AKT-Independent Signaling Downstream of Oncogenic PIK3CA Mutations in Human Cancer

Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA.
Cancer cell (Impact Factor: 23.52). 08/2009; 16(1):21-32. DOI: 10.1016/j.ccr.2009.04.012
Source: PubMed


Dysregulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway occurs frequently in human cancer. PTEN tumor suppressor or PIK3CA oncogene mutations both direct PI3K-dependent tumorigenesis largely through activation of the AKT/PKB kinase. However, here we show through phosphoprotein profiling and functional genomic studies that many PIK3CA mutant cancer cell lines and human breast tumors exhibit only minimal AKT activation and a diminished reliance on AKT for anchorage-independent growth. Instead, these cells retain robust PDK1 activation and membrane localization and exhibit dependency on the PDK1 substrate SGK3. SGK3 undergoes PI3K- and PDK1-dependent activation in PIK3CA mutant cancer cells. Thus, PI3K may promote cancer through both AKT-dependent and AKT-independent mechanisms. Knowledge of differential PI3K/PDK1 signaling could inform rational therapeutics in cancers harboring PIK3CA mutations.

Download full-text


Available from: Aysegul A Sahin
  • Source
    • "These phenotypic differences cannot be simply explained by the increased enzymatic activities of the helical and kinase domain mutations. In fact, Vasudevan et al. (2009) observed that the basal levels of phospho-AKT are lower in cancer cell lines expressing p110a helical domain mutations compared to these cell lines expressing the H1047R kinase domain mutation. Consistently, when complexed with p85 regulatory subunit, the p110a H1047R kinase domain mutant protein displays higher lipid kinase activity in vitro than the E545K helical domain mutant (Samuels et al. 2005). "

    Full-text · Dataset · Jul 2014
  • Source
    • "Increased PDK1 expression has also been reported in 45% of patients with acute myeloid leukemia, and PDK1 seems to be a viable target in head and neck cancer, multiple myeloma, pancreatic cancer, and colorectal cancer [3-7]. Vasudevan et al. [8] reported that a subset of breast cancer cell lines with mutations in PIK3CA displayed a reduced dependence on Akt for tumorigenicity, and instead relied on PDK1-dependent activation of another AGC kinase, SGK-3. Breast cancers are known to be a group of diverse diseases, and cellular heterogeneity has been shown to affect disease-free survival in patients with breast cancer [9]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: 3-phosphoinositide-dependent protein kinase-1 (PDK1) functions downstream of phosphoinositide 3-kinase (PIK3) and activates members of the AGC family of protein kinases that are known to play crucial roles in physiological processes associated with cell metabolism, growth, proliferation and survival. Changes in the expression and activity of PDK1 and several AGC kinases have been linked to human disease, including cancer. We used immunohistochemical analysis to determine PDK1 expression in 241 tumors from patients with breast cancer in which we had previously analyzed PIK3CA mutation status. Moderate or high expression of PDK1 was observed in 213 of the 241 cases (88%). There was no correlation between PIK3CA mutation status and PDK1 overexpression. Our findings indicate that PDK1 is independently activated in breast cancer and not only as part of the PIK3CA pathway, suggesting that PDK1 plays a specific and distinct role from the canonical PIK3/Akt pathway and promotes oncogenesis independently of AKT. Our data implicate PDK-1 and downstream components of the PDK-1 signaling pathway as promising therapeutic targets for the treatment of breast cancer.
    Full-text · Article · Apr 2014 · Diagnostic Pathology
  • Source
    • "For example, both breast cancer and ovarian cancer cell lines exhibited a marked negative association between the levels of PTEN and phosphorylated AKT (Akt.pT308). This relationship was expected due to the critical regulation of 3-phopshatidylinositols by the lipid phosphatase activity of PTEN, and has previously been demonstrated as a significant interaction in multiple tumor types [Davies et al. (1998, 1999, 2009), Stemke-Hale et al. (2008), Vasudevan et al. (2009), Park et al. (2010)]. Although this concordance was expected, our analysis also identified a large network of differential protein interactions among the breast and ovarian cancer cell lines [Figure 3 "
    [Show abstract] [Hide abstract]
    ABSTRACT: Reverse-phase protein array (RPPA) analysis is a powerful, rel- atively new platform that allows for high-throughput, quantitative analysis of protein networks. One of the challenges that currently limit the potential of this technology is the lack of methods that al- low for accurate data modeling and identification of related networks and samples. Such models may improve the accuracy of biological sample classification based on patterns of protein network activation and provide insight into the distinct biological relationships underly- ing different types of cancer. Motivated by RPPA data, we propose a Bayesian sparse graphical modeling approach that uses selection priors on the conditional relationships in the presence of class infor- mation. The novelty of our Bayesian model lies in the ability to draw information from the network data as well as from the associated categorical outcome in a unified hierarchical model for classification. In addition, our method allows for intuitive integration of a priori network information directly in the model and allows for posterior inference on the network topologies both within and between classes. Applying our methodology to an RPPA data set generated from pan- els of human breast cancer and ovarian cancer cell lines, we demon- strate that the model is able to distinguish the different cancer cell types more accurately than several existing models and to identify differential regulation of components of a critical signaling network (the PI3K-AKT pathway) between these two types of cancer. This approach represents a powerful new tool that can be used to improve our understanding of protein networks in cancer.
    Full-text · Article · Mar 2014 · The Annals of Applied Statistics
Show more