Comparison of Quantitative Mass Spectrometry Platforms for Monitoring Kinase ATP Probe Uptake in Lung Cancer

Recent developments in instrumentation and bioinformatics have led to new quantitative mass spectrometry platforms including LC–MS/MS with data-independent acquisition (DIA) and targeted analysis using parallel reaction monitoring mass spectrometry (LC–PRM), which provide alternatives to well-established methods, such as LC–MS/MS with data-dependent acquisition (DDA) and targeted analysis using multiple reaction monitoring mass spectrometry (LC–MRM). These tools have been used to identify signaling perturbations in lung cancers and other malignancies, supporting the development of effective kinase inhibitors and, more recently, providing insights into therapeutic resistance mechanisms and drug repurposing opportunities. However, detection of kinases in biological matrices can be challenging; therefore, activity-based protein profiling enrichment of ATP-utilizing proteins was selected as a test case for exploring the limits of detection of low-abundance analytes in complex biological samples. To examine the impact of different MS acquisition platforms, quantification of kinase ATP uptake following kinase inhibitor treatment was analyzed by four different methods: LC–MS/MS with DDA and DIA, LC–MRM, and LC–PRM. For discovery data sets, DIA increased the number of identified kinases by 21% and reduced missingness when compared with DDA. In this context, MRM and PRM were most effective at identifying global kinome responses to inhibitor treatment, highlighting the value of a priori target identification and manual evaluation of quantitative proteomics data sets. We compare results for a selected set of desthiobiotinylated peptides from PRM, MRM, and DIA and identify considerations for selecting a quantification method and postprocessing steps that should be used for each data acquisition strategy

Effect of homogeneous, resource-limited microenvironment on treatment outcomes and heterogeneity.

<p>(A) The growth dynamics of all in silico cells treated with various therapies for three months in a homogeneous and resource-limited microenvironmental condition. A color line at each treatment indicates the most dominant cell after a given therapy. Color-shaded boxes indicate treatments selecting for the same dominant in silico cell. Orange box: dominant cell (cell ID: 342) after treatments of EGFRi/METi, EGFRi/AKTi, METi/AKTi, EGFRi/RAFi, and AKTi/RAFi. Red box: dominant cell (ID: 417) after <i>RAS_m</i>i/METi and AKTi. Yellow box: dominant cell (ID: 337) after EGFRi/MEKi, METi/MEKi, and AKTi/MEKi. Blue box: dominant cell (ID: 36) after RAFi/MEKi, MEKi, RAFi/ERKi, and MEKi/ERKi. Purple box: dominant cell (ID: 156) after EGFRi/ERKi, METi/ERKi, and AKTi/ERKi. Green box: dominant cell (ID: 113) after ERKi and ERKi/<i>RAS_mi</i>. (B) Linear relationship between average relative cell viability (log2 scale) and post-treatment Shannon indexes. Linear correlation constant: 0.65. Square: monotherapy; circle: combination therapy. Red: EGFRi alone or combination with METi, <i>RAS_mi</i>, AKTi, RAFi, MEKi, or ERKi. Blue: METi alone or combination with <i>RAS_m</i>i, AKTi, RAFi, MEKi, or ERKi. Pink: <i>RAS_m</i>i alone or combination with RAFi, AKTi, MEKi, or ERKi. Green: AKTi alone or combination with RAFi, MEKi, or ERKi. Gray: RAFi, RAFi/MEKi, RAFi/ERKi. Yellow: MEKi, MEKi/ERKi. Orange: ERKi. (C) Linear relationship between average relative cell viability (log2 scale) and post-treatment (with HGF stimulation) Shannon indexes. Linear correlation constant: 0.63. Square: monotherapy; circle: combination therapy. Color definitions are the same as panel B. (D) Therapies affected by HGF significantly. Each arrow starts from a point without HGF stimulation to a point with HGF stimulation. Gray: RAFi/ERKi and RAFi/MEKi. Blue: <i>RAS_m</i>i, <i>RAS_m</i>i/AKTi, <i>RAS_m</i>i/ERKi. Red: EGFRi, EGFRi/<i>RAS_m</i>i, EGFRi/AKTi, EGFRi/MEKi, and EGFRi/ERKi. The numerical data used in Fig 6 are included in the fifth sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; corr, linear correlation; EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; HGF, hepatocyte growth factor; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); PI3K, phosphoinositide 3-kinase; RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma; RAS_m, mutated RAS; RAS_w, wild-type RAS.</p

Cell signaling heterogeneity is modulated by both cell-intrinsic and -extrinsic mechanisms: An integrated approach to understanding targeted therapy

<div><p>During the last decade, our understanding of cancer cell signaling networks has significantly improved, leading to the development of various targeted therapies that have elicited profound but, unfortunately, short-lived responses. This is, in part, due to the fact that these targeted therapies ignore context and average out heterogeneity. Here, we present a mathematical framework that addresses the impact of signaling heterogeneity on targeted therapy outcomes. We employ a simplified oncogenic rat sarcoma (<i>RAS</i>)-driven mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway in lung cancer as an experimental model system and develop a network model of the pathway. We measure how inhibition of the pathway modulates protein phosphorylation as well as cell viability under different microenvironmental conditions. Training the model on this data using Monte Carlo simulation results in a suite of in silico cells whose relative protein activities and cell viability match experimental observation. The calibrated model predicts distributional responses to kinase inhibitors and suggests drug resistance mechanisms that can be exploited in drug combination strategies. The suggested combination strategies are validated using in vitro experimental data. The validated in silico cells are further interrogated through an unsupervised clustering analysis and then integrated into a mathematical model of tumor growth in a homogeneous and resource-limited microenvironment. We assess posttreatment heterogeneity and predict vast differences across treatments with similar efficacy, further emphasizing that heterogeneity should modulate treatment strategies. The signaling model is also integrated into a hybrid cellular automata (HCA) model of tumor growth in a spatially heterogeneous microenvironment. As a proof of concept, we simulate tumor responses to targeted therapies in a spatially segregated tissue structure containing tumor and stroma (derived from patient tissue) and predict complex cell signaling responses that suggest a novel combination treatment strategy.</p></div

Model prediction and validation.

<p>(A) Histograms of relative cell viabilities (log2 scale) of all in silico cells after treatments of EGFRi, METi, <i>RAS_m</i>i, AKTi, RAFi, MEKi, and ERKi. First, a bimodal distribution EGFRi-treated in silico cells (one mode: −1.25; second mode: −0.5). Second, a skewed distribution after METi (a skew toward 0; 0: no change). Third, a uniform distribution in response to <i>RAS_m</i>i (almost uniform distribution from −1.5 to 1.5). Fourth, a slight bimodal distribution after AKTi. Fifth, a distributional response after RAFi. Sixth, a normal distribution in response to MEKi. Seventh, a normal distribution after ERKi. (B) Histograms of relative cell viabilities of all in silico cells in log2 scale. First: EGFRi only (blue), EGFRi/ERKi (orange), and EGFRi/MEKi (yellow). Second: METi only (blue), METi/ERKi (orange), and METi/MEKi (yellow). Third: <i>RAS_m</i>i only (blue), <i>RAS_m</i>i/RAFi (green), <i>RAS_m</i>i/ERKi (orange), and METi/MEKi (yellow). Fourth: AKTi only (blue), AKTi/RAFi (green), AKTi/ERKi (orange), and AKTi/MEKi (yellow). Fifth: RAFi only (blue), RAFi/ERKi (orange), and RAFi/MEKi (yellow). (C) Validation. Model predicted relative cell viabilities (red bars) and experimental data (gray bars) after 10 different treatments. First: EGFRi, EGFRi/MEKi, and EGFRi/ERKi. Second: METi, METi/MEKi, METi/ERKi. Third: AKTi, AKTi/RAFi, AKTi/MEKi, AKTi/ERKi. The numerical data used in Fig 3 are included in the second sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; DMSO, Dimethyl sulfoxide (control); EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma.</p

Effect of HGF stimulation on treatment responses.

<p>(A) Heat-map of cell viability changes due to HGF stimulation (log₂ (treated with HGF/untreated)-log₂ (treated without HGF/untreated)). An unbiased hierarchical clustering separated the cell population into 6 different clusters (indicated by colors on the top of heat-map). Each row indicates a treatment, and each column indicates an in silico cell. Gray to red: no change to increase due to HGF. (B) Model validation. Comparisons between experimental data (gray bars) and model predictions (red bars) after three different combinations (AKTi/MEKi, EGFRi /MEKi, and EGFRi/AKTi). Relative change of cell viability in treated-with-HGF condition to one in treated-without-HGF condition (no HGF) is reported. (C) Chord diagrams and weighted network diagrams that visualize weights between two protein nodes in the representative in silico cell <b>a</b> and <b>b</b>. The numerical data used in Fig 5 are included in the fourth sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; HGF, hepatocyte growth factor; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); PI3K, phosphoinositide 3-kinase; RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma; RAS_m, mutated RAS; RAS_w, wild-type RAS; RSK, ribosomal S6 kinase.</p

Effect of microenvironmental heterogeneity on a targeted therapy outcome and combination therapy.

<p>(A) Heterogeneous EGFR activity in a lung squamous cell carcinoma. Representative image of red foci showing EGFR:GRB2 proximity. Tumor cells (green) are stained with a cytokeratin antibody demarcating epithelial origin. Nuclei (blue) are stained with DAPI. Image was acquired at 200x. (B) HGF distribution. The Eq (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.e006" target="_blank">3</a>) was solved assuming the following parameters: <i>γ</i> = 1.0, diffusion rate <i>D</i> = 0.04, and decay rate <i>λ</i> = 0.001. To be consistent with the choice of parameter in the pathway model (Eq [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.e001" target="_blank">1</a>]), we use a scaling factor <i>ω</i> (<i>ω</i> ≡ 10) (i.e., HGF input in the pathway model <i>x</i>₂ = <i>ωH</i>(<i>x</i>,<i>t</i>), <i>H</i>(<i>x</i>,<i>t</i>): solution obtained using the assumed parameters). (C) First, an initial randomized configuration of cells (domain size: 50 cells × 38 cells). Color represents different in silico cells. Second, a snapshot of <i>RAS_m</i> inhibitor simulation (day 180). (D) Simulated protein activity after 180 days of <i>RAS_m</i> inhibition. The activities of MET, EGFR, RAS_w, and RAF are heterogeneous (yellow to blue color), while those of AKT, MEK, ERK, and RSK are less heterogeneous (blue color). (E) Comparison of combination therapies for 400 days. First, a snapshot of simulation (day 400) after a sequential therapy of <i>RAS_m</i>i for the first 200 days and then RAFi for the rest 200 days (200 days of <i>RAS_m</i>i → 200 days of RAFi). Color represents different in silico cells. Second, a snapshot of simulation (day 400) after a sequential therapy of RAFi for the first 200 days, then <i>RAS_m</i>i for the remaining 200 days (200 days of RAFi → 200 days of <i>RAS_m</i>i). Third: the number of cells over time during the two sequential therapies of <i>RAS_m</i>i and RAFi (blue: <i>RAS_m</i>i → RAFi, red: RAFi → <i>RAS_m</i>i) and a concurrent therapy of the two (green: <i>RAS_m</i>i/RAFi). The numerical data used in Fig 7 are included in the sixth sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; DAPI, 4',6-diamidino-2-phenylindole; EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; GRB2, growth factor receptor bound protein 2; HGF, hepatocycte growth factor; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma; RSK, ribosomal S6 kinase.</p

Signaling pathway model development and model calibration.

<p>(A) Simplified Signaling Network Model. Two inputs (growth factor and HGF), signaling protein nodes (EGFR, MET, <i>RAS_m</i>, RAS_w, PI3K/AKT, RAF, MEK, ERK, RSK), and one output (cell viability). Of note, <i>RAS_m</i> indicates a mutant <i>RAS</i>, while RAS_w indicates a wild-type RAS. A green line represents a positive relation (stimulation) and red line represents a negative relation (inhibition). (B) pMET (Y1234/5), pMEK, pERK, pAKT (both T308 and S473), and pRSK (T359) expression after different inhibitors (1 μM), MET inhibitor (METi, PHA665752), EGFR inhibitor (EGFRi, Erlotinib), RAF inhibitor (RAFi, LY3009120), MEK inhibitor (MEKi, GDC0623), ERK inhibitor (ERKi, SCH772984), and AKT inhibitor (AKTi, MK2208) in both control medium (DMSO) and after 2-hour stimulation-by-HGF (50 ng/mL) condition (DMSO plus HGF). (C) Relative cell viabilities after treatments. Cells were treated inhibitors (1 μM) for 72 hours. Cell viabilities were assessed by CellTiter-Glo assay (Promega). Representative triplicates (± SD) are presented, which showed similar results at least three times. (D) The western blots were quantified using ImageJ and relative changes (log2 scale) are reported. Average values of relative cell viabilities are also reported in log2 scale. All the data are normalized to the treatment-naïve control condition. pAKT (T308) readouts are quantified and used in the model. Of note, we didn’t quantify total protein levels because our primary interest was protein activity (protein phosphorylation). The effects of all inhibitors are modeled by assuming very small activity of a target protein (i.e., 1/16 of control, Methods section), and therefore pMET under METi is set be a very small number (i.e., 1/16 of control) for consistency. (E) Comparison between model predictions (gray box plots) and experimental data (black dots). A log2 fold change of pMET, pMEK, pAKT, pERK, pRSK, and cell viability after treatments of different inhibitors (METi, EGFRi, MEKi, ERKi, AKTi) in both a control medium and HGF-stimulated conditions. RMSE of each protein is following. pMET: 0.03; pMEK: 0.49; pAKT:0.33; pERK: 0.96; pRSK: 0.57; and cell viability: 0.47. The numerical data used in Fig 2 are included in the first sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; DMSO, Dimethyl sulfoxide (control); EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; HGF, hepatocyte growth factor; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); PI3K, phosphoinositide 3-kinase; RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma; RAS_m, mutated RAS; RAS_w, wild-type RAS; RMSE, root-mean-squared-error; RSK, ribosomal S6 kinase.</p

Model predicted combination therapy effect.

<p>(A) Hierarchical clustering and heat-map of relative cell viabilities after 28 different treatments. Simulated treatment responses (cell viability changes) were clustered using an unbiased hierarchical method with a Euclidian distance function, resulting in 7 different clusters (pink, black, blue, green, purple, cyan, and orange color) indicated by color bar on the top of heat-map. Each row indicates an individual therapy. Each column indicates an individual in silico cell. Blue to yellow bars: decrease to increase. The asterisk [*] indicates relative cell viability after a combination therapy of AKTi with MEKi. (B–C) Chord diagrams and weighted network diagrams of the representative in silico cells <b>a</b> and <b>b</b>. Chord diagram: each node in the circle represents each protein node in the network model, represented by different colors. The thickness of chord between two protein nodes represents a weight between two protein nodes (weight, <i>w</i>_<i>ij</i>). The chords are directed, colored by originating sector color. For example, the interaction between <i>RAS_m</i> and RAF is depicted as a light blue chord because the direction is from <i>RAS_m</i> to RAF (<i>RAS_m</i> → RAF; color of <i>RAS_m</i> sector: light blue). Weighted network diagram: the width of each edge represents the weight. A thicker edge represents a larger weight between two proteins. The numerical data used in Fig 4 are included in the third sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; HGF, hepatocyte growth factor; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); PI3K, phosphoinositide 3-kinase; RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma; RAS_m, mutated RAS; RAS_w, wild-type RAS; RSK, ribosomal S6 kinase.</p

APOSTL: An Interactive Galaxy Pipeline for Reproducible Analysis of Affinity Proteomics Data

With continuously increasing scale and depth of coverage in affinity proteomics (AP–MS) data, the analysis and visualization is becoming more challenging. A number of tools have been developed to identify high-confidence interactions; however, a cohesive and intuitive pipeline for analysis and visualization is still needed. Here we present Automated Processing of SAINT Templated Layouts (APOSTL), a freely available Galaxy-integrated software suite and analysis pipeline for reproducible, interactive analysis of AP–MS data. APOSTL contains a number of tools woven together using Galaxy workflows, which are intuitive for the user to move from raw data to publication-quality figures within a single interface. APOSTL is an evolving software project with the potential to customize individual analyses with additional Galaxy tools and widgets using the R web application framework, Shiny. The source code, data, and documentation are freely available from GitHub (https://github.com/bornea/APOSTL) and other sources

A Pilot Proteogenomic Study with Data Integration Identifies MCT1 and GLUT1 as Prognostic Markers in Lung Adenocarcinoma

<div><p>We performed a pilot proteogenomic study to compare lung adenocarcinoma to lung squamous cell carcinoma using quantitative proteomics (6-plex TMT) combined with a customized Affymetrix GeneChip. Using MaxQuant software, we identified 51,001 unique peptides that mapped to 7,241 unique proteins and from these identified 6,373 genes with matching protein expression for further analysis. We found a minor correlation between gene expression and protein expression; both datasets were able to independently recapitulate known differences between the adenocarcinoma and squamous cell carcinoma subtypes. We found 565 proteins and 629 genes to be differentially expressed between adenocarcinoma and squamous cell carcinoma, with 113 of these consistently differentially expressed at both the gene and protein levels. We then compared our results to published adenocarcinoma versus squamous cell carcinoma proteomic data that we also processed with MaxQuant. We selected two proteins consistently overexpressed in squamous cell carcinoma in all studies, MCT1 (SLC16A1) and GLUT1 (SLC2A1), for further investigation. We found differential expression of these same proteins at the gene level in our study as well as in other public gene expression datasets. These findings combined with survival analysis of public datasets suggest that MCT1 and GLUT1 may be potential prognostic markers in adenocarcinoma and druggable targets in squamous cell carcinoma. Data are available via ProteomeXchange with identifier PXD002622.</p></div