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. 2009 Nov 18;4(11):e7887.
doi: 10.1371/journal.pone.0007887.

Profiling critical cancer gene mutations in clinical tumor samples

Laura E MacConaill  1 , Catarina D Campbell, Sarah M Kehoe, Adam J Bass, Charles Hatton, Lili Niu, Matt Davis, Keluo Yao, Megan Hanna, Chandrani Mondal, Lauren Luongo, Caroline M Emery, Alissa C Baker, Juliet Philips, Deborah J Goff, Michelangelo Fiorentino, Mark A Rubin, Kornelia Polyak, Jennifer Chan, Yuexiang Wang, Jonathan A Fletcher, Sandro Santagata, Gianni Corso, Franco Roviello, Ramesh Shivdasani, Mark W Kieran, Keith L Ligon, Charles D Stiles, William C Hahn, Matthew L Meyerson, Levi A Garraway
Affiliations

Profiling critical cancer gene mutations in clinical tumor samples

Laura E MacConaill et al. PLoS One. .

Erratum in

  • PLoS One. 2010;5(9). doi: 10.1371/annotation/3a0c8fee-57ef-43ed-b6c2-55b503e6db5e
  • PLoS One. 2010;5(9). doi: 10.1371/annotation/613c7509-e4c9-42ac-82fb-fc504400d9e0

Abstract

Background: Detection of critical cancer gene mutations in clinical tumor specimens may predict patient outcomes and inform treatment options; however, high-throughput mutation profiling remains underdeveloped as a diagnostic approach. We report the implementation of a genotyping and validation algorithm that enables robust tumor mutation profiling in the clinical setting.

Methodology: We developed and implemented an optimized mutation profiling platform ("OncoMap") to interrogate approximately 400 mutations in 33 known oncogenes and tumor suppressors, many of which are known to predict response or resistance to targeted therapies. The performance of OncoMap was analyzed using DNA derived from both frozen and FFPE clinical material in a diverse set of cancer types. A subsequent in-depth analysis was conducted on histologically and clinically annotated pediatric gliomas. The sensitivity and specificity of OncoMap were 93.8% and 100% in fresh frozen tissue; and 89.3% and 99.4% in FFPE-derived DNA. We detected known mutations at the expected frequencies in common cancers, as well as novel mutations in adult and pediatric cancers that are likely to predict heightened response or resistance to existing or developmental cancer therapies. OncoMap profiles also support a new molecular stratification of pediatric low-grade gliomas based on BRAF mutations that may have immediate clinical impact.

Conclusions: Our results demonstrate the clinical feasibility of high-throughput mutation profiling to query a large panel of "actionable" cancer gene mutations. In the future, this type of approach may be incorporated into both cancer epidemiologic studies and clinical decision making to specify the use of many targeted anticancer agents.

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Conflict of interest statement

Competing Interests: In terms of financial disclosures, Levi Garraway is a consultant for and/or receives sponsored research from Novartis, Inc. None of these relationships constitute a conflict of interest for this work. This does not alter his adherence to all PLoS ONE policies on data sharing and materials.

Figures

Figure 1
Figure 1. The OncoMap process and performance in fresh frozen and FFPE-derived DNA.
A. An overview of the OncoMap process from tumor to mutation profile. See text for details. B. Receiver operator characteristic curves (ROCs) show the sensitivity and specificity for various cutoff values on the sample score of the validation samples. ROCs are plotted for fresh frozen (left) and FFPE-derived (right) DNAs, using bidirectional KRAS assays and Illumina data as a truth-set (see Methods S1).
Figure 2
Figure 2. BRAFV600E mutations detected in archival samples of pediatric gliomas.
Abbreviations: PA - pilocytic astrocytoma, WHO grade I; LGG, nos – low-grade glioma, not otherwise specified, WHO grade I or II; GG – ganglioglioma; A2 – astrocytoma, WHO grade II; HG – high-grade glioma, WHO grade III or IV. Parentheses indicate total number of samples (in chart) or number of samples with indicated mutation (outside chart).
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