Cell Lines, Microarrays, Drugs and Disease: Trying to Predict Response to Chemotherapy
Over the past few years, microarray experiments have supplied much information about the disregulation of biological pathways associated with various types of cancer. Many studies focus on identifying subgroups of patients with particularly agressive forms of disease, so that we know who to treat. A corresponding question is how to treat them. Given the treatment options available today, this means trying to predict which chemotherapeutic regimens will be most effective.
We can try to predict response to chemo with microarrays by defining signatures of drug sensitivity. In establishing such signatures, we would really like to use samples from cell lines, as these can be (a) grown in abundance, (b) tested with the agents under controlled conditions, and (c) assayed without poisoning patients. Recent studies have suggested how this approach might work using a widely-used panel of cell lines, the NCI60, to assemble the response signatures for several drugs. Unfortunately, ambiguities associated with analyzing the data have made these results difficult to reproduce.
In this talk, we will discuss the steps involved in attacking response prediction, and describe how we have analyzed the data. We will cover some specific ambiguities we have encountered, and in some cases how these can be resolved. Finally, we will describe methods for making such analyses more reproducible, so that progress can be made more steadily.