约翰霍普金斯大学的研究人员设计出一种全新的计算机软件，该软件能同时对几百个基因突变进行筛选，并将最可能导致癌症的DNA突变筛选出来，该方法被命名为CHASM(Cancer-specific High-throughput Annotation of Somatic Mutations)，将使科学家将更多的注意力集中到引发肿瘤的突变上。
该研究报告发表在8月15日出版的Cancer Research杂志上。
这项新方法着重对错义突变(missense mutations)进行研究，课题组首次利用计算机方法缩小到600个左右的疑似脑部肿瘤突变，并分选出那些突变在引发癌症过程中起“主导(drivers)”和“随从(passengers)”的突变。“主导突变”即能引发并促进肿瘤生长的突变。“随从突变”即在肿瘤生长过程中出现但对肿瘤的形成和生长没有影响的突变。
在分选之前，研究人员利用机器阅读技术把癌症相关的约50种突变的特征输入到系统中，然后研究人员Karchin和Carter采用Random Forest classifier的数学方法将主导突变和随从突变分开，在这一步里，每一个突变都要经过500个计算“决定树(decision trees)”以区分该突变是否具有引发癌症的特征。
最有可能的突变——主导突变——将被放在名单前列，而将随从突变置后。这样，研究人员在该软件的帮助下，就可以更省时省力的找出引发癌症的最有可能的突变体。（生物谷Bioon.com）
生物谷推荐原始出处：
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.
