Multiscale Gene Networks Dissect the Complexity of Breast Cancer

Despite the enormous amounts of molecular, cellular, and clinical data that are increasingly available for many different types of cancer, it remains a challenge to integrate different dimensions of data to construct mechanistic models that can robustly distinguish key driver genes from passenger genes, predict tumor progression, and tailor therapies optimally for individual patients. We present an integrative biology approach to constructing and analyzing multiscale regulatory networks of breast cancer. We systematically uncover not only known and novel gene subnetworks (modules) linked to breast cancer, but also their key drivers, the majority of which are not transcription factors or signaling molecules. A number of independent lines of evidence support that the predicted key drivers play central roles in breast cancer biology. We predict and experimentally validate ARF1 as a key driver of breast tumor phenotypes, and then demonstrate the ARF1-controlled subnetwork is a novel regulator of intra- and inter- cellular vesicle dynamics involved in epithelial-mesenchymal transition (EMT).

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