Title: High accuracy mutation detection in leukemia on a selected panel of cancer genes
Authors: Kalender Atak, Zeynep *
De Keersmaecker, Kim *
Gianfelici, Valentina
Geerdens, Ellen
Vandepoel, Roel
Pauwels, Daphnie
Porcu, Michaƫl
Lahortiga, Idoya
Brys, Vanessa
Dirks, Willy G
Quentmeier, Hilmar
Cloos, Jacqueline
Cuppens Harry, Harry
Uyttebroeck, Anne
Vandenberghe, Peter
Cools, Jan #
Aerts, Stein # ×
Issue Date: Jun-2012
Publisher: Public Library of Sciene
Series Title: PLoS One vol:7 issue:6 pages:e38463
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.
ISSN: 1932-6203
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Department of Human Genetics - miscellaneous
Laboratory of Molecular Biology of Leukemia
Laboratory of Computational Biology
* (joint) first author
× corresponding author
# (joint) last author

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