Kevin White

Quantitative genetic epistasis has been hypothesized to be an important factor in the

development and progression of complex diseases. Cancers in particular are driven by the accumulation of mutations that may act epistatically during the course of the disease. However, as cancer mutations are uncovered at an unprecedented rate, determining which combinations of genetic alterations interact to produce cancer phenotypes remains a challenge. Here we show that by using combinatorial RNAi screening in cell culture, dense and often previously undetermined interactions among cancer genes were revealed by assessing gene pairs that are frequently co-altered in primary breast cancers. These interacting gene pairs are significantly associated with survival time when co-altered in patients, indicating that genetic interaction mapping may be leveraged to improve risk assessment. As many of these interacting gene pairs involve known drug targets, personalized treatment regimens may be improved by overlaying genetic interactions with mutational profiling.