Applications of Systems Biology to Identify Mechanisms of Adaptive Response to Targeted Single and Combinatorial Drug Treatment in Cancer

A central premise in modern cancer treatment is that better characterization of the genetics of each cancer should lead to better treatments and therefore better patient outcomes. Several improved high-throughput assays and functional genomics analyses currently exist to probe and characterize cancers by pinpointing specific genetic aberrations and their functional implications. But so far, few unambiguously positive outcomes have been observed.
Among all cancer types, melanoma has one of the highest mutational burdens. Although almost all melanomas are driven by mutated components of the mitogen activated protein kinase (MAPK) pathway, the genetic complexity and diversity of the disease creates multiple mechanisms to circumvent or overcome the effects of pathway blockade. Consequently, almost all melanoma patients relapse within 12 months of receiving treatment. Combination therapy has been shown to be effective in overcoming relapses, and some research indicates that combination therapy may be able to delay future relapses. However, effective combinations have thus far proved to be difficult to identify and no known combination of targeted agents provides more than a few months of additional life.
We employed a multipronged approach, combining functional genomics with high-throughput combination screening, to develop classification methods aimed not only at identifying effective therapeutic combinations, but also at determining potential biomarkers for responsiveness to these combinations, and at discerning mechanisms of synergy for some of these combinations. The tools we have developed for melanoma have also been applied to mantle cell lymphoma and chronic lymphocytic leukemia, to substantial effect.