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Reference

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

Paper Id
COSP28920
Authors
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 and Aerts S
Affiliation
Center for Human Genetics, KU Leuven, Leuven, Belgium.
Journal
PloS one 2012;7(6):e38463
ISSN:1932-6203
PUBMED:22675565
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.
Paper Status
Curated
Genes Analysed
122
Mutated Samples
41
Total No. of Samples
41
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Genes Samples CDS Mutation AA Mutation
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Non-Mutant Genes Gene Id (COSG)
This tab shows samples without mutations in the selected study/paper [more details]
Non-Mutant Samples Sample Id (COSS)
This tab shows mutated samples in the selected study/paper [more details]
Sample Name Mutation Count
This tab shows non coding variant in the selected study/paper [more details]
Sample ID Sample Name ID NCV Annotation Zygosity Chromosome Genome start Genome stop Genome version Strand WT seq Mut seq
This tab shows the gene expression and copy number variation data for this study. [more details]

Table Information

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The table currently shows only high value (numeric) copy number data. Copy number segments are excluded if the total copy number and minor allele values are unknown.

Click here to include all copy number data. For more detailed information about copy number data and gain/loss definitions click here.

Sample Gene Expression Expr Level (Z-Score)

Over Expressed; Z-Score > 2.0

Under Expressed; Z-Score < -2.0

Normal; Z-Score within the range -2.0 to 2.0

CN Type Minor Allele Copy Number CN Segment Posn. Average Ploidy

1. N/A represents cases where the average ploidy value is not available( mostly ICGC samples). For some TCGA samples where the minor allele information is not available the average ploidy value could not be calculated.

2. For TCGA samples, the ASCAT algorithm was used to calculate the average ploidy.

3. For CGP samples, the PICNIC algorithm was used to calculate the average ploidy.

CNV
This tab shows the fusion mutations observed in this sample [more details]
Gene Sample Name Id Sample(COSS) CDS Mutation Somatic status Zygosity Validated Type