The Systems Medicine Working Group brings together faculty members from multiple Moffitt Cancer Center programs and departments (Experimental Therapeutics, Molecular Oncology, Cancer Epidemiology, Drug Discovery, Biomedical Informatics, and Biostatistics) who realize the importance of integrated, team-based approaches and network level views of human cancers.
Significant advances in cancer treatment have taken place within the past 20 years. Successes in targeted therapy were solidly grounded in our understanding of the molecular biology of cancer. The identification of specific targets of critical importance to the cancer cell’s physiology coupled to rational drug design allowed physicians and patients an edge against cancer. Despite significant responses, however, relapse is a common event due to the emergence of drug resistance. Recent results suggest that combination therapy holds promise to overcome drug resistance. This is consistent with what we have learned in the development of highly effective treatments against HIV and tuberculosis. Thus, an approach that provides rapid identification of a large number of high-quality targets has the potential to make a significant contribution to cancer treatment.
Recent technological advances have transformed cancer biology from a data-poor to a data-rich science. Complete genomes, exomes, and transcriptomes are now available for many cancer types, and several groups and consortia are undertaking large massively-parallel sequencing and expression analysis projects. The aim of these efforts is to identify all changes (in structure, function and gene expression) accumulated by individual cancers. The identification of these changes has the potential to reveal a large number of candidate targets for cancer treatment. In many cases the studies will merely determine whether a target is high-quality (meaning it is of critical importance to the cancer cell’s physiology). Clearly, a strategy that relies on in-depth studies of individual targets is going to be incremental only.
The Systems Medicine Working Group proposes that a systems biology framework can generate much faster and higher quality targets than the traditional approach. Systems biology can be loosely defined as an interdisciplinary approach to understand complex interactions in biological systems using large datasets. More importantly, it also can be understood as a paradigm that seeks a holistic or integrated view of a system rather than a reductionist view.
Ann Chen, PhD
Steven Eschrich, PhD
Eric Haura, MD
John Koomen, PhD