Pedro Casado

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

Computational analysis of phosphoproteomics data predicts the sensitivity of leukemia cells to kinase inhibitors. Therapeutic Targeting with Phosphoproteomics Because oncogenic mutations frequently occur in genes encoding kinases, kinase inhibitors are often used as cancer therapeutics. However, because of the complexity of kinase signaling networks and the heterogeneous nature of oncogenic mutations, it is difficult to predict how cancer cells will respond to a given kinase inhibitor. Casado et al. performed phosphoproteomic analysis of human acute myeloid leukemia (AML) cell lines and used a computational approach of “signal averaging” to reduce the noise in the phosphoproteomics data, thus monitoring global kinase activation patterns as well as the effects of select inhibitors. In addition to identifying kinase networks associated with AML, this approach accurately predicted the relative sensitivities of patient-derived AML cells to inhibitors. Such profiling of kinase networks could be applied to stratify cancers based on their predicted responses to kinase inhibition. 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.

Characterization of a TiO2 enrichment method for label-free quantitative phosphoproteomics

Phosphorylation is a protein post-translational modification with key roles in the regulation of cell biochemistry and signaling. In-depth analysis of phosphorylation using mass spectrometry is permitting the investigation of processes controlled by phosphorylation at the system level. A critical step of these phosphoproteomics methods involves the isolation of phosphorylated peptides from the more abundant unmodified peptides produced by the digestion of cell lysates. Although different techniques to enrich for phosphopeptides have been reported, there are limited data on their suitability for direct quantitative analysis by MS. Here we report a TiO2 based enrichment method compatible with large-scale and label-free quantitative analysis by LC–MS/MS. Starting with just 500 μg of protein, the technique reproducibly isolated hundreds of peptides, >85% of which were phosphorylated. These results were obtained by using relatively short LC–MS/MS gradient runs (45 min) and without any previous separation step. In order to characterize the performance of the method for quantitative analyses, we employed label-free LC–MS/MS using extracted ion chromatograms as the quantitative readout. After normalization, phosphopeptides were quantified with good precision (coefficient of variation was 20% on average, n = 900 phosphopeptides), linearity (correlation coefficients >0.98) and accuracy (deviations <20%). Thus, phosphopeptide ion signals correlated with the concentration of the respective phosphopeptide in samples, making the approach suitable for in-depth relative quantification of phosphorylation by label-free LC–MS/MS.

A Self-validating Quantitative Mass Spectrometry Method for Assessing the Accuracy of High-content Phosphoproteomic Experiments

Protein kinase pathways play pivotal roles in cell signaling and biology. The phosphoproteome is a reflection of protein kinase pathway activation and therefore there is considerable interest in its quantification as a means to assess the wiring of signaling networks. Although different approaches for quantitative phosphoproteomics have been described, there is no data on how accurate these are for each quantified phosphorylated site. We report a liquid chromatography-MS approach to objectively assess data quality in high-content comparison of phosphoproteomes in which samples to be compared are mixed at different proportions. The experimental data is then used to derive a linear regression function that allows calculating correlation values, linearity, and accuracy. We applied the technique to investigate phosphorylation in P31/Fuj and Kasumi-1, two leukemia cells lines showing strikingly different sensitivities to scr and PI3K inhibitors. We found that phosphopeptides quantified with accuracy were not always quantified with precision because of low ion statistics contributing to variability. Thus our approach was complementary to standard methods for calculating the precision of replicate measurements based on the coefficient of variation and provided additional information on data quality for each quantified phosphopeptide. We quantified > 2250 phosphorylation sites across cell lines with different levels of sensitivity to kinase inhibitors, of which 1847 showed an accuracy variation of < 30% (with an overall mean of 22%). Hundreds of phosphorylation sites on proteins with diverse function (including kinases, transcription, and translation factors) showed significantly distinct intensities across sensitive and resistant cells lines, indicating that kinase pathways are differentially regulated in cancer cells of distinct sensitivity to signaling inhibitors.

Phosphoproteomic Analysis of Leukemia Cells under Basal and Drug-treated Conditions Identifies Markers of Kinase Pathway Activation and Mechanisms of Resistance

Protein kinase signaling is fundamental to cell homeostasis and is deregulated in all cancers but varies between patients. Understanding the mechanisms underlying this heterogeneity is critical for personalized targeted therapies. Here, we used a recently established LC-MS/MS platform to profile protein phosphorylation in acute myeloid leukemia cell lines with different sensitivities to kinase inhibitors. The compounds used in this study were originally developed to target Janus kinase, phosphatidylinositol 3-kinase, and MEK. After further validation of the technique, we identified several phosphorylation sites that were inhibited by these compounds but whose intensities did not always correlate with growth inhibition sensitivity. In contrast, several hundred phosphorylation sites that correlated with sensitivity/resistance were not in general inhibited by the compounds. These results indicate that markers of pathway activity may not always be reliable indicators of sensitivity of cancer cells to inhibitors that target such pathways, because the activity of parallel kinases can contribute to resistance. By mining our data we identified protein kinase C isoforms as one of such parallel pathways being more active in resistant cells. Consistent with the view that several parallel kinase pathways were contributing to resistance, inhibitors that target protein kinase C, MEK, and Janus kinase potentiated each other in arresting the proliferation of multidrug-resistant cells. Untargeted/unbiased approaches, such as the one described here, to quantify the activity of the intended target kinase pathway in concert with the activities of parallel kinase pathways will be invaluable to personalize therapies based on kinase inhibitors.

Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors

BackgroundTumor classification based on their predicted responses to kinase inhibitors is a major goal for advancing targeted personalized therapies. Here, we used a phosphoproteomic approach to investigate biological heterogeneity across hematological cancer cell lines including acute myeloid leukemia, lymphoma, and multiple myeloma.ResultsMass spectrometry was used to quantify 2,000 phosphorylation sites across three acute myeloid leukemia, three lymphoma, and three multiple myeloma cell lines in six biological replicates. The intensities of the phosphorylation sites grouped these cancer cell lines according to their tumor type. In addition, a phosphoproteomic analysis of seven acute myeloid leukemia cell lines revealed a battery of phosphorylation sites whose combined intensities correlated with the growth-inhibitory responses to three kinase inhibitors with remarkable correlation coefficients and fold changes (> 100 between the most resistant and sensitive cells). Modeling based on regression analysis indicated that a subset of phosphorylation sites could be used to predict response to the tested drugs. Quantitative analysis of phosphorylation motifs indicated that resistant and sensitive cells differed in their patterns of kinase activities, but, interestingly, phosphorylations correlating with responses were not on members of the pathway being targeted; instead, these mainly were on parallel kinase pathways.ConclusionThis study reveals that the information on kinase activation encoded in phosphoproteomics data correlates remarkably well with the phenotypic responses of cancer cells to compounds that target kinase signaling and could be useful for the identification of novel markers of resistance or sensitivity to drugs that target the signaling network.

Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data

Mass spectrometry is widely used to probe the proteome and its modifications in an untargeted manner, with unrivalled coverage. Applied to phosphoproteomics, it has tremendous potential to interrogate phospho-signalling and its therapeutic implications. However, this task is complicated by issues of undersampling of the phosphoproteome and challenges stemming from its high-content but low-sample-throughput nature. Hence, methods using such data to reconstruct signalling networks have been limited to restricted data sets and insights (for example, groups of kinases likely to be active in a sample). We propose a new method to handle high-content discovery phosphoproteomics data on perturbation by putting it in the context of kinase/phosphatase-substrate knowledge, from which we derive and train logic models. We show, on a data set obtained through perturbations of cancer cells with small-molecule inhibitors, that this method can study the targets and effects of kinase inhibitors, and reconcile insights obtained from multiple data sets, a common issue with these data.

Environmental Stress Affects the Activity of Metabolic and Growth Factor Signaling Networks and Induces Autophagy Markers in MCF7 Breast Cancer Cells

Phosphoproteomic techniques are contributing to our understanding of how signaling pathways interact and regulate biological processes. This technology is also being used to characterize how signaling networks are remodeled during disease progression and to identify biomarkers of signaling pathway activity and of responses to cancer therapy. A potential caveat in these studies is that phosphorylation is a very dynamic modification that can substantially change during the course of an experiment or the retrieval and processing of cellular samples. Here, we investigated how exposure of cells to ambient conditions modulates phosphorylation and signaling pathway activity in the MCF7 breast cancer cell line. About 1.5% of 3,500 sites measured showed a significant change in phosphorylation extent upon exposure of cells to ambient conditions for 15 min. The effects of this perturbation in modifying phosphorylation patterns did not involve random changes due to stochastic activation of kinases and phosphatases. Instead, exposure of cells to ambient conditions elicited an environmental stress reaction that involved a coordinated response to a metabolic stress situation, which included: (1) the activation of AMPK; (2) the inhibition of PI3K, AKT, and ERK; (3) an increase in markers of protein synthesis inhibition at the level of translation elongation; and (4) an increase in autophagy markers. We also observed that maintaining cells in ice modified but did not completely abolish this metabolic stress response. In summary, exposure of cells to ambient conditions affects the activity of signaling networks previously implicated in metabolic and growth factor signaling. Mass spectrometry data have been deposited to the ProteomeXchange with identifier PXD000472.

Global profiling of protein kinase activities in cancer cells by mass spectrometry.

Protein kinases have important functions in the control of cell biology and are implicated in several diseases including cancer. Here we describe a technique to quantify protein kinase activity in a global fashion and without preconception of the kinases that may be active in the cell or tissue under investigation. In Global Kinase Activity Profiling (GKAP), protein kinases present in experimental cell lysates phosphorylate endogenous substrates, also present in the lysate, under defined conditions. Reaction products are then quantified using standard phosphoproteomic techniques based on LC-MS/MS. The technique thus allows measuring the combined activities of kinases targeting common substrates, which are detected as phosphopeptides by LC-MS/MS. Almost four hundred kinase reactions could be quantified in a human epithelial cell line, 177 of which increased in response to EGF treatment while others decreased in cells exposed to the kinase inhibitors LY294002 or U0126. GKAP also detected marked differences in the patterns of kinase activities in human leukemia cell lines with different sensitivities to kinase inhibitors. These results reveal that GKAP detects and quantifies hundreds of kinase activities modulated by growth factors or pharmacological inhibitors, and that these activities correlate with the phenotypes of cancer cells and their responses to kinase inhibitors.

Impact of phosphoproteomics in the translation of kinase-targeted therapies

Signaling pathways driven by protein and lipid kinases are altered in most human diseases. Therefore, pharmacological inhibitors of cell signaling are one of the most intensively pursued therapeutic approaches for the treatment of diseases such as cancer, neurodegeneration, and metabolic syndromes. Phosphoproteomics is a technique that measures the products of kinase activities and, with the appropriate bioinformatics techniques, the methodology can also provide measures of kinase pathway activation and network circuitry. Hence, due to recent technological advantages, LC‐MS‐based quantitative phosphoproteomics provides relevant information for the design and implementation of kinase inhibitor based therapies. Here, we review how phosphoproteome profiling is being used in translational research as a means to identify drug targets and biomarkers for personalizing therapies based on kinase inhibitors.

Approaches for measuring signalling plasticity in the context of resistance to targeted cancer therapies.

The ability of cells in multicellular organisms to respond to signals in their environment is critical for their survival, development and differentiation. Once differentiated and occupying their functional niche, cells need to maintain phenotypic stability while responding to diverse extracellular perturbations and environmental signals (such as nutrients, temperature, cytokines and hormones) in a co-ordinated manner. To achieve these requirements, cells have evolved numerous intracellular signalling mechanisms that confer on them the ability to resist, respond and adapt to external changes. Although fundamental to normal biological processes, as is evident from their evolutionary conservation, such mechanisms also allow cancer cells to evade targeted therapies, a problem of immediate clinical importance. In the present article, we discuss the role of signalling plasticity in the context of the mechanisms underlying both intrinsic and acquired resistance to targeted cancer therapies. We then examine the emerging analytical techniques and theoretical paradigms that are contributing to a greater understanding of signalling on a global and untargeted scale. We conclude with a discussion on how integrative approaches to the study of cell signalling have been used, and could be used in the future, to advance our understanding of resistance mechanisms to therapies that target the kinase signalling network.