Purpose:

Glioblastoma (GBM) is the most common primary malignant tumor in the central nervous system. Our recent preclinical work has suggested that PD-1/PD-L1 plays an important immunoregulatory role to limit effective antitumor T-cell responses induced by active immunotherapy. However, little is known about the functional role that PD-1 plays on human T lymphocytes in patients with malignant glioma.

Experimental Design: In this study, we examined the immune landscape and function of PD-1 expression by T cells from tumor and peripheral blood in patients with malignant glioma.

Results:

We found several differences between PD-1+ tumor-infiltrating lymphocytes (TIL) and patient-matched PD-1+ peripheral blood T lymphocytes. Phenotypically, PD-1+ TILs exhibited higher expression of markers of activation and exhaustion than peripheral blood PD-1+ T cells, which instead had increased markers of memory. A comparison of the T-cell receptor variable chain populations revealed decreased diversity in T cells that expressed PD-1, regardless of the location obtained. Functionally, peripheral blood PD-1+ T cells had a significantly increased proliferative capacity upon activation compared with PD-1 T cells.

Conclusions:

Our evidence suggests that PD-1 expression in patients with glioma reflects chronically activated effector T cells that display hallmarks of memory and exhaustion depending on its anatomic location. The decreased diversity in PD-1+ T cells suggests that the PD-1–expressing population has a narrower range of cognate antigen targets compared with the PD-1 nonexpression population. This information can be used to inform how we interpret immune responses to PD-1–blocking therapies or other immunotherapies.

Translational Relevance

Inhibition of the PD-1/PD-L1 axis is currently being explored as a potential immunotherapy for patients with malignant gliomas. Typically, PD-1 expression on T cells in the context of cancer has been exclusively thought of as a marker of exhaustion, and current PD-1 checkpoint therapies are thought to be effective due to their ability to reactivate exhausted T-cell populations. In our study, we sought out to define and characterize the PD-1 expression on T cells in the context of malignant gliomas. We found that PD-1 expression on T cells in the tumor reflects both exhaustion and chronic activation. However, in peripheral blood T cells, PD-1 expression reflects antigen experience and chronic activation more than exhaustion. The results from this study may help inform our use of PD-1 as a biomarker and also be used to assess the clinical efficacy of using PD-1 checkpoint blockade in this malignancy.

Glioblastoma (GBM) is the most common primary brain tumor in adults and remains one of the most lethal of all human cancers (1). Current standard of care includes surgical resection, chemotherapy, and radiotherapy and is associated with a median survival of 12 to 18 months (2–4). The dismal outcome of this disease clearly highlights the need for new approaches in therapy. Novel treatment strategies, such as GBM-targeted immunotherapy and vaccination therapy, have shown promise in both preclinical and clinical trials (5–9). However, the survival benefit has been indeterminate. Antitumor immune responses induced by immunotherapy and, more specifically active vaccination in GBM, may in turn induce an adaptive immune resistance mechanism mediated by immune checkpoints such as signaling of programmed death 1 and its ligand (PD-1/PD-L1; refs. 10–12).

PD-1 is a cell surface coinhibitory receptor that is expressed on T cells after activation and limits the immune response, resulting in what are traditionally considered functionally “exhausted” T cells (13, 14). In normal physiologic states, the function of this immune checkpoint protein is to inhibit or reduce autoimmunity and to maintain homeostasis during chronic infections (15, 16). In the setting of cancer, the PD-1/PD-L1 pathway may be an adaptive mechanism by the tumor to evade antitumor immune attack. Although PD-1 has previously been considered to be a marker of terminally differentiated exhausted T cells, PD-1 expression may in fact be a marker of continued effector or memory function needed to maintain pathogen or tumor control while minimizing tissue damage in a chronic state (17). In fact, a number of recent studies have shown that PD-1 expression of CD8+ T cells early after activation may temporally coincide with rapid proliferation and production of signature effector cytokines and be an important signaling pathway in programming of memory CD8+ T cells (18–20).

In this study, we set out to define and characterize PD-1 expression in tumor-infiltrating lymphocytes (TIL) and peripheral blood from patients diagnosed with malignant glioma. Numerous studies have shown PD-L1 expression in GBM as well as PD-1 expression in a subset of GBM TILs (10, 12, 13). PD-1 expression on malignant glioma TILs in comparison with peripheral blood has not been investigated, nor has the function of these malignant glioma PD-1+ TILs been examined in this detail. We found that the proportion of TILs expressing PD-1 was significantly elevated when compared with T cells in patient peripheral blood and T cells from healthy controls. Furthermore, we found increased levels of activation and exhaustion markers on PD-1+ TILs when compared with PD-1+ peripheral blood T cells, while we found increased markers of memory/antigen experience on the peripheral blood PD-1+ T cells. Our results highlight the complex relationship between PD-1 expression on T cells and its relation to exhaustion and chronic activation within human malignant brain tumors.

Patients

We collected 52 patient tumor and blood specimen samples and an additional 14 peripheral blood samples from healthy donors at University of California, Los Angeles (Los Angeles, CA). In parallel, matched TILs and peripheral blood were obtained from 6 consenting patients with glioblastoma who presented to the University of Heidelberg (Heidelberg, Germany) neurosurgery service for surgical removal of their tumors. All patients provided written informed consent, and this study was conducted in accordance with recognized ethical guidelines and under Institutional Review Board–approved protocols from both institutions.

Collection of PBMCs for immune monitoring

Peripheral blood was drawn from patients prior to surgical resection of their gliomas. Once drawn, we diluted peripheral blood at 1:1 dilution of RPMI media (Thermo Fisher Scientific catalog no. MT10041CV); peripheral blood mononuclear cells (PBMC) were harvested through subsequent extraction with Ficoll (Thermo Fisher Scientific catalog no. 45-001-750) gradient. After a series of two washes in RPMI media, we placed cells in freezing media made of 90% Human AB serum (Thermo Fisher Scientific catalog no. MT35060CI) and 10% DMSO (Sigma catalog no. C6295-50ML) and stored in liquid nitrogen.

Collection of TILs

TILs were collected in two ways. In the first method, we collected tumor and digested the tissue via incubation with collagenase (1 mg/mL; Advance Biofactures Corporation) and DNAse (0.02 mg/mL; Sigma catalog no. D4263-5VL) in RPMI media overnight on the day of resection. TILs were isolated using Percoll (Sigma catalog no. P4937) gradient and washed in RPMI twice. In the second method, patient-resected tumors were first digested using Miltenyi's Brain Tumor Dissociation Kit (Miltenyi Biotec catalog no. 130-095-42) and gentleMACS Dissociator (catalog no. 130-093-235) and labeled with CD45+ microbeads (catalog no. 130-045-801). CD45+ cells were positively selected for using Miltenyi LS columns (catalog no. 130-042-401) and MidiMACS Separator. TILs were frozen in the same freezing media as the PBMCs.

Flow cytometric analysis of PBMCs and TILs

We thawed donor PBMCs and patient PBMCs and TILs at 37°C for 3 minutes in a water bath and immediately washed with RPMI media to remove DMSO. We resuspended cells at 1 × 106 cells/100 μL PBS.

In the characterization of PD-1 expression of TILs and PBMCs, we first stained cells with live-dead Zombie Yellow stain (BioLegend, catalog no. 423104). In two separate experimental reactions, we stained cells with a 7-antibody and an 8-antibody panel. Two antibody panels were necessary to investigate all of the surface markers of interest due to the spectral limitations of the flow cytometer. The first antibody cocktail included seven antibodies: CD3 Pe-Cy7 (eBioscience, catalog no. 25-0038-42), CD4 Alexa Fluor 700 (BD Biosciences, catalog no. 557922), CD8 Pacific Blue (BD Biosystems, catalog no. 558207), CD16 FITC (BD Biosystems, catalog no. 555406), CTLA-4 PE (BD Biosystems, catalog no. 555853), PD-1 AF647 (eBioscience, catalog no. 51-9969-73), and CD69 PerCP-CY5.5 (BD Biosystems, catalog no. 560738) antibodies where each antibody was diluted to a 1:40 antibody to buffer ratio. The second 8-antibody cocktail contained CD3 Pe-Cy7 (eBioscience, catalog no. 25-0038-42), CD4 Alexa Fluor 700 (BD Biosystems, catalog no. 557922), CD8 Pacific Blue (BD Biosystems, catalog no. 558207), CD16 FITC (BD Biosystems, catalog no. 555406), CD19 PE (BD Biosystems, catalog no. 555413), CD25 APC-Cy7 (BD Biosystems, catalog no. 557753), CD127 AF647 (BD Biosystems, catalog no. 558598), and CD69 PE-Cy5 (BD Biosystems, catalog no. 555532) antibodies at the same dilution as above.

We used a BD LSRII flow cytometer and the BD FACS diva software to analyze all cell samples. Cytometer settings were restricted to the nine colors used. We used unstained controls to adjust FSC and SSC voltages such that the lymphocyte population became visible and then acquired single-color compensation controls, adjusting voltages such that the stained cells did not exceed 105. We set gates on stained controls in comparison with the negative control and recorded data from all samples, while subsequently applying compensation controls. We recorded staining expression at 80,000 to 100,000 events per sample, as the quantity of T cells was variable from specimen to specimen. Thus, we analyzed the data acquired from the flow cytometer by using frequency measurements rather than absolute numbers to compare sample expression profiles using FlowJo.

Mass cytometry

Patient PBMCs and TILs were collected as described above. Between 1 to 2 million patient PBMCs and TILs were labeled with Cell-ID Cisplatin (Fluidigm, catalog no. 201064) to assess for live/dead and then labeled with a variety of metal-conjugated antibodies. From the patient PBMCs, 1 × 106 cells were set aside and labeled with a smaller panel containing only cell lineage markers to aid in setting the proper gates of other markers. After labeling, cells were fixed using MaxPar Fix and Perm buffer (Fluidigm, catalog no. 201067) and labeled for single-cell discrimination with Cell-ID Intercalator-Ir (Fluidigm, CA 201192A) and run on the Helios CyTOF system.

The collected mass cytometry data were analyzed as described previously (21). Briefly, samples were normalized utilizing a bead standard, then manually gated to remove debris and dead cells. Raw marker intensities were transformed using hyperbolic inverse sine with cofactor of 5. Cell population identification was carried out utilizing the FlowSOM (22) and ConsensusClusterPlus packages in R using 11 lineage markers, with the number of clusters determined by elbow criterion. For differential analysis of cell population abundance and functional marker expression, we fit fixed and mixed linear models to our data and performed testing using the general linear hypothesis function glht in R. To account for multiple testing, we applied the Benjamini–Hochberg adjustment with a false discovery rate of 5%.

FACS of CD3+ PD-1+ and CD3+ PD-1 T-cell populations

As described above, we thawed frozen donor PBMCs and patient PBMCs and TILs, stained samples with CD3 Pe-Cy7 (eBioscience, catalog no. 25-0038-42) and PD-1 AF647 (BD Biosystems, catalog no. 558598) and sorted samples with the Aria II cell sorter (Becton Dickinson) into PD-1+ and PD-1 T-cell populations. We defined PD-1+ T cells as CD3+ and PD1+ and PD-1 T cells as CD3+ and PD1.

RNA gene expression

We used the NanoString nCounter Dx Analysis System, which measures the expression of 511 human immune function genes through digital readouts of the abundance of mRNA transcripts to analyze cell lysates of PD-1–expressing and PD-1 void cells (23). The nCounter Dx Analysis System uses gene-specific probe pairs that hybridize directly to mRNA samples. A Reporter Probe carries the fluorescent signal; a Capture Probe allows the complex to be immobilized for data collection. We included, in each assay, six positive quality controls comprised of a linear titration of in vitro transcribed RNA transcripts and corresponding probes, and eight negative quality controls consisting of probes with no sequence homology to human RNA sequences. Each Human Immunology assay run includes a reference sample consisting of in vitro transcribed RNAs of the 13 targets that are used for normalization purposes.

TCR V-β sequencing

We extracted genomic DNA from sorted CD3+ PD-1+ and CD3+ PD-1 T cells from malignant glioma patient TILs and PBMCs and from healthy donor PBMCs using DNeasy Blood & Tissue Kit (Qiagen catalog no. 69504) and sequenced DNA at the T-cell receptor (TCR) V-β CDR3 region using high-throughput sequencing in collaboration with Adaptive Biotechnologies, as we have recently published (24).

IFNγ activation assay

CD3+ PD-1 and CD3+ PD-1+ cells were sorted in a similar manner described above. In a 96-well plate, we added 2 × 105 cells into each well at a concentration of 1 × 106 cells/mL. For the cells that were to be activated, we added 50 ng/mL anti-CD3 Ab (BioLegend, catalog no. 317304) and 1 μg/mL anti-CD28 Ab (BD Biosciences, catalog no. 555725). In all wells, we added 50 IU/mL IL2. After 24 hours, the supernatant was collected and fresh media with 50 IU/mL IL2 were added in each well, and the supernatant was collected 24 hours later (total of 48 hours after activation). To measure IFNγ levels, we used the Human IFNγ Uncoated ELISA Kit (Thermo Fisher Scientific, catalog no. 88-7613-86).

Statistical analysis

The percentage of T lymphocytes expressing the various T-cell markers studied were compared between patient PBMCs, TILs, and donor PBMCs. Unpaired Welch t test was applied to compare the means of the frequencies of expression with two-sided P values (*, P < 0.05; **, P < 0.005; ***, P < 0.0005).

Glioma-infiltrating T cells have elevated expression of PD-1 and other markers of T-cell exhaustion and activation compared with peripheral T cells

We recently demonstrated a critical role for PD-L1 on T cells infiltrating murine gliomas, as well as analogous human tumors (10, 12). To examine the role of PD-1 in human glioma, we first analyzed the PD-1 expression profile using traditional flow cytometric approaches in TILs and matched peripheral blood from 14 patients with malignant glioma undergoing surgical resection at UCLA Medical Center (Los Angeles, CA; Fig. 1A). The clinical and pathologic characteristics of our patient population are outlined in Table 1. Similarly, we simultaneously analyzed the expression of PD-1 in the peripheral blood T cells of 14 healthy donors (Supplementary Fig. S1A).

Figure 1.

CD3+ T cells from patients with malignant glioma demonstrate significantly elevated expression of PD-1 compared with healthy donor PBMCs and glioma patient PBMCs. A, Percent of PD-1 expression on CD3+ T cells from all the patient samples as measured by flow cytometry (*, P < 0.05; **, P ≤ 0.005; *** P ≤0.0005). B, The percentage of PD-1–expressing CD3+ T cells from a validation cohort of patients with malignant glioma from the University of Heidelberg (*, P ≤ 0.05). C, Histogram of the expression levels of the different exhaustion markers (PD-1, TIM3, LAG3, CTLA-4). D, Percentage of CD3+ T cells that express one or more markers of exhaustion (PD-1, TIM3, LAG3, CTLA-4). E, The percentage of cells expressing different combinations of the various exhaustion markers (**, P ≤ 0.005; ***, P ≤ 0.0005). Blue bars, healthy donor PBMC samples; red bars, glioma patient PBMC samples; and green bars, glioma patient TIL samples.

Figure 1.

CD3+ T cells from patients with malignant glioma demonstrate significantly elevated expression of PD-1 compared with healthy donor PBMCs and glioma patient PBMCs. A, Percent of PD-1 expression on CD3+ T cells from all the patient samples as measured by flow cytometry (*, P < 0.05; **, P ≤ 0.005; *** P ≤0.0005). B, The percentage of PD-1–expressing CD3+ T cells from a validation cohort of patients with malignant glioma from the University of Heidelberg (*, P ≤ 0.05). C, Histogram of the expression levels of the different exhaustion markers (PD-1, TIM3, LAG3, CTLA-4). D, Percentage of CD3+ T cells that express one or more markers of exhaustion (PD-1, TIM3, LAG3, CTLA-4). E, The percentage of cells expressing different combinations of the various exhaustion markers (**, P ≤ 0.005; ***, P ≤ 0.0005). Blue bars, healthy donor PBMC samples; red bars, glioma patient PBMC samples; and green bars, glioma patient TIL samples.

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Table 1.

The clinical and pathological characterization of our patient population

Flow cytometry/gene expression/TCR sequencingMass cytometryActivation IFNγ studiesAll studies
Total number 20 28 52 
Mean age at diagnosis 51.3 years 51.4 years 43.1 years 50.7 years 
Diagnosis Number (percent) Number (percent) Number (percent) Number (percent) 
 Glioblastoma 18 (90.0%) 20 (71.4%) 3 (75.0%) 41 (78.8%) 
 Gliosarcoma 0 (0%) 1 (3.6%) 0 (0%) 1 (1.9%) 
 Anaplastic oligodendroglioma 0 (0%) 3 (10.7%) 0 (0%) 3 (5.8%) 
 Anaplastic astrocytoma 2 (10.0%) 4 (14.3%) 1 (25.0%) 7 (13.5%) 
Sex 
 Male 13 (65.0%) 17 (60.7%) 4 (100%) 34 (65.4%) 
 Female 7 (35.0%) 11 (39.3%) 9 (0%) 27 (51.9%) 
IDH 
 Mutated 4 (20.0%) 7 (25.0%) 2 (50.0%) 13 (25.0%) 
 Wild type 15 (75.0%) 21 (75.0%) 2 (50.0%) 38 (73.1%) 
 Unknown 1 (5.0%)   1 (1.9%) 
MGMT 
 Methylated 10 (50.0%) 11 (39.3%) 0 (0%) 21 (40.4%) 
 Nonmethylated 10 (50.0%) 16 (57.1%) 3 (75.0%) 29 (55.8%) 
 Unknown  1 (3.6%) 1 (25.0%) 2 (3.8%) 
Presentation 
 New diagnosis 14 (70.0%) 16 (57.1%) 0 (0%) 30 (57.7%) 
 Recurrent 6 (40.0%) 12 (42.9%) 4 (100%) 22 (42.3%) 
Flow cytometry/gene expression/TCR sequencingMass cytometryActivation IFNγ studiesAll studies
Total number 20 28 52 
Mean age at diagnosis 51.3 years 51.4 years 43.1 years 50.7 years 
Diagnosis Number (percent) Number (percent) Number (percent) Number (percent) 
 Glioblastoma 18 (90.0%) 20 (71.4%) 3 (75.0%) 41 (78.8%) 
 Gliosarcoma 0 (0%) 1 (3.6%) 0 (0%) 1 (1.9%) 
 Anaplastic oligodendroglioma 0 (0%) 3 (10.7%) 0 (0%) 3 (5.8%) 
 Anaplastic astrocytoma 2 (10.0%) 4 (14.3%) 1 (25.0%) 7 (13.5%) 
Sex 
 Male 13 (65.0%) 17 (60.7%) 4 (100%) 34 (65.4%) 
 Female 7 (35.0%) 11 (39.3%) 9 (0%) 27 (51.9%) 
IDH 
 Mutated 4 (20.0%) 7 (25.0%) 2 (50.0%) 13 (25.0%) 
 Wild type 15 (75.0%) 21 (75.0%) 2 (50.0%) 38 (73.1%) 
 Unknown 1 (5.0%)   1 (1.9%) 
MGMT 
 Methylated 10 (50.0%) 11 (39.3%) 0 (0%) 21 (40.4%) 
 Nonmethylated 10 (50.0%) 16 (57.1%) 3 (75.0%) 29 (55.8%) 
 Unknown  1 (3.6%) 1 (25.0%) 2 (3.8%) 
Presentation 
 New diagnosis 14 (70.0%) 16 (57.1%) 0 (0%) 30 (57.7%) 
 Recurrent 6 (40.0%) 12 (42.9%) 4 (100%) 22 (42.3%) 

PD-1 was significantly elevated in the CD3+ TIL population (mean ± SE: 51.7% ± 7.2%) compared with the CD3+ glioma patient PBMC population (19.7% ± 1.8%, P = 0.0005) and healthy donor PBMCs (17.0% ± 2.4%, P = 0.02; Fig. 1A). The proportion of TILs expressing PD-1 was significantly elevated in the CD3+CD4+ T-cell population (mean ± SE: 53.2% ± 6.8%) as compared with patient peripheral blood (17.6% ± 2.6%, P < 0.0004) or donor peripheral blood (17.3% ± 1.9%, P < 0.0001; Supplementary Fig. S1B). Similarly, the percentage of CD3+CD8+ cells expressing PD-1 was also significantly higher in TILs (51.9% ± 7.39%) than on patient peripheral blood T cells (22.5% ± 3.13%; P < 0.0023) or donor peripheral blood T cells (15.9% ± 2.24%; P < 0.0005; Supplementary Fig. S1C). To validate these findings independently, PD-1 expression was analyzed on GBM TILs, matched patient peripheral blood T cells, and normal controls from a separate cohort of 6 patients with GBM and 6 healthy controls at the University of Heidelberg (Heidelberg, Germany). Similarly, PD-1 expression was significantly increased in the GBM TIL population when compared with matched patient peripheral blood T cells and healthy patients' T cells (Fig. 1B).

Because PD-1 has typically been associated with an exhausted phenotype, in addition to PD-1, the expression of three other exhaustion markers (CTLA-4, TIM-3, and LAG-3) was measured in the patients from our German validation cohort (Fig. 1C). The GBM TIL population showed greater expression of TIM-3 and LAG-3 and marginal increase in expression of CTLA-4 compared with both healthy donor PBMCs and patient PBMCs. To examine this further, we noted the percentage of cells in each population that showed one more of the exhaustion markers (Fig. 1D). The GBM TIL population showed the largest subpopulation of cells that expressed at least one of the exhaustion markers and the only population that contained cells that expressed more than two of the exhaustion markers. In contrast, peripheral blood T cells from matched patients rarely had up to two markers of exhaustion and T cells from healthy controls almost uniformly had no markers of exhaustion except for a small percentage with only one marker. In addition, we examined the proportion of CD3+ cells within each combination of exhaustion markers. There was a greater percentage of CD3+ TILs expressing two or more exhaustion markers across all the different combinations of exhaustion markers than PBMCs from the patients or controls (Fig. 1E). Our results suggest that malignant glioma patients possess elevated frequencies of PD-1 and other exhaustion markers on T cells from the tumor and in the peripheral blood compared with normal healthy volunteers.

To explore further the findings from our standard fluorescent antibody–based flow cytometry, we conducted mass cytometry (21, 25, 26) on an additional 28 matching high-grade glioma patient TILs and PBMCs. A panel of immune lineage, memory, trafficking, activation, and exhaustion markers were used to stain these paired immune cell populations simultaneously (Supplementary Table S1). To analyze the CD3+ PD-1+ population from this set of patients, we employed FlowSom (22), an R package that uses machine learning algorithms to analyze mass cytometry data in an unbiased manner. We ran a principal component analysis using the median expression levels of all the T-cell markers from each individual patient as variables. An unsupervised clustering algorithm was used to generate a tSNE map of phenotypically defined population clusters. The principal component analysis showed a clear separation between the CD3+ PD-1+ PBMCs and the CD3+ PD-1+ TILs (Fig. 2A). The unsupervised clustering algorithm grouped a majority of the CD3+ PD-1+ TILs in distinct clusters from the CD3+ PD-1+ PBMCs (Fig. 2B; Supplementary Fig. S2A).

Figure 2.

High dimensional analysis of mass cytometry data shows elevated expression of markers of activation and exhaustion on CD3+ PD-1+ TILs and markers of activation and antigen experience on CD3+ PD-1+ PBMCs from patients with malignant glioma. A, A PCA plot showing CD3+ PD-1+ TILs (green dots) and PBMCs (red dots). Each dot represents an individual patient. B, tSNE plots showing which clusters the CD3+ PD-1+ TILs were grouped into (green box) and which clusters the CD3+ PD-1+ PBMCs were grouped into (red box) by the clustering algorithm. C, The median expression levels of various activation markers in TILs (green dots) and PBMCs (red dots). The same tSNE plot as B was colored to show which cells expressed the particular marker (*, P ≤ 0.05; **, P ≤ 0.005; ***, P ≤ 0.0005). D, Antigen experience/memory markers. E, Exhaustion markers. F, A heatmap of the median expression pattern for each of the exhaustion, activation, and memory/antigen experience markers for each individual patient.

Figure 2.

High dimensional analysis of mass cytometry data shows elevated expression of markers of activation and exhaustion on CD3+ PD-1+ TILs and markers of activation and antigen experience on CD3+ PD-1+ PBMCs from patients with malignant glioma. A, A PCA plot showing CD3+ PD-1+ TILs (green dots) and PBMCs (red dots). Each dot represents an individual patient. B, tSNE plots showing which clusters the CD3+ PD-1+ TILs were grouped into (green box) and which clusters the CD3+ PD-1+ PBMCs were grouped into (red box) by the clustering algorithm. C, The median expression levels of various activation markers in TILs (green dots) and PBMCs (red dots). The same tSNE plot as B was colored to show which cells expressed the particular marker (*, P ≤ 0.05; **, P ≤ 0.005; ***, P ≤ 0.0005). D, Antigen experience/memory markers. E, Exhaustion markers. F, A heatmap of the median expression pattern for each of the exhaustion, activation, and memory/antigen experience markers for each individual patient.

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We next looked at each individual marker and its median expression levels in all the PBMCs and TILs. For each individual marker, we highlighted the regions on the generated tSNE map where the marker was most highly expressed. For the T-cell exhaustion markers, there was significant increase in TIM-3, PD-L1, and CTLA-4 in the TIL population compared with the PBMC population (Fig. 2C). Among T-cell activation markers, there was a significant increase in expression of CD38 and HLA-DR in the TIL population compared with the PBMC population (Fig. 2D). Meanwhile, among the antigen experience and T-cell memory markers: CD45RA, CD27, and CD127, there was a significant decrease in the TIL population compared with the PBMC population (Fig. 2E). In 4 recent patients, two additional T-cell exhaustion markers CD39 and TIGIT were incorporated into our panel, and the expression levels were measured between the PBMC and TIL groups. We found a significant increase in CD39 in the TIL population (Supplementary Fig. S2C) but a nonsignificant difference in TIGIT expression between the two groups (Supplementary Fig. S2d). A heatmap of each analyzed patient's marker expression level was also generated. There was increased expression of various exhaustion and activation markers in the TIL population and increased expression of antigen experience markers in the PBMC population (Fig. 2F). The results generated from the mass cytometry data strongly suggest phenotypic differences exist between CD3+ PD-1+ cells from malignant glioma patient TILs and PBMCs.

To further investigate the possible relationship of PD-1 expression with T-cell exhaustion, activation, and memory phenotypes, we sought to characterize the gene expression of the PD-1 and PD-1+ cells by performing a tailored comparison panel on the PBMCs and TILs from three randomly selected patients and PBMCs from one healthy individual using RNA hybridization probe technology (23). When the normalized RNA counts of PD-1+ T cells and PD-1 T cells were divided to create a ratio, we found that the ratio of PD-1+/PD-1 gene expression in malignant glioma TILs had an elevated fold change for markers of exhaustion (TIM-3, CTLA-4, TIGIT) when compared with the peripheral blood T cells (Supplementary Fig. S3A). Gene expression analysis of the same samples uncovered an increase in several activation markers (OX-40, 4-1BB, CD25, BIM, ICOS) of TILs when compared with patient peripheral blood T cells (Supplementary Fig. S3B). We saw a reversal of this PD1+/ PD1 fold change for markers of memory (Supplementary Fig. S3C). These data suggest that PD-1 expression reflects activation and exhaustion in TILs and may be a marker of antigen experience in peripheral blood T cells of malignant glioma patients, similar to our findings from our phenotypic analyses.

PD-1+ T cells demonstrate decreased TCR diversity

We used high-throughput DNA sequencing of the TCR V-β region to determine the TCR diversity of CD3+ PD-1+ and CD3+ PD-1 T cells FACS-sorted from one matched patient tumor and PBMC samples, two additional distinct patient PBMC samples, and normal donor PBMCs. Because our data suggested that PD-1 reflects antigen experience, activation, and exhaustion in our malignant glioma patient samples, it was unclear what the TCR repertoire would reflect. In fact, we saw an increase in the diversity of PD-1 T cells from all populations of sorted T cells (Fig. 3A). As expected, we saw a trend toward the least amount of TCR diversity in the TIL population, but our limited number of sorted TIL precludes a clear assessment. Among the predicted productive TCR rearrangements, we also saw an expansion of clonal T-cell populations in the PBMC and TIL populations in patients with malignant glioma, possibly signifying expansion of tumor antigen-specific T cells not seen in healthy donors (Fig. 3B). These findings suggest that PD-1+ T cells are enriched for a restricted TCR population, regardless of where the cells were obtained.

Figure 3.

PD-1+ T cells have decreased TCR diversity compared with PD-1 cells. A, The number of estimated unique TCR sequences in PD-1 and PD-1+ populations in healthy donor PBMCs, glioma patient PBMCs, and glioma patient TILs. Each point represents a single patient. Circle points, healthy donor PBMCs; square points, glioma patient PBMCs; triangle points, glioma patient TILs (**, P ≤ 0.005). B, The frequency of a specific TCR rearrangement in the populations. The populations shown are the CD3+ PD-1 or CD3+ PD-1+ populations from healthy donor PBMCs (dotted bar), glioma patient PBMCs (the checkerboard bar), and glioma patient TILs (slanted stripes bar).

Figure 3.

PD-1+ T cells have decreased TCR diversity compared with PD-1 cells. A, The number of estimated unique TCR sequences in PD-1 and PD-1+ populations in healthy donor PBMCs, glioma patient PBMCs, and glioma patient TILs. Each point represents a single patient. Circle points, healthy donor PBMCs; square points, glioma patient PBMCs; triangle points, glioma patient TILs (**, P ≤ 0.005). B, The frequency of a specific TCR rearrangement in the populations. The populations shown are the CD3+ PD-1 or CD3+ PD-1+ populations from healthy donor PBMCs (dotted bar), glioma patient PBMCs (the checkerboard bar), and glioma patient TILs (slanted stripes bar).

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PD-1+ peripheral T cells of patients with malignant gliomas exhibit an increased rate of activation compared with PD-1 T cells

To examine the functional differences between these T-cell populations, we sorted PD-1+ and PD-1 CD3+ peripheral blood T cells from patients with malignant glioma. Because of limited numbers of PD-1+ T cells in nonexpanded PBMCs, we pooled together PBMCs from multiple patients. The sorted T cells were placed in culture and activated using agonistic CD3/28 antibodies. At 24 and 48 hours postactivation, supernatants were collected and the IFNγ levels measured. At 24 hours, the amount of IFNγ produced by the CD3+ PD-1+ cells was 2.4× higher than the IFNγ produced by the CD3+ PD-1 cells (Fig. 4a). By 48 hours poststimulation, the amount of IFNγ produced by the CD3+ PD-1+ cells was 8.8× higher than the IFNγ produced by the CD3+ PD-1 cells (Fig. 4b).

Figure 4.

CD3+ PD-1+ T cells from pooled glioma patient PBMCs produce higher levels of IFNγ compared to the CD3+ PD-1 T cell population after T cell activation. A, The amount of IFNγ produced in CD3+ PD-1, and CD3+ PD-1+ T cells from four glioma patient PBMCs 24 hours after CD3/CD28 activation. The dotted bar represents CD3+ PD-1 T cells that were not activated. The checkerboard bar represents CD3+ PD-1 T cells that were activated. The horizontal stripes bar represents CD3+ PD-1+ T cells that were activated. The results presented are from a representative experiment that has been repeated multiple times with similar findings. B, IFNγ produced at 48 hours from the same set of cells as in A.

Figure 4.

CD3+ PD-1+ T cells from pooled glioma patient PBMCs produce higher levels of IFNγ compared to the CD3+ PD-1 T cell population after T cell activation. A, The amount of IFNγ produced in CD3+ PD-1, and CD3+ PD-1+ T cells from four glioma patient PBMCs 24 hours after CD3/CD28 activation. The dotted bar represents CD3+ PD-1 T cells that were not activated. The checkerboard bar represents CD3+ PD-1 T cells that were activated. The horizontal stripes bar represents CD3+ PD-1+ T cells that were activated. The results presented are from a representative experiment that has been repeated multiple times with similar findings. B, IFNγ produced at 48 hours from the same set of cells as in A.

Close modal

We previously demonstrated that the PD-1/PD-L1 axis plays an important immunoregulatory role in antitumor T-cell function induced by DC vaccination (10, 12). Several other studies have described T-cell exhaustion and dysfunction within the tumor microenvironment and in relation to PD-1 expression. Thommen and colleagues showed in non–small cell lung cancer patients that PD-1 expressing T cells from the tumor had higher expression of various exhaustion markers, had higher upregulation of genes related to proliferation and activation, and were more clonal (27). Analyzing TILs from both human GBM and murine tumor models, Woroniecka and colleagues showed that TILs had a more prominent exhaustion signature compared with the peripheral blood from patients with GBM and from healthy donor PBMCs (28). However, a comprehensive, high-dimensional analysis of PD-1 expression by human T lymphocytes in patients with malignant glioma has not been described. In our study, we found several differences in both phenotypic characteristics and function between PD-1+ TILs and matched PD-1+ peripheral blood T lymphocytes extracted from patients with malignant glioma. Notably, we found that PD-1 expression in the glioma TIL context may reflect T-cell activation in addition to T-cell exhaustion, but in the periphery, PD-1 expression on T cells may indicate activation and antigen experience (Fig. 5).

Figure 5.

A schematic indicating what PD-1 expression means in the context of malignant glioma.

Figure 5.

A schematic indicating what PD-1 expression means in the context of malignant glioma.

Close modal

Expression of PD-1 on peripheral blood T cells of patients with malignant tumors, including malignant glioma, has previously been reported (29–34). In most of these previous studies, PD-1 was described as a marker of exhaustion by terminally differentiated effector T cells. But these reports failed to describe the phenotypic and functional differences between PD-1+ T cells in the peripheral blood and PD-1+ TILs. In our study, we performed a comprehensive analysis of PD-1 expression on T lymphocytes isolated from the tumors and peripheral blood of patients with malignant glioma. We found that PD-1 was significantly elevated in glioma patient TILs when compared with T cells in glioma patient peripheral blood and T cells from healthy controls. PD-1+ TILs are enriched for markers of activation and exhaustion (CTLA-4, TIM-3, HLA-DR, and CD38) when compared with PD-1+ peripheral blood T cells. In contrast, peripheral blood PD-1+ T cells express a complete array of central memory, effector memory, and even the CD45RA+ effector memory T-cell subsets (CD45RO, CD45RA, CD27, and CD127), suggesting diverging functionality between PD-1+ T cells obtained from distinct anatomic locale.

In the setting of chronic viral infections, similar trends have been observed. PD-1 expression has been associated with T-cell exhaustion in both mouse LCMV models and humans infected with HIV (35, 36). In studies examining the transcriptional profile of the exhausted T-cell population from a chronic LCMV mouse model, the exhausted population remained distinct from the memory T-cell population and had increased transcription of various exhaustion markers such as PD-1, TIM-3, and CTLA-4 and activation markers such as ICOS and OX40 (37, 38). In contrast, previous reports have shown that in healthy controls, PD-1+ peripheral CD8+ T cells do not show exhausted characteristics, but instead exhibit hallmarks of effector memory T cells (39). In a subset of the patients, CD45RO was included in the mass cytometry analysis, which allowed a more thorough exploration of the T-cell memory subsets. We found that the expression CD45RO is uniformly expressed on all CD3+ PD1+ T cells from the tumor but variably expressed by the peripheral blood population. In the peripheral blood, there are multiple populations of PD-1+ T cells that do not express CD45RO and are likely long-lived stem memory TSCM or effector memory TEMRA cells. Thus, our data support the conclusion that the phenotype of PD-1+ T cells obtained from tumor tissue represents a chronically activated, exhausted effector T-cell population (CD45RO+ CD27 IL-7R HLA-DR+ CD38+ TIM-3+ LAG-3+ CTLA-4+), while the PD-1+ T-cell population obtained from peripheral blood represents the full complement of memory T-cell populations (TCM, CD45RO+ CD27+ CD45RA IL7R+; TEM, CD45RO+ CD45RA CD27 IL7R; and TEMRA, CD45RO CD27 CD45RA+ IL7R). Therefore, our data would suggest that PD-1 expression by peripheral blood T cells may not be a good surrogate or indicator for exhaustion or tumor immune evasion. Such information must be considered when applied as a biomarker in clinical immunotherapy trials.

Interestingly, we found that PD-1 expression was associated with increased TCR clonality and decreased diversity in both the TIL population and peripheral blood T cells of patients with malignant glioma. As such, increased clonal expansion of the PD-1+ T-cell population may reflect the activation and expansion of specific antitumor antigen T cells when compared with PD-1 T cells, although future studies are needed to confirm this. In addition, our mass cytometry data revealed a significant increase in expression of CD39 in the CD3+ PD-1+ TIL population compared with the CD3+ PD-1+ PBMC population. CD39, an extracellular ATPase commonly associated with regulatory T cells, has been shown to be coexpressed with PD-1 on antigen-specific but exhausted CD8+ T cells in a chronic viral setting (40). More recently, CD39 has been proposed to be expressed by tumor-specific T cells (41, 42). The increase of clonality and CD39 expression in the PD-1+ TILs further supports their dual function phenotype of antitumor specific activation and exhaustion. Increased TCR clonality has been correlated with better outcomes after anti–PD-1 therapy in melanoma and has been associated with higher PD-1 expression in soft tissue sarcomas (11, 43). Further investigation is needed to study the association between TCR clonality and PD-1 expression in the context of tumor antigen-specific T lymphocytes.

Finally, to characterize PD-1+ peripheral blood T cells in patients with malignant glioma, we investigated the functional activation differences between PD-1+ and PD-1 peripheral blood T cells from patients with GBM. Traditionally, it has been held that effector, memory, and naïve T cells have different response rates, as measured by amount of IFNγ produced, after activation. We found that PD-1+ malignant glioma patient PBMC T cells exhibited an increased rate of IFNγ production when compared with PD-1 PBMC T cells from the same patients. Increased IFNγ is consistent with an activated memory T-cell phenotype, further highlighting that PD-1 expression in peripheral T cells is not a marker of exhaustion as we would expect in the PD-1+ population in the tumor microenvironment.

In conclusion, we demonstrate that PD-1 expression in patients with malignant glioma reflects chronically activated effector T cells that display both hallmarks of memory and exhaustion depending on its location. The use of anti–PD-1/PD-L1 antibody blockade is currently being studied in malignant glioma clinical trials. Further exploration of the effect of these checkpoint inhibitors on PD-1–expressing T cells in different anatomical environments is warranted both to understand the use of PD-1 expression as a therapeutic biomarker as well as its importance for clinical efficacy.

N. Kotecha holds ownership interest (including patents) in Cytobank Inc. W.H. Yong reports receiving commercial research grants from Amgen. No potential conflicts of interest were disclosed by the other authors.

Conception and design: T.B. Davidson, M. Hsu, S. Sedighim, A. Garcia, L.M. Liau, C.C. Herold-Mende, R.M. Prins

Development of methodology: A. Lee, M. Hsu, J. Antonios, A. Garcia, A. Wang, R. Everson, L.M. Liau, C.C. Herold-Mende, R.M. Prins

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A. Lee, M. Hsu, S. Sedighim, J. Orpilla, J. Treger, M. Mastall, S. Roesch, M. Galvez, A. Mochizuki, A. Garcia, D. Nathanson, A. Wang, R. Everson, W.H. Yong, T.F. Cloughesy, C.C. Herold-Mende, R.M. Prins

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): T.B. Davidson, A. Lee, M. Hsu, S. Sedighim, J. Orpilla, M. Mastall, S. Roesch, C. Rapp, M. Galvez, A. Mochizuki, N. Kotecha, N. Bayless, A. Wang, C.C. Herold-Mende, R.M. Prins

Writing, review, and/or revision of the manuscript: T.B. Davidson, A. Lee, M. Hsu, S. Sedighim, A. Mochizuki, N. Bayless, A. Wang, R. Everson, W.H. Yong, T.F. Cloughesy, L.M. Liau, C.C. Herold-Mende, R.M. Prins

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): T.B. Davidson, S. Sedighim, J. Orpilla, J. Antonios, A. Garcia, N. Kotecha, T.F. Cloughesy, C.C. Herold-Mende, R.M. Prins

Study supervision: T.B. Davidson, J. Antonios, L.M. Liau, C.C. Herold-Mende, R.M. Prins

This work was supported in part by NIH/NCI grants R21-CA186004 and R01CA154256 (R.M. Prins), RO1 CA125244 (L.M. Liau), KL2 ULITR001881 (T.B. Davidson), R25 NS079198 and the UCLA SPORE in Brain Cancer P50 CA211015 (R.M. Prins and L.M. Liau). We also acknowledge support from the Parker Institute for Cancer Immunotherapy (PICI; grant number 20163828), the Musella Foundation for Brain Tumor Research (R.M. Prins), the Joseph Drown Foundation (M. Hsu and T.B. Davidson), UCLA Children's Discovery and Innovation Institute Today's and Tomorrow's Children Fund (TTCF) (TBD), and the UCLA Medical Scientist Training Program (MSTP) (M. Galvez). A. Lee is a pre-doctoral fellow supported by the UCLA Tumor Immunology Training Grant (USHHS Ruth L. Kirschstein Institutional National Research Service Award # T32 CA009056). We would like to thank the Janis V. Giorgi Flow Cytometry Core Laboratory for their assistance in constructing our mass cytometry panel and running the Helios CyTOF system. We would like to thank the Center for Systems Biomedicine (Integrated Molecular Technologies Core) for their technical assistance with the NanoString studies, which is supported by CURE/P30DK41301. We also thank the UCLA Jonsson Comprehensive Cancer Center (JCCC) and Center for AIDS Research Flow Cytometry Core Facility that is supported by NIH awards P30 CA016042 and 5P30 AI028697. We would also like to thank the UCLA Brain Tumor Translational Resource (BTTR) and the Translational Pathology Core Laboratory (TPCL) for their assistance with paraffin-embedding and histology. Finally, we would like to express our deepest gratitude to the patients and their families for donating the samples that allowed this project to be successful.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Ostrom
QT
,
Gittleman
H
,
Xu
J
,
Kromer
C
,
Wolinsky
Y
,
Kruchko
C
, et al
CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2009-2013
.
Neuro Oncol
2016
;
18
:
v1
v75
.
2.
Preusser
M
,
de Ribaupierre
S
,
Wohrer
A
,
Erridge
SC
,
Hegi
M
,
Weller
M
, et al
Current concepts and management of glioblastoma
.
Ann Neurol
2011
;
70
:
9
21
.
3.
Stupp
R
,
Mason
WP
,
van den Bent
MJ
,
Weller
M
,
Fisher
B
,
Taphoorn
MJ
, et al
Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma
.
N Engl J Med
2005
;
352
:
987
96
.
4.
Miranda
A
,
Blanco-Prieto
M
,
Sousa
J
,
Pais
A
,
Vitorino
C
. 
Breaching barriers in glioblastoma. Part II: targeted drug delivery and lipid nanoparticles
.
Int J Pharm
2017
;
531
:
389
410
.
5.
Liau
LM
,
Prins
RM
,
Kiertscher
SM
,
Odesa
SK
,
Kremen
TJ
,
Giovannone
AJ
, et al
Dendritic cell vaccination in glioblastoma patients induces systemic and intracranial T-cell responses modulated by the local central nervous system tumor microenvironment
.
Clin Cancer Res
2005
;
11
:
5515
25
.
6.
Prins
RM
,
Soto
H
,
Konkankit
V
,
Odesa
SK
,
Eskin
A
,
Yong
WH
, et al
Gene expression profile correlates with T-cell infiltration and relative survival in glioblastoma patients vaccinated with dendritic cell immunotherapy
.
Clin Cancer Res
2011
;
17
:
1603
15
.
7.
Prins
RM
,
Wang
X
,
Soto
H
,
Young
E
,
Lisiero
DN
,
Fong
B
, et al
Comparison of glioma-associated antigen peptide-loaded versus autologous tumor lysate-loaded dendritic cell vaccination in malignant glioma patients
.
J Immunother
2013
;
36
:
152
7
.
8.
Heimberger
AB
,
Crotty
LE
,
Archer
GE
,
Hess
KR
,
Wikstrand
CJ
,
Friedman
AH
, et al
Epidermal growth factor receptor VIII peptide vaccination is efficacious against established intracerebral tumors
.
Clin Cancer Res
2003
;
9
:
4247
54
.
9.
Kanaly
CW
,
Ding
D
,
Heimberger
AB
,
Sampson
JH
. 
Clinical applications of a peptide-based vaccine for glioblastoma
.
Neurosurg Clin N Am
2010
;
21
:
95
109
.
10.
Antonios
JP
,
Soto
H
,
Everson
RG
,
Orpilla
J
,
Moughon
D
,
Shin
N
, et al
PD-1 blockade enhances the vaccination-induced immune response in glioma
.
JCI Insight
2016
;
1
:
e87059
.
11.
Tumeh
PC
,
Harview
CL
,
Yearley
JH
,
Shintaku
IP
,
Taylor
EJ
,
Robert
L
, et al
PD-1 blockade induces responses by inhibiting adaptive immune resistance
.
Nature
2014
;
515
:
568
71
.
12.
Antonios
JP
,
Soto
H
,
Everson
RG
,
Moughon
D
,
Orpilla
JR
,
Shin
NP
, et al
Immunosuppressive tumor-infiltrating myeloid cells mediate adaptive immune resistance via a PD-1/PD-L1 mechanism in glioblastoma
.
Neuro Oncol
2017
;
19
:
796
807
.
13.
Berghoff
AS
,
Kiesel
B
,
Widhalm
G
,
Rajky
O
,
Ricken
G
,
Wohrer
A
, et al
Programmed death ligand 1 expression and tumor-infiltrating lymphocytes in glioblastoma
.
Neuro Oncol
2015
;
17
:
1064
75
.
14.
McDermott
DF
,
Atkins
MB
. 
PD-1 as a potential target in cancer therapy
.
Cancer Med
2013
;
2
:
662
73
.
15.
Maxwell
R
,
Jackson
CM
,
Lim
M
. 
Clinical trials investigating immune checkpoint blockade in glioblastoma
.
Curr Treat Options Oncol
2017
;
18
:
51
.
16.
Topalian
SL
,
Taube
JM
,
Anders
RA
,
Pardoll
DM
. 
Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy
.
Nat Rev Cancer
2016
;
16
:
275
87
.
17.
Speiser
DE
,
Utzschneider
DT
,
Oberle
SG
,
Munz
C
,
Romero
P
,
Zehn
D
. 
T cell differentiation in chronic infection and cancer: functional adaptation or exhaustion?
Nat Rev Immunol
2014
;
14
:
768
74
.
18.
Yuzefpolskiy Y
BF
,
Penny
L
,
Kalia
V
,
Sarkar
S
. 
PD-1 signals instruct a critical metabolic switch for maintenance of T cell memory
.
J Immunol
2017
;
198(1 supplement)
.
19.
Charlton
JJ
,
Chatzidakis
I
,
Tsoukatou
D
,
Boumpas
DT
,
Garinis
GA
,
Mamalaki
C
. 
Programmed death-1 shapes memory phenotype CD8 T cell subsets in a cell-intrinsic manner
.
J Immunol
2013
;
190
:
6104
14
.
20.
Hong
JJ
,
Amancha
PK
,
Rogers
K
,
Ansari
AA
,
Villinger
F
. 
Re-evaluation of PD-1 expression by T cells as a marker for immune exhaustion during SIV infection
.
PLoS One
2013
;
8
:
e60186
.
21.
Nowicka
M
,
Krieg
C
,
Weber
LM
,
Hartmann
FJ
,
Guglietta
S
,
Becher
B
, et al
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets
.
F1000Res
2017
;
6
:
748
.
22.
Van Gassen
S
,
Callebaut
B
,
Van Helden
MJ
,
Lambrecht
BN
,
Demeester
P
,
Dhaene
T
, et al
FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data
.
Cytometry A
2015
;
87
:
636
45
.
23.
Geiss
GK
,
Bumgarner
RE
,
Birditt
B
,
Dahl
T
,
Dowidar
N
,
Dunaway
DL
, et al
Direct multiplexed measurement of gene expression with color-coded probe pairs
.
Nat Biotechnol
2008
;
26
:
317
25
.
24.
Hsu
MS
,
Sedighim
S
,
Wang
T
,
Antonios
JP
,
Everson
RG
,
Tucker
AM
, et al
TCR sequencing can identify and track glioma-infiltrating T cells after DC vaccination
.
Cancer Immunol Res
2016
;
4
:
412
8
.
25.
Bendall
SC
,
Simonds
EF
,
Qiu
P
,
Amir el
AD
,
Krutzik
PO
,
Finck
R
, et al
Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum
.
Science
2011
;
332
:
687
96
.
26.
Leipold
MD
,
Maecker
HT
. 
Mass cytometry: protocol for daily tuning and running cell samples on a CyTOF mass cytometer
.
J Vis Exp
2012(69)
:
e4398
.
27.
Thommen
DS
,
Koelzer
VH
,
Herzig
P
,
Roller
A
,
Trefny
M
,
Dimeloe
S
, et al
A transcriptionally and functionally distinct PD-1(+) CD8(+) T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade
.
Nat Med
2018
;
24
:
994
1004
.
28.
Woroniecka
K
,
Chongsathidkiet
P
,
Rhodin
K
,
Kemeny
H
,
Dechant
C
,
Farber
SH
, et al
T-cell exhaustion signatures vary with tumor type and are severe in glioblastoma
.
Clin Cancer Res
2018
;
24
:
4175
86
.
29.
Waki
K
,
Yamada
T
,
Yoshiyama
K
,
Terazaki
Y
,
Sakamoto
S
,
Matsueda
S
, et al
PD-1 expression on peripheral blood T-cell subsets correlates with prognosis in non-small cell lung cancer
.
Cancer Sci
2014
;
105
:
1229
35
.
30.
Zheng
H
,
Liu
X
,
Zhang
J
,
Rice
SJ
,
Wagman
M
,
Kong
Y
, et al
Expression of PD-1 on CD4+ T cells in peripheral blood associates with poor clinical outcome in non-small cell lung cancer
.
Oncotarget
2016
;
7
:
56233
40
.
31.
Rutkowski
J
,
Cyman
M
,
Slebioda
T
,
Bemben
K
,
Rutkowska
A
,
Gruchala
M
, et al
Evaluation of peripheral blood T lymphocyte surface activation markers and transcription factors in patients with early stage non-small cell lung cancer
.
Cell Immunol
2017
;
322
:
26
33
.
32.
Wang
W
,
Shen
G
,
Wu
S
,
Song
S
,
Ni
Y
,
Suo
Z
, et al
PD-1 mRNA expression in peripheral blood cells and its modulation characteristics in cancer patients
.
Oncotarget
2017
;
8
:
50782
91
.
33.
MacFarlane
AWt
,
Jillab
M
,
Plimack
ER
,
Hudes
GR
,
Uzzo
RG
,
Litwin
S
, et al
PD-1 expression on peripheral blood cells increases with stage in renal cell carcinoma patients and is rapidly reduced after surgical tumor resection
.
Cancer Immunol Res
2014
;
2
:
320
31
.
34.
Wei
B
,
Wang
L
,
Zhao
X
,
Du
C
,
Guo
Y
,
Sun
Z
. 
The upregulation of programmed death 1 on peripheral blood T cells of glioma is correlated with disease progression
.
Tumour Biol
2014
;
35
:
2923
9
.
35.
Barber
DL
,
Wherry
EJ
,
Masopust
D
,
Zhu
B
,
Allison
JP
,
Sharpe
AH
, et al
Restoring function in exhausted CD8 T cells during chronic viral infection
.
Nature
2006
;
439
:
682
7
.
36.
Day
CL
,
Kaufmann
DE
,
Kiepiela
P
,
Brown
JA
,
Moodley
ES
,
Reddy
S
, et al
PD-1 expression on HIV-specific T cells is associated with T-cell exhaustion and disease progression
.
Nature
2006
;
443
:
350
4
.
37.
Crawford
A
,
Angelosanto
JM
,
Kao
C
,
Doering
TA
,
Odorizzi
PM
,
Barnett
BE
, et al
Molecular and transcriptional basis of CD4(+) T cell dysfunction during chronic infection
.
Immunity
2014
;
40
:
289
302
.
38.
Wherry
EJ
,
Ha
SJ
,
Kaech
SM
,
Haining
WN
,
Sarkar
S
,
Kalia
V
, et al
Molecular signature of CD8+ T cell exhaustion during chronic viral infection
.
Immunity
2007
;
27
:
670
84
.
39.
Duraiswamy
J
,
Ibegbu
CC
,
Masopust
D
,
Miller
JD
,
Araki
K
,
Doho
GH
, et al
Phenotype, function, and gene expression profiles of programmed death-1(hi) CD8 T cells in healthy human adults
.
J Immunol
2011
;
186
:
4200
12
.
40.
Gupta
PK
,
Godec
J
,
Wolski
D
,
Adland
E
,
Yates
K
,
Pauken
KE
, et al
CD39 expression identifies terminally exhausted CD8+ T Cells
.
PLoS Pathog
2015
;
11
:
e1005177
.
41.
Duhen
T
,
Duhen
R
,
Montler
R
,
Moses
J
,
Moudgil
T
,
de Miranda
NF
, et al
Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors
.
Nat Commun
2018
;
9
:
2724
.
42.
Simoni
Y
,
Becht
E
,
Fehlings
M
,
Loh
CY
,
Koo
SL
,
Teng
KWW
, et al
Bystander CD8(+) T cells are abundant and phenotypically distinct in human tumour infiltrates
.
Nature
2018
;
557
:
575
9
.
43.
Pollack
SM
,
He
Q
,
Yearley
JH
,
Emerson
R
,
Vignali
M
,
Zhang
Y
, et al
T-cell infiltration and clonality correlate with programmed cell death protein 1 and programmed death-ligand 1 expression in patients with soft tissue sarcomas
.
Cancer
2017
;
123
:
3291
304
.

Supplementary data