System properties of ErbB receptor signaling for the understanding of cancer progression

Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network

High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities. Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a refining tool in combinatorial anti-cancer drug discovery.

GPR54 (KISS1R) transactivates EGFR to promote breast cancer cell invasiveness

Kisspeptins (Kp), peptide products of the Kisspeptin-1 (KISS1) gene are endogenous ligands for a G protein-coupled receptor 54 (GPR54). Previous findings have shown that KISS1 acts as a metastasis suppressor in numerous cancers in humans. However, recent studies have demonstrated that an increase in KISS1 and GPR54 expression in human breast tumors correlates with higher tumor grade and metastatic potential. At present, whether or not Kp signaling promotes breast cancer cell invasiveness, required for metastasis and the underlying mechanisms, is unknown. We have found that kisspeptin-10 (Kp-10), the most potent Kp, stimulates the invasion of human breast cancer MDA-MB-231 and Hs578T cells using Matrigel-coated Transwell chamber assays and induces the formation of invasive stellate structures in three-dimensional invasion assays. Furthermore, Kp-10 stimulated an increase in matrix metalloprotease (MMP)-9 activity. We also found that Kp-10 induced the transactivation of epidermal growth factor receptor (EGFR). Knockdown of the GPCR scaffolding protein, β-arrestin 2, inhibited Kp-10-induced EGFR transactivation as well as Kp-10 induced invasion of breast cancer cells via modulation of MMP-9 secretion and activity. Finally, we found that the two receptors associate with each other under basal conditions, and FRET analysis revealed that GPR54 interacts directly with EGFR. The stability of the receptor complex formation was increased upon treatment of cells by Kp-10. Taken together, our findings suggest a novel mechanism by which Kp signaling via GPR54 stimulates breast cancer cell invasiveness.

Ligand stimulation of ErbB4 and a constitutively-active ErbB4 mutant result in different biological responses in human pancreatic tumor cell lines

Pancreatic cancer is the fourth leading cause of cancer death in the United States. Indeed, it has been estimated that 37,000 Americans will die from this disease in 2010. Late diagnosis, chemoresistance, and radioresistance of these tumors are major reasons for poor patient outcome, spurring the search for pancreatic cancer early diagnostic and therapeutic targets. ErbB4 (HER4) is a member of the ErbB family of receptor tyrosine kinases (RTKs), a family that also includes the Epidermal Growth Factor Receptor (EGFR/ErbB1/HER1), Neu/ErbB2/HER2, and ErbB3/HER3. These RTKs play central roles in many human malignancies by regulating cell proliferation, survival, differentiation, invasiveness, motility, and apoptosis. In this report we demonstrate that human pancreatic tumor cell lines exhibit minimal ErbB4 expression; in contrast, these cell lines exhibit varied and in some cases abundant expression and basal tyrosine phosphorylation of EGFR, ErbB2, and ErbB3. Expression of a constitutively-dimerized and -active ErbB4 mutant inhibits clonogenic proliferation of CaPan-1, HPAC, MIA PaCa-2, and PANC-1 pancreatic tumor cell lines. In contrast, expression of wild-type ErbB4 in pancreatic tumor cell lines potentiates stimulation of anchorage-independent colony formation by the ErbB4 ligand Neuregulin 1β. These results illustrate the multiple roles that ErbB4 may be playing in pancreatic tumorigenesis and tumor progression.

Systems pharmacology and genome medicine: a future perspective

Genome medicine uses genomic information in the diagnosis of disease and in prescribing treatment. This transdisciplinary field brings together knowledge on the relationships between genetics, pathophysiology and pharmacology. Systems pharmacology aims to understand the actions and adverse effects of drugs by considering targets in the context of the biological networks in which they exist. Genome medicine forms the base on which systems pharmacology can develop. Experimental and computational approaches enable systems pharmacology to obtain holistic, mechanistic information on disease networks and drug responses, and to identify new drug targets and specific drug combinations. Network analyses of interactions involved in pathophysiology and drug response across various scales of organization, from molecular to organismal, will allow the integration of the systems-level understanding of drug action with genome medicine. The interface of the two fields will enable drug discovery for personalized medicine. Here we provide a perspective on the questions and approaches that drive the development of these new interrelated fields.