Abstract
A key question in cancer genomics is how to distinguish “driver” mutations, which contribute to tumorigenesis, from functionally neutral “passenger” mutations. Driver mutation is critically important for understanding the molecular mechanisms of cancer development and progression, which will ultimately help tailor more targeted and effective treatments for patients. In this chapter, we introduce recent developments in computational methods for identifying driver mutations. We summarize existing methods into several major categories and discuss challenges in discovering the whole spectrum of driver mutations in cancer for future computational and systems biology studies.
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