Perturbation of the mutated EGFR interactome identifies vulnerabilities and resistance mechanisms

Affinity purification mass spectrometry characterisation of the interactome of receptor tyrosine kinase proline-rich motifs in cancer

Receptor tyrosine kinase (RTK) overexpression is linked to the development and progression of multiple cancers. RTKs are classically considered to initiate cytoplasmic signalling pathways via ligand-induced tyrosine phosphorylation, however recent evidence points to a second tier of signalling contingent on interactions mediated by the proline-rich motif (PRM) regions of non-activated RTKs. The presence of PRMs on the C-termini of >40 % of all RTKs and the abundance of PRM-binding proteins encoded by the human genome suggests that there is likely to be a large number of previously unexplored interactions which add to the RTK intracellular interactome. Here, we explore the RTK PRM interactome and its potential significance using affinity purification mass spectrometry and in silico enrichment analyses. Peptides comprising PRM-containing C-terminal tail regions of EGFR, FGFR2 and HER2 were used as bait to affinity purify bound proteins from different cancer cell line lysates. 490 unique interactors were identified, amongst which proteins with metabolic, homeostatic and migratory functions were overrepresented. This suggests that PRMs from RTKs may sustain a diverse interactome in cancer cells. Since RTK overexpression is common in cancer, RTK PRM-derived signalling may be an important, but as yet underexplored, contributor to negative cancer outcomes including resistance to kinase inhibitors.

Multiscale photocatalytic proximity labeling reveals cell surface neighbors on and between cells

Proximity labeling proteomics (PLP) strategies are powerful approaches to yield snapshots of protein neighborhoods. Here, we describe a multiscale PLP method with adjustable resolution that uses a commercially available photocatalyst, Eosin Y, which upon visible light illumination activates different photo-probes with a range of labeling radii. We applied this platform to profile neighborhoods of the oncogenic epidermal growth factor receptor and orthogonally validated more than 20 neighbors using immunoassays and AlphaFold-Multimer prediction. We further profiled the protein neighborhoods of cell-cell synapses induced by bispecific T cell engagers and chimeric antigen receptor T cells. This integrated multiscale PLP platform maps local and distal protein networks on and between cell surfaces, which will aid in the systematic construction of the cell surface interactome, revealing horizontal signaling partners and reveal new immunotherapeutic opportunities.

Recent Advances in Molecular and Cellular Functions of S100A10

S100A10 (p11, annexin II light chain, calpactin light chain) is a multifunctional protein with a wide range of physiological activity. S100A10 is unique among the S100 family members of proteins since it does not bind to Ca2+, despite its sequence and structural similarity. This review focuses on studies highlighting the structure, regulation, and binding partners of S100A10. The binding partners of S100A10 were collated and summarized.

EGFR-T790M Mutation-Derived Interactome Rerouted EGFR Translocation Contributing to Gefitinib Resistance in Non-Small Cell Lung Cancer

Secondary mutation, T790M, conferring tyrosine kinase inhibitors (TKIs) resistance beyond oncogenic epidermal growth factor receptor (EGFR) mutations presents a challenging unmet need. Although TKI-resistant mechanisms are intensively investigated, the underlying responses of cancer cells adapting drug perturbation are largely unknown. To illuminate the molecular basis linking acquired mutation to TKI resistance, affinity purification coupled mass spectrometry was adopted to dissect EGFR interactome in TKI-sensitive and TKI-resistant non-small cell lung cancer cells. The analysis revealed TKI-resistant EGFR-mutant interactome allocated in diverse subcellular distribution and enriched in endocytic trafficking, in which gefitinib intervention activated autophagy-mediated EGFR degradation and thus autophagy inhibition elevated gefitinib susceptibility. Alternatively, gefitinib prompted TKI-sensitive EGFR translocating toward cell periphery through Rab7 ubiquitination which may favor efficacy to TKIs suppression. This study revealed that T790M mutation rewired EGFR interactome that guided EGFR to autophagy-mediated degradation to escape treatment, suggesting that combination therapy with TKI and autophagy inhibitor may overcome acquired resistance in non-small cell lung cancer.

Radius measurement via super-resolution microscopy enables the development of a variable radii proximity labeling platform

The elucidation of protein interaction networks is critical to understanding fundamental biology as well as developing new therapeutics. Proximity labeling platforms (PLPs) are state-of-the-art technologies that enable the discovery and delineation of biomolecular networks through the identification of protein-protein interactions. These platforms work via catalytic generation of reactive probes at a biological region of interest; these probes then diffuse through solution and covalently "tag" proximal biomolecules. The physical distance that the probes diffuse determines the effective labeling radius of the PLP and is a critical parameter that influences the scale and resolution of interactome mapping. As such, by expanding the degrees of labeling resolution offered by PLPs, it is possible to better capture the various size scales of interactomes. At present, however, there is little quantitative understanding of the labeling radii of different PLPs. Here, we report the development of a superresolution microscopy-based assay for the direct quantification of PLP labeling radii. Using this assay, we provide direct extracellular measurements of the labeling radii of state-of-the-art antibody-targeted PLPs, including the peroxidase-based phenoxy radical platform (269 ± 41 nm) and the high-resolution iridium-catalyzed µMap technology (54 ± 12 nm). Last, we apply these insights to the development of a molecular diffusion-based approach to tuning PLP resolution and introduce a new aryl-azide-based µMap platform with an intermediate labeling radius (80 ± 28 nm).

Multi-scale network targeting: A holistic systems-biology approach to cancer treatment

The vulnerabilities of cancer at the cellular and, recently, with the introduction of immunotherapy, at the tissue level, have been exploited with variable success. Evaluating the cancer system vulnerabilities at the organismic level through analysis of network topology and network dynamics can potentially predict novel anti-cancer drug targets directed at the macroscopic cancer networks. Theoretical work analyzing the properties and the vulnerabilities of the multi-scale network of cancer needs to go hand-in-hand with experimental research that uncovers the biological nature of the relevant networks and reveals new targetable vulnerabilities. It is our hope that attacking cancer on different spatial scales, in a concerted integrated approach, may present opportunities for novel ways to prevent treatment resistance.

Determination of the glycoprotein specificity of lectins on cell membranes through oxidative proteomics

The cell membrane is composed of a network of glycoconjugates including glycoproteins and glycolipids that presents a dense matrix of carbohydrates playing critical roles in many biological processes. Lectin-based technology has been widely used to characterize glycoconjugates in tissues and cell lines. However, their specificity toward their putative glycan ligand and sensitivity in situ have been technologically difficult to study. Additionally, because they recognize primarily glycans, the underlying glycoprotein targets are generally not known. In this study, we employed lectin proximity oxidative labeling (Lectin PROXL) to identify cell surface glycoproteins that contain glycans that are recognized by lectins. Commonly used lectins were modified with a probe to produce hydroxide radicals in the proximity of the labeled lectins. The underlying polypeptides of the glycoproteins recognized by the lectins are oxidized and identified by the standard proteomic workflow. As a result, approximately 70% of identified glycoproteins were oxidized in situ by all the lectin probes, while only 5% of the total proteins were oxidized. The correlation between the glycosites and oxidation sites demonstrated the effectiveness of the lectin probes. The specificity and sensitivity of each lectin were determined using site-specific glycan information obtained through glycomic and glycoproteomic analyses. Notably, the sialic acid-binding lectins and the fucose-binding lectins had higher specificity and sensitivity compared to other lectins, while those that were specific to high mannose glycans have poor sensitivity and specificity. This method offers an unprecedented view of the interactions of lectins with specific glycoproteins as well as protein networks that are mediated by specific glycan types on cell membranes.

Extracellular Vesicles from Neurosurgical Aspirates Identifies Chaperonin Containing TCP1 Subunit 6A as a Potential Glioblastoma Biomarker with Prognostic Significance

Glioblastoma, WHO-grade IV glioma, carries a dismal prognosis owing to its infiltrative growth and limited treatment options. Glioblastoma-derived extracellular vesicles (EVs; 30-1000 nm membranous particles) influence the microenvironment to mediate tumor aggressiveness and carry oncogenic cargo across the blood-brain barrier into the circulation. As such, EVs are biomarker reservoirs with enormous potential for assessing glioblastoma tumors in situ. Neurosurgical aspirates are rich sources of EVs, isolated directly from glioma microenvironments. EV proteomes enriched from glioblastoma (n = 15) and glioma grade II-III (n = 7) aspirates are compared and 298 differentially-abundant proteins (p-value < 0.00496) are identified using quantitative LC-MS/MS. Along with previously reported glioblastoma-associated biomarkers, levels of all eight subunits of the key molecular chaperone, T-complex protein 1 Ring complex (TRiC), are higher in glioblastoma-EVs, including CCT2, CCT3, CCT5, CCT6A, CCT7, and TCP1 (p < 0.00496). Analogous increases in TRiC transcript levels and DNA copy numbers are detected in silico; CCT6A has the greatest induction of expression and amplification in glioblastoma and shows a negative association with survival (p = 0.006). CCT6A is co-localized with EGFR at 7p11.2, with a strong tendency for co-amplification (p < 0.001). Immunohistochemistry corroborates the CCT6A proteomics measurements and indicated a potential link between EGFR and CCT6A tissue expression. Putative EV-biomarkers described here should be further assessed in peripheral blood.

Novel HDAC11 inhibitors suppress lung adenocarcinoma stem cell self-renewal and overcome drug resistance by suppressing Sox2

Non-small cell lung cancer (NSCLC) is known to have poor patient outcomes due to development of resistance to chemotherapy agents and the EGFR inhibitors, which results in recurrence of highly aggressive lung tumors. Even with recent success in immunotherapy using the checkpoint inhibitors, additional investigations are essential to identify novel therapeutic strategies for efficacious treatment for NSCLC. Our finding that high levels of histone deacetylase 11 (HDAC11) in human lung tumor tissues correlate with poor patient outcome and that depletion or inhibition of HDAC11 not only significantly reduces self-renewal of cancer stem cells (CSCs) from NSCLC but also decreases Sox2 expression that is essential for maintenance of CSCs, indicates that HDAC11 is a potential target to combat NSCLC. We find that HDAC11 suppresses Sox2 expression through the mediation of Gli1, the Hedgehog pathway transcription factor. In addition, we have used highly selective HDAC11 inhibitors that not only target stemness and adherence independent growth of lung cancer cells but these inhibitors could also efficiently ablate the growth of drug-insensitive stem-like cells as well as therapy resistant lung cancer cells. These inhibitors were found to be efficacious even in presence of cancer associated fibroblasts which have been shown to contribute in therapy resistance. Our study presents a novel role of HDAC11 in lung adenocarcinoma progression and the potential use of highly selective inhibitors of HDAC11 in combating lung cancers.

Turning liabilities into opportunities: Off-target based drug repurposing in cancer

Targeted drugs and precision medicine have transformed the landscape of cancer therapy and significantly improved patient outcomes in many cases. However, as therapies are becoming more and more tailored to smaller patient populations and acquired resistance is limiting the duration of clinical responses, there is an ever increasing demand for new drugs, which is not easily met considering steadily rising drug attrition rates and development costs. Considering these challenges drug repurposing is an attractive complementary approach to traditional drug discovery that can satisfy some of these needs. This is facilitated by the fact that most targeted drugs, despite their implicit connotation, are not singularly specific, but rather display a wide spectrum of target selectivity. Importantly, some of the unintended drug "off-targets" are known anticancer targets in their own right. Others are becoming recognized as such in the process of elucidating off-target mechanisms that in fact are responsible for a drug's anticancer activity, thereby revealing potentially new cancer vulnerabilities. Harnessing such beneficial off-target effects can therefore lead to novel and promising precision medicine approaches. Here, we will discuss experimental and computational methods that are employed to specifically develop single target and network-based off-target repurposing strategies, for instance with drug combinations or polypharmacology drugs. By illustrating concrete examples that have led to clinical translation we will furthermore examine the various scientific and non-scientific factors that cumulatively determine the success of these efforts and thus can inform the future development of new and potentially lifesaving off-target based drug repurposing strategies for cancers that constitute important unmet medical needs.

A yeast display immunoprecipitation screen for targeted discovery of antibodies against membrane protein complexes

Yeast display immunoprecipitation is a combinatorial library screening platform for the discovery and engineering of antibodies against membrane proteins using detergent-solubilized membrane fractions or cell lysates as antigen sources. Here, we present the extension of this method for the screening of antibodies that bind to membrane protein complexes, enabling discovery of antibodies that target antigens involved in a functional protein-protein interaction of interest. For this proof-of-concept study, we focused on the receptor-mediated endocytosis machinery at the blood-brain barrier, and adaptin 2 (AP-2) was chosen as the functional interaction hub. The goal of this study was to identify antibodies that bound to blood-brain barrier (BBB) membrane protein complexes containing AP-2. Screening of a nonimmune yeast display antibody library was carried out using detergent-solubilized BBB plasma membranes as an antigen pool, and antibodies that could interact with protein complexes containing AP-2 were identified. Downstream characterization of isolated antibodies confirmed targeting of proteins known to play important roles in membrane trafficking. This functional yeast display immunoprecipitation screen may be applied to other systems where antibodies against other functional classes of protein complexes are sought.

Functional RNAi Screens Define Distinct Protein Kinase Vulnerabilities in EGFR-Dependent HNSCC Cell Lines

The inhibitory epidermal growth factor receptor (EGFR) antibody, cetuximab, is an approved therapy for head and neck squamous cell carcinoma (HNSCC). Despite tumor response observed in some HNSCC patients, cetuximab alone or combined with radio- or chemotherapy fails to yield long-term control or cures. We hypothesize that a flexible receptor tyrosine kinase coactivation signaling network supports HNSCC survival in the setting of EGFR blockade, and that drugs disrupting this network will provide superior tumor control when combined with EGFR inhibitors. In this work, we submitted EGFR-dependent HNSCC cell lines to RNA interference-based functional genomics screens to identify, in an unbiased fashion, essential protein kinases for growth and survival as well as synthetic lethal targets for combined inhibition with EGFR antagonists. Mechanistic target of rapamycin kinase (MTOR) and erythroblastosis oncogene B (ERBB)3 were identified as high-ranking essential kinase hits in the HNSCC cell lines. MTOR dependency was confirmed by distinct short hairpin RNAs (shRNAs) and high sensitivity of the cell lines to AZD8055, whereas ERBB3 dependency was validated by shRNA-mediated silencing. Furthermore, a synthetic lethal kinome shRNA screen with a pan-ERBB inhibitor, AZD8931, identified multiple components of the extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase pathway, consistent with ERK reactivation and/or incomplete ERK pathway inhibition in response to EGFR inhibitor monotherapy. As validation, distinct mitogen-activated protein kinase kinase (MEK) inhibitors yielded synergistic growth inhibition when combined with the EGFR inhibitors, gefitinib and AZD8931. The findings identify ERBB3 and MTOR as important pharmacological vulnerabilities in HNSCC and support combining MEK and EGFR inhibitors to enhance clinical efficacy in HNSCC. SIGNIFICANCE STATEMENT: Many cancers are driven by nonmutated receptor tyrosine kinase coactivation networks that defy full inhibition with single targeted drugs. This study identifies erythroblastosis oncogene B (ERBB)3 as an essential protein kinase in epidermal growth factor receptor-dependent head and neck squamous cell cancer (HNSCC) cell lines and a synthetic lethal interaction with the extracellular signal-regulated kinase mitogen-activated protein kinase pathway that provides a rationale for combining pan-ERBB and mitogen-activated protein kinase inhibitors as a therapeutic approach in subsets of HNSCC.

A Cross-Linking-Aided Immunoprecipitation/Mass Spectrometry Workflow Reveals Extensive Intracellular Trafficking in Time-Resolved, Signal-Dependent Epidermal Growth Factor Receptor Proteome

Ligand binding to the cell surface receptors initiates signaling cascades that are commonly transduced through a protein-protein interaction (PPI) network to activate a plethora of response pathways. However, tools to capture the membrane PPI network are lacking. Here, we describe a cross-linking-aided mass spectrometry workflow for isolation and identification of signal-dependent epidermal growth factor receptor (EGFR) proteome. We performed protein cross-linking in cell culture at various time points following EGF treatment, followed by immunoprecipitation of endogenous EGFR and analysis of the associated proteins by quantitative mass spectrometry. We identified 140 proteins with high confidence during a 2 h time course by data-dependent acquisition and further validated the results by parallel reaction monitoring. A large proportion of proteins in the EGFR proteome function in endocytosis and intracellular protein transport. The EGFR proteome was highly dynamic with distinct temporal behavior; 10 proteins that appeared in all time points constitute the core proteome. Functional characterization showed that loss of the FYVE domain-containing proteins altered the EGFR intracellular distribution but had a minor effect on EGFR proteome or signaling. Thus, our results suggest that the EGFR proteome include functional regulators that influence EGFR signaling and bystanders that are captured as the components of endocytic vesicles. The high-resolution spatiotemporal information of these molecules facilitates the delineation of many pathways that could determine the strength and duration of the signaling, as well as the location and destination of the receptor.

Functional Proteomics and Deep Network Interrogation Reveal a Complex Mechanism of Action of Midostaurin in Lung Cancer Cells

Lung cancer is associated with high prevalence and mortality, and despite significant successes with targeted drugs in genomically defined subsets of lung cancer and immunotherapy, the majority of patients currently does not benefit from these therapies. Through a targeted drug screen, we found the recently approved multi-kinase inhibitor midostaurin to have potent activity in several lung cancer cells independent of its intended target, PKC, or a specific genomic marker. To determine the underlying mechanism of action we applied a layered functional proteomics approach and a new data integration method. Using chemical proteomics, we identified multiple midostaurin kinase targets in these cells. Network-based integration of these targets with quantitative tyrosine and global phosphoproteomics data using protein-protein interactions from the STRING database suggested multiple targets are relevant for the mode of action of midostaurin. Subsequent functional validation using RNA interference and selective small molecule probes showed that simultaneous inhibition of TBK1, PDPK1 and AURKA was required to elicit midostaurin's cellular effects. Immunoblot analysis of downstream signaling nodes showed that combined inhibition of these targets altered PI3K/AKT and cell cycle signaling pathways that in part converged on PLK1. Furthermore, rational combination of midostaurin with the potent PLK1 inhibitor BI2536 elicited strong synergy. Our results demonstrate that combination of complementary functional proteomics approaches and subsequent network-based data integration can reveal novel insight into the complex mode of action of multi-kinase inhibitors, actionable targets for drug discovery and cancer vulnerabilities. Finally, we illustrate how this knowledge can be used for the rational design of synergistic drug combinations with high potential for clinical translation.

Correlation analysis of mesenchymal-epithelial transition factor protein and human epidermal growth receptor 2 protein expression in 1479 cases of lung adenocarcinoma in China

BACKGROUND

To investigate the correlation between mesenchymal-epithelial transition factor (C-Met) and human epidermal growth receptor 2 (HER2) protein expression in primary lung adenocarcinoma tissues.

METHOD

A total of 1479 resected primary lung adenocarcinoma patients were enrolled in the present study for detecting of C-Met and HER2 protein by immunohistochemistry, and correlation analysis was made between the above two biomarkers and related clinicopathological features.

RESULT

Both C-Met and HER2 proteins were found to stain highly positive in lung adenocarcinomas, and a positive correlation was found between them (χ2  = 118.5, P = 2.707 × 10-21 ). In addition, HER2 protein expression was correlated with sex, pathological stage, lymph node metastasis, and major subtypes; and C-Met was correlated with sex (P < 0.05).

CONCLUSION

The expression of C-Met and HER2 protein in lung adenocarcinoma is highly correlated, and whether it is synergistic in the targeted therapy of lung adenocarcinoma deserves further study.

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

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 (RAS)-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.

A signaling pathway is a network of molecules in a cell that is typically initiated by stimuli (e.g., microenvironmental cues) acting on receptors and internal signaling molecules to determine cell fate. Signaling pathways in cancer cells are different from those in normal cells, and this difference helps cancer cells to grow and thrive indefinitely. Drugs that target the aberrant signaling pathways in cancer cells (often referred to as targeted therapy) are promising for improving treatment outcomes of many different cancers in patients. However, most patients eventually develop resistance to these drugs. Resistance may already be present in the tumor or may emerge via mutation or via microenvironmental mediation. Tumor heterogeneity, which is characterized by subtle or dramatic differences among tumor cells, plays a key role in the development of drug resistance. Some tumor cells respond well to therapy, while others may adapt to the stress induced by the drug within the microenvironment. Moreover, removal of drug-sensitive cells may result in the competitive release of drug-resistant cells. Here, we present mathematical models to assess the impact of heterogeneity in signaling pathways within tumor cells on the outcomes of targeted therapy. We consider a simplified version of two well-known signaling pathways that modulate the growth of lung cancer cells. By using different targeted therapies, we quantify the effect of pathway inhibition on protein activity and cell viability and developed a mathematical model of the network, which is trained to reproduce these data and to develop a panel of heterogeneous in silico cells. The model predicts potential mechanisms of drug resistance and proposes combination therapies that are effective across the panel. We validate these combination therapies experimentally using the lung cancer cells and integrated the in silico cells into a computational lung tissue model that explicitly captures the microenvironment of lung cancer. Our results suggest that heterogeneity in both the tumor and microenvironment impacts treatment response in different ways and suggest a novel combination therapy for a better response.

Transcriptionally inducible Pleckstrin homology-like domain, family A, member 1, attenuates ErbB receptor activity by inhibiting receptor oligomerization

Feedback control is a key mechanism in signal transduction, intimately involved in regulating the outcome of the cellular response. Here, we report a novel mechanism by which PHLDA1, Pleckstrin homology-like domain, family A, member 1, negatively regulates ErbB receptor signaling by inhibition of receptor oligomerization. We have found that the ErbB3 ligand, heregulin, induces PHILDA1 expression in MCF-7 cells. Transcriptionally-induced PHLDA1 protein directly binds to ErbB3, whereas knockdown of PHLDA1 increases complex formation between ErbB3 and ErbB2. To provide insight into the mechanism for our time-course and single-cell experimental observations, we performed a systematic computational search of network topologies of the mathematical models based on receptor dimer-tetramer formation in the ErbB activation processes. Our results indicate that only a model in which PHLDA1 inhibits formation of both dimers and tetramer can explain the experimental data. Predictions made from this model were further validated by single-molecule imaging experiments. Our studies suggest a unique regulatory feature of PHLDA1 to inhibit the ErbB receptor oligomerization process and thereby control the activity of receptor signaling network.

CAR-T cells and combination therapies: What's next in the immunotherapy revolution?

Cancer immunotherapies are dramatically reshaping the clinical management of oncologic patients. For many of these therapies, the guidelines for administration, monitoring, and management of associated toxicities are still being established. This is especially relevant for adoptively transferred, genetically-modified T cells, which have unique pharmacokinetic properties, due to their ability to replicate and persist long-term, following a single administration. Furthermore, in the case of CAR-T cells, the use of synthetic immune receptors may impact signaling pathways involved in T cell function and survival in unexpected ways. We, herein, comment on the most salient aspects of CAR-T cell design and clinical experience in the treatment of solid tumors. In addition, we discuss different possible scenarios for combinations of CAR-T cells and other treatment modalities, with a special emphasis on kinase inhibitors, elaborating on the strategies to maximize synergism. Finally, we discuss some of the technologies that are available to explore the molecular events governing the success of these therapies. The young fields of synthetic and systems biology are likely to be major players in the advancement of CAR-T cell therapies, providing the tools and the knowledge to engineer patients' T lymphocytes into intelligent cancer-fighting micromachines.

MET-GRB2 Signaling-Associated Complexes Correlate with Oncogenic MET Signaling and Sensitivity to MET Kinase Inhibitors

Purpose: Targeting MET in cancer is hampered by lack of diagnostics that accurately reflect high MET signaling and dependence. We hypothesized that assays reflecting MET signaling associated protein complexes could redefine tumors dependent on MET and could add additional precision beyond genomic assessments.Experimental Design: We used biochemical approaches, cellular viability studies, and proximity ligation assays to assess MET dependence. We examined MET signaling complexes in lung cancer patient specimens (N = 406) and patient-derived xenograft (PDX) models of solid tumors (N = 308). We evaluated response to crizotinib in a MET-amplified cohort of PDX models of lung cancer (N = 6) and provide a case report of a lung cancer patient harboring a Δexon14 MET splice variant.Results: We found the interaction of MET with the adaptor protein GRB2 is necessary for oncogenic survival signaling by MET. MET-GRB2 complexes were identified only within MET-amplified PDX models and patient specimens but exhibit substantial variability. Lack of MET-GRB2 complexes was associated with lack of response to MET TKI in cell lines and PDX models. Presence of MET-GRB2 complexes can further subtype tumors with Δexon14 MET splice variants. Presence of these complexes correlated with response to crizotinib in one patient with Δexon14 MET lacking MET gene amplification.Conclusions: Proximity assays measuring MET-GRB2 signaling complexes provide novel insights into MET-mediated signaling and could complement current clinical genomics-based assay platforms. Clin Cancer Res; 23(22); 7084-96. ©2017 AACR.

Exploiting Synthetic Lethality and Network Biology to Overcome EGFR Inhibitor Resistance in Lung Cancer

Despite the recent approval of third-generation therapies, overcoming resistance to epidermal growth factor receptor (EGFR) inhibitors remains a major challenge in non-small cell lung cancer. Conceptually, synthetic lethality holds the promise of identifying non-intuitive targets for tackling both acquired and intrinsic resistance in this setting. However, translating these laboratory findings into effective clinical strategies continues to be elusive. Here, we provide an overview of the synthetic lethal approaches that have been employed to study EGFR inhibitor resistance and review the oncogene and non-oncogene signalling mechanisms that have thus far been unveiled by synthetic lethality screens. We highlight the potential challenges associated with progressing these discoveries into the clinic including context dependency, signalling plasticity, and tumour heterogeneity, and we offer a perspective on emerging network biology and computational solutions to exploit these phenomena for cancer therapy and biomarker discovery. We conclude by presenting a number of tangible steps to bolster our understanding of fundamental synthetic lethality mechanisms and advance these findings beyond the confines of the laboratory.

Identification of Topological Network Modules in Perturbed Protein Interaction Networks

Biological networks consist of functional modules, however detecting and characterizing such modules in networks remains challenging. Perturbing networks is one strategy for identifying modules. Here we used an advanced mathematical approach named topological data analysis (TDA) to interrogate two perturbed networks. In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after protein complex components were deleted from the genome. In the second, we reanalyzed previously published data demonstrating the disruption of the human Sin3 network with a histone deacetylase inhibitor. Here we show that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks. We define TMNs as proteins that occupy close network positions depending on their coordinates in a topological space. TNMs provide new insight into networks by capturing proteins from different categories including proteins within a complex, proteins with shared biological functions, and proteins disrupted across networks.

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.

Coupling an EML4-ALK-centric interactome with RNA interference identifies sensitizers to ALK inhibitors

Patients with lung cancers harboring anaplastic lymphoma kinase (ALK) gene fusions benefit from treatment with ALK inhibitors, but acquired resistance inevitably arises. A better understanding of proximal ALK signaling mechanisms may identify sensitizers to ALK inhibitors that disrupt the balance between prosurvival and proapoptotic effector signals. Using affinity purification coupled with mass spectrometry in an ALK fusion lung cancer cell line (H3122), we generated an ALK signaling network and investigated signaling activity using tyrosine phosphoproteomics. We identified a network of 464 proteins composed of subnetworks with differential response to ALK inhibitors. A small hairpin RNA screen targeting 407 proteins in this network revealed 64 and 9 proteins that when knocked down sensitized cells to crizotinib and alectinib, respectively. Among these, knocking down fibroblast growth factor receptor substrate 2 (FRS2) or coiled-coil and C2 domain-containing protein 1A (CC2D1A), both scaffolding proteins, sensitized multiple ALK fusion cell lines to the ALK inhibitors crizotinib and alectinib. Collectively, our data set provides a resource that enhances our understanding of signaling and drug resistance networks consequent to ALK fusions and identifies potential targets to improve the efficacy of ALK inhibitors in patients.

Synthetic lethality in lung cancer and translation to clinical therapies

Lung cancer is a heterogeneous disease consisting of multiple histological subtypes each driven by unique genetic alterations. Despite the development of targeted therapies that inhibit the oncogenic mutations driving a subset of lung cancer cases, there is a paucity of effective treatments for the majority of lung cancer patients and new strategies are urgently needed. In recent years, the concept of synthetic lethality has been established as an effective approach for discovering novel cancer-specific targets as well as a method to improve the efficacy of existing drugs which provide partial but insufficient benefits for patients. In this review, we discuss the concept of synthetic lethality, the various types of synthetic lethal interactions in the context of oncology and the approaches used to identify these interactions, including recent advances that have transformed the ability to discover novel synthetic lethal combinations on a global scale. Lastly, we describe the specific synthetic lethal interactions identified in lung cancer to date and explore the pharmacological challenges and considerations in translating these discoveries to the clinic.

Aberrant expression of miR-21, miR-376c and miR-145 and their target host genes in Merkel cell polyomavirus-positive non-small cell lung cancer

Merkel Cell Polyoma Virus (MCPyV) infection has been associated with non-small cell lung cancer (NSCLC). Viruses can manipulate cellular miRNAs or have a profound impact on cellular miRNA expression to control host regulatory pathways. In this study, we evaluated the expression profiles of cancer-associated and virally affected host microRNAs miR-21, miR-145, miR-146a, miR-155, miR-302c, miR-367 and miR-376c in a series of NSCLC tissue samples as well as in samples from “healthy” sites, distant from the tumour region that were either positive or negative for MCPyV DNA. miR-21 and miR-376c were significantly upregulated whereas miR-145 was significantly downregulated in the MCPyV+ve samples compared to the MCPyV-ve tumour samples. Overall, miR-21 and miR-376c expression was higher in tumour compared to healthy tissue samples. No association was observed between the miR-155, miR-146a, miR-302c and miR-367 levels and the presence of MCPyV. The expression of miR-21 target genes (Pten, Bcl-2, Daxx, Pkr, Timp3), miR-376c (Grb2, Alk7, Mmp9) and miR-145 (Oct-4, Sox2, Fascin1) and their associated pathways (Braf, Akt-1, Akt-2, Bax, Hif1a, p53) was altered between MCPyV+ve tumor samples and their corresponding controls. These results show a novel association between miR-21, miR-376c and miR-145 and their host target genes with the presence of MCPyV, suggesting a mechanism of virus-specific microRNA signature in NSCLC.

Gli1-Mediated Regulation of Sox2 Facilitates Self-Renewal of Stem-Like Cells and Confers Resistance to EGFR Inhibitors in Non-Small Cell Lung Cancer

Non–small cell lung cancer (NSCLC) patients have very low survival rates because the current therapeutic strategies are not fully effective. Although EGFR tyrosine kinase inhibitors are effective for NSCLC patients harboring EGFR mutations, patients invariably develop resistance to these agents. Alterations in multiple signaling cascades have been associated with the development of resistance to EGFR inhibitors. Sonic Hedgehog and associated Gli transcription factors play a major role in embryonic development and have recently been found to be reactivated in NSCLC, and elevated Gli1 levels correlate with poor prognosis. The Hedgehog pathway has been implicated in the functions of cancer stem cells, although the underlying molecular mechanisms are not clear. In this context, we demonstrate that Gli1 is a strong regulator of embryonic stem cell transcription factor Sox2. Depletion of Gli1 or inhibition of the Hedgehog signaling significantly abrogated the self-renewal of stem-like side-population cells from NSCLCs as well as vascular mimicry of such cells. Gli1 was found to transcriptionally regulate Sox2 through its promoter region, and Gli1 could be detected on the Sox2 promoter. Inhibition of Hedgehog signaling appeared to work cooperatively with EGFR inhibitors in markedly reducing the viability of NSCLC cells as well as the self-renewal of stem-like cells. Thus, our study demonstrates a cooperative functioning of the EGFR signaling and Hedgehog pathways in governing the stem-like functions of NSCLC cancer stem cells and presents a novel therapeutic strategy to combat NSCLC harboring EGFR mutations.

Multilayered proteomics reveals molecular switches dictating ligand-dependent EGFR trafficking

A fascinating conundrum in cell signaling is how stimulation of the same receptor tyrosine kinase with distinct ligands generates specific outcomes. To decipher the functional selectivity of EGF and TGF-α, which induce epidermal growth factor receptor (EGFR) degradation and recycling, respectively, we devised an integrated multilayered proteomics approach (IMPA). We analyzed dynamic changes in the receptor interactome, ubiquitinome, phosphoproteome, and late proteome in response to both ligands in human cells by quantitative MS and identified 67 proteins regulated at multiple levels. We identified RAB7 phosphorylation and RCP recruitment to EGFR as switches for EGF and TGF-α outputs, controlling receptor trafficking, signaling duration, proliferation, and migration. By manipulating RCP levels or phosphorylation of RAB7 in EGFR-positive cancer cells, we were able to switch a TGF-α-mediated response to an EGF-like response or vice versa as EGFR trafficking was rerouted. We propose IMPA as an approach to uncover fine-tuned regulatory mechanisms in cell signaling.

Target Identification in Small Cell Lung Cancer via Integrated Phenotypic Screening and Activity-Based Protein Profiling

To overcome hurdles in identifying key kinases in small cell lung cancer (SCLC), we integrated a target-agnostic phenotypic screen of kinase inhibitors with target identification using activity-based protein profiling (ABPP) in which a desthiobiotin-ATP probe was used. We screened 21 SCLC cell lines with known c-MYC amplification status for alterations in viability using a chemical library of 235 small-molecule kinase inhibitors. One screen hit compound was interrogated with ABPP, and, through this approach, we reidentified Aurora kinase B as a critical kinase in MYC-amplified SCLC cells. We next extended the platform to a second compound that had activity in SCLC cell lines lacking c-MYC amplification and identified TANK-binding kinase 1, a kinase that affects cell viability, polo-like kinase-1 signaling, G2-M arrest, and apoptosis in SCLC cells lacking MYC amplification. These results demonstrate that phenotypic screening combined with ABPP can identify key disease drivers, suggesting that this approach, which combines new chemical probes and disease cell screens, has the potential to identify other important targets in other cancer types. Mol Cancer Ther; 15(2); 334-42. ©2016 AACR.

Comparison of gene expression patterns across 12 tumor types identifies a cancer supercluster characterized by TP53 mutations and cell cycle defects

Transcriptional profile-based subtypes of cancer are often viewed as identifying different diseases from the same tissue origin. Understanding the mechanisms driving the subtypes may be key in development of novel therapeutics but is challenged by lineage-specific expression signals. Using a t-test statistics approach, we compared gene expression subtypes across 12 tumor types, which identified eight transcriptional superclusters characterized by commonly activated disease pathways and similarities in gene expression. One of the largest superclusters was determined by the upregulation of a proliferation signature, significant enrichment in TP53 mutations, genomic loss of CDKN2A (p16(ARF)), evidence of increased numbers of DNA double strand breaks and high expression of cyclin B1 protein. These correlations suggested that abrogation of the P53-mediated apoptosis response to DNA damage results in activation of cell cycle pathways and represents a common theme in cancer. A second consistent pattern, observed in 9 of 11 solid tumor types, was a subtype related to an activated tumor-associated stroma. The similarity in transcriptional footprints across cancers suggested that tumor subtypes are commonly unified by a limited number of molecular themes.

Tumor stratification by a novel graph-regularized bi-clique finding algorithm

Due to involved disease mechanisms, many complex diseases such as cancer, demonstrate significant heterogeneity with varying behaviors, including different survival time, treatment responses, and recurrence rates. The aim of tumor stratification is to identify disease subtypes, which is an important first step towards precision medicine. Recent advances in profiling a large number of molecular variables such as in The Cancer Genome Atlas (TCGA), have enabled researchers to implement computational methods, including traditional clustering and bi-clustering algorithms, to systematically analyze high-throughput molecular measurements to identify tumor subtypes as well as their corresponding associated biomarkers. In this study we discuss critical issues and challenges in existing computational approaches for tumor stratification. We show that the problem can be formulated as finding densely connected sub-graphs (bi-cliques) in a bipartite graph representation of genomic data. We propose a novel algorithm that takes advantage of prior biology knowledge through a gene-gene interaction network to find such sub-graphs, which helps simultaneously identify both tumor subtypes and their corresponding genetic markers. Our experimental results show that our proposed method outperforms current state-of-the-art methods for tumor stratification.

Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data

The driver genetic aberrations collectively regulate core cellular processes underlying cancer development. However, identifying the modules of driver genetic alterations and characterizing their functional mechanisms are still major challenges for cancer studies. Here, we developed an integrative multi-omics method CMDD to identify the driver modules and their affecting dysregulated genes through characterizing genetic alteration-induced dysregulated networks. Applied to glioblastoma (GBM), the CMDD identified a core gene module of 17 genes, including seven known GBM drivers, and their dysregulated genes. The module showed significant association with shorter survival of GBM. When classifying driver genes in the module into two gene sets according to their genetic alteration patterns, we found that one gene set directly participated in the glioma pathway, while the other indirectly regulated the glioma pathway, mostly, via their dysregulated genes. Both of the two gene sets were significant contributors to survival and helpful for classifying GBM subtypes, suggesting their critical roles in GBM pathogenesis. Also, by applying the CMDD to other six cancers, we identified some novel core modules associated with overall survival of patients. Together, these results demonstrate integrative multi-omics data can identify driver modules and uncover their dysregulated genes, which is useful for interpreting cancer genome.

Annotation of human cancers with EGFR signaling-associated protein complexes using proximity ligation assays

Strategies to measure functional signaling-associated protein complexes have the potential to augment current molecular biomarker assays, such as genotyping and expression profiling, used to annotate diseases. Aberrant activation of epidermal growth factor receptor (EGFR) signaling contributes to diverse cancers. We used a proximity ligation assay (PLA) to detect EGFR in a complex with growth factor receptor-bound protein 2 (GRB2), the major signaling adaptor for EGFR. We used multiple lung cancer cell lines to develop and characterize EGFR:GRB2 PLA and correlated this assay with established biochemical measures of EGFR signaling. In a panel of patient-derived xenografts in mice, the intensity of EGFR:GRB2 PLA correlated with the reduction in tumor size in response to the EGFR inhibitor cetuximab. In tumor biopsies from three cohorts of lung cancer patients, positive EGFR:GRB2 PLA was observed in patients with and without EGFR mutations, and the intensity of EGFR:GRB2 PLA was predictive of overall survival in an EGFR inhibitor-treated cohort. Thus, we established the feasibility of using PLA to measure EGFR signaling-associated protein complexes in patient-based materials, suggesting the potential for similar assays for a broader array of receptor tyrosine kinases and other key signaling molecules.

Building and exploring an integrated human kinase network: global organization and medical entry points

Biological matter is organized in functional networks of different natures among which kinase-substrate and protein-protein interactions play an important role. Large public data collections allowed us to compile an important corpus of interaction data around human protein kinases. One of the most interesting observations analyzing this network is that coherence in kinase functional activity relies on kinase substrate interactions primarily and not on which protein complexes are formed around them. Further dissecting the two types of interactions at the level of kinase groups (CMGCs, Tyrosine kinases, etc.) we show a prevalence of intra-group interconnectivity, which we can naturally relate to current scenarios of evolution of biological networks. Tracking publication dates we observe high correlation of kinase interaction research focus with general kinase research. We find a similar bias in the targets of kinase inhibitors that feature high redundancy. Finally, intersecting kinase inhibitor specificity with sets of kinases located at specific positions in the kinase network, we propose alternative options for future therapeutic strategies using these compounds.

BIOLOGICAL SIGNIFICANCE

Despite its importance for cellular regulation and the fact that protein kinases feature prominent targets of modern therapeutic approaches, the structure and logic of the global, integrated protein phosphorylation network have not been investigated intensively. To focus on the regulatory skeleton of the phosphorylation network, we contemplated a network consisting of kinases, their substrates, and publicly available physical protein interactions. Analysis of this network at multiple levels allowed establishing a series of interesting properties such as prevalence of kinase substrate interactions as opposed to general protein-protein interactions for establishing a holistic control over kinases activities. Kinases controlling many or a few only other kinases, in addition to non-kinases, were distributed in cellular compartments differently. They were also targeted by kinase inhibitors with distinct success rates. Non-kinases tightly regulated by a large number of kinases were involved in biological processes both specific and shared with their regulators while being preferably localized in the nucleus. Collectively, these observations may provide for a new perspective in the elaboration of pharmacological intervention strategies. We complemented our study of kinase interactions with a perspective of how this type of data is generated in comparison with general research about those enzymes. Namely, what was the temporal evolution of the research community attention for interaction versus non-interaction-based kinase experiments. This article is part of a Special Issue entitled: "20years of Proteomics" in memory of Viatliano Pallini" Guest Editors: Luca Bini, Juan J. Calvete, Natacha Turck, Denis Hochstrasser and Jean-Charles Sanchez.

Integrin β3 links therapy resistance and cancer stem cell properties

Heterogeneity in tumour cell properties underlies many treatment failures. Understanding the sources of such heterogeneity has proved to be challenging, but remains critical to improving patient outcomes. Integrin α(v)β₃ expression in multiple types of solid tumour stem cells is now shown to control a pro-survival pathway that contributes to therapy resistance.

The mammalian-membrane two-hybrid assay (MaMTH) for probing membrane-protein interactions in human cells

Cell signaling, one of key processes in both normal cellular function and disease, is coordinated by numerous interactions between membrane proteins that change in response to stimuli. We present a split ubiquitin-based method for detection of integral membrane protein-protein interactions (PPIs) in human cells, termed mammalian-membrane two-hybrid assay (MaMTH). We show that this technology detects stimulus (hormone or agonist)-dependent and phosphorylation-dependent PPIs. MaMTH can detect changes in PPIs conferred by mutations such as those in oncogenic ErbB receptor variants or by treatment with drugs such as the tyrosine kinase inhibitor erlotinib. Using MaMTH as a screening assay, we identified CRKII as an interactor of oncogenic EGFR(L858R) and showed that CRKII promotes persistent activation of aberrant signaling in non-small cell lung cancer cells. MaMTH is a powerful tool for investigating the dynamic interactomes of human integral membrane proteins.