約翰霍普金斯大學(xué)的研究人員設(shè)計出一種全新的計算機軟件,，該軟件能同時對幾百個基因突變進行篩選,，并將最可能導(dǎo)致癌癥的DNA突變篩選出來,，該方法被命名為CHASM(Cancer-specific High-throughput Annotation of Somatic Mutations)，將使科學(xué)家將更多的注意力集中到引發(fā)腫瘤的突變上,。
該研究報告發(fā)表在8月15日出版的Cancer Research雜志上,。
這項新方法著重對錯義突變(missense mutations)進行研究，課題組首次利用計算機方法縮小到600個左右的疑似腦部腫瘤突變,，并分選出那些突變在引發(fā)癌癥過程中起“主導(dǎo)(drivers)”和“隨從(passengers)”的突變,。“主導(dǎo)突變”即能引發(fā)并促進腫瘤生長的突變。“隨從突變”即在腫瘤生長過程中出現(xiàn)但對腫瘤的形成和生長沒有影響的突變,。
在分選之前,，研究人員利用機器閱讀技術(shù)把癌癥相關(guān)的約50種突變的特征輸入到系統(tǒng)中，然后研究人員Karchin和Carter采用Random Forest classifier的數(shù)學(xué)方法將主導(dǎo)突變和隨從突變分開,，在這一步里,，每一個突變都要經(jīng)過500個計算“決定樹(decision trees)”以區(qū)分該突變是否具有引發(fā)癌癥的特征。
最有可能的突變——主導(dǎo)突變——將被放在名單前列,，而將隨從突變置后,。這樣，研究人員在該軟件的幫助下,，就可以更省時省力的找出引發(fā)癌癥的最有可能的突變體,。（生物谷Bioon.com）
生物谷推薦原始出處：
Cancer Research 69, 6660, August 15, 2009. doi: 10.1158/0008-5472.CAN-09-1133
Cancer-Specific High-Throughput Annotation of Somatic Mutations: Computational Prediction of Driver Missense Mutations
Hannah Carter1, Sining Chen2,3, Leyla Isik1, Svitlana Tyekucheva3, Victor E. Velculescu4, Kenneth W. Kinzler4, Bert Vogelstein4 and Rachel Karchin1
1 Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University; 2 Department of Environmental Health Sciences and Department of Biostatistics, Johns Hopkins School of Public Health; and 3 Department of Oncology and 4 Ludwig Center for Cancer Genetics and Therapeutics and Howard Hughes Medical Institute, Johns Hopkins Kimmel Cancer Center, Baltimore, Maryland
Large-scale sequencing of cancer genomes has uncovered thousands of DNA alterations, but the functional relevance of the majority of these mutations to tumorigenesis is unknown. We have developed a computational method, called Cancer-specific High-throughput Annotation of Somatic Mutations (CHASM), to identify and prioritize those missense mutations most likely to generate functional changes that enhance tumor cell proliferation. The method has high sensitivity and specificity when discriminating between known driver missense mutations and randomly generated missense mutations (area under receiver operating characteristic curve, >0.91; area under Precision-Recall curve, >0.79). CHASM substantially outperformed previously described missense mutation function prediction methods at discriminating known oncogenic mutations in P53 and the tyrosine kinase epidermal growth factor receptor. We applied the method to 607 missense mutations found in a recent glioblastoma multiforme sequencing study. Based on a model that assumed the glioblastoma multiforme mutations are a mixture of drivers and passengers, we estimate that 8% of these mutations are drivers, causally contributing to tumorigenesis.
