High-throughput oncogene mutation profiling in human cancer.
作者信息
Thomas Roman K, Baker Alissa C, Debiasi Ralph M, Winckler Wendy, Laframboise Thomas, Lin William M, Wang Meng, Feng Whei, Zander Thomas, MacConaill Laura, Lee Jeffrey C, Nicoletti Rick, Hatton Charlie, Goyette Mary, Girard Luc, Majmudar Kuntal, Ziaugra Liuda, Wong Kwok-Kin, Gabriel Stacey, Beroukhim Rameen, Peyton Michael, Barretina Jordi, Dutt Amit, Emery Caroline, Greulich Heidi, Shah Kinjal, Sasaki Hidefumi, Gazdar Adi, Minna John, Armstrong Scott A, Mellinghoff Ingo K, Hodi F Stephen, Dranoff Glenn, Mischel Paul S, Cloughesy Tim F, Nelson Stan F, Liau Linda M, Mertz Kirsten, Rubin Mark A, Moch Holger, Loda Massimo, Catalona William, Fletcher Jonathan, Signoretti Sabina, Kaye Frederic, Anderson Kenneth C, Demetri George D, Dummer Reinhard, Wagner Stephan, Herlyn Meenhard, Sellers William R, Meyerson Matthew, Garraway Levi A
机构信息
Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 44 Binney Street, Boston, Massachusetts 02115, USA.
出版信息
Nat Genet. 2007 Mar;39(3):347-51. doi: 10.1038/ng1975. Epub 2007 Feb 11.
Systematic efforts are underway to decipher the genetic changes associated with tumor initiation and progression. However, widespread clinical application of this information is hampered by an inability to identify critical genetic events across the spectrum of human tumors with adequate sensitivity and scalability. Here, we have adapted high-throughput genotyping to query 238 known oncogene mutations across 1,000 human tumor samples. This approach established robust mutation distributions spanning 17 cancer types. Of 17 oncogenes analyzed, we found 14 to be mutated at least once, and 298 (30%) samples carried at least one mutation. Moreover, we identified previously unrecognized oncogene mutations in several tumor types and observed an unexpectedly high number of co-occurring mutations. These results offer a new dimension in tumor genetics, where mutations involving multiple cancer genes may be interrogated simultaneously and in 'real time' to guide cancer classification and rational therapeutic intervention.