ArticlePloS one2012
High accuracy mutation detection in leukemia on a selected panel of cancer genes.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 63 citations in OpenAlex.
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- Distribution based Fuzzy Estimate Spectral Clustering for Cancer Detection with Protein Sequence and Structural MotifsAsian Pacific journal of cancer prevention : APJCP · 2018Article
- Infrequent occurrence of TET1, TET3, and ASXL2 mutations in myelodysplastic/myeloproliferative neoplasms.Blood cancer journal · 2018Article
- STAT5BN642H is a driver mutation for T cell neoplasia.The Journal of clinical investigation · 2018Article
- The NF1 somatic mutational landscape in sporadic human cancers.Human genomics · 2017Review
- Whole-Exome Sequencing of ETV6/RUNX1 in Four Childhood Acute Lymphoblastic Leukaemia CasesAsian Pacific journal of cancer prevention : APJCP · 2017Article
- Germline activating TYK2 mutations in pediatric patients with two primary acute lymphoblastic leukemia occurrences.Leukemia · 2017Article
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- RNA sequencing unravels the genetics of refractory/relapsed T-cell acute lymphoblastic leukemia. Prognostic and therapeutic implications.Haematologica · 2016Article
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Authors and funding
17 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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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