Research Paper Volume 10, Issue 12 pp 4224—4240

ΔNp63 promotes IGF1 signalling through IRS1 in squamous cell carcinoma

Valentina Frezza 1, , Claudia Fierro 1, , Elena Gatti 2, , Angelo Peschiaroli 3, , Anna Maria Lena 1, , Margherita Annicchiarico Petruzzelli 4, , Eleonora Candi 1, 4, , Lucia Anemona 1, , Alessandro Mauriello 1, , Pier Giuseppe Pelicci 2, , Gerry Melino 1, 5, , Francesca Bernassola 1, ,

  • 1 Department of Experimental Medicine, TOR University of Rome "Tor Vergata", Rome 00133, Italy
  • 2 Department of Experimental Oncology European Institute of Oncology, Milan 20139, Italy
  • 3 National Research Council of Italy Institute of Translational Pharmacology (IFT-CNR), Rome 00133, Italy
  • 4 Istituto Dermopatico dell'Immacolata, IRCCS,, Rome 00163, Italy
  • 5 Medical Research Council, Toxicology Unit, University of Cambridge, Leicester LE1 9HN, UK

received: October 24, 2018 ; accepted: December 12, 2018 ; published: December 28, 2018 ;

https://doi.org/10.18632/aging.101725
How to Cite

Copyright: Frezza et al. This is an open‐access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Accumulating evidence has proved that deregulation of ΔNp63 expression plays an oncogenic role in head and neck squamous cell carcinomas (HNSCCs). Besides p63, the type 1-insulin-like growth factor (IGF) signalling pathway has been implicated in HNSCC development and progression. Most insulin/IGF1 signalling converges intracellularly onto the protein adaptor insulin receptor substrate-1 (IRS-1) that transmits signals from the receptor to downstream effectors, including the PI3K/AKT and the MAPK kinase pathways, which, ultimately, promote proliferation, invasion, and cell survival. Here we report that p63 directly controls IRS1 transcription and cellular abundance and fosters the PI3K/AKT and MAPK downstream signalling pathways. Inactivation of ΔNp63 expression indeed reduces tumour cell responsiveness to IGF1 stimulation, and inhibits the growth potential of HNSCC cells. In addition, a positive correlation was observed between p63 and IRS1 expression in human HNSCC tissue arrays and in publicly available gene expression data. Our findings indicate that aberrant expression of ΔNp63 in HNSSC may act as an oncogenic stimulus by altering the IGF signalling pathway.

Introduction

Head and neck squamous cell carcinoma (HNSCC), the sixth most frequent malignancy worldwide, is a heterogeneous disease that develops from the stratified epithelium of the upper aerodigestive tract [1]. Despite recent diagnostic and therapeutic advances, the prognosis and survival of HNSCC patients remain poor. In response to the increase in the number of new HNSCC cases worldwide, further knowledge of tumour biology and the identification of novel clinical biomarkers are needed to improve prognostic stratification and optimise the anti-cancer therapies.

The p53 family of transcription factors includes p53, p63 and p73, which are all involved in tumorigenesis [28] as well as in fertility [9,10], metabolism [11], and aging [1217] regulation. In addition, a developmental or differentiation function has been described for all the family members [1832]. Many of these features are compatible with the genetic [3337] and metabolic [3845] events described in aging [4655]. A common feature of the p53 family members is the presence of two distinct promoters that can be differentially used to transcribe the full-length (TA isoforms) or the N-terminal-shorter (ΔN isoforms) proteins, exerting distinct functions [56]. ΔNp63 is the predominant isoform transcribed from the TP63 gene. It is expressed in the basal compartments of several ectoderm-derived tissues [5760], in which, it acts as a master regulator of epithelial development and maintenance [61,62]. In addition, ΔNp63 promotes the survival and sustains the self-renewal potential of epithelial cancer stem cells [63,64]. Genomic amplification and overexpression of TP63, as a result of decreased methylation of CpGs at the ΔNp63 promoter, is frequent in HNSCCs [6567], in which ΔNp63 promotes cancer cell survival, proliferation and chemoresistance [59,66,68,69]. ΔNp63 overexpression is an adverse prognostic factor for HNSCC patients [66,70]. Although some ΔNp63 targets and molecular pathways relevant for its pro-tumorigenic activities have been identified [59,66,71,72], there still need to uncover the molecular basis of its oncogenic function.

The Insulin-like Growth Factor (IGF) axis is composed of the tyrosine kinase IGF type 1 receptor (IGF1R), its ligands, insulin, IGF1 and IGF2, the adaptor protein Insulin Receptor Substrate 1 (IRS1) and a family of six ligand-binding proteins (IGFBPs) that regulate the bioavailability and half-life of circulating IGF1. Among the IGFBPs, IGFBP3 binds to more than 95% of circulating IGF. The IGF1R binds IGF1 and IGF2 with high affinity, as well as insulin, though with lower affinity. Upon ligation, IGF1R recruits and phosphorylates IRS1. Phosphorylated IRS1 acts as docking site for intracellular adaptor proteins that, ultimately, activate two downstream signalling cascades: the PI3K/AKT and the MAPK pathways, both of which have mitogenic and pro-survival roles.

Reduced responsiveness to IGF1/insulin can occur as a result of diminished total or phosphorylated amounts of IGF1R or IRS1, and results in decreased activation of the PI3K/AKT and MAPK pathways [73].

Deregulation of the IGF axis has been implicated in the development and progression of several human cancers. Elevated serum IGF1 levels, and increased levels or constitutive activation of IGF1R and IRS1 are associated with increased risk of a variety of epithelial cancers, metastasis and therapeutic resistance [7480]. Hence, it has been proposed that reduction of IGF signalling may have a prognostic impact and a therapeutic benefit in some cancer types. In HNSCC, IGF1R overexpression is associated with adverse survival, HPV negativity and high tumour T-stage [76,81,82]. High IGF1 levels and both lower and higher levels of IGFBP3 are predictor risk factors for secondary tumour development in patients with HNSCC [83,84]. In addition, increased IRS1 expression was found in nasopharyngeal carcinoma patients, where it correlated with lymph node metastasis [80].

Notably, the existence of a possible crosstalk between p63 and the IGF system has been reported. The tumour suppressive TAp63 isoforms negatively control Igf1r transcription [85], whereas Igfbp3 is a target of transcriptional repression by ΔNp63 [86]. Here we report, that Np63 affects the transcription and the cellular abundance of Irs1 in HNSCC cells and, that, as a result, p63 down-regulation impairs the activation of the intracellular signalling pathways following IGF1R stimulation.

Results

p63 controls IRS1 expression levels in HNSCC cells

By exploiting RNA sequencing (RNA-seq) transcriptome profiling, we identified genes regulated by ΔNp63 in normal human epidermal keratinocytes (NHEKs) (E.C. unpublished data). The analysis of RNA-Seq data obtained from p63-depleted cells revealed almost 50% reduction in IRS1 expression levels relatively to control cells (Fig. 1A). We then sought to test whether IRS1 expression is regulated by p63 in HNSCC cells. HNSCC cell lines display moderate/high levels of p63 expression (Fig. 1B, upper panel). In particular, the predominantly expressed isoform of p63 in HNSCC cells is ΔNp63, with TAp63 being undetectable in the majority of the cell lines (Fig. 1C). We observed a consistent correlation between ΔNp63 protein levels and the gene expression pattern of IRS1 in most of the HNSCC cell lines analysed (Fig. 1B, lower panel). Validation of RNA-seq data showed that, following p63 knockdown, IRS1 transcript and protein levels were reduced in NHEK and in a panel of HNSCC cell lines (Fig. 1D).

IRS1 expression is decreased upon down-regulation of p63 in HNSCC cell lines. (A) Relative expression levels of Irs1 as measured by RNA-Seq analysis of p63-depleted NHEK. Cells were transfected with p63 (sip63#1) or scrambled control (siScr) siRNAs. P-value = 0,005. (B) The amount of p63 was measured in NHEK and HNSCC cell lines by western blot analysis (upper panel). IRS1 transcript levels were analysed by RT-qPCR (lower panel). RT-qPCR was performed in duplicate. IRS1 expression was normalized on Tbp housekeeper and plotted relative to NHEK cells (mean ± s.d.). (C) The transcript levels of TAp63 (black box) and ΔNp63 (grey box) were measured in NHEK and HNSCC cell lines by RT-qPCR. RT-qPCR was performed as above. Gene expression was normalized on Tbp housekeeper and plotted relative to NHEK cells (mean ± s.d.). (D) RT-qPCR analysis (upper panels) of two independent experiments performed in duplicates for Irs1 transcripts in NHEK and HNSCC cells transfected with scrambled control (siScr) or p63 (sip63#1) siRNAs. Cells were harvested 48 h after transfection. qRT-PCR was performed as above. Values are normalized to Tbp and plotted relative to control cells (mean ± s.d.). Western blot analysis for IRS1 and p63 in HNSCC cells transfected as above. Cells were harvested 48 h after transfection. β-actin served as loading control.

Figure 1. IRS1 expression is decreased upon down-regulation of p63 in HNSCC cell lines. (A) Relative expression levels of Irs1 as measured by RNA-Seq analysis of p63-depleted NHEK. Cells were transfected with p63 (sip63#1) or scrambled control (siScr) siRNAs. P-value = 0,005. (B) The amount of p63 was measured in NHEK and HNSCC cell lines by western blot analysis (upper panel). IRS1 transcript levels were analysed by RT-qPCR (lower panel). RT-qPCR was performed in duplicate. IRS1 expression was normalized on Tbp housekeeper and plotted relative to NHEK cells (mean ± s.d.). (C) The transcript levels of TAp63 (black box) and ΔNp63 (grey box) were measured in NHEK and HNSCC cell lines by RT-qPCR. RT-qPCR was performed as above. Gene expression was normalized on Tbp housekeeper and plotted relative to NHEK cells (mean ± s.d.). (D) RT-qPCR analysis (upper panels) of two independent experiments performed in duplicates for Irs1 transcripts in NHEK and HNSCC cells transfected with scrambled control (siScr) or p63 (sip63#1) siRNAs. Cells were harvested 48 h after transfection. qRT-PCR was performed as above. Values are normalized to Tbp and plotted relative to control cells (mean ± s.d.). Western blot analysis for IRS1 and p63 in HNSCC cells transfected as above. Cells were harvested 48 h after transfection. β-actin served as loading control.

p63 induces IRS1 expression by binding directly to the regulatory region of the Irs1 gene

Genome-wide profiling of p63 binding sites by Chromatin IP Sequencing (ChIP-seq) analysis of NHEKs [87] revealed peaks of p63 binding to regions downstream the Irs1 locus (Fig. 2A). The algorithm p63scan identified a putative p63 responsive element (RE) in the most distant enriched peak. To validate direct interaction of p63 with this putative RE, we examined p63 occupancy at the site identified in the ChIP-seq analysis. By ChIP experiments in HNSCC cells, we found binding of p63 to a regulatory region located downstream the Irs1 locus (+148 kbps from the TSS) (Fig. 2B). In addition, by performing luciferase activity reporter assays in H1299 cells, we found that the ΔNp63 isoforms activate a luciferase reporter gene driven by the p63 RE located in the regulatory region of the Irs1 locus (Fig. 2C). Site-specific mutagenesis of the p63 RE almost completely abrogated the transactivating ability of ΔNp63 (Fig. 2C). Overall, these data demonstrate that Irs1 is a direct transcriptional target of ΔNp63.

p63 binds to the regulatory region of theIrs1gene. (A) p63 DNA-binding profiles in the Irs1 locus, obtained in NHEKs by ChIP-sequencing (ChIP-seq) using 4A4 and H129 anti-p63 antibodies in two normal human primary keratinocyte cell lines (K1 and K2) [87]. (B) ChIP analysis of p63 occupancy at the regulatory regions of the Irs1 gene. ChIP assays were performed in Fadu HNSCC cells using H129 anti-p63 antibody and control IgGs. PCR validation was performed using primers spanning the p63-binding sites located within the genomic regions identified by ChIP-seq assays. (C) Luciferase reporter assays of Irs1 regulatory regions (left panel). The pGL3 reporter vector (30 ng) and the pRL-CMV-Renilla luciferase plasmid (5 ng) were cotransfected with the empty pcDNA-HA vector or plasmids coding ΔNp63α, ΔNp63β, and ΔNp63γ (150 ng) into the p53 null human H1299 cell line. The luciferase activities of cellular extracts were measured 24 h after transfection. Cellular lysates were also analysed by western blot (right panel). Data are presented as mean ± SD and are representative of three independent experiments.

Figure 2. p63 binds to the regulatory region of theIrs1gene. (A) p63 DNA-binding profiles in the Irs1 locus, obtained in NHEKs by ChIP-sequencing (ChIP-seq) using 4A4 and H129 anti-p63 antibodies in two normal human primary keratinocyte cell lines (K1 and K2) [87]. (B) ChIP analysis of p63 occupancy at the regulatory regions of the Irs1 gene. ChIP assays were performed in Fadu HNSCC cells using H129 anti-p63 antibody and control IgGs. PCR validation was performed using primers spanning the p63-binding sites located within the genomic regions identified by ChIP-seq assays. (C) Luciferase reporter assays of Irs1 regulatory regions (left panel). The pGL3 reporter vector (30 ng) and the pRL-CMV-Renilla luciferase plasmid (5 ng) were cotransfected with the empty pcDNA-HA vector or plasmids coding ΔNp63α, ΔNp63β, and ΔNp63γ (150 ng) into the p53 null human H1299 cell line. The luciferase activities of cellular extracts were measured 24 h after transfection. Cellular lysates were also analysed by western blot (right panel). Data are presented as mean ± SD and are representative of three independent experiments.

p63 inactivation impairs cellular sensitivity of HNSCC cells to IGF1/insulin stimulation

We next tested whether knockdown of ΔNp63 affects the level of activated IRS1. To assess whether down-regulation of IRS1 in p63-depletd HNSCC cells would impair cellular responsiveness to receptor stimulation, we treated serum starved Fadu cells with both IGF1 and insulin. Stimulation of the IGF1R resulted in reduced levels of phosphorylated (Ser612) IRS1 in p63-depleted relatively to control cells (Fig. 3A). To rule out off-target effects two independent siRNAs against p63 and one specific ΔNp63 siRNA were employed (Fig. 3A, Fig. S1A and S1B).

Depletion of p63 reduces the responsiveness of HNSCC cells to ligand stimulation. (A) Fadu cells were transfected with siScr or different p63 (sip63#1, sip63#2, siΔNp63) siRNAs. Forty-eight h after transfection, cells were serum starved for 4 h, and then stimulated with 5 nM IGF1 (upper panel) or 500 ng/ml insulin (lower panel) for 10 min. Protein amounts of p63, IRS1 and p-IRS1 were detected by western blot analysis. β-actin served as loading control. Blots are representative of three individual experiments. (B) Fadu cells were transfected with siScr, sip63#1 and sip63#2, serum starved for 4 h and then stimulated with 5 nM IGF1 for 10 min. Cellular extracts were analysed with the following antibodies: anti-IRS1, anti-p-IRS1, anti-p-AKT, anti-AKT, anti-p-S6 Ribosomal Protein, anti-S6, anti-p44/42 MAPK (p-ERK1/2), anti-ERK1/2, p63 and β-actin as loading control. Blots are representative of three individual experiments. (C) Fadu cells were transfected with siScr or siIRS1 (upper panel) and with sip63#1, ΔNp63, or siScr (lower panel). Forty-eight h after transfection, cells were seeded in 6-cm plates at 500,000/plate and growth was followed until day 6.

Figure 3. Depletion of p63 reduces the responsiveness of HNSCC cells to ligand stimulation. (A) Fadu cells were transfected with siScr or different p63 (sip63#1, sip63#2, siΔNp63) siRNAs. Forty-eight h after transfection, cells were serum starved for 4 h, and then stimulated with 5 nM IGF1 (upper panel) or 500 ng/ml insulin (lower panel) for 10 min. Protein amounts of p63, IRS1 and p-IRS1 were detected by western blot analysis. β-actin served as loading control. Blots are representative of three individual experiments. (B) Fadu cells were transfected with siScr, sip63#1 and sip63#2, serum starved for 4 h and then stimulated with 5 nM IGF1 for 10 min. Cellular extracts were analysed with the following antibodies: anti-IRS1, anti-p-IRS1, anti-p-AKT, anti-AKT, anti-p-S6 Ribosomal Protein, anti-S6, anti-p44/42 MAPK (p-ERK1/2), anti-ERK1/2, p63 and β-actin as loading control. Blots are representative of three individual experiments. (C) Fadu cells were transfected with siScr or siIRS1 (upper panel) and with sip63#1, ΔNp63, or siScr (lower panel). Forty-eight h after transfection, cells were seeded in 6-cm plates at 500,000/plate and growth was followed until day 6.

To examine whether alteration of IRS1 and phospho-IRS1 levels induced by p63 silencing may affect downstream IGF signal transduction, we measured the activation of PI3K/AKT and MAPK downstream signalling pathways in p63-depleted cells. Upon p63 knock-down, we observed desensitization of HNSCC cells to IGF1 stimulation, as assessed by decreased amounts of phospho-AKT (Ser473) and phospho-S6 (Ser235/236) (Fig. 3B). Activation of MAPK (phospho-Erk1/2; Thr202/Tyr204) signalling pathway in response to IGF-1 stimulation was also markedly reduced in p63-depleted cells (Fig. 3B), further proving that p63 affects cellular sensitivity to IGF1/insulin stimulation through the regulation of IRS1 cellular abundance. Notably, knockdown of IRS1 hampered the proliferation of HNSCC cells, mimicking the effect of p63 inactivation (Fig. 3C). These findings indicate that the p63/IRS1 functional axis positively regulates the growth potential of HNSCC cells.

p63 and IRS1 expression correlates in HNSCC patients

To examine possible correlations between the expression levels of ΔNp63 and IRS1 in NHSCC primary tumours, clinical NHSCC tumour specimens samples and related benign controls were examined for p63 and IRS1 staining on tissue microarray slides. Consistent with previous reports [65,66], the majority of the patients (63%) showed high levels of p63 expression (Fig. 4A; representative staining patterns are seen in Fig. 4C, top panels). High and moderate tumour cell IRS1 expression was observed in 27% and 10%, respectively of the cases (Fig. 4B, representative staining patterns are seen in Fig. 4C, bottom panels). Significant correlation of p63 and IRS1 expression was observed in 23 out of 60 HNSCC samples (38%, Fig. 4C and Table S1).

p63 and IRS1 expression patterns are positively correlated in HNSCC patients. (A-C) Tissue sections were stained with anti-p63 and IRS1 antibodies. The scores for the percentage of p63 and IRS1 positive cells and those for the expression intensities were combined to calculate immunoreactive scores (IRSs) summarised in Table S1. According to the IRS, patients were assigned to five groups: 0 (no expression), 0,5 (low expression), 1 (moderate expression), 1,5-2 (high expression), and 2,5-3 (very high expression). The distribution of p63 and IRS1 expression in HNSCC specimens and normal tissues is shown in A, B. (C) Representative micrographs of immunohistochemical staining of p63 and IRS1 in HNSCC primary tumours (magnification: 40x). (D-E) Density distribution of RPKM values in normal (n=44; blue) and tumour (n=522: red) samples for p63 and IRS1 transcripts. Top right panels show an enlargement of the overlapping curves. Vertical black line indicates the optimal cutpoint between tumour and normal RPKM distributions, identified using OptimalCutpoints R package [96] P-value P-value F) Pie charts representing the proportion of tumour samples with IRS1 up- or down-regulated in p63 up (left) and p63 down-regulated (right) tumour samples. Statistical significance of the contingency table represented by the 4 sub-dataset of tumour samples is p-value= 4.293e-07 (Fisher’s exact test). Statistical tests (Wilcoxon and Fisher’s exact tests) on TCGA gene expression data were performed in R.

Figure 4. p63 and IRS1 expression patterns are positively correlated in HNSCC patients. (A-C) Tissue sections were stained with anti-p63 and IRS1 antibodies. The scores for the percentage of p63 and IRS1 positive cells and those for the expression intensities were combined to calculate immunoreactive scores (IRSs) summarised in Table S1. According to the IRS, patients were assigned to five groups: 0 (no expression), 0,5 (low expression), 1 (moderate expression), 1,5-2 (high expression), and 2,5-3 (very high expression). The distribution of p63 and IRS1 expression in HNSCC specimens and normal tissues is shown in A, B. (C) Representative micrographs of immunohistochemical staining of p63 and IRS1 in HNSCC primary tumours (magnification: 40x). (D-E) Density distribution of RPKM values in normal (n=44; blue) and tumour (n=522: red) samples for p63 and IRS1 transcripts. Top right panels show an enlargement of the overlapping curves. Vertical black line indicates the optimal cutpoint between tumour and normal RPKM distributions, identified using OptimalCutpoints R package [96] P-value < 1.074e-10 for p63 and P-value < 0.004 for IRS1. (F) Pie charts representing the proportion of tumour samples with IRS1 up- or down-regulated in p63 up (left) and p63 down-regulated (right) tumour samples. Statistical significance of the contingency table represented by the 4 sub-dataset of tumour samples is p-value= 4.293e-07 (Fisher’s exact test). Statistical tests (Wilcoxon and Fisher’s exact tests) on TCGA gene expression data were performed in R.

To further investigate the expression levels of p63 and IRS1, we analysed publicly available transcriptome sequencing data of 522 HNSCC patients from the TCGA repository [88]. Overall, the transcript levels of p63 and IRS1 are significantly higher in tumour specimens than in normal samples (Fig. 4D, and 4E). On the basis of their expression levels, for each gene, we stratified tumour patients into two distinct groups, displaying either up-regulation or down-regulation relatively to normal subjects. Notably, we found that 66.2% of tumour samples with p63 up-regulation also exhibited high levels of IRS1 expression A similar correlation was observed in samples with p63 down-regulation, in which 58% of the patients concomitantly displayed low levels of IRS1 transcripts (Fig. 4F, p-value= 4.293e-07). Overall, these findings demonstrate that a statistical significant positive association exists between p63 and IRS1 expression in HNSCC patients.

Discussion

It has been originally hypothesized that, ΔNp63 mainly exerts its oncogenic functions by acting as a dominant negative repressor of the tumour suppressive members of the p53 family, including TAp63. As a consequence of preventing access to their DNA binding sites, ΔNp63 would impinge on the transcription of genes involved in cell cycle control and cell death. In addition, emerging evidence has unveiled key tumour-related signalling pathways that are transcriptionally regulated by ΔNp63, in a p53 independent manner [89]. For instance, by acting in concert with the chromatin remodelling factor ACTL6A, p63 controls chromatin accessibility and functions as a direct transcriptional repressor of the Hippo/YAP regulator WWC1 in SCC [66]. Furthermore, ΔNp63 controls a transcriptional program comprising the hyaluronic acid (HA) synthase HAS3 and two hyaluronidase genes, HYAL-1 and HYAL-3, thus sustaining the pro-tumorigenic HA metabolism and signalling [59]. Abraham and collaborators [90] have recently identified components of the transforming growth factor-β signalling and the RHOA GTPase as targets and mediators of ΔNp63-dependent cell proliferation in SCCs.

Our data demonstrate that IRS1 is a direct target of transcriptional activation by ΔNp63 in HNSCC cells. Coherently, ΔNp63 and IRS1 expression patterns are positively correlated in primary tumours, suggesting that the interaction of p63 and IRS1 might contribute to the pathogenesis of HNSCC. More broadly, in ΔNp63 overexpressing SSC tumours, unbalanced expression of the TAp63/ΔNp63 isoforms may lead to enhanced Igf-r1/Irs1 transcriptional activation, resulting in augmented protein abundance of IGFR1 and IRS1, which may increase sensitivity of cancer cells to growth factor stimulation (Figure 5). In addition, overexpression of ΔNp63 prevents expression of Igfbp3 thus, ultimately, enhancing circulating IGF-1 (Fig. 5). Thus, the existence of a crosstalk between p63 and the IGF1 system may represent a mechanism by which tumours that overexpress ΔNp63 escape apoptosis and acquire a proliferative advantage.

A model depicting the crosstalk between p63 and the IGF system. While the tumour suppressive TAp63 isoforms negatively control Igf1r transcription, the oncogenic ΔNp63 variants induce and repress the expression of the Irs1 and Igfbp3 genes, respectively. In HNSCC cells overexpressing ΔNp63, the transcription of Igf1r would be stimulated as a result of the unbalanced ratio between the TA/ΔN p63 proteins. Aberrant accumulation of IGF1R and its docking protein IRS1 would enhance signalling activation in response to receptor stimulation. On the other hand, reduced expression levels of Igfbp3 would increase the availability of circulating IGF1 that could further potentiate receptor activation.

Figure 5. A model depicting the crosstalk between p63 and the IGF system. While the tumour suppressive TAp63 isoforms negatively control Igf1r transcription, the oncogenic ΔNp63 variants induce and repress the expression of the Irs1 and Igfbp3 genes, respectively. In HNSCC cells overexpressing ΔNp63, the transcription of Igf1r would be stimulated as a result of the unbalanced ratio between the TA/ΔN p63 proteins. Aberrant accumulation of IGF1R and its docking protein IRS1 would enhance signalling activation in response to receptor stimulation. On the other hand, reduced expression levels of Igfbp3 would increase the availability of circulating IGF1 that could further potentiate receptor activation.

Elevated levels of IRS1 have been reported to contribute to cancer development and progression [78,80,91,92]. Notably, high expression of IRS1 in breast cancer cells was positively correlated with aberrant phosphorylation of AKT, which was significantly associated with lymph node metastasis [93]. Coherently, we observed reduced ligand-stimulated activation of AKT in p63-depleted cells, implying that reducing the cellular abundance of IRS1 could be a strategy to diminish the mitogenic potential of the IGF system. Acting as an adaptor protein that conveys signals originating from different receptors to multiple downstream signalling molecules, IRS1 represents a potentially relevant predictive clinical biomarkers for cancers susceptible to IGF-IR targeting.

Several reports have showed a correlation between the IGF pathway and HNSCC clinical parameters [8084]. On the other hand, IGF pathway-related proteins have not been implemented as cancer biomarkers yet, due to the existence of contradictory findings on their prognostic impact. Indeed, negative correlations between levels of IGF1/IGF1R and clinical outcomes have been also reported in HNSCC [94]. Thus, in addition to unveiling alterations within the IGF system in tumours, the identification of alterations outside the IGF axis (e.g. upstream regulators), which may affect the IGF signalling, would be relevant to establish additional predictive markers for patient stratification and clinical management.

The controversial clinical data on the prognostic impact of the IGF system in HNSCC may reflect the high histopathological heterogeneity within this disease that includes tumours arising from various anatomical sites. Whether or not deregulation of the IGF signalling network and the positive regulation of IRS1 by ΔNp63 may have a prognostic significance for HNSCC are still relevant questions in the field, and further studies are needed to clarify these issues.

Materials and Methods

Cells and culture conditions

Neonatal normal human epidermal keratinocytes (NHEKs, Life Technologies) were cultured in EpiLife medium with human keratinocyte growth supplements added (Life Technologies). FaDu (pharynx squamous cell carcinoma), SSC-090 (oral squamous cell carcinoma) and SSC-154 (tongue squamous cell carcinoma) cells were grown in Eagle’s minimum essential medium (EMEM, Lonza, Basel, Switzerland); A253 cells (submaxillary salivary gland carcinoma) were cultured in McCoy’s medium (Gibco, Invitrogen); SCC-9 cells (tongue squamous cell carcinoma) were cultured in1:1 mixture of Dulbecco's Modified Eagle's Medium (DMEM) and Ham’s F-12 medium (DMEM F12, Gibco, Invitrogen) supplemented with 400 ng/mL hydrocortisone; CAL27 (tongue squamous cell carcinoma) and SCC-104 (oral squamous cell carcinoma) cells were grown in DMEM. All media were supplemented with 10% FBS, 100 U penicillin, and 100 μg/mL streptomycin (Gibco, Life Technologies). All HNSCC cell lines were purchased from ATCC and routinely tested for mycoplasma contaminations.

For p63 siRNA-mediated knockdown, NHEK and HNSCC cells were transfected with the following siRNAs: sip63#1 (SASI_Hs02_00326864) and sip63#2 (SASI_Hs02_00326867) oligos were purchased from Sigma-Aldrich; the sense strand of the siΔNp63 is: 5’- GAAGAAAGGACAGCAGCATTG -3’. The Negative Control siRNA (Qiagen, AATTCTCCGAACGTGTCACGT) was used as a silencing control. All transfections were performed using the Lipofectamine RNAiMAX transfection reagent (Invitrogen) according to manufacturer's protocols.

RNA sequencing

RNA sequencing was performed as previously described [95]. Briefly, total RNA was extracted using a mirVana miRNA isolation kit (Thermo Fisher). rRNA was removed from each RNA extraction before proceeding with RNA seq library construction. Sequencing was performed on a SOLiD sequencer 5500XL (Applied Biosystems) with 75-base-pair single-end reads by Genomnia s.r.l. (Milan, Italy). Sequencing reads in SOLID “xsq” format were mapped to the hg19 genome built and analysed with the Lifetech Lifescope 2.5.1 Whole Transcriptomic analysis pipeline with the Integromics Seqsolve software and proprietary Genomnia procedures.

Real-time qPCR

Total RNA was extracted by the RNAeasy kit (Qiagen, Hilden, Germany). Total RNA (500ng) was used for reverse transcription using GoScript Reverse Transcription System kit (Promega, Fritchburg, WI, USA) following the manufacturer's instructions. Human Tbp mRNA was used as housekeeping gene for quantity normalization. qRT-PCR was performed using the Platinum SYBR Green qPCR SuperMix UDG (Invitrogen), using the following primer pairs: Irs1 5′- CTCAACTGGACATCACAGCAG -3′ (sense) and 5′- AGGTCCTAGTTGTGAATCATG -3′ (antisense); TAp63 5′- TCAGAAGATGGTGCGACAAAC -3′ (sense) and 5′- GTTCAGGAGCCCCAGGTTCG-3′ (antisense); ΔNp63 5′- GAAGAAAGGACAGCAGCATTG -3′ (sense) and 5′- GGGACTGGTGGACGAGGAG -3′ (antisense). The PCR was monitored by a melting curve protocol according to the specifications of the ABI 7500 instrument (Applied Biosystems). Relative quantification of gene expression was calculated according to the 2−ΔΔCt method. IRS1 expression was normalized on Tbp housekeeper.

Western blot analysis

Cells were lysed in SDS lysis buffer (100 mM Tris рН 8.8, 1% SDS, 5 mM EDTA, 20 mM DTT, and 2 mM AEBSF). Total protein extracts were resolved in SDS polyacrylamide gel and blotted onto a Hybond PVDF membrane (GE Healthcare, Chicago, IL, USA). After being blocked with PBST 5% non-fat dry milk (Bio-Rad), membranes were incubated over night with primary antibodies at +4°C, washed and hybridized for 1 h at room temperature using the appropriate horseradish peroxidase-conjugated secondary antibody (rabbit and mouse, Bio-Rad, Hercules, California, USA). Detection was performed with the ECL chemiluminescence kit (Perkin Elmer, Waltham, Massachusetts, USA). The following antibodies were used: anti-IRS1 (D32G12, Cell Signaling), anti-p63 (clone 4A4, Santa Cruz Biotechnology), anti-p-IRS1 (Ser612, clone C15H5, Cell Signaling), anti-p-AKT (Ser473, clone D9E, Cell Signaling), anti-AKT (clone #9272, Cell Signaling), anti-p-S6 Ribosomal Protein (Ser235/236, clone #2211, Cell Signaling), anti-S6 (Clone 5G10, Cell Signaling), anti-p44/42 MAPK (p-ERK1/2) (Thr202/Tyr204, Clone E10, Cell Signaling), anti-ERK1/2 (Clone 137F5, Cell Signaling).

Chromatin immunoprecipitation assay

Fadu cells were used for ChIP assay. Cells were collected, fixed in 1% formaldehyde, and subjected to sonication for DNA shearing. The ChIP assay was performed with an anti-p63 antibody (H129, Santa Cruz Biotechnology) or unspecific immunoglobulin G (IgG) (Invitrogen) using a ChIP assay Kit (Invitrogen). PCR validation was performed using primers spanning the p63-binding sites located within the genomic regions identified by ChIP-seq assays.

Luciferase assay

For luciferase assays, a total of 1.2×106 H1299 cells were seeded in 12-well dishes 24 h before transfection. In total, 30 ng of pGL3 reporter vector, 5 ng of pRL-CMV-Renilla luciferase vector (Promega) and 150 ng of HA-ΔNp63α expression vectors or empty pcDNA-HA vector (as a control) were cotransfected using the Effectene transfection reagent according to the manufacturer's instructions (Qiagen). The luciferase activities of cellular extracts were measured 24 h after transfection using a Dual Luciferase Reporter Assay System (Promega). The light emission was measured over 10 sec using a Lumat LB9507 luminometer (EG&GBerthold). The transfection efficiency was normalized to Renilla luciferase activity. The p63 RE was mutated by site-directed mutagenesis using the forward primer: 5’-ATAAGGGCCTTCCTGTTCCGAGGCAGCCGTGTGAACCACC-3’ and reverse primer: 5’- GGTGGTTCACACGGCTGCCTCGGAACAGGAAGGCCCTTAT-3’.

Human HNSCC tumour tissues

Tissue arrays including paraffin-embedded HNSCC (n=60), adenoid cystic carcinomas (n=7), adenocarcinomas (n=1), mucoepidermoid carcinomas (n=1), and normal tissues. (n=10) were purchased from US Biomax, Inc. (Rockville, MD). Samples were deparaffinized and rehydrated in 1x PBS for 10 min. Tissue sections were stained with anti-p63 (clone 4A4, Ventana) and IRS1 (Clone EP263Y, Abcam) antibodies. Immunoistochemistry (IHC) scoring was performed by two independent pathologists. The percentage of positive cells was rated as follows: 0, negative; 1, 1–25% positive cells; 2, 26–50% positive cells; 3, 51–75% positive cells; and 4, >75% positive cells. The staining intensity was scored as 0, negative; 1, weak; 2, moderate; and 3, intensive. For both stainings, the scores for the percentage of positive cells and those for the expression intensities were combined to calculate immunoreactive scores (IRSs) that are summarised in Table S1. According to the IRS, we grouped patients into five groups: 0 (no expression), 0,5 (low expression), 1 (moderate expression), 1,5-2 (high expression), and 2,5-3 (very high expression). Non squamous cell carcinomas of the head and neck were excluded from the analysis.

Computational methods

Publicly available gene expression data from TCGA were downloaded from the Genomic Data Commons (GDC) Data Portal and pre-processed via the TCGAbiolinks R package [96]. Harmonized and normalized RPKM data from GDC was downloaded for Head and Neck Squamous Cell Carcinomas (TCGA-HNSC).

RPKM distributions for each of the genes of interest have been analyzed either for tumor and normal samples in the HNSC cohort. A discrimination cutpoint between tumor and normal expression distributions either for TP63 and IRS1 has been detected using OptimalCutpoints R package [97]. The identified optimal cutoffs represent the RPKM values that maximize the separation of gene expression distributions in tumor and normal samples. Data mining, statistical tests (Wilcoxon and Fisher’s exact tests) and plots on TCGA gene expression data were performed in R.

Statistical analysis

The significance of differences between two experimental groups was calculated using the two-tailed Student’s t-test. Values with P<0.05 were considered significant.

Supplementary Materials

Supplementary File

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This work was partially supported by “Mission Sustainability“ 2017 (“Head and neck squamous cell tumours: the identification of new biomarkers”) to FB, by MFAG 2014 Id.15523 to AP, by the Medical Research Council (to GM), Associazione Italiana per la Ricerca contro il Cancro (AIRC) to GM IG#20473 (2018-2022), Fondazione Luigi Maria Monti IDI-IRCCS (RC to EC), Ministry of Health Italy-China cooperation grant. Work has been also supported by Regione Lazio through Lazio Innova Progetto Gruppo di Ricerca n 85-2017-14986.

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