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PLoS By Category | Recent PLoS Articles
Hematology

High Accuracy Mutation Detection in Leukemia on a Selected Panel of Cancer Genes
Published: Monday, June 04, 2012
Author: Zeynep Kalender Atak et al.

by Zeynep Kalender Atak, Kim De Keersmaecker, Valentina Gianfelici, Ellen Geerdens, Roel Vandepoel, Daphnie Pauwels, Michaël Porcu, Idoya Lahortiga, Vanessa Brys, Willy G. Dirks, Hilmar Quentmeier, Jacqueline Cloos, Harry Cuppens, Anne Uyttebroeck, Peter Vandenberghe, Jan Cools, Stein Aerts

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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