Identification and modulation of drug targets for precision medicine in breast, lung and ovarian
cancer subtypes
Stutvoet, Thijs
DOI:
10.33612/diss.144705120
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Publication date:
2020
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Stutvoet, T. (2020). Identification and modulation of drug targets for precision medicine in breast, lung and
ovarian cancer subtypes. University of Groningen. https://doi.org/10.33612/diss.144705120
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MAPK pathway activity plays a key role in PD-L1
expression of lung adenocarcinoma cells
Thijs S. Stutvoet Arjan Kol Elisabeth G.E. de Vries Marco de Bruyn Rudolf S.N. Fehrmann Anton G.T. Terwisscha van Scheltinga
Steven de Jong
Chapter 3
ABSTRACT
Immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and programmed death-ligand 1 (PD-L1) have improved the survival of patients with non-small cell lung cancer (NSCLC). Still, many patients do not respond to these inhibitors. PD-L1 (CD274) expression, one of the factors that influences efficacy of immune checkpoint inhibitors, is dynamic. Here, we studied the regulation of PD-L1 expression in NSCLC without targetable genetic alterations in EGFR, ALK, BRAF, ROS1, MET, ERBB2, and RET. Analysis of RNA sequencing data from these NSCLC tumors revealed that inferred IFNγ, EGFR, and MAPK signaling correlated with CD274 gene expression in lung adenocarcinoma. In a representative lung adenocarcinoma cell line panel, stimulation with EGF or IFNγ strongly increased CD274 mRNA, and PD-L1 protein and membrane expression, which were further enhanced by combining EGF and IFNγ. Similarly, tumor cell PD-L1 membrane expression increased after coculture with activated peripheral blood mononuclear cells. Inhibition of the MAPK pathway, using EGFR inhibitors cetuximab and erlotinib or MEK 1 and 2 inhibitor selumetinib, prevented EGF and IFNγ-induced
CD274 mRNA, and PD-L1 protein, and membrane upregulation, but had no effect on
IFNγ-induced major histocompatibility complex 1 (MHC-I) upregulation. Interestingly, although IFNγ increases transcriptional activity of CD274, MAPK signaling also increased stabilization of CD274 mRNA. In conclusion, MAPK pathway activity plays a key role in EGF- and IFNγ-induced PD-L1 expression in lung adenocarcinoma without targetable genetic alterations and may present a target to improve efficacy of immunotherapy.
3
INTRODUCTION
After years of limited progress in the treatment of advanced non-small cell lung cancer (NSCLC), a major leap forward has been made with the introduction of programmed cell death protein 1 (PD-1)/ programmed death-ligand 1 (PD-L1) targeting immune checkpoint inhibitors. These have greatly improved overall survival of patients with advanced NSCLC, especially in patients without targetable genetic alterations, accounting for almost 60% of NSCLC.1–3 Patients with PD-L1 positive tumors generally
respond better to PD-1 targeted immune checkpoint inhibition. However, discrepancies between observed PD-L1 expression and benefit from treatment often occur.4 Even
in a preselected patient population with >50% PD-L1 positive tumor cells, only 45-55% of patients respond to therapy.5 The limited value of tumor PD-L1 expression
as a biomarker may be caused by the highly dynamic expression of PD-L1 due to the influence of multiple factors.6 The best characterized inducer of PD-L1 expression in
NSCLC is the pro-inflammatory IFNγ, which is secreted by T cells.7,8 PD-L1 expressed
on tumor cells in return binds to PD-1 on T cells, disrupting T cell function and thereby preventing an effective tumor immune response.9 Oncogenic driver mutations, such
as mutations in EGFR, ALK and BRAF, are known inducers of PD-L1 expression in NSCLC cells. In these oncogene-activated cells, the PI3K/mTOR, JAK/STAT, and MAPK pathway are the main drivers of PD-L1 expression.10–13
Interestingly, in patients EGFR wild-type NSCLC tumors have higher levels of PD-L1 expression and tumor infiltrating lymphocytes, and respond better to PD-1/PD-L1 targeted therapy compared to EGFR mutant NSCLC.1,2 However, there is only limited
data about the regulation of PD-L1 expression in NSCLC without targetable genetic alterations.14–16 Better understanding of PD-L1 regulation may provide a rationale to
combine immune checkpoint inhibitors with other targeted agents. In the present study, we aimed to identify pathways regulating CD274 (PD-L1) expression in this NSCLC subtype by using RNA sequencing data from The Cancer Genome Atlas (TCGA) lung adenocarcinoma and squamous cell lung carcinoma data sets. We functionally validated our findings using adenocarcinoma cell lines and cocultures with PBMCs. Our results indicate that growth factor-dependent MAPK signaling plays an important role in basal and IFNγ-induced PD-L1 expression of lung adenocarcinoma without targetable genetic alterations.
METHODS
TCGA data retrieval and analysis
TCGA RNA sequencing V2 and mutation data for lung adenocarcinoma and squamous cell carcinoma17,18 were obtained from the cBioportal19 for Cancer Genomics on October
14th 2018. We selected 230 adenocarcinoma and 178 squamous cell carcinoma
samples with complete RNA sequencing and whole exome sequencing data. Data was analyzed and visualized using R (available from https://www.r-project.org/) and the R studio interface 1.1.453 (available from https://www.rstudio.com/) and ggplot2 package for R 3.5.1 (available from http://ggplot2.tidyverse.org). Our analysis was performed in 159 lung adenocarcinoma, and 166 squamous cell lung carcinoma samples without targetable alterations in EGFR, ALK, ROS1, BRAF, ERBB2, MET, or RET.20 TCGA RNA
sequencing data was normalized in two steps. Each expression value was first log10-transformed, and next Z-score normalized by subtracting the mean expression of each gene and dividing by the standard deviation. Next, MAPK pathway activation was inferred according to the methods of previously described gene signatures for Rat Sarcoma (RAS) and MEK activation.21,22 Inferred pathway activities were calculated as described in
the original papers. IFNγ signaling was inferred using IRF1 and STAT1 gene expression, STAT3 signaling by using STAT3 gene expression. The correlation of CD274 (PD-L1) gene expression with these signature scores was calculated using Spearman correlation. To complement this analysis, gene set enrichment analysis (GSEA) was performed on the same samples, using the hallmark PI3K and IFNG signatures in addition to the previously mentioned signatures. Furthermore, we used the C6 oncogenic signaling MEK and EGFR signatures, and an alternative PI3K signature. However, the authors doubt whether their signatures, developed for estrogen receptor positive breast cancer, can be used for other tumor types (http://software.broadinstitute.org/gsea/index.jsp).23,24 GSEA was
performed with 1000 permutations with Z-score normalized CD274 gene expression as a continuous phenotype label. Genes were ranked based on the Pearson Metric.
Cell culture
The human NSCLC cell lines HCC827, H292, A549, H358 and H460 were obtained from the American Type Culture Collection (ATCC). H322 was obtained from Sigma-Aldrich. All cell lines are from the adenocarcinoma histological subtype, except H292 which is an adenocarcinoma-like mucoepidermoid carcinoma. Cells were quarantined until screening for microbial contamination and mycoplasma was performed and proven to be negative. Cells were tested and authenticated using short tandem repeat (STR) profiling. Cells were grown in RPMI with 10% FCS, with 2 mM glutamine added for H322 cells. All cells were incubated in a humidified atmosphere with 5% CO2 and at 37°C.
Antibodies and treatments
For flow cytometry, mouse anti-PD-L1 (clone 29E.2A3, BioLegend), MHC-I (Clone W6/32, BioLegend), and secondary antibodies against mouse IgG (polyclonal goat anti-mouse PE, SouthernBiotec) were used. For Western blotting, membranes were incubated with 1:250 EGFR (#2232), 1:500 pEGFR (#3777), and 1:1000 PD-L1 (#13684), pERK1/2 (#9106), ERK1/2 (#9102), pSTAT1Ser727 (#8826), STAT1 (#9172), pSTAT3Tyr705 (#9145), STAT3
(#12640), pAKTs473 (#9271), pAKTthr308 (#9275), pS6Ser235/236 (#2211), S6 (#2217) antibodies
(Cell Signaling Technology), 0.4 µg/mL CMTM6 antibody (HPA026980, Atlas Antibodies), 1:1000 GAPDH antibody (128915, Abcam), 1:10000 β-actin antibody (#69100, MP Biochemicals) and secondary HRP-anti-mouse or HRP-anti-rabbit antibodies at 1:1500 (Dako). Detection was performed using Lumi-Light Western blotting substrate (Roche Diagnostics). Cells were treated under normal culture conditions with EGF (R&D systems), HGF (R&D systems), IFNγ (R&D Systems), erlotinib (LC Laboratories), cetuximab (Merck KGaA), selumetinib (AZD6244, Axon Medchem), XL147 (LC Laboratories), everolimus (Selleckchem), BMS911543 (Selleckchem), and actinomycin D (Sigma-Aldrich).
siRNA-transfection
Cells were transiently transfected with an siRNA targeting STAT3 (Eurogentec), or a negative control siRNA (12935300, Invitrogen) using oligofectamine (11252011, Invitrogen) in Opti-MEM (51985, Invitrogen) according to the manufacturer’s instructions. Twenty-four hours after transfection, cells were treated with indicated ligands and treatments. After a total of 48 hours STAT3 knockdown efficiency and proteins of interest were analyzed by Western blotting or flow cytometry. All experiments were performed in triplicate.
Flow cytometric analysis
Cells were harvested using trypsin and kept on ice and in PBS with 2% FCS during the entire protocol. Cells were incubated with anti-PD-L1 antibodies at 10 µg/mL for 45 minutes. Bound antibody was detected by incubating cells with goat anti-mouse IgG at a 1:50 dilution for 45 minutes. Measurements were performed on a BD Accuri C6 flow cytometer (BD Biosciences). Data analysis was performed with FlowJo v10 (Tree Star) and surface receptor expression was expressed as mean fluorescence intensity (MFI). Measurements were corrected for background fluorescence and unspecific binding of the secondary antibody. Unless stated otherwise, all experiments were performed in triplicate.
Western blot
Lysates from cells were made using mammalian protein extraction reagent with protease and phosphatase inhibitors diluted 1:100 (Thermo Fisher Scientific). Proteins were separated using SDS-PAGE. Target proteins were stained with the earlier
mentioned antibodies. Images were captured using a digital imaging system (Bio-Rad). All presented bands were observed around the size specified in supplementary table 2A. Densitometric quantitation of target proteins was calculated using imageJ relative to loading controls β-actin or GAPDH depending on target protein size.
Viability assays
For the viability assays H292 (8000 cells/well), H358 (20000 cells/well), A549 (2000 cells/ well), H322 (10000 cells/well) and H460 (2000 cells/well) cells were plated in 96 well plates in their respective media and after six hours erlotinib or selumetinib were added in concentrations ranging from 0.01 to 10 µM. After 96 hours treatment, cells were fixated using 3.7% formaldehyde and stained using crystal violet. After washing, bound crystal violet was dissolved using 10% ethanoic acid and absorption was measured at a wavelength of 590 nm. Cell survival was calculated as percentage of untreated control. All proliferation assays were performed three times in triplicate.
RNA sample collection and qRT-PCR
Total RNA was extracted using Trizol reagent (Invitrogen) and possible DNA contamination was removed using TURBO DNase ambion (Life technologies, AM2238). Next, RNA was reverse transcribed to cDNA with M-MLV reverse transcriptase (Thermo Fisher Scientific, 28025013). Real-time PCR was performed using IQ SYBR Green Supermix (Bio-Rad, 1708886) according to manufacturer’s instructions. The following primers were used: CD274 forward 5’-CAATGTGACCAGCACACTGAGAA-3’, reverse 5’- GGCATAATAAGATGGCTCCCAGAA-3’; GAPDH forward 5’-CCCACTCCTCCACCTTTGAC-3’, reverse 5’-CCACCACCCTGTTGCTGTAG-3’. The relative gene expression was calculated using the double delta CT method and GAPDH as loading control.25 All qPCR experiments
were performed three times in duplicate.
Coculture experiments
Human PBMCs were isolated from whole blood by Ficoll-Paque density centrifugation (Ficoll-Paque PLUS, GE Healthcare Life Sciences) from peripheral blood donated by healthy volunteers. The acquired PBMCs were activated for 72 hours using human T-activator CD3/28 beads (Thermo Fisher Scientific) and 100 IU/mL IL-2 (Proleukin, Novartis) in the presence of tumor cells. Separately, tumor cells were seeded into 96-well plates at a density of 1 x 104 cells/well for 48 hours. Then, the pre-activated PBMCs
were added into the coculture system at a 5:1 ratio of PBMCs to tumor cells. During coculture, cells were treated with EGF (20 ng/mL), erlotinib (10 µM) and selumetinib (10 µM). After 24 hours of coculture cell-free supernatant was collected for IFNγ analysis by ELISA (Sino Biological). Cells were harvested for flow cytometric measurement of PD-L1
3
membrane expression. In separate experiments, tumor cells were cultured in cell-free supernatant from activated PBMCs. After 24 hours PD-L1 membrane expression was determined using flow cytometry.
Statistics
Cell line experiments were assessed for differences with unpaired two-tailed Student’s t-test or two-way ANOVA followed by Bonferroni post-hoc or Dunnett’s test. Results are represented as means ± SD. A P-value < 0.05 was considered statistically significant. Statistical analyses were performed using GraphPad Prism software (version 6.0 GraphPad software).
RESULTS
MAPK pathway activation correlates with PD-L1 gene expression in lung adenocarcinoma
To study which EGFR-related signaling pathways regulate CD274 expression in NSCLC without targetable genetic alterations, we collected RNA sequencing data of 159 lung adenocarcinoma and 166 squamous cell lung carcinoma samples from TCGA excluding samples with driver mutations in EGFR, ALK, BRAF, ROS1, MET, ERBB2, and RET. Activating
KRAS mutations were present in 75 of the lung adenocarcinoma and 1 of the squamous
cell carcinoma samples. Activation of the MAPK pathway was determined using validated signatures for RAS or MEK activation.21,22 MAPK pathway activation scores were significantly
higher in KRAS mutant samples (Fig. S1A). In addition, there was a moderate correlation between RAS and MEK activation scores (Fig. S1B). Interestingly, in adenocarcinomas, but not in squamous cell carcinomas, RAS and MEK activation scores correlated with CD274 gene expression (Fig. 1A, table 1). Subset analysis showed that these correlations were strongest in KRAS wild-type lung adenocarcinomas (Fig. 1B, S1C). STAT3 gene expression did not correlate with CD274 in any subset (table 1). In both histological subtypes gene expression of STAT1 and IRF1, important mediators of IFNγ, correlated with CD274 (Fig. 1C, S1D), CD8A (rs = 0.73, rs = 0.72), and IFNG (rs = 0.71, rs = 0.74) gene expression, respectively.
Inferred MEK and RAS activities were not significantly correlated with STAT1 expression (rs 0.12, p = 0.27; rs 0.1, p = 0.36). Interestingly, a combined score for MAPK and IFNγ signaling, created by adding up 0 to 1 rescaled MEK activation scores and STAT1 expression levels, correlated more strongly with PD-L1 expression levels (Fig. S1E). GSEA using RAS, MEK and the Hallmark IFNG signatures largely confirmed these findings. In addition, GSEA of the PI3K hallmark signature suggested a link between PI3K/mTOR signaling and CD274 expression (Supplementary table 1). This suggests that activation of the MAPK, PI3K/mTOR,
and IFNγ pathway are related to increased CD274 expression in lung adenocarcinomas without targetable genetic alterations.
Table 1. Correlation between inferred MAPK, PI3K/mTOR and IFNγ pathway activity, and CD274 gene expression
in NSCLC subtypes Adenocarcinoma LUSC Non-targetable EGFRmt Total n = 28 Non-targetable KRASwt n = 166 Total n = 159 KRASmt n = 73 KRASwtn = 86 RAS-score rs p 4.1 x 100.42-8 0.32 0.006 7.6 x 100.47-6 0.52 0.005 -0.06ns MEK-score rs p 2.0 x 100.40-7 0.21 ns 7.5 x 100.51-7 0.13 ns 0.04ns STAT3 rs p -0.15ns -0.20ns -0.10ns -.08ns 0.11ns STAT1 r p < 2.2 x 100.54-16 0.66 < 2.2 x 10-16 0.48 4.9 x 10-6 0.33 ns 1.6 x 100.43-8 IRF1 r p 8.8 x 100.49-11 0.43 2.0 x 10-4 0.53 2.0 x 10-7 0.47 0.01 2.1 x 100.33-5
Abbreviations: LUSC = squamous cell lung carcinoma; wt = wild-type; mt = mutant; rs = Spearman’s rho; ns = not
significant
Figure 1. MAPK and IFNγ signaling correlate with CD274 expression in lung adenocarcinoma tumors without
targetable genetic alterations. RNA sequencing data from all TCGA lung adenocarcinoma and squamous cell lung carcinoma samples without targetable genetic alterations was collected. RNA sequencing data was Z-score normalized after 10log transformation. (A) In all samples the correlation between CD274 expression and
RAS-activation score or MEK-RAS-activation score was calculated using Spearman correlation. (B) In the KRAS wild-type
lung adenocarcinoma samples, the correlation between CD274 expression and RAS-activation score or MEK-activation score was calculated using Spearman correlation. (C) In all collected samples, the correlation between
STAT1 and CD274 expression was calculated using Spearman correlation. LUSC = squamous cell lung carcinoma.
3
EGF increases IFNγ-induced PD-L1 expression in EGFR wild-type NSCLC cells
A panel of lung adenocarcinoma cell lines without targetable genetic alterations, including a KRAS wild-type (H322), 3 KRAS mutant (A549, H358, and H460), and a KRAS wild-type adenocarcinoma-like mucoepidermoid carcinoma cell line (H292),26 was selected to
further investigate the relation between EGFR and IFNγ pathway activation, and PD-L1 expression. Membrane PD-PD-L1 expression was observed in all cell lines, irrespective of KRAS mutation status (Fig. S2A). The highest levels were found in H292, H358 and H460 cells. Levels were comparable to PD-L1 membrane levels of HCC827 EGFR mutant NSCLC cells (Fig. S2A). We wondered whether EGF, a known activator of the EGFR, PI3K/ mTOR and MAPK pathway, and IFNγ would increase PD-L1 expression in our panel. Treatment with EGF or IFNγ for 24 hours using physiologically relevant concentrations (20 ng/mL)27,28 increased PD-L1 membrane expression in both KRAS wild-type and KRAS
mutant cells (Fig. 2A,B). Moreover, exposure of cells to EGF combined with IFNγ resulted in a further increase in PD-L1 expression compared to IFNγ alone. Prolonged incubation up to 72 hours further enhanced PD-L1 expression in H292 and H358 (Fig. 2C).
To gain insight in the underlying mechanism of the increase in PD-L1 surface expression, we measured PD-L1 protein and CD274 mRNA and levels. The EGF- and IFNγ-induced increase in surface expression was reflected in a strong induction of both mRNA and total protein levels (Fig. 2D,E). EGF stimulated the MAPK and PI3K/mTOR pathway, as signified by increased levels of phosphorylated ERK 1 and 2 (pERK1/2) and phosphorylated ribosomal S6 protein (pS6), respectively (Fig. 2E, S2B). IFNγ strongly increased STAT1 and phosphorylated STAT1 (pSTAT1) levels for up to 72 hours. Taken together, these results indicate that EGF and IFNγ activate the MAPK, PI3K/mTOR, and STAT1 pathway, and concurrently increase in PD-L1 mRNA, protein and membrane levels.
EGFR inhibition prevents EGF- and IFNγ-induced PD-L1 upregulation
To analyze the regulation of PD-L1 membrane expression by EGFR and STAT1 signaling, H292 and H358 cells were treated with EGF and IFNγ in the presence of anti-EGFR monoclonal antibody cetuximab or EGFR small molecule inhibitor erlotinib. Interestingly, cetuximab and erlotinib prevented not only EGF-induced but also IFNγ-induced upregulation of CD274 mRNA levels, which was reflected in reduced PD-L1 membrane and total protein expression levels (Fig. 3A,B, S3A). Data from multiple experiments showed that erlotinib reduced basal PD-L1 membrane levels in H358 but not H292 cells (Fig. S3B). Erlotinib had a similar effect on EGF- and IFNγ-induced PD-L1 membrane expression in two other cell lines, but not in the erlotinib-resistant H460 cell line, as expected (Fig. S4A). EGFR inhibition effectively reduced EGF-dependent MAPK and PI3K/mTOR signaling, and modestly decreased IFNγ-induced upregulation of (p)STAT1 expression (Fig. 3B).
These results indicate that EGF- and IFNγ-induced CD274 mRNA, and PD-L1 protein and membrane expression levels are dependent on EGFR-mediated signaling.
Figure 2. EGF and IFNγ induce PD-L1 in NSCLC cell lines. (A, B) A panel of NSCLC cell lines without targetable
genetic alterations was treated with 20 ng/mL EGF, 20 ng/mL IFNγ, or both. After 24 hours, PD-L1 membrane expression was measured using flow cytometry. (Student’s t test ns = not significant, * P < 0.05, ** P < 0.01 compared to IFNγ, n = 3-11, combined data from all other figures). (C) Cells were treated with EGF and IFNγ for
24 or 72 hours. PD-L1 membrane expression was measured using flow cytometry. (Represented as MFI / mean
corresponding control MFI, n = 3) (D) H292 and H358 were treated with 20 ng/mL EGF or IFNγ, or both. After 24
hours CD274 mRNA levels were measured using RT-qPCR. Data was analyzed using the double delta CT method and GAPDH as a loading control. (E) H292 and H358 were treated with 20 ng/mL EGF, 20 ng/mL IFNγ, or both.
After 1, 24, 48, and 72 hours, protein levels were measured using Western blotting. Actin was used as loading control. Con = untreated control.
Figure 3. EGFR inhibition prevents EGF- and IFNγ-induced PD-L1 expression. H292 and H358 cells were treated
with 20 μg/mL cetuximab or 10 μM erlotinib with and without co-treatment with 20 ng/mL EGF, 20 ng/mL IFNγ, or both. (A) After 24 hours, PD-L1 membrane expression was measured using flow cytometry. (Two-way ANOVA
with Dunnett’s multiple comparisons test ** P < 0.01, *** P < 0.001 compared to untreated control). (B) After 24
hours cellular protein levels were measured using Western blotting. Actin was used as loading control. Data from a representative experiment are shown. C = untreated control, E + I = EGF + IFNγ.
MAPK pathway inhibition prevents PD-L1 expression induction by EGF and IFNγ
Next, we assessed the involvement of MAPK and PI3K/mTOR signaling in PD-L1 upregulation. Selumetinib, an inhibitor of MEK1/2, almost completely suppressed induction of CD274 mRNA by EGF and IFNγ in H292 and H358, and diminished the induction of protein and membrane expression levels (Fig. 4A,B, S3A). Moreover, selumetinib decreased basal PD-L1 membrane expression of H292 and H358 cells (Fig. S3B). Selumetinib effectively inhibited MEK1/2 activity, as reflected in the reduction in pERK1/2 levels, and had a moderate effect on pSTAT1 levels. The effect of selumetinib on PD-L1 membrane expression was confirmed in additional cell lines (Fig. S4A). PI3K inhibitor XL147 and mTORC1 inhibitor everolimus partially suppressed EGF- and IFNγ-induced PD-L1 protein expression in H292 cells, but they had no effect on the induction of PD-L1 membrane expression (Fig. 4A,B). At these concentrations, both drugs effectively inhibited PI3K/mTOR pathway activity, as indicated by reduced pAKT and pS6 levels (Fig. 4B).
We pursued studying the MAPK pathway because of its major influence on PD-L1 expression. A wide range of selumetinib and erlotinib concentrations was used to determine if lower drug concentrations also reduce PD-L1 expression. After 24 hours even the lowest concentration (0.1 µM) selumetinib strongly reduced pERK levels, as well as PD-L1 protein and membrane expression levels of H292 and H358 cells in both control and EGF- and IFNγ-stimulated cells (Fig. S3C). Treatment with this selumetinib concentration for 96 hours resulted in a growth reduction of 30-50% (Fig. S3D), indicating that PD-L1 expression can be manipulated with a MAPK activity inhibitor using concentrations that only partially reduce cell growth. In line with these results, selumetinib had a similar moderate effect on growth in the other three cell lines (Fig. S4B). Similar results were observed with erlotinib. Concluding, MAPK pathway inhibitors suppresses EGF- and IFNγ-induced CD274 mRNA, and PD-L1 protein and membrane expression at concentrations that have a small effect on growth.
Hepatocyte growth factor induces PD-L1 surface expression via the MAPK pathway
We investigated whether activation of the MAPK pathway via hepatocyte growth factor receptor (cMET), has a similar effect on PD-L1 expression as MAPK activation by EGFR. Overexpression of cMET and its ligand HGF occur frequently in lung adenocarcinoma tumors.29 Moreover, upon binding of hepatocyte growth factor (HGF), cMET is known to
activate PI3K/mTOR, MAPK, and JAK/STAT pathways, similar to EGFR.30 HGF enhanced
PD-L1 expression and augmented IFNγ-induced PD-PD-L1 expression in the cMET-positive (data not shown) H292 and H358 cell lines (Fig. 4C). Combining HGF and EGF had no additional effect on PD-L1 membrane expression compared to single EGF or HGF treatment. Also in this case, selumetinib effectively prevented HGF-induced effects on PD-L1 expression levels in both cell lines, while erlotinib only showed efficacy in H292 cells. Taken together, these results demonstrate that, irrespective of the upstream growth factor receptor, MAPK pathway activation is essential for PD-L1 membrane expression.
MAPK pathway inhibition does not interfere with IFNγ-induced MHC-I upregulation
Expression of major histocompatibility complex I (MHC-I) is critical for tumor cell antigen presentation and the anti-tumoral immune response.31 Because both MAPK and IFNγ
signaling can influence MHC-I expression, we wondered whether EGFR and MEK1/2 blockade could interfere with its expression in our cell lines.32–34 IFNγ, but not EGF,
increased MHC-I membrane expression in 4 out of 5 cell lines (Fig. 4D, S4C). Moreover, MAPK pathway inhibition using erlotinib and selumetinib did not influence IFNγ-induced upregulation of MHC-I expression, suggesting that MHC-I-mediated tumor cell antigen
3
presentation will not be impaired by these drugs.
MAPK signaling increases stability of CD274 mRNA
To investigate the role of STAT signaling in MHC-I and PD-L1 expression after IFNγ or EGF treatment, we inhibited STAT1 and STAT3, major transcription factors downstream of IFNγ and EGFR signaling respectively.6,35 Suppression of STAT1 signaling using JAK2
inhibitor BMS911543 prevented IFNγ-induced PD-L1 and MHC-I expression, but not EGF-induced PD-L1 expression (Fig. 5A, S5A). Also, inhibition of STAT3 using an siRNA had no influence on PD-L1 regulation by MAPK signaling (Fig. S5B). Therefore, we hypothesized that the MAPK pathway may regulate PD-L1 at a posttranscriptional level. KRAS mutations were recently shown to be involved in posttranscriptional regulation of basal PD-L1 levels through modulation of CD274 mRNA stability.36 To study whether
MAPK signaling controls stability of IFNγ-induced CD274 mRNA, KRAS wild-type and mutant cells were pretreated with IFNγ followed by the addition of the transcriptional blocker actinomycin D.37 Blocking transcription for 90 minutes halved CD274 levels (Fig.
5B). Interestingly, degradation of CD274 was counteracted by EGF-induced activation of MAPK signaling. Accordingly, inhibition of MAPK signaling with selumetinib accelerated
CD274 degradation and decreased the stabilization by EGF. These results show that
MAPK signaling influences stability of CD274 mRNA, contributing to regulation of PD-L1 protein and membrane expression.
MAPK pathway inhibition decreases PBMC-induced PD-L1 surface expression
To study the relation between immune cell activation and PD-L1 expression of tumor cells, we performed cocultures of PBMCs and NSCLC cells. After 24 hours of coculture, membrane expression of PD-L1 and MHC-I were strongly induced in tumor cells (Fig. 6A, B). Conditioned medium from activated PBMCs contained IFNγ (30 ng/mL) in comparable levels to our other experiments and also strongly induced PD-L1 membrane expression, indicating IFNγ may be involved in this PBMC-induced PD-L1 expression (Fig. S6A,B). Similar to our previous experiments, EGF further enhanced tumor cell MAPK pathway activity and PD-L1 expression, which was counteracted by erlotinib and selumetinib, without influencing MHC-I expression (Fig. 6A,B, S6A). Our results indicate that MAPK pathway inhibition can reduce tumor cell PD-L1 expression in a more complex coculture system, without interfering with MHC-I induction in tumor cells, potentially improving immunogenicity of these cells.
Figure 4. Selumetinib effectively decreases growth factor- and IFNγ-induced PD-L1 expression. H292 and H358
cells were treated with 10 μM XL147, 10 μM everolimus, or 10 μM selumetinib, with and without co-treatment with 20 ng/mL EGF, 20 ng/mL IFNγ, or both. (A) After 24 hours, PD-L1 membrane expression was measured using
flow cytometry. (Two-way ANOVA with Dunnett’s multiple comparisons test, * P < 0.05, *** P < 0.001 compared to ligand-stimulated control) (B) After 24 hours, cellular protein levels were measured using Western blotting. Actin
was used as loading control. Data are from a representative experiment. N = 2. (C) H292 and H358 cells were
treated with 10 μM erlotinib or 10 μM selumetinib, with and without co-treatment with 20 ng/mL EGF, 20 ng/mL HGF, 20 ng/mL IFNγ, or a combination. After 24 hours, PD-L1 tumor cell membrane expression was measured using flow cytometry. (Two-way ANOVA with Dunnett’s multiple comparisons test * P < 0.05, ** P < 0.01, *** P < 0.001 compared to ligand-stimulated control). (D) Cells were treated with 10 μM selumetinib or 10 μM erlotinib
with and without co-treatment with 20 ng/mL EGF, 20 ng/mL IFNγ, or both. After 24 hours, MHC-I expression was measured using flow cytometry (Student’s t test ** P < 0.01). ns = not significant, C = untreated control, XL = XL147, Ev = everolimus, Selu = selumetinib. H+E+I = HGF + EGF + IFNγ
Figure 5. MAPK signaling increases CD274 mRNA stability. (A) Cells were treated with BMS911543 in the presence
of 20 ng/mL EGF and IFNγ. After 24 hours PD-L1 membrane expression was measured using flow cytometry and cellular protein levels were measured using Western blotting. Blot from a representative experiment. N = 2. (B)
H292 and H358 were treated with IFNγ for 24 hours. After washing, cells were treated with 5 µg/mL actinomycin for 10 minutes, after which 20 ng/mL IFNγ, 20 ng/mL EGF, or 10µM selumetinib were added for 80 minutes. RNA was harvested and CD274 mRNA levels were measured using RT-qPCR. Data was analyzed using the double delta CT method and GAPDH as a loading control. (Two-way ANOVA with Tukey test * P < 0.05, ** P < 0.01, *** P < 0.001). Con = untreated control, E+I = EGF + IFNγ, ActD = actinomycin D.
Figure 6. Erlotinib and selumetinib prevent PBMC-induced PD-L1, but not MHC-I expression in NSCLC cells. (A) H292 and H358 cells were cocultured with 72-hour pre-activated PBMCs from healthy volunteers at a
ratio of 5 PBMCs per tumor cell. During coculture cells were treated with 20 ng/mL EGF, and 10 μM erlotinib or 10 μM selumetinib. After 24 hours, PD-L1 membrane expression (Two-way ANOVA with Bonferroni’s multiple comparisons method ** P < 0.01, *** P < 0.01 compared to control + pre-activated PBMCs) (B) and
MHC-I membrane expression were measured using flow cytometry (Two-way ANOVA * P < 0.05, ** P < 0.01 compared to control). (C) Proposed model for the role of IFNγ and MAPK signaling in PD-L1 regulation of lung
adenocarcinoma. IFNγ derived from tumor infiltrating immune cells induces transcription of CD274 in tumor cells through activation of JAK/STAT-signaling. CD274 mRNA is translated into PD-L1 protein, which is transported to the cell membrane. Growth receptor- and KRAS mutation-induced MAPK signaling increases STAT signaling, potentially adding to transcriptional activity. Also, MAPK signaling increases stability of CD274 mRNA, resulting in increased mRNA and protein levels, and subsequently increasing PD-L1 membrane expression. R = resting PBMCs, A = activated PBMCs.
DISCUSSION
In this study we reveal a correlation between MAPK pathway activation and CD274 expression in lung adenocarcinomas without targetable genetic alterations using TCGA RNA sequencing data. Subsequently, we demonstrate the importance of MAPK signaling in the upregulation of PD-L1 by growth factors and IFNγ in lung adenocarcinoma cell lines, which was mediated through CD274 mRNA stability and STAT1 activation (Fig. 6C). Inhibition of the MAPK pathway prevents growth factor-, IFNγ-, and PBMC-induced PD-L1 upregulation, whereas it does not interfere with MHC-I expression. Taken together, these results indicate that MAPK pathway inhibition may improve tumor cell immunogenicity
3
of lung adenocarcinomas without targetable genetic alterations, comprising almost 60% of all lung adenocarcinoma tumors in the Western world.3
Our TCGA analysis suggests MAPK pathway activity and CD274 gene expression are primarily connected in lung adenocarcinoma, but not in squamous cell carcinoma of the lung. Targeting the MAPK pathway is especially interesting in lung adenocarcinoma, because these tumors have a more active MAPK pathway and more frequently harbor KRAS mutations compared to squamous cell carcinoma tumors.38,39 Intriguingly, this is also
the subtype where tumor cell PD-L1 expression has predictive and prognostic value.40,41
Our study shows that MAPK pathway inhibition prevents the induction of CD274 mRNA by EGF and IFNγ through two separate mechanisms (Fig. 6C). Firstly, a moderate dose-dependent reduction of STAT1 and pSTAT1 levels upon inhibition of EGFR or MEK1/2 may lower CD274 transcription (Fig. S3C). This might be due to the inhibition of the eukaryotic initiation factor 4F (eIF4F) translation initiation complex, which is a downstream effector of the MAPK pathway and essential for cap-dependent translation of STAT1.39,42 Secondly, we
observed reduced CD274 mRNA stability upon inhibition of MEK1/2 or EGFR. This expands earlier data on basal PD-L1 expression in KRAS mutant cell lines, where MEK1/2 inhibition activates tristetraprolin (TTP), resulting in CD274 mRNA degradation.36 Although CD274
transcription and MHC-I-related transcription are both regulated by STAT1,6,35 MAPK
inhibition, in contrast to JAK2 inhibition, does not affect MHC-I expression, suggesting that MAPK activity primarily regulates CD274 mRNA stability in lung adenocarcinoma cells. At the protein level, PD-L1 expression can be affected by several mechanisms such as glycosylation, ubiquitination, and stabilization at the cell membrane.15,43–45 However,
we found no direct effect of EGF or IFNγ on CKLF-like MARVEL transmembrane domain containing protein 6 (CMTM6) protein levels in H292 or H358 cells (data not shown). Our experiments demonstrated that both EGFR and MEK1/2 inhibitors decrease EGF- and IFNy-induced PD-L1 expression, potentially increasing immunogenicity of lung adenocarcinoma cells. Nevertheless, because the MAPK pathway is downstream of a plethora of growth factor receptors, downstream inhibition with MEK1/2 inhibitors may be more effective to modulate PD-L1 expression than inhibition of specific growth factor receptors. This is supported by our finding that MEK1/2 inhibition, but not EGFR inhibition, prevented HGF-induced PD-L1 expression and by earlier findings in renal cell carcinoma.46 In vitro we observed PD-L1 downregulation at selumetinib concentrations
that had a small effect on cell growth. Although in vitro experiments do not perfectly model the tumor microenvironment, our results suggest that an immunomodulatory effect may already be present at lower selumetinib doses than previously utilized in NSCLC patients.47,48 The immunomodulatory role of MAPK signaling is increasingly
being recognized.49 Multiple studies using in vivo colon cancer models showed that
MEK inhibition potentiates the anti-tumor immune response by preventing T cell apoptosis and decreasing levels of myeloid suppressor cells and regulatory T cells. This resulted in sustained tumor regression when combined with PD-L1, PD-1, or cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) blocking treatment.50–52 In phase II and
III studies of selumetinib in NSCLC patients, disappointing efficacy was observed.47,48
However, clear immune modulating effects were observed, indicating that MAPK pathway inhibition may increase efficacy of immunotherapy. Modulating PD-L1 expression is especially interesting in NSCLC tumors without targetable genetic alterations, because these have a more inflamed tumor microenvironment and respond better to immune checkpoint inhibitors than tumors with targetable genetic alterations, such as EGFR mutations.1,2 These combination strategies are currently being tested in NSCLC patients
(NCT03600701, NCT03299088).
In conclusion, our results show the importance of growth factor induced MAPK pathway signaling in PD-L1 expression in lung adenocarcinoma without targetable genetic alterations. This provides a rationale to explore the combination of MAPK pathway inhibitors with immunotherapy in this lung cancer subtype.
Conflicts of interest
The authors declare that they have no competing interests.
Acknowledgements
This work is supported by a POINTING grant of the Dutch Cancer Society to E.G.E. de Vries. T.S. Stutvoet is supported by a fellowship of the Junior Scientific Master Class (JSM) of the University of Groningen.
Author contributions
TS, AK, AT, and SJ designed, performed and interpreted the experiments. EV gave valuable input on structuring the experiments; MB and RF respectively guided the coculture and in silico experiments. TS and AK wrote the manuscript and put the figures together; EV, MB, RF, AT, and SJ made revisions and proofread the manuscript. All authors read and approved the final manuscript.
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SUPPLEMENTARY FIGURES
Supplementary Figure 1. Supplementary TCGA analyses. All RNA sequencing data from the TCGA
adenocarcinoma and squamous cell carcinoma datasets from samples without targetable genetic alterations was collected. (A) In the selected lung adenocarcinoma samples the difference between MAPK pathway activation in
EGFR and KRAS wild-type and mutant tumors was determined using a RAS and MEK activation score. Statistical analysis was performed using Wilcoxon rank-sum. (B) In the selected lung adenocarcinoma and squamous cell
carcinoma samples the correlation between a RAS and MEK activation score was calculated using Spearman correlation. (C) In KRAS mutant adenocarcinoma samples, the correlation between RAS activation scores and
CD274 expression was calculated using Spearman correlation. (D) In all collected samples the correlation of IRF1
and CD274 expression was calculated using Spearman correlation. (E) A combined RAS or MEK and STAT1 score
was devised for all KRAS wild-type adenocarcinoma samples by adding up 0-1 rescaled RAS or MEK activation scores and STAT1 expression. Correlation with CD274 expression was calculated using Spearman correlation. wt = wild-type, mt = mutant, NSCLC = non-small cell lung cancer, LUSC = squamous cell lung carcinoma.
Supplementary Figure 2. Basal PD-L1 expression and short-term stimulation of the EGFR and IFNγ pathway. (A) PD-L1 membrane expression of lung adenocarcinoma cells was measured during the exponential growth
phase using flow cytometry. Results from a representative experiment. (B) H292 and H358 were treated with
20 ng/ml EGF, 20 ng/mL IFNγ, or both. After 5, 15, and 60 minutes, protein levels were measured using Western blotting. Actin was used as a loading control. Results from a representative experiment.
Supplementary Figure 3. MAPK pathway inhibition decreases EGF- and IFNγ-induced PD-L1 expression at
concentrations that only partially reduce cell growth. (A) H292 and H358 were treated with 20 ng/mL EGF and
IFNγ, and 10 µM erlotinib or selumetinib. After 24 hours CD274 levels were measured using RT-qPCR. Data was analyzed using the double delta CT method and GAPDH as a loading control. (B) H292 and H358 cells were
treated with 10 µM erlotinib or selumetinib for 24 hours. Next, PD-L1 membrane expression was measured using flow cytometry (Student’s T test compared to control * P < 0.05, *** P < 0.001, n = 8). (C) H292 and H358
cells were treated with varying concentrations of erlotinib or selumetinib with and without co-treatment with 20 ng/mL EGF and IFNγ. After 24 hours, PD-L1 membrane expression was measured using flow cytometry and protein levels were measured using Western blotting. Actin was used as a loading control. (D) Cells were plated
in 96-well plates and treated with different concentrations of erlotinib or selumetinib for 96 hours to ensure that at least four rounds of cell division had occurred in the control wells. Cell density was measured using crystal violet absorption. Pictures are from a representative experiment. Conc = concentration, Erlo = erlotinib, Selu = selumetinib.
Supplementary Figure 4. Effects of erlotinib and selumetinib on PD-L1 and MHC-I expression, and
prolifera-tion in a panel of NSCLC cell lines. (A) Cells were treated with 10 µM erlotinib or 10 µM selumetinib with and
without co-treatment with 20 ng/mL EGF, 20 ng/mL IFNγ, or both. After 24 hours PD-L1 membrane expression was measured using flow cytometry (Two-way ANOVA * P < 0.05, ** P < 0.01, *** P < 0.001 compared to ligand-treated control). (B) Cells were plated in 96-well plates and treated with different concentrations of
erlo-tinib or selumeerlo-tinib for 96 hours to ensure at least 4 rounds of cell division had occurred in the control wells. Cell density was measured using crystal violet absorption. Pictures are from a representative experiment. (C)
Cells were treated with 20 ng/mL EGF, 20 ng/mL IFNγ, or both. After 24 hours MHC-I expression was measured using flow cytometry (Student’s T test, * P < 0.05, *** P < 0.001). Conc = concentration, Erlo = erlotinib, Selu = selumetinib.
Supplementary Figure 5. JAK2 and STAT3 influence MHC-I, but not PD-L1 expression. (A) Cells were treated
with BMS911543 in the presence of 20 ng/mL IFNγ. After 24 hours PD-L1 membrane expression was measured using flow cytometry (Student’s T test compared to control: ns = not significant, * P < 0.05, *** P < 0.001). (B)
Cells were transfected with siRNAs targeting STAT3. After 24 hours cells were treated with the indicated ligands and treatments. After a total of 48 hours, PD-L1 membrane expression was measured using flow cytometry. PD-L1, pSTAT3, pERK1/2, and GAPDH protein levels were detected using Western blotting. Blots are from a rep-resentative experiment. n = 3. C = untreated control, siC = non-targeting siRNA control, siSTAT3 = STAT3 siRNA, Erlo = erlotinib, Selu = selumetinib, si = STAT3 siRNA.
Supplementary Figure 6. PBMC-derived IFNγ may play a role in PD-L1 induction. (A) H292 and H358 were
cultured in supernatant from activated PBMCs, with or without EGF and selumetinib. After 24 hours, MAPK path-way activity and PD-L1 expression were assessed using Western blotting, and PD-L1 membrane expression was measured using flow cytometry. (B) IFNγ concentration was measured in medium after coculture using ELISA.
3
SUPPLEMENTARY TABLES
Table S1. Antibodies used in the study
Antibody Dilutions Catalog number/ clone Vendor
Flow cytometry
PD-L1 1:50 29E.2A3 BioLegend (San Diego, CA, USA)
MHC-I 1:50 W6/32 BioLegend
Anti-mouse-PE 1:50 1030-09S Southernbiotech (Birmingham, AL, USA)
Western blot
EGFR 1:250 2232 Cell Signaling Technology (Danvers, MA, USA)
pEGFR 1:500 3777 Cell Signaling Technology
PD-L1 1:1000 13686 Cell Signaling Technology
ERK 1:1000 9102 Cell Signaling Technology
pERK1/2 1:1000 9106 Cell Signaling Technology
STAT1 1:1000 9172 Cell Signaling Technology
pSTAT1Ser727 1:1000 8826 Cell Signaling Technology
STAT3 1:1000 12640 Cell Signaling Technology
pSTAT3Tyr705 1:1000 9145 Cell Signaling Technology
AKT 1:1000 9275 Cell Signaling Technology
pAKTThr308 1:1000 9271 Cell Signaling Technology
S6 1:1000 2217 Cell Signaling Technology
pS6 1:1000 2211 Cell Signaling Technology
CMTM6 0.4 µg/ml HPA026980 Atlas Antibodies (Bromma, Sweden)
GAPDH 1:1000 128915 Abcam (Cambridge, UK)
β-actin 1:10000 69100 MP Biochemicals (Santa Ana, CA, USA)
HRP-anti-mouse 1:1500 P0260 Dako (Glostrup, Denmark)
HRP-anti-rabbit 1:1500 P0448 Dako
Table S2.
(A) Protein band sizes that were considered as specific staining for the target protein
PD-L1 40–50 kDa pEGFR 180 kDa EGFR 175 kDa pSTAT1 90 kDa STAT1 90 kDa pSTAT3 90 kDa STAT3 90 kDa pERK1/2 42/44 kDa ERK1/2 42/44 kDa pAKTS473 60 kDa pAKTT308 60 kDa AKT 60 kDa pS6 23 kDa S6 23 kDa Actin 42 kDa GAPDH 38 kDa
(B) Quantification of densitometry of all presented western blotting results
Figure 2E
H292 Control 1 h
EGF 24 hEGF 48 hE + I 72 hEGF IFN1 h 24 hIFN 48 hIFN 72 hIFN E + I1 h 24 hE + I 48 hE + I 72 hE + I PD-L1 1.00 1.76 3.42 4.17 4.84 1.53 2.54 4.91 5.61 1.39 6.74 8.50 8.17 pERK1/2 1.00 2.48 2.40 1.87 2.01 0.66 0.27 0.69 1.15 2.00 1.74 1.44 0.95 ERK1/2 1.00 0.87 0.98 0.75 1.02 1.10 0.80 0.78 0.85 0.81 0.99 0.92 0.88 pSTAT1 1.00 4.52 2.48 2.03 2.34 6.17 6.11 5.82 4.96 5.62 8.74 10.14 8.79 STAT1 1.00 1.45 1.31 1.17 1.29 1.50 3.78 3.58 2.70 1.39 3.65 3.52 3.21 pSTAT3 1.00 0.56 0.23 0.14 0.15 0.93 0.44 0.05 0.04 0.25 0.03 0.06 0.12 STAT3 1.00 1.82 1.48 1.16 1.21 1.47 1.23 1.21 0.97 1.00 1.30 1.19 1.31 pS6 1.00 11.98 9.07 5.52 4.14 1.77 1.67 5.19 6.03 5.15 6.44 5.12 5.18 S6 1.00 1.63 1.50 0.92 1.04 0.90 0.70 0.81 0.76 1.02 1.45 1.28 1.18 H358 Control 1 h
EGF 24 hEGF 48 hE + I 72 hEGF IFN1 h 24 hIFN 48 hIFN 72 hIFN E + I1 h 24 hE + I 48 hE + I 72 hE + I PD-L1 1.00 0.90 1.07 1.32 1.50 1.12 3.38 4.33 3.85 1.00 3.29 4.18 4.21 pERK1/2 1.00 6.37 5.02 6.55 2.71 2.83 5.94 6.75 4.86 5.47 3.95 6.74 3.42 ERK1/2 1.00 0.95 0.95 1.01 0.88 0.98 0.94 1.03 0.83 0.98 0.80 0.82 0.92 pSTAT1 1.00 1.33 0.88 0.81 0.79 3.21 2.90 4.19 3.82 3.83 3.54 3.67 3.24 STAT1 1.00 1.12 1.15 1.18 0.90 1.09 1.95 2.27 2.02 0.89 1.99 2.44 1.57 pSTAT3 1.00 0.55 0.53 0.55 0.95 0.80 0.97 0.91 1.02 0.69 0.31 0.45 0.38 STAT3 1.00 0.97 1.05 1.07 1.18 1.33 1.23 1.35 1.21 1.02 1.12 0.97 0.86 pS6 1.00 2.64 2.37 1.93 1.15 2.28 2.26 1.53 1.43 1.51 0.95 0.98 1.23 S6 1.00 1.00 1.07 1.11 1.16 1.30 1.05 1.58 1.43 1.40 1.11 0.87 0.76
3
Figure 3B H292 Control Control Control Control Cetuximab Cetuximab Cetuximab Cetuximab Erlotinib Erlotinib Erlotinib Erlotinib Control EGF IFN EGF + IFN Control EGF IFN EGF + IFN Control EGF IFN EGF + IFN PD-L1 1.00 1.70 2.47 5.64 1.23 0.91 1.74 3.17 0.92 0.66 1.45 1.93 pEGFR 1.00 0.26 0.47 0.15 1.58 2.13 2.53 1.82 0.04 0.03 0.06 0.06 EGFR 1.00 0.34 0.78 0.28 0.40 0.43 0.47 0.29 0.19 0.16 0.24 0.20 pERK1/2 1.00 4.89 1.54 3.76 0.23 1.49 0.26 1.32 0.22 0.12 0.10 0.09 ERK1/2 1.00 0.60 0.71 0.65 0.78 0.74 0.77 0.76 0.77 0.74 0.57 0.63 pSTAT1 1.00 2.36 6.39 5.80 0.85 1.19 3.97 4.94 0.62 0.53 4.24 5.55 STAT1 1.00 1.04 2.68 2.14 0.80 0.85 2.59 2.48 0.88 0.76 2.03 2.67 pSTAT3 1.00 0.17 1.09 0.33 1.53 0.40 1.68 0.45 1.16 1.37 1.24 1.26 STAT3 1.00 0.78 0.88 0.77 0.69 0.69 0.72 1.03 0.68 0.80 0.68 0.84 pS6 1.00 3.12 2.37 12.15 0.89 1.85 1.07 10.50 0.67 0.29 0.34 0.55 S6 1.00 0.79 0.79 0.71 0.78 0.75 0.70 0.78 0.56 0.75 0.57 0.56 H358 Control Control Control Control Cetuximab Cetuximab Cetuximab Cetuximab Erlotinib Erlotinib Erlotinib Erlotinib Control EGF IFN EGF + IFN Control EGF IFN EGF + IFN Control EGF IFN EGF + IFN PD-L1 1.00 1.11 2.63 2.82 0.64 0.80 1.23 3.19 1.19 1.01 1.62 1.41 pEGFR 1.00 0.69 0.34 0.40 2.78 1.56 1.42 0.55 0.13 0.04 0.08 0.09 EGFR 1.00 0.87 0.91 0.71 0.97 1.07 0.98 1.06 1.26 1.01 1.14 0.87 pERK1/2 1.00 1.23 0.34 0.72 0.51 1.25 0.22 0.75 0.14 0.09 0.11 0.15 ERK1/2 1.00 0.89 0.88 1.04 1.13 1.13 0.97 0.95 1.22 1.07 1.08 0.91 pSTAT1 1.00 1.29 3.92 4.34 1.15 1.38 3.09 3.94 1.11 1.17 4.76 3.99 STAT1 1.00 1.08 2.66 2.57 0.78 0.75 1.72 1.58 0.65 0.61 1.88 2.01 pSTAT3 1.00 0.57 0.15 0.14 0.96 0.39 0.61 0.25 2.35 2.59 2.59 1.56 STAT3 1.00 0.95 1.03 1.08 0.99 1.01 0.98 0.99 1.12 1.16 1.33 1.23 pS6 1.00 0.92 1.09 1.22 0.62 1.09 0.41 0.95 0.14 0.09 0.11 0.12 S6 1.00 0.91 0.93 0.89 0.79 0.78 0.70 0.84 1.01 1.16 1.10 0.96
3
Figure 4B H292 Control Control Control Control EGF EGF EGF EGF IFN IFN IFN IFN E + I E + I E + I E + I Control XL147 Everolimus Selu Control XL147 Everolimus Selu Control XL147 Everolimus Selu Control XL147 Everolimus Selu PD-L1 1.00 1.11 1.38 0.48 3.52 1.56 1.89 0.64 1.82 2.90 4.05 3.38 10.01 7.55 8.12 3.17 pERK1/2 1.00 1.00 1.18 0.10 2.96 1.71 1.73 0.11 0.48 0.50 0.70 0.07 1.82 1.56 1.89 0.14 ERK1/2 1.00 1.14 1.01 1.26 1.48 0.91 0.86 1.08 1.10 1.12 1.04 1.10 0.75 0.71 0.66 0.70 pSTAT1 1.00 1.14 1.10 1.40 2.06 1.10 1.02 0.57 3.33 3.95 3.98 4.13 5.50 5.57 4.89 2.56 STAT1 1.00 0.94 0.76 0.91 0.94 0.74 0.53 0.64 2.53 2.51 2.48 2.84 2.06 2.69 1.80 2.33 pSTAT3 1.00 1.61 1.27 1.84 0.34 0.37 0.30 1.28 1.04 1.47 1.51 1.87 0.19 0.29 0.38 1.17 STAT3 1.00 1.10 0.96 1.23 0.96 0.56 0.42 0.50 0.69 0.63 0.71 0.76 0.82 0.62 0.63 0.68 pAKTS473 1.00 1.01 1.48 1.12 3.97 0.72 1.52 3.84 2.67 0.63 2.02 2.48 6.11 1.55 1.90 4.65 pAKTT308 1.00 0.45 1.22 0.97 1.42 0.22 0.56 1.33 0.79 0.14 0.88 0.57 2.22 0.34 1.21 1.77 AKT 1.00 1.41 1.03 1.48 1.07 0.82 0.61 0.70 0.77 0.73 0.75 0.80 0.78 0.80 0.66 0.58 pS6 1.00 0.27 0.31 0.50 1.44 0.75 0.38 0.69 0.70 0.14 0.16 0.37 1.34 0.82 0.34 0.74 S6 1.00 0.84 0.63 1.00 0.87 0.49 0.40 0.47 0.45 0.53 0.53 0.52 0.53 0.46 0.39 0.51 H358 Control Control Control Control EGF EGF EGF EGF IFN IFN IFN IFN E + I E + I E + I E + I Control XL147 Everolimus Selu Control XL147 Everolimus Selu Control XL147 Everolimus Selu Control XL147 Everolimus Selu PD-L1 1.00 1.21 1.45 0.49 2.66 1.78 1.67 1.52 5.12 3.98 5.85 2.91 6.28 6.29 6.19 2.28 pERK1/2 1.00 1.02 2.20 0.05 2.40 1.95 2.12 0.12 1.19 1.13 1.56 0.05 1.13 1.13 1.84 0.18 ERK1/2 1.00 0.94 0.93 0.95 1.09 1.02 0.84 0.79 0.54 0.69 0.98 0.91 0.78 0.95 0.84 0.93 pSTAT1 1.00 0.92 0.97 0.65 0.92 0.73 0.76 0.75 1.13 1.16 1.15 0.85 1.17 1.37 1.35 0.95 STAT1 1.00 1.28 1.83 1.91 2.12 2.24 1.99 2.97 3.36 3.56 3.39 3.62 3.52 4.29 3.57 3.46 pSTAT3 1.00 1.87 0.34 8.44 0.78 2.02 0.62 13.21 0.88 1.49 0.60 6.99 0.46 1.24 0.08 5.43 STAT3 1.00 1.12 1.33 1.10 1.56 1.22 1.08 1.42 1.58 1.56 1.64 1.42 1.77 2.24 1.96 1.54 pAKTS473 1.00 0.06 0.59 0.59 2.19 0.29 0.50 0.76 1.57 0.03 0.47 0.26 1.56 0.15 0.72 0.61 pAKTT308 1.00 0.26 1.05 0.46 0.78 0.08 0.46 0.19 0.67 0.03 0.35 0.14 0.31 0.05 0.24 0.12 AKT 1.00 1.02 0.89 0.93 0.95 0.96 0.73 1.02 0.81 0.96 0.85 0.75 0.57 0.78 0.83 0.66 pS6 1.00 0.58 0.38 0.29 1.13 0.70 0.30 0.51 1.02 0.62 0.28 0.43 0.77 0.72 0.43 0.58 S6 1.00 1.16 1.09 0.94 1.63 1.20 0.96 1.17 1.15 1.06 1.02 1.27 1.19 1.27 0.73 0.82
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Figure 5A
H292 Control Control Control Control BMS911543 BMS911543 BMS911543 BMS911543 Control EGF IFN EGF + IFN Control EGF IFN EGF + IFN PD-L1 1.00 8.42 6.39 34.91 2.81 6.96 1.99 5.87
pSTAT1 1.00 1.68 7.66 6.81 0.67 1.43 2.89 3.35
H358 Control Control Control Control BMS911543 BMS911543 BMS911543 BMS911543 Control EGF IFN EGF + IFN Control EGF IFN EGF + IFN PD-L1 1.00 0.76 1.86 2.54 0.48 0.60 0.60 0.49
pSTAT1 1.00 1.23 2.08 2.40 0.85 0.79 1.59 1.41
Figure S2B
H292 EGF EGF EGF IFN IFN IFN EGF +
IFN EGF + IFN EGF + IFN Control 5 min 15 min 60 min 5 min 15 min 60 min 5 min 15 min 60 min PD-L1 1.00 1.10 1.39 1.62 1.23 0.94 0.84 0.84 0.93 1.01
pEGFR 1.00 10.49 9.45 9.80 1.00 0.94 2.59 9.93 16.89 7.63
pERK1/2 1.00 19.64 16.22 14.40 0.80 0.44 0.74 19.30 18.51 18.09
pSTAT1 1.00 1.81 3.04 4.88 1.17 1.71 5.87 1.33 3.19 7.41
pSTAT3 1.00 2.26 2.12 1.25 2.05 2.34 2.86 2.50 3.80 2.34
H358 EGF EGF EGF IFN IFN IFN EGF +
IFN EGF + IFN EGF + IFN Control 5 min 15 min 60 min 5 min 15 min 60 min 5 min 15 min 60 min PD-L1 1.00 1.64 1.85 2.15 2.00 1.51 1.57 1.82 1.93 1.69 pEGFR 1.00 3.95 3.43 2.53 0.73 0.43 0.63 2.95 4.45 2.00 pERK1/2 1.00 8.34 6.94 3.32 2.24 1.05 1.05 7.31 6.46 1.76 pSTAT1 1.00 1.45 1.67 2.08 1.35 1.09 2.13 1.22 1.47 3.00 pSTAT3 1.00 1.41 1.19 0.95 1.31 1.18 1.08 1.51 1.59 0.58
3
Figure S3C H292 Control Control Control Control Control Control Control EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN Control Erlo Erlo Erlo Selu Selu Selu Control Erlo Erlo Erlo Selu Selu Selu Control 0.1 1 10 0.1 1 10 Control 0.1 1 10 0.1 1 10 PD-L1 1.00 0.90 0.96 1.05 0.72 0.85 1.08 11.10 9.53 2.61 2.52 3.65 1.69 0.69 pEGFR 1.00 0.84 0.85 0.31 0.79 0.70 1.46 1.58 1.73 0.91 0.55 0.63 0.74 0.70 pERK1/2 1.00 0.11 0.01 0.01 0.08 0.07 0.05 5.14 3.79 0.30 0.06 2.82 1.62 0.07 pSTAT1 1.00 1.11 0.86 1.23 1.14 1.31 2.25 27.30 22.93 13.54 14.96 18.87 16.78 12.64 pSTAT3 1.00 0.82 1.05 1.01 1.15 1.42 1.71 0.47 0.17 1.43 1.75 0.35 0.79 2.28 pS6 1.00 0.44 0.28 0.29 0.34 0.54 0.72 5.75 4.34 0.57 0.65 3.25 4.21 2.07 H358 Control Control Control Control Control Control Control EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN Control Erlo Erlo Erlo Selu Selu Selu Control Erlo Erlo Erlo Selu Selu Selu Control 0.1 1 10 0.1 1 10 Control 0.1 1 10 0.1 1 10 PD-L1 1.00 0.87 0.85 0.90 0.77 0.94 0.99 2.88 3.21 1.41 1.18 1.23 1.25 1.44 pEGFR 1.00 2.12 2.29 1.62 1.74 0.93 1.64 1.91 2.06 1.50 1.27 2.51 1.08 0.89 pERK1/2 1.00 0.49 0.14 0.05 0.05 0.03 0.08 1.83 1.54 0.24 0.11 0.24 0.17 0.25 pSTAT1 1.00 1.05 0.93 0.73 0.78 0.86 1.00 3.57 4.23 3.21 2.93 2.65 2.42 2.27 pSTAT3 1.00 2.07 4.99 4.13 4.00 4.64 5.31 0.36 0.59 2.39 2.99 2.67 3.61 4.16 pS6 1.00 0.73 0.40 0.36 0.42 0.40 0.47 1.80 2.15 0.97 0.53 1.09 0.69 0.60
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Figure S5B
H292 Control EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN Control Control siRNA
control STAT3 siRNA Erlo Selu STAT3 siRNA + Erlo STAT3 siRNA + Selu PD-L1 1.00 5.46 5.35 4.12 1.20 1.53 1.51 1.19 pSTAT3 1.00 1.59 1.55 1.14 1.93 2.51 1.27 0.89 pERK1/2 1.00 2.24 2.15 1.33 0.03 0.18 0.08 0.11
H358 Control EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN EGF + IFN Control Control siRNA
control STAT3 siRNA Erlo Selu STAT3siRNA+ Erlo STAT3 siRNA+ Selu PD-L1 1 3.81 4.11 4.21 2.02 1.62 1.86 1.71
pSTAT3 1.00 0.19 0.20 0.06 0.95 1.18 0.56 0.70
pERK1/2 1.00 2.28 2.11 2.19 0.34 0.16 0.05 0.32
Figure S6A
H292 Control Control Control Cond. medium Cond. medium Cond. medium Control EGF Selu Control EGF Selumetinib PD-L1 1.00 4.51 1.11 8.99 12.64 1.94
pERK1/2 1.00 7.55 0.30 7.36 11.75 0.45
H358 Control Control Control Cond. medium Cond. medium Cond. medium Control EGF Selu Control EGF Selumetinib PD-L1 1.00 1.17 0.56 3.04 2.80 0.63
pERK1/2 1.00 1.45 0.08 1.65 1.62 0.05
