An Integrated Systems Biology Approach to Better Understand Cancer

Abstract

Cancer is an umbrella term that encompasses a collection of related diseases. In all types of cancer, a s cells become unregulated, they begin to divide uncontrollably and spread into surrounding tissues. From early to late stages of disease progression, there are many alterations that occur and many criteria that must be met for cancer to proliferate uncontrollable, resist cell death, avoid the immune system, and metastasize. Targeted therapy have been developed for a wide range of dysregulated cancer pathways using different modalities and mechanism of actions. However, due to the diversity of cancer, the same treatment that may be effective for one type of cancer may not be responsive on another. Furthermore, patients may carry resistance forms of the disease. To alleviate this, new strategies have been developed to target specific mutations of known dysregulated proteins and to use combination therapies that target multiple pathways. However, both methods require vast amount of knowledge on the biological interactions and mechanism of actions that takes place within the cell. To address this knowledge gap, we believe that the metabolism can be used as a tool to better understand the dysregulations of signaling and gene expression. There is a great opportunity to study the system as a whole to gain key insights for combination therapies that target different regulatory pathways, such as the metabolism and signaling. In this work, we leveraged our current understanding of signaling transduction, transcription factor, and metabolic networks to develop an integrated systems biology approach to quantitatively unravel the mechanisms that regulate cancer. Ultimately, we were able to establish a computational model that incorporated mechanistic understanding of multiple layers of cellular decision making. We believe this work will be useful in the development and evaluation of new combination therapy across all form of cancers.