Novel method for detection of cancer driver mutations

A new method of cancer genome analysis have led to identification of rare mutations contributing to cancer development.

The main challenge of cancer genomics is statistically significant detection of mutations, as cancer cells are characterized by high mutability. Some mutations are essential for cancer development (called “drivers”), and other just happen spontaneously without critical influence on a disease (called “passengers”). A new model tries to differ driver and passenger mutations by their nearby sequence variation.

The statistical framework was applied to 29 tumor types represented by 12,004 samples. The outcome were 697 gene-cancer pairs, including 423 previously not known.

Among discovered mutational hotspots, HDAC4 was significantly mutated in gastroesophaegal cancer, POLR2A in lung adenocarcinoma, and in various cancers: ANAPC1, FGFR4, IKZF3, PARG, SOX7, ZFX.

All data are available under

More: “Discovery of cancer driver genes based on nucleotide context”, F. Dietlein et al., 2018, doi:10.1101/485292.