Mark Sevecka

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.

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.

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.

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

Combining two strategies enables simultaneous quantification of multiple 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 β-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 β-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.

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.Cancer: Computational model predicts ligand dependent tumorsThe prediction of growth factor induced cancer cell growth was improved significantly by combining a signaling model with machine learning. A team led by Andreas Raue at Merrimack Pharmaceuticals, attempted to better understand growth factor-dependent tumors and their potential treatment with receptor-targeting antibodies. Interestingly, prediction of tumor response improved significantly by adding prior knowledge from a mechanistic signaling model. This conceptually new approach relies solely on publicly available gene expression data and can be readily applied in drug development and development of clinical trials. In patient data, correlation between growth factor expression in the tumor microenvironment and its predicted response were identified. This consolidates the belief of an addiction of tumors to growth factors abundant in the tumor microenvironment, and might enable a more robust patient stratification in the future.

Abstract 5464: In-vitro studies of MM-121/SAR 256212, an anti-ErbB-3 antibody, in combination with erlotinib in EGFR-wild-type NSCLC.

MM-121/SAR 256212 is a fully human anti ErbB3/HER3 antibody that blocks ligand-induced receptor activation. Formation of EGFR/ErbB3 (ErbB1-3) heterodimers has been implicated as a major driver of tumor growth and survival in non-small cell lung cancer (NSCLC). Although erlotinib remains the standard-of-care treatment for patients with EGFR-wild-type NSCLC who have failed platinum combination chemotherapy, clinical benefit is typically modest. To address this unmet medical need, we investigated the combination of erlotinib with MM-121 in pre-clinical models of NSCLC. We initially assembled a panel of 25 EGFR-WT NSCLC cell lines spanning the most common histological subtypes (adenocarcinoma, squamous and large-cell carcinoma). Clinical studies show that tumors harboring activating Ras mutations less commonly respond to ErbB-directed therapies. To explore the effect of Ras mutations on responsiveness to MM‐121, we also selected our cell lines to include a variety of H-/K-/N-Ras genotypes. Using a carefully optimized in-vitro 3D culture system, we then measured cell viability in response to MM-121, erlotinib or a combination of the drugs in the presence or absence of exogenously added epidermal growth factor (EGF) and/or heregulin-β1 (HRG). Our results indicate that MM-121 inhibits HRG-driven cell proliferation in the studied cell lines. In the five cell lines exhibiting dual-EGF-HRG-driven cell proliferation, the combination of MM-121 with erlotinib demonstrated superior inhibition of cell viability over erlotinib alone.To assess the ability of MM-121 to inhibit tumor cell growth independent of exogenously supplied HRG, we established mouse xenografts from three of the cell lines and treated mice with erlotinib, MM-121, or a combination of the two. All three in-vivo models showed greatly enhanced inhibition of tumor cell growth compared to erlotinib alone in a manner consistent with our in-vitro results. Analyzing our in vitro data we found that mutations in Ras genes (HRAS, KRAS, NRAS) do not preclude response to MM-121 or incremental benefit from the combination of MM-121 with erlotinib over erlotinib alone. Non-adenocarcinoma origin likewise did not preclude response to MM-121, although a weak trend towards diminished activity in the ten non-adenocarcinoma NSCLC cell lines was apparent. Merrimack Pharmaceuticals and Sanofi are co-developing MM-121 and a Phase 1-2 study of MM-121 in combination with erlotinib is currently enrolling patients with EGFR-wild-type NSCLC. Citation Format: Marisa J. Wainszelbaum, Mark S. Sevecka, Olga Burenkova, Gabriela Garcia, William Kubasek, Gavin MacBeath. In-vitro studies of MM-121/SAR 256212, an anti-ErbB-3 antibody, in combination with erlotinib in EGFR-wild-type NSCLC. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5464. doi:10.1158/1538-7445.AM2013-5464