Biomedical informatics for cancer research

In the past two decades, the large investment in cancer research led to identification of the complementary roles of genetic mutation and epidenetic change as the fundamental drivers of cancer. With these discoveries, we now recognize the deep heterogeneity in cancer, in which phenotypically similar behaviors in tumors arise from different molecular aberrations. Although most tumors contains many mutations, only a few mutated genes drive carcinogenesis. For cancer treatment, we must identify and target only the deleterious subset of aberrant proteins from these mutated genes to maximze efficacy while minizing harmful side effects. Together, these observations dictate that next-generation treatments for cancer will become hightly individualized, focusing on the specific set of aberrant driver proteins identified in a tumor. This drives a need for informatics in cancer research and treatment far beyond the need in other diseases. For each individual cancer, we must find the molecular aberrations, identify those that re deleterious in the specific tumor, design and computationally model treatments, and monitor the overall health of the individual. This must be done efficiently in order to generate appropriate treatment plans in a cost-effective manner, State-of-the-art techniques to address many of these needs are being devloped in biomedical informatics and are the focus of this volume.