TY - JOUR
T1 - Identifying driver mutations in sequenced cancer genomes
T2 - Computational approaches to enable precision medicine
AU - Raphael, Benjamin J.
AU - Dobson, Jason R.
AU - Oesper, Layla
AU - Vandin, Fabio
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014/1/30
Y1 - 2014/1/30
N2 - High-throughput DNA sequencing is revolutionizing the study of cancer and enabling the measurement of the somatic mutations that drive cancer development. However, the resulting sequencing datasets are large and complex, obscuring the clinically important mutations in a background of errors, noise, and random mutations. Here, we review computational approaches to identify somatic mutations in cancer genome sequences and to distinguish the driver mutations that are responsible for cancer from random, passenger mutations. First, we describe approaches to detect somatic mutations from high-throughput DNA sequencing data, particularly for tumor samples that comprise heterogeneous populations of cells. Next, we review computational approaches that aim to predict driver mutations according to their frequency of occurrence in a cohort of samples, or according to their predicted functional impact on protein sequence or structure. Finally, we review techniques to identify recurrent combinations of somatic mutations, including approaches that examine mutations in known pathways or protein-interaction networks, as well as de novo approaches that identify combinations of mutations according to statistical patterns of mutual exclusivity. These techniques, coupled with advances in high-throughput DNA sequencing, are enabling precision medicine approaches to the diagnosis and treatment of cancer.
AB - High-throughput DNA sequencing is revolutionizing the study of cancer and enabling the measurement of the somatic mutations that drive cancer development. However, the resulting sequencing datasets are large and complex, obscuring the clinically important mutations in a background of errors, noise, and random mutations. Here, we review computational approaches to identify somatic mutations in cancer genome sequences and to distinguish the driver mutations that are responsible for cancer from random, passenger mutations. First, we describe approaches to detect somatic mutations from high-throughput DNA sequencing data, particularly for tumor samples that comprise heterogeneous populations of cells. Next, we review computational approaches that aim to predict driver mutations according to their frequency of occurrence in a cohort of samples, or according to their predicted functional impact on protein sequence or structure. Finally, we review techniques to identify recurrent combinations of somatic mutations, including approaches that examine mutations in known pathways or protein-interaction networks, as well as de novo approaches that identify combinations of mutations according to statistical patterns of mutual exclusivity. These techniques, coupled with advances in high-throughput DNA sequencing, are enabling precision medicine approaches to the diagnosis and treatment of cancer.
UR - http://www.scopus.com/inward/record.url?scp=84893346030&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893346030&partnerID=8YFLogxK
U2 - 10.1186/gm524
DO - 10.1186/gm524
M3 - Review article
C2 - 24479672
AN - SCOPUS:84893346030
VL - 6
JO - Genome Medicine
JF - Genome Medicine
SN - 1756-994X
IS - 1
M1 - 5
ER -