Breast Cancer Progression Roadmap Inferred from Static Sample Data
Major efforts continue to catalog the genomic events associated with breast cancer, but interpretation and extrapolation of the accumulating data to provide insights into the dynamic aspects of the disease remain a major challenge. Here, we present a computational strategy that enables the construction of a progression tree of breast cancer using static tumor sample data. Our findings support a linear, branching model with two distinct trajectories for malignant progression. The validity of the model was demonstrated in 27 independent breast cancer datasets (n = 9,281), 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 development of breast cancer.
Y. Sun*, J. Yao, L. Yang, R. Chen, N. Nowak, S. Goodison*, Computational Approach for Deriving Cancer Progression Roadmaps from Static Sample Data, Nucleic Acids Research, in press.
- Main paper
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- Supplementary data (113 pages)
- Supplementary figure 10
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