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Feature Articles : Feb 15, 2009 ( )
Pathway Knowledge Can Benefit Cancer Therapeutics
Analytical Methodologies Show Promise in Identifying Points for Intervention!--h2>
Four scientists who will take the stage later this month at CHI’s “International Molecular Medicine Tri-Conference” to describe their approach to pathway analysis as applied to cancer diagnostics and therapeutic optimization recently gave GEN a preview of their presentations.
Craig Giroux, Ph.D., director of systems and computational biology at the Karmanos Cancer Institute at Wayne State University, works with clinical samples to measure expression profiles in individual tumors. Dr. Giroux and his team have taken on the challenge of developing a diagnostic for cancer disease states using genetic signatures. Using a breast cancer model, they have taken a systems-level approach to their analysis of individual breast cancer cells expressing HER2. Despite the wealth of knowledge about this aggressive class of breast cancer and the established therapeutic choices that are available, there is still much to learn about tumor cell heterogeneity in the population.
Tumor cells are a complex and unstable system. According to Dr. Giroux, tumor cells are best described as a dynamic network with unique patterns of modular activity within a network of pathway related genes. The genes associated with tumor progression have been found to be related to each other in response to a stimulus or therapeutic treatment. The connector genes that link the different response modules are apparently made up of chaperones and heat shock pathways that comprise the cellular stress and homeostasis maintenance processes. These linkages enable local activation of different modules in concert.
“Our goal is to understand how to best use static snapshots based on gene signatures to monitor the moving target that is tumor cell progression. The list of 50–70 genes involved in tumor progression has been vetted,” Dr. Giroux indicates. “We now need to deal with the confounding effects of tumor cell heterogeneity to help us identify and distinguish those genes that are the drivers of tumor progression from those genes that are passengers in the process.
“We have found that these drivers are not random, but rather reside in localized centers within the cellular network. Given our current level of understanding, we are now asking ourselves how we can best apply this to cancer therapeutics?”
Working in collaboration with his clinical colleagues at the Karmanos Cancer Institute, Dr. Giroux is testing the utility of a combinatorial therapeutic approach to HER2-positive breast cancer. His lab has found this combinatorial approach to be the best option, given that delivery of a single therapeutic has been reproducibly shown to lose potency over time. The combinatorial approach will affect multiple pathways that are activated in concert in the HER2-positive breast cancer cells.
Dr. Giroux will highlight his approach using graphical network based methods to demonstrate that the gene-expression activity patterns of individual tumor types can be mapped to the global cellular interaction network. He will describe this systems-level approach to the analysis of individual HER2-positive breast tumors and extrapolate to other oncogene-driven tumor models.
Fei Hua, Ph.D., principal scientist in the systems biology group at Pfizer, is studying cancer pathways at the protein level. Dr. Fei and her lab have been studying the phosphatidylinositol 3-kinase (PI3K)/AKT pathway by looking at protein levels and phosphorylation status in the pathway. PI3 kinase is a ubiquitous lipid kinase involved in receptor signal transduction by receptor tyrosine kinases and has been linked to many cellular functions, including cell growth, proliferation, differentiation, motility, survival, and intracellular trafficking. Hyper-activation of this pathway has been found in more than half of the solid tumors. This pathway is very complex due to multiple feedback loops and interactions with other pathways.
The approach that Dr. Hua’s group is taking is to measure effects of inhibiting at different points of the pathway to improve our understanding about the transduction of signals along the PI3K pathway. The goal is also to prioritize a group of drug candidates that have been designed and selected to inhibit the pathway. The team is looking at a set of 10 different inhibitors that work both upstream and downstream of the pathway.
“We use a reverse-phase protein array to monitor 75 different parameters from each lysate taken from our established cell line,” notes Dr. Hua. “Using a reverse-phase protein array, we spot hundreds of cell lysates on 75 different slides and then probe each slide with a different biomarker-specific antibody. We prefer this approach to the standard protein array wherein the antibodies are printed on the slides and the cell lysate is floated over it. Not only does this approach allow us to conserve the consumption of cell lysate but it also minimizes the impact of the significant differences in antibody affinities for their respective targets. Our assays are more robust, and we can rely on the comparison data between lysates for each parameter.”
Dr. Hua and her team have found that the systematic approach that they are taking to study expression profiles of multiple proteins in the presence of various inhibitors along the PI3K pathway provides more insight into the function of the target pathway than can be determined using a single inhibitor. It also provides additional information that cannot be obtained from a genomics approach. Among other things, dosage effects and selectivity of the inhibitor present great challenges to extract a consistent pathway structure from the inhibition data.
At the Van Andel Research Institute, Craig Webb, Ph.D., director of the program of translational medicine, and his team are building a roadmap to personalized medicine based on a multidisciplinary approach that includes research laboratory, informatics, and outcomes in the clinic.
Since joining the Van Andel Institute, Dr. Webb has been building on the wealth of knowledge from genomics and biomarkers to develop a practical approach to optimal therapeutic selection for individualized molecular-based medicine for oncology patients.
Specifically, in his presentation at the “Tri-Conference,” Dr. Webb will outline his team’s ongoing efforts to utilize molecular information derived from individual patient tumors in conjunction with knowledge of biomarker-drug interaction to predict treatments for an improved therapeutic index. Using network theory and mathematical modeling to develop a signature-based targeted-expression pattern, Dr. Webb and his colleagues are working to understand why some therapies work or others don’t for patients in the current clinical trial studies the team is involved in. Results from their clinical experiences are rolled into their ongoing preclinical research effort that is focused on understanding the underlying mechanisms involved in the developing cancer state.
“We have developed a software application suite, XB-BioIntegration Suite (XB-BIS), to support our multidisciplinary translational research. The application serves as a common interface for the consolidation of the clinical, preclinical, and molecular data, and a variety of analytical, visualization and reporting tools,” Dr. Webb reports. “The software allows for bidirectional flow of real-time data and information between multiple clinical and research components of the project. This portal provides our internal team and our clinical collaborators with a forum to share data and visualize analytical outcomes.”
Multidimensional Pathways in Cancer
Michael Leibman, Ph.D., president and managing director of Strategic Medicine, outlined what his company is focused on.
Strategic Medicine, a team of six principals, was founded to ask some hard questions, including the question of whether or not the medical community is practicing medicine optimally. Partnering with consultants, as well as basic and clinical medical researchers, Dr. Leibman and his team are taking a look at the bigger picture to determine whether we’re ready for personalized medicine. Given the complexities of how diseases present in the clinic and the observation that patients rarely present with a single disease, do we know enough about disease pathways to diagnose and practice medicine optimally?
“Disease is a process, not a chronic state. We continually observe dynamic changes in a patient with disease and in response to the therapeutic regime they’re given. These changes impact the presentation of the disease throughout the course of the therapy,” Dr. Leibman explains. “The ways in which we describe the process impacts our ability to stratify our patients and provide them with the most efficacious therapy available.
“We are currently looking at approximately 800 variables in an attempt to extract the most critical ones that tie back to the underlying biology of the disease state. There is a richness of qualitative data that exists, and we’re working toward making that data more quantifiable.”
Dr. Leibman and his team are building their models on the knowledge base that exists for breast cancer. In breast cancer, there is a wealth of knowledge at the molecular level and extensive clinical experience that exists for the disease. But to fully understand the breast cancer disease state, Dr. Leibman reports that they need to develop network models that look beyond a single-dimensional view and incorporate all forms of molecular data from gene-expression levels to protein networks to metabolic data from actual patients. The analysis then takes a look at the gaps in understanding to determine what’s left out in the model.
Strategic Medicine works on a global basis to monitor the impact of drugs on patients around the world. It is also working with pharmaceutical firms to change how they approach drug discovery. It hopes to balance the biological screening process with the economics involved to determine how best to integrate new drug candidates into the standard practice used by physicians today. By way of example, understanding off-target risks of drug candidates that can be anticipated and eliminated early in the drug discovery process is key to avoiding failures in clinical trials, or worse, post-market release.
The Strategic Medicine approach is really disease agnostic—the current modeling being done with breast cancer will be extrapolated to other disease states. While it is continuing to make inroads toward stratified medicine, Strategic Medicine indicated that the medical community is not yet ready to deliver personalized medicine.
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