Date: 28.04.2016, Thursday
Place: Room M2, Quantum
Speaker: Jing Tang
Title: Mathematical modeling for the rational selection of personalized cancer drug combinations
Abstract: Making cancer treatment more personalized and effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We critically need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. Utilizing pharmacological screening data from cancer samples, we have developed a logic-based network modeling approach called TIMMA to predict effective drug combinations. The TIMMA algorithm starts by identifying a set of essential drug targets that are most predictive of monotherapy responses. A drug combination is then treated as a combination of the essential targets, the effect of which can be estimated based on the set relationships with the observed target profiles. The TIMMA approach has been applied on the MDA-MB-231 triple-negative breast cancer cell line using 41 drugs and 384 targets. The predicted drug synergy scores were found significantly correlated with the experimental validation results. To further facilitate the statistical testing of drug combination experiment data, we have also developed a novel mathematical model called ZIP to score the drug interactions. Compared to the existing models such as Loewe additive and Bliss independence models, the ZIP model captures the drug interaction relationships by comparing the change in the potency and shape of the dose-response curves between individual drugs and their combination. We utilized a Delta score to quantify the deviation from the expectation of zero interaction, and proved that a non-interaction is equivalent to both probabilistic independence and dose additivity. Using data from a large-scale anticancer drug combination experiment, we demonstrated how the ZIP model captures the experimentally confirmed drug synergy while keeping the number of false positive lower than with the other scoring models. Further, rather than relying on a single parameter to assess drug interaction, we proposed the use of an interaction landscape over the full dose-response matrix to identify and quantify synergistic and antagonistic dose regions. Taken together, the computational-experimental pipeline for drug combination discovery offers an increased power to predict and test the most potential drug combinations and finally translate into treatment options by clinical collaborators.
All interested are warmly welcome!