Predicting ligand-dependent tumors from multi-dimensional signaling features.

Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo.

Enhanced Targeting of the EGFR Network with MM-151, an Oligoclonal Anti-EGFR Antibody Therapeutic.

Although EGFR is a validated therapeutic target across multiple cancer indications, the often modest clinical responses to current anti-EGFR agents suggest the need for improved therapeutics. Here, we demonstrate that signal amplification driven by high-affinity EGFR ligands limits the capacity of monoclonal anti-EGFR antibodies to block pathway signaling and cell proliferation and that these ligands are commonly coexpressed with low-affinity EGFR ligands in epithelial tumors. To develop an improved antibody therapeutic capable of overcoming high-affinity ligand-mediated signal amplification, we used a network biology approach comprised of signaling studies and computational modeling of receptor-antagonist interactions. Model simulations suggested that an oligoclonal antibody combination may overcome signal amplification within the EGFR:ERK pathway driven by all EGFR ligands. Based on this, we designed MM-151, a combination of three fully human IgG1 monoclonal antibodies that can simultaneously engage distinct, nonoverlapping epitopes on EGFR with subnanomolar affinities. In signaling studies, MM-151 antagonized high-affinity EGFR ligands more effectively than cetuximab, leading to an approximately 65-fold greater decrease in signal amplification to ERK. In cell viability studies, MM-151 demonstrated antiproliferative activity against high-affinity EGFR ligands, either singly or in combination, while cetuximab activity was largely abrogated under these conditions. We confirmed this finding both in vitro and in vivo in a cell line model of autocrine high-affinity ligand expression. Together, these preclinical studies provide rationale for the clinical study of MM-151 and suggest that high-affinity EGFR ligand expression may be a predictive response marker that distinguishes MM-151 from other anti-EGFR therapeutics.

Receptor tyrosine kinases fall into distinct classes based on their inferred signaling networks.

Although many anticancer drugs that target receptor tyrosine kinases (RTKs) provide clinical benefit, their long-term use is limited by resistance that is often attributed to increased abundance or activation of another RTK that compensates for the inhibited receptor. To uncover common and unique features in the signaling networks of RTKs, we measured time-dependent signaling in six isogenic cell lines, each expressing a different RTK as downstream proteins were systematically perturbed by RNA interference. Network models inferred from the data revealed a conserved set of signaling pathways and RTK-specific features that grouped the RTKs into three distinct classes: (i) an EGFR/FGFR1/c-Met class constituting epidermal growth factor receptor, fibroblast growth factor receptor 1, and the hepatocyte growth factor receptor c-Met; (ii) an IGF-1R/NTRK2 class constituting insulin-like growth factor 1 receptor and neurotrophic tyrosine receptor kinase 2; and (iii) a PDGFRß class constituting platelet-derived growth factor receptor ß. Analysis of cancer cell line data showed that many RTKs of the same class were coexpressed and that increased abundance of an RTK or its cognate ligand frequently correlated with resistance to a drug targeting another RTK of the same class. In contrast, abundance of an RTK or ligand of one class generally did not affect sensitivity to a drug targeting an RTK of a different class. Thus, classifying RTKs by their inferred networks and then therapeutically targeting multiple receptors within a class may delay or prevent the onset of resistance.

A dual array-based approach to assess the abundance and posttranslational modification state of signaling proteins.

A system-wide analysis of cell signaling requires detecting and quantifying many different proteins and their posttranslational modification states in the same cellular sample. Here, we present Protocols for two miniaturized, array-based methods, one of which provides detailed information on a central signaling protein and the other of which provides a broad characterization of the surrounding signaling network. We describe a bead-based array and its use in characterizing the different forms and functions of ß-catenin, as well as lysate microarrays (reverse-phase protein arrays) and their use in detecting and quantifying proteins involved in the canonical and noncanonical Wnt signaling pathways. As an application of this dual approach, we characterized the state of ß-catenin signaling in cell lysates and linked these molecule-specific data with pathway-wide changes in signaling. The Protocols described here provide detailed instructions for cell culture methods, bead arrays, and lysate microarrays and outline how to use these complementary approaches to obtain insight into a complex network at a systems level.

Lysate microarrays enable high-throughput, quantitative investigations of cellular signaling.

Lysate microarrays (reverse-phase protein arrays) hold great promise as a tool for systems-level investigations of signaling and multiplexed analyses of disease biomarkers. To date, however, widespread use of this technology has been limited by questions concerning data quality and the specificity of detection reagents. To address these concerns, we developed a strategy to identify high-quality reagents for use with lysate microarrays. In total, we tested 383 antibodies for their ability to quantify changes in protein abundance or modification in 20 biological contexts across 17 cell lines. Antibodies yielding significant differences in signal were further evaluated by immunoblotting and 82 passed our rigorous criteria. The large-scale data set from our screen revealed that cell fate decisions are encoded not just by the identities of proteins that are activated, but by differences in their signaling dynamics as well. Overall, our list of validated antibodies and associated protocols establish lysate microarrays as a robust tool for systems biology.

Dissecting protein function and signaling using protein microarrays.

Although many methods exist to study the recognition and signaling properties of proteins in isolation, it remains a challenge to perform these investigations on a system-wide or proteome-wide scale and within the context of biological complexity. Protein microarray technology provides a powerful tool to assess the selectivity of protein-protein interactions in high-throughput and to quantify the abundances and post-translational modification states of many different proteins in complex mixtures. Here, we provide an overview of the various applications of protein microarray technology and compare the strengths and technical challenges associated with each approach. Overall, we conclude that if this technology is to have a substantial impact on our understanding of cell biology and physiology, increased emphasis must be placed on obtaining rigorously controlled quantitative data from protein function microarrays and on assessing the selectivity of reagents used in conjunction with protein-detecting microarrays.

Linear combinations of docking affinities explain quantitative differences in RTK signaling.

Receptor tyrosine kinases (RTKs) process extracellular cues by activating a broad array of signaling proteins. Paradoxically, they often use the same proteins to elicit diverse and even opposing phenotypic responses. Binary, 'on-off' wiring diagrams are therefore inadequate to explain their differences. Here, we show that when six diverse RTKs are placed in the same cellular background, they activate many of the same proteins, but to different quantitative degrees. Additionally, we find that the relative phosphorylation levels of upstream signaling proteins can be accurately predicted using linear models that rely on combinations of receptor-docking affinities and that the docking sites for phosphoinositide 3-kinase (PI3K) and Shc1 provide much of the predictive information. In contrast, we find that the phosphorylation levels of downstream proteins cannot be predicted using linear models. Taken together, these results show that information processing by RTKs can be segmented into discrete upstream and downstream steps, suggesting that the challenging task of constructing mathematical models of RTK signaling can be parsed into separate and more manageable layers.

State-based discovery: a multidimensional screen for small-molecule modulators of EGF signaling.

As an alternative to conventional, target-oriented drug discovery, we report a strategy that identifies compounds on the basis of the state that they induce in a signaling network. Immortalized human cells are grown in microtiter plates and treated with compounds from a small-molecule library. The target network is then activated and lysates derived from each sample are arrayed onto glass-supported nitrocellulose pads. By probing these microarrays with antibodies that report on the abundance or phosphorylation state of selected proteins, a global picture of the target network is obtained. As proof of concept, we screened 84 kinase and phosphatase inhibitors for their ability to induce different states in the ErbB signaling network. We observed functional connections between proteins that match our understanding of ErbB signaling, indicating that state-based screens can be used to define the topology of signaling networks. Additionally, compounds sort according to the multidimensional phenotypes they induce, suggesting that state-based screens may inform efforts to identify the targets of biologically active small molecules.

Uncovering quantitative protein interaction networks for mouse PDZ domains using protein microarrays.

One of the principal challenges in systems biology is to uncover the networks of protein-protein interactions that underlie most biological processes. To date, experimental efforts directed at this problem have largely produced only qualitative networks that are replete with false positives and false negatives. Here, we describe a domain-centered approach--compatible with genome-wide investigations--that enables us to measure the equilibrium dissociation constant (K(D)) of recombinant PDZ domains for fluorescently labeled peptides that represent physiologically relevant binding partners. Using a pilot set of 22 PDZ domains, 4 PDZ domain clusters, and 20 peptides, we define a gold standard dataset by determining the K(D) for all 520 PDZ-peptide combinations using fluorescence polarization. We then show that microarrays of PDZ domains identify interactions of moderate to high affinity (K(D) < or = 10 microM) in a high-throughput format with a false positive rate of 14% and a false negative rate of 14%. By combining the throughput of protein microarrays with the fidelity of fluorescence polarization, our domain/peptide-based strategy yields a quantitative network that faithfully recapitulates 85% of previously reported interactions and uncovers new biophysical interactions, many of which occur between proteins that are co-expressed. From a broader perspective, the selectivity data produced by this effort reveal a strong concordance between protein sequence and protein function, supporting a model in which interaction networks evolve through small steps that do not involve dramatic rewiring of the network.