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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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Cancer genes identified in 4742-tumor dataset. X-axis indicates the q-value (FDR) in the most significant of the 21 tumor types. Y-axis indicates the q-value when the 4742 tumors are analyzed as a combined cohort. Genes in the upper left quadrant reached significance only in the combined analysis. Genes in the lower right quadrant reached significance only in one or more single-type analyses. Genes in the upper right quadrant were significant in both the combined set and in individual tumor types. Color of gene names is as in Figure 2.
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Figure 3: Cancer genes identified in 4742-tumor dataset. X-axis indicates the q-value (FDR) in the most significant of the 21 tumor types. Y-axis indicates the q-value when the 4742 tumors are analyzed as a combined cohort. Genes in the upper left quadrant reached significance only in the combined analysis. Genes in the lower right quadrant reached significance only in one or more single-type analyses. Genes in the upper right quadrant were significant in both the combined set and in individual tumor types. Color of gene names is as in Figure 2.

Mentions: We then analyzed the combined set, which yielded 114 genes (Supplementary Table 2). While 84 of these genes were already identified from analysis of individual tumor types, the remaining 30 achieved significance based only on the frequency of mutations across tumor types – underscoring the value of cross-tumor-type analysis. Conversely, 140 of the 224 genes found in analysis of individual tumor types did not reach significance when analyzing the combined set (Figure 3, lower right quadrant), consistent with the observation that many genes show strong enrichment in only one or a few tumor types.


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)

Cancer genes identified in 4742-tumor dataset. X-axis indicates the q-value (FDR) in the most significant of the 21 tumor types. Y-axis indicates the q-value when the 4742 tumors are analyzed as a combined cohort. Genes in the upper left quadrant reached significance only in the combined analysis. Genes in the lower right quadrant reached significance only in one or more single-type analyses. Genes in the upper right quadrant were significant in both the combined set and in individual tumor types. Color of gene names is as in Figure 2.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: Cancer genes identified in 4742-tumor dataset. X-axis indicates the q-value (FDR) in the most significant of the 21 tumor types. Y-axis indicates the q-value when the 4742 tumors are analyzed as a combined cohort. Genes in the upper left quadrant reached significance only in the combined analysis. Genes in the lower right quadrant reached significance only in one or more single-type analyses. Genes in the upper right quadrant were significant in both the combined set and in individual tumor types. Color of gene names is as in Figure 2.
Mentions: We then analyzed the combined set, which yielded 114 genes (Supplementary Table 2). While 84 of these genes were already identified from analysis of individual tumor types, the remaining 30 achieved significance based only on the frequency of mutations across tumor types – underscoring the value of cross-tumor-type analysis. Conversely, 140 of the 224 genes found in analysis of individual tumor types did not reach significance when analyzing the combined set (Figure 3, lower right quadrant), consistent with the observation that many genes show strong enrichment in only one or a few tumor types.

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