Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks.

Protein posttranslational modifications (PTMs) have typically been studied independently, yet many proteins are modified by more than one PTM type, and cell signaling pathways somehow integrate this information. We coupled immunoprecipitation using PTM-specific antibodies with tandem mass tag (TMT) mass spectrometry to simultaneously examine phosphorylation, methylation, and acetylation in 45 lung cancer cell lines compared to normal lung tissue and to cell lines treated with anticancer drugs. This simultaneous, large-scale, integrative analysis of these PTMs using a cluster-filtered network (CFN) approach revealed that cell signaling pathways were outlined by clustering patterns in PTMs. We used the t-distributed stochastic neighbor embedding (t-SNE) method to identify PTM clusters and then integrated each with known protein-protein interactions (PPIs) to elucidate functional cell signaling pathways. The CFN identified known and previously unknown cell signaling pathways in lung cancer cells that were not present in normal lung epithelial tissue. In various proteins modified by more than one type of PTM, the incidence of those PTMs exhibited inverse relationships, suggesting that molecular exclusive "OR" gates determine a large number of signal transduction events. We also showed that the acetyltransferase EP300 appears to be a hub in the network of pathways involving different PTMs. In addition, the data shed light on the mechanism of action of geldanamycin, an HSP90 inhibitor. Together, the findings reveal that cell signaling pathways mediated by acetylation, methylation, and phosphorylation regulate the cytoskeleton, membrane traffic, and RNA binding protein-mediated control of gene expression.

Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data.

Most tools developed to visualize hierarchically clustered heatmaps generate static images. Clustergrammer is a web-based visualization tool with interactive features such as: zooming, panning, filtering, reordering, sharing, performing enrichment analysis, and providing dynamic gene annotations. Clustergrammer can be used to generate shareable interactive visualizations by uploading a data table to a web-site, or by embedding Clustergrammer in Jupyter Notebooks. The Clustergrammer core libraries can also be used as a toolkit by developers to generate visualizations within their own applications. Clustergrammer is demonstrated using gene expression data from the cancer cell line encyclopedia (CCLE), original post-translational modification data collected from lung cancer cells lines by a mass spectrometry approach, and original cytometry by time of flight (CyTOF) single-cell proteomics data from blood. Clustergrammer enables producing interactive web based visualizations for the analysis of diverse biological data.

Quantitative Profiling of Post-translational Modifications by Immunoaffinity Enrichment and LC-MS/MS in Cancer Serum without Immunodepletion.

A robust method was developed and optimized for enrichment and quantitative analysis of posttranslational modifications (PTMs) in serum/plasma samples by combining immunoaffinity purification and LC-MS/MS without depletion of abundant proteins. The method was used to survey serum samples of patients with acute myeloid leukemia (AML), breast cancer (BC), and nonsmall cell lung cancer (NSCLC). Peptides were identified from serum samples containing phosphorylation, acetylation, lysine methylation, and arginine methylation. Of the PTMs identified, lysine acetylation (AcK) and arginine mono-methylation (Rme) were more prevalent than other PTMs. Label-free quantitative analysis of AcK and Rme peptides was performed for sera from AML, BC, and NSCLC patients. Several AcK and Rme sites showed distinct abundance distribution patterns across the three cancer types. The identification and quantification of posttranslationally modified peptides in serum samples reported here can be used for patient profiling and biomarker discovery research.

Measurement of PIP3 levels reveals an unexpected role for p110ß in early adaptive responses to p110α-specific inhibitors in luminal breast cancer.

BYL719, which selectively inhibits the alpha isoform of the phosphatidylinositol 3-kinase (PI3K) catalytic subunit (p110a), is currently in clinical trials for the treatment of solid tumors, especially luminal breast cancers with PIK3CA mutations and/or HER2 amplification. This study reveals that, even among these sensitive cancers, the initial efficacy of p110α inhibition is mitigated by rapid re-accumulation of the PI3K product PIP3 produced by the p110ß isoform. Importantly, the reactivation of PI3K mediated by p110ß does not invariably restore AKT phosphorylation, demonstrating the limitations of using phospho-AKT as a surrogate to measure PI3K activation. Consistently, we show that the addition of the p110ß inhibitor to BYL719 prevents the PIP3 rebound and induces greater antitumor efficacy in HER2-amplified and PIK3CA mutant cancers.

Integrative genomic and proteomic analyses identify targets for Lkb1-deficient metastatic lung tumors.

In mice, Lkb1 deletion and activation of Kras(G12D) results in lung tumors with a high penetrance of lymph node and distant metastases. We analyzed these primary and metastatic de novo lung cancers with integrated genomic and proteomic profiles, and have identified gene and phosphoprotein signatures associated with Lkb1 loss and progression to invasive and metastatic lung tumors. These studies revealed that SRC is activated in Lkb1-deficient primary and metastatic lung tumors, and that the combined inhibition of SRC, PI3K, and MEK1/2 resulted in synergistic tumor regression. These studies demonstrate that integrated genomic and proteomic analyses can be used to identify signaling pathways that may be targeted for treatment.

A predictive phosphorylation signature of lung cancer.

BACKGROUND: Aberrant activation of signaling pathways drives many of the fundamental biological processes that accompany tumor initiation and progression. Inappropriate phosphorylation of intermediates in these signaling pathways are a frequently observed molecular lesion that accompanies the undesirable activation or repression of pro- and anti-oncogenic pathways. Therefore, methods which directly query signaling pathway activation via phosphorylation assays in individual cancer biopsies are expected to provide important insights into the molecular "logic" that distinguishes cancer and normal tissue on one hand, and enables personalized intervention strategies on the other. RESULTS: We first document the largest available set of tyrosine phosphorylation sites that are, individually, differentially phosphorylated in lung cancer, thus providing an immediate set of drug targets. Next, we develop a novel computational methodology to identify pathways whose phosphorylation activity is strongly correlated with the lung cancer phenotype. Finally, we demonstrate the feasibility of classifying lung cancers based on multi-variate phosphorylation signatures. CONCLUSIONS: Highly predictive and biologically transparent phosphorylation signatures of lung cancer provide evidence for the existence of a robust set of phosphorylation mechanisms (captured by the signatures) present in the majority of lung cancers, and that reliably distinguish each lung cancer from normal. This approach should improve our understanding of cancer and help guide its treatment, since the phosphorylation signatures highlight proteins and pathways whose phosphorylation should be inhibited in order to prevent unregulated proliferation.

Signaling networks assembled by oncogenic EGFR and c-Met.

A major question regarding the sensitivity of solid tumors to targeted kinase inhibitors is why some tumors respond and others do not. The observation that many tumors express EGF receptor (EGFR), yet only a small subset with EGFR-activating mutations respond clinically to EGFR inhibitors (EGFRIs), suggests that responsive tumors uniquely depend on EGFR signaling for their survival. The nature of this dependence is not understood. Here, we investigate dependence on EGFR signaling by comparing non-small-cell lung cancer cell lines driven by EGFR-activating mutations and genomic amplifications using a global proteomic analysis of phospho-tyrosine signaling. We identify an extensive receptor tyrosine kinase signaling network established in cells expressing mutated and activated EGFR or expressing amplified c-Met. We show that in drug sensitive cells the targeted tyrosine kinase drives other RTKs and an extensive network of downstream signaling that collapse with drug treatment. Comparison of the signaling networks in EGFR and c-Met-dependent cells identify a "core network" of approximately 50 proteins that participate in pathways mediating drug response.

Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer.

Despite the success of tyrosine kinase-based cancer therapeutics, for most solid tumors the tyrosine kinases that drive disease remain unknown, limiting our ability to identify drug targets and predict response. Here we present the first large-scale survey of tyrosine kinase activity in lung cancer. Using a phosphoproteomic approach, we characterize tyrosine kinase signaling across 41 non-small cell lung cancer (NSCLC) cell lines and over 150 NSCLC tumors. Profiles of phosphotyrosine signaling are generated and analyzed to identify known oncogenic kinases such as EGFR and c-Met as well as novel ALK and ROS fusion proteins. Other activated tyrosine kinases such as PDGFRalpha and DDR1 not previously implicated in the genesis of NSCLC are also identified. By focusing on activated cell circuitry, the approach outlined here provides insight into cancer biology not available at the chromosomal and transcriptional levels and can be applied broadly across all human cancers.