Medical research institutions have generated massive amounts of biological data by genetically profiling hundreds of cancer cell lines. In parallel, academic labs have conducted genetic screens on small numbers of cancer cell lines under custom experimental conditions. In order to share information between these two scientific approaches, this article proposes a frequentist assisted by Bayes (FAB) procedure for hypothesis testing that allows historical information from genomics datasets to increase the power of hypothesis tests in specialized studies. The exchange of information takes place through a novel probability model for multimodal genomics data, which connects biological hypotheses pertaining to cancer cell lines and genes across a variety of experimental contexts. If the explanatory power of the model is high, then the resulting FAB tests can be more powerful than the corresponding classical tests. Otherwise, the FAB tests yield as many discoveries as the classical tests. Simulations and practical investigations demonstrate that the FAB testing procedure can increase the number of effects discovered in genomics studies while still maintaining strict type I error control.