Mutational heterogeneity in cancer and the search for new cancer-associated genes View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-06-16

AUTHORS

Michael S. Lawrence, Petar Stojanov, Paz Polak, Gregory V. Kryukov, Kristian Cibulskis, Andrey Sivachenko, Scott L. Carter, Chip Stewart, Craig H. Mermel, Steven A. Roberts, Adam Kiezun, Peter S. Hammerman, Aaron McKenna, Yotam Drier, Lihua Zou, Alex H. Ramos, Trevor J. Pugh, Nicolas Stransky, Elena Helman, Jaegil Kim, Carrie Sougnez, Lauren Ambrogio, Elizabeth Nickerson, Erica Shefler, Maria L. Cortés, Daniel Auclair, Gordon Saksena, Douglas Voet, Michael Noble, Daniel DiCara, Pei Lin, Lee Lichtenstein, David I. Heiman, Timothy Fennell, Marcin Imielinski, Bryan Hernandez, Eran Hodis, Sylvan Baca, Austin M. Dulak, Jens Lohr, Dan-Avi Landau, Catherine J. Wu, Jorge Melendez-Zajgla, Alfredo Hidalgo-Miranda, Amnon Koren, Steven A. McCarroll, Jaume Mora, Ryan S. Lee, Brian Crompton, Robert Onofrio, Melissa Parkin, Wendy Winckler, Kristin Ardlie, Stacey B. Gabriel, Charles W. M. Roberts, Jaclyn A. Biegel, Kimberly Stegmaier, Adam J. Bass, Levi A. Garraway, Matthew Meyerson, Todd R. Golub, Dmitry A. Gordenin, Shamil Sunyaev, Eric S. Lander, Gad Getz

ABSTRACT

As the sample size in cancer genome studies increases, the list of genes identified as significantly mutated is likely to include more false positives; here, this problem is identified as stemming largely from mutation heterogeneity, and a new analytical methodology designed to overcome this problem is described.

PAGES

214-218

References to SciGraph publications

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  • Journal

    TITLE

    Nature

    ISSUE

    7457

    VOLUME

    499

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nature12213

    DOI

    http://dx.doi.org/10.1038/nature12213

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1021761707

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/23770567


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