The search for improved molecular cancer diagnostics is a challenge for which systems approaches show great promise. As is becoming increasingly clear, cancer is a perpetually-evolving, highly multi-factorial disease. With next generation sequencing providing an ever-increasing amount of high-throughput data, the need for analytical tools that can provide meaningful context is critical. Systems approaches have demonstrated an ability to separate meaningful signal from noise that arises from population heterogeneity, heterogeneity within and across tumors, and multiple sources of technical variation when sufficient sample sizes are obtained and standardized measurement technologies are used. The ability to develop clinically useful molecular cancer diagnostics will be predicated on advancements on two major fronts: 1) more comprehensive and accurate measurements of multiple endpoints, and 2) more sophisticated analytical tools that synthesize high-throughput data into meaningful reflections of cellular states. To this end, systems approaches that have integrated transcriptomic data onto biomolecular networks have shown promise in their ability to classify tumor subtypes, predict clinical progression, and inform treatment options. Ultimately, the success of systems approaches will be measured by their ability to develop molecular cancer diagnostics through distilling complex, systems-wide information into actionable information in the clinic.
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