Cancer: A Systems Biology disease

Abstract

Cancer research has focused on the identification of molecular differences between cancerous and healthy cells. The emerging picture is overwhelmingly complex. Molecules out of many parallel signal transduction pathways are involved. Their activities appear to be controlled by multiple factors. The action of regulatory circuits, cross-talk between pathways and the non-linear reaction kinetics of biochemical processes complicate the understanding and prediction of the outcome of intracellular signaling. In addition, interactions between tumor and other cell types give rise to a complex supra-cellular communication network. If cancer is such a complex system, how can one ever predict the effect of a mutation in a particular gene on a functionality of the entire system? And, how should one go about identifying drug targets?

Here, we argue that one aspect is to recognize, where the essence resides, i.e. recognize cancer as a Systems Biology disease. Then, more cancer biologists could become systems biologists aiming to provide answers to some of the above systemic questions. To this aim, they should integrate the available knowledge stemming from quantitative experimental results through mathematical models. Models that have contributed to the understanding of complex biological systems are discussed. We show that the architecture of a signaling network is important for determining the site at which an oncologist should intervene. Finally, we discuss the possibility of applying network-based drug design to cancer treatment and how rationalized therapies, such as the application of kinase inhibitors, may benefit from Systems Biology.

Introduction

During the micro-evolutionary process of malignant transformation, cancer cells accumulate multiple genetic alterations that provide them with several capabilities (Cahill et al., 1999). The latter include the escape from normal growth control, evasion of the suicidal apoptotic program, induction of sustained angiogenesis, the ability to metastasise, and to invade healthy tissues (Hanahan and Weinberg, 2000). In the past decades, cancer researchers have collected an enormous amount of information about the differences between cancer cells and their healthy counterparts, with the ultimate goal of identifying drug targets. This has, for instance, led to the identification of genes that are causally implicated in human cancer and to the discovery of the mutations in those genes. A cancer gene census was recently compiled (Futreal et al., 2004), which currently contains 300 genes. The vast majority of these genes function in signal transduction processes within or between cells, govern cell cycle progression, apoptosis, angiogenesis, and infiltration (Vogelstein and Kinzler, 2004).

Although knowledge of the molecular cell biology of cancer is enormous, at the same time, the emerging complexity of the entire ‘cancer system’ overwhelms us, leaving an enormous gap in our understanding and predictive power. In this paper, we will discuss aspects of this complexity, and how one can deal with it to answer questions that are relevant for the treatment of cancer.