Breast Tumor Cells with PI3K Mutation or HER2 Amplification Are Selectively Addicted to Akt Signaling

Dysregulated PI3K/Akt signaling occurs commonly in breast cancers and is due to HER2 amplification, PI3K mutation or PTEN inactivation. The objective of this study was to determine the role of Akt activation in breast cancer as a function of mechanism of activation and whether inhibition of Akt signaling is a feasible approach to therapy.A selective allosteric inhibitor of Akt kinase was used to interrogate a panel of breast cancer cell lines characterized for genetic lesions that activate PI3K/Akt signaling: HER2 amplification or PI3K or PTEN mutations in order to determine the biochemical and biologic consequences of inhibition of this pathway. A variety of molecular techniques and tissue culture and in vivo xenograft models revealed that tumors with mutational activation of Akt signaling were selectively dependent on the pathway. In sensitive cells, pathway inhibition resulted in D-cyclin loss, G1 arrest and induction of apoptosis, whereas cells without pathway activation were unaffected. Most importantly, the drug effectively inhibited Akt kinase and its downstream effectors in vivo and caused complete suppression of the growth of breast cancer xenografts with PI3K mutation or HER2 amplification, including models of the latter selected for resistance to Herceptin. Furthermore, chronic administration of the drug was well-tolerated, causing only transient hyperglycemia without gross toxicity to the host despite the pleiotropic normal functions of Akt.These data demonstrate that breast cancers with PI3K mutation or HER2 amplification are selectively dependent on Akt signaling, and that effective inhibition of Akt in tumors is feasible and effective in vivo. These findings suggest that direct inhibition of Akt may represent a therapeutic strategy for breast and other cancers that are addicted to the pathway including tumors with resistant to Herceptin

An integrative approach for building personalized gene regulatory networks for precision medicine

Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare