Future of personalized medicine in oncology: a systems biology approach

Abstract

The development of cost-effective technologies able to comprehensively assess DNA, RNA, protein, and metabolites in patient tumors has fueled efforts to tailor medical care. Indeed validated molecular tests assessing tumor tissue or patient germline DNA already drive therapeutic decision making. However, many theoretical and regulatory challenges must still be overcome before fully realizing the promise of personalized molecular medicine. The masses of data generated by high-throughput technologies are challenging to manage, visualize, and convert to the knowledge required to improve patient outcomes. Systems biology integrates engineering, physics, and mathematical approaches with biologic and medical insights in an iterative process to visualize the interconnected events within a cell that determine how inputs from the environment and the network rewiring that occurs due to the genomic aberrations acquired by patient tumors determines cellular behavior and patient outcomes. A cross-disciplinary systems biology effort will be necessary to convert the information contained in multidimensional data sets into useful biomarkers that can classify patient tumors by prognosis and response to therapeutic modalities and to identify the drivers of tumor behavior that are optimal targets for therapy. An understanding of the effects of targeted therapeutics on signaling networks and homeostatic regulatory loops will be necessary to prevent inadvertent effects as well as to develop rational combinatorial therapies. Systems biology approaches identifying molecular drivers and biomarkers will lead to the implementation of smaller, shorter, cheaper, and individualized clinical trials that will increase the success rate and hasten the implementation of effective therapies into the clinical armamentarium.

Fig 1.

A systems approach integrating genomic and functional proteomic data to identify molecular strategies for the treatment of breast cancer. A comprehensive list of aberrant genes identified by copy number and sequence studies is filtered using expression arrays and functional proteomics. The found targets most likely represent important drivers of oncogenesis and can be mapped using network and functional analysis approaches into interconnecting molecular pathways. This pathway analysis can then be used to design rational therapeutic approaches in individual patients. Bottom panel reproduced with permission from Leary et al. Copyright (2008) National Academy of Sciences, U.S.A. FGF, fibroblast growth factor; EGFR, epidermal growth factor receptor; PI3K, phosphatidylinositol-3-OH kinase.

Fig 2.

Schema of a neoadjuvant phase II molecular marker–driven randomized study. Patients have a baseline biopsy and molecular imaging, and are stratified (STRATA) according to the marker, then adaptively randomly assigned (AR) to standard therapy versus standard therapy plus a targeted therapy directed to the marker of interest. A second biopsy and molecular imaging are performed at 2 weeks of treatment to study pharmacodynamic markers in both arms and correlate the pharmacodynamic changes and marker status with response at the time of surgery. This is a modified “Marker by Treatment Interaction Design” in which there is an assumption that a hypothesized marker splits the population into groups in which the efficacy of a particular treatment will differ. This design can be viewed as a classical randomized clinical trial with upfront stratification for the marker. Importantly, patients with and without the marker have the same chance of being randomly assigned to either therapy until the adaptive part of the study begins. Once enough information is gathered (safety or efficacy according with the end points of the study), the group with the advantage is enriched. Strict early stopping rules are built in. The main goal of these studies is to generate a better hypothesis to be tested in an adequately powered clinical trial. Sx, surgery.