Computational approach for deriving cancer progression roadmaps from static sample data

Yijun Sun, Jin Yao, Yang Le, Runpu Chen, Norma J. Nowak, Steven Goodison

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

As with any biological process, cancer development is inherently dynamic. While major efforts continue to catalog the genomic events associated with human cancer, it remains difficult to interpret and extrapolate the accumulating data to provide insights into the dynamic aspects of the disease. Here, we present a computational strategy that enables the construction of a cancer progression model using static tumor sample data. The developed approach overcame many technical limitations of existing methods. Application of the approach to breast cancer data revealed a linear, branching model with two distinct trajectories for malignant progression. The validity of the constructed model was demonstrated in 27 independent breast cancer data sets, and through visualization of the data in the context of disease progression we were able to identify a number of potentially key molecular events in the advance of breast cancer to malignancy.

Original languageEnglish (US)
Article numbere69
JournalNucleic Acids Research
Volume45
Issue number9
DOIs
StatePublished - May 19 2017

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Neoplasms
Breast Neoplasms
Biological Phenomena
Disease Progression
Linear Models
Datasets

ASJC Scopus subject areas

  • Genetics

Cite this

Computational approach for deriving cancer progression roadmaps from static sample data. / Sun, Yijun; Yao, Jin; Le, Yang; Chen, Runpu; Nowak, Norma J.; Goodison, Steven.

In: Nucleic Acids Research, Vol. 45, No. 9, e69, 19.05.2017.

Research output: Contribution to journalArticle

Sun, Yijun ; Yao, Jin ; Le, Yang ; Chen, Runpu ; Nowak, Norma J. ; Goodison, Steven. / Computational approach for deriving cancer progression roadmaps from static sample data. In: Nucleic Acids Research. 2017 ; Vol. 45, No. 9.
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