Analysis of growth factor signaling in genetically diverse breast cancer lines

Background: Soluble 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. Results: We 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. Conclusions: Responses 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/

Mathematische Modellierung der Signaltransduktion in tierischen Zellen am Beispiel der EGF induzierten MAP Kinase Kaskade und des TNF Rezeptor Crosstalks

In this work we apply mathematical modeling to signal transduction networks in mammalian cells. In particular, we have developed models for a survival pathway, the EGF induced MAP kinase cascade and an apoptotic signal trans-duction network the TNF receptor crosstalk. The models presented here are based on and were validated with own experimental data. In the field of signal transduction the major proteins involved and their interactions are fairly well known and biochemically characterized . One characteristic of signal transduction networks is that they are highly interconected by positive and negative feedbacks. Therefore, the dynamics of these networks can not be understood by intuition alone. Mathematical modeling has proven to be a valuable tool in engineering that deals effectively with complexity. In both casese were able to verify hypotheses, which were obtained by the models, experimentally. This work shows that mathematical modeling in combination with quantitative experimental data can give new insights into the potential mechanisms of intracellular signal transduction and regulation.nnerhalb der vorliegende Dissertation wurden mathematische Modelle fuer die EGF induzierte MAP-Kinase-Kaskade und fuer die TNF induzierte Apoptose entwickelt und analysiert. Bei der Modellentwicklung wurde darauf geachtet, dass die mathematischen Modelle auf experimentellen Daten beruhen und soweit moeglich, auch experimentell validiert wurden. Die hier entwickelten Modelle implizieren den derzeitigen Stand des Wissens ueber die jeweiligen Signaltransduktionsnetzwerke und bilden somit ein Forum fuer das publizierte Detailwissen. Waehrend die interagierenden Molekuele in Signaltransduktionsnetzwerken meist strukturell und biochemisch gut charakterisiert sind, ist es nicht moeglich, das Systemverhalten des Netzwerks aufgrund der positiven und negativen Feedback-Regulation rein intuitiv zu verstehen. In dieser Arbeit wird gezeigt, dass es moeglich ist, mathematische Modelle fuer grosse Signalnetzwerke mit praediktiven Charakter zu entwickeln. Wir konnten Hypothesen, die mit Hilfe des Modells gewonnen wurden, experimentell verifizieren und somit die Modelle innerhalb ihrer Grenzen validieren. Die in dieser Arbeit entwickelten Modelle ermoeglichen somit ein ganzheitliches Verstaendnis dieser komplexen biologisch Signalrtansduktions- systeme und die Identifizierung regulatorischer Mechanismen innerhalb der Netzwerke

Quantitative Systems Pharmacology models as a key to translational medicine

Recent advances in experimentation and computation have allowed us to build multiscale models of unprecedented scale. Quantitative Systems Pharmacology (QSP) models are multiscale models that link protein-interaction or drug-interaction kinetics to cellular response in the context of animal or human (disease) physiology. One of the areas where QSP models hold the most promise is in translating preclinical science into the clinic. In the following, I describe recent examples of multiscale models such as bacterial whole-cell models, multiscale tumor growth inhibition models and human disease models. Challenges of multiscale models and emerging solutions will be discussed

Navigating Between Right, Wrong, and Relevant: The Use of Mathematical Modeling in Preclinical Decision Making.

The goal of this mini-review is to summarize the collective experience of the authors for how modeling and simulation approaches have been used to inform various decision points from discovery to First-In-Human clinical trials. The article is divided into a high-level overview of the types of problems that are being aided by modeling and simulation approaches, followed by detailed case studies around drug design (Nektar Therapeutics, Genentech), feasibility analysis (Novartis Pharmaceuticals), improvement of preclinical drug design (Pfizer), and preclinical to clinical extrapolation (Merck, Takeda, and Amgen)

Human Cytochrome P450 1, 2, 3 Families as Pharmacogenes with Emphases on Their Antimalarial and Antituberculosis Drugs and Prevalent African Alleles

Precision medicine gives individuals tailored medical treatment, with the genotype determining the therapeutic strategy, the appropriate dosage, and the likelihood of benefit or toxicity. Cytochrome P450 (CYP) enzyme families 1, 2, and 3 play a pivotal role in eliminating most drugs. Factors that affect CYP function and expression have a major impact on treatment outcomes. Therefore, polymorphisms of these enzymes result in alleles with diverse enzymatic activity and drug metabolism phenotypes. Africa has the highest CYP genetic diversity and also the highest burden of malaria and tuberculosis, and this review presents current general information on CYP enzymes together with variation data concerning antimalarial and antituberculosis drugs, while focusing on the first three CYP families. Afrocentric alleles such as CYP2A6*17, CYP2A6*23, CYP2A6*25, CYP2A6*28, CYP2B6*6, CYP2B6*18, CYP2C8*2, CYP2C9*5, CYP2C9*8, CYP2C9*9, CYP2C19*9, CYP2C19*13, CYP2C19*15, CYP2D6*2, CYP2D6*17, CYP2D6*29, and CYP3A4*15 are implicated in diverse metabolic phenotypes of different antimalarials such as artesunate, mefloquine, quinine, primaquine, and chloroquine. Moreover, CYP3A4, CYP1A1, CYP2C8, CYP2C18, CYP2C19, CYP2J2, and CYP1B1 are implicated in the metabolism of some second-line antituberculosis drugs such as bedaquiline and linezolid. Drug–drug interactions, induction/inhibition, and enzyme polymorphisms that influence the metabolism of antituberculosis, antimalarial, and other drugs, are explored. Moreover, a mapping of Afrocentric missense mutations to CYP structures and a documentation of their known effects provided structural insights, as understanding the mechanism of action of these enzymes and how the different alleles influence enzyme function is invaluable to the advancement of precision medicine

Autocrine signaling is a key regulatory element during osteoclastogenesis

Osteoclasts are responsible for bone destruction in degenerative, inflammatory and metastatic bone disorders. Although osteoclastogenesis has been well-characterized in mouse models, many questions remain regarding the regulation of osteoclast formation in human diseases. We examined the regulation of human precursors induced to differentiate and fuse into multinucleated osteoclasts by receptor activator of nuclear factor kappa-B ligand (RANKL). High-content single cell microscopy enabled the time-resolved quantification of both the population of monocytic precursors and the emerging osteoclasts. We observed that prior to induction of osteoclast fusion, RANKL stimulated precursor proliferation, acting in part through an autocrine mediator. Cytokines secreted during osteoclastogenesis were resolved using multiplexed quantification combined with a Partial Least Squares Regression model to identify the relative importance of specific cytokines for the osteoclastogenesis outcome. Interleukin 8 (IL-8) was identified as one of RANKL-induced cytokines and validated for its role in osteoclast formation using inhibitors of the IL-8 cognate receptors CXCR1 and CXCR2 or an IL-8 blocking antibody. These insights demonstrate that autocrine signaling induced by RANKL represents a key regulatory component of human osteoclastogenesis