Cancer progression modeling using static sample data

As molecular profiling data continue 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.

• Publication year

  2014

• Venue

  Genome Biology

• Publication date

  2014-08-01

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1186/s13059-014-0440-0PMID 25155694PMCID 4196119
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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