Abstract The Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an
unprecedented opportunity to comprehensively characterize a vast set of uniformly …

The dichotomous model of" drivers" and" passengers" in cancer posits that only a few
mutations in a tumor strongly affect its progression, with the remaining ones being …

Cancer progression is an evolutionary process during which thousands of somatic variants
are accumulated within an individual. In the classic view of cancer progression, a handful of …

The advance of personalized cancer medicine requires the accurate identification of the
mutations driving each patient's tumor. However, to date, we have only been able to obtain …

Background Transforming patient-specific molecular data into clinical decisions is
fundamental to personalized medicine. Despite massive advancements in cancer genomics …

A key challenge in cancer genomics is understanding the functional relationships and
dependencies between combinations of somatic mutations that drive cancer development …

Driver mutations are the genetic variants responsible for oncogenesis, but how specific
somatic mutational events arise in cells remains poorly understood. Mutational signatures …

While recently emergent driver mutation data sets are available for developing
computational methods to predict cancer mutation effects, benchmark sets focusing on …

Cancer driver genes can be under positive selection for various types of genetic alterations,
including gain-of-function or loss-of-function point mutations (single-nucleotide variants …

Many cancer genomes contain large numbers of somatic mutations, but few of these
mutations drive tumor development. Current approaches to identify cancer driver genes are …