Broad and Conserved Immune Regulation by Genetically ......Michalik (Center for Integrative...

Microenvironment and Immunology

Broad and Conserved Immune Regulation byGenetically Heterogeneous Melanoma CellsNatalie J. Neubert1, Laure Till�e1, David Barras2, Charlotte Soneson2,Petra Baumgaertner1, Donata Rimoldi1, David Gfeller1,2, Mauro Delorenzi1,2,Silvia A. Fuertes Marraco1, and Daniel E. Speiser1

Abstract

Although mutations drive cancer, it is less clear to what extentgenetic defects control immune mechanisms and confer resis-tance to T-cell-based immunotherapy. Here, we studied the reac-tions of malignant and benign melanocyte lines to cytotoxicCD8þ T cells (CTL) using flow cytometry and gene expressionanalyses. We found rapid and broad upregulation of immune-regulatory genes, essentially triggered by CTL-derived IFNg andaugmented by TNFa. These reactions were predominantlyhomogenous, independent of oncogenic driver mutations, and

similar in benign and malignant cells. The reactions exhibitedboth pro- and antitumorigenic potential and primarily corre-sponded to mechanisms that were conserved, rather thanacquired, by mutations. Similar results were obtained from directex vivo analysis of the tumor microenvironment. Thus, immuneregulation in the tumor landscape may often be driven by con-served mechanisms, which may explain why T-cell–based immu-notherapy can provide durable benefits with relatively infrequentescape. Cancer Res; 77(7); 1623–36. �2017 AACR.

IntroductionMelanoma has a high prevalence of somatic mutations (1).

Although the majority are putative neutral "passenger" muta-tions, a subset of "driver" mutations is thought to be responsiblefor malignancy, and is consequently used for the molecularclassification of melanoma (2, 3). The most frequent oncogenicdrivers in melanoma, BRAF and NRAS, belong to the MAPKsignaling cascade (3). The identification of these oncogenic dri-vers led to great innovations in targeted therapy against melano-ma (4, 5). Patients treated with small-molecule inhibitors ofoncogenic drivers frequently experience tumor regressions andclinical responses. Unfortunately, however, most tumors relapsebecause of various resistance mechanisms (6), calling for moredurable therapies.

"Checkpoint" blockers represent a major recent breakthroughin immunotherapy of solid cancers. This drug class blocksimmune inhibitory receptor-ligand interactions thus empoweringantitumor immune responses, capable of inducing durable clin-ical responses in increasing numbers of cancer patients (7, 8). Atthe center of immune responses against cancer and the conse-

quent success of many immunotherapies are the cancer-specificcytotoxic CD8þ T cells (CTL; "antitumor T cells"). However, theregulators of T-cell responses and immune escape in cancerpatients remain only partly understood.

A central challenge is to know whether resistance to CTL-basedimmunotherapy arises from properties that tumor cells acquiredby genetic alterations. Examples of mutational mechanismsinclude the constitutive activation of the Wnt/b-catenin signalingpathway in melanoma cells suppressing the recruitment of den-dritic cells and subsequent T-cell activation (9). Furthermore,impairment of T-cell responses may be associated with BRAFmutations or loss of PTEN, and with production of cytokinessuch as IL1 and VEGF (10, 11). Moreover, PDL1 can be consti-tutively expressed by cancer cells, as found inHodgkin lymphomapatients (12) and in a model of pancreatic cancer (13).

Besides these mutational mechanisms, there is an alternativeexplanation for the inefficiency of antitumor T cells (14). Phys-iologic, evolutionary conserved mechanisms of T-cell inhibitionoperate inmanynonmalignant conditions of prolonged infectionand/or inflammation. The aim of these mechanisms is to atten-uate lymphocyte cytotoxicity to avoid extended tissue damage,that is, immunopathology. In healthy tissues, the majority oftissue cells such as epithelial and stromal cells, but also endo-thelial cells and melanocytes assure tissue integrity and function.In infectious diseases, their destruction by CTLs must be limited,particularly in strong, broad and/or long-lasting immuneresponses such as chronic infections. Cancer cells arise fromnormal tissue cells, and thus already possess such immune-regulatory properties (15, 16). Therefore, cancer cells do notnecessarily need to acquire immune-suppressive mechanisms bygenetic modification.

A common feature of the nonmutational immune-regulatorymechanisms is that they are stimulated when tissue cells areattacked by antigen-specific CTLs. For instance, IFNg producedby CTLs triggers PDL1 expression and secretion of IDO (17, 18),

1Ludwig Cancer Research and Department of Oncology, University of Lausanne,Lausanne, Switzerland. 2SIB Swiss Institute of Bioinformatics, Lausanne,Switzerland.

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

Current address for C. Soneson: Institute ofMolecular Life Sciences, University ofZurich, and SIB Swiss Institute of Bioinformatics, Zurich 8057, Switzerland.

Corresponding Author: Daniel E. Speiser, Ludwig Cancer Research, Universityof Lausanne, Biopole 3-02DB92, CH-1066 Epalinges, Switzerland. Phone: 41-21-314-0182; Fax: 41-21-692-5995; E-mail: [email protected]

doi: 10.1158/0008-5472.CAN-16-2680

�2017 American Association for Cancer Research.

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both attenuating CTLs.However, there is no comprehensive studycharacterizing immune-regulatory responses of tumor cells toCTLs. We set out to explore the impact of CTL attack onmelanomacells, with the goal to identify their spectrum of inflammatory andimmune-regulatory reactions, that is, tumor cell–intrinsic immuneresponses. Indeed, the tumor cells produced many proinflamma-tory factors, chemokines and immune-inhibitory molecules. Weconsidered the relevance of mutational heterogeneity by studying19 melanoma cell lines featuring different oncogenic driver muta-tions, and in addition, comparing melanoma cells with benignmelanocytes. Our results show a surprising broadness and pre-dominant homogeneity of the reactions to CTL attack.

Materials and MethodsCell lines and CTLs

Melanoma cell lines (Supplementary Table S1) were generatedfrom HLA-A2þ patients with histologically proven metastaticmelanoma. Patients consented to the study approved by the localethics committee. All melanoma cell lines had been cultured lessthan 6 months since their generation at the time of the experi-ments. Melanoma cell lines used for CTL-melanoma cell cocul-tures were MelanA-positive (confirmed by FACS). They werecultured in RPMI1640 þ GlutaMAX-I, complemented with10% heat-inactivated FCS (PAA), 1.1 mmol/L Arginine (SigmaAldrich), 0.48 mmol/L Asparagine (Sigma-Aldrich), 11.25 mmol/LGlutamine (Gibco), 10 mmol/L HEPES (Gibco), and 100 U/mLpenicillin/streptomycin (Gibco). Where indicated cultures con-tained IFNg (222U/ml; Peprotech) and/or TNFa [50ng/mLwhenused alone (19) or 10 ng/mL when used with IFNg (20);PeproTech]. All melanoma cell lines were authenticated usingthe PowerPlex 21 system (Promega) or PCR-SSO in November/December 2016 (details see Supplementary Methods).

Autologous or HLA/antigen-matchedMelanA-specific CD8þ T-cell clones (CTLs) were isolated from melanoma patient bloodand amplified by restimulation with PHA, irradiated feeder cells,and human recombinant IL2 (21). Yellow fever–specific CTLswere cloned from blood of healthy donors and used as negativecontrol in coculture experiments.

The melanocyte lines 20BO4/KRW/DJS and NHM were kindgifts fromG. Ghanem (Universit�e Libre de Bruxelles; 2007) and L.Michalik (Center for Integrative Genomics, University of Lau-sanne; 2014), respectively. Melanocyte lines were not authenti-cated, but melanocyte origin was confirmed by analyses of mel-anosomal RNA/protein expression (data not shown). 20BO4,KRW and NHM melanocyte lines were cultured in Media M2("Ready-to-use"; PromoCell). The DJS melanocyte line was cul-tured in F-10-Nut-Mix(1x)þGlutaMAX-I (Gibco) supplementedwith 5% FCS (PAA), 2.3% Melanocyte Growth Medium M2SupplementMix (PromoCell), 8 mmol/L Hepes (Gibco) and10 ng/mL PMA (Sigma). For 20BO4, KRW and DJS, melanocytecocultures analyzed for protein expressionwere done in F-10-Nut-Mix(1x)þGlutaMAX-I (Gibco) supplemented with 5% FCS(PAA), 5%MelanoMax (Gentaur) and 8 mmol/L Hepes (Gibco).All cell lines and CTLs were confirmed negative for mycoplasmaby PCR with mycoplasma-specific primers, as described in theSupplementary Methods.

Cocultures with CTLsWe established a systemwhere bothmelanocytic cells andCTLs

coexisted for at least 3 days, and that is suitable for protein and

transcriptomic analyses (22). Briefly, melanoma cells/melano-cytes were stained with 1 mmol/L CFSE (CellTrace CFSE CellProliferation Kit, Molecular Probes) and seeded in 24-well plates3 days before coculturewithCTLs. CTLswere stainedwith 1mmol/L Violet Tracker (CellTrace Violet Cell Proliferation Kit, MolecularProbes) and added at a 1:1 ratio to the adherent melanoma cell ormelanocyte cultures. Coculture media were supplemented with30 U/mL IL2 (Proleukin, Roche).

Flow cytometryMelanocytic cells and CTLs were distinguished by their pre-

culture green-fluorescent (melanoma cells/melanocytes) and vio-let-fluorescent (CTLs) labels described in the previous section. Forprotein analyses, intracellular and surface stainings were per-formed as specified in the Supplementary Methods and Supple-mentary Table S2. For cell sorting, cultures were harvested after 24hours, washed once with PBS and stained with the dead cellmarker DAPI. Live CFSE-labeled melanoma cells/melanocyteswere sorted (gating shown in Supplementary Fig. S1A) with anAstrios cytometer (BeckmanCoulter) directly into cell suspensionmedia (PBS, 10% BSA, 0.05M EDTA) and immediately processedfor RNA extraction. Purity of sorted populations was determinedby analyzing aliquots before and after FACS sorting (Supplemen-tary Fig. S1B).

Gene-expression analysisGene expression was measured with the NanoString Technol-

ogy according to the manufacturer's instructions. Specificationsconcerning the design of the custom-made gene set and the dataanalysis are described in the Supplementary Methods and Sup-plementary Fig. S2.

Statistical analysisStatistical analyses were performed with GraphPad Prism

V6.0d. Wilcoxon matched-paired signed-rank tests were used toassess the difference between untreated and cytokine treatedsamples. Kruskal–Wallis tests were performed to assess the differ-ences between themutational groups.P values and FDRs forGSEAwere calculated with 1,000 sample permutations using the Gene-Pattern web interface from the Broad Institute (genepattern.broadinstitute.org). The Hallmark collection (H), the GeneOntology collection (C5) and the Immunologic signatures col-lection (C7) were selected from the MSigDB portal (http://www.broadinstitute.org/gsea/msigdb; version 5.0 and 5.1).

Data and materials availabilityThe microarray data (Supplementary Fig. S3, Supplementary

Table S3) have been deposited in the NCBI Gene ExpressionOmnibus database with the accession number GEO: GSE79991.

ResultsMelanoma cells respond to CTL attack by upregulation ofimmune-regulatory genes

To study the immediate CTL-melanoma cell interaction, wecocultured melanoma cells from different patients (Supplemen-tary Table S1)with antigen-specific CTLs at a 1:1 ratio for 24 hours(22). We used CTLs recognizing HLA-A2/MLANA (MelanA/MART-1) presented by HLA-A2–positive melanoma cells andmelanocytes. As expected, with increasing CTL:melanoma cellratio, CTLs killed increasing fractions but not all melanoma cells.We studied the surviving cells following fluorescence-activated

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http://www.broadinstitute.org/gsea/msigdbhttp://www.broadinstitute.org/gsea/msigdbhttp://cancerres.aacrjournals.org/

cell sorting. First, we characterized the gene expression profiles ofthreemelanoma cell lines bymicroarrays. At baseline (melanomacells before coculture with CTLs, Supplementary Fig. S3A) theprofiles were heterogeneous in accordance with the previouslyreported genetic heterogeneity of melanoma (2, 3). In contrast,the changes induced by the CTLs were remarkably homogenous,with several hundreds of differentially expressed genes in com-mon between the three cell lines (Supplementary Fig. S3A). Geneset enrichment analysis (GSEA) assessing the degree to which thegenes in a particular gene set are coordinately up- or down-regulated (Supplementary Fig. S3B–S3E; Supplementary TableS3) showed that the most enriched signatures were "defenseresponse," "immune response," and "inflammatory response"for the Ontology collection (Supplementary Fig. S3B and S3C),and "IFNg response," "IFNa response," and "TNFa signaling viaNFKB" for the Hallmark collection (Supplementary Fig. S3D andS3E). These results show that melanoma cells responded to CTLswith gene expression changes of immune-regulatory genes.

Next, we quantified the gene expression changes using Nano-String.We curated a panel of 181 immune-related genes that wereeither differentially expressed in the above-mentionedmicroarray(>4-fold increased or >2-fold decreased in average in the threecocultured vs. untreated melanoma cell lines) and/or have aknown role in inflammatory mechanisms of the TME. We gavepriority to genes that are predicted to be secreted or expressed atthe cell surface (the selection is detailed in the SupplementaryMethods and Supplementary Table S4). The expression of the181-gene panel was analyzed in four different HLA-A2-positivemelanoma cell lines: the three previously used cell lines and oneadditional cell line, allowing two autologous (T1185B, T1015A)and two nonautologous cocultures (Me275, Me290) with HLA-A2-restricted MelanA-specific CTLs. Melanoma cells were charac-terized at baseline and after 24 hours of exposure to melanoma-specific CTLs, negative control CTLs (specific for the yellow-fever-virus-antigen A2/NS4b) or the cytokines IFNg and TNFa, knownto be secreted by activated CTLs (Fig. 1A).

First, hierarchical clustering of the samples showed that expo-sure to MelanA-specific CTLs induced upregulation of the major-ity of the 181-gene immune panel (Fig. 1B; Supplementary TableS5). In contrast, negative control CTLs upregulated only one gene(CCL5) by more than 2-fold in all four melanoma cell lines,compared to at least 34-fold increase after exposure to MelanA-specific CTLs (Supplementary Table S5). Thus, differential geneexpression predominantly depended on antigen-specific interac-tion of CTLs with melanoma cells.

Second, among the most upregulated genes, there were mole-cules associatedwith immune suppression such asPDL1 (CD274)and IDO1 but also molecules that support immune cell recruit-ment (CCL2 and CCL5), and even the chemokines CXCL9/10/11that are well known to promote T-cell responses (Fig. 1C). Thedifferentially expressed genes were significantly enriched in themolecular signatures of "IL6-JAK-STAT3 signaling," "allograftrejection," "IFNa response," and "IFNg response" (Fig. 1D). Thus,upon exposure to cognate antigen-specific CTLs, melanoma cellsthemselves express many immune-regulatory genes that areknown to shape the tumor microenvironment (TME).

IFNg and TNFa mediate the induction of immune-regulatorygenes in melanoma cells

The third finding was that MelanA-specific CTLs induced amolecular IFNg-response signature in melanoma cells (Fig. 1D).

The inflammatory cytokines IFNg and TNFa trigger expression ofimmune-related genes and thus remodel the surrounding tissue(23, 24). Therefore, we askedwhether IFNg and TNFa alone couldinduce the above-described changes. Indeed, the melanoma cellsexposed to these twocytokines showeda similar expressionpattern(Fig. 1B), with most upregulated genes in common with CTL-exposed cells (Fig. 2A). In fact, at least 4-fold upregulation of 43genes was shared in all four melanoma cell lines under each of theeight exposures to cytokines or melanoma-specific CTLs (Supple-mentary Fig. S4A–S4C), indicating that all fully shared reactionswere regulated by the cytokines.

Next, we exposed the melanoma cell lines to IFNg or TNFaseparately and analyzed the protein expression of several of themost differentially expressed genes detected in the NanoString(Fig. 2B). TNFa alone had only little impact. IFNg alone led tostrong upregulation of two proteins (PDL1, HLA-DR) and mildupregulationof another twoproteins (CXCL9andCXCL10)whileothers remained low (CCL2, CCL5). Addition of TNFa increasedthe effects of IFNg to a very close mimic of the effects of MelanA-specific CTLs. Analysis of coculture supernatants revealed thatMelanA-specific CTLs secreted more IFNg than TNFa (Supple-mentary Fig. S4D). In line with these findings, Ingenuity analysis(in silico) of the genes upregulated in melanoma cells exposed toMelanA-specific CTLs predicted IFNg to be responsible for theobserved effects, and to act via STAT1 (Fig. 2C). Altogether, IFNgand TNFa synergistically induce changes in melanoma cells.

Direct ex vivo single-cell profiling shows thatHLA expression bymelanoma cells correlates with T-cell activation

To evaluate the in vivo relevance of our observations, weanalyzed the gene expression of single T cells and single tumorcells. We took advantage of a recently published large-scale studycharacterizing single cells from melanoma tumors by RNAsequencing (RNA-seq; ref. 25). In this dataset, genes that couldbe reliably detected in melanoma cells include genes with con-stitutive expression in untreated melanoma cell lines and higherexpression upon exposure to CTLs or their cytokines. This was thecase for HLA Class I (HLA-A, -B and -C), whose expression incancer cells showed strong correlation with the activation ofintratumoral T-cells, as revealed by IFNG and PD1 expression byT-cells (Fig. 3A). The expression of many genes (such as CXCL9/10/11 and PDL1) was hardly detectable, likely due to technicallimitations of the single-cell RNA-seq technology such as highrates of transcript dropout (Fig. 3B). Thus, also in vivo the presenceof activated T cells is associated with HLA Class I upregulation intumor cells, but limited sensitivity of this novel technology stillemphasizes the need for in vitro studies.

Shared responses of 16 melanoma cell lines, irrespective ofBRAF or NRAS mutations

Despite the knownmutational heterogeneity of melanoma, wefound surprisingly homogenous reactions to antigen-specificCTLs or to their prototypic cytokines. Some of the most upregu-lated genes were CXCL9/10, CCL2/5, IDO1, PDL1, HLA-DR, andHLA Class I (Fig. 4A). To test whether these observations at theRNA level also hold true at the protein level, and for a largernumber of patients, we analyzed 16 melanoma cell lines from 16patients (15 new cell lines and the previously used T1015A). Thismelanoma panel consisted of lines carrying mutated BRAF orNRAS, and lines that were double wild-type for BRAF/NRAS(Supplementary Table S1). CXCL9/10, CCL2/5, IDO1, PDL1,

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HLA-DR and HLA Class I proteins were again potently increasedupon cytokine treatment (Fig. 4B and C; Supplementary Fig. S5A;Supplementary Table S6). Most of these proteins were notexpressed in the untreated cell lines but induced with treatment.At baseline,HLA-DRwas expressed at diverse levels, and increasedupon cytokine exposure. One cell line (T975A) showed highbaseline expression and a slight decrease upon cytokine exposure;in two independent repetitions of the experiment, this cell lineslightly increased HLA-DR (data not shown). Thus, HLA-DRexpression of this cell line was rather stable, without significantchanges induced by IFNgþTNFa. HLAClass I was expressed by alluntreated cell lines but further increased upon exposure to cyto-kines (Supplementary Fig. S5B). Importantly, we found no pat-

tern characteristic for the BRAF or NRAS status, neither whenleft untreated nor when exposed to cytokines (Fig. 4D and E).Thus, these two oncogenic drivers had no major influence onthe response of melanoma cells to CTL-derived cytokines. Insummary, all cell lines except Me215 and T1349A respondedsurprisingly similarly to cytokine treatment, sharing the profile oftheir responses despite differences in the magnitude of changes(Fig. 4C–E).

Benign melanocytes respond similarly as melanoma cellsWe then asked whether the mechanisms responsible for the

observed melanoma cell responses to CTL attack are conserved oracquired bymutation. We analyzed benignmelanocytes that, like

Figure 1.

Exposure to MelanA-specific CTLs altered the expression of many immune-regulatory genes in melanoma cells. A, Experimental setup. B, Two-way hierarchicalclustering of expression values of 181 genes from melanoma cell lines cultured in duplicates or triplicates for 24 hours under the indicated conditions. Redindicates high and blue indicates low expression relative to the mean. Gray fields in the heat map indicate expression below background cutoff value. C, Top30 differentially expressed genes in melanoma cells exposed to MelanA-specific CTLs versus untreated. D, Top four gene sets of the Hallmark collection that areenriched inmelanoma cells exposed toMelanA-specific CTLs/cytokines comparedwith untreated/control CTL-treatedmelanoma cells, based onGSEAanalysis. FDR< 0.25 and P < 0.05 (except Interferon_gamma_response, P < 0.1).

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melanoma cells, express MelanA and can be recognized by Mel-anA-specific CTLs (Supplementary Fig. S6A and S6B).We culturedfourmelanocyte lineswithMelanA-specificCTLs, negative controlCTLs or IFNgþTNFa, and analyzed gene expression of our 181-gene immune panel. Interestingly, the melanocytes clusteredaccording to treatment, with a clear separation between theuntreated/control CTL-exposed samples and the cytokine/Mel-anA-specific CTL-exposed samples (Fig. 5A; Supplementary

Table S7). Gene expression was similar in control CTL-exposedmelanocytes and in untreatedmelanocytes. MelanA-specific CTLsinduced similar but slightly weaker gene expression changescompared to cytokines. Globally, all four melanocyte lines hadcomparable expression patterns. The most strongly upregulatedgenes upon coculture with MelanA-specific CTLs includedthe chemokines CXCL9/10/11 and CCL2/5, and the immune-suppressive molecules IDO1 and PDL1 (Fig. 5B).

Figure 2.

IFNg and TNFamediate CTL-induced gene expression changes in melanoma cells. A, Venn diagram comparing genes with at least 4-fold change in four melanomacell lines treated with IFNgþTNFa or MelanA-specific CTLs, compared with untreated. B, Protein expression of a representative melanoma cell line, Me275,after 48 hours of the indicated treatment (mean� SD, n¼ 3; MelanA-specific CTL condition, n¼ 2).C, Ingenuity analysis of geneswith at least 4-fold change (averageof all four cell lines treated with MelanA-specific CTLs vs. untreated).






Next, we investigated whether benign melanocytes behavedsimilarly to malignant melanoma cells when cultured with CTLsor cytokines. We first clustered the average log2 fold-changes ofidentical replicates for treated versus untreated samples (completelinkage method based on Euclidean distance). Melanoma cellsandmelanocytes were interspersed and clustered according to thetreatment, showing a clear separation of cells exposed to negativecontrol CTLs versus MelanA-specific CTLs or cytokines (Fig. 5C).The latter two displayed similarly strong changes, the former

virtually no changes. Subsequently, we looked for differencesbetween the benign andmalignant cells (Fig. 5D and E). Remark-ably, average responses to cytokine treatment (Fig. 5E) werealmost identical for the two cell types [R¼ 0.89; 95% confidenceinterval (CI); 0.85–0.91]. Responses of melanocytes to MelanA-specific CTLs were also similar but with a slightly smaller mag-nitude (R ¼ 0.87; 95% CI, 0.30–0.90; Fig. 5D).

Only 23 genes differed between melanocytes and melanomacells (log2 fold-changes; adjusted P < 0.05) upon exposure to

Figure 3.

Markers of T-cell activation correlatewithHLA-A, -B and -C expression bymalignant cells in direct single-cell analysis ofmelanoma specimens. RNA-seq data and celltype annotations of each single cell (T ormalignant) fromTirosh and colleagues (25) were downloaded fromGEO (GSE72056). For each patient with data frommorethan five T-cells and more than five malignant cells, expression values were averaged within each cell type and each patient (red squares in A and blackcircles in B; n ¼ 11). A, Markers that are expressed in untreated melanoma cells and that are further increased after cytokine exposure in vitro are reliablydetectable. Actual single-cell expression values (log of transcripts-per-million) are shown for each patient (expression in single T cells versus averageexpression in malignant cells are shown vertically, while expression in single malignant cells versus average expression in T cells are shown horizontally). Averageexpression values for each cell type and patient were used to compute the correlation and the linear fit (red line). B, Markers that are not expressed inuntreated melanoma cells but triggered by IFNg are barely detectable in single malignant cells analyzed by Tirosh and colleagues (ref. 25; average transcripts-per-million of cells from each patient).

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Figure 4.

Mutational status of BRAF and NRAS does not discriminate melanoma responses. A, Differential gene expression of highly upregulated genes in melanomacell lines exposed to MelanA-specific CTLs or cytokines (mean � SD of RNA log2-fold change assessed by NanoString; n ¼ 4). B, Experimental setup toanalyze protein expression of melanoma cell lines. C,Differences in protein expression of melanoma cell lines cultured alone or in presence of cytokines for 48 hours(flow cytometry; Wilcoxon matched-paired signed-rank tests). MFI, median fluorescence intensity. �� , P� 0.01; ��, P� 0.001; ��*, P� 0.0001. D, Same data as C,with the cell lines grouped bymutational status (mean� 95% confidence interval, Kruskal–Wallis tests). E,Heatmap summarizing protein expression (gray diagonalline, missing data).






Figure 5.

Melanoma cells and benignmelanocytes show similar responses. RNA expressionwasmeasured using NanoString.A, Two-way hierarchical clustering of expressionvalues of 181 genes from melanocytes cultured for 24 hours under the indicated conditions. Red indicates high and blue indicates low expression relativeto the mean. Gray fields in heat map indicate expression below background cutoff value. B, Top 30 differentially expressed genes in melanocytes exposed toMelanA-specific CTLs versus untreated. C, Two-way hierarchical clustering of differential gene expression of 181 genes and 91 samples. Expression ofbiological replicates was averaged and differential gene expression was calculated as log2-fold change (treated/untreated). Red indicates overexpressionand blue indicates lower expression relative to the untreated sample. D and E, Average of the log2-fold changes for the four melanocyte lines versus the fourmelanoma cell lines after exposure to MelanA-specific CTLs (D) or to IFNg and TNFa (E). CI, 95% confidence interval. F, Venn diagram comparing the atleast 4-fold increased genes in all four melanocyte lines upon exposure with IFNg and TNFa or with MelanA-specific CTLs.

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MelanA-specific CTLs (Supplementary Table S8). Some of thesegenes were upregulated in both groups of cells but with a greatermagnitude in melanoma cells (e.g., CXCL10, BIRC3, TAP1, HLA-B, and GRP39) or differed in baseline expression (e.g., IFI27,ISG15, EPSTI1, and IRF7). Of note, only one gene, SNAI1,behaved differently: CTLs triggered SNAI1 expression only inmelanoma cell lines but not in melanocytes.

Even though the MelanA-specific CTLs produced cytokines incocultures withmelanocytes, the quantity of cytokine productionwas lower than in cocultures with melanoma cells (Supplemen-tary Fig. S6C). This possibly explains the slightly weaker mela-nocyte response to CTLs and the lower correlation compared tothe exposure to the cytokines (Fig. 5D and E). Nevertheless, bothcorrelations were statistically highly significant. All genes thatwere �4-fold increased after exposure to MelanA-specific CTLswere also �4-fold increased in cytokine-exposed melanocytes,suggesting a dose-dependent effect of the cytokines (Fig. 5F),supported by studies describing IFNg dose-dependent inductionof immune genes such as PDL1 and CCL5 (26, 27). In summary,melanocytes and melanoma cells showed a broad and largelysimilar response to cytokines and CTLs.

We next investigated how the key upregulated genes in mela-noma cells behaved inmelanocytes, by focusing on the 43 at least4-fold differentially expressed genes described above (Supple-mentary Fig. S4C). Hierarchical clustering revealed that melano-ma cells and melanocytes clustered together, depending on treat-ment (Fig. 6A). Twenty of these genes were increased in all fourmelanocyte lines upon exposure to MelanA-specific CTLs (Fig.6B). However, these calculations might underestimate the simi-larity, because CTLs expressed less IFNg in cultures with melano-cytes than in cultures with melanoma cells (Supplementary Fig.S6C). To analyze gene expression under the same treatmentconditions, we compared melanocytes and melanoma cells cul-tured with equal concentrations of IFNg and TNFa in the absenceof CTLs. Indeed, under these conditions over 80% of the upre-gulated genes in melanoma cells were also increased in melano-cytes (Fig. 6C).

We then studied protein expression. Genes encoding proteinsthat we had previously confirmed to be differentially expressed inmelanoma cells were also expressed very similarly inmelanocytes(Fig. 6D).Our flow cytometry data from a 3-day time course showthat the four melanocyte lines expressed CXCL9/10 and HLA-DRwith similar kinetics and to a similar degree as a referencemelanoma cell line (Fig. 6E). Exposure to MelanA-specific CTLsor cytokines induced strong protein expression changes. Theexpression of CXCL9/10 reached a peak after 24 hours exposureto MelanA-specific CTLs, HLA-DR reached a plateau after 48hours. Negative control CTLs did not induce protein expressionchanges.

Taken together, melanocytes responded to specific CTL andcytokine exposure with similar RNA and protein expressionchanges as melanoma cells. Our results indicate that theimmune-regulatory responses of melanoma cells correspondto conserved mechanisms that are already present in benignmelanocytes.

The induced immune-regulatory genes reflect physiologicinflammatory reactions

To interpret the melanoma cell responses to cytokines orMelanA-specific CTLs, we compared them with the Immuno-logic Signatures collection (version 5.1) of the publicly avail-

able MSigDB database using GSEA, as previously done forthe microarray (Supplementary Fig. S3). The majority of thetop 10 immunologic signatures identified by GSEA are anno-tated as generally occurring during immune reactions suchas TLR9 agonist stimulation, viral infection or IFNg stimulation(Fig. 7A and B). Most of these gene signatures were derivedfrom reactions that arise within a few hours or days, in line withthe duration of our treatment.

As a second approach, we used the literature-based Ingenuitysoftware to identify functions associated with our upregulatedgenes (Fig. 7C). Some of the most differentially expressed geneswere annotated to stimulate themovement ofmyeloid cells, a celltype that can potentially be transformed into protumorigenicmacrophages in the TME. However, this chemokine profile alsofacilitates migration of neutrophils and T cells, two immune celltypes that cannot be clearly classified as pro- or antitumorigenic.In general, the pattern of upregulated molecules resembled pat-terns associated with inflammatory and immune reactions innonmalignant pathologies (e.g., viral infections) and not partic-ularly the patterns described for protumorigenic immune-sup-pressive mechanisms. Thus, the melanoma cell lines still pos-sessed conserved mechanisms to react to CTL attack, despite thatthese malignant cells were derived from patients with advancedhighly mutated metastatic melanoma.

DiscussionWe demonstrate that melanoma cells collectively respond to

CTL attack by immediatemodulation of a broad array of immune-regulatory factors, and that this modulation is conserved amonggenetically heterogeneous melanoma cells and similar in benignmelanocytes.

In our coculture model, CTLs killed a fraction but not allmelanoma cells. This experimental "coexistence" is based onthe notion that melanoma cells can persist over extendedperiods of time in patients, despite that tumor-specific CTLsinfiltrate tumors and may destroy some melanoma cells (28).Long-term cocultures of cancer cells with cancer-specific CTLsselect CTL-resistant clones, for example, cancer cells withincreased expression levels of antiapoptotic genes or loosenedCTL-cancer cell interaction due to a modified actin cytoskeleton(29, 30). In our experiments, CTLs killed a fraction of themelanoma cells, whereas the remaining cells remained viableand showed rapid gene expression changes that were mostlyunrelated to cell death. The changes were highly similar tothose induced by the cytokines IFNg and TNFa and in line withobservations of bystander effects on neighboring cells in theTME (16, 31). Therefore, CTLs not only shape cancer by tumorcell killing and selection, but also by inducing rapid anddynamic gene expression changes. Our data are compatiblewith the model of "adaptive resistance mechanisms" (16, 31)and with preclinical models of intratumoral immune attack byCTLs showing rapid gene expression changes in the tumors(32). Although the CTL-induced gene expression changes mightbe short-lived and potentially reversible, they may also havelong-term effects, particularly when CTL activity is sustained.The upregulated genes and proteins included several chemo-tactic cytokines that can increase migration of additionalimmune cells and alter the TME. Thus, the CTL-inducedchanges in melanoma cells may support and shape continuousinflammatory and immunological activities in the TME.






Figure 6.

The top differentially expressed factors behave similarly in melanoma cells and melanocyte lines at RNA and protein levels. A, Two-way hierarchical clusteringof 43 genes separates MelanA-specific CTL- and cytokine-exposed samples from control CTL-exposed samples (log2-fold change). Red, overexpressionrelative to the untreated sample.B and C,Venn diagrams comparing genes that are at least 4-fold increased inmelanoma ormelanocytes upon exposure to MelanA-specific CTLs and IFNgþTNFa (B) and upon exposure to IFNgþTNFa only (C). D, Two-way hierarchical clustering of genes whose protein expression hadbeen analyzed in Fig. 4. Red indicates high and cyan indicates low expression relative to the mean. RNA expression was measured by NanoString. E, CXCL9/10 andHLA-DR protein expression of four melanocyte lines and one representative melanoma cell line. HLA-DR expression was quantified by FACS surface stainingand CXCL9/10 were quantified by intracellular FACS staining.

Neubert et al.





A major aim of our study was to determine whether theseimmune-regulatory reactions of melanoma cells correspond tomechanisms that are acquired by mutation or to conservedproperties. Therefore, we investigated a large array of immune-regulatory genes, a relatively large panel of melanoma cells, andincluded a control population of benign melanocytes. In mela-noma, up to several thousand mutations can be detected in onetumor specimen (2), suggesting alterations in a significant frac-tion of the known 20,000–25,000 protein-coding genes of the

human genome (33). If the immune-regulatory reactions ofmelanoma cells are predominantly due to mechanisms that areacquired bymutation, one expects them to be heterogeneous andto differ depending on theirmutational status and load.However,the melanoma cells behaved with remarkable homogeneityand were independent of BRAF or NRAS mutations. The latterresult is compatible with studies of melanoma patients show-ing that BRAF and NRAS mutations do not significantly influ-ence responses to immunotherapy (34, 35).

Figure 7.

Differential gene expression of CTL- and cytokine-exposed melanoma cells resembles the inflammatory profile of nonmalignant cells. A and B, StandardGSEA revealing the relative overexpression of inflammatory signatures associated with the differentially expressed genes in melanoma cells exposed toIFNgþTNFaorMelanA-specificCTLs.A,Top 10 enriched datasets from the collection "Immunologic signatures." Somedatasets contained several enrichedgene sets.Shown is the most enriched gene set for each of the top 10 datasets. FDR < 0.25; P < 0.05. B, The differentially expressed genes in the NanoString areenriched in gene sets reflecting immune reactions, for example, viral infection, TLR9 agonist or IFNg stimulation. Shown are the top four enriched gene sets. Genes tothe right of the rank-ordered gene list are overexpressed in melanoma cell lines after exposure to melanoma-specific CTLs or cytokines. C, Ingenuity analysisof genes with at least 4-fold increase (average of all four cell lines treated with MelanA-specific CTLs vs. untreated).






Despite the large numbers of mutations in melanoma cells, wefound similar reactions in melanoma cells and melanocytes. Theresponses resembled situations of normal tissue inflammation orinfection. For example, CTL-derived IFNg can induce a plethora ofdownstream factors, to help infected tissue to control infection.One of them is HLA upregulation, enhancing antigen-presenta-tion by the infected cells. In contrast, induction of immune-regulatory molecules such as PDL1 attenuates the immuneresponse to avoid overwhelming T-cell responses and consequentimmunopathology (36, 37).

The finding that most reactions were shared between the vastmajority of melanoma cell lines supports the notion that mela-noma cells react to immune attack mostly with conservedmechanisms. In fact, benign and malignant diseases may sharemany mechanisms of immune regulation. This does not excludethe possibility that mutations of immune-relevant genes couldlead to melanoma-specific immune-regulatory reactions,although this might be less frequent. A recent study of approxi-mately 8,500 TCGA solid tumor samples estimated that immuneregulation by mutations or gene copy-number alterations occursin approximately 10%of patients (2, 38). For several tumor types,the authors did not find significant correlations between suchgenetic alterations and patient survival. As an example, geneticalterations can cause PDL1 expression bymelanoma cells, but thisis only observed in about 4% of patients (2). Structural variationsof the 30UTR of PDL1 can lead to increased levels of aberrant,stabilized PDL1, but this type of modification was only found in0.6%(3/470)ofmelanomasamples (39). In contrast, in vivoPDL1upregulation is observed in the majority of T-cell–infiltratedtumors (16). In agreement with these studies, only one of our19 tested melanoma cell lines expressed PDL1 constitutively (atbaseline),whereas PDL1was inducible by IFNg and TNFa inmostof the remaining cell lines. Furthermore, two of our 19melanomacell lines (Me215/T1349A, Fig. 4E) were poorly responsive to thecytokines, reminiscent of the recently reported findings of inter-feron nonresponsiveness due to JAK1/JAK2 mutations found intwomelanoma patients with secondary resistance in a study of 78patients treated with anti–PD-1 antibody (40). Altogether, thecurrent evidence suggests that, in reaction to CTL infiltration,conserved mechanisms of immune regulation may predominateover mutation-driven immune regulation.

Our results are from in vitro-cultured cells, and therefore maynot directly reflect situations in vivo. Nevertheless, many of thedifferentially expressed genes are also upregulated in vivo.Analysisof whole tumor tissue has previously revealed a 6-chemokinesignature in biopsies from melanoma metastases that is prefer-entially expressed in tumors with CTL infiltration (41). Five out ofthese six genes matched with our observations (CCL2, CCL4/5,and CXCL9/10) in CTL-treated melanoma cells, suggesting thatmelanoma cells responding to CTL attack can contribute to theprevalence of these chemokines in the TME. In addition, a study in45 melanoma patients treated with Ipilimumab found 170 genesupregulated in clinical responders compared to nonresponders(42). A large number of these genes overlapped with the genes weidentified in our study (CD40, FAS, CXCL9/10/11, CCL4/5, IDO1,DRAM1, RARRES3, IRF1, STAT1, TAP1/2, BIRC3, ICAM1, CD74,APOL1, UBE2L6, FAS, HLA-B, TAPBP). Several of the downregu-lated genes coincided with our results (MITF, MLANA, SNAI2,TYR, TYRP1), confirming that the gene expression observed in ourstudy upon confrontation with melanoma-specific CTLs corre-sponds to a good part to the gene expression observed in vivo.

Furthermore, new technical advances allow us to study separatelygene expression in T cells and inmalignant cells from fresh surgeryspecimens. Although the single-cell RNA-seq data of Tirosh andcolleagues (25) support some of our findings, this analysis canonly pinpoint correlations and still faces technical challenges(detection limits), highlighting the need for in vitro studies likeours to characterize melanoma cells in presence of activated CTLsin a controlled environment. Overall, these similarities betweenfindings in whole tumor tissue and in tumor cells themselvessupport the view that immune-regulators produced by tumor cells(the prime targets of CTLs) may significantly contribute to theshaping of the immune landscape in the TME.

Are these cellular responses protumorigenic? A plethora ofstudies shows the capacities of melanoma to produce immuno-suppressive molecules (15, 16), supporting the notion that mel-anoma frequently inhibits antitumor immune responses. This isconfirmed by our finding that MelanA-specific CTL-exposed mel-anoma cells increased PDL1, IDO1, and IL6 molecules that areclearly associated with immune suppression (16, 43–45). How-ever, we also found increased expression of CXCL9/10/11 andHLA Class I that promote T-cell recruitment and tumor antigenrecognition (46–49). Finally, many of the upregulated genes havepreviously been associated with both potentially pro- and anti-tumorigenic effects, namely IL8 (50), TGFb (51), CCL2 (52),CCL5 (53), and HLA-DR (54, 55). Another possible effect ofCTL-derived TNFa on melanoma cells is dedifferentiation (viaincreased NGFR), possibly leading to resistance to T-cell–basedtherapy (19). However, in our experimental system we did notobserve substantial modulation of NGFR (Supplementary TableS5). In fact, our analyses in silico did not show a preferentialenrichment for gene sets associated with either pro- or antitu-morigenic profiles or functions. Accordingly, Jin and colleagues(56) found that human mesenchymal stroma cells exposed tomelanoma antigen-specific tumor-infiltrating lymphocytesinduce both proinflammatory and immunosuppressive mole-cules (e.g., CXCL9/10/11, IDO1/2, CCL2/5, IRF1), with morethan half of the genes (66 of 139 genes, Class B in theirSupplementary Fig. S4) overlapping with genes upregulated inour study. Taken together, current data support the notion thatCTL attack (via IFNg � TNFa) induces a complex program ofimmune-regulatory events by melanoma cells that may promotetumor growth or act against it. This lack of specialization may berelated to the observation that immunotherapy can provide long-lasting benefits, with lower escape likelihood than other cancertherapies (8).

ConclusionOur results demonstrate that melanoma cells display a broad

and immediate immune-regulatory response to CTL attack. Thisresponse is predominantly based on conserved mechanisms ofimmune regulation, and features factors that are potentially pro-and/or antitumorigenic. Distinguishing mechanisms that differfor pro- versus antitumorigenic factors will be subject of futureresearch. Therapeutic targeting of shared, nonmutationalmechanisms may be beneficial, because it can circumvent thechallenges of the well-known vast genetic interpatient and alsointrapatient heterogeneity.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.


Neubert et al.




Authors' ContributionsConception and design: N.J. Neubert, D. Rimoldi, S.A. Fuertes Marraco,D.E. SpeiserDevelopment ofmethodology:N.J. Neubert, S.A. Fuertes Marraco, D.E. SpeiserAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): N.J. Neubert, L. Till�e, D.E. SpeiserAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): N.J. Neubert, L. Till�e, D. Barras, C. Soneson,D. Rimoldi, D. Gfeller, S.A. Fuertes Marraco, D.E. SpeiserWriting, review, and/or revision of the manuscript: N.J. Neubert, L. Till�e,D.Barras,C. Soneson,D.Rimoldi,M.Delorenzi, S.A. FuertesMarraco,D.E. SpeiserAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): N.J. Neubert, P. Baumgaertner, D.E. SpeiserStudy supervision: M. Delorenzi, S.A. Fuertes Marraco, D.E. Speiser

AcknowledgmentsWe thank J. Joyce, F. Sallusto, T. Petrova, M. De Palma, and R. Debets for

discussions and advice, A. Wilson, C. Fumey, S. Winkler, T. Pillonel, N.Montandon, T. Murray, R. Panes, and K. M€uhlethaler for technical help,

L. Michalik & G. Ghanem for melanocyte lines, and B. van den Eynde for theIDO-specific antibody.

Grant SupportThis project was supported by grants from the ISREC Foundation (on

"Cancer and Immunology"), the Emma Muschamp Foundation, the SwissCancer Research Foundation (3507-08-2014), the Swiss National ScienceFoundation (CRSII3_160708, 320030_152856), SwissTransMed (KIP 18), theMedic Foundation and Alfred and Annemarie von Sick (all Switzerland), theWilhelm Sander Foundation (Germany), the Cancer Research Institute, LudwigCancer Research, and the Campbell Family Institute (Canada).

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received October 5, 2016; revised December 14, 2016; accepted December28, 2016; published OnlineFirst January 19, 2017.

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2017;77:1623-1636. Published OnlineFirst January 19, 2017.Cancer Res Natalie J. Neubert, Laure Tillé, David Barras, et al. Heterogeneous Melanoma CellsBroad and Conserved Immune Regulation by Genetically

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