Limits...
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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Number of samples needed to detect significantly mutated genes, as a function of a tumor type’s median background mutation frequency of (x-axis) and a cancer gene’s mutation rate above background (the various curves). Y-axis shows the number of samples needed to achieve 90% power for 90% of genes. Grey vertical lines indicate tumor type median background mutation frequencies. Black dots indicate sample sizes in the current study. For most tumor types, the current sample size is inadequate to reliably detect genes mutated at 5% or less above background. See also Supplementary Figure 9.
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Figure 5: Number of samples needed to detect significantly mutated genes, as a function of a tumor type’s median background mutation frequency of (x-axis) and a cancer gene’s mutation rate above background (the various curves). Y-axis shows the number of samples needed to achieve 90% power for 90% of genes. Grey vertical lines indicate tumor type median background mutation frequencies. Black dots indicate sample sizes in the current study. For most tumor types, the current sample size is inadequate to reliably detect genes mutated at 5% or less above background. See also Supplementary Figure 9.

Mentions: Figure 5 shows that the current collection lacks the desired power to detect genes mutated at 5% above the background rate for 17 of the 21 tumor types and even at 10% for 7 of the tumor types. These results are consistent with the down-sampling analysis showing that candidate cancer genes with frequency ≥ 20% are approaching saturation, while the number of candidate cancer genes at lower frequencies is continuing to grow rapidly with sample size.


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)

Number of samples needed to detect significantly mutated genes, as a function of a tumor type’s median background mutation frequency of (x-axis) and a cancer gene’s mutation rate above background (the various curves). Y-axis shows the number of samples needed to achieve 90% power for 90% of genes. Grey vertical lines indicate tumor type median background mutation frequencies. Black dots indicate sample sizes in the current study. For most tumor types, the current sample size is inadequate to reliably detect genes mutated at 5% or less above background. See also Supplementary Figure 9.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 5: Number of samples needed to detect significantly mutated genes, as a function of a tumor type’s median background mutation frequency of (x-axis) and a cancer gene’s mutation rate above background (the various curves). Y-axis shows the number of samples needed to achieve 90% power for 90% of genes. Grey vertical lines indicate tumor type median background mutation frequencies. Black dots indicate sample sizes in the current study. For most tumor types, the current sample size is inadequate to reliably detect genes mutated at 5% or less above background. See also Supplementary Figure 9.
Mentions: Figure 5 shows that the current collection lacks the desired power to detect genes mutated at 5% above the background rate for 17 of the 21 tumor types and even at 10% for 7 of the tumor types. These results are consistent with the down-sampling analysis showing that candidate cancer genes with frequency ≥ 20% are approaching saturation, while the number of candidate cancer genes at lower frequencies is continuing to grow rapidly with sample size.

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