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

Editorial: Special section on multiscale cancer modeling

The papers in this special section focus on the use of multiscale modeling in the field of cancer research. Cancer is a complex, heterogeneous disease, characterized by many interaction processes on, and across, multiple scales in time and space that act in concert to drive cancer formation, progression, invasion, and metastasis. These processes range from molecular reactions to cell-cell interactions, to tumor growth and invasion on the tissue-scale, and even to larger scales, such as the physiology, pathophysiology, and population scales. In addition, many cancer properties (including, e.g., size, cell density, extracellular ligands, cellular receptors, mutation type(s), phenotypic distribution, vasculature status, blood vessel permeability, and treatment prognosis) are dynamic and patient-dependent, changing and evolving with both time and treatments. For example, cell death rate may change over time due to chemotherapy. All these dynamically changing cancer properties make development of effective cancer therapies extremely difficult. Computational modeling has the potential to predict complex behaviors of cancer, elucidate regulatory mechanisms, and help inform experimental design. Everyone would agree that computer simulations are usually more cost-effective, efficient, and tractable, relative to laboratory experiments

Editorial: Special section on multiscale cancer modeling

The papers in this special section focus on the use of multiscale modeling in the field of cancer research. Cancer is a complex, heterogeneous disease, characterized by many interaction processes on, and across, multiple scales in time and space that act in concert to drive cancer formation, progression, invasion, and metastasis. These processes range from molecular reactions to cell-cell interactions, to tumor growth and invasion on the tissue-scale, and even to larger scales, such as the physiology, pathophysiology, and population scales. In addition, many cancer properties (including, e.g., size, cell density, extracellular ligands, cellular receptors, mutation type(s), phenotypic distribution, vasculature status, blood vessel permeability, and treatment prognosis) are dynamic and patient-dependent, changing and evolving with both time and treatments. For example, cell death rate may change over time due to chemotherapy. All these dynamically changing cancer properties make development of effective cancer therapies extremely difficult. Computational modeling has the potential to predict complex behaviors of cancer, elucidate regulatory mechanisms, and help inform experimental design. Everyone would agree that computer simulations are usually more cost-effective, efficient, and tractable, relative to laboratory experiments