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High accuracy mutation detection in leukemia on a selected panel of cancer genes.

Kalender Atak Z, De Keersmaecker K, Gianfelici V, Geerdens E, Vandepoel R, Pauwels D, Porcu M, Lahortiga I, Brys V, Dirks WG, Quentmeier H, Cloos J, Cuppens H, Uyttebroeck A, Vandenberghe P, Cools J, Aerts S - PLoS ONE (2012)

Bottom Line: We tested nine combinations of different mapping and variant-calling methods, varied the variant calling parameters, and compared the predicted mutations with a large independent validation set obtained by capillary re-sequencing.We confirmed mutations in known T-ALL drivers, including PHF6, NF1, FBXW7, NOTCH1, KRAS, NRAS, PIK3CA, and PTEN.Finally, we re-sequenced a small set of 39 candidate genes and identified recurrent mutations in TET1, SPRY3 and SPRY4.

View Article: PubMed Central - PubMed

Affiliation: Center for Human Genetics, KU Leuven, Leuven, Belgium.

ABSTRACT
With the advent of whole-genome and whole-exome sequencing, high-quality catalogs of recurrently mutated cancer genes are becoming available for many cancer types. Increasing access to sequencing technology, including bench-top sequencers, provide the opportunity to re-sequence a limited set of cancer genes across a patient cohort with limited processing time. Here, we re-sequenced a set of cancer genes in T-cell acute lymphoblastic leukemia (T-ALL) using Nimblegen sequence capture coupled with Roche/454 technology. First, we investigated how a maximal sensitivity and specificity of mutation detection can be achieved through a benchmark study. We tested nine combinations of different mapping and variant-calling methods, varied the variant calling parameters, and compared the predicted mutations with a large independent validation set obtained by capillary re-sequencing. We found that the combination of two mapping algorithms, namely BWA-SW and SSAHA2, coupled with the variant calling algorithm Atlas-SNP2 yields the highest sensitivity (95%) and the highest specificity (93%). Next, we applied this analysis pipeline to identify mutations in a set of 58 cancer genes, in a panel of 18 T-ALL cell lines and 15 T-ALL patient samples. We confirmed mutations in known T-ALL drivers, including PHF6, NF1, FBXW7, NOTCH1, KRAS, NRAS, PIK3CA, and PTEN. Interestingly, we also found mutations in several cancer genes that had not been linked to T-ALL before, including JAK3. Finally, we re-sequenced a small set of 39 candidate genes and identified recurrent mutations in TET1, SPRY3 and SPRY4. In conclusion, we established an optimized analysis pipeline for Roche/454 data that can be applied to accurately detect gene mutations in cancer, which led to the identification of several new candidate T-ALL driver mutations.

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Related in: MedlinePlus

Mutations in the 97 genes.Coding mutations in known cancer genes (A) and candidate genes (B) are indicated with different color codes. Panel A is further subdivided into (I) genes that are known to be drivers in T-ALL, and (II) the genes that have recurrent somatic mutations in various human cancers. The cell lines are located to the left of the table, and the patient samples are located to the right. Genes are ranked according to the frequency of protein altering mutations in the patient samples.
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pone-0038463-g002: Mutations in the 97 genes.Coding mutations in known cancer genes (A) and candidate genes (B) are indicated with different color codes. Panel A is further subdivided into (I) genes that are known to be drivers in T-ALL, and (II) the genes that have recurrent somatic mutations in various human cancers. The cell lines are located to the left of the table, and the patient samples are located to the right. Genes are ranked according to the frequency of protein altering mutations in the patient samples.

Mentions: We applied the optimized pipeline determined above, consisting of the SSAHA2+BWA-SW combination for read mapping, and Atlas-SNP2 for variation calling, to identify mutations in a panel of 58 “cancer genes” across 18 T-ALL cell lines and 15 primary T-ALL patient samples. This set of genes consists of 13 T-ALL drivers (Figure 2.A.I) and 45 other genes involved in a variety of cancers (Figure 2.A.II). All of these genes are present in the Census [2] database of cancer genes except for the recently discovered cancer genes ATOH1 and PHF6 [13], [14]. Since PHF6 mutations are involved in T-ALL we added PHF6 to our list of T-ALL drivers.


High accuracy mutation detection in leukemia on a selected panel of cancer genes.

Kalender Atak Z, De Keersmaecker K, Gianfelici V, Geerdens E, Vandepoel R, Pauwels D, Porcu M, Lahortiga I, Brys V, Dirks WG, Quentmeier H, Cloos J, Cuppens H, Uyttebroeck A, Vandenberghe P, Cools J, Aerts S - PLoS ONE (2012)

Mutations in the 97 genes.Coding mutations in known cancer genes (A) and candidate genes (B) are indicated with different color codes. Panel A is further subdivided into (I) genes that are known to be drivers in T-ALL, and (II) the genes that have recurrent somatic mutations in various human cancers. The cell lines are located to the left of the table, and the patient samples are located to the right. Genes are ranked according to the frequency of protein altering mutations in the patient samples.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3366948&req=5

pone-0038463-g002: Mutations in the 97 genes.Coding mutations in known cancer genes (A) and candidate genes (B) are indicated with different color codes. Panel A is further subdivided into (I) genes that are known to be drivers in T-ALL, and (II) the genes that have recurrent somatic mutations in various human cancers. The cell lines are located to the left of the table, and the patient samples are located to the right. Genes are ranked according to the frequency of protein altering mutations in the patient samples.
Mentions: We applied the optimized pipeline determined above, consisting of the SSAHA2+BWA-SW combination for read mapping, and Atlas-SNP2 for variation calling, to identify mutations in a panel of 58 “cancer genes” across 18 T-ALL cell lines and 15 primary T-ALL patient samples. This set of genes consists of 13 T-ALL drivers (Figure 2.A.I) and 45 other genes involved in a variety of cancers (Figure 2.A.II). All of these genes are present in the Census [2] database of cancer genes except for the recently discovered cancer genes ATOH1 and PHF6 [13], [14]. Since PHF6 mutations are involved in T-ALL we added PHF6 to our list of T-ALL drivers.

Bottom Line: We tested nine combinations of different mapping and variant-calling methods, varied the variant calling parameters, and compared the predicted mutations with a large independent validation set obtained by capillary re-sequencing.We confirmed mutations in known T-ALL drivers, including PHF6, NF1, FBXW7, NOTCH1, KRAS, NRAS, PIK3CA, and PTEN.Finally, we re-sequenced a small set of 39 candidate genes and identified recurrent mutations in TET1, SPRY3 and SPRY4.

View Article: PubMed Central - PubMed

Affiliation: Center for Human Genetics, KU Leuven, Leuven, Belgium.

ABSTRACT
With the advent of whole-genome and whole-exome sequencing, high-quality catalogs of recurrently mutated cancer genes are becoming available for many cancer types. Increasing access to sequencing technology, including bench-top sequencers, provide the opportunity to re-sequence a limited set of cancer genes across a patient cohort with limited processing time. Here, we re-sequenced a set of cancer genes in T-cell acute lymphoblastic leukemia (T-ALL) using Nimblegen sequence capture coupled with Roche/454 technology. First, we investigated how a maximal sensitivity and specificity of mutation detection can be achieved through a benchmark study. We tested nine combinations of different mapping and variant-calling methods, varied the variant calling parameters, and compared the predicted mutations with a large independent validation set obtained by capillary re-sequencing. We found that the combination of two mapping algorithms, namely BWA-SW and SSAHA2, coupled with the variant calling algorithm Atlas-SNP2 yields the highest sensitivity (95%) and the highest specificity (93%). Next, we applied this analysis pipeline to identify mutations in a set of 58 cancer genes, in a panel of 18 T-ALL cell lines and 15 T-ALL patient samples. We confirmed mutations in known T-ALL drivers, including PHF6, NF1, FBXW7, NOTCH1, KRAS, NRAS, PIK3CA, and PTEN. Interestingly, we also found mutations in several cancer genes that had not been linked to T-ALL before, including JAK3. Finally, we re-sequenced a small set of 39 candidate genes and identified recurrent mutations in TET1, SPRY3 and SPRY4. In conclusion, we established an optimized analysis pipeline for Roche/454 data that can be applied to accurately detect gene mutations in cancer, which led to the identification of several new candidate T-ALL driver mutations.

Show MeSH
Related in: MedlinePlus