Cancer progression modeling using static sample data.
Bottom Line: 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.
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.
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Mentions: There have been two major conceptual models proposed regarding the origins of breast cancer subtypes and associated biological mechanisms (see Figure 6 in ). One model proposes a distinct-path scenario where each subtype follows a path of initiation and progression independently of the others. The alternative is a linear evolution model, which proposes that tumors gradually evolve from normal cells to malignant states through the accumulation of genetic alterations. While both models embrace the notion of cancer evolution, an important implication from the first model is that the subtypes are considered as different diseases, and the alternative theory proposes that subtypes are different stages of the same disease. Clarifying this issue could have a profound impact as research strategies used in the two scenarios could be very different. The bifurcation structure revealed in our model supports the linear evolution model as a representation of the breast cancer progression process. We should emphasize that our method is a generic approach without making any model assumption on data. If the four major subtypes evolve directly from normal cells, as proposed by the discrete evolution model, in a population study with a large number of samples, we should be able to detect four independent paths connecting normal samples with the four subtypes, but this was not the case (Figures 2, 5 and 7). Our result suggests that basal subtypes are derived from the luminal subtypes, an idea that has been recently suggested through experimentation . The idea that HER2+ phenotypes are derived from luminal B tumors also makes biological sense. Through association of CNA data and putative driver gene expression (data not shown), we found that the copy numbers of the genes involved in the HER2 signaling pathway are significantly amplified in HER2+ samples relative to luminal B samples, suggesting that the HER2+ phenotype develops from luminal B through gene copy number variation, and that this event is distinct from progression to basal phenotypes. This result echoes recent studies that demonstrated that cancer subtypes are not hard-wired, and genotypes and phenotypes can shift over time .Figure 7