BACOM2.0 facilitates absolute normalization and quantification of somatic copy number alterations in heterogeneous tumor View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-11

AUTHORS

Yi Fu, Guoqiang Yu, Douglas A. Levine, Niya Wang, Ie-Ming Shih, Zhen Zhang, Robert Clarke, Yue Wang

ABSTRACT

Most published copy number datasets on solid tumors were obtained from specimens comprised of mixed cell populations, for which the varying tumor-stroma proportions are unknown or unreported. The inability to correct for signal mixing represents a major limitation on the use of these datasets for subsequent analyses, such as discerning deletion types or detecting driver aberrations. We describe the BACOM2.0 method with enhanced accuracy and functionality to normalize copy number signals, detect deletion types, estimate tumor purity, quantify true copy numbers, and calculate average-ploidy value. While BACOM has been validated and used with promising results, subsequent BACOM analysis of the TCGA ovarian cancer dataset found that the estimated average tumor purity was lower than expected. In this report, we first show that this lowered estimate of tumor purity is the combined result of imprecise signal normalization and parameter estimation. Then, we describe effective allele-specific absolute normalization and quantification methods that can enhance BACOM applications in many biological contexts while in the presence of various confounders. Finally, we discuss the advantages of BACOM in relation to alternative approaches. Here we detail this revised computational approach, BACOM2.0, and validate its performance in real and simulated datasets. More... »

PAGES

13955

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/srep13955

DOI

http://dx.doi.org/10.1038/srep13955

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1013233279

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26350498


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