Quantitative Phosphoproteomic Data Research Articles

Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer

Precision oncology approaches for patients with colorectal cancer (CRC) continue to lag behind other solid cancers. Functional precision oncology-a strategy that is based on perturbing primary tumor cells from cancer patients-could provide a road forward to personalize treatment. We extend this paradigm to measuring proteome activity landscapes by acquiring quantitative phosphoproteomic data from patient-derived organoids (PDOs). We show that kinase inhibitors induce inhibitor- and patient-specific off-target effects and pathway crosstalk. Reconstruction of the kinase networks revealed that the signaling rewiring is modestly affected by mutations. We show non-genetic heterogeneity of the PDOs and upregulation of stemness and differentiation genes by kinase inhibitors. Using imaging mass-cytometry-based profiling of the primary tumors, we characterize the tumor microenvironment (TME) and determine spatial heterocellular crosstalk and tumor-immune cell interactions. Collectively, we provide a framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno-) oncology in CRC.

Phosphoproteome Profiling Using an Isobaric Carrier without the Need for Phosphoenrichment.

Phosphorylation is a crucial component of cellular signaling cascades. It controls a variety of biological cellular functions, including cell growth and apoptosis. Owing to the low stoichiometry of phosphorylated proteins, the enrichment of phosphopeptides prior to LC-MS/MS is necessary for comprehensive phosphoproteome analysis, and quantitative phosphoproteomic workflows are typically limited by the amount of sample required. To address this issue, we developed an easy-to-establish, widely applicable, and reproducible strategy to increase phosphoproteomic signals from a small amount of sample without a phosphoenrichment step. By exploiting the multiplexing nature of isobaric labeling to generate a merged signal from multiple samples, and using a larger amount of enriched phosphopeptides as a carrier, we were able to increase trace amounts of phosphopeptides in the unpurified sample to an identifiable level and perform quantification using the reporter ion intensity of the isobaric tag. Our results showed that >1400 phosphopeptides were quantified from 250 ng of tryptic peptides prepared from cells. In a proof-of-concept of our strategy, we distinguished three types of lung cancer cell lines based on their quantitative phosphoproteomic data and identified changes in the phosphoproteome induced by drug treatment.

Transitions in the Proteome and Phospho-Proteome During <i>Xenopus laevis</i> Development

Vertebrate development from an egg to a complex multi-tissue million-cell organism is supported by multiple phases of genome-scale remodeling of the repertoire of proteins and their post-translational modifications, yet so far we know little about these phases. In this paper we present comprehensive characterization of these processes reflected by eleven time points, approximately eleven thousand proteins, and six thousand phospho-forms in two replicates. We find that the most dramatic changes to the proteome occur during the transition to functional organ systems, which occurs as the embryo becomes a tadpole. At that time the absolute amount of non-yolk protein increases two-fold, and there is a shift in the balance of expression from proteins regulating gene expression to receptors, ligands, and proteins involved in cell-cell and cell-environment interactions. Between the early and late tadpole, the median increase of membrane and secreted proteins is substantially higher than that of nuclear proteins. For the first time, we have included quantitative phosphoproteomic data across the same developmental stages. In contrast to the significant protein changes that are concentrated at the end of the time series, the most significant phosphorylation changes are concentrated in the very early stages of development. A clear exception are phosphorylations of proteins involved in gene expression; these increase just after fertilization, with patterns that are highly correlated with the underlying protein changes. To improve our interpretation of this unique data set, we created a pipeline for identifying homologous human phosphorylations from the measured Xenopus phospho-proteome, and we estimated, where possible, the occupancy of phosphorylation sites. Overall, we detected many profound temporal transitions, which suggest concerted changes in developmental strategies in the embryo that are particularly pronounced once early patterning and specification are complete.

QPhos: a database of protein phosphorylation dynamics in humans.

Temporal and spatial protein phosphorylation dynamically orchestrates a broad spectrum of biological processes and plays various physiological and pathological roles in diseases and cancers. Recent advancements in high-throughput proteomics techniques greatly promoted the profiling and quantification of phosphoproteome. However, although several comprehensive databases have reserved the phosphorylated proteins and sites, a resource for phosphorylation quantification still remains to be constructed. In this study, we developed the qPhos (http://qphos.cancerbio.info) database to integrate and host the data on phosphorylation dynamics. A total of 3 537 533 quantification events for 199 071 non-redundant phosphorylation sites on 18 402 proteins under 484 conditions were collected through exhaustive curation of published literature. The experimental details, including sample materials, conditions and methods, were recorded. Various annotations, such as protein sequence and structure properties, potential upstream kinases and their inhibitors, were systematically integrated and carefully organized to present details about the quantified phosphorylation sites. Various browse and search functions were implemented for the user-defined filtering of samples, conditions and proteins. Furthermore, the qKinAct service was developed to dissect the kinase activity profile from user-submitted quantitative phosphoproteome data through annotating the kinase activity-related phosphorylation sites. Taken together, the qPhos database provides a comprehensive resource for protein phosphorylation dynamics to facilitate related investigations.

Reconstructing phosphorylation signalling networks from quantitative phosphoproteomic data.

Cascades of phosphorylation between protein kinases comprise a core mechanism in the integration and propagation of intracellular signals. Although we have accumulated a wealth of knowledge around some such pathways, this is subject to study biases and much remains to be uncovered. Phosphoproteomics, the identification and quantification of phosphorylated proteins on a proteomic scale, provides a high-throughput means of interrogating the state of intracellular phosphorylation, both at the pathway level and at the whole-cell level. In this review, we discuss methods for using human quantitative phosphoproteomic data to reconstruct the underlying signalling networks that generated it. We address several challenges imposed by the data on such analyses and we consider promising advances towards reconstructing unbiased, kinome-scale signalling networks.

Heat stress induced, ligand-independent MET and EGFR signalling in hepatocellular carcinoma

Purpose: The aims of the present study were 2-fold: first, to test the hypothesis that heat stress induces MET and EGFR signalling in hepatocellular carcinoma (HCC) cells and inhibition of this signalling decreases HCC clonogenic survival; and second, to identify signalling pathways associated with heat stress induced MET signalling.Materials and Methods: MET+ and EGFR+ HCC cells were pre-treated with inhibitors to MET, EGFR, PI3K/mTOR or vehicle and subjected to heat stress or control ± HGF or EGF growth factors and assessed by colony formation assay, Western blotting and/or quantitative mass spectrometry. IACUC approved partial laser thermal or sham ablation was performed on orthotopic N1S1 and AS30D HCC tumours and liver/tumour assessed for phospho-MET and phospho-EGFR immunostaining.Results: Heat-stress induced rapid MET and EGFR phosphorylation that is distinct from HGF or EGF in HCC cells and thermal ablation induced MET but not EGFR phosphorylation at the HCC tumour ablation margin. Inhibition of the MET and EGFR blocked both heat stress and growth factor induced MET and EGFR phosphorylation and inhibition of MET decreased HCC clonogenic survival following heat stress. Pathway analysis of quantitative phosphoproteomic data identified downstream pathways associated with heat stress induced MET signalling including AKT, ERK, Stat3 and JNK. However, inhibition of heat stress induced MET signalling did not block AKT signalling.Conclusions: Heat-stress induced MET and EGFR signalling is distinct from growth factor mediated signalling in HCC cells and MET inhibition enhances heat stress induced HCC cell killing via a PI3K/AKT/mTOR-independent mechanism.

Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib.

Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature — integrin β4 (ITGB4) — was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.

Temporal Profiling of Lapatinib-suppressed Phosphorylation Signals in EGFR/HER2 Pathways

Lapatinib is a clinically potent kinase inhibitor for breast cancer patients because of its outstanding selectivity for epidermal growth factor receptor (EGFR) and EGFR2 (also known as HER2). However, there is only limited information about the in vivo effects of lapatinib on EGFR/HER2 and downstream signaling targets. Here, we profiled the lapatinib-induced time- and dose-dependent phosphorylation dynamics in SKBR3 breast cancer cells by means of quantitative phosphoproteomics. Among 4953 identified phosphopeptides from 1548 proteins, a small proportion (5-7%) was regulated at least twofold by 1-10 μm lapatinib. We obtained a comprehensive phosphorylation map of 21 sites on EGFR/HER2, including nine novel sites on HER2. Among them, serine/threonine phosphosites located in a small region of HER2 (amino acid residues 1049-1083) were up-regulated by the drug, whereas all other sites were down-regulated. We show that cAMP-dependent protein kinase is involved in phosphorylation of this particular region of HER2 and regulates HER2 tyrosine kinase activity. Computational analyses of quantitative phosphoproteome data indicated for the first time that protein-protein networks related to cytoskeletal organization and transcriptional/translational regulation, such as RNP complexes (i.e. hnRNP, snRNP, telomerase, ribosome), are linked to EGFR/HER2 signaling networks. To our knowledge, this is the first report to profile the temporal response of phosphorylation dynamics to a kinase inhibitor. The results provide new insights into EGFR/HER2 regulation through region-specific phosphorylation, as well as a global view of the cellular signaling networks associated with the anti-breast cancer action of lapatinib.

Phosphosignature Predicts Dasatinib Response in Non-small Cell Lung Cancer

Targeted drugs are less toxic than traditional chemotherapeutic therapies; however, the proportion of patients that benefit from these drugs is often smaller. A marker that confidently predicts patient response to a specific therapy would allow an individual therapy selection most likely to benefit the patient. Here, we used quantitative mass spectrometry to globally profile the basal phosphoproteome of a panel of non-small cell lung cancer cell lines. The effect of the kinase inhibitor dasatinib on cellular growth was tested against the same panel. From the phosphoproteome profiles, we identified 58 phosphorylation sites, which consistently differ between sensitive and resistant cell lines. Many of the corresponding proteins are involved in cell adhesion and cytoskeleton organization. We showed that a signature of only 12 phosphorylation sites is sufficient to accurately predict dasatinib sensitivity. Four of the phosphorylation sites belong to integrin β4, a protein that mediates cell-matrix or cell-cell adhesion. The signature was validated in cross-validation and label switch experiments and in six independently profiled breast cancer cell lines. The study supports that the phosphorylation of integrin β4, as well as eight further proteins comprising the signature, are candidate biomarkers for predicting response to dasatinib in solid tumors. Furthermore, our results show that identifying predictive phosphorylation signatures from global, quantitative phosphoproteomic data is possible and can open a new path to discovering molecular markers for response prediction.

Abstract 4808: Discovery of a phospho-signature predicting response to dasatinib in non-small cell lung cancer

Abstract Since targeted drugs selectively block molecular pathways that are typically over-activated in tumour cells, they are more precise and show fewer adverse effects than traditional chemotherapeutic agents. At the same time the proportion of patients that benefit from targeted drugs is smaller. Therefore, predictive molecular markers are needed to confidently predict the patient's response to a specific therapy. Such markers would facilitate therapy personalization, where the selected therapy is based on the molecular profile of the patient. We sought to identify a signature of protein phosphorylations that predicts the response to dasatinb in non-small cell lung cancer cell lines. A panel of cell lines were profiled in a global, unbiased, phosphoproteomics study yielding quantitative information for roughly 25,000 phosphorylation sites. The identical cell lines were tested for their response to dasatinib. From the phosphoproteome profiles, we identified a signature of twelve phosphorylation sites that can accurately predict dasatinib sensitivity. Four of the phosphorylation sites belong to integrin α4, a protein that mediates cell-matrix or cell-cell adhesion. We evaluated the performance of this signature in a cross-validation set-up and investigated the robustness of the selected predictive features. Finally, we confirmed the predictive power of the signature in an independent set of breast cancer cell lines. We showed that the phosphorylations of integrin β4 as well as eight further proteins are candidate biomarkers for predicting response to dasatinib in solid tumours. Furthermore, we demonstrated that identifying predictive phosphorylation signatures from global, quantitative phosphoproteomic data is possible, and opens a new path to discovering molecular markers for response prediction. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4808. doi:1538-7445.AM2012-4808