotyrosine (pY) and global phosphoproteome (pS/T/Y) enrichment, respectively. Analysis of phosphoproteomics data identified increased phosphorylation of multiple RTKs including EGFR, HER2, HER3, IGF1R, AXL and PDGFRA, including others. RAS-GTP pull-down show increased levels of RAS-GTP and NRAS-GTP in the resistant cells. RAS isoform quantification using parallel reaction monitoring (MRM) mass spectrometry further confirmed increased activity of NRAS in pull-down samples. Transcriptomic analysis revealed elevated genes responsible for anti-apoptosis pathways mediated by PI3K/AKT signaling. Enrichment of transcription factors such as AP1 and AP-2A could drive enhanced EGF expression and EGFR phosphorylation in sotorasib resistant cells. Consistent with enhanced RTK and RAS activity, Western blots confirmed higher phosphorylation of ERK and AKT in LU65-AMGR cells. We show higher EGFR phosphorylation in resistant cells when compared to the parental counterpart. While LU65 cells viability is reduced markedly by sotorasib combination with afatinib, an irreversible HER family inhibitor, resistant cells showed lesser effect on cell viability with afatinib combination. Consistent with our in vitro observations, the sotorasib and afatinib combination treatment significantly regressed tumor growth only in LU65 xenografts, but not in LU65-AMGR xenografts. Conclusions: Our data suggest that activation of multiple RTKs maintains RAS activity and PI3K signaling in LU65-AMGR cells. Thus, dual pan-RAS and PI3K inhibition could serve as promising strategy of treatment in relapsed tumors identified with WT RAS and PI3K signaling activation. Citation Format: Denis Imbody, Hitendra S. Solanki, Bina Desai, Paul A. Stewart, Yaakov Stern, Ryoji Kato, Anurima Majumder, Liznair Bridenstine, Aobuli Xieraili, Bin Fang, Lancia Darville, Fumi Kinose, John M. Koomen, Uwe Rix, Andriy Marusyk, Eric B. Haura. Wildtype RAS activity and PI3K signaling as new vulnerabilities in cells with acquired resistance to sotorasib [abstract]. In: Proceedings of the AACR Special Conference: Targeting RAS; 2023 Mar 5-8; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Res 2023;21(5_Suppl):Abstract nr B015.

Abstract 5726: Pan-cancer proteogenomics expands the landscape of therapeutic targets

Abstract Background: Molecularly targeted therapies are critical for improving cancer treatment. Since proteins are the targets of these therapies and functional effectors of genomic aberrations, proteogenomics data from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) provides an unprecedented opportunity to characterize existing and future therapeutic targets for cancer treatment. Approach: CPTAC proteogenomics data from >1000 cancer patients spanning 10 cancer types was used to evaluate current and potential therapeutic targets curated from four databases. Cell line data from DepMap was further integrated to distinguish causations from associations. Computational pipelines were deployed to identify synthetic lethality for targeting tumor suppressor loss and to prioritize tumor associated antigens as immunotherapy targets. Results: We systematically collected 3050 druggable proteins and classified them into 5 tiers to facilitate different applications such as companion diagnostics, drug repurposing, and new therapy development. Many druggable proteins showed poor mRNA-protein correlation, including secreted proteins and proteins whose abundance was correlated with their interaction partners instead of cognate mRNA, highlighting the necessity of direct proteomic quantification of drug targets. 618 druggable proteins showed both overexpression in tumors compared to normal and significant dependency in CRISPR-Cas9 screens of cell lines of the same lineage. Notably, PAK1, a kinase targeted by investigational drugs, demonstrated both overexpression and dependency in all cancer types. A similar analysis of phosphoproteomics data focusing on known activating sites of druggable proteins further revealed targetable dependencies driven by protein hyperactivation. The phosphosite pS50 on PTPN1, a phosphatase targeted by experimental drugs, was increased in 7 cancer types and PTPN1 demonstrated dependency in related cancer cell lines. Based on tumor proteogenomic data and cell line CRISPR-Cas9 screen data, we identified synthetic lethality for difficult to target tumor suppressor losses, revealing TP53 mutations as a candidate biomarker to select breast cancer patients for CHEK1 inhibition, and endometrial cancer patients for treatment with doxorubicin. We identified 140 proteins whose expression was restricted in normal tissues but abnormal in tumors. Experimental analysis of peptides predicted to have high binding affinity to the most common allotype HLA-A02 for 7 prioritized proteins identified 21 peptides from 5 proteins with both strong binding affinity and immunogenicity which could be further investigated as immunotherapy targets. Conclusion: We generate a comprehensive resource of protein and peptide targets that covers multiple therapeutic modalities. This unique resource will pave the way for repurposing of currently available drugs and developing new drugs for cancer treatment. Citation Format: Jonathan T. Lei, Sara R. Savage, Xinpei Yi, Bo Wen, Hongwei Zhao, Lauren K. Somes, Paul W. Shafer, Yongchao Dou, Qiang Gao, Valentina Hoyos, Bing Zhang. Pan-cancer proteogenomics expands the landscape of therapeutic targets. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5726.

Dual data and motif clustering improves the modeling and interpretation of phosphoproteomic data

SummaryCell signaling is orchestrated in part through a network of protein kinases and phosphatases. Dysregulation of kinase signaling is widespread in diseases such as cancer and is readily targetable through inhibitors. Mass spectrometry-based analysis can provide a global view of kinase regulation, but mining these data is complicated by its stochastic coverage of the proteome, measurement of substrates rather than kinases, and the scale of the data. Here, we implement a dual data and motif clustering (DDMC) strategy that simultaneously clusters peptides into similarly regulated groups based on their variation and their sequence profile. We show that this can help to identify putative upstream kinases and supply more robust clustering. We apply this clustering to clinical proteomic profiling of lung cancer and identify conserved proteomic signatures of tumorigenicity, genetic mutations, and immune infiltration. We propose that DDMC provides a general and flexible clustering strategy for the analysis of phosphoproteomic data.

Phosphoproteomics Identifies PI3K Inhibitor-selective Adaptive Responses in Pancreatic Cancer Cell Therapy and Resistance.

The PI3K pathway is highly active in human cancers. The four class I isoforms of PI3K are activated by distinct mechanisms leading to a common downstream signaling. Their downstream redundancy is thought to be responsible for treatment failures of PI3K inhibitors. We challenged this concept, by mapping the differential phosphoproteome evolution in response to PI3K inhibitors with different isoform-selectivity patterns in pancreatic cancer, a disease currently without effective therapy. In this cancer, the PI3K signal was shown to control cell proliferation. We compared the effects of LY294002 that inhibit with equal potency all class I isoenzymes and downstream mTOR with the action of inhibitors with higher isoform selectivity toward PI3Kα, PI3Kβ, or PI3Kγ (namely, A66, TGX-221 and AS-252424). A bioinformatics global pathway analysis of phosphoproteomics data allowed us to identify common and specific signals activated by PI3K inhibitors supported by the biological data. AS-252424 was the most effective treatment and induced apoptotic pathway activation as well as the highest changes in global phosphorylation-regulated cell signal. However, AS-252424 treatment induced reactivation of Akt, therefore decreasing the treatment outcome on cell survival. Reversely, AS-252424 and A66 combination treatment prevented p-Akt reactivation and led to synergistic action in cell lines and patient organoids. The combination of clinically approved α-selective BYL-719 with γ-selective IPI-549 was more efficient than single-molecule treatment on xenograft growth. Mapping unique adaptive signaling responses to isoform-selective PI3K inhibition will help to design better combinative treatments that prevent the induction of selective compensatory signals.

Using Multilayer Heterogeneous Networks to Infer Functions of Phosphorylated Sites.

Mass spectrometry-based quantitative phosphoproteomics has become an essential approach in the study of cellular processes such as signaling. Commonly used methods to analyze phosphoproteomics datasets depend on generic, gene-centric annotations such as Gene Ontology terms, which do not account for the function of a protein in a particular phosphorylation state. Analysis of phosphoproteomics data is hampered by a lack of phosphorylated site-specific annotations. We propose a method that combines shotgun phosphoproteomics data, protein–protein interactions, and functional annotations into a heterogeneous multilayer network. Phosphorylation sites are associated to potential functions using a random walk on the heterogeneous network (RWHN) algorithm. We validated our approach against a model of the MAPK/ERK pathway and functional annotations from PhosphoSitePlus and were able to associate differentially regulated sites on the same proteins to their previously described specific functions. We further tested the algorithm on three previously published datasets and were able to reproduce their experimentally validated conclusions and to associate phosphorylation sites with known functions based on their regulatory patterns. Our approach provides a refinement of commonly used analysis methods and accurately predicts context-specific functions for sites with similar phosphorylation profiles.

PhosR enables processing and functional analysis of phosphoproteomic data

Mass spectrometry (MS)-based phosphoproteomics has revolutionized our ability to profile phosphorylation-based signaling in cells and tissues on a global scale. To infer the action of kinases and signaling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalization by using a set of "stably phosphorylated sites" and in functional analysis for inferring active kinases and signaling pathways. In particular, we introduce a "signalome" construction method for identifying a collection of signaling modules to summarize and visualize the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating biological knowledge from MS-based phosphoproteomic data.

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep-learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT, and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep-learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

Computational Analysis of Phosphoproteomics Data in Multi-Omics Cancer Studies.

Multiple types of molecular data for the same set of clinical samples are increasingly available and may be analyzed jointly in an integrative analysis to maximize comprehensive biological insight. This analysis is important as separate analyses of individual omics data types usually do not fully explain disease phenotypes. An increasing number of studies have now been focusing on multi-omics data integration, yet not many studies have included phosphoproteomics data, an important layer for understanding signaling pathways. Multi-omics integration methods with phosphoproteomics data are reviewed in the context of cancer research as well as multi-omics methods papers that would be promising to apply to phosphoproteomics data. Analysis of individual data types is still the major approach even in large cohort proteogenomics studies. Hence, a section is dedicated on possible integrative methods for multi-omics and phosphoproteomics data. In summary, this review provides the readers with both currently used integrative methods previously applied to phosphoproteomics and multi-omics data integration and other algorithms for multi-omics data integration promising for future application to phosphoproteomics data.

PhosR Enables Processing and Functional Analysis of Phosphoproteomic Data

Mass spectrometry (MS)-based phosphoproteomics has revolutionised our ability to profile phosphorylation-based signalling in cells and tissues on a global scale. To infer the action of kinases and signalling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalisation using a novel set of ‘stably phosphorylated sites’, and in functional analysis for inferring active kinases and signalling pathways. In particular, we introduce a ‘signalome’ construction method for identifying a collection of signalling modules to summarise and visualise the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating novel biological knowledge from MS-based phosphoproteomic data.

Proteomic and Phosphoproteomic Analysis Reveals that Neurokinin-1 Receptor (NK1R) Blockade with Aprepitant in Human Keratinocytes Activates a Distinct Subdomain of EGFR Signaling: Implications for the Anti-Pruritic Activity of NK1R Antagonists.

Background: Epidermal growth factor receptor (EGFR) inhibitors can cause serious cutaneous toxicities, including pruritus and papulopustular acneiform skin eruptions. Increasingly, the neurokinin-1 receptor (NK1R) antagonist aprepitant is being utilized as an anti-pruritic agent in the treatment of EGFR-inhibitor induced pruritus. Aprepitant is believed to reduce itching by blocking NK1R on the surface of dermal mast cells. However, the effects of aprepitant on human keratinocytes remains unexplored. Methods: Herein, we examine the effects of aprepitant on EGFR stimulation in HaCaT cells using a phosphoproteomic approach including reverse phase protein arrays and Ingenuity Pathway Analysis. Changes in EGFR phosphorylation were visualized using Western blotting and the effect of EGF and aprepitant on the growth of HaCaT cells was determined using the WST-1 Cell Proliferation Assay System. Results: We found that aprepitant increased the phosphorylation of EGFR, as well as 10 of the 23 intracellular proteins phosphorylated by EGF. Analysis of phosphoproteomic data using Ingenuity Pathway Analysis software revealed that 5 of the top 10 pathways activated by EGF and aprepitant are shared. Conclusions: We propose that aprepitant produces its antipruritic effects by partially activating EGFR. Activation of EGFR by aprepitant was also seen in primary human keratinocytes. In addition to itch reduction through partial activation of shared EGFR pathways, aprepitant exerts a dose-dependent cytotoxicity to epithelial cells, which may contribute to its antitumor effects.

Abstract C088: Chemical phosphoproteomics systematically identifies circuitries of kinase networks in cancer cells and predicts their response to kinase inhibitors

Abstract Introduction: Responses to targeted drugs are highly variable across patients and current genomic biomarkers are often ineffective at stratification. Here, we tested the hypothesis that direct quantification of kinase activity markers (using phosphoproteomics) would predict kinase inhibitor efficacy in cancer with greater accuracy than proxies of pathway activation (such as genetic mutations). Experimental procedures We designed an approach (based on computational analysis of phosphoproteomics data) that (i) systematically identifies markers of kinase activity and (ii) measures these markers in primary cancer cells with precision and accuracy. To test the performance of the method, we carried out LC-MS/MS phosphoproteomics analysis of three different cancer cell lines treated with 60 different kinase inhibitors. In parallel, we determined the selectivity profile of the same 60 compounds against 460 kinases. An algorithm was designed to compare the in vitro specificity profiles of these kinase inhibitors with their effects on cellular phosphoproteomes. We then statistically assessed kinase activity enrichment in primary breast tumors from 86 cases and 36 primary acute myeloid leukaemia (AML) blast specimens, by measuring our markers of kinase and signaling activity in these tumors. Models to predict sensitivity to kinase inhibitors were constructed using methods based on multivariate regression (partial least squares) and machine learning (random forest and neural networks). Unpublished data We quantified 22,000 unique protein phosphorylation sites in three cell lines treated with 60 kinase inhibitors each in quadruplicate, resulting in the acquisition of 15.8M quantitative data points. Using our newly developed computational tools, we identified 6,206 kinase activity markers for 106 kinases and 1,508 network edges (kinase-kinase relationships). These markers of kinase activity and network circuitry correlated with the impact that several kinase inhibitors had in reducing the viability of primary AML cases. Machine learning models, that used our markers of kinase activity as input, predicted responses to inhibitors with high accuracy (RMSE <0.15). As an example, our approach was twice more accurate at determining response to midostaurin than the FDA approved FLT3-ITD mutation biomarkers. Quantification of our activity markers in 86 primary breast tumors revealed an anti-correlation between MAPK and PI3K activity markers. Consistent with this observation, MAPK pathway inhibitors decreased the viability of cells with wild-type PIK3CA to a greater extent than those harboring PIK3CA mutations in helical or kinase domains. Our data suggest that absence of PIK3CA mutations, in combination with our MAPK pathway activity markers, should be investigated as a predictive signature for MAPK pathway inhibitors. Conclusion: Our study provides insights into kinase network regulation and represents a unique resource to investigate the relationships between kinase network topology and drug response versus resistance. Crucially, we demonstrate that our approach predicts efficacy of targeted therapies with greater accuracy than mutational analysis. Citation Format: Pedro Rodriguez Cutillas, Mauran Hijazi, Ryan Smith, Conrad Bessant, David Britton. Chemical phosphoproteomics systematically identifies circuitries of kinase networks in cancer cells and predicts their response to kinase inhibitors [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C088. doi:10.1158/1535-7163.TARG-19-C088

PKCε contributes to lipid-induced insulin resistance through cross talk with p70S6K and through previously unknown regulators of insulin signaling

Insulin resistance drives the development of type 2 diabetes (T2D). In liver, diacylglycerol (DAG) is a key mediator of lipid-induced insulin resistance. DAG activates protein kinase C ε (PKCε), which phosphorylates and inhibits the insulin receptor. In rats, a 3-day high-fat diet produces hepatic insulin resistance through this mechanism, and knockdown of hepatic PKCε protects against high-fat diet-induced hepatic insulin resistance. Here, we employed a systems-level approach to uncover additional signaling pathways involved in high-fat diet-induced hepatic insulin resistance. We used quantitative phosphoproteomics to map global in vivo changes in hepatic protein phosphorylation in chow-fed, high-fat-fed, and high-fat-fed with PKCε knockdown rats to distinguish the impact of lipid- and PKCε-induced protein phosphorylation. This was followed by a functional siRNA-based screen to determine which dynamically regulated phosphoproteins may be involved in canonical insulin signaling. Direct PKCε substrates were identified by motif analysis of phosphoproteomics data and validated using a large-scale in vitro kinase assay. These substrates included the p70S6K substrates RPS6 and IRS1, which suggested cross talk between PKCε and p70S6K in high-fat diet-induced hepatic insulin resistance. These results identify an expanded set of proteins through which PKCε may drive high-fat diet-induced hepatic insulin resistance that may direct new therapeutic approaches for T2D.

Abstract 2262: Inference of kinase activity for cancer phosphoproteomics using substrate prediction scores

Abstract Background: Quantification of global protein phosphorylation abundance levels with residue-level resolution is feasible with mass spectrometry and phosphopeptide enrichment methods. Researchers have inferred kinase activity from phosphoproteomics data using a variety of methods to extract interpretable and biologically relevant information, but analysis of phosphoproteomics data is limited by the fact that detected phosphorylation sites often have unknown function and relevance. Methods: We developed a novel method for aggregating and interpreting phosphoproteomics data that combines kinase prediction scores from NetworKIN with experimental summary statistics to estimate abundance changes in predicted kinase substrates. By weighting student t-statistic scores by kinase prediction scores and summing cumulative scores across all phosphopeptides detected in a given screen, we generated kinase activity scores that reflect the overall direction, magnitude and significance of observed changes in predicted kinase substrates. These kinase scores were assessed using permutation testing to determine their significance versus changes that would be expected from random chance. To evaluate our method, we applied it to a shotgun phosphoproteomic dataset generated to study phosphorylation responses to rapamycin, dasatinib and combination treatment in MDA-MB-231 breast cancer cells. Results: We were able to confirm phosphoproteomic perturbations in expected “hallmark” kinase activities using prediction-based kinase activity inference scores, including decreased phosphorylation of Src and Abl substrates in response to dasatinib treatment, as well as decreases in p70S6K substrate phosphorylation in response to rapamycin. Compared to KSEA, we identified changes in substrate abundance for a variety of potential downstream kinases including DNAPK, PAK2, CDK3, and multiple members of the MAPK family that were observed to be downregulated in single-agent and in combination treatment. Conclusions: Our novel method provides a representation of phosphoproteomic changes based on abundance of predicted kinase substrates. Using predicted substrates allows for a global view of phosphoproteomic changes that extends beyond the relatively small number of known kinase-substrate relationships. Using a drug combination with potential application in breast cancer as an example, we demonstrate that this method can be used to identify phosphoproteomic changes and can potentially be used to rationally design and investigate novel drug combinations. Citation Format: Peter Liao, Jennifer Yori, Ruth Keri, Mehmet Koyuturk, Jill Barnholtz-Sloan. Inference of kinase activity for cancer phosphoproteomics using substrate prediction scores [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2262.

DynaPho: a web platform for inferring the dynamics of time-series phosphoproteomics.

Large-scale phosphoproteomics studies have improved our understanding of dynamic cellular signaling, but the downstream analysis of phosphoproteomics data is still a bottleneck. We develop DynaPho, a useful web-based tool providing comprehensive and in-depth analyses of time-course phosphoproteomics data, making analysis intuitive and accessible to non-bioinformatics experts. The tool currently implements five analytic modules, which reveal the transition of biological pathways, kinase activity, dynamics of interaction networks and the predicted kinase-substrate associations. These features can assist users in translating their larger-scale time-course phosphoproteomics data into valuable biological discoveries. DynaPho is freely available at http://dynapho.jhlab.tw/ . hsuancheng@ym.edu.tw or yukijuan@ntu.edu.tw . Supplementary data are available at Bioinformatics online.

Nuclear phosphoproteomics analysis reveals that CDK1/2 are involved in EGF-regulated constitutive pre-mRNA splicing in MDA-MB-468 cells

The epidermal growth factor (EGF) receptor (EGFR) pathway is one of the most dysregulated and extensively investigated signaling pathways in human cancers and plays important roles in the regulation of nuclear functions through both cytoplasmic and nuclear EGFR pathways. However, the current understanding of the nuclear phosphorylation responses to activated EGFR pathways remains limited. In the present study, phosphoproteomics analysis revealed the increased phosphorylation of 90 nuclear proteins, primarily involved in RNA processing, pre-mRNA splicing and cell cycle regulation, upon EGF stimulation in MDA-MB-468 cells. Cellular splicing assays of the β-globin (HBB) minigene confirmed that EGF induced constitutive pre-mRNA splicing. Further analysis of phosphoproteomics data identified multiple CDK1/2 substrates in pre-mRNA splicing-related proteins, and both CDK1/2 inhibitors and CDK1/2 knockdowns reduced EGF-regulated pre-mRNA splicing. In conclusion, the results of the present study provide evidence that CDK1/2 participate in the regulation of constitutive pre-mRNA splicing by EGF stimulation in MDA-MB-468 cells. SignificanceIn this study, we successfully carried out a survey of nuclear phosphorylation changes in response to EGF stimulation. The results from the functional category analysis and pre-mRNA splicing assay strongly indicated that EGFR activation increased constitutive pre-mRNA splicing in MDA-MB-468 cells, revealing additional role of EGFR on regulation of mRNA maturation beyond alternative pre-mRNA splicing reported by previous studies. Furthermore, we found that CDK1/2 participated in constitutive pre-mRNA splicing regulation by EGF in MDA-MB-468 cells. Our study provides new knowledge for understanding the regulation of constitutive pre-mRNA splicing by EGF stimulation.

SELPHI: correlation-based identification of kinase-associated networks from global phospho-proteomics data sets.

While phospho-proteomics studies have shed light on the dynamics of cellular signaling, they mainly describe global effects and rarely explore mechanistic details, such as kinase/substrate relationships. Tools and databases, such as NetworKIN and PhosphoSitePlus, provide valuable regulatory details on signaling networks but rely on prior knowledge. They therefore provide limited information on less studied kinases and fewer unexpected relationships given that better studied signaling events can mask condition- or cell-specific ‘network wiring’.SELPHI is a web-based tool providing in-depth analysis of phospho-proteomics data that is intuitive and accessible to non-bioinformatics experts. It uses correlation analysis of phospho-sites to extract kinase/phosphatase and phospho-peptide associations, and highlights the potential flow of signaling in the system under study. We illustrate SELPHI via analysis of phospho-proteomics data acquired in the presence of erlotinib—a tyrosine kinase inhibitor (TKI)—in cancer cells expressing TKI-resistant and -sensitive variants of the Epidermal Growth Factor Receptor. In this data set, SELPHI revealed information overlooked by the reporting study, including the known role of MET and EPHA2 kinases in conferring resistance to erlotinib in TKI sensitive strains. SELPHI can significantly enhance the analysis of phospho-proteomics data contributing to improved understanding of sample-specific signaling networks. SELPHI is freely available via http://llama.mshri.on.ca/SELPHI.

Confident and sensitive phosphoproteomics using combinations of collision induced dissociation and electron transfer dissociation

We present a workflow using an ETD-optimised version of Mascot Percolator and a modified version of SLoMo (turbo-SLoMo) for analysis of phosphoproteomic data. We have benchmarked this against several database searching algorithms and phosphorylation site localisation tools and show that it offers highly sensitive and confident phosphopeptide identification and site assignment with PSM-level statistics, enabling rigorous comparison of data acquisition methods. We analysed the Plasmodium falciparum schizont phosphoproteome using for the first time, a data-dependent neutral loss-triggered-ETD (DDNL) strategy and a conventional decision-tree method. At a posterior error probability threshold of 0.01, similar numbers of PSMs were identified using both methods with a 73% overlap in phosphopeptide identifications. The false discovery rate associated with spectral pairs where DDNL CID/ETD identified the same phosphopeptide was < 1%. 72% of phosphorylation site assignments using turbo-SLoMo without any score filtering, were identical and 99.8% of these cases are associated with a false localisation rate of < 5%. We show that DDNL acquisition is a useful approach for phosphoproteomics and results in an increased confidence in phosphopeptide identification without compromising sensitivity or duty cycle. Furthermore, the combination of Mascot Percolator and turbo-SLoMo represents a robust workflow for phosphoproteomic data analysis using CID and ETD fragmentation.Biological significanceProtein phosphorylation is a ubiquitous post-translational modification that regulates protein function. Mass spectrometry-based approaches have revolutionised its analysis on a large-scale but phosphorylation sites are often identified by single phosphopeptides and therefore require more rigorous data analysis to unsure that sites are identified with high confidence for follow-up experiments to investigate their biological significance. The coverage and confidence of phosphoproteomic experiments can be enhanced by the use of multiple complementary fragmentation methods. Here we have benchmarked a data analysis pipeline for analysis of phosphoproteomic data generated using CID and ETD fragmentation and used it to demonstrate the utility of a data-dependent neutral loss triggered ETD fragmentation strategy for high confidence phosphopeptide identification and phosphorylation site localisation.

Kinase-Substrate Enrichment Analysis Provides Insights into the Heterogeneity of Signaling Pathway Activation in Leukemia Cells

Kinases determine the phenotypes of many cancer cells, but the frequency with which individual kinases are activated in primary tumors remains largely unknown. We used a computational approach, termed kinase-substrate enrichment analysis (KSEA), to systematically infer the activation of given kinase pathways from mass spectrometry-based phosphoproteomic analysis of acute myeloid leukemia (AML) cells. Experiments conducted in cell lines validated the approach and, furthermore, revealed that DNA-dependent protein kinase (DNA-PK) was activated as a result of inhibiting the phosphoinositide 3-kinase (PI3K)-mammalian target of rapamycin (mTOR) signaling pathway. Application of KSEA to primary AML cells identified PI3K, casein kinases (CKs), cyclin-dependent kinases (CDKs), and p21-activated kinases (PAKs) as the kinase substrate groups most frequently enriched in this cancer type. Substrates phosphorylated by extracellular signal-regulated kinase (ERK) and cell division cycle 7 (CDC7) were enriched in primary AML cells that were resistant to inhibition of PI3K-mTOR signaling, whereas substrates of the kinases Abl, Lck, Src, and CDK1 were increased in abundance in inhibitor-sensitive cells. Modeling based on the abundances of these substrate groups accurately predicted sensitivity to a dual PI3K and mTOR inhibitor in two independent sets of primary AML cells isolated from patients. Thus, our study demonstrates KSEA as an untargeted method for the systematic profiling of kinase pathway activities and for increasing our understanding of diseases caused by the dysregulation of signaling pathways.

In Vivo SILAC-Based Proteomics Reveals Phosphoproteome Changes during Mouse Skin Carcinogenesis

Cancer progresses through distinct stages, and mouse models recapitulating traits of this progression are frequently used to explore genetic, morphological, and pharmacological aspects of tumor development. To complement genomic investigations of this process, we here quantify phosphoproteomic changes in skin cancer development using the SILAC mouse technology coupled to high-resolution mass spectrometry. We distill protein expression signatures from our data that distinguish between skin cancer stages. A distinct phosphoproteome of the two stages of cancer progression is identified that correlates with perturbed cell growth and implicates cell adhesion as a major driver of malignancy. Importantly, integrated analysis of phosphoproteomic data and prediction of kinase activity revealed PAK4-PKC/SRC network to be highly deregulated in SCC but not in papilloma. This detailed molecular picture, both at the proteome and phosphoproteome level, will prove useful for the study of mechanisms of tumor progression.