Dynamic Response Networks
“There are scientific and practical reasons for pursuing individualized treatment of cancer,” said Serhiy Souchelnytskyi, Ph.D., associate professor at the Karolinska Institute. “My primary activity involves proteome profiling of human breast cancer to allow generation of networks suitable for systemic analysis. Initially, it was not my intent to focus on breast cancer, as this could very well be applicable to other cancers, but breast cancer proved to be a good model.”
Dr. Souchelnytskyi’s methodology of choice is Dynamic Response Network (DRNet). “DRNet is a tool that can be used in the clinic to manage genetic and proteomic information to predict disease development and response to treatments,” he explained. “Among other things, DRNet can discriminate malignant vs. benign neoplasia—up to 70 percent of breast neoplasia are not malignant. This provides possibilities for clinical applications such as the individualization of treatments.”
Dr. Souchelnytskyi presented two case studies demonstrating the clinical applications of DRNet that focused on improved diagnostics and selection of treatment in one case and selection of treatment of resistant breast cancer in the other.
“The case-studies indicated that individual differences are so significant that selection of the most efficient treatment requires assessment of each patient individually,” said Dr. Souchelnytskyi. “Tumorigenesis studies indicated specific network features of the main steps of tumorigenesis. Proteomics is the most reliable in reflecting the functional status of cells and tumors, which is required for DRNet building.”
When a treatment is only 80% effective, noted Dr. Souchelnytskyi, you don’t want to be in that other 20%. However, with first-line treatment, there are strict guidelines to treat. “With second-line, there are more options, but less certainty, and this is where we come into the picture. Cancer can be aggressive or indolent. If it grows, go after it; with malignancy you have to be careful. I’ve seen cases where tumors have been activated to metastasis. We step up and suggest ways for treatment and let the clinician decide.”
Researchers have a range of protein-profiling methodologies at their disposal. In his presentation, William M. Gallagher, Ph.D., associate professor of cancer biology at University of California Davis, talked about how tissue microarrays and digital slide scanning technologies can greatly speed up biomarker development.
“We have actually stopped most of our omics-based discovery programs and shifted more of our efforts toward validation of our findings via high-throughput antibody-based assays on tissue microarrays—which, in a sense, can also be included within the proteomics sphere,” said Dr. Gallagher.
A core activity within Dr. Gallagher’s lab centers on the creation and use of tissue microarrays to provide a means for screening large numbers of clinical samples on a simultaneous basis. “This assay affords its own problems, namely surrounding downstream analysis via pathologist-based interpretation.”
“Accordingly, we have developed an automated approach to assist investigators to quantify expression of biomarkers that have been assessed via immunohistochemistry.” This approach, termed IHC-MARK, can discriminate tumor-specific expression of biomarkers at different subcellular levels, e.g., nuclear, cytoplasmic, and membranous. “We are currently in the process of commercializing this technology via our spin-out company, OncoMark.
“IHC-MARK has been road-tested so far on multiple tumor and marker types,” explained Dr. Gallagher, who is also CSO of OncoMark.
One of the key bottlenecks that Dr. Gallagher’s group initially faced was the difficulty in performing downstream validation of identified biomarkers of interest. “This required us to refocus our efforts on this crucial phase and develop new solutions such as the automated image-analysis approach to overcome these problems.
“Our findings suggest that such immunohistochemistry-based surrogates may provide more clinically applicable assays than complex gene-expression signatures. The result is a comprehensive biomarker development pathway that extends from discovery through validation on tissue microarrays, which is yielding clinically relevant biomarker panels for predicting outcome in breast cancer.”