Single-Cell Lymphocyte Heterogeneity inAdvanced Cutaneous T-cell Lymphoma SkinTumorsAlyxzandria M. Gaydosik1, Tracy Tabib1, Larisa J. Geskin2, Claire-Audrey Bayan2,James F. Conway3, Robert Lafyatis1, and Patrizia Fuschiotti1
Abstract
Purpose: The heterogeneity of tumor cells presents a majorchallenge to cancer diagnosis and therapy. Cutaneous T-celllymphomas (CTCL) are a group of T lymphocytemalignanciesthat primarily affect skin. Lack of highly specific markers formalignant lymphocytes prevents early diagnosis, while onlylimited treatment options are available for patients withadvanced stage CTCL. Droplet-based single-cell transcriptomeanalysis of CTCL skin biopsies opens avenues for dissectingpatient-specific T lymphocyte heterogeneity, providing abasis for identifying specific markers for diagnosis and cureof CTCL.
Experimental Design: Single-cell RNA-sequencing wasperformed by Droplet-based sequencing (10X Genomics),focusing on 14,056 CD3þ lymphocytes (448 cells from nor-mal and 13,608 cells from CTCL skin samples) from skinbiopsies of 5 patients with advanced-stage CTCL and 4 healthydonors. Protein expression of identified genes was validated in
advanced stage CTCL skin tumors by immunohistochemistryand confocal immunofluorescence microscopy.
Results:Our analysis revealed a large inter- and intratumorgene expression heterogeneity in the T lymphocyte subset,as well as a common gene expression signature in highlyproliferating lymphocytes that was validated in multipleadvanced-stage skin tumors. In addition, we established theimmunologic state of reactive lymphocytes and found hetero-geneity in effector and exhaustion programs across patientsamples.
Conclusions: Single-cell analysis of CTCL skin tumor sam-ples reveals patient-specific landscapes of malignant and reac-tive lymphocytes within the local microenvironment of eachtumor, giving an unprecedented view of lymphocyte hetero-geneity and identifying tumor-specific molecular signatures,with important implications for diagnosis and personalizeddisease treatment.
IntroductionCutaneous T-cell lymphomas (CTCL) are a heterogeneous
group of malignancies characterized by chronic inflammationand accumulation of malignant T lymphocytes in the skin (1).CTCL encompasses diverse presentations including Sezary syn-drome where patients present with erythroderma, lymphadenop-athy, and circulatingmalignant T lymphocytes, as well as mycosisfungoides inwhichmalignant cells resideprimarily in the skin (2).Mycosis fungoides is the most common form of CTCL andtypically runs an indolent course with an excellent 5-year survivalrate in early stages, but significantly decreased survival inadvanced disease (3). In the early stages, most T cells reside in
the skin and only a few circulate in peripheral blood and lymphnodes. However, a small number of patients progress, and tumorcells may involve other sites of the body with a fatal outcome (4).About 20% of patients progress to advanced-stage mycosis fun-goides (stages IIB to IV; ref. 5), and the prognosis for patients withwidespread CTCL manifestation beyond the skin is poor with a5-year survival rate of only 40% (6). Large cell transformationoccurs in 56% to 67% of patients with advanced stage mycosisfungoides (6) and is accompanied by clinically aggressive diseaseand shortened survival. Diagnosis of mycosis fungoides is diffi-cult, especially in the early stages, due to the absence of specificmarkers for malignant lymphocytes that distinguish them fromnonmalignant tumor infiltrating T lymphocytes (TIL). Diagnosisis usually based on clinicopathologic correlation, and the averagetime-to-diagnosis is 7 years (7). Delays prevent timely treatmentand result in poorer clinical outcomes, while the treatmentoptions for patients with aggressive forms of mycosis fungoidesare limited, reflecting our poor understanding of diseasepathogenesis.
Lymphocyte proliferation in CTCL is largely restricted to theskin, implying thatmalignant cells are dependent on their specificcutaneous microenvironment. Cytokines and other immuno-modulator factors produced by malignant lymphocytes andTILs (8, 9) as well as by other immune and stromal cells (10)affect cutaneous inflammation (1, 8) and are important consti-tuents of tumor local microenvironments, fostering survival,proliferation, and suppression of tumor cell immunosurveil-lance (8, 11). In this context, reactive TILs are exposed to
1Department of Medicine, Division of Rheumatology and Clinical Immunology,University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.2Columbia University Medical Center, New York, New York. 3Department ofStructural Biology, University of Pittsburgh School of Medicine, Pittsburgh,Pennsylvania.
Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).
Corresponding Author: Patrizia Fuschiotti, University of Pittsburgh School ofMedicine, S709 BST, 200 Lothrop Street, Pittsburgh, PA 15261. Phone: 412-648-9385; Fax: 412-383-8753; E-mail: [email protected]
multiple immunosuppressive pressures, including negativeregulatory pathways and upregulation of inhibitory receptorssuch as PD1, CTLA4, LAG3, TIM3, and TIGIT that render themdysfunctional (12–14) and unable to elaborate their full effectorfunctions for ultimately killing tumor cells (12, 13). However, theheterogeneity and immunologic state of malignant and reactivelymphocytes within CTCL skin tumors remain incompletelycharacterized.
Recent advances in single cell transcriptome technology,including droplet-based single-cell RNA-sequencing (scRNA-seq;ref. 15), profile gene expression across thousands of individualcells from a large heterogeneous population (16, 17) such as apatient biopsy. This high-resolution analysis of cellular hetero-geneity reveals individual cell functions in the context of theirmicroenvironment and provides striking insights into the com-plex cellular composition of normal and diseased tissue. Here, wereport scRNA-seq analysis of skin tumor cells from patients withadvanced stage CTCL. This analysis provides an unprecedentedview of lymphocyte heterogeneity within the skin-microenviron-ment of individual CTCL tumors by identifying molecular sig-natures that are unique for each tumor. We also established acommon gene expression signature in highly proliferating lym-phocytes as well as the immunological state of TILs within eachtumor. Together, these data provide important implications forpersonalized disease management.
Materials and MethodsSubjects and skin biopsies
Skin samples were obtained at the Comprehensive SkinCancer Center, Columbia University Medical Center, from10 patients with confirmed diagnoses of advanced CTCL (stageIIB–IVA; described in Supplementary Methods; SupplementaryTableS1) and stagedaccording to themost recent consensus (4, 5).Five patient samples were used for scRNA-seq and all ten for IHC.Participants gave written informed consent. Human researchprotocols were approved by the Institutional Review Board,ColumbiaUniversity. Controls includedhumannormal skin (NS,
n¼ 8; 4 each for scRNA-seq and IHC) and atopic dermatitis (AD,n¼ 4; all for IHC) obtained fromTheHealth Sciences Tissue Bank,University of Pittsburgh (Pittsburgh, PA). This study was con-ducted in accordance with the Declaration of Helsinki. Experi-mental procedures followed established techniques using theChromium Single Cell 30 Library V2 Kit (10x Genomics; ref. 18).Briefly, cell suspensions fromenzymatically digested skinbiopsieswere loaded into the Chromium instrument (10X Genomics),and the resultingbarcoded cDNAswere used to construct libraries.RNA-seq was performed on each sample (approximately 200million reads/sample). Cell-gene unique molecular identifiercounting matrices generated were analyzed using Seurat (19) toidentify distinct cell populations using Louvain clustering (15).See Supplementary Methods for details. All scRNA-seq data havebeen deposited in the GEO database (accession GSE128531).
Multicolor IHCSingle and dual antibody staining using tyramide signal ampli-
fication (Thermo Fisher Scientific) were performed on formalin-fixed, paraffin-embedded skin samples as described previous-ly (18). Antibodies were all purchased from Sigma. IHC imageswere obtained with an Evos FL Auto microscope (Life Technol-ogies). Confocal images were captured on an Olympus Fluoview1000 confocal microscope using an oil immersion 100�objective.
ResultsSingle-cell transcriptome profiles from advanced-stage CTCLskin tumors and healthy control skin
We used scRNA-seq to profile gene expression in cells obtainedfrom the enzymatically digested skin of five advanced-stage CTCLskin tumors and four healthy control skin samples (Supplemen-tary Table S1). Fig. 1A depicts histologic features of the tumorsstudied, as described in Supplementary Table S2. A 3-mm skinbiopsy from each donor yielded 3,607 to 9,272 cells from skintumor samples and 2,200 to 4,847 cells from healthy skin. Afterreverse transcription from each cell, we constructed cDNA librar-ies and performed massive parallel sequencing, obtaining anaverage of 47,894 mapped reads per cell and a median of1,261 unique genes detected per cell, comparable with previousstudies (18). Cells were grouped according to their expressionprofiles byprincipal components analysis (PCA) and t-distributedstochastic neighbor embedding (t-SNE) dimensional reduc-tion (20). Comparison of whole skin cell distribution from eachtumor samplewith the four control skin samples showsoverlap inthe transcriptional profiles of cells from healthy skin samples butgenerally no overlap between cells from the tumor samples andthe healthy samples (Fig. 1B). Strikingly, the combination of fivetumors and four controls show that the tumor sample profiles donot overlapwith eachother, thus exhibiting significant intertumorheterogeneity (Fig. 1C). Unsupervised graph-based Louvain clus-tering by Seurat (19) identified 26 clusters of cells (Fig. 1D)whosetypes were identified by the expression of cell-specific markergenes (18) (Fig. 1E).We found the greatest heterogeneity betweentumors and controls, as well as across tumors, was at the level oflymphocytes, keratinocytes, fibroblasts, and macrophages. Inaddition to PCA, canonical correlation analysis showed compa-rable results (19). Thus, scRNA-seq analysis characterizes detailsof the large intertumor cell transcriptional heterogeneity inadvanced CTCL skin samples.
Translational Relevance
Advances in single-cell gene expression profiling of patientsamples open new avenues for dissecting tumor cell hetero-geneity, which is a central feature of precision medicine. Weemployed scRNA-seq technology to profile the transcriptomesof thousands of individual cells from advanced stage CTCLskin tumors. Our analysis revealed a large inter- and intratu-mor gene expression heterogeneity, particularly in the T lym-phocyte subset, aswell as a commongene expression signaturein highly proliferating lymphocytes that was validated inmultiple advanced stage skin tumors. In addition, we estab-lished the immunologic state of tumor-infiltrating lympho-cytes and found heterogeneity in effector and exhaustionprograms across patient samples. Thus, single-cell analysisprovides an unprecedented view of all major cellular compo-nents simultaneously and their individual gene expressionstates. New developments in single-cell transcriptome profil-ing are highly relevant for discovering clinically relevant bio-markers of disease and for tailoring patient-specific treatment.
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T lymphocyte transcriptional profiles of the tumors overlappedminimally with profiles of lymphocytes purified from healthyskin (Fig. 2A). Strikingly, we also observed impressive inter-tumorheterogeneity by the marginal overlap between transcriptionalprofile of tumor-derived lymphocytes (Fig. 2B). Comparison ofthe transcriptomes of each lymphocyte subset from the tumorsand control skin samples identified 11 clusters (Fig. 2C). Somelymphocyte clusters were unique to individual tumors, such ascluster 8 (CTCL-2), clusters 2 and 3 (CTCL-5), cluster 4 (CTCL-6),cluster 6 (CTCL-8), and cluster 5 (CTCL-12), while clusters 1 and10 included lymphocytes derived from all tumor and healthy skinsamples.
Wedetermined thedifferential expression (DE)of genes in eachof the unique clusters from each tumor by comparing geneexpression from each cell in the cluster to that of all other cellsin the dataset, using a cut-off of P < 0.05 and further requiringexpression of the gene from >25%of cells in the cluster. Thus, DE-identified genes are expressed either uniquely or by a largeproportion of cells within each cluster compared to all otherclusters. Examples of themost highly significantDE genes for eachcluster are highlighted by heatmap (Fig. 2D) and the proportionof cells and the scaled average expression of these genes by alltumors and controls show strong and specific expression withinindividual tumors (Fig. 2E). Uniquely expressed genes includedRDH10, CXCL13, SCG2 (CTCL-2), FGR, IGFBP2/P6, NEFM(CTCL-5), ANO1, TNP1, CES4A, ZDHHC14 (CTCL-6), LGALS7,SERPINB3/B4, SPRR2A (CTCL-8), NTRK2, TMPRSS3 (CTCL-12).From these and other DE genes (Supplementary Table S3), weidentified distinct gene expression signatures for each tumor-specific cluster, including expression of eukaryotic initiation fac-tors (eIF) andoncogenes (CTCL-2);NK-cell receptor and signalingmolecules (CTCL-5); genes associated with tumor cell survival,proliferation, and metastasis (CTCL-6); members of the serpin,S100, and galectin families (CTCL-8), and genes associated withincreased cell motility and invasiveness (CTCL-12). IngenuityPathway Analysis (IPA) (21) identified activation of key molec-ular pathways in these patient-specific tumor clusters. Highlysignificant examples of distinct pathways activated in each tumorwere unrelatedbetweenpatient samples (Fig. 2F) but followed thegene expression signatures identified above. These included eIF2,eIF4, and mTOR signaling (CTCL-2); NK-cell signaling and virusentry via endocytic pathways (CTCL-5); tumorigenic pathwayscommon to glioma and non-small cell lung cancer (CTCL-6);pathways related to skin inflammation and skin-barrierdysfunction (CTCL-8); and pathways associated with epithelial–mesenchymal transition (CTCL-12).
TOX (thymus high-mobility group box) is considered amarkerof malignant lymphocytes in CTCL tumors (9, 22, 23). Strikingly,clustering of the TOXþ cells from each tumor sample (Supple-mentary Fig. S1) revealed a large overlap of gene expression withthe corresponding tumor-specific clusters identified above(Fig. 2G). In addition, in these tumor-specific clusters, we foundsignificant but heterogeneous overexpression of genes associatedwith tumorigenesis, tumor-cell proliferation, and resistance toapoptosis (Supplementary Fig. S2). Although the single-cell geneexpression approach employed (30 Library Kit) does not allowTCR repertoire profiling, and therefore the identification ofmalig-nant lymphocytes by alpha-betaTCR clonality (24), we still coulddetect strong expansions of TRBC1 or TRBC2 genes in these
tumor-specific clusters (Supplementary Fig. S3), consistent withthe occurrence of alpha-betaTCR clonality. Newly availablescRNA-seq tools will allow characterization of this clonality infuturework aswas demonstrated in a recent study onCD4þ T cellsfrom the peripheral blood of a Sezary patient (25). Nonetheless,we conclude from our lines of evidence that the heterogeneousbut tumor-specific signatures confirmed in TOXþ cells representpatient-specific gene expression of malignant lymphocytes thatmay have implications for personalized therapy focusing onspecific pathways.
A gene expression signature identifies highly proliferatinglymphocytes in advanced stage CTCL skin tumors
Cell-cycle analysis identified actively proliferating lymphocytesby expression ofG2–Mand S-phase genes (Fig. 3A andB). Thefiftyhighest DE genes for each cluster of the five tumors analyzed(Fig. 3C) show specific clusters characterized by strong geneexpression signatures, such as clusters 1 and 7 (CTCL-2), cluster3 and 4 (CTCL-5), cluster 4 (CTCL-6), cluster 3 (CTCL-8), andcluster 7 (CTCL-12). Strikingly, these clusters corresponded to thehighly proliferating lymphocytes identified in Fig. 3A and B andhighly expressed genes involved in cell-cycle progression (e.g.,PCNA, CDK6, CCND1, NUSAP1, CENPE, CCNA2, HMMR,CDCA8, CDK1, CENPM, CDC20, ATP5C1), proliferation (e.g.,MIK67,KIAA0101,TOP2A,NPM1, IGF2,PLK1,MYC, FOS,NPM1,PRDX1, PIM2, RAN), and survival (e.g., BCL2, BIRC5, BIRC3,BCL2L12, MCTS1, TSC22; Supplementary Table S4), thereforelikely representing highly proliferating malignant lymphocytes.Comparison of these clusters identified a 17-gene expressionsignature common to all five tumors tested (Fig. 3D). Highexpression by these genes was detected in all tumors while onlyfewpositive cells were found in the lymphocyte clusters of healthycontrols for most genes identified (Supplementary Fig. S4). Strik-ingly, this 17-gene expression signature was also found in TOXþ
cells from all patient samples but not from controls (Fig. 3E andF). We focused on three of these common genes, PCNA, ATP5C1,and NUSAP1, that presented low expression in normal lympho-cytes. These were further investigated by IHC in the five tumorsanalyzed by scRNA-seq (Fig. 4A) as well as in additional samplesfrom patients with advanced stage CTCL (Supplementary Fig.S5). Staining in tumor samples was compared with normal(NS) and atopic dermatitis (AD) skin. Results showed thatapart from scant PCNAþ cells in the epidermis, NS and ADskin were negative for expression of these markers, while alltumor samples tested exhibited high numbers of positive cellsboth in the epidermis and in the dermis for all three markerstested. By multicolor immunofluorescence microscopy, we nextdemonstrated that these markers colocalized with TOX(Fig. 4B). Thus, we have identified a gene expression signatureof highly proliferating malignant lymphocytes that is commonto all tumors tested and could be developed as a marker for thediagnosis of CTCL.
Tumor-infiltrating CD8þ T lymphocytes exhibit heterogeneityon effector and exhaustion programs across patients
TILs, particularly CD8þ T cells, are the major effector cell-typefor fighting and killing cancer cells (12, 13). To define themolecular signature of CD8þ TILs in the tumor microenviron-ment of advanced stage CTCL skin tumors, we examined geneexpression of effector molecules, checkpoint receptor inhibitors,and markers of T regulatory (Treg) cells in CD8þ T cells from the
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tumor and control skin samples. Transcriptome profiles weredistinct and nonoverlapping for CTCL-5 and CTCL-6 (Fig. 5A),while most CD8þ T cells from tumors and controls appear tooverlap in cluster 1 (Fig. 5B). The cell composition of each CD8þ
cluster is reported in Supplementary Table S5. Thus, the cells incluster 1 appeared to reflect reactive CD8þ T cells, that is, TILs, asthey did not express tumor-associated genes (Fig. 5C,bottom, and D), while CD8þ T cells from the other clustersexpressed genes associated with tumors likely indicating malig-nant lymphocytes but which are also associated with dysregula-tion. CD8þ TILs in cluster 1 expressed markers of skin-residency,such as CD69 and ITGAE, and of memory cells (CD27). Multiplecoinhibitory receptors were expressed by cells in this cluster,although we found variability across tumors (Fig. 5C, top, and5E), including expression of PD1, CTLA4, TIM3, LAG3, and TIGITby most CD8þ TILs from CTCL-6; PD1 and LAG3 expression byseveral cells from CTCL-2, and expression by few cells from theother tumors. However, coinhibitory receptors were alsoexpressed by CD8þ lymphocytes from tumor-specific clusterssuch as PD1 and TIM3 (clusters 0, 2, and 5: CTCL-5) as well asTIM3 and TIGIT (clusters 3 and 4: CTCL-6; Fig. 5C). Similarly, weobserved variable expression of coinhibitory receptors byCD3þCD4þ T cells across all samples and within tumor-specificclusters (Supplementary Fig. S6). Multicolor immunofluores-cencemicroscopy shows representative expression of coinhibitoryreceptors by CD8þ lymphocytes in advanced CTCL tumors(Fig. 5F). No cells in any CD8þ clusters expressed Treg markerssuch as FOXP3 and only cells in clusters 0, 2, and 3 expressedIL2RA. Conversely, we were able to identify specific CD3þCD4þ
lymphocyte clusters that coexpressed FOXP3, IL2RA, PD1,CTLA4,LAG3, and TIGIT, which likely identified Treg TILs (Supplemen-tary Fig. S6C).
Analysis of effector molecule expression indicated that severalCD8þ TILs in cluster 1 expressed granzyme A (GZMA), while onlyfew cells expressed granzyme B (GZMB) and perforin (PRF1).CTCL-6CD8þT cells from clusters 3 and4highly expressedGZMBand PRF1, or PRF1 only, respectively. None of the other CD8þ
clusters contained significant numbers of cells expressing cytolyticmolecules (Fig. 5C). However, we detected a variable andmodestup-regulation of FASL in most clusters, potentially providingan alternate cytolytic mechanism. CD8þ lymphocytes from clus-ters 1 and 2 expressed IFNG while cells from most clustersexpressed TNFA and IL1B. Interestingly, specific CD4þ clustersexpressed IFNG and TNFA as well as immunosuppressive cyto-kines such as IL10, TGFB1, IL4 and IL13 (SupplementaryFig. S6C). Finally, we detected no IL2 production by any clustersof CD8þ or CD4þ T cells.
Together, these results reveal a complex landscape of CD8þ,CD4þ, and Treg TIL gene expression characterized by differentlevels of effector molecules and a variable combination of
coinhibitory receptors likely impairing an effective antitumorresponse.
DiscussionTumor cellular heterogeneity poses challenges to cancer diag-
nosis and treatment. Advances in single-cell gene expressionprofiling of patient samples open new avenues for dissecting thisheterogeneity, which is a central feature of precisionmedicine.Weemployed scRNA-seq technology to profile the transcriptomes ofthousands of individual cells from advanced-stage CTCL skintumors. Our analysis revealed a large inter- and intratumor geneexpression heterogeneity, particularly in the T lymphocyte subset,as well as a common gene expression signature in highly prolif-erating lymphocytes that was validated in multiple advanced-stage skin tumors. In addition, we established the immunologicalstate of TILs and found heterogeneity in effector and exhaustionprograms across patient samples. Thus, single-cell analysis pro-vides an unprecedented view of all major cellular componentssimultaneously and their individual gene expression states, withimportant implications for diagnosis and personalized diseasetreatment.
Large numbers of malignant and nonmalignant reactive lym-phocytes are often found infiltrating CTCL skin tumors. However,specific markers have been lacking for identifying the malignantlymphocytes since the tumor cells cannot be reliably isolated fromthe lesional skin of patients. This limitation prevents character-izing the transcriptional profile and heterogeneity of malignantlymphocytes or distinguishing them from benign reactive lym-phocytes that might block tumor growth. Furthermore, it delaysthe diagnosis of CTCL and complicates development of effectivetreatments. Characterizing single-cell transcriptomes overcomesthese problems while providing an unbiased and comprehensivemap of rare lymphocyte populations and cell states within eachtumor sample. We found that the transcriptional profiles oflymphocytes isolated from healthy control skin were similar toeach other, while those from tumor skin appearedmostly distinctfrom the healthy skin profiles and overlap only partially with eachother. This variation reflects both the different subtypes of thesamples studied as well as any tumor-specific expression uniqueto individual patients. Significantly, we could identify at least oneunique lymphocyte cluster for eachCTCL tumor sample analyzed,and their malignant phenotype was confirmed by the overlap ingene expressionwith cells from the same tumor expressing TOX, amarker previously shown to identify malignant lymphocytes inCTCL. Thus, we have demonstrated a novel basis for identifyingtumor cell heterogeneity that may be developed for personalizedtherapies.
The unique transcriptional pattern of tumor-specific lym-phocyte clusters observed for each tumor sample indicates
Figure 1.Grouping of CTCL and normal skin populations. Transcriptomes of 44,842 cells from four normal (14,179 cells) and five advanced-CTCL (30,663 cells) skinbiopsies clustered using Seurat (19). A, Hematoxylin and eosin staining (H&E) of skin biopsies from representative normal skin (NS) and the five tumors analyzedby scRNA-seq: top row at 200�, bottom at 400�. B, Two-dimensional t-SNE shows dimensional reduction of reads from single cells, revealing grouping in eachCTCL sample compared to all healthy control skin samples. Cells from each subject are indicated by different colors. C, All samples in (B) are combined. D,Distinct gene expression signatures are represented by the clustering of knownmarkers for multiple cell types and visualized using t-SNE. Clusters belonging toeach cell type are color coded. E, Cell types in skin cell suspensions were identified by cell-specific marker as previously described (18), including AIF1 –macrophages; VWF – endothelial cells; TPSAB1 –mast cells; SCGB1B2P – secretory (glandular) cells; RGS5 – pericytes; PMEL –melanocytes; MS4A1 – B cells; KRT1– keratinocytes; DES – smooth muscle cells; COL1A1 – fibroblasts; CD3D – T lymphocytes; and CD1C – dendritic cells. Intensity of purple color indicates thenormalized level of gene expression. Cell-type specific clusters are indicated by an arrow and gates are drawn around each cluster.
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Transcriptional profiles of lymphocytes from CTCL tumors and normal skin samples.A and B, Transcriptomes of 14,056 cells (448 cells from normal and 13,608cells from CTCL skin samples) expressing CD3 from original t-SNE of all cells (Fig. 1D and E) were reanalyzed (color coded by subject) and represented asin Fig. 1B and C, revealing 11 discrete Louvain clusters using Seurat (C; ref. 19). D, Heatmap showing examples of the most highly significant differentiallyexpressed genes (n¼ 10) for each cluster from C. Cluster numbers are indicated at the top, while the cell source is indicated on the right side. Each columnrepresents a cell. E, Dot-plot showing the proportion of cells and the scaled average gene expression of the DE genes selected inD. F, Pathway analysis byIngenuity of the most significant genes from the tumor-specific clusters.G, Percentage of DEGs shared between tumor-specific clusters and TOXþ cells from thesame tumor.
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Gene expression signature of proliferating lymphocytes. A, t-SNE analysis of CTCL and normal T lymphocytes in the cell cycle. Expression of S and G2 geneshighlights proliferating cells. B, Louvain clusters from T lymphocytes of individual CTCL tumors. C, Heat maps of lymphocyte transcriptomes from individualtumors showing 50 examples of highly significantly DE genes in each of the clusters in (B). Cluster numbers are indicated at the top. Each column represents acell. D, Venn diagram showing overlap of expressed genes in highly proliferating lymphocytes from clusters 1, 7 (CTCL-2); clusters 3, 4 (CTCL-5); cluster 4(CTCL-6); cluster 3 (CTCL-8), and cluster 7 (CTCL-12). E, Transcriptomes of TOXþ T lymphocytes from patient tumors and healthy control skin samples. F,Dot-plot shows the proportion of cells and the scaled average gene DE expression of the 17 common genes identified inD.
Single Cell Heterogeneity in CTCL Skin Tumors
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High numbers of ATP5C1þTOXþ, PCNAþTOXþ, andNUSAP1þTOXþ T cells accumulate in the skin tumorsof patients with advanced stage CTCL. A,Immunohistochemical stain from skin biopsies ofnormal skin (NS, n¼ 4), atopic dermatitis (AD, n¼ 4),and advanced stage CTCL (n¼ 5) used in scRNA-seqexperiments, each at 200� (left) and 400� (right). B,Representative examples from 3 patient samplestested of double color immunofluorescence stainingfor ATP5C1/TOX, PCNA/TOX, and NUSAP1/TOX, asindicated, at 1,000�. DAPI stains nuclei.
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activation of specific tumor-associated signaling pathways.Some expressed genes had not been previously associated withCTCL while the expression of others that had been linked toCTCL varied across patient samples from high level expressionby many cells in some tumors to little or no expression inothers. For example, the cluster unique for CTCL-2, a samplefrom a stage IVA Sezary syndrome patient, overexpressedchemokine genes such as CXCL13, CCR7 and CCR4 that conferenhanced migratory ability to memory Sezary cells (26). Fur-thermore, multiple eIF proteins were upregulated in this tumorsample, showing that the eIF2 and eIF4 signaling pathways areactivated as well as the mTOR signaling cascade, a majorregulator of eIF4 and ribosomal protein S6 kinase (27). Inter-estingly, deregulation or altered expression of eIFs leads totranslational reprogramming and promotes several oncogenicprocesses, including tumor cell survival, proliferation, neovas-cularization, and metastasis (27). In contrast, lymphocytes ofthe two clusters unique to CTCL-5 (representing a CD8þ
aggressive cytotoxic CTCL) expressed genes involved in NKcell-signaling as well as several NK-cell receptors, includingkiller-cell immunoglobulin-like receptors and CLEC12A thatnegatively regulate NK-mediated cytotoxicity against tumorcells (28, 29). We also detected upregulation of several genesinvolved with virus entry via endocytic pathways, which isintriguing in view of the potential role for persistent viralinfections in the etiology of CTCL (30). A third pattern char-acteristic of cells from the CTCL-6–specific cluster was expres-sion of genes involved with tumor cell survival, proliferation,and metastasis, some common to tumorigenic pathways spe-cific to glioma and non-small cell lung cancer (e.g., CDK6,KRAS, PA2G4, PIK3R1, RB1, RRAS2, RXRA, TFDP1).
Parallel to upregulation of genes associatedwith carcinogenesisandmetastasis such asTPT1 (31) andMALAT1 (32), cells from theCTCL-8–specific cluster expressed the cysteine-protease inhibitorsSERPINB3 and SERPINB4, which are expressed by various tumorsand involved in inactivating granzyme M, an enzyme that killstumor cells (33). Moreover, SERPINB3/B4 promotes tumor-celltransformation, migration, and drug resistance (33) and contri-butes to inflammation and barrier dysfunction in inflammatoryskin diseases (34). Indeed, we found that cells in this clusterupregulated expression of genes from the psoriasis-like pathwayas well as those associated with skin-barrier dysfunction, which isa striking match to the histopathologic characterization of theCTCL-8 sample (Supplementary Table S3). Previous studies haveshown that malignant T lymphocytes drive the morphologicaland histopathological changes observed in mycosis fungoidesskin lesions, including keratinocyte hyperproliferation and com-promised skin barrier function (35). These changes lead to down-regulation of keratinocyte differentiation markers and increasedintercellular distance, enhancing skin permeability and contrib-uting to the increased susceptibility to skin infections in CTCLpatients, particularly in advanced-stage disease (36). The clusterunique to CTCL-12, from a patient with mycosis fungoides andlarge-cell transformation, overexpressed genes associated withepithelial–mesenchymal transition (EMT). This process allowstumor cells to acquire migratory, invasive, and stem-like proper-ties, promoting tumor infiltration and metastasis (37, 38).Although originally associated with epithelial-derived tumors,EMT is also implicated in hematologic cancers (39). During ETM,cancer cells change morphology by disruption of intercellularjunctions, loss of cell polarity, reorganization of the cytoskeleton,
Figure 5.
Expression of effector and exhaustion genes by CD8þ T cells across patientskin tumors. A, Transcriptomes of CD8þ T lymphocytes from individual CTCLtumors and normal skin samples (color coded by subject), revealing 7discrete Louvain clusters (B). C,Gene expression from the 7 discrete Louvainclusters in (B), showing DE of coinhibitory receptors and effector molecules(top) and of tumor-associated genes (bottom). Cluster numbers areindicated in the middle. Each column represents a cell. D, Dot-plot shows theproportion of cells and the scaled average gene DE expression of the tumor-associated genes selected in (C, bottom). E, Violin plots show expression ofco-inhibitory receptors by CD8þ T lymphocytes from cluster 1. F,Immunofluorescence microscopy shows coexpression of CD8 andcoinhibitory receptors, as indicated, in advanced stage CTCL skin tumors.A representative experiment is shown at 1000� (top) and zoomed-in by3� (bottom).
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and increased cell motility necessary for invasion (37, 38). Inparticular, we observed upregulation of genes associated with theremodeling of adherens junctions, changes in cell adhesion,activation of small GTPases of the Rho family such as RAC1,CDC42, and RHOA, and reorganization of the actin cytoskeleton(Supplementary Table S3; refs. 37, 38, 40). We also found upre-gulation of TMPRSS3, a type II transmembrane serine proteasethat contributes to EMT in other human cancers via activation ofthe ERK1/2 signaling pathway (41).
While characterizing this vast intertumor heterogeneity in geneexpression that may be essential for tailoring personalized med-icine, we also searched for gene expression common to all tumorsthat could be used to guide diagnosis, design new medicationsthat treat all CTCL tumor subtypes, and monitor treatmentefficacy. We found that highly proliferating T lymphocytes ineach tumor expressed a very defined and strong gene expressionsignature involving cell-cycle progression, proliferation, resis-tance to apoptosis, and metabolic processes. Strikingly, we foundthat these signatures had 17 genes in common thatwe found to bealso expressed by TOXþ cells in all tumors. We validated theprotein coexpression of three of them (PCNA, ATP5C1, NUSPA1)with TOX in multiple patients with advanced-stage CTCL. Thus,these results strongly indicate that both the common and hetero-geneous patterns of gene expression canbe exploited for diagnosisand treatment of CTCL.
Expression of checkpoint inhibitory receptors renders CD8þ
TILs incapable of mounting an efficient antitumor response, asmanifested by impaired degranulation and reduced proinflam-matory cytokine production (12, 13). Thus, recent cancerimmunotherapies have focused on enhancing CD8þ T-cellantitumor responses by targeting the inhibitory receptors (12),yielding major clinical benefits (42). However, only a subset ofpatients exhibited clear long-term responses, while mostpatients with different types of tumors failed to respond. Thisfailure likely results from the expression of multiple inhibitoryreceptors on TILs that may synergistically modulate antitumorresponses by different pathways. Preclinical and clinical (43)studies have indicated that full antitumor immunity mayrequire several inhibitory receptors to be blocked. Consistentwith these studies, our analysis demonstrates that co-inhibitoryreceptors are simultaneously but heterogeneously expressed onboth CD8þ and CD4þ T lymphocytes. A particularly strikingexample is the overexpression of TIGIT, LAG3, and TIM3 byCD8þ T cells in CTCL-6, and LAG3 in CTCL-2, indicating astrong patient-specific signature that may be exploited forindividualized targeting. Although co-inhibitory receptorsappeared to be expressed mostly by reactive TILs, we alsoobserved their expression by T lymphocytes expressingtumor-associated genes. In some cases, these cells were clearlyidentifiable as malignant, while in others they may insteadrepresent exhausted T lymphocytes acquiring a malignant phe-notype or nonconventional Tregs such as CD4þ Tregs lackingFOXP3 and/or IL2RA expression.
TIGIT expression by CD4þ T cells from the peripheral bloodof patients with advanced stage Sezary syndrome was recentlyassociated with reduced IFNG and IL2 production (44). Wefound that TIGIT is also highly expressed by both CD8þ andCD4þ TILs from advanced stage CTCL skin tumors, and inparallel with expression of other coinhibitory receptors, mostnotably TIM3, PD1, LAG3, and to a lesser extent CTLA4.Because TIGIT can foster an immunosuppressive tumor micro-
environment by promoting Treg function and maintenance aswell as by inhibiting cytotoxic T-cell activity (45), we con-firmed TIGIT expression by Tregs in the samples tested andfound a correlation with lack of GZMB and perforin expressionin cluster 1 CD8þ TILs that characterizes exhaustion. Interest-ingly, we found that CTCL-6–specific CD8þTIGITþ lympho-cytes from cluster 3 expressed GZMB and perforin as well astumor-associated genes, likely representing cytotoxic malig-nant lymphocytes. CD8þ TILs showed no IL2 expression whilemost cells expressed TNFA and IFNG, which was associatedwith EOMES expression, consistent with an exhausted CD8þ
T-cell phenotype (46). Such cells are defective in IFNG pro-duction and cytotoxicity but continue expressing IFNG andgranzyme mRNAs (46), consistent with our findings. Likewise,we observed that CD4þTILs also expressed IFNG and TNFA aswell as TGFB1, which is likely produced in combination withIL10 by FOXP3þ cytotoxic Tregs (47). Furthermore, variabilityobserved in TGFB1, IL10, IL4, and IL13 cytokine productionfrom CD4þ T cells of the CTCL-12–specific and CTCL-8–specific clusters likely reflects FOXP3�Tregs or malignant lym-phocytes, or CD4þFOXP3þ malignant lymphocytes with sup-pressive activity (48), respectively. We conclude that multiplecoinhibitor receptors are expressed by malignant and reactivelymphocytes in advanced CTCL skin tumors, conferring to thelatter a dysfunctional phenotype. Understanding the hetero-geneity in coinhibitory receptor expression may be essentialfor developing patient-specific therapy and for guiding check-point inhibitor blocking to permit effective killing of tumorcells by TILs.
In conclusion, single-cell transcriptome profiling provides nov-el insights into CTCL disease heterogeneity by revealing patient-specific landscapes ofmalignant and reactive lymphocytes. Recentreports applying single-cell RNA-seq to peripheral blood of Sezarypatients (25, 49) also demonstrated heterogeneity in the malig-nant T-cell population and, in addition, genetic heterogeneitywithin the same patient over time has been observed (50).Although studies with small subsets of patients need to beexpanded to confirm and extend general trends, we nonethelessdemonstrate the ability to detect gene expression patterns amongsingle skin tumor cells that can provide a framework for improv-ing CTCL diagnosis and treatment, thus realizing the goal ofprecision medicine.
Disclosure of Potential Conflicts of InterestL.J. Geskin reports receiving speakers bureau honoraria from Helsinn, is a
consultant/advisory board member for Helsinn and Therakos, and reportsreceiving commercial research grants from Mallinckrodt and Actelion. Nopotential conflicts of interest were disclosed by the other authors.
Authors' ContributionsConception and design: L.J. Geskin, R. Lafyatis, P. FuschiottiDevelopment of methodology: R. LafyatisAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): L.J. Geskin, C.-A. Bayan, P. FuschiottiAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): A.M. Gaydosik, T. Tabib, L.J. Geskin, J.F. Conway,R. Lafyatis, P. FuschiottiWriting, review, and/or revision of the manuscript: A.M. Gaydosik, T. Tabib,L.J. Geskin, C.-A. Bayan, J.F. Conway, R. Lafyatis, P. FuschiottiAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): A.M. Gaydosik, T. Tabib, L.J. Geskin,C.-A. Bayan, J.F. Conway, R. Lafyatis, P. FuschiottiStudy supervision: L.J. Geskin, P. Fuschiotti
Gaydosik et al.
Clin Cancer Res; 25(14) July 15, 2019 Clinical Cancer Research4452
AcknowledgmentsWe thank Christina Morse for technical support for immunofluorescence
microscopy. This work was supported by NIH/NCI grant R21 CA209107-02 toP. Fuschiotti.
The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby marked
advertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.
Received January 13, 2019; revised March 21, 2019; accepted April 12, 2019;published first April 22, 2019.
References1. Girardi M, Heald PW, Wilson LD. The pathogenesis of mycosis fungoides.
N Engl J Med 2004;350:1978–88.2. Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, et al. The
2016 revision of theWorldHealthOrganization classification of lymphoidneoplasms. Blood 2016;127:2375–90.
3. Agar NS, Wedgeworth E, Crichton S, Mitchell TJ, Cox M, Ferreira S, et al.Survival outcomes and prognostic factors in mycosis fungoides/Sezarysyndrome: validation of the revised International Society for CutaneousLymphomas/EuropeanOrganisation for Research andTreatment ofCancerstaging proposal. J Clin Oncol 2010;28:4730–9.
4. Scarisbrick JJ, Kim YH, Whittaker SJ, Wood GS, Vermeer MH, Prince HM,et al. Prognostic factors, prognostic indices and staging in mycosis fun-goides and Sezary syndrome: where are we now? Br J Dermatol 2014;170:1226–36.
5. Scarisbrick JJ, Prince HM, Vermeer MH, Quaglino P, Horwitz S, Porcu P,et al. Cutaneous Lymphoma International Consortium Study of Outcomein Advanced Stages of Mycosis Fungoides and Sezary Syndrome: Effect ofSpecific Prognostic Markers on Survival and Development of a PrognosticModel. J Clin Oncol 2015 doi 10.1200/JCO.2015.61.7142.
6. Arulogun SO, Prince HM, Ng J, Lade S, Ryan GF, Blewitt O, et al. Long-termoutcomes of patients with advanced-stage cutaneous T-cell lymphoma andlarge cell transformation. Blood 2008;112:3082–7.
7. Scarisbrick JJ,QuaglinoP, PrinceHM, Papadavid E,HodakE, BagotM, et al.The PROCLIPI international registry of early stage Mycosis Fungoidesidentifies substantial diagnostic delay in most patients. Br J Dermatol2018doi 10.1111/bjd.17258.
8. Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation.Nature 2008;454:436–44.
9. Geskin LJ, Viragova S, Stolz DB, Fuschiotti P. Interleukin-13 is over-expressed in cutaneous T-cell lymphoma cells and regulates their prolif-eration. Blood 2015doi blood-2014-07-590398 [pii]
10. Komohara Y, Takeya M. CAFs and TAMs: maestros of the tumour micro-environment. J Pathol 2017;241:313–5.
11. Guenova E, Watanabe R, Teague JE, Desimone JA, Jiang Y, Dowlatshahi M,et al. TH2 cytokines from malignant cells suppress TH1 responses andenforce a global TH2 bias in leukemic cutaneous T-cell lymphoma.Clin Cancer Res 2013;19:3755–63.
12. Crespo J, Sun H, Welling TH, Tian Z, Zou W. T cell anergy, exhaustion,senescence, and stemness in the tumor microenvironment. Curr OpinImmunol 2013;25:214–21.
13. Wherry EJ, Kurachi M. Molecular and cellular insights into T cell exhaus-tion. Nat Rev Immunol 2015;15:486–99.
14. AndersonAC, JollerN, KuchrooVK. Lag-3, Tim-3, and TIGIT: Co-inhibitoryReceptors with Specialized Functions in Immune Regulation. Immunity2016;44:989–1004.
15. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al.Highly Parallel Genome-wide Expression Profiling of Individual CellsUsing Nanoliter Droplets. Cell 2015;161:1202–14.
16. Schelker M, Feau S, Du J, Ranu N, Klipp E, MacBeath G, et al. Estimation ofimmune cell content in tumour tissue using single-cell RNA-seq data.Nature Communications 2017;8:2032.
17. Venteicher AS, Tirosh I, Hebert C, Yizhak K, Neftel C, Filbin MG, et al.Decoupling genetics, lineages, and microenvironment in IDH-mutantgliomas by single-cell RNA-seq. Science 2017;355.
18. Tabib T, Morse C, Wang T, Chen W, Lafyatis R. SFRP2/DPP4 and FMO1/LSP1 Define Major Fibroblast Populations in Human Skin. J Invest Der-matol 2018;138:802–10.
19. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction ofsingle-cell gene expression data. Nat Biotechnol 2015;33:495–502.
20. BushatiN, Smith J, Briscoe J,WatkinsC.An intuitive graphical visualizationtechnique for the interrogation of transcriptome data. Nucleic Acids Res2011;39:7380–9.
21. Kramer A, Green J, Pollard J Jr., Tugendreich S. Causal analysis approachesin Ingenuity Pathway Analysis. Bioinformatics 2014;30:523–30.
22. Zhang Y, Wang Y, Yu R, Huang Y, Su M, Xiao C, et al. Molecular markers ofearly-stage mycosis fungoides. J Invest Dermatol 2012;132:1698–706.
23. Dulmage B, Geskin L, Guitart J, Akilov OE. The biomarker landscape inmycosis fungoides and Sezary syndrome. Exp Dermatol 2017;26:668–76.
24. de Masson A, O'Malley JT, Elco CP, Garcia SS, Divito SJ, Lowry EL, et al.High-throughput sequencing of the T cell receptor beta gene identifiesaggressive early-stage mycosis fungoides. Sci Transl Med 2018;10.
25. Borcherding N, Voigt AP, Liu V, Link BK, Zhang W, Jabbari A. Single-cellprofiling of cutaneous T-cell lymphoma reveals underlying heterogeneityassociated with disease progression. Clin Cancer Res 2019 doi 10.1158/1078-0432.CCR-18-3309.
26. Picchio MC, Scala E, Pomponi D, Caprini E, Frontani M, Angelucci I, et al.CXCL13 is highly produced by Sezary cells and enhances their migratoryability via a synergistic mechanism involving CCL19 and CCL21 chemo-kines. Cancer Res 2008;68:7137–46.
27. de la Parra C, Walters BA, Geter P, Schneider RJ. Translation initiationfactors and their relevance in cancer. Curr Opin Genet Dev 2018;48:82–8.
28. Wechsler J, Bagot M, Nikolova M, Parolini S, Martin-Garcia N, Boumsell L,et al. Killer cell immunoglobulin-like receptor expression delineates in situSezary syndrome lymphocytes. J Pathol 2003;199:77–83.
29. Benson DM Jr., Caligiuri MA. Killer immunoglobulin-like receptors andtumor immunity. Cancer Immunol Res 2014;2:99–104.
30. Litvinov IV, Shtreis A, Kobayashi K, Glassman S, Tsang M, Woetmann A,et al. Investigating potential exogenous tumor initiating and promotingfactors for Cutaneous T-Cell Lymphomas (CTCL), a rare skin malignancy.Oncoimmunology 2016;5:e1175799.
31. LucibelloM, Gambacurta A, Zonfrillo M, Pierimarchi P, Serafino A, Rasi G,et al. TCTP is a critical survival factor that protects cancer cells fromoxidative stress-induced cell-death. Exp Cell Res 2011;317:2479–89.
32. Kim SH, Kim SH, Yang WI, Kim SJ, Yoon SO. Association of the long non-coding RNAMALAT1 with the polycomb repressive complex pathway in Tand NK cell lymphoma. Oncotarget 2017;8:31305–17.
33. Sun Y, Sheshadri N, Zong WX. SERPINB3 and B4: From biochemistry tobiology. Semin Cell Dev Biol 2017;62:170–7.
34. Izuhara K, Yamaguchi Y, Ohta S, Nunomura S, Nanri Y, Azuma Y, et al.Squamous cell carcinoma antigen 2 (SCCA2, SERPINB4): an emergingbiomarker for skin inflammatory diseases. Int J Mol Sci 2018;19.
35. Thode C, Woetmann A, Wandall HH, Carlsson MC, Qvortrup K, KauczokCS, et al. Malignant T cells secrete galectins and induce epidermal hyper-proliferation and disorganized stratification in a skin model of cutaneousT-cell lymphoma. J Invest Dermatol 2015;135:238–46.
36. Willerslev-Olsen A, Krejsgaard T, Lindahl LM, Bonefeld CM, Wasik MA,Koralov SB, et al. Bacterial toxins fuel disease progression in cutaneousT-cell lymphoma. Toxins 2013;5:1402–21.
37. da Silva-Diz V, Lorenzo-Sanz L, Bernat-Peguera A, Lopez-Cerda M,Munoz P. Cancer cell plasticity: Impact on tumor progression andtherapy response. Semin Cancer Biol 2018 doi 10.1016/j.semcancer.2018.08.009.
38. Christiansen JJ, Rajasekaran AK.Reassessing epithelial to mesenchymaltransition as a prerequisite for carcinoma invasion and metastasis.Cancer Res 2006;66:8319–26.
39. Chen SC, Liao TT, Yang MH. Emerging roles of epithelial-mesenchymaltransition in hematological malignancies. J Biomed Sci 2018;25:37.
www.aacrjournals.org Clin Cancer Res; 25(14) July 15, 2019 4453
40. Takeichi M. Dynamic contacts: rearranging adherens junctions to driveepithelial remodelling. Nat Rev Mol Cell Biol 2014;15:397–410.
41. Wang JY, Jin X, Li XF. Knockdown of TMPRSS3, a Transmembrane SerineProtease, Inhibits Proliferation, Migration, and Invasion in Human Naso-pharyngeal Carcinoma Cells. Oncol Res 2018;26:95–101.
42. Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: acommon denominator approach to cancer therapy. Cancer Cell 2015;27:450–61.
43. Wolchok JD, Chiarion-Sileni V, Gonzalez R, Rutkowski P, Grob JJ, CoweyCL, et al. Overall Survival with Combined Nivolumab and Ipilimumab inAdvanced Melanoma. N Engl J Med 2017;377:1345–56.
44. Jariwala N, Benoit B, Kossenkov AV, Oetjen LK, Whelan TM, Cornejo CM,et al. TIGIT and Helios Are Highly Expressed on CD4(þ) T Cells in SezarySyndrome Patients. J Invest Dermatol 2017;137:257–60.
45. Dougall WC, Kurtulus S, Smyth MJ, Anderson AC. TIGIT and CD96: newcheckpoint receptor targets for cancer immunotherapy. Immunol Rev2017;276:112–20.
46. Wherry EJ, Ha SJ, Kaech SM,HainingWN, Sarkar S, Kalia V, et al. Molecularsignature of CD8þ T cell exhaustion during chronic viral infection. Immu-nity 2007;27:670–84.
47. Akhmetzyanova I, Zelinskyy G, Littwitz-Salomon E, Malyshkina A, DietzeKK, Streeck H, et al. CD137 Agonist Therapy Can Reprogram Regulatory TCells into Cytotoxic CD4þ T Cells with Antitumor Activity. J Immunol2016;196:484–92.
48. Heid JB, Schmidt A, Oberle N, Goerdt S, Krammer PH, Suri-Payer E, et al.FOXP3þCD25- tumor cells with regulatory function in Sezary syndrome.J Invest Dermatol 2009;129:2875–85.
49. Buus TB, Willerslev-Olsen A, Fredholm S, Blumel E, Nastasi C, Gluud M,et al. Single-cell heterogeneity in Sezary syndrome. Blood Adv 2018;2:2115–26.
50. Litvinov IV, Tetzlaff MT, Thibault P, Gangar P, Moreau L, Watters AK, et al.Gene expression analysis in Cutaneous T-Cell Lymphomas (CTCL) high-lights disease heterogeneity and potential diagnostic and prognostic indi-cators. Oncoimmunology 2017;6:e1306618.
Clin Cancer Res; 25(14) July 15, 2019 Clinical Cancer Research4454
2019;25:4443-4454. Published OnlineFirst April 22, 2019.Clin Cancer Res Alyxzandria M. Gaydosik, Tracy Tabib, Larisa J. Geskin, et al. T-cell Lymphoma Skin TumorsSingle-Cell Lymphocyte Heterogeneity in Advanced Cutaneous
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