Complex diseases like cancers are heterogeneous (unique both within and across individuals) and dynamic (changing over time) diseases that arise from perturbations to multiple biological networks at different levels of organization. One of the ways we profile multiple systems at once is using various -omic technologies to profile an individual in many different ways. The central idea is that we need to personalize our treatments to each individual. Ultimately, we want to be able to provide the right therapies in the right dosages at the right time. We argue that the use of deep phenotyping — especially in a longitudinal manner over time — to define a baseline for each individual will help elucidate mechanisms underlying the onset and progression of cancers.
Among others, we are targeting two research areas:
- We need to develop a good way to identify individuals with a high risk of developing cancers (e.g., with polygenic risk scores) and monitor them to detect disease at the earliest stage so that we can intervene immediately. The unfortunate truth with many diseases (including cancers) is that by the time a clinical diagnosis is reached, there are so many consequences of the original disease manifestation that it is often too late to cure the original disease. Thus, we can help ameliorate these effects by detecting cancer earlier and giving us a chance to intervene early in the disease progression.
- We need to understand the mechanisms underlying the onset and development of cancers. An understanding of the underlying biological mechanisms will help improve the identification of therapeutic targets. For example, if we knew that a specific biological pathway was perturbed during the initial development and progression of leukemia, then potential diagnostics and therapies might be developed that could target that pathway.
Presently we have on-going research projects in glioblastoma and breast cancer in collaboration with Swedish Caner Institute.
Predicting Cancer Phenotypes
This project aims to significantly advance our ability to study the complex mechanistic underpinnings of cancer using integrated multi-omic network models. Towards this goal, we are building metabolic networks relevant to cancer biology to serve as a mechanistic basis on which to ground statistical analyses of experimental multi-omic data. This collaborative systems-based approach for integrating and analyzing multi-omic information from regulatory network topology, signaling and metabolic pathways will lead to…