Revealing tumor heterogeneity of breast cancer by utilizing the linkage between somatic and germline mutations.


Journal

Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837

Informations de publication

Date de publication:
27 11 2019
Historique:
received: 08 04 2018
revised: 07 06 2018
accepted: 26 06 2018
pubmed: 22 9 2018
medline: 2 9 2020
entrez: 22 9 2018
Statut: ppublish

Résumé

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/.

Identifiants

pubmed: 30239581
pii: 5091279
doi: 10.1093/bib/bby084
pmc: PMC6954402
doi:

Substances chimiques

TP53 protein, human 0
Tumor Suppressor Protein p53 0
Class I Phosphatidylinositol 3-Kinases EC 2.7.1.137
PIK3CA protein, human EC 2.7.1.137

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2306-2315

Subventions

Organisme : NHGRI NIH HHS
ID : R01 HG008759
Pays : United States

Informations de copyright

© The Author(s) 2018. Published by Oxford University Press.

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Auteurs

Meng Zou (M)

School of Mathematics and Statistics, Huazhong University of Science and Technology.

Rui Jin (R)

Department of Statistics, University of Iowa.

Kin Fai Au (KF)

School of Mathematics and Statistics, Huazhong University of Science and Technology.
Department of Biomedical Informatics, Ohio State University.

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Classifications MeSH