Revealing tumor heterogeneity of breast cancer by utilizing the linkage between somatic and germline mutations. (18th September 2018)

• Record Type: Journal Article
• Authors: Zou, Meng; Jin, Rui; Au, Kin Fai
• Is Part Of: Briefings in bioinformatics. Volume 20:Number 6(2020)
• Page Start: 2306
• Page End: 2315
• Publication Date: 2018-09-18
• Subjects: tumor heterogeneity -- subclone inference -- VAF -- somatic mutation -- germline mutation; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bby084
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: The intra-tumor heterogeneity is associated with cancer progression and therapeutic resistance, such as in breast cancer. While the existing methods for studying tumor heterogeneity only analyze variant allele frequency (VAF), the genotype of variant is also informative for inferring subclones, which can be detected by long reads or paired-end reads. We developed GenoClone to integrate VAF with the genotype of variant innovatively, so it showed superior performance of inferring the number of subclones, estimating the fractions of subclones and identifying somatic single-nucleotide variants composition of subclones. When GenoClone was applied to 389 TCGA breast cancer samples, it revealed extensive intra-tumor heterogeneity. We further found that a few somatic mutations were relevant to the late stage of tumor evolution, including the ones at the oncogene PIK3CA and the tumor suppress gene TP53. Moreover, 52 subclones that were identified from 167 samples shared high similarity of somatic mutations, which were clustered into three groups with the sizes of 24, 14 and 14. It is helpful for understanding the development of breast cancer in certain subgroups of people and the drug development for population level. Furthermore, GenoClone also identified the tumor heterogeneity in different aliquots of the same samples. The implementation of GenoClone is available at http://www.healthcare.uiowa.edu/labs/au/GenoClone/ .