Context-dependent signal integration by the GLI code: the oncogenic load, pathways, modifiers and implications for cancer therapy

Abstract

Canonical Hedgehog (HH) signaling leads to the regulation of the GLI code: the sum of all positive and negative functions of all GLI proteins. In humans, the three GLI factors encode context-dependent activities with GLI1 being mostly an activator and GLI3 often a repressor. Modulation of GLI activity occurs at multiple levels, including by co-factors and by direct modification of GLI structure. Surprisingly, the GLI proteins, and thus the GLI code, is also regulated by multiple inputs beyond HH signaling. In normal development and homeostasis these include a multitude of signaling pathways that regulate proto-oncogenes, which boost positive GLI function, as well as tumor suppressors, which restrict positive GLI activity. In cancer, the acquisition of oncogenic mutations and the loss of tumor suppressors - the oncogenic load - regulates the GLI code toward progressively more activating states. The fine and reversible balance of GLI activating GLI(A) and GLI repressing GLI(R) states is lost in cancer. Here, the acquisition of GLI(A) levels above a given threshold is predicted to lead to advanced malignant stages. In this review we highlight the concepts of the GLI code, the oncogenic load, the context-dependency of GLI action, and different modes of signaling integration such as that of HH and EGF. Targeting the GLI code directly or indirectly promises therapeutic benefits beyond the direct blockade of individual pathways.

Keywords: Cancer; Development; GLI transcription factors; Hedgehog-GLI signaling; Oncogenes; Signal transduction; Signaling integration; Stem cells.

Fig. 1

Model for the GLI code and its morphogenetic activity leading to the creation of context-dependent diversity. A gradient of HH ligands is interpreted, canonically, by a combinatorial and context-specific distribution of repressor and activator activities of the three GLI proteins, the GLI code. Note that GLI1 and GLI2 have strong activating action and GLI3 is a strong repressor in many contexts. Combinatorial GLI activities are then modified by positive or negative modifiers leading to differential regulation of target genes, which may either respond to create graded levels of expression of specific genes or induce specific genes in given thresholds. The output of these genetic changes is then the creation of spatially and/or temporally distinct outputs and behaviors.

Fig. 2

Control of the GLI code by the oncogenic load. (A) Under normal homeostatic conditions a fine-tuned balance of HH signaling as well as of parallel proto-oncogenic (e.g., EGF, FGF, PDGF, etc.) and tumor-suppressive pathways leads to precisely controlled levels of GLI^A/GLI^R. The balance can be tipped one way or another, thus allowing for the highly controlled ON-OFF switch. For simplicity, feed-forward and feedback regulatory loops are not included. (B) In cancer, the loss of tumor suppressors and the presence of mutant oncogenes lead to the massive deregulation of the GLI code and to a constitutively active ON state (GLI^A). Note that given the stable genetic changes resulting from gene mutation, the GLI code is no longer under homeostatic control.

Fig. 3

A working framework for the GLI code as a node for signal integration. Multiple signaling inputs from diverse pathways, including but not restricted to HH, EGF, FGF, TGFβ, can converge on GLI regulation, changing the GLI code. Integration can also take place above, through crosstalk (gray arrows). The position of the different components is not related to each other but shown as examples of the types of components involved in the signaling cascades. The GLI code, a transcriptional regulatory node, is then modulated by additional context-dependent inputs (arrow and T bar, such as ZIC proteins) that include a negative feedback loop with p53 and a positive feed-forward regulatory loop with NANOG . The outcome, through differential regulation of target genes, is context-dependent and includes change in stemness, survival, proliferation migration and metabolic regulation. This framework can help not only to conceptualize cell behavior resulting from multiple signaling events but also design multi-target therapies to increase efficiency and prevent resistance. Note that each input also has divergent pathways not shown in the scheme.

Fig. 4

Modes of HH-EGF signaling integration. (A) Canonical HH-GLI signaling activated by binding of SHH to its receptor PTCH results in ciliary localization of SMOH and subsequent GLI activation (GLI^A). HH-GLI signaling alone only activates classical GLI targets including HHIP and GLI1 but fails to induce HH-EGFR cooperation target genes. (B) Concomitant activation of HH-GLI and EGF/PDGF signaling (EGFR or PDGFRA) can lead to synergistic interactions . Such interactions can result in (i) cross talk between SHH and EGFR in neural stem cells , (ii) enhancement of GLI1 activity by RAS/MEK signaling in melanomas and other tumor cells , and/or (iii) synergistic promotion of basal cell carcinoma and pancreatic cancer by selective activation of HH-EGFR target genes such as CXCR4, FGF19, SOX9 and TGFA . In the latter case, integration of HH-EGFR signaling occurs at the level of common target gene promoters. Activation of EGF signaling induces the RAS/RAF/MEK/ERK cascade eventually leading to activation of GLI1 or/and of the JUN/AP1 transcription factor. JUN synergizes with GLI activator forms by co-occupying selected target gene promoters leading to synergistic transcriptional activation of HH-EGFR targets and enhanced tumorigenesis (e.g., BCC and pancreatic cancer).

Fig. 5

GLI DNA binding and context-dependent target gene regulation. (A) Consensus 9-mer GLI DNA binding motif calculated from experimentally validated GLI binding sites. The motif was generated with a set of 22 experimentally validated GLI binding sites using WebLogo3 . Positions 4C and 6C are essential for DNA binding while basically all other positions allow a certain degree of sequence variation resulting in distinct target gene activation efficiencies. (B) 3D model of the GLI DNA binding domain composed of five zinc fingers and its interaction with the consensus binding sequence. Note that fingers 4 and 5 form extensive base contacts thereby determining binding specificity (source: Protein Databank ID 2GLI; [119]). (C) Non-exhaustive models of context-dependent target gene activation. Here, GLI activator (GLI^A) and GLI repressor forms (GLI^R) binding the same target sequences refer to the GLI code. (i) Classical target gene activation model with GLI^A binding to the promoters of canonical targets such as PTCH1 or HHIP. (ii) Context-dependent interactions of GLI^A with co-activators (CoA) or (iii) of GLI^R with co-repressors (CoR) modifies the GLI code and expression of HH-GLI targets. (iv) Context-dependent combinatorial binding of GLI^A and cooperating transcription factors (TF) (e.g., JUN, SOX2) to common target promoters can also result in synergistic modulation of gene expression.

Fig. 6

Post-translational modifications regulate GLI transcriptional activity. Fine-tuning of GLI activity by phosphorylation/dephosphorylation and acetylation/deacetylation. Left: fully activated GLI transcription factor with multiple phosphorylated serine/threonine residues in the N-terminal region and the DNA binding domain. In addition, de-acetylation promotes DNA binding affinity and transcriptional activity, respectively. Several kinases (MAPK, S6K, aPKC) and deacetylases catalyze the activation of GLI, while phosphatases, PKA and acetyltransferases negatively regulate GLI activity. Note that PKA phosphorylation of the two amino acid residues C-terminal of the DNA binding domain negatively regulates the transcriptional activity of GLI without affecting processing or stability . ncPKA: non-consensus PKA phosphorylation sites involved in GLI activation.