A key challenge in cancer genomics is to distinguish driver mutations, mutations important for cancer development, from random passenger mutations that have accumulated in somatic cells but do not have functional consequences.
A key challenge in interpreting these data is to distinguish driver mutations, mutations important for cancer development, from passenger mutations that have accumulated in somatic cells but without functional consequences.
TP53 mutation, mutations in TP53 called from Illumina sequencing.
In order to collect experimentally verified LoF and GoF mutational information, we obtained 816 LoF mutations and 474 GoF mutations from a literature text-mining process.
Recently, Samocha et al.31 developed a framework for the interpretation of de novo mutations by distinguishing disease-related mutations from background variations.
The failure to distinguish class 1 mutations from class 2 mutations via in silico approaches provided the impetus for us to pursue a biochemical approach, guided by structural insight obtained via X-ray crystallography.
In this study, we use somatic insertional mutagenesis in mice as a model to demonstrate that low abundance mutations that are only rarely found as clonal mutations in advanced-stage disease can be effectively employed to identify known cancer drivers and differentiate rare disease-causing mutations from passenger mutations.
It includes cultures of wild-type contributed from a number of laboratories and a few thousand derivatives carrying one or up to 29 mutations from among 3500 mutations in (or included in deletions spanning) more than 1300 different loci.
Sidransky's team suspected that because cellular debris from the bladder sometimes ends up in urine, they might look for the mtDNA mutation there as well; likewise, mutations from neck and head tumors might show up in saliva, and from lung tumors in lung fluid.
Before his work, it was impossible to separate the contributions of such mutations from those of mutations in nuclear DNA.
