Background: Checkpoint inhibitors act on exhausted CD8+ T cells and restore their effector function in chronic infections and cancer. The underlying mechanisms of action appear to differ between different types of cancer and are not yet fully understood. Methods: Here, we established a new orthotopic HCC model to study the effects of checkpoint blockade on exhausted CD8+ tumor-infiltrating lymphocytes (TILs). The tumors expressed endogenous levels of HA, which allowed the study of tumor-specific T cells. Results: The induced tumors developed an immune-resistant TME in which few T cells were found. The few recovered CD8+ TILs were mostly terminally exhausted and expressed high levels of PD-1. PD-1/CTLA-4 blockade resulted in a strong increase in the number of CD8+ TILs expressing intermediate amounts of PD-1, also called progenitor-exhausted CD8+ TILs, while terminally exhausted CD8+ TILs were almost absent in the tumors of treated mice. Although transferred naïve tumor-specific T cells did not expand in the tumors of untreated mice, they expanded strongly after treatment and generated progenitor-exhausted but not terminally exhausted CD8+ TILs. Unexpectedly, progenitor-exhausted CD8+ TILs mediated the antitumor response after treatment with minimal changes in their transcriptional profile. Conclusion: In our model, few doses of checkpoint inhibitors during the priming of transferred CD8+ tumor-specific T cells were sufficient to induce tumor remission. Therefore, PD-1/CTLA-4 blockade has an ameliorative effect on the expansion of recently primed CD8+ T cells while preventing their development into terminally exhausted CD8+ TILs in the TME. This finding could have important implications for future T-cell therapies.

Liver cancer is the sixth most frequently diagnosed cancer and the fourth most common cause of cancer-related death worldwide. The prognosis for patients with liver cancer remains poor as therapeutic options are limited [1]. Inhibition of receptors that negatively regulate T-cell activation, such as cytotoxic T lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1), with monoclonal antibodies (checkpoint blockade) is an efficient alternative for the treatment of tumors that are resistant to traditional therapies. CTLA-4 and PD-1 are normally involved in the maintenance of immune homeostasis, as they regulate inflammatory immune responses to pathogens and tolerance to self-antigens antigens [2]. During chronic infections and cancer, the expression of these receptors is associated with T-cell exhaustion, a state of dysfunctionality defined by poor effector function, low proliferative capacity, and survival after antigen stimulation [2‒5].

Checkpoint blockade is thought to reinvigorate exhausted CD8+ T cells recovering their effector function. Although the mechanisms underlying this process are still not completely understood, previous studies have shown that CD8+ T-cell subsets with varying degrees of exhaustion respond differentially to PD-1 blockade. During chronic infection with murine lymphocytic choriomeningitis virus (LCMV), terminally exhausted CD8+ T cells expressing high levels of PD-1 (CD8+PD-1high) were refractory to blockade of the PD-1 pathway, while progenitor-exhausted CD8+ T cells expressing intermediate amounts of PD-1 (CD8+PD-1int) were responsive [3]. CD8+PD-1int tumor-infiltrating lymphocytes (TILs) in mouse melanoma were also responsive to PD-1 or PD-1/CTLA-4 blockade. Additionally, the duration of response to PD-1/CTLA-4 blockade in patients with melanoma correlated with the frequency of CD8+PD-1int TILs detected in biopsies collected prior to treatment, indicating that this subset responded to the treatment [6]. In contrast, in patients with non-small-cell lung carcinoma, the presence of CD8+PD-1high but not of CD8+PD-1int TILs in biopsies prior to anti-PD-1 therapy predicted response, indicating that CD8+PD-1high TILs may also respond to anti-PD-1 treatment [7]. Therefore, analysis of the effect of checkpoint blockade on CD8+ TIL subsets in different cancers may help to identify predictors of response and improve therapeutic protocols.

Here, we established an orthotopic liver cancer model to study the effect of PD-1/CTLA-4 blockade on exhausted CD8+ TILs. Treatment induced the expansion of CD8+PD-1int TILs but not of CD8+PD1high TILs. Similar expansion was also observed after adoptive transfer of naïve CD8+PD1neg tumor-specific T cells and PD-1/CTLA-4 blockade. Tumor control correlated with the migration of the transferred cells in the tumor microenvironment (TME), expansion, and infiltration of CD8+PD-1int TILs into the tumor. Interestingly, no changes were observed in the transcriptional profile of PD-1int TILs due to the therapy. In conclusion, PD-1/CTLA-4 blockade improves the expansion of recently primed CD8+ T cells, promotes tumor infiltration, and prevents the development of primed cells into CD8+PD-1high TILs in the TME. Additionally, our data demonstrate that PD-1/CTLA-4 blockade can significantly improve the effectiveness of adoptive T-cell therapies (ACTs). These findings are relevant for future clinical interventions.

Mice

Tumors were induced in 6- to 8-week-old BALB/cJHanZtm mice. HA-specific T cells were obtained from congenic CD45.1+ BALB-Ptprca-Tg(TCR/C-TCR(HA/PR8/34))CL4/Rlf mice [8]. Albumin-cre x Rosa-26HA F1 mice were utilized for the generation of liver tumor cells. All animals were bred and maintained under pathogen-free conditions at the animal facility of Hannover Medical School, Germany.

Generation of HA-Expressing Murine HCC Cell Lines

Liver tumor cells expressing the influenza hemagglutinin HA peptide were generated by inducing autochthonous liver tumors in Albumin-cre x Rosa-26HA F1 mice [8] by hydrodynamic cotransfection [9] of plasmids carrying oncogenes and Sleeping Beauty transposase (pPGK SB13 Kras g12V and pT/p53-246 sh p53). Under these conditions, HA is expressed at endogenous levels (see online suppl. Methods; for all online suppl. material, see www.karger.com/doi/10.1159/000526899). Tumor cell lines were obtained either after homogenization of tumor-bearing tissues or by overgrowth of tumor cells from a small tumor piece. The obtained cell lines were cultured in DMEM medium containing 10% FCS and penicillin/streptomycin. Clonal tumor cell lines were obtained by a limiting dilution assay, and HA-expressing clones were selected based on their capacity to stimulate HA-specific CD8+ T cells in vitro. The clone Hep-HA-STK-1 was utilized for the experiments.

In vitro Restimulation Assay

Tumor-isolated lymphocytes (1 × 106) were stimulated with irradiated (30 Gy) Hep-HA-STK-1 cells (5 × 104 cells/well) in the presence of suboptimal concentrations of anti-CD3 (0.5 μg/mL) and anti-CD28 antibodies (2 μg/mL) as described by Thommen et al. [7]. To measure degranulation, stimulation was performed in the presence of an anti-CD107a-BV421 antibody (0.25 μg/106 cells) and brefeldin A for 6 h as previously described [10]. Unstimulated cells were incubated for the same time in the presence of anti-CD107a-BV421 antibody and brefeldin A only. After incubation, cells were recovered and stained for CD8 and PD-1. They were then fixed, stained for intracellular TNF-α and IFN-γ, and analyzed by flow cytometry.

Adoptive Cell Transfer

CD8+ T cells expressing an HA-specific TCR were isolated from the spleens of CD45.1+ BALB-Ptprca-Tg(TCR/C-TCR(HA/PR8/34))CL4/Rlf transgenic mice and transferred on day 10 after tumor induction. Isolation was performed using the MojoSort Mouse CD8 T-Cell Isolation Kit (BioLegend) according to manufacturer’s instructions. For transfer, the isolated cells (1 × 106/100 μL in 0.9% NaCl solution) were injected into the tail vein of the mice. The cell purity was controlled by flow cytometry prior to transfer.

Tumor Induction and in vivo Treatments

Hep-HA-STK-1 cells (5 × 105 in 10 μL PBS) were injected into the left liver lobes of BALB/c mice [11]. Mock-operated mice received a 10 μL injection of PBS. Eleven days after tumor induction, tumor-bearing mice were randomized and treated either with 100 μg of α-PD-1 plus 100 μg of α-CTLA-4 antibodies or the same concentrations of the corresponding isotype controls (rat IgG2a plus polyclonal Syrian hamster IgG) by intraperitoneal injections at days 11, 13, and 17. Mock-treated mice received isotype control antibodies. For the histological analysis, untreated mice received PBS. After killing, the length and width of the tumors were measured with a digital caliper. The calculation of the tumor area was performed according to the following formula: area = a/2 × b/2 × π, where a = length (largest tumor diameter) and b = width (perpendicular tumor diameter).

Immunofluorescence and H&E Staining

The liver was perfused with PBS and fixed in 2% formaldehyde. For immunofluorescence, the tissues were equilibrated in 30% sucrose, embedded in OCT medium and frozen. Immunofluorescence microscopy was performed as described before [12, 13]. Briefly, cryosections were rehydrated, blocked, and stained. For sequential staining, a further blocking step was performed between the stains. For the H&E staining, the tissues were embedded in paraffin and sectioned and stained as described before [14, 15]. All images were acquired using an AxioImagerM1 microscope with corresponding Zen2.3 Imaging software (Carl Zeiss). The quantification of the data was done by counting 5 microscopic fields (×20 magnification) from 5 sections per mice and time point.

TCR – Sequencing

RNA isolation was performed with the RNeasy Plus Micro Kit (Qiagen). After isolation, RNAs were converted into cDNAs using the SuperScript III Reverse Transcriptase Kit (Thermo Fisher Scientific). Amplicon libraries of rearranged TRAV12 (Vα8 family) CDR3 regions were generated pars pro toto as previously described [16‒18]. Amplicons were purified after agarose gel electrophoresis using the QIAquick Gel Extraction Kit (Qiagen) and subjected to an index PCR for MiSeq analysis (Nextera Index Kit, Illumina). Index PCR amplicons were purified using the AMPure XP PCR purification kit (Beckman Coulter). The Quant-iTTM PicoGreenTM dsDNA Assay Kit (Thermo Fisher Scientific) was used for DNA quantification. PCR amplicons were processed according to Illumina’s “Denature and Dilute Library guidelines” to generate a 4 nM library. Sequencing was performed according to Illumina MiSeq analysis. FASTQ files and a corresponding quality report (FASTQC tool, Babraham Institute) were generated. The sequences were annotated using IMGT/HighV-Quest [19]. TCR clones were identified based on identic CDR3 sequences, and clone frequencies were calculated using in-house bash and R scripts (https://figshare.com/s/c7f85bcbca2be425bd25). The Shannon indices (HS) were calculated using the following formula:

graphic

RNA – Sequencing and Analysis

RNA was isolated as above described. The quality and integrity of the RNA were controlled with a 2,100 Bioanalyzer (Agilent Technologies). Only RNA samples with an RIN ≥8 were used. The samples were purified with the Dynabeads mRNA DIRECT Micro Kit (Thermo Fisher Scientific), and a library was prepared using the NEBNext Ultra II Directional RNA Library Prep Kit (New England BioLabs). Sequencing was performed on a NovaSeq 600 using a NovaSeq 6000 S1 Reagent Kit (Illumina). FASTQ files and a corresponding quality report (Babraham Institute) were generated. The quality and adapter trimming of the FASTQ files were performed using Trim Galore! Wrapper (Babraham Institute), and reads <20 bp were removed. The trimmed sequences were aligned to the reference genome using the STAR tool [20]. Feature counts were performed using the R package Rsubread [21]. Only genes that occurred at least five times in two of the samples were selected. Gene annotation and log2 transformation as well as normalization beyond the 50th percentile were performed by using the R packages bioMaRT [22, 23] and the quartile normalization of edgeR [24], respectively. Differential gene expression was depicted using Qlucore Omics Explorer v3.5 (Qlucore). For gene ontology (GO) analyses, the Generic GO Term Finder was used [25].

Flow Cytometry

Lymphocytes were isolated from the tumors and liver lobes as described in the online supplementary methods to obtain a cell suspension. Cells were pelleted, resuspended (106 cells/100 μL) in PBE containing 20% rat serum and 4% FcR blocking reagent (Miltenyi Biotec), and incubated on ice for 20 min. After blocking nonspecific binding sites, cells were stained with the appropriate concentration of labeled antibodies for 20 min. After staining, cells were washed with PBE, then incubated with DAPI for 1 min, washed again, resuspended in PBE, and measured in a flow cytometer. For intracellular staining, cells were fixed with Fixation Buffer after surface staining and permeabilized with Permeabilization Wash Buffer (Biolegend) prior to staining. Dead cells were stained with the Fixable Viability Dye eFluorTM 780 (Thermo Fisher Scientific) prior to fixation. For sorting, lymphocytes isolated from 5 to 10 individual tumor-bearing liver lobes were pooled, washed, and resuspended in PBE at a concentration of 106 cells/100 μL. Blocking of nonspecific binding sites and staining were performed as above described above. The analysis was done in an LSRII (BD). Sorting was performed using a FACSAria Fusion, a FACSAria Ilu (both from BD), or a MoFloXDP sorter (Beckman Coulter). Analysis was performed using FACSDiva software (BD).

Statistics

Analysis was performed using GraphPad Prism versions 5.01 and 9 (GraphPad Software, La Jolla, CA, USA). All statistical analyses and p values are described in the figure legends. p < 0.05 was considered significant. For a box-whisker plot, the box represents the interquartile range value, the line in the box represents the median, and the whisker represents 1.5 × interquartile range values (Tukey).

New Liver Cancer Model and Effect of PD-1/CTLA-4 Blockade on Tumor Growth

Liver tumor cells expressing endogenous levels of an influenza hemagglutinin HA-peptide (Hep-HA-STK-1 cells) were gained from autochthonous liver tumors induced in Albumin-cre x Rosa-26HA F1 mice. To induce localized orthotopic tumors, Hep-HA-STK-1 cells were injected into the liver parenchyma (Fig. 1a). Monitoring 11 days after tumor induction showed the presence of visible tumors in the liver (Fig. 1b). In addition, an accumulation of exhausted CD8+PD-1high TILs was observed in tumor-bearing liver lobes (Fig. 1c). Histological analysis demonstrated that the tumors expressed PD-L1 and Glypcan-3 (online suppl. Fig. 1a). Unless otherwise mentioned, therapy response was assessed on day 21 in mice that received either PD-1/CTLA-4 blockade or isotype control antibodies on days 11, 13, and 17 (Fig. 1d). In some experiments, mice received 1 × 106 CD8+CD45.1+ HA-specific T cells intravenously on day 10 after tumor induction. Despite high intragroup variability, the tumor size was significantly reduced in mice receiving PD-1/CTLA-4 blockade (Fig. 1e, f). Adoptive T-cell transfer (ACT) without checkpoint blockade had no significant effect on tumor growth. In contrast, PD-1/CTLA-4 blockade caused a comparable reduction of tumor mass independent of the transfer of tumor-specific CD8+ T cells (Fig. 1f).

Fig. 1.

PD-1/CTLA-4 blockade leads to tumor regression. a Autochthonous HA-expressing liver tumors were generated by hydrodynamic cotransfection of oncogenes and sleeping beauty transposase (SB13)-bearing plasmids into Albumin-cre x Rosa-26HA F1 mice. Tumor cells (Hep-HA) were gained from the tumors and cloned. Hep-HA-STK-1 cells were injected into the left liver lobe of BALB/c mice to induce orthotopic tumors. The arrow shows a tumor cell depot after injection. b Representative image of the tumor-bearing liver lobe 11 days after induction. c Flow cytometry plots of endogenous CD8+ TILs 11 days after tumor induction. FMO (fluorescence minus one), Mock (PBS-injected liver lobe). d Experimental design. Treatment (arrows), time of analysis (). e Representative images of the tumor-bearing liver lobes of untreated (isotype controls) or treated (PD-1/CTLA-4 blockade) mice 21 days after tumor induction. f Tumor size in untreated and treated mice in the absence (−ACT) or presence (+ACT) of adoptively transferred HA-specific CD8+ T cells. Shown are the pooled data from 3 representative experiments. One-way ANOVA with Bonferroni post hoc test; n= 10 each group; *p< 0.05. g Hematoxylin-eosin and h immunofluorescence staining of tumor sections of untreated and treated mice. Tumor (T), invasive margin (IM), and liver (L). CD4 (green), CD8 (red), and CD44 (blue). Arrows show the presence of tertiary lymphoid organs in the vicinity of the tumors. Bars: 500 μm (HE staining); 50 μm (immunofluorescence).

Fig. 1.

PD-1/CTLA-4 blockade leads to tumor regression. a Autochthonous HA-expressing liver tumors were generated by hydrodynamic cotransfection of oncogenes and sleeping beauty transposase (SB13)-bearing plasmids into Albumin-cre x Rosa-26HA F1 mice. Tumor cells (Hep-HA) were gained from the tumors and cloned. Hep-HA-STK-1 cells were injected into the left liver lobe of BALB/c mice to induce orthotopic tumors. The arrow shows a tumor cell depot after injection. b Representative image of the tumor-bearing liver lobe 11 days after induction. c Flow cytometry plots of endogenous CD8+ TILs 11 days after tumor induction. FMO (fluorescence minus one), Mock (PBS-injected liver lobe). d Experimental design. Treatment (arrows), time of analysis (). e Representative images of the tumor-bearing liver lobes of untreated (isotype controls) or treated (PD-1/CTLA-4 blockade) mice 21 days after tumor induction. f Tumor size in untreated and treated mice in the absence (−ACT) or presence (+ACT) of adoptively transferred HA-specific CD8+ T cells. Shown are the pooled data from 3 representative experiments. One-way ANOVA with Bonferroni post hoc test; n= 10 each group; *p< 0.05. g Hematoxylin-eosin and h immunofluorescence staining of tumor sections of untreated and treated mice. Tumor (T), invasive margin (IM), and liver (L). CD4 (green), CD8 (red), and CD44 (blue). Arrows show the presence of tertiary lymphoid organs in the vicinity of the tumors. Bars: 500 μm (HE staining); 50 μm (immunofluorescence).

Close modal

Histological analysis revealed that only few CD4+ and CD8+ T cells were found in the tumors of untreated mice. These were predominantly located at the invasive margin and in the outer areas of the tumors, indicating that T cells did not effectively infiltrate the tumor. Impairment of T-cell infiltration, also called T-cell exclusion, is an immune evasion mechanism frequently observed in solid tumors [26] and has been previously observed in experimental HCC [27]. In contrast, residual tumor areas present in treated mice were heavily infiltrated with T cells (Fig. 1g, h). Tertiary lymphoid organs were observed in both groups but were much more frequent in treated animals (online suppl. Fig. 1b). Therefore, PD-1/CTLA-4 blockade induced a strong antitumor response.

PD-1/CTLA-4 Blockade Results in Activation and Proliferation of T Cells

To characterize T cells infiltrating the TME after PD-1/CTLA-4 blockade, we analyzed tumor sections from untreated and treated mice killed at different times after tumor induction and treatment. While effector CD8+ and CD4+Foxp3 T cells were found almost exclusively in the tumors of treated mice, CD4+Foxp3+ regulatory T cells (Tregs) were present in the tumors of both mouse groups (Fig. 2a). The number of both CD8+ and CD4+ T cells significantly increased over time in treated animals. These increases occurred at comparable rates so that no significant changes in the CD8+/CD4+ T-cell ratio were observed between untreated and treated animals (Fig. 2b, upper panels). While the number of CD4+Foxp3 effector T cells significantly increased after therapy, the numbers of CD4+Foxp3+ Tregs remained constant. This led to a significant increase in the CD4+Foxp3/CD4+Foxp3+ T-cell ratio in treated animals (Fig. 2b, lower panels). Consequently, tumor remission was not due to depletion of Tregs by the anti-CTLA-4 antibody as described by others [28], but the expansion of effector T cells shifted the ratio of effectors to regulators in favor of a proinflammatory response.

Fig. 2.

PD-1/CTLA-4 blockade leads to intratumoral expansion of CD8+ TILs. Representative staining of tumor sections 21 days after tumor induction (a) CD4, CD8, and FoxP3 and (c) CD4, CD8, CD44, and PD-1. Quantitative analysis of tumor sections at different time points after tumor induction and treatment. (b) CD4, CD8, and FoxP3 and (d) CD8, CD44, and PD-1. e Representative staining for CD8, PD-1, and Ki67 14 and 21 days after tumor induction. f Quantitative analysis at the same time points. Shown are the pooled data from 3 experiments with 1 to 2 mice per time point and group: untreated (n= 4) and treated (n= 5) per time point. Mean ± SD; two-sided Mann-Whitney test; *p< 0.05. g Quantification of Ki67 expression in CD8+ TIL subsets 14 days after tumor induction by flow cytometry. Mean ± SD; one-way ANOVA with Bonferroni post hoc test; n= 3 each group.

Fig. 2.

PD-1/CTLA-4 blockade leads to intratumoral expansion of CD8+ TILs. Representative staining of tumor sections 21 days after tumor induction (a) CD4, CD8, and FoxP3 and (c) CD4, CD8, CD44, and PD-1. Quantitative analysis of tumor sections at different time points after tumor induction and treatment. (b) CD4, CD8, and FoxP3 and (d) CD8, CD44, and PD-1. e Representative staining for CD8, PD-1, and Ki67 14 and 21 days after tumor induction. f Quantitative analysis at the same time points. Shown are the pooled data from 3 experiments with 1 to 2 mice per time point and group: untreated (n= 4) and treated (n= 5) per time point. Mean ± SD; two-sided Mann-Whitney test; *p< 0.05. g Quantification of Ki67 expression in CD8+ TIL subsets 14 days after tumor induction by flow cytometry. Mean ± SD; one-way ANOVA with Bonferroni post hoc test; n= 3 each group.

Close modal

The few CD8+ TILs found in the tumors of untreated mice were CD44 and PD-1+. In contrast, the majority of the CD8+ TILs in the tumors of the treated mice were CD44+ and PD-1 (Fig. 2c). PD-1/CTLA-4 blockade induced a stable increase in the numbers and proportions of these cells (Fig. 2d), indicating that treatment led to priming and activation of naïve CD8+ T cells in the TME. In murine LCMV infection and human HCC, CD8+PD-1int cells express large amounts of CD44. In contrast, terminally exhausted CD8+PD-1high cells express low levels of CD44 [3, 29]. Given the detection limit of the immunofluorescence, it is likely that only cells expressing high levels of PD-1 and CD44 were recognized as positive. Therefore, CD8+ T cells identified as PD-1+CD44 most likely correspond to CD8+PD-1highCD44low TILs, whereas cells identified as PD-1 correspond to CD8+PD-1negCD44+ and CD8+PD-1intCD44+ TILs.

Treatment also led to a transient increase in the expression of the proliferation marker Ki67, which was observed on day 14 but not on day 21 (Fig. 2e, f). Co-staining of Ki67 and PD-1 revealed that both CD8+PD-1 and CD8+PD-1+ TILs proliferated in response to the treatment, although CD8+PD-1+ TILs showed a high proliferative rate (Fig. 2f). Since immunofluorescence staining did not discriminate between the CD8+PD-1 TIL subsets, we analyzed the coexpression of CD8, Ki67, and PD-1 by flow cytometry. Both CD8+PD-1high and CD8+PD1int TILs expressed Ki67. However, in agreement with previously published data [6, 7], the majority of the CD8+Ki67+ TILs were PD-1high (Fig. 2g, upper panel). In addition, the percentages of Ki67+ cells belonging to the different PD-1 subsets were similar despite treatment (Fig. 2g, lower panel). Although a high percentage of CD8+PD1high TILs expressing Ki67 was detected in the tumor of treated mice on day 14, the absolute number of CD8+PD-1+ TILs detected by immunofluorescence remained constant over time (Fig. 2d, lower left panel). This is surprising since cells expressing high levels of PD-1 are recognized as PD-1+ by immunofluorescence. This result indicated that CD8+PD-1high-proliferating cells either left the tumor or died after stimulation. The latter possibility is consistent with the observation that CD8+PD-1high TILs in experimental melanoma proliferate more efficiently than CD8+PD-1int TILs but undergo a limited number of division cycles and have a high death rate after TCR stimulation [6].

PD-1/CTLA4 Blockade Results in the Expansion of a Few Tumor-Specific T-Cell Clones

Blockade of both CTLA-4 and PD-1 pathways leads to expansion of the CD8+ T-cell pool. While PD-1 blockade leads to an increase in the clonality of the TIL repertoire, CTLA-4 blockade broadens the entire peripheral TCR repertoire [30‒32]. Broadening of the peripheral TCR repertoire results from the polyclonal expansion of nontumor-specific TCR clones and correlates more with the toxicity of the therapy than with the antitumor response [30, 31]. As demonstrated in Figure 3, the frequencies of the most common clonotypes were much higher in the tumors than in the spleens regardless of treatment, demonstrating that PD-1/CTLA-4 blockade did not expand the peripheral TCR repertoire.

Fig. 3.

PD-1/CTLA-4 blockade leads to clonal expansion of CD8+ TILs. Mean clone frequency occupancy by (a) clone rank (a) and clone frequency (b) in the tumors and spleens of untreated (n= 4) and treated (n= 4) mice on day 21 after induction. c Shannon index. Two-sided Mann-Whitney test. d Averaged cumulative frequency distributions of the top 500 TCR clonotypes in comparison to high- and low-clonality distribution models. Shown are the pooled data from 2 independent experiments.

Fig. 3.

PD-1/CTLA-4 blockade leads to clonal expansion of CD8+ TILs. Mean clone frequency occupancy by (a) clone rank (a) and clone frequency (b) in the tumors and spleens of untreated (n= 4) and treated (n= 4) mice on day 21 after induction. c Shannon index. Two-sided Mann-Whitney test. d Averaged cumulative frequency distributions of the top 500 TCR clonotypes in comparison to high- and low-clonality distribution models. Shown are the pooled data from 2 independent experiments.

Close modal

T cells sorted from the tumors 21 days after induction were highly clonal, with four clones accounting for 60% of the CD8+ TIL repertoire of untreated mice. The treatment led to further clonal expansion, as 60% of the CD8+ TIL repertoire of treated mice corresponded to a single clone (Fig. 3a, b). Although the TCR clonality of the CD8+ TILs clearly increased after treatment, the differences in the calculated Shannon indices were not significant between untreated and treated mice (p = 0.11, Fig. 3c). Nevertheless, the distribution of the cumulative frequencies of the 500 most frequent clones indicated a rather clonal TCR repertoire after treatment (Fig. 3d). In conclusion, the treatment increased the clonality of the CD8+ TIL repertoire without leading to a broadening of the peripheral CD8+ T-cell repertoire.

PD-1/CTLA-4 Blockade Supports Priming of Naïve CD8+ T Cells in the TME and Induces the Local Expansion of CD8+PD-1int TILs

To clarify which CD8+ TIL subsets expanded in the tumors after therapy, we analyzed the phenotype of the CD8+ TILs isolated from untreated and treated mice on day 21 after tumor induction by flow cytometry. The fate of naive tumor-specific T cells (CD8+CD45.1+ HA-specific T cells) adoptively transferred 10 days after tumor induction was also investigated.

As expected, the treatment resulted in an expansion of CD8+ TILs. Similar results were observed in the rest of the liver but not in the tumor-draining lymph nodes or the spleen (Fig. 4a, upper panels), confirming the localized activation of T cells. Transferred naïve CD8+CD45.1+ HA-specific T cells behaved similar to endogenous CD8+CD45.2+ T cells (Fig. 4a, lower panels).

Fig. 4.

PD-1/CTLA-4 blockade results in the expansion of endogenous and transferred tumor-specific CD8+PD-1int TILs. a Total numbers of CD8+ T cells (upper panels). Numbers of endogenous (CD45.2+) and tumor-specific (CD45.1+) CD8+ T cells (lower panels). b Representative flow cytometry plots of CD8+ TILs 21 days after tumor induction. c Quantitative analysis of total CD8+PD-1neg, CD8+PD-1int, and CD8+PD-1high TILs (upper panels). Quantitative analysis discriminating endogenous (CD45.2+) and tumor-specific (CD45.1+) CD8+PD-1neg, CD8+PD-1int and CD8+PD-1high TILs (lower panels). Shown are the pooled data from 2 representative experiments with 5 females and 3 males per group, respectively; n= 8 each group; Mean ± SD; one-way ANOVA with Bonferroni post hoc test; *p< 0.05, **p< 0.01, ***p< 0.001. Tumor-bearing liver lobes or left liver lobes (LL), rest of the liver (liver), and dLN.

Fig. 4.

PD-1/CTLA-4 blockade results in the expansion of endogenous and transferred tumor-specific CD8+PD-1int TILs. a Total numbers of CD8+ T cells (upper panels). Numbers of endogenous (CD45.2+) and tumor-specific (CD45.1+) CD8+ T cells (lower panels). b Representative flow cytometry plots of CD8+ TILs 21 days after tumor induction. c Quantitative analysis of total CD8+PD-1neg, CD8+PD-1int, and CD8+PD-1high TILs (upper panels). Quantitative analysis discriminating endogenous (CD45.2+) and tumor-specific (CD45.1+) CD8+PD-1neg, CD8+PD-1int and CD8+PD-1high TILs (lower panels). Shown are the pooled data from 2 representative experiments with 5 females and 3 males per group, respectively; n= 8 each group; Mean ± SD; one-way ANOVA with Bonferroni post hoc test; *p< 0.05, **p< 0.01, ***p< 0.001. Tumor-bearing liver lobes or left liver lobes (LL), rest of the liver (liver), and dLN.

Close modal

Flow cytometric analysis confirmed that PD-1/CTLA-4 blockade led to sustained expansion of CD8+PD-1int but not of CD8+PD-1high TILs (Fig. 4b, c, upper panel). In addition, a significant expansion of transferred CD45.1+CD8+PD-1neg TILs was also observed (Fig. 4c, lower panel). In general, transferred CD8+PD-1neg and CD8+PD-1int TILs expanded more efficiently than their endogenous counterparts after treatment, as the number of CD45.1+CD8+PD-1neg and CD45.1+CD8+PD-1int TILs increased more than 200-fold compared to mock-operated mice. In contrast, endogenous CD45.2+CD8+PD-1neg TILs did not expand, and the number of CD45.2+CD8+PD-1int TILs was only 7-fold higher in comparison to mock-operated mice. In summary, the therapy induced the preferential expansion of tumor-specific T-cell clones. Importantly, treatment enabled the priming of transferred naïve tumor-specific CD8+PD-1neg T cells, resulting in their expansion and differentiation into CD8+PD-1int cells.

CD8+PD-1high TILs in Liver Cancer Shown a T-Cell Exhaustion Signature

To determine the transcriptional signature of CD8+ T cells naturally occurring in tumors, we analyzed the mRNA profiles of sorted CD8+PD-1neg, CD8+PD-1int, and CD8+PD-1high TILs of untreated mice at day 21 (gating strategy and purity, online suppl. Fig. 2a). As demonstrated in Figure 5a, the three CD8+ TIL subsets clearly segregated from each other. Multigroup comparison analysis identified 151 differentially expressed transcripts between the three CD8+ T-cell subsets (online suppl. Table 1). Two main clusters were identified corresponding to transcripts that were up- or downregulated in CD8+PD-1high TILs compared to the other two subsets (Fig. 5b). GO enrichment analysis revealed that processes involved in cell death, regulation of signal transduction, and regulation of response to stimuli were upregulated, while processes involved in cell proliferation and immune response were downregulated in CD8+PD-1high TILs (Fig. 5c; online suppl. Table 2).

Fig. 5.

Transcriptional signatures of CD8+ TIL subsets. Shown are three independent biological samples for each PD-1 subset. Each sample was obtained using pooled CD8+ TILs from 5 to 10 mice. a Principal component analysis (PCA). b Unsupervised clustering analysis of the three TIL subsets; one-way ANOVA; Q< 0.05. c GO analysis. Upregulated processes (red) and downregulated processes (blue). The percentages of genes corresponding to each GO term among 76 upregulated and 75 downregulated genes are shown. d Venn diagram showing shared and differentially expressed transcripts between the three PD-1 subsets. Volcano plots showing up- and downregulated transcripts between: e CD8+PD-1high and CD8+PD-1neg TILs. Transcripts regulated at least 15-fold are annotated. f CD8+PD-1high and CD8+PD-1int TILs. Transcripts regulated at least 5-fold are annotated. Two-sided Student’s t-test; Q≤ 0.05; log2 fold change ≥1. g Expression of exhaustion markers with a calculated Q>0.05: Lag 3(Q= 0.081), Entpd1(Q= 0.089), Tnfrsf18(Q= 0.131), and CD200(Q= 0.164). Mean ± SD; one-way ANOVA with Bonferroni post hoc test; *p< 0.05, **p< 0.01. h Volcano plot showing up- and downregulated transcripts between CD8+PD-1int and CD8+PD-1neg.Two-sided Student’s t-test; Q≤ 0.05; log2 fold change ≥1.

Fig. 5.

Transcriptional signatures of CD8+ TIL subsets. Shown are three independent biological samples for each PD-1 subset. Each sample was obtained using pooled CD8+ TILs from 5 to 10 mice. a Principal component analysis (PCA). b Unsupervised clustering analysis of the three TIL subsets; one-way ANOVA; Q< 0.05. c GO analysis. Upregulated processes (red) and downregulated processes (blue). The percentages of genes corresponding to each GO term among 76 upregulated and 75 downregulated genes are shown. d Venn diagram showing shared and differentially expressed transcripts between the three PD-1 subsets. Volcano plots showing up- and downregulated transcripts between: e CD8+PD-1high and CD8+PD-1neg TILs. Transcripts regulated at least 15-fold are annotated. f CD8+PD-1high and CD8+PD-1int TILs. Transcripts regulated at least 5-fold are annotated. Two-sided Student’s t-test; Q≤ 0.05; log2 fold change ≥1. g Expression of exhaustion markers with a calculated Q>0.05: Lag 3(Q= 0.081), Entpd1(Q= 0.089), Tnfrsf18(Q= 0.131), and CD200(Q= 0.164). Mean ± SD; one-way ANOVA with Bonferroni post hoc test; *p< 0.05, **p< 0.01. h Volcano plot showing up- and downregulated transcripts between CD8+PD-1int and CD8+PD-1neg.Two-sided Student’s t-test; Q≤ 0.05; log2 fold change ≥1.

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The transcriptional signature of CD8+PD-1high TILs differed more strongly from that of CD8+PD-1neg TILs, as 122 transcripts were differentially expressed between these two subsets. In addition, 27 transcripts were exclusively up- or downregulated in this subset. In contrast, only 76 transcripts were differentially expressed between CD8+PD-1high and CD8+PD-1int TILs. CD8+PD-1int TILs and CD8+PD-1neg TILs were more similar, as only 46 transcripts were differentially expressed between these two subsets (Fig. 5d). Transcripts related to CD8+ T-cell exhaustion and dysfunctionality, such as Pdcd1 (PD-1), Havcr2 (TIM-3), Tnfrsf9 (4-1BB), Tigit, Csf1, Dusp4, Klri2, and Iftm1 [6, 7, 33‒35], were significantly upregulated in CD8+PD-1high TILs (Fig. 5e, f; online suppl. Table 2). Some transcripts frequently expressed by exhausted CD8+ T cells, such as Lag-3, Entpd1 (CD39), Tnfrsf18 (GITR), and CD200, tended to be upregulated, but their calculated q values were greater than 0.05 (Fig. 5g). Transcripts for PD-1 and TIM-3 were also upregulated in CD8+PD-1int TILs compared to CD8+PD-1neg TILs; however, their expression levels were significantly lower than those in CD8+PD-1high TILs, most likely reflecting the activated status of these cells (Fig. 5h). Transcripts for CCR7, L-Selectin (Sell), Lef1, and S1P1 (S1pr1) were downregulated in CD8+PD-1high TILs, indicating that these cells were activated, while CD8+PD-1neg cells most likely represented naïve T cells (Fig. 5e, f; online suppl. Table 3). In conclusion, CD8+PD-1high TILs show similarities to exhausted T cells found in both chronic LCMV infection and cancer models [3, 4, 6].

PD-1/CTLA-4 Blockade Only Modestly Affects the Transcriptional Signatures of the Responding CD8+ TIL Subtypes

To assess how checkpoint inhibition affects the different subsets of CD8+ TILs, we compared their transcriptional profiles in untreated and treated mice. Due to the low number of CD8+PD-1high TILs in the tumors of the treated mice, we could not sort this subset for analysis (online suppl. Fig. 2b). Multigroup comparison analysis of the five analyzed subsets identified 376 significant differentially expressed transcripts (Fig. 6a and online suppl. Table 4). Eleven clusters of genes displaying distinct expression profiles between CD8+PD-1high TILs and at least one of the other CD8+ TIL subsets were identified. Four of these clusters (C1-C4) contained transcripts that were significantly enriched in the GO analysis (Fig. 6b; online suppl. Table 5). The expression profiles of transcripts belonging to C2 and C4 were very similar between untreated CD8+PD-1high and treated CD8+PD-1int TILs. Transcripts in C2 identified upregulated processes involved in the regulation of response to stimulus, cell communication, cell proliferation, immunological response, signal transduction, and apoptosis. Transcripts in C4 identified downregulated processes involved in T-cell receptor rearrangement, T-cell activation and differentiation, cell migration, and proliferation. Surprisingly, when the CD8+PD-1high subset was excluded from the analysis, only a few significant differences in the transcriptional signatures of the remaining CD8+ TIL subsets were observed between untreated and treated mice (Fig. 6c). In addition, no significant differences in the expression of transcripts for effector molecules and cytokines were observed between CD8+PD-1int TILs of untreated and CD8+PD-1int TILs of treated mice, indicating that these cells had comparable functionalities.

Fig. 6.

Transcriptional signatures of the CD8+ TIL subsets in untreated and treated mice. Shown are three independent biological samples for each PD-1 subset. Each sample was obtained using pooled CD8+ TILs from 5 to 10 mice. a Unsupervised clustering analysis of differentially expressed transcripts. One-way ANOVA, Q< 0.05. Cluster with significant up- or downregulation of biological processes (C1-C4). b GO analysis of C1-C4. Shown are the percentages of genes corresponding to each GO term among 36 genes in C1, 115 genes in C2, 51 genes in C3, and 70 genes in C4. c Unsupervised clustering analysis excluding the CD8+PD-1high subset. One-way ANOVA; Q< 0.05 (left panel). Comparison of CD8+PD-1neg (middle panel) and CD8+PD-1int (right panel) TILs of untreated and treated mice; two-sided Student’s t-test; Q≤ 0.05, log2 fold change ≥1. d Venn diagram showing shared and differentially expressed transcripts between PD-1high versus untreated CD8+PD-1neg + PD-1int TILs and PD-1high versus treated CD8+PD-1neg + PD-1int TILs. e, f Volcano plots showing up- and downregulated transcripts between PD-1high versus untreated CD8+PD-1neg + PD-1int and PD-1high versus treated CD8+PD-1neg + PD-1int TILs. Transcripts that were upregulated at least 10-fold or downregulated at least 5-fold are annotated. g Unsupervised clustering analysis of CD45.2+CD8+PD-1int TILs from untreated and treated mice and CD45.1+CD8+PD-1int TILs from treated mice; one-way ANOVA, Q< 0.05 (left). Comparison of CD45.2+CD8+PD-1int TILs from untreated mice and CD45.1+CD8+PD-1int TILs from treated mice (middle panel). Comparison of CD45.2+CD8+PD-1int and CD45.1+CD8+PD-1int TILs from treated mice (right panel). Two-sided Student’s t-test; Q≤ 0.05, log2 fold change ≥1.

Fig. 6.

Transcriptional signatures of the CD8+ TIL subsets in untreated and treated mice. Shown are three independent biological samples for each PD-1 subset. Each sample was obtained using pooled CD8+ TILs from 5 to 10 mice. a Unsupervised clustering analysis of differentially expressed transcripts. One-way ANOVA, Q< 0.05. Cluster with significant up- or downregulation of biological processes (C1-C4). b GO analysis of C1-C4. Shown are the percentages of genes corresponding to each GO term among 36 genes in C1, 115 genes in C2, 51 genes in C3, and 70 genes in C4. c Unsupervised clustering analysis excluding the CD8+PD-1high subset. One-way ANOVA; Q< 0.05 (left panel). Comparison of CD8+PD-1neg (middle panel) and CD8+PD-1int (right panel) TILs of untreated and treated mice; two-sided Student’s t-test; Q≤ 0.05, log2 fold change ≥1. d Venn diagram showing shared and differentially expressed transcripts between PD-1high versus untreated CD8+PD-1neg + PD-1int TILs and PD-1high versus treated CD8+PD-1neg + PD-1int TILs. e, f Volcano plots showing up- and downregulated transcripts between PD-1high versus untreated CD8+PD-1neg + PD-1int and PD-1high versus treated CD8+PD-1neg + PD-1int TILs. Transcripts that were upregulated at least 10-fold or downregulated at least 5-fold are annotated. g Unsupervised clustering analysis of CD45.2+CD8+PD-1int TILs from untreated and treated mice and CD45.1+CD8+PD-1int TILs from treated mice; one-way ANOVA, Q< 0.05 (left). Comparison of CD45.2+CD8+PD-1int TILs from untreated mice and CD45.1+CD8+PD-1int TILs from treated mice (middle panel). Comparison of CD45.2+CD8+PD-1int and CD45.1+CD8+PD-1int TILs from treated mice (right panel). Two-sided Student’s t-test; Q≤ 0.05, log2 fold change ≥1.

Close modal

We additionally tried to identify genes potentially involved in the abolishment of T-cell exhaustion after therapy by comparing untreated CD8+PD-1high TILs with the other two CD8+ subsets of untreated and treated mice. As shown in Figure 6d, untreated PD-1high TILs were more similar to treated PD-1neg + PD-1int TILs than to untreated PD-1neg + PD-1int TILs (37 vs. 118 exclusively regulated transcripts). As expected, transcripts related to T-cell exhaustion were upregulated in PD-1high TILs compared to untreated PD-1neg + PD-1int TILs (Fig. 6e; online suppl. Table 6). In contrast, Spp1 (osteopontin), Sprr1a (Cornifin-A), Serpine1, Vcan (Versican), Krt8 (Cytokeratin8), and Tm4sf1 were upregulated in PD-1high TILs compared to treated PD-1neg + PD-1int TILs (Fig. 6f; online suppl. Table 2). Osteopontin, also called Eta-1 (early T-lymphocyte activator-1), is a T-cell cytokine that is expressed following TCR signaling. The expression of osteopontin in activated CD8+ T cells is regulated by T-bet, and its expression is essential for the development of effector Tc1 CD8+ T cells. Osteopontin is also highly expressed in a subset of senescent CD4+PD-1+ memory T cells, which hardly proliferate after TCR stimulation [36]. Furthermore, expression of osteopontin has been recently demonstrated for CD8+PD1+ TILs isolated from mammary carcinoma of mice feed with high-fat diet [37]. Therefore, high osteopontin expression could be a common feature of dysfunctional T cells. The other differentially expressed transcripts were atypical for T cells, and their functions remain to be clarified.

Finally, to compare the transcriptional signatures of endogenous (CD45.2+) and tumor-specific (CD45.1+) CD8+PD-1int TILs, we transferred CD8+CD45.1+ HA-specific T cells in tumor-bearing mice 10 days after tumor induction and treated them subsequently as described. Endogenous CD8+CD45.2+ and transferred CD8+CD45.1 PD-1int TILs were sorted on day 21 and used for mRNA analysis. Due to the very low numbers of CD45.1+CD8+PD-1int TILs in the tumors of untreated mice, we compared only the three successfully sorted TIL subtypes. Only minor differences were observed between the analyzed TIL subtypes, independent of their origin or treatment, indicating that endogenous and transferred tumor-specific TILs had similar functions (Fig. 6g).

In summary, the treatment seems not significantly altering the effector capacity of the CD8+ TILs, making it unlikely that PD-1/CTLA-4 blockade reinvigorated exhausted CD8+ T cells. To prove this hypothesis, we examined the ability of these cells to produce TNF-α and IFN-γ after in vitro restimulation with Hep-HA-STK-1 cells plus anti-CD3 and anti-CD28 antibodies. Consistent with the mRNA data, a comparable proportion of CD8+ TILs able to produce TNF-α were found in the tumors of untreated and treated mice (online suppl. Fig. 4a, c). Moreover, comparable proportions of cells belonging to the three CD8+ TIL subsets produced TNF-α, regardless of therapy (online suppl. Fig. 4b, d). However, the total number of CD8+ TILs that produced TNF-α+ after restimulation was significantly increased in treated animals (online suppl. Fig. 4e). Remarkably, this increase was due to an increase in the number of TNF-expressing CD8+PD-1int TILs (online suppl. Fig. 4, f). In addition, part of these cells coexpressed IFN-γ and membrane-bound CD107a (online suppl. Fig. 5 a, b), indicating that these cells but not CD8+PD-1neg TILs are able to degranulate and exert the effector function after therapy.

In the present work, we aimed to investigate the effect of PD-1/CTLA-4 blockade on exhausted CD8+ TILs in liver cancer. For this purpose, we established a model in which the induced tumors developed an inflamed TME, as evidenced by the presence of tertiary lymphoid organs and the accumulation of immune cells surrounding the tumor nodes. In agreement with previous studies, we observed three subtypes of CD8+ T cells (PD-1neg, PD-1int, and PD-1high) in the TME, although terminally exhausted CD8+PD-1high TILs predominated [6, 7].

Which subset of CD8+ T cells responds to checkpoint blockade remains controversial. In all cases, however, the response to therapy correlated with proliferation. Previous studies demonstrated that CD8+PD-1high T cells express higher levels of the proliferation marker Ki67 than CD8+PD-1int cells. However, despite a robust proliferation after in vitro stimulation, they only modestly proliferate in vivo. In contrast, CD8+PD-1int cells strongly expanded after adoptive transfer. In the majority of the studies, checkpoint blockade increased the expansion of CD8+PD-1int but not of CD8+PD-1high T cells [3, 4, 6]. In line with these observations, almost all CD8+PD-1high TILs in our model expressed Ki67 after checkpoint blockade, but no expansion of this subset was observed. Therefore, the expression of Ki67 seems to correlate with the ability of individual cells to divide but not with their ability to maintain proliferation and survive after TCR stimulation, which is essential for maintenance of the antitumor response.

In non-small-cell lung carcinoma, ex vivo-isolated CD8+PD-1high TILs are dysfunctional [7]. Although they can regain their effector function after in vitro cultivation, it is not clear whether they can do the same in vivo. In contrast, in experimental melanoma, CD8+PD-1high TILs show a higher cytotoxic capacity in vitro than CD8+PD-1int TILs. However, due to their limited proliferative capacity, they show a low ability to control tumor growth after adoptive transfer. In addition, transferred CD8+PD-1high cells cannot differentiate into CD8+PD-1int cells. In contrast, transferred CD8+PD-1int TILs differentiated into highly cytotoxic CD8+PD-1high TILs in vivo. Since increased numbers of CD8+PD-1high TILs were observed in the tumor of mice that received CD8+PD-1int TILs and checkpoint blockade, the authors proposed that PD-1 and PD-1/CTLA-4 blockades induce not only the expansion of CD8+PD-1int TILs but also their differentiation into CD8+PD-1high TILs [6].

In our model, only CD8+PD-1int TILs expanded in response to PD-1/CTLA-4 blockade. CD8+PD-1high TILs disappeared after treatment, showing that CD8+PD-1int TILs did not differentiate into CD8+PD-1high TILs. In addition, we did not observe the upregulation of transcripts for Granzyme A and B and effector cytokines in CD8+PD-1high compared to CD8+PD-1int TILs, as described for experimental melanoma [6]. As demonstrated by immunofluorescence, treatment increased the number of activated CD8+ TILs expressing CD44. Consistent with this observation, the total number of CD8+ TILs producing TNF-α and IFN-γ and exhibiting surface expression of CD107a after in vitro restimulation was also increased in treated mice. Interestingly, although CD8+PD-1neg and CD8+PD-1int cells were capable of producing cytokines, translocation of CD107a was virtually restricted to the PD-1int subset. It is therefore likely that in our model, CD8+PD-1int TILs perform the effector function. Since the transferred tumor-specific CD8+ T cells in our model responded to therapy with strong expansion of CD8+PD-1neg and CD8+PD-1int but not CD8+PD-1high TILs, we believe that treatment promotes the recruitment of naïve T cells into the TME, leading to their priming, activation, and proliferation. Due to activation, the expression of inhibitory receptors increases, resulting in the emergence of CD8+PD-1int cells. This hypothesis is consistent with previously published data, demonstrating that the response to PD-1 blockade is mediated by T-cell clones that have recently entered the tumor [38].

In conclusion, our data suggest that the amount of effector cells in the tumor plays a key role in tumor remission. Therefore, antitumor response after therapy depends on the proliferation of CD8+ TILs. There is increasing evidence that exhausted T cells exhibit metabolic insufficiencies that impedes proliferation after activation, and it is known that PD-1 blockade can lead to metabolic reprogramming that likely restores proliferation [39, 40]. Our GO analysis showed that several metabolic pathways were altered in CD8+PD-1high TILs compared with the other PD-1 subsets from untreated and treated mice. Therefore, it would be possible that metabolic reprogramming supports the expansion of CD8+PD-1int TILs in our model.

Besides proliferation, infiltration of activated CD8+ TILs into the tumor is a critical step required for tumor remission. Interestingly, we observed a slight but significant increase in the concentration of CCL2 in tumor lysates of treated mice compared to untreated mice (online suppl. Fig. 3). Elevated concentrations of CCL-2 after PD-1 blockade correlated with tumor infiltration by CD8+ T cells in a model of metastatic breast cancer [41]. In addition, nitration of CCL2 by reactive nitrogen species in the inflamed TME led to trapping of T cells in the stroma surrounding the tumor. Inhibition of CCL2 nitration resulted in tumor infiltration [42]. Nitration of CCL2 is not recognized by anti-CCL2 antibodies, so that nitration inhibition increases the CCL2 levels detected in the tumors. It is therefore plausible that checkpoint blockade inhibited the nitration of CCL2, resulting in the elevated levels of CCL2 observed in our model. In this case, CCL2 would be the crucial molecule involved in T-cell exclusion.

One limitation associated with checkpoint inhibitor treatment is that the presence of preexisting tumor-specific T cells is required for therapeutic success [43]. Genetically engineered T cells displaying chimeric antigen receptors have been successfully used in the treatment of B-cell malignancies [44, 45]. Unfortunately, the same success has not been observed in the treatment of solid tumors [44]. The failure of CAR-T-cell therapy in the treatment of solid tumors has been associated with poor migration to the tumor site, lack of tumor infiltration, and cell survival after transfer [46, 47]. In our liver cancer model, few doses of checkpoint inhibitors subsequent to T-cell transfer were sufficient to overcome these problems. This finding could have important implications for future therapies.

We would like to acknowledge the assistance of the Cell Sorting Core Facility of the Hannover Medical School and Prof. Dr. Christine Falk for providing analysis software and for constructive discussion of the data.

Animal care and experiments were performed in accordance with institutional and national guidelines. All animal experiments were performed according to protocols approved by the Animal Welfare Commission of the Hannover Medical School and Local Ethics Animal Review Board (#33.12-42502-04-15/1779; April 20, 2015 and #33.19-42502-04-18/3063; May 10, 2019; Lower Saxony State Office for Consumer Protection and Food Safety, Oldenburg, Germany).

The authors have no conflicting interests.

This work was supported by the Wilhelm Sander-Stiftung (Grant No. 2017.070.1 to Elmar Jaeckel and Ana C. Davalos-Misslitz), the Government of Canada’s New Frontiers in Research Fund (NFRF), NFRFT-2020-00787, and Deutsche Forschungsgemeinschaft (BU2722/2-3 to Laura Elisa Buitrago-Molina and HA6880/2-1 to Matthias Hardtke-Wolenski).

Conceptualization: Elmar Jaeckel and Ana C. Davalos-Misslitz; methodology: Ana C. Davalos-Misslitz, Elmar Jaeckel, and Matthias Hardtke-Wolenski; investigation: Sandra Bufe, Artur Zimmermann, Sarina Ravens, Robert Geffers, and Ana C. Davalos-Misslitz; formal analysis: Sandra Bufe, Ana C. Davalos-Misslitz, Sarina Ravens, Robert Geffers, and Steven R. Talbot; validation: Elmar Jaeckel, Ana C. Davalos-Misslitz, Immo Prinz; resources, Norman Woller, Florian Kühnel, Michael Peter Manns, and Heiner Wedemeyer; visualization: Sandra Bufe, Ana C. Davalos-Misslitz, Laura Elisa Buitrago-Molina, and Fatih Noyan; writing – original draft: Ana C. Davalos-Misslitz and Sandra Bufe; writing – review and editing: Matthias Hardtke-Wolenski, Laura Elisa Buitrago-Molina, Sarina Ravens, Immo Prinz, Norman Woller, Fatih Noyan, Florian Kühnel, Steven R. Talbot, Michael Peter Manns, Heiner Wedemeyer, and Elmar Jaeckel; supervision: Ana C. Davalos-Misslitz, Elmar Jaeckel, and Matthias Hardtke-Wolenski; and funding acquisition: Elmar Jaeckel, Ana C. Davalos-Misslitz, Laura Elisa Buitrago-Molina, and Matthias Hardtke-Wolenski.

All data generated or analyzed during this study are included in this article and its supplementary material files. Further enquiries can be directed to the corresponding author.

1.
Sung
H
,
Ferlay
J
,
Siegel
RL
,
Laversanne
M
,
Soerjomataram
I
,
Jemal
A
,
.
Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
.
CA A Cancer J Clin
.
2021
;
71
(
3
):
209
49
. https://doi.org/10.3322/caac.21660.
2.
Wherry
EJ
.
T cell exhaustion
.
Nat Immunol
.
2011
;
12
(
6
):
492
9
. https://doi.org/10.1038/ni.2035.
3.
Blackburn
SD
,
Shin
H
,
Freeman
GJ
,
Wherry
EJ
.
Selective expansion of a subset of exhausted CD8 T cells by αPD-L1 blockade
.
Proc Natl Acad Sci U S A
.
2008
;
105
(
39
):
15016
21
. https://doi.org/10.1073/pnas.0801497105.
4.
Paley
MA
,
Kroy
DC
,
Odorizzi
PM
,
Johnnidis
JB
,
Dolfi
DV
,
Barnett
BE
.
Progenitor and terminal subsets of CD8+ T cells cooperate to contain chronic viral infection
.
Science
.
2012
;
338
(
6111
):
1220
5
. https://doi.org/10.1126/science.1229620.
5.
Zarour
HM
.
Reversing T-cell dysfunction and exhaustion in cancer
.
Clin Cancer Res
.
2016
;
22
(
8
):
1856
64
. https://doi.org/10.1158/1078-0432.ccr-15-1849.
6.
Miller
BC
,
Sen
DR
,
Al Abosy
R
,
Bi
K
,
Virkud
YV
,
LaFleur
MW
.
Subsets of exhausted CD8(+) T cells differentially mediate tumor control and respond to checkpoint blockade
.
Nat Immunol
.
2019
;
20
(
3
):
326
36
. https://doi.org/10.1038/s41590-019-0312-6.
7.
Thommen
DS
,
Koelzer
VH
,
Herzig
P
,
Roller
A
,
Trefny
M
,
Dimeloe
S
.
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
(
7
):
994
1004
. https://doi.org/10.1038/s41591-018-0057-z.
8.
Dywicki
J
,
Noyan
F
,
Misslitz
AC
,
Hapke
M
,
Galla
M
,
Schlue
J
.
Hepatic T cell tolerance induction in an inflammatory environment
.
Dig Dis
.
2018
;
36
(
2
):
156
66
. https://doi.org/10.1159/000481341.
9.
Ju
HL
,
Han
KH
,
Lee
JD
,
Ro
SW
.
Transgenic mouse models generated by hydrodynamic transfection for genetic studies of liver cancer and preclinical testing of anti-cancer therapy
.
Int J Cancer
.
2016
;
138
(
7
):
1601
8
. https://doi.org/10.1002/ijc.29703.
10.
Wolint
P
,
Betts
MR
,
Koup
RA
,
Oxenius
A
.
Immediate cytotoxicity but not degranulation distinguishes effector and memory subsets of CD8+ T cells
.
J Exp Med
.
2004
;
199
(
7
):
925
36
. https://doi.org/10.1084/jem.20031799.
11.
Kollmar
O
,
Schilling
MK
,
Menger
MD
.
Experimental liver metastasis: standards for local cell implantation to study isolated tumor growth in mice
.
Clin Exp Metastasis
.
2004
;
21
(
5
):
453
60
. https://doi.org/10.1007/s10585-004-2696-3.
12.
Hardtke-Wolenski
M
,
Fischer
K
,
Noyan
F
,
Schlue
J
,
Falk
CS
,
Stahlhut
M
.
Genetic predisposition and environmental danger signals initiate chronic autoimmune hepatitis driven by CD4+ T cells
.
Hepatology
.
2013
;
58
(
2
):
718
28
. https://doi.org/10.1002/hep.26380.
13.
Krege
J
,
Seth
S
,
Hardtke
S
,
Davalos-Misslitz
ACM
,
Forster
R
.
Antigen-dependent rescue of nose-associated lymphoid tissue (NALT) development independent of LTβR and CXCR5 signaling
.
Eur J Immunol
.
2009
;
39
(
10
):
2765
78
. https://doi.org/10.1002/eji.200939422.
14.
Romermann
D
,
Ansari
N
,
Schultz-Moreira
AR
,
Michael
A
,
Marhenke
S
,
Hardtke-Wolenski
M
.
Absence of Atg7 in the liver disturbed hepatic regeneration after liver injury
.
Liver Int
.
2020
;
40
(
5
):
1225
38
. https://doi.org/10.1111/liv.14425.
15.
Dywicki
J
,
Buitrago-Molina
LE
,
Pietrek
J
,
Lieber
M
,
Broering
R
,
Khera
T
.
Autoimmune hepatitis induction can occur in the liver
.
Liver Int
.
2020
;
40
(
2
):
377
81
. https://doi.org/10.1111/liv.14296.
16.
Huang
YJ
,
Haist
V
,
Baumgartner
W
,
Fohse
L
,
Prinz
I
,
Suerbaum
S
,
.
Induced and thymus-derived Foxp3(+) regulatory T cells share a common niche
.
Eur J Immunol
.
2014
;
44
(
2
):
460
8
. https://doi.org/10.1002/eji.201343463.
17.
Yang
BH
,
Hagemann
S
,
Mamareli
P
,
Lauer
U
,
Hoffmann
U
,
Beckstette
M
.
Foxp3+ T cells expressing RORγt represent a stable regulatory T-cell effector lineage with enhanced suppressive capacity during intestinal inflammation
.
Mucosal Immunol
.
2016
;
9
(
2
):
444
57
. https://doi.org/10.1038/mi.2015.74.
18.
Dywicki
J
,
Buitrago-Molina
LE
,
Noyan
F
,
Davalos-Misslitz
AC
,
Hupa-Breier
KL
,
Lieber
M
,
.
The detrimental role of regulatory T cells in nonalcoholic steatohepatitis
.
Hepatol Commun
;
2021
;
6
(
2
):
320
33
.
19.
Brochet
X
,
Lefranc
MP
,
Giudicelli
V
.
IMGT/V-QUEST: the highly customized and integrated system for IG and TR standardized V-J and V-D-J sequence analysis
.
Nucleic Acids Res
.
2008
;
36
(
Web Server
):
W503
8
. https://doi.org/10.1093/nar/gkn316.
20.
Dobin
A
,
Davis
CA
,
Schlesinger
F
,
Drenkow
J
,
Zaleski
C
,
Jha
S
,
.
STAR: ultrafast universal RNA-seq aligner
.
Bioinformatics
.
2013
;
29
(
1
):
15
21
. https://doi.org/10.1093/bioinformatics/bts635.
21.
Liao
Y
,
Smyth
GK
,
Shi
W
.
The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads
.
Nucleic Acids Res
.
2019
;
47
(
8
):
e47
. https://doi.org/10.1093/nar/gkz114.
22.
Durinck
S
,
Moreau
Y
,
Kasprzyk
A
,
Davis
S
,
De Moor
B
,
Brazma
A
,
.
BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis
.
Bioinformatics
.
2005
;
21
(
16
):
3439
40
. https://doi.org/10.1093/bioinformatics/bti525.
23.
Durinck
S
,
Spellman
PT
,
Birney
E
,
Huber
W
.
Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt
.
Nat Protoc
.
2009
;
4
(
8
):
1184
91
. https://doi.org/10.1038/nprot.2009.97.
24.
McCarthy
DJ
,
Chen
Y
,
Smyth
GK
.
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation
.
Nucleic Acids Res
.
2012
;
40
(
10
):
4288
97
. https://doi.org/10.1093/nar/gks042.
25.
Boyle
EI
,
Weng
S
,
Gollub
J
,
Jin
H
,
Botstein
D
,
Cherry
JM
,
.
GO::TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes
.
Bioinformatics
.
2004
;
20
(
18
):
3710
5
. https://doi.org/10.1093/bioinformatics/bth456.
26.
Joyce
JA
,
Fearon
DT
.
T cell exclusion, immune privilege, and the tumor microenvironment
.
Science
.
2015
;
348
(
6230
):
74
80
. https://doi.org/10.1126/science.aaa6204.
27.
Chen
Y
,
Ramjiawan
RR
,
Reiberger
T
,
Ng
MR
,
Hato
T
,
Huang
Y
.
CXCR4 inhibition in tumor microenvironment facilitates anti-programmed death receptor-1 immunotherapy in sorafenib-treated hepatocellular carcinoma in mice
.
Hepatology
.
2015
;
61
(
5
):
1591
602
. https://doi.org/10.1002/hep.27665.
28.
Simpson TR, Li F, Montalvo-Ortiz W, Sepulveda MA, Bergerhoff K, Arce F, et al. Fc-dependent depletion of tumor-infiltrating regulatory T cells co-defines the efficacy of anti-CTLA-4 therapy against melanoma. J Exp Med. 2013 Aug 26;210(9):1695–710.
29.
Ma
J
,
Zheng
B
,
Goswami
S
,
Meng
L
,
Zhang
D
,
Cao
C
.
PD1(Hi) CD8(+) T cells correlate with exhausted signature and poor clinical outcome in hepatocellular carcinoma
.
J Immunother Cancer
.
2019
;
7
(
1
):
331
. https://doi.org/10.1186/s40425-019-0814-7.
30.
Hopkins
AC
,
Yarchoan
M
,
Durham
JN
,
Yusko
EC
,
Rytlewski
JA
,
Robins
HS
.
T cell receptor repertoire features associated with survival in immunotherapy-treated pancreatic ductal adenocarcinoma
.
JCI Insight
.
2018
;
3
(
13
):
122092
. https://doi.org/10.1172/jci.insight.122092.
31.
Robert
L
,
Tsoi
J
,
Wang
X
,
Emerson
R
,
Homet
B
,
Chodon
T
.
CTLA4 blockade broadens the peripheral T-cell receptor repertoire
.
Clin Cancer Res
.
2014
;
20
(
9
):
2424
32
. https://doi.org/10.1158/1078-0432.ccr-13-2648.
32.
Tumeh
PC
,
Harview
CL
,
Yearley
JH
,
Shintaku
IP
,
Taylor
EJM
,
Robert
L
.
PD-1 blockade induces responses by inhibiting adaptive immune resistance
.
Nature
.
2014
;
515
(
7528
):
568
71
. https://doi.org/10.1038/nature13954.
33.
Waugh
KA
,
Leach
SM
,
Moore
BL
,
Bruno
TC
,
Buhrman
JD
,
Slansky
JE
.
Molecular profile of tumor-specific CD8+ T cell hypofunction in a transplantable murine cancer model
.
J
.
2016
;
197
(
4
):
1477
88
. https://doi.org/10.4049/jimmunol.1600589.
34.
Li H, van der Leun AM, Yofe I, Lubling Y, Gelbard-Solodkin D, van Akkooi ACJ, et al. Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma. Cell. 2020 Apr 30;181(3):747.
35.
Wirth TC, Xue HH, Rai D, Sabel JT, Bair T, Harty JT, et al. Repetitive antigen stimulation induces stepwise transcriptome diversification but preserves a core signature of memory CD8(+) T cell differentiation. Immunity. 2010 Jul 23;33(1):128–40.
36.
Shimatani
K
,
Nakashima
Y
,
Hattori
M
,
Hamazaki
Y
,
Minato
N
.
PD-1 + memory phenotype CD4 + T cells expressing C/EBPα underlie T cell immunodepression in senescence and leukemia
.
Proc Natl Acad Sci U S A
.
2009
;
106
(
37
):
15807
12
. https://doi.org/10.1073/pnas.0908805106.
37.
Kado
T
,
Nawaz
A
,
Takikawa
A
,
Usui
I
,
Tobe
K
.
Linkage of CD8(+) T cell exhaustion with high-fat diet-induced tumourigenesis
.
Sci Rep
.
2019
;
9
(
1
):
12284
. https://doi.org/10.1038/s41598-019-48678-0.
38.
Yost
KE
,
Satpathy
AT
,
Wells
DK
,
Qi
Y
,
Wang
C
,
Kageyama
R
.
Clonal replacement of tumor-specific T cells following PD-1 blockade
.
Nat Med
.
2019
;
25
(
8
):
1251
9
. https://doi.org/10.1038/s41591-019-0522-3.
39.
Bengsch
B
,
Johnson
AL
,
Kurachi
M
,
Odorizzi
PM
,
Pauken
KE
,
Attanasio
J
.
Bioenergetic insufficiencies due to metabolic alterations regulated by the inhibitory receptor PD-1 are an early driver of CD8(+) T cell exhaustion
.
Immunity
.
2016
;
45
(
2
):
358
73
. https://doi.org/10.1016/j.immuni.2016.07.008.
40.
Franco
F
,
Jaccard
A
,
Romero
P
,
Yu
YR
,
Ho
PC
.
Metabolic and epigenetic regulation of T-cell exhaustion
.
Nat Metab
.
2020
;
2
(
10
):
1001
12
. https://doi.org/10.1038/s42255-020-00280-9.
41.
Peranzoni E, Lemoine J, Vimeux L, Feuillet V, Barrin S, Kantari-Mimoun C, et al. Macrophages impede CD8 T cells from reaching tumor cells and limit the efficacy of anti-PD-1 treatment. Proc Natl Acad Sci U S A. 2018 Apr 24;115(17):E4041–E50.
42.
Molon B, Ugel S, Del Pozzo F, Soldani C, Zilio S, Avella D, et al. Chemokine nitration prevents intratumoral infiltration of antigen-specific T cells. J Exp Med. 2011 Sep 26;208(10):1949–62.
43.
Fares
CM
,
Van Allen
EM
,
Drake
CG
,
Allison
JP
,
Hu-Lieskovan
S
.
Mechanisms of resistance to immune checkpoint blockade: why does checkpoint inhibitor immunotherapy not work for all patients
.
Am Soc Clin Oncol Educ Book
.
2019
;
39
:
147
64
. https://doi.org/10.1200/edbk_240837.
44.
Hou
B
,
Tang
Y
,
Li
W
,
Zeng
Q
,
Chang
D
.
Efficiency of CAR-T therapy for treatment of solid tumor in clinical trials: a meta-analysis
.
Dis Markers
.
2019
;
2019
:
3425291
11
. https://doi.org/10.1155/2019/3425291.
45.
Shah
NN
,
Maatman
T
,
Hari
P
,
Johnson
B
.
Multi targeted CAR-T cell therapies for B-cell malignancies
.
Front Oncol
.
2019
;
9
:
146
. https://doi.org/10.3389/fonc.2019.00146.
46.
Ma S, Li X, Wang X, Cheng L, Li Z, Zhang C, et al. Current Progress in CAR-T Cell Therapy for Solid Tumors. Int J Biol Sci. 2019;15(12):2548–60.
47.
Martinez M, Moon EK. CAR T cells for solid tumors: new strategies for finding, infiltrating, and surviving in the tumor microenvironment. Front Immunol. 2019;10:128.

Additional information

Elmar Jaeckel and Ana C. Davalos-Misslitz contributed equally to this work.