Hannah Johnson

Molecular Characterization of EGFR and EGFRvIII Signaling Networks in Human Glioblastoma Tumor Xenografts

Glioblastoma multiforme (GBM) is a malignant primary brain tumor with a mean survival of 15 months with the current standard of care. Genetic profiling efforts have identified the amplification, overexpression, and mutation of the wild-type (wt) epidermal growth factor receptor tyrosine kinase (EGFR) in ∼50% of GBM patients. The genetic aberration of wtEGFR is frequently accompanied by the overexpression of a mutant EGFR known as EGFR variant III (EGFRvIII, de2–7EGFR, ΔEGFR), which is expressed in 30% of GBM tumors. The molecular mechanisms of tumorigenesis driven by EGFRvIII overexpression in human tumors have not been fully elucidated. To identify specific therapeutic targets for EGFRvIII driven tumors, it is important to gather a broad understanding of EGFRvIII specific signaling. Here, we have characterized signaling through the quantitative analysis of protein expression and tyrosine phosphorylation across a panel of glioblastoma tumor xenografts established from patient surgical specimens expressing wtEGFR or overexpressing wtEGFR (wtEGFR+) or EGFRvIII (EGFRvIII+). S100A10 (p11), major vault protein, guanylate-binding protein 1(GBP1), and carbonic anhydrase III (CAIII) were identified to have significantly increased expression in EGFRvIII expressing xenograft tumors relative to wtEGFR xenograft tumors. Increased expression of these four individual proteins was found to be correlated with poor survival in patients with GBM; the combination of these four proteins represents a prognostic signature for poor survival in gliomas. Integration of protein expression and phosphorylation data has uncovered significant heterogeneity among the various tumors and has highlighted several novel pathways, related to EGFR trafficking, activated in glioblastoma. The pathways and proteins identified in these tumor xenografts represent potential therapeutic targets for this disease.

Qualitatively different T cell phenotypic responses to IL-2 versus IL-15 are unified by identical dependences on receptor signal strength and duration.

IL-2 and IL-15 are common γ-chain family cytokines involved in regulation of T cell differentiation and homeostasis. Despite signaling through the same receptors, IL-2 and IL-15 have non-redundant roles in T cell biology, both physiologically and at the cellular level. The mechanisms by which IL-2 and IL-15 trigger distinct phenotypes in T cells remain elusive. To elucidate these mechanisms, we performed a quantitative comparison of the phosphotyrosine signaling network and resulting phenotypes triggered by IL-2 and IL-15. This study revealed that the signaling networks activated by IL-2 or IL-15 are highly similar and that T cell proliferation and metabolism are controlled in a quantitatively distinct manner through IL-2/15R signal strength independent of the cytokine identity. Distinct phenotypes associated with IL-2 or IL-15 stimulation therefore arise through differential regulation of IL-2/15R signal strength and duration because of differences in cytokine–receptor binding affinity, receptor expression levels, physiological cytokine levels, and cytokine–receptor intracellular trafficking kinetics. These results provide important insights into the function of other shared cytokine and growth factor receptors, quantitative regulation of cell proliferation and metabolism through signal transduction, and improved design of cytokine based clinical immunomodulatory therapies for cancer and infectious diseases.

Computer aided manual validation of mass spectrometry-based proteomic data

Advances in mass spectrometry-based proteomic technologies have increased the speed of analysis and the depth provided by a single analysis. Computational tools to evaluate the accuracy of peptide identifications from these high-throughput analyses have not kept pace with technological advances; currently the most common quality evaluation methods are based on statistical analysis of the likelihood of false positive identifications in large-scale data sets. While helpful, these calculations do not consider the accuracy of each identification, thus creating a precarious situation for biologists relying on the data to inform experimental design. Manual validation is the gold standard approach to confirm accuracy of database identifications, but is extremely time-intensive. To palliate the increasing time required to manually validate large proteomic datasets, we provide computer aided manual validation software (CAMV) to expedite the process. Relevant spectra are collected, catalogued, and pre-labeled, allowing users to efficiently judge the quality of each identification and summarize applicable quantitative information. CAMV significantly reduces the burden associated with manual validation and will hopefully encourage broader adoption of manual validation in mass spectrometry-based proteomics.

Phosphotyrosine Profiling of NSCLC Cells in Response to EGF and HGF Reveals Network Specific Mediators of Invasion

Growth factor signaling is deregulated in cancer and often leads to invasion, yet receptor tyrosine kinase signaling pathways driving invasion under different growth factor conditions are not well understood. To identify specific signaling molecules regulating invasion of A549 non-small cell lung carcinoma (NSCLC) cells downstream of the epidermal growth factor receptor (EGFR) and Met, quantitative site-specific mass spectrometric analysis of tyrosine phosphorylation was performed following epidermal growth factor (EGF) or hepatocyte growth factor (HGF) stimulation, at three different growth factor concentrations and at two time points. Through this analysis, the temporal and concentration dependent phosphorylation profiles were obtained for 131 and 139 sites downstream of EGF and HGF stimulation, respectively. To characterize the effect of these signaling network alterations, we quantified 3D cell migration/invasion through Matrigel. Partial least-squares regression (PLSR) analysis was performed to identify the tyrosine phosphorylation sites most strongly correlated with EGF and/or HGF mediated invasion. Potential common and specific signaling events required for driving invasion downstream of EGFR and Met were identified using either a combined or two independent PLSR models, based on the quantitative EGF or HGF data. Our data highlight the integration and compartmentalization of signaling required for invasion in cancer.

Quantitative analysis of signaling networks across differentially embedded tumors highlights interpatient heterogeneity in human glioblastoma.

Glioblastoma multiforme (GBM) is the most aggressive malignant primary brain tumor, with a dismal mean survival even with the current standard of care. Although in vitro cell systems can provide mechanistic insight into the regulatory networks governing GBM cell proliferation and migration, clinical samples provide a more physiologically relevant view of oncogenic signaling networks. However, clinical samples are not widely available and may be embedded for histopathologic analysis. With the goal of accurately identifying activated signaling networks in GBM tumor samples, we investigated the impact of embedding in optimal cutting temperature (OCT) compound followed by flash freezing in LN2 vs immediate flash freezing (iFF) in LN2 on protein expression and phosphorylation-mediated signaling networks. Quantitative proteomic and phosphoproteomic analysis of 8 pairs of tumor specimens revealed minimal impact of the different sample processing strategies and highlighted the large interpatient heterogeneity present in these tumors. Correlation analyses of the differentially processed tumor sections identified activated signaling networks present in selected tumors and revealed the differential expression of transcription, translation, and degradation associated proteins. This study demonstrates the capability of quantitative mass spectrometry for identification of in vivo oncogenic signaling networks from human tumor specimens that were either OCT-embedded or immediately flash-frozen.

Quantitative Analysis of Signaling Networks across Differentially Embedded Tumors Highlights Interpatient Heterogeneity in Human Glioblastoma

Glioblastoma multiforme (GBM) is the most aggressive malignant primary brain tumor, with a dismal mean survival even with the current standard of care. Although in vitro cell systems can provide mechanistic insight into the regulatory networks governing GBM cell proliferation and migration, clinical samples provide a more physiologically relevant view of oncogenic signaling networks. However, clinical samples are not widely available and may be embedded for histopathologic analysis. With the goal of accurately identifying activated signaling networks in GBM tumor samples, we investigated the impact of embedding in optimal cutting temperature (OCT) compound followed by flash freezing in LN2 vs immediate flash freezing (iFF) in LN2 on protein expression and phosphorylation-mediated signaling networks. Quantitative proteomic and phosphoproteomic analysis of 8 pairs of tumor specimens revealed minimal impact of the different sample processing strategies and highlighted the large interpatient heterogeneity present in these tumors. Correlation analyses of the differentially processed tumor sections identified activated signaling networks present in selected tumors and revealed the differential expression of transcription, translation, and degradation associated proteins. This study demonstrates the capability of quantitative mass spectrometry for identification of in vivo oncogenic signaling networks from human tumor specimens that were either OCT-embedded or immediately flash-frozen.

Molecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion mice

Metabolic syndrome describes a set of obesity-related disorders that increase diabetes, cardiovascular, and mortality risk. Studies of liver-specific protein-tyrosine phosphatase 1b (PTP1b) deletion mice (L-PTP1b(-/-)) suggest that hepatic PTP1b inhibition would mitigate metabolic-syndrome through amelioration of hepatic insulin resistance, endoplasmic-reticulum stress, and whole-body lipid metabolism. However, the altered molecular-network states underlying these phenotypes are poorly understood. We used mass spectrometry to quantify protein-phosphotyrosine network changes in L-PTP1b(-/-) mouse livers relative to control mice on normal and high-fat diets. We applied a phosphosite-set-enrichment analysis to identify known and novel pathways exhibiting PTP1b- and diet-dependent phosphotyrosine regulation. Detection of a PTP1b-dependent, but functionally uncharacterized, set of phosphosites on lipid-metabolic proteins motivated global lipidomic analyses that revealed altered polyunsaturated-fatty-acid (PUFA) and triglyceride metabolism in L-PTP1b(-/-) mice. To connect phosphosites and lipid measurements in a unified model, we developed a multivariate-regression framework, which accounts for measurement noise and systematically missing proteomics data. This analysis resulted in quantitative models that predict roles for phosphoproteins involved in oxidation-reduction in altered PUFA and triglyceride metabolism.

Network Modeling Identifies Patient-specific Pathways in Glioblastoma.

Glioblastoma is the most aggressive type of malignant human brain tumor. Molecular profiling experiments have revealed that these tumors are extremely heterogeneous. This heterogeneity is one of the principal challenges for developing targeted therapies. We hypothesize that despite the diverse molecular profiles, it might still be possible to identify common signaling changes that could be targeted in some or all tumors. Using a network modeling approach, we reconstruct the altered signaling pathways from tumor-specific phosphoproteomic data and known protein-protein interactions. We then develop a network-based strategy for identifying tumor specific proteins and pathways that were predicted by the models but not directly observed in the experiments. Among these hidden targets, we show that the ERK activator kinase1 (MEK1) displays increased phosphorylation in all tumors. By contrast, protein numb homolog (NUMB) is present only in the subset of the tumors that are the most invasive. Additionally, increased S100A4 is associated with only one of the tumors. Overall, our results demonstrate that despite the heterogeneity of the proteomic data, network models can identify common or tumor specific pathway-level changes. These results represent an important proof of principle that can improve the target selection process for tumor specific treatments.

A Thermodynamic-Based Interpretation of Protein Expression Heterogeneity in Different Glioblastoma Multiforme Tumors Identifies Tumor-Specific Unbalanced Processes.

We describe a thermodynamic-motivated, information theoretic analysis of proteomic data collected from a series of 8 glioblastoma multiforme (GBM) tumors. GBMs are considered here as prototypes of heterogeneous cancers. That heterogeneity is viewed here as manifesting in different unbalanced biological processes that are associated with thermodynamic-like constraints. The analysis yields a molecular description of a stable steady state that is common across all tumors. It also resolves molecular descriptions of unbalanced processes that are shared by several tumors, such as hyperactivated phosphoprotein signaling networks. Further, it resolves unbalanced processes that provide unique classifiers of tumor subgroups. The results of the theoretical interpretation are compared against those of statistical multivariate methods and are shown to provide a superior level of resolution for identifying unbalanced processes in GBM tumors. The identification of specific constraints for each GBM tumor suggests tumor-specific combination therapies that may reverse this imbalance.

Quantitative Analysis of Tyrosine Kinase Signaling Across Differentially Embedded Human Glioblastoma Tumors

Glioblastoma is the most aggressive primary brain tumor with a poor mean survival even with the current standard of care. Kinase signaling analyses of clinical glioblastoma samples provide a physiologically relevant view of oncogenic signaling networks. Here, we describe the methods that enable the quantification of protein expression profiles and phosphotyrosine signaling across flash frozen and optimal cutting temperature (OCT) compound embedded tumor specimens. The data derived from these experiments can be used to identify the intra- and inter-patient heterogeneity present in these tumors. Correlation and functional analyses on the quantitative protein expression and phosphotyrosine signaling data obtained from clinical samples can be used to identify tyrosine kinase signaling networks present in these tumors and reveal the differential expression of functionally related proteins. This chapter provides the quantitative mass spectrometry methods required for the identification of in vivo oncogenic signaling networks from human tumor specimens.