Cancer progression modeling using static sample data

Yijun Sun, Jin Yao, Norma J. Nowak, Steven Goodison

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

As molecular profiling data continues to accumulate, the design of integrative computational analyses that can provide insights into the dynamic aspects of cancer progression becomes feasible. Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples. We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model for breast cancer progression. An interactive model facilitates the identification of key molecular events in the advance of disease to malignancy.

Original languageEnglish (US)
Number of pages1
JournalGenome Biology
Volume15
Issue number8
DOIs
StatePublished - Jan 1 2014
Externally publishedYes

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cancer
breast neoplasms
neoplasms
modeling
taxonomic keys
Neoplasms
Breast Neoplasms
sampling
branching
tumor
Linear Models
methodology
method

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

Cite this

Cancer progression modeling using static sample data. / Sun, Yijun; Yao, Jin; Nowak, Norma J.; Goodison, Steven.

In: Genome Biology, Vol. 15, No. 8, 01.01.2014.

Research output: Contribution to journalArticle

Sun, Yijun ; Yao, Jin ; Nowak, Norma J. ; Goodison, Steven. / Cancer progression modeling using static sample data. In: Genome Biology. 2014 ; Vol. 15, No. 8.
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