Birgit Schoeberl

Input–output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data

The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB‐activated pathways we have constructed, trained and analyzed a mass action model of immediate‐early signaling involving ErbB1–4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log‐linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the Kd for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch‐like responses. Thus, key system‐wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation.

Therapeutically Targeting ErbB3: A Key Node in Ligand-Induced Activation of the ErbB Receptor–PI3K Axis

Computational modeling of the ErbB signaling network affirms ErbB3 as a therapeutic target. Zooming In on ErbB3 Aberrant signaling involving the ErbB family of receptors, which can signal as homo- or heterodimers to activate the phosphatidylinositol 3-kinase (PI3K) signaling pathway, has been implicated as contributing to various cancers. Using a systems approach, Schoeberl et al. implicated ErbB3—a member of the ErbB family that is catalytically inactive—as critical to signaling stimulated by ligands that bind either ErbB1 or ErbB3. Computational analysis suggested that inhibiting ligand binding to ErbB3 might represent a more successful approach to treating cancers associated with ligand-induced stimulation of ErbB-PI3K signaling mediated by combinatorial receptor activation than do current therapies that target overexpressed or mutationally activated ErbB-family receptors. Moreover, experimental analysis revealed that a monoclonal antibody developed on the basis of this strategy could stop the growth of tumors grafted into immunodeficient mice. The signaling network downstream of the ErbB family of receptors has been extensively targeted by cancer therapeutics; however, understanding the relative importance of the different components of the ErbB network is nontrivial. To explore the optimal way to therapeutically inhibit combinatorial, ligand-induced activation of the ErbB–phosphatidylinositol 3-kinase (PI3K) axis, we built a computational model of the ErbB signaling network that describes the most effective ErbB ligands, as well as known and previously unidentified ErbB inhibitors. Sensitivity analysis identified ErbB3 as the key node in response to ligands that can bind either ErbB3 or EGFR (epidermal growth factor receptor). We describe MM-121, a human monoclonal antibody that halts the growth of tumor xenografts in mice and, consistent with model-simulated inhibitor data, potently inhibits ErbB3 phosphorylation in a manner distinct from that of other ErbB-targeted therapies. MM-121, a previously unidentified anticancer therapeutic designed using a systems approach, promises to benefit patients with combinatorial, ligand-induced activation of the ErbB signaling network that are not effectively treated by current therapies targeting overexpressed or mutated oncogenes.

An ErbB3 Antibody, MM-121, Is Active in Cancers with Ligand-Dependent Activation

ErbB3 is a critical activator of phosphoinositide 3-kinase (PI3K) signaling in epidermal growth factor receptor (EGFR; ErbB1), ErbB2 [human epidermal growth factor receptor 2 (HER2)], and [hepatocyte growth factor receptor (MET)] addicted cancers, and reactivation of ErbB3 is a prominent method for cancers to become resistant to ErbB inhibitors. In this study, we evaluated the in vivo efficacy of a therapeutic anti-ErbB3 antibody, MM-121. We found that MM-121 effectively blocked ligand-dependent activation of ErbB3 induced by either EGFR, HER2, or MET. Assessment of several cancer cell lines revealed that MM-121 reduced basal ErbB3 phosphorylation most effectively in cancers possessing ligand-dependent activation of ErbB3. In those cancers, MM-121 treatment led to decreased ErbB3 phosphorylation and, in some instances, decreased ErbB3 expression. The efficacy of single-agent MM-121 was also examined in xenograft models. A machine learning algorithm found that MM-121 was most effective against xenografts with evidence of ligand-dependent activation of ErbB3. We subsequently investigated whether MM-121 treatment could abrogate resistance to anti-EGFR therapies by preventing reactivation of ErbB3. We observed that an EGFR mutant lung cancer cell line (HCC827), made resistant to gefitinib by exogenous heregulin, was resensitized by MM-121. In addition, we found that a de novo lung cancer mouse model induced by EGFR T790M-L858R rapidly became resistant to cetuximab. Resistance was associated with an increase in heregulin expression and ErbB3 activation. However, concomitant cetuximab treatment with MM-121 blocked reactivation of ErbB3 and resulted in a sustained and durable response. Thus, these results suggest that targeting ErbB3 with MM-121 can be an effective therapeutic strategy for cancers with ligand-dependent activation of ErbB3.

Antitumor activity of a novel bispecific antibody that targets the ErbB2/ErbB3 oncogenic unit and inhibits heregulin-induced activation of ErbB3

The prevalence of ErbB2 amplification in breast cancer has resulted in the heavy pursuit of ErbB2 as a therapeutic target. Although both the ErbB2 monoclonal antibody trastuzumab and ErbB1/ErbB2 dual kinase inhibitor lapatinib have met with success in the clinic, many patients fail to benefit. In addition, the majority of patients who initially respond will unfortunately ultimately progress on these therapies. Activation of ErbB3, the preferred dimerization partner of ErbB2, plays a key role in driving ErbB2-amplified tumor growth, but we have found that current ErbB2-directed therapies are poor inhibitors of ligand-induced activation. By simulating ErbB3 inhibition in a computational model of ErbB2/ErbB3 receptor signaling, we predicted that a bispecific antibody that docks onto ErbB2 and subsequently binds to ErbB3 and blocks ligand-induced receptor activation would be highly effective in ErbB2-amplified tumors, with superior activity to a monospecific ErbB3 inhibitor. We have developed a bispecific antibody suitable for both large scale production and systemic therapy by generating a single polypeptide fusion protein of two human scFv antibodies linked to modified human serum albumin. The resulting molecule, MM-111, forms a trimeric complex with ErbB2 and ErbB3, effectively inhibiting ErbB3 signaling and showing antitumor activity in preclinical models that is dependent on ErbB2 overexpression. MM-111 can be rationally combined with trastuzumab or lapatinib for increased antitumor activity and may in the future complement existing ErbB2-directed therapies to treat resistant tumors or deter relapse. Mol Cancer Ther; 11(3); 582–93. ©2012 AACR.

MM-141, an IGF-IR– and ErbB3-Directed Bispecific Antibody, Overcomes Network Adaptations That Limit Activity of IGF-IR Inhibitors

Although inhibition of the insulin-like growth factor (IGF) signaling pathway was expected to eliminate a key resistance mechanism for EGF receptor (EGFR)-driven cancers, the effectiveness of IGF-I receptor (IGF-IR) inhibitors in clinical trials has been limited. A multiplicity of survival mechanisms are available to cancer cells. Both IGF-IR and the ErbB3 receptor activate the PI3K/AKT/mTOR axis, but ErbB3 has only recently been pursued as a therapeutic target. We show that coactivation of the ErbB3 pathway is prevalent in a majority of cell lines responsive to IGF ligands and antagonizes IGF-IR–mediated growth inhibition. Blockade of the redundant IGF-IR and ErbB3 survival pathways and downstream resistance mechanisms was achieved with MM-141, a tetravalent bispecific antibody antagonist of IGF-IR and ErbB3. MM-141 potency was superior to monospecific and combination antibody therapies and was insensitive to variation in the ratio of IGF-IR and ErbB3 receptors. MM-141 enhanced the biologic impact of receptor inhibition in vivo as a monotherapy and in combination with the mTOR inhibitor everolimus, gemcitabine, or docetaxel, through blockade of IGF-IR and ErbB3 signaling and prevention of PI3K/AKT/mTOR network adaptation. Mol Cancer Ther; 13(2); 410–25. ©2013 AACR.

Profiles of Basal and Stimulated Receptor Signaling Networks Predict Drug Response in Breast Cancer Lines

Activity of receptor tyrosine kinase networks may serve as an effective means to classify breast cancers and predict their sensitivity to therapeutic drugs. Hybrid Biomarkers Changes in genes only tell part of the story in cancer. Cancer cells also exhibit altered signaling networks and can rewire their signaling pathways in response to either endogenous stimuli or drug therapy. Niepel et al. combined information about breast cancer clinical subtype, genetic status, receptor abundance, and signaling pathway activity to create hybrid biomarkers that predicted the effectiveness of various targeted breast cancer therapies. This approach may prove a better way to customize treatments for cancer patients because it accounts for heterogeneity in tumors with similar genetic alterations and because the multitude of genetic changes observed in cancer appear to result in a smaller set of dysregulated signaling states. Identifying factors responsible for variation in drug response is essential for the effective use of targeted therapeutics. We profiled signaling pathway activity in a collection of breast cancer cell lines before and after stimulation with physiologically relevant ligands, which revealed the variability in network activity among cells of known genotype and molecular subtype. Despite the receptor-based classification of breast cancer subtypes, we found that the abundance and activity of signaling proteins in unstimulated cells (basal profile), as well as the activity of proteins in stimulated cells (signaling profile), varied within each subtype. Using a partial least-squares regression approach, we constructed models that significantly predicted sensitivity to 23 targeted therapeutics. For example, one model showed that the response to the growth factor receptor ligand heregulin effectively predicted the sensitivity of cells to drugs targeting the cell survival pathway mediated by PI3K (phosphoinositide 3-kinase) and Akt, whereas the abundance of Akt or the mutational status of the enzymes in the pathway did not. Thus, basal and signaling protein profiles may yield new biomarkers of drug sensitivity and enable the identification of appropriate therapies in cancers characterized by similar functional dysregulation of signaling networks.

Rapid optimization and prototyping for therapeutic antibody-like molecules

Multispecific antibody-like molecules have the potential to advance the standard-of-care in many human diseases. The design of therapeutic molecules in this class, however, has proven to be difficult and, despite significant successes in preclinical research, only one trivalent antibody, catumaxomab, has demonstrated clinical utility. The challenge originates from the complexity of the design space where multiple parameters such as affinity, avidity, effector functions, and pharmaceutical properties need to be engineered in concurrent fashion to achieve the desired therapeutic efficacy. Here, we present a rapid prototyping approach that allows us to successfully optimize these parameters within one campaign cycle that includes modular design, yeast display of structure focused antibody libraries and high throughput biophysical profiling. We delineate this approach by presenting a design case study of MM-141, a tetravalent bispecific antibody targeting two compensatory signaling growth factor receptors: insulin-like growth factor 1 receptor (IGF-1R) and v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (ErbB3). A MM-141 proof-of-concept (POC) parent molecule did not meet initial design criteria due to modest bioactivity and poor stability properties. Using a combination of yeast display, structured-guided antibody design and library-scale thermal challenge assay, we discovered a diverse set of stable and active anti-IGF-1R and anti-ErbB3 single-chain variable fragments (scFvs). These optimized modules were reformatted to create a diverse set of full-length tetravalent bispecific antibodies. These re-engineered molecules achieved complete blockade of growth factor induced pro-survival signaling, were stable in serum, and had adequate activity and pharmaceutical properties for clinical development. We believe this approach can be readily applied to the optimization of other classes of bispecific or even multispecific antibody-like molecules.

Analysis of growth factor signaling in genetically diverse breast cancer lines

BackgroundSoluble growth factors present in the microenvironment play a major role in tumor development, invasion, metastasis, and responsiveness to targeted therapies. While the biochemistry of growth factor-dependent signal transduction has been studied extensively in individual cell types, relatively little systematic data are available across genetically diverse cell lines.ResultsWe describe a quantitative and comparative dataset focused on immediate-early signaling that regulates the AKT (AKT1/2/3) and ERK (MAPK1/3) pathways in a canonical panel of well-characterized breast cancer lines. We also provide interactive web-based tools to facilitate follow-on analysis of the data. Our findings show that breast cancers are diverse with respect to ligand sensitivity and signaling biochemistry. Surprisingly, triple negative breast cancers (TNBCs; which express low levels of ErbB2, progesterone and estrogen receptors) are the most broadly responsive to growth factors and HER2amp cancers (which overexpress ErbB2) the least. The ratio of ERK to AKT activation varies with ligand and subtype, with a systematic bias in favor of ERK in hormone receptor positive (HR+) cells. The factors that correlate with growth factor responsiveness depend on whether fold-change or absolute activity is considered the key biological variable, and they differ between ERK and AKT pathways.ConclusionsResponses to growth factors are highly diverse across breast cancer cell lines, even within the same subtype. A simple four-part heuristic suggests that diversity arises from variation in receptor abundance, an ERK/AKT bias that depends on ligand identity, a set of factors common to all receptors that varies in abundance or activity with cell line, and an “indirect negative regulation” by ErbB2. This analysis sets the stage for the development of a mechanistic and predictive model of growth factor signaling in diverse cancer lines. Interactive tools for looking up these results and downloading raw data are available at http://lincs.hms.harvard.edu/niepel-bmcbiol-2014/.

Impact of Intrinsic Affinity on Functional Binding and Biological Activity of EGFR Antibodies

Aberrant expression and activation of EGF receptor (EGFR) has been implicated in the development and progression of many human cancers. As such, targeted therapeutic inhibition of EGFR, for example by antibodies, is a promising anticancer strategy. The overall efficacy of antibody therapies results from the complex interplay between affinity, valence, tumor penetration and retention, and signaling inhibition. To gain better insight into this relationship, we studied a panel of EGFR single-chain Fv (scFv) antibodies that recognize an identical epitope on EGFR but bind with intrinsic monovalent affinities varying by 280-fold. The scFv were converted to Fab and IgG formats, and investigated for their ability to bind EGFR, compete with EGF binding, and inhibit EGF-mediated downstream signaling and proliferation. We observed that the apparent EGFR-binding affinity for bivalent IgG plateaus at intermediate values of intrinsic affinity of the cognate Fab, leading to a biphasic curve describing the ratio of IgG to Fab affinity. Mathematical modeling of antibody–receptor binding indicated that the biphasic effect results from nonequilibrium assay limitations. This was confirmed by further observation that the potency of EGF competition for antibody binding to EGFR improved with both intrinsic affinity and antibody valence. Similarly, both higher intrinsic affinity and bivalent binding improved the potency of antibodies in blocking cellular signaling and proliferation. Overall, our work indicates that higher intrinsic affinity combined with bivalent binding can achieve avidity that leads to greater in vitro potency of antibodies, which may translate into greater therapeutic efficacy. Mol Cancer Ther; 11(7); 1467–76. ©2012 AACR.

Optimizing Properties of Antireceptor Antibodies Using Kinetic Computational Models and Experiments

Monoclonal antibodies are valuable as anticancer therapeutics because of their ability to selectively bind tumor-associated target proteins like receptor tyrosine kinases. Kinetic computational models that capture protein-protein interactions using mass action kinetics are a valuable tool for understanding the binding properties of monoclonal antibodies to their targets. Insights from the models can be used to explore different formats, to set antibody design specifications such as affinity and valence, and to predict potency. Antibody binding to target is driven by both intrinsic monovalent affinity and bivalent avidity. In this chapter, we describe a combined experimental and computational method of assessing the relative importance of these effects on observed drug potency. The method, which we call virtual flow cytometry (VFC), merges experimental measurements of monovalent antibody binding kinetics and affinity curves of antibody-antigen binding into a kinetic computational model of antibody-antigen interaction. The VFC method introduces a parameter χ, the avidity factor, which characterizes the ability of an antibody to cross-link its target through bivalent binding. This simple parameterization of antibody cross-linking allows the model to successfully describe and predict antibody binding curves across a wide variety of experimental conditions, including variations in target expression level and incubation time of antibody with target. We further demonstrate how computational models of antibody binding to cells can be used to predict target inhibition potency. Importantly, we demonstrate computationally that antibodies with high ability to cross-link antigen have significant potency advantages. We also present data suggesting that the parameter χ is a physical, epitope-dependent property of an antibody, and as a result propose that determination of antibody cross-linking and avidity should be incorporated into the screening of antibody panels for therapeutic development. Overall, our results suggest that antibody cross-linking, in addition to monovalent binding affinity, is a key design parameter of antibody performance.