It is already possible to characterize tumors across multiple molecular factors; the challenge comes in being able to analyze and integrate the large amounts of information available.
Dr. Trey Ideker and his team have tackled that problem for head and neck squamous cell carcinoma (HNSCC): their semi-automated method allows for the rapid development of predictive models estimating patient survival based on their tumor characteristics. This method is able to analyze simultaneously data on the scale of the genome related to a number of molecular features, like gene expression or mutation analysis.
This technology promises to transform patient management for HNSCC by identifying patients whose prognosis is poor and thus may benefit from a more intense therapeutic regimen.
Images via the Ideker Laboratory web page: http://healthsciences.ucsd.edu/som/medicine/research/labs/ideker/Pages/default.aspx