Molecular profiles of matched primary and metastatic tumor samples support a linear evolutionary model of breast cancer

Runpu Chen, Steve Goodison, Yijun Sun

Research output: Contribution to journalArticle

Abstract

The interpretation of accumulating genomic data with respect to tumor evolution and cancer progression requires integrated models. We developed a computational approach that enables the construction of disease progression models using static sample data. Application to breast cancer data revealed a linear, branching evolutionary model with two distinct trajectories for malignant progression. Here, we used the progression model as a foundation to investigate the relationships between matched primary and metastasis breast tumor samples. Mapping paired data onto the model confirmed that molecular breast cancer subtypes can shift during progression and supported directional tumor evolution through luminal subtypes to increasingly malignant states. Cancer progression modeling through the analysis of available static samples represents a promising breakthrough. Further refinement of a roadmap of breast cancer progression will facilitate the development of improved cancer diagnostics, prognostics, and targeted therapeutics.

Original languageEnglish (US)
Pages (from-to)170-174
Number of pages5
JournalCancer research
Volume80
Issue number2
DOIs
StatePublished - Jan 1 2020

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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