To identify key signals that promote the invasive transition during cancer, we used a computational approach in which we built and mined literature-based molecular interaction networks representing focal adhesions and invadopodia. We further combined initial hub results from centralities and random walk analyses of the networks with experimental RPPA results from human HNSCC tumors to identify potential signaling patterns associated with tumor aggressiveness. By following up on a potential “PI3K high/PKCα-low” pattern associated with bad prognosis, we discovered that the combination of increased PI3K with low PKCα activity indeed promotes an “invasive state” characterized by enhanced formation and activity of invadopodia along with changes in the organization of adhesions. By contrast, in the absence of either PIK3CA activating mutations or loss of PTEN, PKCα inhibition instead diminished invadopodia formation and activity. These results point to the potential value of analyzing invasive signaling in a “network” approach and suggest that combinations of PI3K and PKCα signaling may be useful as biomarkers for cancer aggressiveness.
Molecular network models can be used for various purposes and are typically constructed from either high-throughput datasets or from the literature (16
). Cancer is a particularly appropriate application due to the deregulation of signaling networks and the large number of genetic and epigenetic changes (58
) that lead to diverse network configurations. Although networks are typically constructed based on datasets, signaling pathways or statistical relationships, we instead used networks to represent physical cytoskeletal structures (22
) and constrained the edges to represent only direct molecular binding interactions. Whereas use of literature-derived networks such as ours can bias to “rediscovery”, addition of first-neighbor binding partners and comparison of these networks to human tumor data allowed us to generate and then subsequently test hypotheses about potential signaling hub combinations that govern focal adhesion-invadopodia transitions. Indeed, the interaction between PI3K pathway mutations and PKCα activity was unexpected and affected invadopodia formation and activity. We expect that future use of these networks, in combination with various biological datasets, will lead to additional insights into activation of the invasive state. Our network approach may also be useful for modeling of other subcellular structures.
Our data indicate that the combination of PI3K activation and PKCα inhibition promotes focal adhesion-invadopodia switching behavior and invasiveness, as assessed by Transwell invasion assays. Consistent with our random walk analysis, PI3K activity was most closely associated with invadopodia induction, whereas PKCα activity led to more organized focal adhesions, including those surrounding invadopodia. Furthermore, network representations show more binding interactions to invadopodia proteins from PI3K and its lipid products than from PKCα (Fig S7
). These data are consistent with our hypothesis that the invasive transition involves both adhesion reorganization and acquisition of new properties that allow formation of invadopodia, such as generation of PI(3,4)P2
. These findings also suggest that there may be other combinations of focal adhesion and invadopodia hub states that either activate or shut down the invasive state.
In vascular smooth muscle and endothelial cells, the PKC-activating and tumor promoting drug phorbol myristate acetate (PMA) is a potent promoter of both focal adhesion disassembly and podosome formation (13
). Indeed, in cancer cells that did not harbor PI3K or PTEN mutations, we found that inhibition of PKCα shut down invadopodia formation, possibly due to prevention of focal adhesion turnover (13
). We speculate that deregulated PI3K activity in cells expressing PI3K mutants alters this dynamic by bypassing the need for focal adhesion turnover in order to organize new invadopodia. Under that circumstance, inhibition of PKCα could then promote invadopodia formation by preventing negative feedback to PI3K (55
). This model is supported by our findings that knockdown of PKCα in cells expressing the constitutively active H1047R PI3K mutant leads to an increase in the cellular ratio of PI(3,4,5)P3
and that expression of the nonphosphorylatable S361/652A p85α mutant enhances invadopodia-associated ECM degradation (). Because the serine phosphorylation sites targeted by PKCs in the SH2 domains of p85α affect binding to phosphotyrosine-containing peptides (56
), we speculate that at least one mechanism whereby PKCα may limit PI3K activity in cells with mutations in the PI3K pathway is by minimizing the binding time of PI3K to phosphotyrosine-containing binding partners at the plasma membrane.
Both increased and decreased abundance of PKCα have been reported in various human cancer types, including breast cancer (59
). In HNSCC, increased abundance of PKCα correlates with decreased survival; however the relationship with PI3K was not examined (63
). Our finding that PKCα silencing has opposite effects depending on the activation state of PI3K indicates that the overall network state is likely to be critical in accurately predicting prognosis and treatment. We expect that appropriate biomarkers may be critical for effective use of PKC inhibitors in the clinic (62
). Because we used siRNA, our findings are specific to PKCα. However, given that chemical inhibitors may potentially crossreact, some consideration should probably also be given to the role of other PKC isoforms that we have not addressed in this study, such as PKCβ, which shows increased abundance in colon cancer and lymphoma and decreased abundance in melanoma (65
In summary, we used a combination of molecular interaction network building and analysis to discover signaling states that promote a specific invasive phenotype: focal adhesion reorganization and invadopodia formation. We found that the combination of high, deregulated PI3K activity with low PKCα activity optimally promotes invadopodia formation in combination with adhesion reorganization. Interesting questions for the future include: (i) whether additional aggressive cancer cell phenotypes such as cell survival are augmented by the PI3K high/PKCα low signaling combination; (ii) whether other tumor types coregulate PI3K and PKCα activities; and (iii) whether we can use our subcellular network approach to identify additional signaling states that are associated with a specific phenotype.