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 PDGFRβ class constituting platelet-derived growth factor receptor β. 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.W. M. Keck FoundationNational Institutes of Health (U.S.) (R21 CA126720)National Institutes of Health (U.S.) (P50 GM068762)National Institutes of Health (U.S.) (RC1 HG005354)National Institutes of Health (U.S.) (U54-CA112967)National Institutes of Health (U.S.) (R01-CA096504)Alfred and Isabel Bader (Fellowship)Jacques-Emile Dubois (fellowship

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

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

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 PDGFRβ class constituting platelet-derived growth factor receptor β. 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. Sci Signal. 2013 Jul 16;6(284):ra5

Abstract 1312: Predicting ligand-dependent tumors from multi-dimensional signaling features [Abstract]

Receptor tyrosine kinases (RTKs) are high-affinity cell surface receptors for growth factors that are frequently deregulated in cancer. Signaling through these receptors has been associated with increased cancer cell proliferation and resistance to cytotoxic therapies. To block this detrimental signaling, many companies are developing inhibitory antibodies against various RTKs. A key challenge in clinical studies is the optimal stratification of patients who may benefit from these therapies. For an RTK targeted antibody, the detection of the respective growth factor in the tumor microenvironment may be an important bio-marker. Beyond the physical presence of the growth factor, the decision whether a cancer cell will respond to growth factor-induced signals is governed by complex intra-cellular signaling networks. We compared different approaches to predict cellular responses and will highlight a hybrid approach that combines mechanistic modeling based on ordinary differential equations with a machine learning algorithm. The models are trained on in vitro drug response screens and then applied to predict response in patient samples. The mechanistic models are trained on quantitative data from signal transduction studies as well as RNAseq data for cellular characterization. Using the hybrid approach, a correlation between growth factor expression in the tumor microenvironment and its predicted response was identified. This supports the hypothesis of addiction of tumors to growth factors abundant in the tumor microenvironment, and might enable more robust patient stratification in the future