Hebrew University, Israel
The mass of publicly available genomic datasets, incorporate within them valid data that can be very beneficial for clinical decisions. The challenge is to decipher the true signals from the noise. We developed a specially designed method for scanning datasets of responders and non-responders to different treatments and finding the most statistically powerful predictive genomic signatures.
The signature found for predicting response to Interferon in HCV patients ,was found to be consistent in similar triggering of the innate response as in Dengue virus, Influenza, Poliovirus, Western Nile and PBMC of healthy people.
Realizing that the signature genes are sentto battle with the virus challenge ,mathematical equations can be assigned to describe the battle dynamics, andbiological simulations can be carried out to evaluate each individual response, based on these measured geneexpressions.
This has the benefit of selecting an optimal dose strategy per each patient. Hence these genes servenot only as biomarkers for predicting responders or non responders ,but can enable manipulating the treatmentbased on the individual ‘s signature to change the fate of the non responders.