Monday, 21 October 2013

Mutational landscape and significance across 12 major cancer types

(c) Zephyris at the English language Wikipedia
This article has been written by a team of the Washington University in St Louis (USA) and Brown University (Providence, USA).
The authors are: Cyriac Kandoth, Michael D. McLellan, Fabio Vandin, Kai Ye, Beifang Niu, Charles Lu, Mingchao Xie, Qunyuan Zhang, Joshua F. McMichael, Matthew A. Wyczalkowski, Mark D. M. Leiserson, Christopher A. Miller, John S. Welch, Matthew J. Walter, Michael C. Wendl, Timothy J. Ley, Richard K. Wilson, Benjamin J. Raphael & Li Ding.

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase, Wnt/β-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.

This is an open access article; you can read the full text following the link
Nature  502, 333–339

 
Affiliations:

The Genome Institute, Washington University in St Louis, Missouri 63108, USA
Cyriac Kandoth, Michael D. McLellan, Kai Ye, Beifang Niu, Charles Lu, Mingchao Xie, Qunyuan Zhang, Joshua F. McMichael, Matthew A. Wyczalkowski, Christopher A. Miller, Michael C. Wendl, Timothy J. Ley, Richard K. Wilson & Li Ding

Department of Computer Science, Brown University, Providence, Rhode Island 02912, USA
Fabio Vandin, Mark D. M. Leiserson & Benjamin J. Raphael

Department of Genetics, Washington University in St Louis, Missouri 63108, USA
Kai Ye, Qunyuan Zhang, Michael C. Wendl, Timothy J. Ley, Richard K. Wilson & Li Ding

Department of Medicine, Washington University in St Louis, Missouri 63108, USA
John S. Welch, Matthew J. Walter, Timothy J. Ley & Li Ding
Siteman Cancer Center, Washington University in St Louis, Missouri 63108, USA
John S. Welch, Matthew J. Walter, Timothy J. Ley, Richard K. Wilson & Li Ding

Department of Mathematics, Washington University in St Louis, Missouri 63108, USA
Michael C. Wend