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Discovery and saturation analysis of cancer genes across 21 tumour types.

Lawrence MS, Stojanov P, Mermel CH, Robinson JT, Garraway LA, Golub TR, Meyerson M, Gabriel SB, Lander ES, Getz G - Nature (2014)

Bottom Line: We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types.Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies.We estimate that near-saturation may be achieved with 600-5,000 samples per tumour type, depending on background mutation frequency.

View Article: PubMed Central - PubMed

Affiliation: Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA.

ABSTRACT
Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2-20%). To explore the feasibility of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exome sequences from 4,742 human cancers and their matched normal-tissue samples across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types. Our analysis also identified 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600-5,000 samples per tumour type, depending on background mutation frequency. The results may help to guide the next stage of cancer genomics.

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Mutation patterns for one known and two novel cancer genes. EGFR shows distinctive tumor-type-specific concentrations of mutations in different regions of the gene. RHEB, which encodes a small GTPase in the RAS superfamily, shows a mutational hotspot in the effector domain. RHOA, another a member of the RAS superfamily, also shows a mutational hotspot in the effector domain. Colored bars after tumor type names are copy-ratio distributions for the gene, when available (red=amplified, blue=deleted). See also Supplementary Figure 4. Similar diagrams for all genes are available at http://www.tumorportal.org.
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Figure 1: Mutation patterns for one known and two novel cancer genes. EGFR shows distinctive tumor-type-specific concentrations of mutations in different regions of the gene. RHEB, which encodes a small GTPase in the RAS superfamily, shows a mutational hotspot in the effector domain. RHOA, another a member of the RAS superfamily, also shows a mutational hotspot in the effector domain. Colored bars after tumor type names are copy-ratio distributions for the gene, when available (red=amplified, blue=deleted). See also Supplementary Figure 4. Similar diagrams for all genes are available at http://www.tumorportal.org.

Mentions: Data and results are posted at http://www.tumorportal.org/. The site includes graphical displays of the mutations in each of the 18,388 genes studied; see examples in Figure 1 and Supplementary Figure 4. The site also includes tables of mutational data for each significant gene) and Q-Q plots for each statistical test.


Discovery and saturation analysis of cancer genes across 21 tumour types.

Lawrence MS, Stojanov P, Mermel CH, Robinson JT, Garraway LA, Golub TR, Meyerson M, Gabriel SB, Lander ES, Getz G - Nature (2014)

Mutation patterns for one known and two novel cancer genes. EGFR shows distinctive tumor-type-specific concentrations of mutations in different regions of the gene. RHEB, which encodes a small GTPase in the RAS superfamily, shows a mutational hotspot in the effector domain. RHOA, another a member of the RAS superfamily, also shows a mutational hotspot in the effector domain. Colored bars after tumor type names are copy-ratio distributions for the gene, when available (red=amplified, blue=deleted). See also Supplementary Figure 4. Similar diagrams for all genes are available at http://www.tumorportal.org.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4048962&req=5

Figure 1: Mutation patterns for one known and two novel cancer genes. EGFR shows distinctive tumor-type-specific concentrations of mutations in different regions of the gene. RHEB, which encodes a small GTPase in the RAS superfamily, shows a mutational hotspot in the effector domain. RHOA, another a member of the RAS superfamily, also shows a mutational hotspot in the effector domain. Colored bars after tumor type names are copy-ratio distributions for the gene, when available (red=amplified, blue=deleted). See also Supplementary Figure 4. Similar diagrams for all genes are available at http://www.tumorportal.org.
Mentions: Data and results are posted at http://www.tumorportal.org/. The site includes graphical displays of the mutations in each of the 18,388 genes studied; see examples in Figure 1 and Supplementary Figure 4. The site also includes tables of mutational data for each significant gene) and Q-Q plots for each statistical test.

Bottom Line: We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types.Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies.We estimate that near-saturation may be achieved with 600-5,000 samples per tumour type, depending on background mutation frequency.

View Article: PubMed Central - PubMed

Affiliation: Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA.

ABSTRACT
Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2-20%). To explore the feasibility of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exome sequences from 4,742 human cancers and their matched normal-tissue samples across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types. Our analysis also identified 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600-5,000 samples per tumour type, depending on background mutation frequency. The results may help to guide the next stage of cancer genomics.

Show MeSH
Related in: MedlinePlus