Dynamic Targeting in Cancer Treatment

With the advent of personalized medicine, design and development of anti-cancer drugs that are specifically targeted to individual or sets of genes or proteins has been an active research area in both academia and industry. The underlying motivation for this approach is to interfere with several pathological crosstalk pathways in order to inhibit or at the very least control the proliferation of cancer cells. However, after initially conferring beneficial effects, if sub-lethal, these artificial perturbations in cell function pathways can inadvertently activate drug-induced up- and down-regulation of feedback loops, resulting in dynamic changes over time in the molecular network structure and potentially causing drug resistance as seen in clinics. Hence, the targets or their combined signatures should also change in accordance with the evolution of the network (reflected by changes to the structure and/or functional output of the network) over the course of treatment. This suggests the need for a “dynamic targeting” strategy aimed at optimizing tumor control by interfering with different molecular targets, at varying stages. Understanding the dynamic changes of this complex network under various perturbed conditions due to drug treatment is extremely challenging under experimental conditions let alone in clinical settings. However, mathematical modeling can facilitate studying these effects at the network level and beyond, and also accelerate comparison of the impact of different dosage regimens and therapeutic modalities prior to sizeable investment in risky and expensive clinical trials. A dynamic targeting strategy based on the use of mathematical modeling can be a new, exciting research avenue in the discovery and development of therapeutic drugs

Cracking the context-specific PI3K signaling code.

Specificity in signal transduction is determined by the ability of cells to “encode” and subsequently “decode” different environmental signals. Akin to computer software, this “signaling code” governs context-dependent execution of cellular programs through modulation of signaling dynamics and can be corrupted by disease-causing mutations. Class IA phosphoinositide 3-kinase (PI3K) signaling is critical for normal growth and development and is dysregulated in human disorders such as benign overgrowth syndromes, cancer, primary immune deficiency, and metabolic syndrome. Despite decades of PI3K research, understanding of context-dependent regulation of the PI3K pathway and of the underlying signaling code remains rudimentary. Here, we review current knowledge on context-specific PI3K signaling and how technological advances now make it possible to move from a qualitative to quantitative understanding of this pathway. Insight into how cellular PI3K signaling is encoded or decoded may open new avenues for rational pharmacological targeting of PI3K-associated diseases. The principles of PI3K context-dependent signal encoding and decoding described here are likely applicable to most, if not all, major cell signaling pathways

DECIPHERING CELL SIGNALING REWIRING IN HUMAN DISORDERS

The knowledge of cell molecular mechanisms implicated in human diseases is expanding and should be converted into guidelines for deciphering pathological cell signaling and suggesting appropriate treatment. The basic assumption is that during a pathological transformation, the cell does not create new signaling mechanisms, but rather it hijacks the existing molecular programs. This affects not only intracellular functions, but also a crosstalk between different cell types resulting in a new, yet pathological status of the system. There is a certain combination of molecular characteristics dictating specific cell signaling states that sustains the pathological disease status. Identifying and manipulating the key molecular players controlling these cell signaling states, and shifting the pathological status toward the desired healthy phenotype, are the major challenge for molecular biology of human diseases

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems’ level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research

Development and Application of Lysate Microarray Technology for Quantitative Analysis of Human Disease

Reductionist biology has yielded tremendous insight into the basis of biochemistry and genetic disease. However, the remarkable failure of reductionist biology to explain complex problems, especially cancer, has led to the development of systems biology. The vast complexity of biological systems remains the most difficult problem in biology today. In order to understand this complexity, we need tools to massively multiplex measurements of a signaling network. Therefore, we developed lysate microarray technology to fill this need. In this work, we discuss three ways in which lysate microarrays were applied to human disease. In the first work, we discuss a key stage in malaria development. The liver-stage malaria parasite represents a promising target for intervention, and we present the first use of lysate microarray technology as a screening tool for host-parasite interactions in an infectious disease. We identified three cancer-related pathways that are modified in malaria infection, and studied the p53 pathway in depth. Our finding that the parasite downregulates p53 and that treatment with Nutlin-3 strongly decreases parasite load may lead to the development of a prophylactic malaria vaccine. In the second work, we began by screening drug combinations and varying dosing schedule in triple-negative breast cancers (TNBCs). We systematically explored stimulation space and collected a large lysate microarray dataset, which was used for statistical analysis. We identified a sensitization effect when a growth factor signaling inhibitor was presented before a genotoxic agent. This sensitization was generalizable among a subset of TNBCs and may generally be important for cancers driven by growth factor signaling, as we found the effect extends to nonTNBC cancers. We hope this data will be useful in guiding cancer treatment strategies in patients. In the third work, we study the changing role of the DNA Damage Response (DDR) as a cell line evolves towards cancer. We used the MCF10A progression series and studied how these cell lines respond to genotoxic agents. We identified differences in cell fates after treatment, and collected a large lysate microarray dataset for statistical analysis. Early analysis of the data indicates gross rewiring within the DDR between the MCF10A cell lines