体细胞
生物
变色
遗传学
癌症
拷贝数变化
基因组
癌症的体细胞进化
癌变
外显子组测序
人类基因组
基因
癌症研究
基因组不稳定性
突变
DNA
DNA损伤
作者
Scott L. Carter,Kristian Cibulskis,Elena Helman,Aaron McKenna,Hui Shen,Travis Zack,Peter W. Laird,Robert C. Onofrio,Wendy Winckler,Barbara A. Weir,Rameen Beroukhim,David Pellman,Douglas A. Levine,Eric S. Lander,Matthew Meyerson,Gad Getz
摘要
Tumors vary in their ratio of normal to cancerous cells and in their genomic copy number. Carter et al. describe an analytic method for inferring the purity and ploidy of a tumor sample, enabling longitudinal studies of subclonal mutations and tumor evolution. We describe a computational method that infers tumor purity and malignant cell ploidy directly from analysis of somatic DNA alterations. The method, named ABSOLUTE, can detect subclonal heterogeneity and somatic homozygosity, and it can calculate statistical sensitivity for detection of specific aberrations. We used ABSOLUTE to analyze exome sequencing data from 214 ovarian carcinoma tumor-normal pairs. This analysis identified both pervasive subclonal somatic point-mutations and a small subset of predominantly clonal and homozygous mutations, which were overrepresented in the tumor suppressor genes TP53 and NF1 and in a candidate tumor suppressor gene CDK12. We also used ABSOLUTE to infer absolute allelic copy-number profiles from 3,155 diverse cancer specimens, revealing that genome-doubling events are common in human cancer, likely occur in cells that are already aneuploid, and influence pathways of tumor progression (for example, with recessive inactivation of NF1 being less common after genome doubling). ABSOLUTE will facilitate the design of clinical sequencing studies and studies of cancer genome evolution and intra-tumor heterogeneity.
科研通智能强力驱动
Strongly Powered by AbleSci AI