Causal reasoning identifies mechanisms of sensitivity for a novel AKT kinase inhibitor, GSK690693

Inappropriate activation of AKT signaling is a relatively common occurrence in human tumors, and can be caused by activation of components of, or by loss or decreased activity of inhibitors of, this signaling pathway. A novel, pan AKT kinase inhibitor, GSK690693, was developed in order to interfere with the inappropriate AKT signaling seen in these human malignancies. Causal network modeling is a systematic computational analysis that identifies upstream changes in gene regulation that can serve as explanations for observed changes in gene expression. In this study, causal network modeling is employed to elucidate mechanisms of action of GSK690693 that contribute to its observed biological effects. The mechanism of action of GSK690693 was evaluated in multiple human tumor cell lines from different tissues in 2-D cultures and xenografts using RNA expression and phosphoproteomics data. Understanding the molecular mechanism of action of novel targeted agents can enhance our understanding of various biological processes regulated by the intended target and facilitate their clinical development.

Causal network modeling on transcriptomic and proteomic data identified molecular networks that are comprised of activated or inhibited mechanisms that could explain observed changes in the sensitive cell lines treated with GSK690693. Four networks common to all cell lines and xenografts tested were identified linking GSK690693 inhibition of AKT kinase activity to decreased proliferation. These networks included increased RB1 activity, decreased MYC activity, decreased TFRC activity, and increased FOXO1/FOXO3 activity.

AKT is involved in regulating both cell proliferation and apoptotic pathways; however, the primary effect with GSK690693 appears to be anti-proliferative in the cell lines and xenografts evaluated. Furthermore, these results indicate that anti-proliferative responses to GSK690693 in either 2-D culture or xenograft models may share common mechanisms within and across sensitive cell lines.