Research ArticleCancer Immunology

IL-33 and ST2 mediate FAK-dependent antitumor immune evasion through transcriptional networks

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Sci. Signal.  05 Dec 2017:
Vol. 10, Issue 508, eaan8355
DOI: 10.1126/scisignal.aan8355
  • Fig. 1 Nuclear FAK regulates expression of IL-33 and its receptor ST2.

    (A) Gene ontology enrichment analysis (cellular component terms) on the significantly down-regulated set of genes in the FAK−/− SCC transcriptome relative to the wild-type [WT; percentage false-positives (pfp) < 0.05]. Genes annotated with the overrepresented term (extracellular region; Benjamini-Hochberg–corrected hypergeometric test) were used to seed a protein interaction network based on direct physical interactions (gray lines). Color of each node (circle) is proportional to the log-transformed fold change in gene expression. The largest connected graph component is displayed. (B and C) Abundance of IL-33 at the mRNA level [(B); by qRT-PCR] and protein level [(C); by Western blot] in FAK-WT, FAK−/−, FAK-nls (a mutant that is largely excluded from the nucleus), and FAK-kd (a kinase-deficient mutant) SCC cells. (D and E) Abundance of IL33 mRNA [(D); by qRT-PCR] and IL-33 protein [(E); by Western blot] in FAK-WT SCC cells treated with control [dimethyl sulfoxide (DMSO)] or VS4718 (250 nM; for 24 hours). Western blot additionally assessed in FAK−/− SCC cells for reference. (F) Analysis of enzyme-linked immunosorbent assay (ELISA) for IL-33 in conditioned media from FAK-WT, FAK−/−, FAK-nls, and FAK-kd SCC cells. (G) qRT-PCR analysis of ST2 expression in FAK-WT, FAK−/−, FAK-nls, and FAK-kd SCC cells. (H) Analysis of ELISA for sST2 in conditioned media from FAK-WT, FAK−/−, FAK-nls, and FAK-kd SCC cells. (I) qRT-PCR analysis of ST2 expression in FAK-WT SCC cells treated with control (DMSO) or VS4718 (250 nM; for 24 hours). (J) Analysis of ELISA for sST2 in conditioned media from FAK-WT SCC cells treated with control (DMSO) or VS4718 (250 nM; for 24 hours). Data are means ± SEM. n = 3 experiments. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001 by Tukey’s corrected one-way analysis of variance (ANOVA) (B, G, and H) or two-tailed unpaired t test (D, I, and J).

  • Fig. 2 Nuclear FAK and IL-33 cooperate to regulate chemokine expression.

    (A) Representative Western blot of IL-33 abundance in FAK-WT, FAK-WT pLKO, and FAK-WT IL-33 short hairpin RNA 1 (shRNA1) SCC cells. PARP, poly(ADP-ribose) polymerase. (B) qRT-PCR analysis of IL33 expression in FAK-WT pLKO, FAK−/−, and FAK-WT IL-33–shRNA1 SCC cells. (C and D) qRT-PCR analysis of IL33 (C) and Ccl5 (D) expression in primary keratinocytes and FAK-WT SCC cells. (E) qRT-PCR analysis of Ccl5 expression in FAK-WT pLKO, FAK−/−, and FAK-WT IL-33–shRNA1 cells. (F) NanoString analysis of chemokine expression in FAK-WT pLKO, FAK−/−, and FAK-WT IL-33–shRNA1 SCC cells. Log10-transformed expression levels for chemokines with at least 50 counts were hierarchically clustered and displayed as a heat map. Pearson correlation coefficient between expression profiles in FAK−/− and FAK-WT IL-33–shRNA1 SCC cells is shown. Log2-transformed fold changes (over SCC FAK-WT pLKO) are also displayed. (G) qRT-PCR analysis of Ccl5 expression in SCC FAK−/− and FAK−/−+ IL-33 cells. Data are means ± SEM. n = 3 for all experiments. *P ≤ 0.05, **P ≤ 0.01,***P ≤ 0.001, ****P ≤ 0.0001 by Tukey’s corrected one-way ANOVA (B and E) or two-tailed unpaired t test (C, D, and G).

  • Fig. 3 IL-33 and sST2 support SCC tumor growth through suppressing the antitumor immune response.

    (A and B) Representative growth of FAK-WT, FAK−/−, and FAK-WT IL-33–shRNA1 (A) or FAK-WT IL-33–CRISPR (clustered regularly interspaced short palindromic repeats) (B) SCC tumors after orthotopic subcutaneous implantation. (C) qRT-PCR analysis of ST2 expression in FAK-WT pLKO, FAK-WT ST2-shRNA1, and FAK-WT ST2-shRNA2 SCC cells. (D) Growth of FAK-WT ST2-shRNA (pools 1 and 2) SCC tumors after orthotopic subcutaneous implantation. (E) CD45+ cells as a percentage of live cells isolated from FAK-WT SCC tumors 12 days after implantation. (F) Abundance of different immune cell populations as a percentage of CD45+ cells. Marker sets used to identify cell populations are listed in fig. S3. (G) Percentage ST2+ cells in different immune cell populations. (H) Mean fluorescent intensity (MFI) of ST2 expression in different immune cell populations. MFO, fluorescence minus one. (I) Representative growth of FAK-WT, FAK-WT ST2-shRNA1, and FAK-WT ST2-shRNA2 SCC tumors receiving treatment with either a CD8-depleting antibody (Ab) or isotype control (Ctrl) Ab. Statistics in (C): ****P ≤ 0.0001 by Tukey’s corrected one-way ANOVA. Data are means ± SEM. n = 3 for qRT-PCR, n = 6 to 8 tumors.

  • Fig. 4 Nuclear IL-33 interacts with an extensive network of transcriptional regulators.

    (A) Representative Western blot of IL-33 abundance in cytoplasmic (Cyto), nuclear (Nuc), and chromatin (Chr) fractions from FAK-WT and FAK−/− SCC cells. (B) Representative Western blot of IL-33 abundance in FAK-WT IL-33–CRISPR/BirA-E.V. and FAK-WT IL-33–CRISPR/IL-33–BirA SCC cells. (C) Western blot analysis of lysates from nuclear fractionations of IL-33–BirA fusion protein expression in FAK-WT IL-33–CRISPR/E.V. (empty vector) or FAK-WT IL-33–CRISPR/IL-33–BirA SCC cells. (D) Functional interaction network analysis of the IL-33 interactome. Direct physical interactions (solid gray lines) and functional association with transcription (dashed gray lines) are shown. Node style indicates association with additional relevant overrepresented functions (P < 0.0001 by Benjamini-Hochberg–corrected hypergeometric test). Components of the PTW/PP1 phosphatase, Baf-type, and TFIID complexes are highlighted. (E) Western blot analysis of key network components using streptavidin pulldowns from biotinylated lysates of SCC FAK-WT IL-33 CRISPR cells expressing either BirA empty vector or IL-33–BirA fusion protein. (F) Representative Western blot of chromatin fractions and whole cell lysates from SCC FAK-WT, FAK-WT pLKO, and FAK-WT IL-33 shRNA1 cells. (G) Interrogation of the IL-33 BioID protein interaction network to identify potential upstream regulators of mCcl5 promoter associated transcription factors (taken from Qiagen ENCODE database). PP, protein-protein interaction; E, expression; PD, protein-DNA interaction; T, transactivation; A, activation. (H) Western blot analysis of IL-33:BRD4, IL-33:FAK, and IL-33:HDAC1 associations using streptavidin pulldowns from biotinylated lysates of FAK-WT IL-33–CRISPR SCC cells expressing either BirA empty vector or IL-33–BirA fusion protein. HDAC1, histone deacetylase 1. (I) qRT-PCR analysis of Ccl5 expression in SCC FAK-WT cells treated with DMSO or JQ1 (200 nM for 48 hours). (J) qRT-PCR analysis of Ccl5 expression in FAK-WT SCC cells treated with DMSO or vorinostat (10 μM for 24 hours). Statistics in (I) and (J): ****P ≤ 0.0001, **P ≤ 0.01 by two-tailed unpaired t test. n = 3 for all experiments.

  • Fig. 5 Nuclear FAK regulates IL-33/ST2 signaling to control the antitumor immune response.

    Model of the mechanism. Nuclear FAK regulates IL33 expression (“1”) through interaction with transcription factors (TFs) and transcriptional regulators (TRs). Nuclear FAK and IL-33 cooperate to drive expression of Ccl5 and sST2 (“2” and “3,” respectively) through interaction with transcription factors and transcriptional regulators. Ccl5 and sST2 are secreted from SCC cancer cells, promoting immune evasion. We have previously reported a CCL5-CCR1, 3, and 5 paracrine signaling axis between FAK-WT SCC cells and tumor-infiltrating regulatory T (Treg) cells that contributes to immune evasion. We propose that sST2 contributes to immune evasion through competitive inhibition of IL-33/ST2 signaling on cytotoxic CD8+ T cells (“4”), resulting in tumor tolerance.

Supplementary Materials

  • www.sciencesignaling.org/cgi/content/full/10/508/eaan8355/DC1

    Fig. S1. Identification of common upstream regulators of IL33 and ST2 promoter–associated transcription factors.

    Fig. S2. CRIPSR knockout of IL33 reduces Ccl5 expression.

    Fig. S3. Gene ontology enrichment analysis.

    Table S1. Immune cell population markers.

    Table S2. Primer sequences.

  • Supplementary Materials for:

    IL-33 and ST2 mediate FAK-dependent antitumor immune evasion through transcriptional networks

    Bryan Serrels,* Niamh McGivern, Marta Canel, Adam Byron, Sarah C. Johnson, Henry J. McSorley, Niall Quinn, David Taggart, Alex Von Kreigsheim, Stephen M. Anderton, Alan Serrels,* Margaret C. Frame*

    *Corresponding author. Email: m.frame{at}ed.ac.uk (M.C.F.); b.serrels{at}ed.ac.uk (B.S.); a.serrels{at}ed.ac.uk (A.S.)

    This PDF file includes:

    • Fig. S1. Identification of common upstream regulators of IL33 and ST2 promoter–associated transcription factors.
    • Fig. S2. CRIPSR knockout of IL33 reduces Ccl5 expression.
    • Fig. S3. Gene ontology enrichment analysis.
    • Table S1. Immune cell population markers.
    • Table S2. Primer sequences.

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    Citation: B. Serrels, N. McGivern, M. Canel, A. Byron, S. C. Johnson, H. J. McSorley, N. Quinn, D. Taggart, A. Von Kreigsheim, S. M. Anderton, A. Serrels, M. C. Frame, IL-33 and ST2 mediate FAK-dependent antitumor immune evasion through transcriptional networks. Sci. Signal. 10, eaan8355 (2017).

    © 2017 American Association for the Advancement of Science