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Research Article Immune Proling and Quantitative Analysis Decipher the Clinical Role of Immune-Checkpoint Expression in the Tumor Immune Microenvironment of DLBCL Ziju Y. Xu-Monette 1 , Min Xiao 1 , Qingyan Au 2 , Raghav Padmanabhan 2 , Bing Xu 3 , Nicholas Hoe 2 , Sandra Rodríguez-Perales 4 , Raul Torres-Ruiz 4,5 , Ganiraju C. Manyam 6 , Carlo Visco 7 , Yi Miao 1 , Xiaohong Tan 1 , Hongwei Zhang 1 , Alexandar Tzankov 8 , Jing Wang 6 , Karen Dybkær 9 , Wayne Tam 10 , Hua You 11 , Govind Bhagat 12 , Eric D. Hsi 13 , Maurilio Ponzoni 14 , Andr es J.M. Ferreri 14 , Michael B. Møller 15 , Miguel A. Piris 16 , J. Han van Krieken 17 , Jane N. Winter 18 , Jason R. Westin 19 , Lan V. Pham 1 , L. Jeffrey Medeiros 1 , George Z. Rassidakis 20 , Yong Li 21 , Gordon J. Freeman 22 , and Ken H. Young 1,23 Abstract PD-1/L1 and CTLA-4 blockade immunotherapies have been approved for 13 types of cancers and are being studied in diffuse large B-cell lymphoma (DLBCL), the most com- mon aggressive B-cell lymphoma. However, whether both PD-1 and CTLA-4 checkpoints are active and clinically sig- nicant in DLBCL is unknown. Whether PD-1 ligands expressed by tumor cells or by the microenvironment of DLBCL are critical for the PD-1 immune checkpoint is unclear. We performed immunophenotypic proling for 405 patients with de novo DLBCL using a MultiOmyx immu- nouorescence platform and simultaneously quantitated expression/coexpression of 13 immune markers to identify prognostic determinants. In both training and validation cohorts, results demonstrated a central role of the tumor immune microenvironment, and when its functionality was impaired by deciency in tumor-inltrating T cells and/or natural killer cells, high PD-1 expression (but not CTLA-4) on CD8 þ T cells, or PD-L1 expression on T cells and macro- phages, patients had signicantly poorer survival after ritux- imabCHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone) immunochemotherapy. In contrast, tumor- cell PD-L2 expression was associated with superior survival, as well as PD-L1 þ CD20 þ cells proximal (indicates interac- tion) to PD-1 þ CD8 þ T cells in patients with low PD-1 þ percentage of CD8 þ T cells. Gene-expression proling results suggested the reversibility of T-cell exhaustion in PD-1 þ / PD-L1 þ patients with unfavorable prognosis and implication of LILRA/B, IDO1, CHI3L1, and SOD2 upregulation in the microenvironment dysfunction with PD-L1 expression. This study comprehensively characterized the DLBCL immune landscape, deciphered the differential roles of various checkpoint components in rituximabCHOP resistance in DLBCL patients, and suggests targets for PD-1/PD-L1 block- ade and combination immunotherapies. 1 Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, Texas. 2 NeoGenomics Laboratories, Inc., Aliso Viejo, California. 3 Department of Hematology, The First Afliated Hospital of Xiamen University, Fujian, China. 4 Molecular Cytogenetics Unit, Human Cancer Genetics Pro- gramme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain. 5 Josep Carreras Leukemia Research Institute, Department of Biomedicine, School of Medicine, University of Barcelona, Barcelona, Spain. 6 Department of Bioinformatics and Computational Biology, The University of Texas MD Ander- son Cancer Center, Houston, Texas. 7 San Bortolo Hospital, Vicenza, Italy. 8 Institute of Pathology, University Hospital of Basel, Basel, Switzerland. 9 Aal- borg University Hospital, Aalborg, Denmark. 10 Weill Cornell Medicine, Cornell University, New York, New York. 11 Afliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China. 12 New York Presbyterian Hospital/Columbia University Medical Center, New York, New York. 13 Cleveland Clinic, Cleveland, Ohio. 14 San Raffaele Hospital, Milan, Italy. 15 Odense University Hospital, Odense, Denmark. 16 Hospital Universitario Marqu es de Valdecilla, Santander, Spain. 17 Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, the Netherlands. 18 Feinberg School of Medicine, Northwestern University, Chicago, Illinois. 19 Department of Lympho- ma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas. 20 Department of Oncology and Pathology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden. 21 Department of Cancer Biology, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio. 22 Dana- Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts. 23 Grad- uate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, Texas. Note: Supplementary data for this article are available at Cancer Immunology Research Online (http://cancerimmunolres.aacrjournals.org/). Z.Y. Xu-Monette, M. Xiao, Q. Au, and R. Padmanabhan contributed equally to this article. Corresponding Authors: Ken H. Young, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030. Phone: 713-745- 2598; Fax: 713-792-7273; E-mail: [email protected]; Bing Xu, The First Afliated Hospital of Xiamen University, Fujian, China. Phone: 713-745-2598; Fax: 713-792-7273; E-mail: [email protected]; and Qingyan Au, NeoGenomics Laboratories, Inc., Aliso Viejo, CA. Phone: 713-745-2598; E-mail: [email protected] doi: 10.1158/2326-6066.CIR-18-0439 Ó2019 American Association for Cancer Research. Cancer Immunology Research Cancer Immunol Res; 7(4) April 2019 644 on July 20, 2020. © 2019 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from Published OnlineFirst February 11, 2019; DOI: 10.1158/2326-6066.CIR-18-0439

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Research Article

Immune Profiling and Quantitative AnalysisDecipher the Clinical Role of Immune-CheckpointExpression in the Tumor ImmuneMicroenvironment of DLBCLZiju Y. Xu-Monette1, Min Xiao1, Qingyan Au2, Raghav Padmanabhan2, Bing Xu3,Nicholas Hoe2, Sandra Rodríguez-Perales4, Raul Torres-Ruiz4,5, Ganiraju C. Manyam6,Carlo Visco7,Yi Miao1, Xiaohong Tan1, Hongwei Zhang1, Alexandar Tzankov8, Jing Wang6,KarenDybkær9,WayneTam10, HuaYou11,GovindBhagat12, Eric D. Hsi13, Maurilio Ponzoni14,Andr�es J.M. Ferreri14, Michael B. Møller15, Miguel A. Piris16, J. Han van Krieken17,Jane N.Winter18, Jason R.Westin19, Lan V. Pham1, L. Jeffrey Medeiros1,George Z. Rassidakis20, Yong Li21, Gordon J. Freeman22, and Ken H. Young1,23

Abstract

PD-1/L1 and CTLA-4 blockade immunotherapies havebeen approved for 13 types of cancers and are being studiedin diffuse large B-cell lymphoma (DLBCL), the most com-mon aggressive B-cell lymphoma. However, whether bothPD-1 and CTLA-4 checkpoints are active and clinically sig-nificant in DLBCL is unknown. Whether PD-1 ligandsexpressed by tumor cells or by the microenvironment ofDLBCL are critical for the PD-1 immune checkpoint isunclear. We performed immunophenotypic profiling for405 patients with de novo DLBCL using a MultiOmyx immu-nofluorescence platform and simultaneously quantitatedexpression/coexpression of 13 immune markers to identifyprognostic determinants. In both training and validationcohorts, results demonstrated a central role of the tumorimmune microenvironment, and when its functionality wasimpaired by deficiency in tumor-infiltrating T cells and/ornatural killer cells, high PD-1 expression (but not CTLA-4) on

CD8þ T cells, or PD-L1 expression on T cells and macro-phages, patients had significantly poorer survival after ritux-imab–CHOP (cyclophosphamide, doxorubicin, vincristine,and prednisone) immunochemotherapy. In contrast, tumor-cell PD-L2 expression was associated with superior survival,as well as PD-L1þCD20þ cells proximal (indicates interac-tion) to PD-1þCD8þ T cells in patients with low PD-1þ

percentage of CD8þ T cells. Gene-expression profiling resultssuggested the reversibility of T-cell exhaustion in PD-1þ/PD-L1þpatients with unfavorable prognosis and implicationof LILRA/B, IDO1, CHI3L1, and SOD2 upregulation in themicroenvironment dysfunction with PD-L1 expression. Thisstudy comprehensively characterized the DLBCL immunelandscape, deciphered the differential roles of variouscheckpoint components in rituximab–CHOP resistance inDLBCL patients, and suggests targets for PD-1/PD-L1 block-ade and combination immunotherapies.

1Department of Hematopathology, TheUniversity of TexasMDAndersonCancerCenter, Houston, Texas. 2NeoGenomics Laboratories, Inc., Aliso Viejo, California.3Department of Hematology, The First Affiliated Hospital of Xiamen University,Fujian, China. 4Molecular Cytogenetics Unit, Human Cancer Genetics Pro-gramme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.5Josep Carreras Leukemia Research Institute, Department of Biomedicine,School of Medicine, University of Barcelona, Barcelona, Spain. 6Department ofBioinformatics and Computational Biology, The University of Texas MD Ander-son Cancer Center, Houston, Texas. 7San Bortolo Hospital, Vicenza, Italy.8Institute of Pathology, University Hospital of Basel, Basel, Switzerland. 9Aal-borg University Hospital, Aalborg, Denmark. 10Weill Cornell Medicine, CornellUniversity, New York, New York. 11Affiliated Cancer Hospital and Institute ofGuangzhou Medical University, Guangzhou, China. 12New York PresbyterianHospital/Columbia University Medical Center, New York, New York. 13ClevelandClinic, Cleveland, Ohio. 14San Raffaele Hospital, Milan, Italy. 15Odense UniversityHospital, Odense, Denmark. 16Hospital Universitario Marqu�es de Valdecilla,Santander, Spain. 17Radboud Institute for Molecular Life Sciences, RadboudUniversity Medical Center, Nijmegen, the Netherlands. 18Feinberg School ofMedicine, Northwestern University, Chicago, Illinois. 19Department of Lympho-ma andMyeloma, TheUniversity of TexasMDAnderson Cancer Center, Houston,Texas. 20Department of Oncology and Pathology, Karolinska Institutet and

Karolinska University Hospital, Stockholm, Sweden. 21Department of CancerBiology, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio. 22Dana-Farber Cancer Institute, HarvardMedical School, Boston, Massachusetts. 23Grad-uate School of Biomedical Sciences, The University of Texas Health ScienceCenter at Houston, Houston, Texas.

Note: Supplementary data for this article are available at Cancer ImmunologyResearch Online (http://cancerimmunolres.aacrjournals.org/).

Z.Y. Xu-Monette, M. Xiao, Q. Au, and R. Padmanabhan contributed equally to thisarticle.

Corresponding Authors: Ken H. Young, The University of Texas MD AndersonCancer Center, 1515 Holcombe Boulevard, Houston, TX 77030. Phone: 713-745-2598; Fax: 713-792-7273; E-mail: [email protected]; Bing Xu, The FirstAffiliated Hospital of Xiamen University, Fujian, China. Phone: 713-745-2598;Fax: 713-792-7273; E-mail: [email protected]; and Qingyan Au,NeoGenomics Laboratories, Inc., Aliso Viejo, CA. Phone: 713-745-2598; E-mail:[email protected]

doi: 10.1158/2326-6066.CIR-18-0439

�2019 American Association for Cancer Research.

CancerImmunologyResearch

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IntroductionAntitumor T-cell response is critical for the immune surveil-

lance of cancer but is often damped by immune checkpoints.The checkpoint receptor CTLA-4 inhibits T-cell activation at thepriming phase by competing with CD28 to bind CD80/CD86 (1–3). PD-1 suppresses the function of activated T cellsthrough ligation of PD-1 ligands PD-L1 (4, 5) or PD-L2 (6),mainly at the effector phase (7). PD-L1 can also interact withCD80 on activated T cells, and this interaction is required forT-cell anergy induction and maintenance (8). The CTLA-4/PD-1immune checkpoint can be reversed by CTLA-4/PD-1/PD-L1–blocking antibodies. However, the mechanisms of action arenot completely understood (9, 10), and therapeutic effects ofthese blocking antibodies differ between CD4þ and CD8þ Tcells (11, 12), between effector T and regulatory T cells (Tregs;refs. 13, 14), and between na€�ve T and memory T (Tm)cells (15). In addition, whether PD-L1 expressed by tumorcells or by host cells is essential for the immunotherapeuticefficacy of PD-1/L1 blockade remains controversial (9).

Diffuse large B-cell lymphoma (DLBCL) is the most commonaggressive B-cell lymphoma, including two major molecularsubtypes classified by gene-expression profiling (GEP): germi-nal center B-cell–like (GCB) and activated B-cell–like (ABC)DLBCL. The standard treatment for DLBCL, combinationimmunochemotherapy R-CHOP (rituximab, cyclophospha-mide, doxorubicin, vincristine, and prednisone), is associatedwith a high complete response rate of �80%. However, 10% to15% of DLBCLs are refractory, and 20% to 25% of patientsexperience relapse after an initial response, posing clinicalchallenges (16). PD-1 and CTLA-4 blockade monotherapiesshowed promising efficacy in treating relapsed/refractoryDLBCL in phase I clinical trials (17, 18). However, in a phaseII study, the objective response rate of anti–PD-1 monotherapywas low in patients with relapsed/refractory DLBCL who wereineligible for, or failed with, autologous hematopoietic celltransplantation (10% and 3%, respectively; ref. 19). Phase IIresults of mono/combination immunotherapies in other clin-ical settings are currently unavailable.

Although PD-1/PD-L1 expression in DLBCL has beenstudied by different assessment methods, different cell sourcesof PD-1/PD-L1 expression and functional dependency onligand–receptor interaction obscure the role of the PD-1 check-point in DLBCL (9, 10). Some studies found that high PD-L1expression in tumor cells and soluble PD-L1 in the peripheralblood were associated with poorer survival in DLBCL (20–23)but other studies found no correlation between tumor-cellPD-L1 expression and clinical outcome (23–25). "Microenvi-ronmental" PD-L1 expression was inconsistently associatedwith nonsignificantly poorer or better survival (20, 23);increased tumor-infiltrating PD-1þ lymphocytes were moreoften associated with favorable than with unfavorable prognos-tic effects in DLBCL (10). Low PD-1 expression in DLBCL cellswas also found (26–28), but its prognostic effect is unknown.

Cell-specific and topological analysis of immune-checkpointexpression in patients has become feasible with the fluo-rescent multiplex immunohistochemistry (IHC) and automat-ed quantitation technology (29, 30). In this study, we used aMultiOmyx platform to simultaneously quantify the expres-sion of 13 immune markers (CTLA-4, PD-1, PD-L1, PD-L2,CD20, PAX5, CD3, CD4, CD8, FOXP3, CD45RO, CD56, andCD68) in situ in a large cohort of de novo DLBCL cases.

Checkpoint expression (PD-1, CTLA-4, and PD-L1/L2) intumors and microenvironment components analyzed by threedifferent methods (percentage within a cell type, cell density,and tissue cellularity) and PD-L1/PD-L2 genetic alterationsevaluated by fluorescence in situ hybridization (FISH) werecorrelated to the survival and gene-expression profiles ofpatients, in order to decipher the clinical role of immune-checkpoints and identify prognostic determinants in DLBCL.

Materials and MethodsPatients and molecular characterization

Themulticenter cohort study included405patientswith de novoDLBCL treated with R-CHOP from the International DLBCLR-CHOP Consortium Program (31, 32). Patients with Epstein–Barr virus (EBV) infection, primary mediastinal large B-cell lym-phoma, primary central nervous system DLBCL, primary cutane-ous DLBCL, HIV infection, or transformed DLBCL have beenexcluded. The median age was 63 years; the median follow-upwas 47.5 months. Based on GEP (deposited in Gene-ExpressionOmnibus GSE#31312) of total RNA by Affymetrix GeneChipsHuman Genome U133 Plus 2.0 (32, 33), 176 and 148 cases weredetermined as GCB and ABC subtype, respectively. This study wasconducted in accordance with the Declaration of Helsinki. Datacollection protocols were approved as being ofminimal to no riskor as exempt by the institutional review board of each participat-ing institution.

Antibodies for MultiOmyx fluorescent multiplex IHCFormalin-fixed, paraffin-embedded (FFPE) tissue microar-

rays were stained with 13 antibodies conjugated to eithercyanine 3 (cy3) or cyanine 5 (cy5) and 40,6-diamidino-2-phenylindole (DAPI) via eight staining rounds. The antibo-dies used, by staining order, were mouse anti–CTLA-4(sc376016; Santa Cruz Biotechnology), rabbit anti-CD56(156R-95; Cell Marque), anti–PD-L2 (clone 366C.9E5; pro-vided by G.J. Freeman), rabbit anti-Pax5 (AC-0158; CellMarque), mouse anti-CD45RO (304202; BioLegend), mouseanti-CD8 (M7103; Dako), mouse anti-FOXP3 (320113; Bio-Legend), mouse anti-CD3 (M7254; Dako), rabbit anti–PD-L1(M4420; Spring Bioscience), rabbit anti-CD4 (ab181724;Abcam), rabbit anti–PD-1 (ab186928; Abcam), rabbit anti-CD20 (ab166865; Abcam), and mouse anti-CD68 (MS-397-PABX; Thermo Fisher Scientific). Fluorescently labeled second-ary antibodies were obtained from Jackson ImmunoResearchLaboratories, Inc.

MultiOmyx stainingFFPE tissue arrays were baked at 65�C for 1 hour. Slides were

deparaffinized with xylene, rehydrated by decreasing ethanolconcentration washes, and then processed for antigen retrieval.A two-step antigen retrieval was adopted to allow antibodieswith different antigen retrieval conditions to be used togetheron the same samples (34, 35). Samples were then blockedagainst nonspecific binding with 10% (wt/vol) donkey serumand 3% (wt/vol) bovine serum albumin (BSA) in phosphate-buffered solution (PBS) for 1 hour at room temperature andstained with DAPI for 15 minutes. Directly conjugated primaryantibodies were diluted in PBS supplied with 3% (wt/vol) BSAto optimized concentrations and applied for 1 hour at roomtemperature on a Leica Bond III Stainer. In the case of primary–

PD-1/L1 in Immune (but not DLBCL) Cells Confer Poor Outcome

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secondary antibody staining, samples were incubated withprimary antibody, followed by incubation with species-specificsecondary antibodies conjugated to either cyanine 3 (cy3) orcyanine 5 (cy5).

A total of 8 rounds of antibody staining were performed insequence on the FFPE slides. CTLA-4 and CD56 antibodycocktail was used in round 1, followed by PD-L2, PAX5, andCD45RO staining as single antibody conjugation in round 2,3, and 4, respectively. CD8 and FOXP3 antibody cocktail wasused in round 5, followed by CD3 and PD-L1 staining in round6, CD4 and PD-1 staining in round 7, and CD20 and CD68staining in round 8.

Microscopy and image acquisitionStained images were collected on INCell analyzer 2200

microscope (GE Healthcare Life Sciences) equipped withhigh-efficiency fluorochrome-specific filter sets for DAPI, cy3,and cy5. For multiplexed staining where colocalization wasdesired, the regions of interest (�0.4–0.6 mm2 tissue area) wereimaged, and stage coordinates were saved. The coordinates ofeach image region were then recalled for each subsequentround after minor readjustment using reference points fromthe first-round DAPI image and determining the appropriateoffset. The exposure times were set at a fixed value for all imagesof a given marker. For image analyses, microscopy images wereexported as full-resolution TIFF images in grayscale for eachindividual channel collected.

Dye inactivation in tissueFor the dye inactivation process, following staining round

image acquisition, slides were decoverslipped, and dye inacti-vation was performed as previously described in US patent7,741,045 (36). Briefly, the slides were immersed in alkalinesolution containing H2O2 for 15 minutes with gentle agitationat room temperature. After 15 minutes, the slides were washedwith PBS. The samples were imaged to check the efficacy of thedye inactivation, and then subjected to another round ofstaining.

MultiOmyx image analyticsThe acquired images from sequential rounds were registered

using DAPI images acquired in the first round of staining via arigid registration algorithm for each region of interest. Theparameters of transformation were then applied to the subse-quent rounds, which ensured that the pixel coordinates acrossall the imaging rounds corresponded to the same physicallocations on the tissue. Classification and coexpression analysiswere performed in multiple stages. First, a nuclear segmenta-tion algorithm was applied on the DAPI image to delineate andidentify individual cells. Location information and expressionof all the markers were computed for every cell identified. Then,morphologic image analysis and shape detection were per-formed using proprietary algorithms. (Neogenomics Laborato-ries; https://neogenomics.com/pharma-services/lab-services/multiomyx). These algorithms detect and classify cells as pos-itive or negative for each marker depending on their subcellularlocalization and morphology. A tissue-quality algorithm wasalso applied to the images to ensure image artifacts that aroseowing to tissue folding or tear did not affect cell classification.Coexpression analysis and phenotype identification were per-formed by combining individual marker classification results.

For spatial analysis, two phenotypes of interest were defined:the anchor phenotype and the target phenotype. For each case,every cell of the anchor phenotype (PD-1þCD4þCD3þ or PD-1þCD8þCD3þ), the average distance of K-nearest neighbor(KNN; K ¼ 10) cells of the target phenotype (PD-L1þCD20þ,PD-L1þCD68þ, or PD-L1þCD3þ) was computed and plotted.The mean KNN distance in the study cohort was 128 mm. Basedon the findings from previous studies (30, 37) and consideringour use of a greater K value compared with K ¼ 1 in a previousstudy (30), the cutoff for distant/no interaction was determinedas >100 mm.

PD-L1/L2 FISHTwo sets of FISH probes were used to study the PD-L1 and

PD-L2 genes individually. The RP11-590H20– and CH17-432N17–specific bacterial artificial chromosomes, which mapto the PD-L1 and PD-L2 genes (9p24.1 cytoband), respectively,were purchased from the human BAC clone library at Chil-dren's Hospital Oakland Research Institute, and labeled byNick translation assay with TexasRed to generate locus-specificFISH probes (red). The RP11-145H11 clone (9q21) was labeledwith fluorescein isothiocyanate fluorescence to generate a con-trol probe to enumerate chromosome 9 (green).

FISH analyses were performed according to the manufac-turers' instructions as previously described (38) on 5-mm tissuemicroarray sections mounted on positively charged slides(Superfrost, Thermo Fisher Scientific). Briefly, the FFPE slideswere deparaffinized in xylene and then rehydrated graduallyin a graded series of ethanol. The Histology FISH AccessoryKit (Dako, Agilent) was used following the manufacturer'sinstructions. Briefly, the method consists of pretreatment in2-(N-morpholino) ethanesulfonic acid, followed by proteindigestion performed on pepsin solution. After dehydration,the samples were denatured in the presence of the specificprobe at 66�C for 10 minutes and left overnight for hybrid-ization at 45�C in a Dako hybridizer machine. Finally, theslides were washed with 20 � saline-sodium citrate buffer withTween-20 detergent at 63�C and mounted on fluorescentmounting medium (DAPI). The FISH signals within nucleiin all the cells in tissue were manually enumerated by twoinvestigators working independently. FISH images were alsocaptured using a charge-coupled device camera (SenSys camera;Photometrics) connected to a PC running the CytoVision imageanalysis system (Applied Imaging) with focus motor andZ-stack software.

Four patterns were defined: no alteration, with two greencontrol signals and two red PD-L1/PD-L2 signals; polyploidy,withmore than two green control signals and the same number ofred PD-L1/PD-L2 signals; gain, with the number of PD-L1/PD-L2signals between three to five copies greater than the two controlprobe signals; and amplification, with the number of PD-L1/PD-L2 signals at least six copies greater than that of the control probesignals.

Statistical analysisClinical features were compared by Fisher's exact test. Expres-

sion between groups was compared by the unpaired Student ttest (two-tailed). Survival of two groups of patients with dif-ferent expression status using cutoffs determined by the X-Tilesoftware (version 3.6.1, Yale School of Medicine, New Haven,CT) was compared by the Kaplan–Meier method and log-rank

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test. Cox proportional hazards regression model was used formultivariate analyses. P � 0.05 was considered significant.Multiple testing corrections were performed using the Benja-mini–Hochberg procedure. GEP data were analyzed to identifydifferentially expressed genes between two groups by multipleStudent t tests via control of the false discovery rate as describedpreviously (31–33).

ResultsLack of T-cell and/or NK cell infiltration predicts poor survival

To characterize the tumor immune microenvironment com-position, numbers of DLBCL cells (CD20þ/PAX5þ), tumor-infiltrating T cells (CD3þ), macrophages (CD3�CD68þ), andnatural killer (NK) cells (CD56þCD3�) were quantified. Celldensities (cell counts/mm2) in 405 patients with EBV� DLBCLwere plotted in Fig. 1A. ABC-DLBCL compared with GCB-DLBCLhad higher macrophage (P ¼ 0.0006) and CD8þ T-cell density(P ¼ 0.034; Supplementary Fig. S1A). T cells were further sub-typed by CD4, CD8, FOXP3 (Treg), and CD45RO (Tm) markers.The median CD4þ T-cell:CD8þ T-cell ratio was 1.53; the medianTreg/CD4þ T-cell percentage was 25.5%; and the median Tm cellpercentages were 63.1% and 70.7% for CD4þ T cells and CD8þ Tcells, respectively.

Immune cell infiltration was evaluated by dividing the numberof tumor-infiltrating immune cells by the total number of DLBCLcells, T cells, macrophages, and NK cells in each sample, resultingin a percentage that was slightly higher than the total tissue

cellularity, which used the number of all nucleated cells (DAPIþ)as the denominator. Very low T-cell infiltration (0%–2.6%),referred to as a CD3� "cold" tumor immune microenvironment,was associated with unfavorable clinical parameters includinghigh international prognostic index (Supplementary Table S1)and decreased progression-free survival (PFS) and overall survival(OS) rates (Fig. 1A). Similarly, deficiency in NK cell, CD4þ T-cell,and CD8þ T-cell infiltration (cutoff: 3.0%, 1.6%, and 1.5%,respectively), but not macrophage deficiency or CD4þ T-cell:CD8þ T-cell, Treg:CD4þ T-cell, and Treg:CD8þ T-cell ratios,showed a significant adverse prognostic impact (SupplementaryTable S2). Patients with high (>20%) CD4þ T-cell infiltration hadbetter survival compared with those with low CD4þ T-cell infil-tration; in contrast, high infiltration of CD8þ T cells andCD8þ Tmcells (cutoff: �17.4% and �16.3%, respectively) was associatedwith poorer survival amongCD8þ T-cell patients (SupplementaryFig. S1B).

High PD-1, but not CTLA-4, expression on T cells is associatedwith adverse prognosis

To assess immune-checkpoint expression in each patient withDLBCL, numbers of PD-1þ, PD-L1þ, PD-L2þ, and CTLA-4þ cellsof different cell types were quantitated, and cell type–specificpercentages of expression were calculated by dividing the num-ber of positive cells by the total number of cells within each cellpopulation. PD-1þ, PD-L1þ, PD-L2þ, and CTLA-4þ cell densitiesand percentages of different cell types in the study cohort areplotted in Fig. 1B. The ABC subtype, compared with the GCB

Figure 1.

Immune profiling analysis in 405 patients with de novo DLBCL. A, Top, scatter plot for cell densities of lymphoma cells and immune cells in the study cohort.Bottom, DLBCL patients lacking CD3þ T-cell infiltration had significantly poorer survival. Abbreviations: B, B cells; T, T cells; M�, macrophages. B, Scatter plotsfor PD-1þ, PD-L1þ, PD-L2þ, and CTLA-4þ cell densities and percentages of different cell types in the study cohort. � , P < 0.05; ��, P < 0.005; ��� , P < 0.0001. C,Representative fluorescent multiplex IHC images. Left, PD-L1þ DLBCL cells in respect to PD-1þCD8þ T cells; right, PD-L1þ antigen-presenting cells in respect toPD-1þCD4þ T cells. Original magnification,� 20. D, Activated B-cell–like (ABC) compared with germinal center B-cell–like (GCB) subtype had significantly highermean levels of PD-L1 and PD-1 expression in DLBCL cells. Each dot represents for one patient; black lines show the mean values.

PD-1/L1 in Immune (but not DLBCL) Cells Confer Poor Outcome

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subtype, had significantly higher PD-L1 expression in CD20þ

B cells and CD68þ macrophages (representative images inFig. 1C) and PD-1 expression in CD20þ B cells (Fig. 1D).

A representative image of PD-1 expression in CD4þ T cells andCD8þ T cells is shown in Fig. 2A. A high PD-1þ percentage(PD-1hi; cutoff: �54.8%) in CD8þ T cells was associated withsignificantly poorer survival in overall DLBCL (prevalence:29%; Fig. 2B) and the CD8þ T-cell subcohort (Table 1). PD-1þ

expression in CD4þ T cells (cutoff: �3%; prevalence: 93%) wasassociated with elderly age, high international prognostic index(Supplementary Table S1), and significantly poorer survival(Table 1). However, higher PD-1þ percentage of CD4þ T cellsdid not show further adverse prognostic effect; paradoxically,PD-1hi expression in CD4þ T cells (cutoff: �75%) showed trendstoward better OS (P ¼ 0.067) and PFS (P ¼ 0.056) but the casenumber was small (n ¼ 25; Supplementary Fig. S1C).

As PD-1 signaling is known to affect T-cell proliferation andeffector functions, we examined T-cell infiltration and GEP inDLBCL samples. Patients with PD-1hiCD8þ T cells comparedwith patients with PD-1loCD8þ T cells had increased CD8þ Tmcell infiltration (P ¼ 0.0004), CD4þ Tm cell infiltration (P <0.0001), and a gene signature including CXCL9, GZMK, PRF1,HLA-A, TRBC1 (also included in theCD3þT-cell infiltrationgenesignature), C1QC, and CD5 upregulation (Fig. 2C; Supplemen-tary Table S3), indicating a correlation between PD-1 expressionand T-cell activation. In contrast, patients with PD-1 expressionin conventional helper T cells (Th, CD3þCD4þFOXP3�) cells

compared with those with PD-1– Th cells had decreased infil-trationof na€�veCD4þT cells (P<0.0001) and downregulationofBCL2L1 (encoding antiapoptotic Bcl-XL), CCL17, IL13RA1, andCD200 expression (Supplementary Table S3).

In approximately one third of the cohort, PD-1 was alsoexpressed in CD20þ DLBCL cells (Fig. 2D), but at lower expres-sion than in T cells (Fig. 1B; median expression, 1% vs. 34%).Although high PD-1þ percentage in DLBCL cells (cutoff: �65%)showed adverse prognostic effects (Supplementary Table S2), PD-1hiCD20þ patients were rare (n ¼ 4) and had concurrent PD-1hi

expression in CD8þ T cells and PD-1 expression in CD4þ T cells.Using the mean percentage (�8%) as the PD-1þCD20þ cutoff,PD-1 expression in DLBCL cells (prevalence: 25%) was notprognostic but showed a distinct gene signature that had overlapwith the gene signature for PD-1 expression (�8%) in Tregs(Fig. 2C), including upregulation of MAF, which may increasethe susceptibility of T cells to apoptosis, and GIMAPs, which arecritical for the differentiation and survival of T cells and B cells.PDCD1 was not shown in any of the PD-1 expression genesignatures (Supplementary Table S3).

Unlike the PD-1 receptor, CTLA-4 expression was very low,infrequent (Fig. 1B), mainly in the cytoplasm, and also found inNK cells and macrophages. CTLA-4 expression in T cells wasassociatedwith significantly betterOS inDLBCL (Fig. 2B; Table 1)and upregulation of CTLA4 and ICOS. Tregs had the highestCTLA-4þ percentage (mean: 6.3%; median: 2.3%) among T-cellsubsets, and the expression in Tregs (representative image

Figure 2.

PD-1 and CTLA-4 expression in T cells. A, Representative fluorescent multiplex IHC image for PD-1 expression in CD3þCD8þ cells and CD3þCD4þ

T cells in a DLBCL sample from a patient who died at 15 months of follow-up. B, Survival analysis for high PD-1þ percentage (PD-1hi) in CD8þ T cellsand T-cell CTLA-4þ expression. C, Venn diagrams illustrating the overlaps between gene signatures. D, Representative fluorescent multiplex IHCimage for high PD-1 expression in CD20þ B cells in a DLBCL sample from a patient who died at 12.8 months of follow-up. E, Representativefluorescent multiplex IHC image for CTLA-4 expression in Tregs in a DLBCL sample from a patient who remained alive at 66 months of follow-up.Abbreviations: DET, differentially expressed transcripts; FDR, false discovery rate. Original magnification of images: �20. CD3 marker is not shown inA and E for clarity in viewing.

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in Fig. 2E) was associated with increased Treg:CD4þ T-cell ratios(P < 0.0001) and Treg cellularity (P ¼ 0.0003), better OS, andupregulation of CTLA4, ICOS, FOXP3, and HLA-DOA (Table 1;Supplementary Tables S2–S4).

High PD-L1/PD-L2 expression in DLBCL cells is associatedwith favorable prognosis

PD-L1 expressionwas foundmainly inDLBCL cells andmacro-phages, which had the highest mean PD-L1þ cell density and

Table 1. Univariate and multivariate survival analyses for immune-checkpoint expression in DLBCL evaluated by percentage within a cell type

Univariate analysisMultivariate analysis

for OSMultivariate analysis

for PFSCutoff definition;prevalence

Impact inoverall DLBCL

Impact in T-cell–infiltrated DLBCL HR (95% CI) P HR (95% CI) P

PD-1 expression in a T-cell subtypePD-1hi in CD8þ

T cellsPD-1hi: �54.8%of CD8þ T cells;29.1%

Poorer OS:P ¼ 0.0052; poorerPFS: P ¼ 0.024

Poorer OS:P ¼ 0.0002; poorerPFS: 0.0023 in CD8þ

patients

1.93 (1.25–2.98) 0.003 1.67 (1.11–2.52) 0.013

PD-1þ in CD4þ

T cellsPD-1þ: �3.0%of CD4þ T cells;93.1%

Poorer OS:P ¼ 0.0025; poorerPFS: P ¼ 0.0079

Poorer OS:P ¼ 0.0016; poorerPFS: P ¼ 0.003 inCD4þ patients

2.98 (1.20–7.39) 0.018 2.97 (1.09–8.09) 0.033

PD-L1 expression in a specific cell typePD-L1hi inB cells

PD-L1hi: �40%of CD20þ cells;8.3%

Better OS: P¼ 0.034 (P¼ 0.067 for betterPFS)

Better OS: P ¼ 0.037(P¼ 0.084 for better PFS)in CD3þ patients

0.38 (0.15–0.93) 0.035 0.55 (0.27–1.12) 0.099

In patients withPD-1loCD8þ

T cells: prevalence, 10%

Better OS: P ¼ 0.032;better PFS: P ¼ 0.04

Better OS: P ¼ 0.035; betterPFS: P ¼ 0.053in CD3þ patients

0.26 (0.08–0.83) 0.023 0.39 (0.16–0.96) 0.04

ProximitybetweenPD-L1þCD20þ

B cellsand PD-1þCD8þ

T cells

In patients withPD-1loCD8þ Tcells: prevalence, 72.3%

Better OS:P < 0.0001; betterPFS: P < 0.0001

Better OS:P ¼ 0.0001; better PFS:P ¼ 0.0033 in CD3þ

patients

0.35 (0.20–0.60) <0.001 0.40 (0.24–0.67) <0.001

PD-L1þ inCD68þ

cells

PD-L1þ: �27% ofCD68þ cells;72.7%

Poorer OS: P ¼ 0.02(P ¼ 0.16 for PFS)

Poorer OS: P ¼ 0.015(P ¼ 0.14 for PFS) in CD3þ

patients

1.49 (0.93–2.40) 0.097 1.27 (0.83–1.93) 0.27

In patients with PD-1hiCD8þ

T cells: prevalence, 75.4%Poorer OS:P ¼ 0.0052; poorerPFS: P ¼ 0.024

Poorer OS: P ¼ 0.01; poorerPFS: P ¼ 0.05in CD8þ patients

3.36 (1.18–9.56) 0.023 2.35 (0.97–5.67) 0.058

PD-L1þ inT cells

PD-L1þ: �5.8% of T cells;27.4%

Poorer OS: P ¼ 0.04(P ¼ 0.16 for PFS)

Poorer OS: P ¼ 0.009(P ¼ 0.07 for poorerPFS) in CD3þ

patients

1.48 (0.98–2.23) 0.062 1.37 (0.93–2.02) 0.12

PD-L1þ inNK cells

PD-L1þ: �13.3% of NK cells;41.2%

Poorer OS: P¼0.038 (P¼ 0.069 for poorerPFS)

Poorer OS:P ¼ 0.0078; poorerPFS: P ¼ 0.019 inCD3þ patients

1.24 (0.86–1.80) 0.26 1.36 (0.96–1.93) 0.087

In patients with PD-1hiCD8þ

T cells: prevalence,49.2%

Poorer OS:P ¼ 0.0086; poorerPFS: P ¼ 0.0029

Poorer OS:P ¼ 0.0066; poorerPFS: P ¼ 0.0027 inCD8þ patients

2.58 (1.30–5.11) 0.007 2.51 (1.34–4.71) 0.004

PD-L2 expression in a specific cell typePD-L2þ inB cells

PD-L2þ: �25% ofCD20þ cells; 5.2%

Better OS: P ¼ 0.0018;better PFS:P ¼ 0.0018

Better OS: P ¼ 0.0039;better PFS: P ¼ 0.0046 inCD3þ patients

— 0.96 — 0.96

CTLA-4 expression in a T-cell subtypeCTLA-4þ inTregs

CTLA-4þ: �13%of Tregs; 15.8%

Better OS: P ¼ 0.018(P ¼ 0.098 for PFS)

Better OS: P ¼ 0.023(P ¼ 0.16 for PFS) inCD4þ patients

0.45 (0.25–0.82) 0.009 0.49 (0.27–0.88) 0.018

CTLA-4þ inconventionalTh cells

CTLA-4þ: �1.9%of Th cells; 22.0%

Better OS: P ¼ 0.023;better PFS:P ¼ 0.039

Better OS: P ¼ 0.02;better PFS: P ¼ 0.036in CD4þ patients

0.58 (0.36–0.95) 0.031 0.58 (0.35–0.94) 0.027

NOTE: For multivariate survival analyses, Cox models were used and the factors included high International Prognostic Index score, sex, B-symptoms, >5-cm tumorsize, and individual immune-checkpoint expression. For PD-1/PD-L1 expression in T cells, analyseswere performed in patientswith T-cell infiltration. For expression inother cell types, analyses were performed in the overall DLBCL cohort.Significant P values are in bold.Abbreviations: HR, hazard ratio; CI, confidence interval; NK, natural killer, CD56þCD3�; Th, helper T cells, CD3þCD4þFOXP3� cells; Tregs, regulatory T cells,CD3þCD4þFOXP3þ cells.

PD-1/L1 in Immune (but not DLBCL) Cells Confer Poor Outcome

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percentage, respectively (Fig. 1B and C). The case distribution ofPD-L1 expression with respect to PD-1hiCD8þ T-cell expression isshown in Fig. 3A.

Tumor-cell PD-L1 expression (PD-L1þ; cutoff: �3.0%; preva-lence: 84%) was associated with poorer OS in patients with PD-1hiCD8þ T cells (P ¼ 0.034; Fig. 3B) but not in overall DLBCL.However, this adverse impact lost significance in multivariateanalysis (Supplementary Table S2). PD-L1þ percentage of CD20þ

cells did not show a dose-dependent unfavorable prognosticeffect among PD-1hiCD8þ T-cell patients, and patients withintermediate PD-L1 expression (15%–32%) had a good progno-sis (Supplementary Fig. S1D).

High PD-L1þ percentage of CD20þ cells (PD-L1hi; cutoff:�40%; prevalence: 8.3%) was associated with better survival inpatients with PD-1loCD8þ T cells (P¼ 0.032 for OS, Fig. 3B; P¼0.04 for PFS) and in overall DLBCL (P¼ 0.034 for OS; marginalP ¼ 0.067 for PFS; Table 1) without association with anyfavorable clinical parameter. With a lower cutoff, >30% (20,21), PD-L1hi expression (prevalence: 12%) was associated with

favorable PFS (P ¼ 0.037) and OS (P ¼ 0.06) only in patientswith PD-1loCD8þ T cells but not in overall DLBCL (Supple-mentary Fig. S1E; Supplementary Table S2). We also evaluatedPD-L1þ percentage in double-positive CD20þPAX5þ B cellsand found that B-cell PD-L1hi expression (cutoff: �36%)remained to have favorable prognostic effect in overall DLBCL(P ¼ 0.05) and in patients with PD-1loCD8þ T cells (P ¼ 0.06).GEP analysis showed that PD-L1hi expression in DLBCL cellswas associated with upregulation of CD274, JAK2, STAT1,APOBEC3A (cytidine deaminase), KLRC4 (NK lectin-like recep-tor C4), HAVCR2 (TIM3), and DRAM and downregulation ofIGL@ (Supplementary Table S3).

The mean PD-L1þCD20þ expression in PD-1hiCD8þ T-cellpatientswas similar to that in PD-1loCD8þ T-cell patients. Regard-less of the PD-1hi/PD-1lo status, PD-L1þCD20þ expression wasassociated with increased CD8þ/CD4þ Tm cell infiltration andhigher PD-1þCD8þ T-cell densities, and PD-L1hiCD20þ expres-sion was associated with increased CD8þ Tm and CD68þ cellinfiltration. However, only in patients with PD-1hiCD8þ T cells,

Figure 3.

PD-L1 and PD-L2 expression in CD20þ B cells. A, Distribution of cases with PD-1/PD-L1/L2 expression evaluated by percentage in immune cells or DLBCL cellsand cases with close distance from PD-L1þ B cells to PD-1þCD8þ T cells (PD-L1:PD-1close) in 405 DLBCL patients. Each column represents one patient.Abbreviations: GCB, germinal center B-cell–like; ABC, activated B-cell–like; UC, unclassifiable. B,OS compared between positive and negative PD-L1 expressionin DLBCL cells in patients with high PD-1þ percentage of CD8þ T cells (PD-1hiCD8þ); high and low PD-L1 expression in DLBCL cells in patients without PD-1hiCD8þ

cells (PD-1loCD8þ); and close and far distance from PD-L1þ DLBCL cells to PD-1þCD8þ T cells in patients with low PD-1þ percentage in CD8þ T cells (PD-1lowþ

CD8þ). C, Visualized proximity of PD-L1þDLBCL cells and PD-L1þmacrophages to PD-1þCD8þ T cells in a DLBCL sample with low PD-1þ expression in CD8þ

T cells from a patient who remained alive at 18 months of follow-up. Original magnification,� 20. D, Left, representative fluorescent multiplex IHC image forPD-L2 expression in DLBCL cells in a DLBCL sample from a patient who remained alive at 60 months of follow-up. Right, PD-L2þ expression in DLBCL cells wasassociated with significantly better survival.

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PD-L1hi CD20þ expression was associated with higherPD-1þCD8þ T-cell densities (P ¼ 0.002). In contrast, only inpatients with PD-1loCD8þ T cells, PD-L1hi patients comparedwith PD-L1loDLBCLpatients had a lowermeanPD-1þpercentagein CD8þ T cells (P ¼ 0.0023; Supplementary Table S4).

To determine whether the presence of the PD-L1–PD-1 inter-action underlies the opposing prognostic effects of PD-L1 expres-sion in the PD-1loCD8þ and PD-1hiCD8þ T-cell groups, wequantified the KNN distance from PD-L1þCD20þ B cells toPD-1þCD4þ T cells or to PD-1þCD8þ T cells. We found thatalmost all patients with PD-1hiCD8þ T cells, who had poorersurvival compared with patients with PD-1loCD8þ T cells, hadPD-L1–PD-1 proximity (representative image in Fig. 1C). Incontrast, patients with minimal PD-1 expression in CD8þ T cells(PD-1þ percentage: >0% but <11%; n ¼ 50) had favorableprognosis and significantly lower prevalence of proximitybetween PD-L1þCD20þ B cells and PD-1þCD8þ T cells comparedwith other DLBCL patients (28% vs. 79%), even though thesepatients had higher mean PD-L1þ percentage in DLBCL cells (P¼0.0002). However, patients without any PD-1þCD8þ T-cellexpression (PD-1þ percentage: 0%) and PD-L1–PD-1 interaction(n ¼ 14) had low PD-L1 expression and poor prognosis (Sup-plementary Fig. S1F and S1G). After exclusion of these cases, therest of PD-1loCD8þ cases (referred to as PD-1lowþCD8þ cases, n¼221) also had a high prevalence (72%) of PD-L1þCD20þ B-cell/PD-1þCD8þ T-cell proximity (representative image in Fig. 3C).Contrary toexpectations,PD-1lowþCD8þ caseswithproximalPD-L1to PD-1þCD8þ T cells (but not PD-1þCD4þ T cells) compared withthose with distant PD-L1 had significantly better survival in univar-iate analysis (OS, Fig. 3B; PFS, P < 0.0001) and multivariate analy-sis adjusting for clinical parameters (Table 1). SupplementaryFigure S2A shows two representative KNN distance plots for eachcell inPD-1lowþCD8þ caseswithorwithoutPD-L1–PD-1proximity.

Regardless of the PD-1hi/PD-1lo status, PD-L1þCD20þ B-cell/PD-1þCD8þ T-cell proximity was associated with higher CD8þ/CD4þ T-cell infiltration. Only in patients with PD-1lowþCD8þ

T cells, was PD-L1–PD-1 proximity associated with a significantlyhigher mean PD-1þ percentage in CD8þ T cells (P < 0.0001) andCD4þ T cells (P ¼ 0.0022) and a higher PD-L1þ percentage inDLBCL cells (P ¼ 0.033; Supplementary Table S4), whereas caseswithout PD-L1–PD-1 proximity had a significantly higher frequen-cy of MYCþBCL2þ double expression (31.7%, P ¼ 0.007). Furthermultivariate analysis in patients with PD-1lowþCD8þ T cells foundthat PD-L1þCD20þ B-cell/PD-1þCD8þ T-cell proximity was a signi-ficant favorable prognostic factor independent of MYC/BCL2 dou-ble or single expression (P¼ 0.005 to 0.02). PD-L1–PD-1 proximityin PD-1lowþCD8þ cases had a distinct gene signature (Supplemen-tary Table S3), which essentially is part of the CD3þ T-cell infiltra-tion gene signature (Supplementary Fig. S2B), including upregula-tion of genes involved in T-cell receptor signaling (CD3E, CD3D,and TRBC1) and cytolytic function (GZMK, GZMA, and PRF1).

Another PD-1 ligand, PD-L2, was expressed infrequently. PD-L2þ expression in DLBCL cells (major source, Fig. 1B) and T cellswas associated with significantly better survival by univariate butnot multivariate analysis (Fig. 3D; Table 1). In line with this, PD-L2þ expression (regardless of source) was associatedwith low PD-1 expression in CD4þ T cells and PD-L1hi expression in DLBCLcells (Supplementary Table S4). Despite the low case numbers,PD-L2þ expression in DLBCL cells showed a prominent GEPsignature, including upregulation of PD-L2, CD80, CCL17,IL13RA1 (IL-13 is a typical Th2 cytokine), and SLAMF1 (which

promotes Th2but inhibits Th1 cytokines), anddownregulation ofCD1C, PRKCB, and FOXP1 (Supplementary Table S3).

PD-L1/PD-L2 genetic alterations were analyzed by FISH usingeither a PD-L1 or PD-L2 probe (Fig. 4A). PD-L1 and PD-L2 geneamplification was observed in 3.0% and 3.2% of the cohort,respectively, and associated with higher expression of PD-L1/PD-L2 protein (Fig. 4A) and PD-L1mRNA. In addition, 7.0% to 7.8%of cases had polyploidy or PD-L1/PD-L2 gain. None of these PD-L1/PD-L2 copy-number alterations showed prognostic effects.

PD-L1 expression in tumor-infiltrating immune cells hasadverse prognostic effects

Microenvironment PD-L1 expression in macrophages (pre-valence: 73%), T cells (lowest mean PD-L1þ percentage), andNK cells (lowestmeanPD-L1þ cell density; Fig. 1B)was associatedwith significantly poorer survival in DLBCL patients (Fig. 4B;Table 1), although PD-L1 expression in T cells and NK cells wasassociated with increased CD8þ Tm cell and macrophage infil-tration. PD-L1 expression in T cells was also associated withincreased CD4þ Tm and na€�ve CD8þ T-cell infiltration, andPD-L1 expression in CD4þ T cells was associated with a decreasedmean Treg:CD4þ T-cell ratio (all P < 0.0001; ref. 24). In contrast,PD-L1 expression in macrophages and NK cells was associatedwith decreased infiltration of na€�ve CD4þ T cells (P¼ 0.0006 andP < 0.0001, respectively; Supplementary Table S4).

Multivariate analyses adjusting for clinical parameters indicat-ed that the adverse prognostic impact of PD-L1 expression inmacrophages and NK cells was significant only in patients withPD-1hiCD8þ T cells (Fig. 4B). In patients with PD-1lowþCD8þ

T cells, spatial analysis showed that close distance from PD-L1þ

macrophages or PD-L1þCD8þTmcells to PD-1þCD8þT cellswasassociated with significantly better survival (for OS, P < 0.0001;Supplementary Fig. S2C; for PFS, P ¼ 0.0043 and P ¼ 0.0049,respectively).

Among the PD-L1þ GEP signatures, the signature for PD-L1expression in T cells wasmost prominent, which overlapswith thesignatures for CD3þ T-cell infiltration and PD-1hiCD8þ expres-sion (Fig. 4C). Shared immunosuppressive signatures by PD-L1expression in T cells, macrophages, and NK cells included upre-gulation of IDO1, SOD2, CHI3L1, and LILRBs (Fig. 4C). The genesignature of PD-L1 expression inmacrophages also included IL10,IL18BP, and genes involved inmetabolism and signaling (such asEGFR and RAS; Supplementary Table S3).

Increased PD-1þCD8þ T cells and PD-L1þ T cells andmacrophages are associated with poor survival

Compared with cell percentages, cell density analysis foundfewer significant prognostic factors to stratify patients (Fig. 5A;Supplementary Fig. S2D). High densities of PD-1þCD8þ T cells,PD-L1þ T cells, and PD-L1þ macrophages showed adverse prog-nostic effects (Fig. 5B), whereas high densities of PD-L2þCD20þ

cells, PD-L2þ T cells, and PD-L1þCD20þ cells showed favorableeffects (Table 2; Supplementary Table S2).

Compared with PD-1hi/PD-L1þ percentages, high PD-1þ/PD-L1þ cell densities showed more prominent gene signatures butwith larger overlaps (Fig. 4C), in line with the significant associa-tions between high densities of PD-1þCD8þ T cells, PD-L1þ

T cells, and PD-L1þCD68þ cells (all P < 0.0001), but not betweenPD-1þ percentage in CD8þ T cells and PD-L1þ percentage inT cells/CD68þ cells (Supplementary Table S4).

PD-1/L1 in Immune (but not DLBCL) Cells Confer Poor Outcome

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PD-1þ/PD-L1þ T-cell and PD-L1þ macrophage cellularity isassociated with inferior survival

Tissue cellularity analysis showed additional prognostic factorsincluding the favorable high PD-L1þCD56þ, PD-L2þCD68þ, andPD-1þCD20þ tissue cellularity (Table 2; Supplementary TableS2). However, the adverse effects of high PD-1þ/PD-L1þ T-cellcellularity on OS were only significant after exclusion of patientswith CD3� cold tumors. In contrast, the favorable effects of hightissue cellularity of PD-L1þCD20þ cells, PD-1þCD20þ cells, PD-L2þCD3þ T-cell cells, and PD-L2þCD68þ cells on OS were sig-nificant in overall DLBCL, but not after exclusion of CD3�

patients (Table 2).There were significant positive associations between different

PD-1þ and PD-L1þ immune cell cellularity (SupplementaryFig. S2E) and between PD-1þCD4þ and CTLA-4þ T-cell cellu-larity, whereas negative associations are shown between PD-1þ

T-cell cellularity and PD-L1þ/PD-L2þ B-cell cellularity (Supple-mentary Table S4). Patients with high PD-1þ/PD-L1þ T-cell orPD-L1þ macrophage tissue cellularity showed distinct gene sig-natures but with large overlaps (Fig. 5C). The case distribution of

expression by high tissue cellularity versus by percentage in a celltype is shown in Fig. 5A.

For themultiple comparisonsperformed in theDLBCL cohort,prognostic significance was adjusted by the Benjamini–Hoch-berg procedure (Supplementary Table S5), and further interro-gated by randomly assigning 18 consortium centers into twoindependent cohorts. As shown in Supplementary Fig. S3, inboth training and validation cohorts, CD3�, CD56�, PD-1hi

expression in CD8þ T cells, PD-1 expression in CD4þ T cells,PD-L1 expression in Th cells, and PD-L1 expression in macro-phages in patients with PD-1hiCD8þ T cells had significantadverse prognostic effects, whereas PD-L2þ expression inCD20þ

B cells predicted superior survival, as well as proximity/interac-tion between PD-L1þCD20þ B cells and PD-1þCD8þ T cells inpatients with PD-1lowþCD8þ T cells. The adverse effect of PD-L1expression inNKcells, the favorable effect ofCTLA-4þexpressionin T cells, and the context-dependent prognostic effect of PD-L1hi

and PD-L1þ expression in CD20þ B cells did not reach statisticalsignificance in one cohort but did in the other. Compared withevaluation of expression by percentage within a cell type,

Figure 4.

PD-L1 genetic alteration analysis in DLBCL and prognostic and GEP analysis for PD-L1 expression in the tumor microenvironment. A, Left, representative FISHimages for PD-L1 genetic abnormalities according to the numbers of red PD-L1 signals compared with the control green CEP9 signals. Right, PD-L1 geneamplification was associated with significantly higher mean percentage of DLBCL cells expressing the PD-L1 protein. B, PD-L1 expression in T cells was associatedwith significantly poorer OS in the overall DLBCL cohort. PD-L1 expression in CD68þ cells and CD56þ cells had significant adverse prognostic effects in patientswith high PD-1þ percentage of CD8þ T cells (PD-1hiCD8þ). C, Venn diagrams illustrating the overlaps between various gene signatures for PD-L1þ/PD-1hi

percentage in a cell type and T-cell infiltration (left and middle) and between various gene signatures for high PD-1þ/PD-L1þ cell densities (right). Abbreviation:DET, differentially expressed transcripts.

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assessment of cell abundance by cell density or cellularityshowed less consistent significance in two independent cohorts.However, in both training and validation cohorts, low T-cell/NKcell cellularity and high PD-1/PD-L1 expression in CD8þ/CD4þ

T cells by either cell density or cellularity showed significantlyunfavorable prognostic effects, whereas high PD-L2þCD20þ B-cell cellularity showed significantly favorable prognostic effect.

DiscussionDysfunction of antitumor responses by immune-checkpoint

expression in the tumor microenvironment (rather thanexpression in T cells infiltrating into normal tissues or periph-eral blood; refs. 39–41) needs to be better characterizedby modern immunopathology. Using a 13-marker/14-colorMultiOmyx immunofluorescent platform, this study compre-hensively analyzed the immune landscape of DLBCL biopsiesand deciphered the clinical role of PD-1/CTLA-4 immunecheckpoints in a large number of DLBCL patients. To minimizeeffects of tissue heterogeneity, small sampling, variable tissuefixation, selection bias, and limitation of digital imaging field,we selected immune-infiltrated tumor regions on the tissuemicroarray and used the percentage of positive cells in aspecific cell type as a method to evaluate intratumoral check-point expression. This method showed higher sensitivity toindicate the unfavorable role of checkpoint expression andenhanced ability to identify immunologic determinants than

the traditional methods evaluating immune cell abundance,because numbers of different types of immune cells, but notpercentage of specific cells with immune-checkpoint expres-sion, often increased simultaneously in DLBCL samples. Cellabundance assessment by cell density showed better specificityin prognostication, but the cellularity method is more able toidentify immune desert/cold cases. For cold CD3�/CD56� andhigh CD8þCD45ROþ T-cell infiltration, our method using adifferent denominator to evaluate the tumor immune micro-environment composition showed better prognostic resultscompared with tissue cellularity. The results in this studyprovide valuable insights for future application of multiplexIHC and digital quantitative analysis.

In two independent cohorts, we demonstrate that high PD-1expression in CD8þ T cells and PD-L1 expression in T cells andCD68þ cells had significant adverse prognostic effects inpatients with de novoDLBCL. In contrast, high PD-L2 expressionin DLBCL cells was favorable and associated with lower PD-1expression in CD4þ/CD8þ T cells and CD80 upregulation.Controversially in earlier studies, PD-L2 costimulated T-cellproliferation and cytokine production through unknown recep-tors (42, 43). Proximity (interaction) between PD-L1þCD20þ

B cells and PD-1þCD8þ T cells was associated with significantlyimproved survival in patients with low PD-1þ percentage inCD8þ T cells, suggesting that in this scenario, PD-1–PD-L1interaction indicates T-cell activation and nonexclusion ratherthan exhaustion (9), or that the suppressive effect of PD-L1–

Figure 5.

Cell abundance analysis. A, Top, distribution of cases with high PD-1þ/PD-L1þ/PD-L2þ cell density as indicated by colored columns in 405 DLBCL patients.Bottom, distribution of high PD-1þ/PD-L1þ/PD-L2þ tissue cellularity and percentage in a specific cell type. Each column represents one patient. B, High densitiesof PD-1þCD8þ T cells, PD-L1þCD3þ cells, and PD-L1þCD68þ cells were associated with significantly poorer OS. C, Venn diagram illustrating the overlaps betweengene signatures for high PD-1þ/PD-L1þ tissue cellularity. Abbreviations: GCB, germinal center B-cell–like; ABC, activated B-cell–like; UC, unclassifiable; DET,differentially expressed transcripts.

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Table 2. Univariate and multivariate survival analyses for immune-checkpoint expression in DLBCL evaluated by cell density and tissue cellularity

Univariate analysis Multivariate analysis for OS Multivariate analysis for PFSCutoff;prevalence

Impact in overallDLBCL

Impact in T-cell–infiltrated DLBCL HR (95% CI) P HR (95% CI) P

Cell density of expression-positive cellsHigh PD-1þ

CD8þ T-celldensity

�671 cells/mm2;21%

Poorer PFS:P ¼ 0.0096(P ¼ 0.066 forpoorer OS)

Poorer OS: P ¼ 0.014;poorer PFS:P ¼ 0.0018 in CD8þ

patients

1.80 (1.14–2.85) 0.011 2.00 (1.31–3.05) 0.001

High PD-L1þ

T-cell density�248 cells/mm2;20.2%

Poorer OS:P ¼ 0.031(P ¼ 0.064 forpoorer PFS)

Poorer OS:P ¼ 0.0075; poorerPFS: P ¼ 0.02 in CD3þ

patients

1.93 (1.23–3.03) 0.004 1.84 (1.20–2.83) 0.006

High PD-L1þ

Th cell density�123 cells/mm2;24.9%

Poorer OS:P¼0.019; poorerPFS: P ¼ 0.043

Poorer OS:P ¼ 0.0028; poorer PFS:P ¼ 0.0097 inCD4þ patients

1.68 (1.10–2.58) 0.017 1.59 (1.06–2.38) 0.025

High PD-L1þ

CD68þ celldensity

�769 cells/mm2;9.9%

Poorer OS: 0.04(P ¼ 0.085 forPFS)

Poorer OS: 0.0092;poorer PFS: 0.023 inCD3þ patients

1.50 (0.86–2.63) 0.15 in CD3þ

patients1.43 (0.84–2.46) 0.19 in CD3þ

patients

High PD-L1þ

CD20þ B-celldensity

�1,467 cells/mm2; 13%

Better PFS:P ¼ 0.023(P ¼ 0.055 forbetter OS)

Better PFS: P ¼ 0.047(P ¼ 0.093 for OS) inCD3þ patients

0.50 (0.27–0.95) 0.034 0.51 (0.28–0.92) 0.024

High PD-L2þ

CD20þ B-celldensity

�1,877 cells/mm2; 3.7%

Better OS:P ¼ 0.0084;better PFS:P ¼ 0.012

Better OS: P ¼ 0.01; betterPFS:P ¼ 0.0048in CD3þ patients

— 0.97 — 0.96

Tissue cellularity of expression-positive cellsHigh tissuecellularity ofPD-1þCD8þ Tcells

>2.2% of DAPIþ

cells;43.5%

Poorer PFS:P ¼ 0.014(P ¼ 0.13 for OS)

Poorer OS: P ¼ 0.01; poorerPFS:P ¼ 0.0008 in CD8þ

patients

1.66 (1.08–2.56) 0.02 1.73 (1.16–2.58) 0.007

High tissuecellularity ofPD-1þCD4þ Tcells

>1.4% of DAPIþ

cells;60%

Poorer PFS:P ¼ 0.043(P ¼ 0.22 for OS)

Poorer OS: P ¼ 0.01; poorerPFS:P ¼ 0.0012 in CD4þ

patients

1.56 (0.98–2.48) 0.059 1.55 (1.01–2.38) 0.046

High tissuecellularity ofPD-L1þ Tcells

�2.0% of DAPIþ

cells; 24%Marginal P forpoorer OS:P ¼ 0.058(P¼0.16 for PFS)

Poorer OS: P ¼ 0.014;poorer PFS: P ¼ 0.053in CD3þ patients

1.71 (1.12–2.61) 0.012 1.65 (1.10–2.46) 0.014

High tissuecellularity ofPD-L1þCD68þ

cells

�6.0% of DAPIþ

cells; 13.3%Poorer OS:P ¼ 0.039;poorer PFS:P ¼ 0.01

Poorer OS: P ¼ 0.012;poorer PFS:P ¼ 0.0035 in CD3þ

patients

1.36 (0.84–2.22) 0.21 in CD3þ

patients1.6 (1.03–2.57) Not significant

in DLBCL.0.038 inCD3þ

patientsPositive tissuecellularity ofPD-1þCD20þ

B cells

�0.22% of DAPIþ

cells; 17.6%Better OS:P ¼ 0.05(P ¼ 0.084for PFS)

Not significant(P ¼ 0.082 for OS andP ¼ 0.17 for PFS) inCD3þ patients

0.58 (0.32–1.03) 0.061 0.71 (0.43–1.19) 0.19

High tissuecellularity ofPD-L1þCD56þ

cells

�1.1% of DAPIþ

cells;4.4%

Better OS: 0.013;better PFS: 0.02

Better OS: P¼0.027; betterPFS: P ¼ 0.027 in CD3þ

patients

0.14 (0.019–0.99) 0.049 0.25 (0.061–1.01) 0.051

High tissuecellularity ofPD-L1þCD20þ

B cells

�16.6% of DAPIþ

cells; 8.6%Better OS:P ¼ 0.05(P ¼ 0.075 forbetter PFS)

Not significant(P ¼ 0.071 for betterOS and P ¼ 0.12 forPFS) in CD3þ

patients

0.39 (0.18–0.84) 0.017 0.52 (0.27–0.99) 0.048

High tissuecellularity ofPD-L2þCD20þ

B cells

>13% of DAPIþ

cells;4.9%

Better OS:P ¼ 0.0082;better PFS:P ¼ 0.0086

Better OS: P ¼ 0.015;better PFS:P ¼ 0.0078in CD3þ patients

0.12 (0.017–0.90) 0.039 0.10 (0.014–0.75) 0.025

High tissuecellularity ofCTLA-4þCD3þ

cells

�0.79%of DAPIþ

cells; 18.5%Better OS:P ¼ 0.007(P ¼ 0.08 forbetter PFS)

Better OS: P ¼ 0.017(PFS: P ¼ 0.16) inCD3þ patients

0.53 (0.29–0.98) 0.044 0.79 (0.48–1.30) 0.35

NOTE: For multivariate survival analyses, Cox models were used and the factors included high International Prognostic Index score, sex, B-symptoms, >5-cm tumorsize, and individual immune-checkpoint expression. For PD-1/PD-L1 expression in T cells, analyseswere performed in patientswith T-cell infiltration. For expression inother cell types (except for PD-L1þCD68þ cell cellularity as indicated), analyses were performed in the overall DLBCL cohort.Significant P values are in bold.Abbreviations: HR, hazard ratio; CI, confidence interval; Th, helper T cells, CD3þCD4þFOXP3� cells.

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PD-1 interaction is exerted only in a minority of T cells,therefore not significantly affecting overall T-cell function. Ourresults support two studies' findings in murine tumor models,which showed that PD-L1 expressed by host antigen-presentingcells, but not by tumor cells, is indispensable for PD-L1 block-ade–mediated tumor regression (44, 45).

These results are opposite to those by Kiyasu and collea-gues (20), that tumor-cell PD-L1hi (�30%) expression, but notmicroenvironmental PD-L1/PD-1 expression,was associatedwithsignificantly poorer OS in 273 DLBCL patients. The differentresults could be attributable to the inclusion of various DLBCLentities (such as EBVþDLBCL of the elderly with a higher PD-L1hi

frequency than de novo DLBCL not otherwise specified) andlimitations in the PD-L1/PAX5 double-immunostaining methodandprognostic analysis in the studybyKiyasu and colleagues. Celldensity of PD-1þ tumor-infiltrating lymphocytes was used toanalyze OS (but not PFS) in overall DLBCL without exclusionof CD3� cases. For microenvironmental PD-L1 analysis, patientswith tumor-cell (and microenvironmental) PD-L1hi expressionwere excluded.

Our GEP analysis showed that GZMs, PRF1, CD5, and CD44,but not IFNG or PDCD1, were upregulated in PD-1hiCD8þ T-cellpatients, which may indicate a "plastic" dysfunctional state ofCD8þ T cells, possibly reversible by PD-1/L1 blockade (9, 46, 47).However, T-cell deficiency was found in 6.4% of DLBCL patientswith poor prognoses, and LILRs, CHI3L1, IDO1, SOD2, IL10,FCGR3B, and CD163 upregulation were associated with pre-valent PD-L1 expression, which could explain the low responserate to anti–PD-1 monotherapy (close to the frequency ofPD-L1hiCD20þ B-cell expression and that of 9p24.1 geneticalterations in this study cohort) in relapsed/refractory DLBCLafter amedian of three prior lines of therapies in a phase II clinicaltrial (19). Also, PD-1 expression was found in B cells, which maycontribute to resistance to PD-1 blockade therapy. One previousstudy showed elevated PD-1 expression in circulating NK cells inDLBCL patients (48), although in our study PD-1 expression wasnot found in intratumoralNK cells ormacrophages in anypatient.Whether PD-1 expression in non-T cells can contribute to ther-apeutic resistance of PD-1 blockade needs to be clarified by futurestudies.

Another finding of this study is the association of CTLA-4expression with favorable prognosis, increased Treg cellularityand Treg:CD4þ T-cell ratio, ICOS, CD40LG, and CD28 upregula-tion, and decreased PD-1 expression in CD4þ T cells. Althoughgermline Ctla4 knockout resulted in the rapid development oflethal lymphoproliferative disorder, Ctla4 deletion in adult miceconferred protection from autoimmune disease and did notenhance antitumor immunity (49, 50), which were attributableto compensatory upregulation of IL-10, LAG-3, and PD-1 expres-sion uponCtla4 deletion and to a lack of tumor-infiltrating CD8þ

T-cell expansion (49).In conclusion, high intratumoral expression of PD-1 on CD8þ

T cells and PD-L1 onmacrophages and T cells, but not expressionof PD-L1/PD-L2 on DLBCL cells or CTLA-4 on T cells, are resis-

tancemechanisms for standard immunochemotherapy inDLBCLand hence are therapeutic targets for PD-1/PD-L1 blockade inpatients with refractory/relapsed DLBCL. Combination therapiesare needed forDLBCLpatients lacking activated tumor-infiltratingT/NK cells or having additional immunosuppressivemechanismsin the tumor microenvironment. Further studies are necessary toestablish prognostic and predictive biomarkers based on immuneinfiltration and PD-1/PD-L1/PD-L2/CTLA-4 expression inDLBCLsamples.

Disclosure of Potential Conflicts of InterestE.D. Hsi reports receiving commercial research funding from Eli Lilly and

AbbVie and is a consultant/advisory board member for Celgene, SeattleGenetics, and Jazz Pharmaceuticals. J.H. van Krieken reports receiving acommercial research grant from Amgen. G.J. Freeman reports receivingcommercial research funding from Bristol-Myers Squibb, Roche/Genentech,Novartis, VCB, and Ipsen; has ownership interest in Novartis, Roche/Gen-entech, Bristol-Myers Squibb, mplimmune/AstraZeneca, Merck, EMD Ser-ono, Boehringer Ingelheim, and Dako; and is a consultant/advisory boardmember for Novartis, Lilly, Roche/Genentech, Bristol-Myers Squibb, BethylLaboratories, Xios Therapeutics, Quiet Therapeutics, and Seattle Genetics.No potential conflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: Z.Y. Xu-Monette, M. Xiao, B. Xu, K.H. YoungDevelopment of methodology: Z.Y. Xu-Monette, M. Xiao, Q. Au, B. Xu,G.J. Freeman, K.H. YoungAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): Z.Y. Xu-Monette, Q. Au, N. Hoe, S. Rodríguez-Perales, R. Torres-Ruiz, C. Visco, X. Tan, A. Tzankov, K. Dybkær, W. Tam,G. Bhagat, E.D. Hsi, M. Ponzoni, A.J.M. Ferreri, M.B. Møller, M.A. Piris,J.H. van Krieken, J.N. Winter, K.H. YoungAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): Z.Y. Xu-Monette, M. Xiao, Q. Au, R. Padmanabhan,B. Xu, N. Hoe, G.C. Manyam, Y. Miao, J. Wang, E.D. Hsi, M.B. Møller,L.J. Medeiros, Y. Li, K.H. YoungWriting, review, and/or revision of the manuscript: Z.Y. Xu-Monette, M. Xiao,Q. Au, R. Padmanabhan, B. Xu, C. Visco, X. Tan, H. Zhang, A. Tzankov,K. Dybkær, W. Tam, G. Bhagat, E.D. Hsi, M. Ponzoni, A.J.M. Ferreri,M.B. Møller, J.H. van Krieken, J.N. Winter, J.R. Westin, L.V. Pham,L.J. Medeiros, G.Z. Rassidakis, Y. Li, G.J. Freeman, K.H. YoungAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): B. Xu, N. Hoe, X. Tan, A. Tzankov, K. Dybkær,G. Bhagat, E.D. Hsi, M.B. Møller, L.J. Medeiros, G.Z. Rassidakis,Study supervision: B. Xu, K.H. YoungOther (organized all collaboration medical centers and support for IRB andMTA approval): K.H. YoungOther (provision of study thought, materials, key reagents, and technology):H. You

AcknowledgmentsThis work is supported by the Sister Institution Network Fund at The

University of Texas MD Anderson Cancer Center.

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received June 29, 2018; revised September 23, 2018; accepted February 5,2019; published first February 11, 2019.

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PD-1/L1 in Immune (but not DLBCL) Cells Confer Poor Outcome

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2019;7:644-657. Published OnlineFirst February 11, 2019.Cancer Immunol Res   Ziju Y. Xu-Monette, Min Xiao, Qingyan Au, et al.   Microenvironment of DLBCLRole of Immune-Checkpoint Expression in the Tumor Immune Immune Profiling and Quantitative Analysis Decipher the Clinical

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