| | + | Cancer is a genomic disease, with enormous heterogeneity in its behavior. In the past, our methods for categorization, prediction of outcome, and treatment selection have relied largely on a morphologic classification of cancer. But new technologies are fundamentally reframing our views of cancer initiation, progression, [https://en.wikipedia.org/wiki/Metastasis metastasis], and response to treatment; moving us towards a molecular classification of cancer. This transformation depends not only on our ability to deeply investigate the cancer genome, but also on our ability to link these specific molecular changes to specific tumor behaviors. As sequencing costs continue to decline at a supra-[https://en.wikipedia.org/wiki/Moore%27s_law Moore’s law] rate, a torrent of cancer genomic data is looming. However, our ability to deeply investigate the cancer genome is outpacing our ability to correlate these changes with the phenotypes that they produce. [https://en.wikipedia.org/wiki/Translational_science Translational investigators] seeking to associate specific genetic, epigenetic, and systems changes with particular tumor behaviors, lack access to detailed observable traits about the cancer (the so called ‘deep phenotype’), which has now become a major barrier to research. |