Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer

Abstract

Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer.

Keywords: Boolean model; Cancer phenotypes; Combinatorial therapy; Discrete dynamical system; Drug resistance; Dynamic model; Network model; Personalized therapy; Signal transduction networks; Targeted cancer therapy.

Figure 1

Illustrative example of a signal transduction network relevant to a cancer hallmark phenotype, uncontrolled proliferation. In the normal context cell proliferation is driven by growth factors that bind to receptor tyrosine kinases (RTKs); yet it can also be an outcome of alterations in signal transduction proteins. In panel A, the six subgraphs that are typically referred to as separate pathways in the biological literature are colored differently. Panels B–D indicate select outcomes of a discrete dynamic model of this network, whose regulatory functions are indicated in the bottom of panel B. The unperturbed system (B) has two possible steady states, a non-proliferative one and one with controlled proliferation (Proliferation = 1), among which it may select depending on environmental signals. (C) Alterations in certain oncogenes or tumor suppressor genes yield a single outcome: uncontrolled proliferation (Proliferation = 2). (D) Targeted inhibition of an oncogene (here, PI3K) may not eliminate the proliferating phenotype. Further details are provided in Box 1.

Figure 2

Illustration of resistance mechanisms to targeted therapies and effective combinatorial treatments. (A) The identified mechanisms of resistance to targeted inhibition of signaling through an oncogene , include the reactivation of the oncogene or of nodes that mediate its effect on a cancer phenotype, alterations that drive the same cancer phenotype through an overlapping or parallel pathway, or alterations that drive different cancer hallmark phenotypes. All of these resistance mechanisms can be mapped to relevant intra-cellular networks through systems biology methods. Panel B illustrates a network-based view of a cancer emergence, evolution and finally eradication trajectory. Cancer cells may develop adaptive (e.g. negative feedback-driven) or acquired resistance to a targeted treatment. Adaptive resistance may also aid the selection of certain alterations in the population of cancer cells. Dynamic modeling of the relevant intra-cellular networks can anticipate these resistance mechanisms and predict the most effective targeted therapy.