Lending PD1 a helping hand
LAG3 is an inhibitory receptor expressed on exhausted T cells that is thought to temper T cell activation by engaging peptide–MHC class II complexes. Here, Andrews et al. have engineered mice expressing a noncleavable form of LAG3 (LAG3NC) that cannot be shed from the cell surface by ADAM family proteases. By generating mouse strains that express LAG3NC ion-distinct T cell types, they found that LAG3 shedding by conventional CD4+ T cells rather than T regulatory cells (Tregs) or CD8+ T cells to be important for driving responsiveness to anti-PD1. Although both CD8+ T cells and Tregs have received considerable attention in the context of immunotherapy, this study highlights the importance of T cell help in promoting antitumor immunity.
Abstract
Mechanisms of resistance to cancer immunotherapy remain poorly understood. Lymphocyte activation gene–3 (LAG3) signaling is regulated by a disintegrin and metalloprotease domain-containing protein–10 (ADAM10)– and ADAM17-mediated cell surface shedding. Here, we show that mice expressing a metalloprotease-resistant, noncleavable LAG3 mutant (LAG3NC) are resistant to PD1 blockade and fail to mount an effective antitumor immune response. Expression of LAG3NC intrinsically perturbs CD4+ T conventional cells (Tconvs), limiting their capacity to provide CD8+ T cell help. Furthermore, the translational relevance for these observations is highlighted with an inverse correlation between high LAG3 and low ADAM10 expression on CD4+ Tconvs in the peripheral blood of patients with head and neck squamous cell carcinoma, which corresponded with poor prognosis. This correlation was also observed in a cohort of patients with skin cancers and was associated with increased disease progression after standard-of-care immunotherapy. These data suggest that subtle changes in LAG3 inhibitory receptor signaling can act as a resistance mechanism with a substantive effect on patient responsiveness to immunotherapy.
INTRODUCTION
The immunotherapeutic blockade of inhibitory receptors, such as programmed cell death-1 (PD1) and cytotoxic T-lymphocyte antigen 4 (CTLA4), that promote tumor-infiltrating lymphocyte (TIL) dysfunction has led to substantive improvements in objective responses across a wide variety of tumor types (1–5). However, only a small proportion of patients (10 to 30%) are responsive to these modalities as a monotherapy, and a full understanding of the mechanisms of resistance remains elusive. Lymphocyte activation gene–3 (LAG3) is an inhibitory receptor that coexpresses with PD1 on intratumoral T cells in several murine tumor models, and dual blockade synergistically limits tumor growth compared with either modality as a monotherapy (6). LAG3 coexpression with PD1 exemplifies a dysfunctional program of CD8+ TILs with a reduced capacity to proliferate and produce cytokines (7). Thus, LAG3 is currently being targeted in the clinic by at least 10 immunotherapeutics in combination with PD1/programmed death-ligand 1 (PDL1)–targeting strategies to capitalize on expected synergy (8).
Altered metalloprotease activity and transmembrane protein shedding have been shown to have a marked effect on signaling activity and cell function for Fas ligand (9, 10). LAG3 expression and consequent function are also regulated by cell surface shedding achieved by a disintegrin and metalloprotease domain–containing protein–10 (ADAM10) and ADAM17. ADAM10/17 cleaves LAG3 at the connecting peptide between the membrane proximal D4 domain and the transmembrane domain, releasing a monomeric soluble form of LAG3 (sLAG3). sLAG3 that is released after metalloprotease-mediated shedding is not known to have any biological activity (11). However, previous in vitro studies have shown that preventing LAG3 shedding by generation of noncleavable form of LAG3 (LAG3NC) mutants affects T cell function by enhancing inhibitory function (12).
Given these observations with LAG3NC mutants in vitro, we generated a conditional knock-in mouse that restricts LAG3NC to Cre-expressing cell populations, a unique tool to address the physiological relevance of LAG3 transmembrane shedding in vivo. Because the impact of LAG3 shedding on antitumor immunity and subsequent response to immunotherapy is unknown, we generated mice with LAG3NC restricted to T cells to assess the impact of LAG3 shedding in MC38 colon adenocarcinoma, a transplantable tumor model sensitive to anti-PD1 (6). We find that LAG3 shedding from the surface of CD4+ conventional T cells (Tconvs), and not CD8+ T cells or CD4+ regulatory T cells (Tregs), is essential for an effective antitumor response mediated by anti-PD1 immunotherapy in mouse models. Single-cell RNA-sequencing (RNA-seq) analysis revealed the transcriptional relevance for LAG3 shedding on CD4+ Tconvs, because this population was transcriptionally altered as a result of LAG3NC. Such CD4+ Tconvs have reduced functionality with an impaired capacity for help, affecting CD8+ T cell function that is required for tumor clearance. The translational relevance for these findings is highlighted by the inverse correlation between LAG3 and ADAM10 expression in the peripheral blood of treatment-naïve patients with head and neck squamous cell carcinoma (HNSCC), demonstrating that LAG3 on CD4+ Tconvs is a prognostic marker of poor disease outcome. Last, a cohort of patients with advanced skin cancer showed that disease progression after standard-of-care (SOC) anti-PD1/CTLA4 immunotherapy was associated with higher LAG3 expression on CD4+ Tconvs. Patients who responded to immunotherapy had a lower LAG3:ADAM10 ratio consistent with the phenotype of the LAG3NC mouse models described herein.
RESULTS
Generation of a LAG3NC conditional knock-in mouse
To assess the impact of LAG3 shedding on T cells, we generated a conditional knock-in mouse that allows for Cre recombinase–mediated, cell type–restricted expression of LAG3NC. Lag3 exon 7, between the membrane-proximal D4 domain and the transmembrane domain, was replaced with an alternate version of the connecting peptide in which 12 amino acid residues were removed (LAG3ESCP) and previously shown to be resistant to metalloprotease-mediated shedding in vitro (Fig. 1A and fig. S1A) (11, 12). In addition, enhanced blue fluorescent protein (EBFP) and ametrine were incorporated as fluorescent reporters for the transcriptional activity of Lag3WT (in the absence of Cre) and Lag3NC (in the presence of Cre), respectively (fig. S1,B and C). Lag3NC.L/L mice were then crossed with several Cre recombinase mouse lines to facilitate cell type–restricted deletion to all T cells (CD4Cre) (13), CD8+ T cells (E8ICre.GFP) (14), CD4+ T cells (tamoxifen-inducible ThPOKCreERT2; fig. S1D), or Tregs (tamoxifen-inducible Foxp3CreERT2.GFP; fig. S1E) (15). Release of sLAG3 in vitro was not detected after stimulation of T cells expressing both the Lag3NC.L/L-floxed allele and Cre recombinase (fig. S1F). However, serum levels of sLAG3 were not substantively altered between LAG3NC mice and controls, due to other innate populations such as plasmacytoid dendritic cells that are a major source of sLAG3 (fig. S1G) (16). Thus, our genetic system allows for the expression of LAG3 to be switched to a mutant noncleavable form to selectively assess the impact of LAG3 shedding on the function of distinct T cell subpopulations within the tumor microenvironment (Fig. 1B).
(A) Schematic of LAG3NC conditional knock-in mouse. (B) LAG3 and ametrine expression on CD4+ Foxp3−, CD4+ Foxp3+, and CD8+ TILs isolated from Lag3NC.L/L and Lag3NC.L/LCD4Cre mice that received 5 × 105 MC38 adenocarcinoma cells subcutaneously. (C) Individual tumor growth curves and (D) survival plot of Lag3NC.L/L and Lag3NC.L/LCD4Cre mice receiving 5 × 105 MC38 adenocarcinoma cells subcutaneously and anti-PD1 or IgG (200 μg) on days 6, 9, and 12 by intraperitoneal injection. (E) Kaplan-Meir curve showing tumor-free animals after secondary MC38 injection (2.5 × 105 cells subcutaneously) of Lag3NC.L/L and Lag3NC.L/LCD4Cre mice after primary MC38 injection (5 × 105 cells subcutaneously) and resection (day 12) or sham control animals. (F) Mean tumor growth curves (left) and survival plot (right) of Lag3NC.L/L and Lag3NC.L/LCD4Cre mice receiving 1.25 × 105 B16-gp100 melanoma cells intradermally and immunized with Amph-gp100 or Amph-E7 vaccine subcutaneously on days 4 and 11 (20 μg) with anti-PD1 or IgG as in (C). Results represent the mean of three (B to D) or two (E and F) independent experiments. *P < 0.05, **P < 0.01, and ***P < 0.001. n.s., not significant by (D to F) log-rank (Mantel-Cox) and (F) two-way ANOVA. Error bars represent the means ± SEM.
Restriction of LAG3NC to T cells affects anti-PD1–mediated tumor regression
Two mouse models of cancer were used to assess the impact of LAG3NC on tumor growth: MC38, a model of adenocarcinoma, and B16-F10, a model of melanoma. No differences in primary tumor growth were observed between Lag3NC.L/LCD4Cre mice and Lag3NC.L/L controls with either model, likely due to the fact that tumor-induced tolerance and checkpoint inhibition were already maximal (fig. S2, A and B). Expression of the inhibitory receptors LAG3, PD1, T cell immunoglobulin and mucin 3 (TIM3), and CD244 (2B4) were unchanged on all T cell populations assessed, although T cell immunoreceptor with immunoglobulin and ITIM domains (TIGIT) was elevated on CD4+Foxp3− and CD8+ TILs from MC38-bearing Lag3NC.L/LCD4Cre mice (fig. S2, C to H). PD1 expression was also increased on CD4+Foxp3− TIL. No difference in proliferation, death, and survival markers was observed in CD4+Foxp3+, CD4+Foxp3−, or CD8+ MC38 TILs (fig. S2, I to K). Furthermore, there was no difference in phosphorylated Akt or S6 by flow cytometry (fig. S2, L and M).
We then assessed the impact of LAG3NC expression on an antitumor immune response after immunotherapy. We used a therapeutic anti-PD1–dosing regimen that results in ~30 to 40% of mice clearing MC38 tumors, as seen here in Lag3NC.L/L control mice and consistent with our previous observations (Fig. 1, C and D, and fig. S3A) (6). Lag3NC.L/LCD4Cre mice exhibited substantially reduced sensitivity to anti-PD1 immunotherapy with <10% clearing tumors. This suggested that LAG3NC on T cells impaired the antitumor immune response elicited by blockade of the PD1 pathway.
To ensure that the restrictive impact of LAG3NC on antitumor immunity was not limited to this model, we evaluated two additional systems. First, using an MC38 rechallenge model in which the primary tumor was resected at day 12 and challenged with MC38 30 days later (fig. S3B), we showed that about half the Lag3NC.L/LCD4Cre mice failed to clear MC38, whereas all the Lag3NC.L/L control mice were protected (Fig. 1E). Second, because B16 is refractory to anti-PD1, we used an immunotherapeutic regimen combining an amphiphile vaccine (Amph-vax) consisting of a gp100 antigen linked to a lipophilic albumin-binding tail by a solubility promoting polar polymer chain (gp100-vax) (17, 18) combined with anti-PD1 treatment. The combination of gp100-vax and anti-PD1 drives a strong immunotherapeutic response in C57BL/6 mice bearing a B16 line that overexpresses gp100 (B16-gp100) but not B16-F10, which expresses very low levels of gp100, compared with isotype control or a control amphiphile delivering a peptide derived from human papilloma virus (HPV)–derived cervical cancer antigen E7 (E7-vax) (fig. S3, C and D). Tumor reduction after combinatorial gp100-vax/anti-PD1 immunotherapy was not observed in Lag3NC.L/LCD4Cre mice compared with Lag3NC.L/L controls, suggesting that LAG3NC on T cells blocks the antitumor immune response elicited by this therapeutic regimen (Fig. 1F). Together, these data suggest that LAG3NC limits T cell–mediated antitumor immunity in multiple mouse models.
Transcriptomic analysis of LAG3NC on T cell populations in the context of PD1 blockade
To further understand which T cell population is selectively modulated by LAG3NC and is causative of the immunotherapeutic resistance shown in this model, we assessed the transcriptional profiles of T cells isolated from MC38 tumors pooled from Lag3NC.L/L and Lag3NC.L/LCD4Cre mice after anti-PD1 immunotherapy, or isotype control, using single-cell transcriptomic analysis (single-cell RNA-seq; fig. S4A). We initially used a single-cell RNA-seq analysis pipeline (see Materials and Methods) to embed all cells in a two-dimensional fast interpolation-based t-distributed stochastic neighborhood embedding (FltSNE) (19) and performed clustering and differential gene expression analysis to classify cell types based on canonical markers (e.g., clusters with high expression of Cd3d and Cd8a were classified as CD8+ T cells). We identified CD8+ T cells, CD4+Foxp3+ Treg, and CD4+Foxp3− Tconv lineages (Fig. 2A). The frequencies of these T cell subsets recovered bioinformatically were similar to those identified by flow cytometry, with CD4+Foxp3+ Tregs as the largest subpopulation recovered (fig. S4B). To assess the magnitude of transcriptional differences between cell types and treatment conditions, we measured the distance between experimental groups in high dimensional space using the Bhattacharyya distance (BD) for the three T cell populations identified (Fig. 2B and table S1). This analysis unexpectedly revealed the greatest transcriptional differences in the CD4+Foxp3− Tconv population, specifically when comparing the isotype versus anti-PD1 treatment in the Lag3NC.L/LCD4Cre mice. This finding suggests that CD4+Foxp3− Tconvs in the Lag3NC.L/LCD4Cre mice change more than other populations after anti-PD1 therapy. Further, this analysis also revealed subtle differences in the CD8+ T cell population, although these differences were largely a result of anti-PD1 treatment across both genotypes. Unexpectedly, this analysis revealed that the CD4+Foxp3+ Treg population was largely similar between samples.
Single-cell RNA-seq analysis of T cells isolated from tumors pooled from Lag3NC.L/L and Lag3NC.L/LCD4Cre mice at day 14 injected with 5 × 105 MC38 adenocarcinoma cells subcutaneously and anti-PD1 or IgG (200 μg) on days 6, 9, and 12 by intraperitoneal injection. (A) FltSNE visualization and DRAGON clustering of all single cells identified CD4+ Tregs (orange), CD4+ Tconvs (purple), and CD8+ T cells (green). (B) Quantification of differences by BD between CD4+ Tconvs, CD4+ Tregs, and CD8+ T cells in Lag3NC.L/L and Lag3NC.L/LCD4Cre mice, receiving anti-PD1 or IgG. (C) Clustering of CD4+ Tconvs by DRAGON revealed a total of seven clusters across all samples. (D) Scaled sample enrichment in clusters identified in (C). (E) Gene set enrichment analysis revealed signature genes associated with each cluster identified in (C).
To address the transcriptional differences of CD4+Foxp3− Tconvs between groups, we bioinformatically isolated and reclustered this population to identify a total of seven clusters, projected by FltSNE (Fig. 2C and fig. S5, A and B). To illustrate the enrichment of different genotypes and treatment conditions across clusters, we generated heat maps to assess the scaled enrichment of sample across the clusters (Fig. 2D). Because CD4+Foxp3− Tconvs changed most in the Lag3NC.L/LCD4Cre mice after anti-PD1 treatment, we sought to evaluate the transcriptional profile associated with this group. We found that cells from cluster 2 had the highest scaled enrichment for this group, representing a higher than anticipated frequency of cells, compared with anti-PD1–treated Lag3NC.L/L controls (fig. S5C). Conversely, cluster 1 was the most highly enriched cluster in the comparator group (Lag3NC.L/L + anti-PD1). Gene set enrichment analysis revealed that cluster 2 was driven by several interferon signaling pathways (Fig. 2E). Furthermore, three members of the interferon-induced transmembrane (IFITM) protein family that are expressed in T cells (Ifitm1 to Ifitm3) were shown to be highly up-regulated (fig. S5D). Recently, it has been shown that IFITM proteins regulate T helper 1 (TH1) differentiation as Ifitm-deficient CD4+ T cells have higher expression of TH1-assoicated genes, including interferon-γ (IFN-γ) (20, 21). This suggests that CD4+ T cells from Lag3NC.L/LCD4Cre mice treated with anti-PD1 may be less TH1-like than the Lag3NC.L/L group.
Bioinformatically isolating CD4+Foxp3+ Tregs and reclustering identified nine clusters (fig. S6A). Fewer differences for enriched clusters between experimental groups were apparent for this population; however, cluster 8 was enriched after anti-PD1 treatment in Lag3NC.L/LCD4Cre mice and also had higher expression of interferon signaling pathways (fig. S6, B to F). Assessment of the transcriptional differences of CD8+ T cells after reclustering showed differences when assessing enrichment of the six clusters identified (fig. S7, A to F). Together, this analysis suggests that the largest transcriptional differences exist in the CD4+Foxp3− Tconv compartment with respect to the difference between wild-type and LAG3NC experimental groups.
LAG3NC intrinsically affects CD4+ T cell functionality
To assess whether LAG3NC on CD4+ T cells affected tumor clearance with immunotherapeutic blockade, we used Lag3NC.L/LThPOKCreERT2 mice in which LAG3NC was restricted to CD4+ T cells after tamoxifen administration. As with Lag3NC.L/LCD4Cre animals, tamoxifen-administered Lag3NC.L/LThPOKCreERT2 mice exhibited reduced survival after MC38 inoculation when treated with anti-PD1 compared with control mice (Fig. 3, A and B, and fig. S8A). To further interrogate the role of LAG3NC on TIL functionality, we assessed cytokines by flow cytometry at day 14 after MC38 inoculation, stratified by responsiveness to anti-PD1 compared with isotype control (responders and nonresponders) (fig. S8B). An increase in IFN-γ+TNF-α+CD4+Foxp3− Tconvs when Lag3NC.L/L controls were treated with anti-PD1 was not evident in Lag3NC.L/LCD4Cre mice (Fig. 3C and fig. S8, C and D). Conversely, killer cell lectin-like receptor G1 (KLRG1), which is reduced after anti-PD1 and has been shown to inhibit CD4+ T cell responses (22), was increased in CD4+Foxp3− Tconvs expressing LAG3NC (fig. S8E). Tumor necrosis factor–α (TNF-α)–producing CD4+ Tconvs, more evident in Lag3NC.L/L control mice, were KLRG1-negative (fig. S8F). Moreover, an increase in interleukin-2 (IL-2) production was observed within CD4+Foxp3− TILs of Lag3NC.L/L control animals receiving anti-PD1, which was not evident in Lag3NC.L/LCD4Cre mice (Fig. 3D). CD4+Foxp3− TILs were also more proliferative by Ki67 staining and 5-bromo-2′-deoxyuridine (BrdU) incorporation after anti-PD1 treatment in Lag3NC.L/L controls than in Lag3NC.L/L CD4Cre mice (Fig. 3E). Whereas a statistically significant increase in cleaved caspase-3 (cCasp3), but not B-cell lymphoma 2 (Bcl2), in CD4+Foxp3− TILs was shown in Lag3NC.L/LCD4Cre mice compared with Lag3NC.L/L controls, there was no statistical difference between these groups after anti-PD1 treatment (fig. S8, G and H).
(A) Individual tumor growth curves and (B) survival plot of Lag3NC.L/L and Lag3NC.L/LThPOKCreERT2 mice receiving 5 × 105 MC38 adenocarcinoma cells subcutaneously and anti-PD1 or IgG (200 μg) on days 6, 9, and 12 by intraperitoneal injection, as well as five consecutive intraperitoneal injections of tamoxifen (1 mg in 5% ethanol/sunflower oil) from days 0 to 4. TIL was harvested at day 14 from Lag3NC.L/L or Lag3NC.L/LCD4Cre mice injected with 5 × 105 MC38 adenocarcinoma cells subcutaneously receiving anti-PD1 or IgG (200 μg) on days 6, 9, and 12 by intraperitoneal injection. (C) IFN-γ and TNF-α, as well as (D) IL-2 from CD4+ Foxp3− TILs were measured after restimulation with PMA and ionomycin for 4 hours in the presence of brefeldin A. Mice that received anti-PD1 were stratified into nonresponders (N) and responders (R) to treatment. (E) BrdU and Ki67 staining was assessed in CD4+ Foxp3− TIL, by intraperitoneal injection of BrdU 12 hours before harvest. (F) Mean clinical scores after EAE induction in Lag3NC.L/L, Lag3NC.L/LCD4Cre, and Lag3NC.L/LE8ICre.GFP mice. (G) Lymphocytes were isolated from the brain at day 14 after immunization and stimulated with MOG35–55 peptide for 20 hours and the last 4 hours with brefeldin A. IL-17A, IFN-γ, and GM-CSF was assessed from CD4+Foxp3− T cells. (H) Ki67 was assessed in CD4+Foxp3−, CD4+Foxp3+, and CD8+ T cells isolated in (G). Results represent the mean of three independent experiments. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. n.s. not significant by (B) log-rank (Mantel-Cox), (C to E) Mann-Whitney test, (F) two-way ANOVA and (G and H) unpaired t test. Error bars represent the means ± SEM.
To further investigate whether the LAG3NC phenotype on CD4+ T cells selectively affects CD4+Foxp3− Tconvs rather than CD4+Foxp3+ Tregs, we used Lag3NC.L/LFoxp3CreERT2.GFP mice, which restricts LAG3NC expression to CD4+Foxp3+ Tregs after tamoxifen administration. In contrast to Lag3NC.L/LThPOKCreERT2 mice, anti-PD1–treated Lag3NC.L/LFoxp3CreERT2.GFP mice exhibited similar tumor clearance to their tamoxifen-treated controls (Foxp3CreERT2.GFP and Lag3NC.L/L) with consequent improved survival (fig. S9, A to C). Moreover, proliferation and frequency of CD4+Foxp3+ Tregs were unchanged between Lag3NC.L/LCD4Cre and control mice treated with anti-PD1 or isotype (fig. S9, D and E). Bcl2 and cCasp3 expression was also unchanged between experimental groups, although cCasp3 was significantly increased in Tregs isolated from isotype-treated Lag3NC.L/LCD4Cre mice compared with controls (fig. S9, F and G). Together, these data suggest that LAG3NC selectively affects CD4+Foxp3− Tconvs and not CD4+Foxp3+ Tregs within the tumor, which in turn limits an effective antitumor immune response after anti-PD1 immunotherapy.
To confirm the selective role for LAG3NC on CD4+Foxp3− Tconvs, we immunized Lag3NC.L/L, Lag3NC.L/LCD4Cre, or Lag3NC.L/LE8ICre.GFP mice with myelin oligodendrocyte glycoprotein (MOG)35–55 peptide to induce experimental autoimmune encephalomyelitis (EAE), a CD4+ TH-mediated disease model. Lag3NC.L/LCD4Cre mice exhibited a reduced severity of EAE compared with Lag3NC.L/L controls and Lag3NC.L/LE8ICre.GFP mice (Fig. 3F). As a result of LAG3NC, there is a reduction of pathogenic IFN-γ+IL-17+GM-CSF+CD4+Foxp3− Tconvs isolated from the brain (day 15 after immunization) (Fig. 3G). Moreover, this T cell population showed a selective reduction of proliferation in Lag3NC.L/LCD4Cre mice compared with Lag3NC.L/L controls, which was not significant in Tregs or CD8+ T cells isolated from the brain (Fig. 3H). Together, these results show a selective role for LAG3NC mediating CD4+ Foxp3− Tconv functionality and proliferation in two different disease models.
LAG3NC extrinsically modulates effector CD8+ T cell functionality
We had initially predicted that LAG3NC would have a dominant effect on CD8+ T cells, particularly given that this population has the greatest expression of LAG3 within the TIL compartment and their central role in antitumor immunity (8). Our transcriptional analysis had shown that any differences observed with CD8+ TIL were primarily due to the impact of anti-PD1 treatment rather than LAG3NC expression. It was unexpected to find that Lag3NC.L/LE8ICre.GFP mice exhibited similar tumor regression and survival to controls, suggesting that LAG3NC expression on CD8+ T cells did not affect response to anti-PD1 (Fig. 4, A and B, and fig. S10A). To further interrogate the role of LAG3NC, we assessed MC38 TILs by flow cytometry at day 14 after inoculation, stratified by responsiveness to anti-PD1 compared with isotype (fig. S10B). Although an intrinsic LAG3NC CD8+ T cell phenotype was not observed in MC38 tumor–bearing, anti-PD1–treated Lag3NC.L/LE8ICre.GFP mice, IFN-γ/TNF-α release from restimulated CD8+ TILs isolated from Lag3NC.L/LCD4Cre mice was reduced compared with controls when treated with anti-PD1 (Fig. 4C and fig. S10, C and D). In contrast, CD8+ TILs isolated from Lag3NC.L/LE8ICre.GFP mice do exhibit increased IFN-γ/TNF-α release after anti-PD1 treatment despite LAG3NC expression on CD8+ T cells, confirming that this is a LAG3NC cell-extrinsic effect. No differences in proliferation or death and survival were observed in CD8+ T cells (fig. S10, E to G).
(A) Individual tumor growth curves and (B) survival plot of Lag3NC.L/L and Lag3NC.L/LE8ICre.GFP mice receiving 5 × 105 MC38 adenocarcinoma cells subcutaneously and anti-PD1 or IgG on days 6, 9, and 12 (200 μg) by intraperitoneal injection. (C) TIL was harvested at day 14 from Lag3NC.L/L, Lag3NC.L/LCD4Cre, or Lag3NC.L/LE8ICre.GFP mice injected with 5 × 105 MC38 adenocarcinoma cells subcutaneously receiving anti-PD1 or IgG (200 μg) on days 6, 9, and 12 by intraperitoneal injection. IFN-γ and TNF-α from CD8+ TIL were measured after restimulation with PMA and ionomycin for 4 hours in the presence of brefeldin A. Mice that received anti-PD1 were stratified into nonresponders (N) and responders (R) to treatment. (D) Lag3NC.L/L or Lag3NC.L/LCD4Cre mice (Thy1.2+) received an adoptive transfer (AT) of 1 × 105 pmel (Thy1.1+) cells the day before inoculation with 1.25 × 105 B16-gp100 melanoma cells intradermally. Mice received anti-PD1 (200 μg) intraperitoneally on days 6, 9, and 12, and tumor volume was measured when sacrificed on day 15 after inoculation. (E) IFN-γ was measured on both Thy1.1+ (pmel) and Thy1.2+ (endogenous) CD8+ T cells from (D) after restimulation as in (C). Results represent the mean of three independent experiments. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. n.s. not significant by (B) log-rank (Mantel-Cox) and (C to E) Mann-Whitney tests.
CD4+Foxp3− Tconvs have been shown to promote the effector function of CD8+ TILs to produce IFN-γ and subsequently enhance antitumor immunity (23). To provide further support for a cell-extrinsic effect on CD8+ T cells, we adoptively transferred gp100-specific Thy1.1+ pmel CD8+ T cells into B16-gp100 tumor-bearing Lag3NC.L/L or Lag3NC.L/LCD4Cre hosts, which also received anti-PD1 (fig. S11A). When transferred into Lag3NC.L/LCD4Cre hosts, tumor size was larger compared with Lag3NC.L/L control hosts and Thy1.1+ pmel cells isolated from the tumor produced less IFN-γ (Fig. 4, D and E). Size of tumor and cytokine release had no relationship to the extent of pmel infiltration (fig. S11, B to E). Together, these data suggest that LAG3NC extrinsically affects CD8+ T cell–mediated clearance of tumors after PD1 blockade.
ADAM-mediated cell surface shedding of LAG3 promotes antitumor immunity
The observations above are a consequence of preventing LAG3 shedding by expression of a LAG3NC mutant. To assess the effect of ADAM-mediated cell surface shedding of LAG3, the expression of Adam10 in sorted T cell populations isolated from B16-F10 tumor-bearing Foxp3Cre-YFP mice was assessed by quantitative real-time polymerase chain reaction (qPCR) and compared with peripheral T cell populations (fig. S12A). CD8+ T cells isolated from B16-F10 tumors, but not CD4+ Tconv or Treg populations, have reduced Adam10 expression compared with the periphery. This may explain why CD8+ TILs have the highest amount of LAG3 expression and why a phenotype was not observed in Lag3NC.L/LE8ICre.GFP mice.
To determine whether the phenotype observed in Lag3NC.L/LCD4Cre mice is synonymous with ADAM-metalloprotease enzymatic activity, an ADAM10 inhibitor (GI254023X) was used that limits LAG3 shedding from CD4+Foxp3− Tconvs and CD8+ T cells in vitro (fig. S12, B and C) (24). To investigate the effect of ADAM10-mediated LAG3 shedding in vivo, we subcutaneously implanted C57BL/6 mice with Alzet osmotic pumps releasing GI254023X (20 mg/kg per day) over 14 days, which resulted in the reduction of sLAG3 in sera by 30% compared with mice receiving vehicle control (fig. S12D). After anti-PD1 treatment, mice receiving GI254023X exhibited a reduced percentage of IFN-γ+–producing CD8+ TILs versus controls, a reduction that was comparable with Lag3NC.L/LCD4Cre mice, with a trend toward larger tumors (fig. S12, E and F). This supports the notion that the phenotype exhibited by expression of LAG3NC on T cells is due to limiting ADAM10-metalloprotease–mediated LAG3 shedding.
Low LAG3:ADAM10 ratio on conventional CD4+ T cells is indicative of patient survival and responsiveness to PD1 blockade
Although substantial clinical success has been achieved with PD1/PDL1-targeting agents, most patients with cancer do not respond to checkpoint blockade, and so targeting coexpressed inhibitory receptors such as LAG3 has been suggested to enhance objective response rates (8). To understand the clinical relevance of the relationship between LAG3 and ADAM10 expression, we assessed CD4+Foxp3− Tconv, CD4+Foxp3+ Treg, and CD8+ T cell populations isolated from fresh tumor tissue and matched peripheral blood lymphocytes (PBLs) from treatment-naïve patients with metastatic melanoma (table S2). Although healthy-donor PBLs did not express surface LAG3, a subset of patients with melanoma did show enhanced LAG3 expression on both CD4+Foxp3− Tconv and CD4+Foxp3+ Treg populations in peripheral blood, with expression further enhanced in corresponding TIL populations (fig. S13, A to D). CD8+ T cells from patient peripheral blood showed a minimal LAG3 expression profile, although this is still greatly enhanced in the tumor compartment (fig. S13, E and F). There was also heterogeneous expression of ADAM10 in patient PBL, particularly on CD4+ T cell populations (fig. S13, G to L). We next assessed the relationship between LAG3 and ADAM10 by paired analysis. For CD4+ T cell populations in tumor and peripheral blood, a group of patients with higher LAG3 expression inversely correlated with lower ADAM10 expression (fig. S13, M to R).
To further understand the clinical relevance of the dichotomy observed between LAG3 and ADAM10, particularly on CD4+Foxp3− Tconvs, we assessed peripheral blood isolated from a second cohort of patients with advanced metastatic melanoma and other skin cancers who had received SOC anti-PD1 or anti-PD1/CTLA4 immunotherapy (table S3). Patients were stratified by response to treatment according to RECIST 1.1 criteria, consisting of 18 patients who showed either initial partial response (PR) or complete response (CR) and 19 patients with initial disease progression. Although there was no difference in LAG3 expression on T cell populations before and after immunotherapy in the cohort of patients who responded to treatment, there was a statistically significant increase in LAG3 expression on CD4+Foxp3− Tconvs in the cohort of patients who progressed, which was not evident for CD4+Foxp3+ Tregs or CD8+ T cells (Fig. 5A and fig. S14, A to E). Although changes in ADAM10 expression before and after treatment were not significant (fig. S14, F to H), paired analysis with LAG3 shows a similar dichotomy of expression compared with the previous cohort (Fig. 5B and fig. S14, I and J). As a result, the LAG3:ADAM10 ratio was found to be statistically increased in CD4+Foxp3− Tconvs in the PBL of patients who progressed after treatment but not in CD4+Foxp3+ Tregs or CD8+ T cells (Fig. 5C and fig. S14, K and L). This suggests that LAG3 on CD4+ Tconvs, in which level of expression is modulated by ADAM10-mediated cell surface shedding, acts as a primary resistance mechanism during immunotherapy and is associated with poor responsiveness to treatment.
(A) Lymphocytes were isolated from peripheral blood of patients with advanced metastatic melanoma before (pre) or after (post) treatment with SOC anti-PD1 ± anti-CTLA4 (n = 37; cohort B; table S3). The change in LAG3 and ADAM10 expression was assessed for CD4+Foxp3− T cells, and patients were stratified by responsiveness to treatment. (B) Paired analysis of LAG3 and ADAM10 expression on CD4+Foxp3− T cells isolated from patients in (A). (C) LAG3:ADAM10 ratio for CD4+Foxp3− T cells of patients in (A). (D) Lymphocytes were isolated from peripheral blood of patients with HNSCC (n = 50; cohort D; table S5), and LAG3 expression on CD4+Foxp3−, CD4+Foxp3+, and CD8+ T cells was assessed by stage of disease. (E) Paired analysis of LAG3 and ADAM10 expression on CD4+Foxp3− T cells. (F) Kaplan-Meier survival curve of patients with advanced disease–stage HNSCC (n = 25) with high LAG3:ADAM10 ratio (≥0.3865) or low LAG3:ADAM10 ratio (<0.3865) expressed on CD4+Foxp3− T cells. *P < 0.05, **P < 0.01, and ****P < 0.0001. n.s. not significant by (A and D) unpaired t test, (C) Wilcoxon test, and (F) log-rank (Mantel-Cox).
The clinical relevance for LAG3 and ADAM10 expression on CD4+ Tconv PBL was further supported by analysis of a third patient cohort with a different tumor type—treatment-naïve HNSCC (table S4). In previous studies, LAG3 over expression on TIL has been shown to be a prognostic factor correlating to higher tumor pathological grades (25–27). Similar observations are shown for treatment-naïve patients with HNSCC compared with the metastatic melanoma cohort, with elevated LAG3 surface expression on CD4+ T cell populations in patient peripheral blood compared with healthy donors (fig. S15, A to C). Again, there is heterogeneous expression of ADAM10 and ADAM17 on patient PBL, although this is inversed correlated to LAG3 on each cell type (fig. S15, D to L). HPV has been identified as the causative agent of a subgroup of patients with HNSCC, which have higher responsiveness toward PD1 blockade (28). Patients with HPV-positive status show minimal expression of LAG3 on CD4+ T cell populations in peripheral blood (fig. S15, M to O).
Given the heterogeneous level of LAG3 expression that was also evident for the HNSCC patient cohort, we wanted to further confirm the clinical relevance of ADAM-mediated cell surface shedding of LAG3 by analyzing a second cohort (fourth in total analyzed in this study) of frozen-banked PBL samples from treatment-naïve patients with HNSCC consisting of 25 early stage disease (T1, T2, T3, and/or N0 and N1) and 25 advanced disease cases (T4 and/or N2B, N2C and N3) for whom clinical outcome data were available (table S5). LAG3 expression on CD4+Foxp3− Tconvs, but not CD4+Foxp3+ Tregs and CD8+ T cells in peripheral blood, was associated with advanced stage disease (Fig. 5D). Despite the difference in LAG3 surface expression on CD4+ T cell populations, sLAG3 in patient sera was also unchanged, suggesting that this may not be a useful clinical biomarker because there are other cell types that shed LAG3, as in mice (fig. S16A). In this cohort, ADAM10 expression was also statistically reduced on Tregs in patients with advanced disease (fig. S16B). As previously shown, the heterogeneity of LAG3 expression in PBL inversely correlated with ADAM10 expression in CD4+ T cell populations but not CD8+ T cells (Fig. 5E and fig. S16, C and D). Assessing all parameters within the advanced disease patient cohort showed that low (<23.5%) LAG3 expression on CD4+Foxp3− Tconvs was the best predictor for survival, with poor prognosis for patients with high (≥23.5%) LAG3 expression (fig. S16E). Because CD4+Foxp3− Tconvs with high LAG3 expression had relatively low ADAM10 expression, patients with a low LAG3:ADAM10 ratio (<0.3865) had better survival outcome than patients with a high LAG3:ADAM10 ratio (≥0.3865) (Fig. 5F).
DISCUSSION
Overall our study suggests four key points. First, ADAM-mediated cell surface shedding of LAG3 is important for effective antitumor immune responses, as demonstrated in vivo with a conditional knock-in mouse model in which clinical relevance of these observations is exemplified, suggesting that LAG3 acts as a mechanism of primary resistance in patients with advanced cancer receiving checkpoint blockade therapy and that LAG3:ADAM10 expression on CD4+Foxp3− Tconvs may act as a systemic prognostic biomarker in a select group of patients. The potential importance of additional co-inhibitory receptors beyond PD1, such as LAG3, that affect responsiveness to immunotherapy, with a subsequent impact on disease progression and survival is evident, particularly given that anti-PD1 as a monotherapy only benefits a proportion of patients.
Second, our observations highlight the unexpected impact that limiting ADAM-mediated cell surface shedding of an inhibitory receptor can have on immune responses, in general, and responsiveness to immunotherapy, specifically. The resistance demonstrated to PD1 blockade in preclinical models and the relationship between a high LAG3:ADAM10 ratio and poor prognosis in HNSCC and responsiveness to immunotherapy in patients with melanoma highlight altered LAG3 shedding as a potential resistance mechanism.
Third, we were surprised to find that limiting LAG3 shedding could affect T cell function without demonstrably affecting cell surface expression as determined by flow cytometry. We speculate that because ADAM10 is recruited to the immunological synapse during T cell activation, it is in this context that the resistance of LAG3 to shedding becomes functionally impactful (29, 30).
Fourth, our results demonstrate the importance of CD4+Foxp3− Tconvs in driving a protective immune response after immunotherapy despite preoccupation in the field with checkpoint blockade directly reinvigorating CD8+ T cells or disabling CD4+Foxp3+ Tregs. CD4+Foxp3− Tconvs may serve as a nexus for more resistance mechanisms to effective immunotherapy than previously appreciated.
MATERIALS AND METHODS
Study design
This study aimed to investigate whether LAG3 shedding from T cell populations is required to generate sufficient antitumor immune responses necessary for tumor clearance in vivo. We generated a conditional knock-in mouse that renders LAG3NC to Cre-expressing T cell populations. We used in vitro and in vivo assays to validate the mice by flow cytometry and enzyme-linked immunosorbent assay (ELISA). We evaluated the effect of LAG3NC on tumor growth and functionality of intratumoral T cells using flow cytometry. Tumors were measured every 3 days with digital calipers, and tumor volume calculated was blinded and randomized for treatment group. Mice were removed from study when tumor growth reached a mean diameter of 1.5 cm or when necrosis was observed. The number of experimental replicates and statistical methods are described in the figure legends. Samples were double-blinded or randomized during experiments or analyses.
All human samples (peripheral blood and tumor) were obtained after approval from the Institutional Review Board of the University of Pittsburgh Medical Center (UPMC) and Johns Hopkins University School of Medicine. All individuals were recruited with written informed consent.
Cell lines and reagents
B16-F10 cells were obtained from M. J. Turk (Dartmouth College, NH). B16-gp100 cells were obtained from P. M. Sondel (University of Wisconsin, WI). MC38 cells were obtained from J. P. Allison (M.D. Anderson Cancer Center, TX). B16 cell lines were cultured in complete RPMI medium (Lonza), and MC38 cell lines were cultured in complete Dulbecco’s modified Eagle’s medium (Lonza), both supplemented with 10% fetal bovine serum (FBS), penicillin (100 U/ml), streptomycin (100 μg/ml), 2 mM glutamine, 1 mM pyruvate, 5 mM Hepes, 100 μM nonessential amino acids, and 2-mercaptoethanol (2ME). B16-gp100 cells were cultured with the addition of Geneticin (0.8 mg/ml; Thermo Fisher Scientific). All cell lines and assay cultures were maintained at 37°C and 5% CO2.
Amphiphile-CpG was produced as previously described (17). Cysteine-modified amphiphilic peptides for gp100 (CAVGALEGPRNQDWLGVPRQL) and HPV-E743–62 (IDGPAGQAEPDRAHYNIVTFC) were conjugated to maleimide-DSPE–polyethylene glycol–2000 in dimethyl formamide at the Peptide Synthesis Core Facility, Northwestern University. Bioconjugations were purified by reverse-phase high-performance liquid chromatography, and peptide amphiphiles were characterized by matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry. The peptide conjugates were then diluted in 10× double-distilled H2O and lyophilized into powder, redissolved in dimethyl sulfoxide (DMSO) and stored at −80°C.
Mice
C57BL/6, Rosa26LSLtdTomato, and pmel mice were obtained from the Jackson Laboratory. CD4Cre mice were obtained from P. Brindle (St. Jude Children’s Research Hospital, TN). E8ICre.GFP mice were previously described (14). ThPOKCreERT2 mice were established in the laboratory of I. Taniuchi using a minigene transgene construct that was constructed by replacing Cre complementary DNA (cDNA) with CreERT2 cDNA in the Thpok-Cre construct (31). Foxp3CreERT2.GFP and Foxp3Cre.YFP mice were obtained from A. Y. Rudensky (Memorial Sloan Kettering Cancer Center, NY). LAG3−/− mice were obtained from Y.-H. Chen (Stanford University, CA) with permission from C. Benoist and D. Mathis. LAG3NC mice were generated in-house as detailed below. All animal experiments were performed in the American Association for the Accreditation of Laboratory Animal Care–accredited, specific pathogen–free facilities in Division of Laboratory Animal Resources, University of Pittsburgh School of Medicine. Female and male mice were used. Mice were used for studies when 4 to 8 weeks old, except for EAE studies, which were used when 8 to 12 weeks old. Animal protocols were approved by the Institutional Animal Care and Use Committees of the University of Pittsburgh.
Generation of LAG3NC mice
The Lag3NC.L/L targeting construct was generated using standard recombineering methods (32). Initially, 15.4 kb of the Lag3 locus was retrieved from a bacterial artificial chromosome plasmid (RP23-408H4) and a Loxp-Neo-Loxp cassette inserted 195–base pair (bp) upstream of exon 7. The Neo was removed via Cre-mediated recombination leaving a single Loxp and an Eco RI restriction site (inserted into the intron of the retrieved Lag3 locus). The following was then inserted: a “self-cleaving” T2A peptide sequence followed by EBFP2 before the stop codon of Lag3 in exon 8, a X baI restriction site with a Frt-Neo-Frt-Loxp cassette 261 bp from the end of exon 8, 160 bp of the splice acceptor site of Lag3 intron 6 followed by alternate versions of exons 7 and 8 of Lag3 that lack the connecting peptide (HSARRISGDLKG), and the self-cleaving P2A peptide followed by ametrine inserted before the stop codon. The linearized targeting construct was electroporated into JM8A3.N1 embryonic stem cells, and neomycin-resistant clones were screened by Southern blot analysis using Eco RI and X baI restriction digests for the 5′ and 3′ ends, respectively. Correctly targeted clones were 100% normal diploid by karyotype analysis and were injected into C57BL/6 blastocysts. Chimeric mice were mated to C57BL/6 mice and transmission of the targeted allele verified by PCR. The mice were crossed with actin flipase mice to remove the Neo cassette.
Human T cell populations
Patients and specimens
All patients were seen either in the Division of Hematology/Oncology at UPMC (cohort A) or in the Department of Oncology at the Sidney Kimmel Comprehensive Cancer Center (SKCCC) and Bloomberg-Kimmel Institute for Cancer Immunotherapy at Johns Hopkins University School of Medicine (cohort B) and the Department of Otolaryngology at UPMC (cohorts C and D) and consented for participation in a specimen collection research protocol at their respective institutions. Cohort A consisted of matched PBL and TIL (n = 14) thawed from patients with metastatic melanoma from the Division of Oncology at UPMC. Cohort B consisted of banked PBL samples collected from patients with metastatic melanoma or other skin cancers before beginning checkpoint blockade therapy (including anti-PD1 therapy or the combination of anti-PD1 and anti-CTLA4 therapy) and collected again at the time of the patient’s initial scans nearing 12 weeks on checkpoint blockade therapy. Patients in this cohort were responders (CR or PR) (n = 18) or progressors (disease progression) (n = 19) from SKCCC. Response and progression were determined by RECIST 1.1 criteria. Cohort C consisted of tumor and PBL samples from patients with HNSCC with primary disease (n = 29). Patients who presented to UPMC with biopsy-proven HNSCC that elected to undergone radiation or chemotherapy and radiation that elected to participate in the research protocol donated blood at time of surgery or at time of clinic visit. Tumor tissue from the primary site of disease was obtained from patients undergoing both biopsy and definitive surgical therapy. Cohort D consisted of a cohort of banked PBL samples from patients with HNSCC with both early-stage (n = 25) and advanced (n = 25) disease for correlation with disease progression and survival.
Isolation of patient blood and TIL samples.
Patient blood samples were drawn into heparinized tubes and centrifuged on a Ficoll-Hypaque gradient (GE Healthcare Bioscience). PBLs were recovered and washed in RPMI medium.
Tumor samples were washed in an antibiotic medium of RPMI with penicillin-streptomycin and amphotericin B for 30 min. Tissue was then mechanically digested and passed through a 100-μm filter. TILs were washed and isolated. Bulk TIL and PBL were stained for flow cytometric analysis.
Antibodies and flow cytometry
For murine experiments, single-cell suspensions were stained with antibodies against T cell receptor β (TCRβ; H57-597, eBioscience), CD4 (GK1.5, BioLegend), CD8α (53-6.7, BioLegend), CD8β (YTS156.7.7, BioLegend), CD45.2 (104, BioLegend), PD1 (RMP1-30, BioLegend), LAG3 (C9B7W, eBioscience), TIM3 (RMT3-23, eBioscience), TIGIT (GIGD7, eBioscience), CD244.2 (m2B4, BioLegend), Foxp3 (FJK-16s, eBioscience), Ki67 (B56, BD Biosciences), BrdU (Bu20A, eBioscience) Bcl2 (BCL/10C4, BioLegend), KLRG1 (2F1, eBioscience), Thy1.1 (OX-7, BioLegend), Thy1.2 (30-H12, BioLegend), IFN-γ (XMG1.2, BioLegend), TNF-α (MP6-XT22, BioLegend), IL-2 (JES6-5H4, BioLegend), IL-17A (TC11-18H10.1, BioLegend), and granulocyte-macrophage colony-stimulating factor (GM-CSF) (MP1-22E9, BioLegend). For human experiments, single-cell suspensions were stained with antibodies against CD3 (SP34-2, BD Biosciences), CD4 (RPA-T4, BioLegend), CD8α (RPA-T8, BioLegend), CD45 (H1100, BioLegend), LAG3 (1408, Bristol-Meyers Squibb), ADAM10 (SHM14, BioLegend), ADAM17 (111633, R&D Systems), and Foxp3 (PCH101, eBioscience).
Surface staining was performed on ice for 15 min. Dead cells were discriminated by staining with Ghost Viability Dye (Tonbo Biosciences) in phosphate-buffered saline (PBS). For cytokine expression analysis, cells were activated with phorbol myristate acetate (PMA; 0.1 μg/ml; Sigma-Aldrich) and ionomycin (0.5 μg/ml; Sigma-Aldrich) in complete RPMI containing 10% FBS and brefeldin A (BFA; eBioscience) for 4 hours. For intracellular staining of cytokines and transcription factors, cells were stained with surface markers, fixed in Fix/Perm buffer (eBioscience) for 30 min, washed in permeabilization buffer (eBioscience) twice, and stained for intracellular factors in permeabilization buffer for 30 min on ice. For cCasp3 staining, cells were incubated with anti-cCasp3 (D175, Cell Signaling Technology) for 30 min and then stained with Alexa Fluor 555 anti-rabbit immunoglobulin G (IgG; Invitrogen) for 30 min at 4°C.
For BrdU labeling, mice were injected with BrdU (Sigma-Aldrich) in 0.2 ml of sterile PBS (100 mg/kg body weight) intraperitoneally 12 hours before harvest. Immunostaining for Ki67 (B56, BD Biosciences) and BrdU (Bu20a, BioLegend) was performed using eBioscience Foxp3 staining and BD Cytofix/Cytoperm kits.
For phosphorylated Akt and S6 staining after surface staining, cells were fixed in 1.5% paraformaldehyde (PFA) in Fix/Perm buffer (eBioscience) for 15 min at room temperature. Cells were washed in permeabilization buffer (eBioscience) twice and stained with pAkt (S473, D9E, Cell Signaling Technology) and pS6 (S235/236, D57.2.2E, Cell Signaling Technology) for 45 min on ice. Cells were sorted on Aria II (BD Biosciences) or analyzed on Fortessa (BD Biosciences), and data analysis was performed on FlowJo (Tree Star Inc.).
For negative selection of CD8+ T cells, biotin-conjugated antibodies against the following targets were used: CD4 (RM4-5, BioLegend), CD11b (M1/70, BioLegend), CD11c (N418, BioLegend), CD16/32 (93, eBioscience), CD19 (6D5, BioLegend), CD25 (PC61, BioLegend), CD49b (DX5, eBioscience), CD105 (MJ7/18, eBioscience), B220 (RA3-6B2, BioLegend), I-Ab (KH74, BioLegend), γδTCR (eBioGL3, eBioscience), Ly-6G/C (RB6-8C5, BioLegend), and Ter119 (TER119, BioLegend).
Tumor models
Mice were injected with MC38 colon adenocarcinoma (5 × 105 cells subcutaneously), B16-F10, or B16-gp100 melanoma [1.25 × 105 cells intradermally (i.d.)]. Tumors were measured every 3 days with digital calipers, and tumor volume calculated was blinded and randomized. Mice were removed from study when tumor growth reached a mean diameter of 1.5 cm or when necrosis was observed. A cohort of mice received a secondary MC38 injection (2.5 × 105 cells subcutaneously) on day 42, after primary MC38 tumor injection and resection at day 12 or sham control animals.
Therapeutic anti-PD1 was administered when tumors were palpable (6 days), and mice received 200 μg of anti-PD1 (clone G4) or Armenian hamster IgG isotype (Bio X Cell) intraperitoneally on days 6, 9 and 12. Tumors, draining and nontumor-draining lymph nodes were collected for analysis on the indicated time point. TILs were prepared with enzymatic [collagenase IV (Worthington Biochemical) and dispase (STEMCELL Technologies), both 1 mg/ml] and mechanical disruption.
For Amph-vax experiments, mice were injected with B16-gp100 or B16-F10 melanoma (1.25 × 105 cells i.d.). On days 4 and 11, mice were immunized with 20 μg of Amph-gp100 or Amph-E7 as a control, subcutaneously at the base of the tail. Mice also received anti-PD1 (clone G4) or Armenian hamster IgG as an isotype control as the therapeutic regimen detailed above. Tumor area was measured with digital calipers over time.
Adoptive transfer of pmel cells
CD8+ T cells were negatively selected from the spleen and lymph nodes of pmel mice by incubation with a biotin-conjugated antibody cocktail (anti-CD4, CD25, CD11b, CD11c, CD16/32, CD19, CD49b, CD105, B220, Ly6-G/C, I-Ab, γδTCR, and Ter119) on ice for 15 min. Cells were then washed and incubated with Pierce Streptavidin Magnetic Beads (Thermo Fisher Scientific) on ice for 15 min. Cells were placed on a magnet, and nonbound cells were extracted resulting in CD8+ T cells with >90% purity. Lag3NC.L/L or Lag3NC.L/L CD4Cre mice received an adoptive transfer of purified pmel cells (1 × 105 cells intravenously). The following day, mice were injected with B16-gp100 melanoma (1.25 × 105 cells i.d.), and tumor size was measured over time.
EAE induction and lymphocyte isolation
Mice were immunized with 100 μg of MOG35–55 (AAPPTEC) peptide emulsified with 500 μg of complete Freund’s adjuvant (Sigma-Aldrich) subcutaneously in either flank. Mice received 400 ng of pertussis toxin (List Biological Laboratories) intraperitoneally on days 0 and 2. Mice were monitored daily, and EAE clinical signs were scored by the following grades: 0, no disease; 1, limp tail; 2, partial hindlimb paralysis; 3, complete hindlimb paralysis; 4, complete hindlimb paralysis and partial front limb paralysis; 5, moribund or death. Mice received DietGel 31M (ClearH2O) during disease course.
Single-cell suspensions were prepared by isolating the brain and spinal cord after perfusion with PBS before tissues were extracted. Tissues were minced and incubated in collagenase D (1 mg/ml; Roche) and deoxyribonuclease I (200 U/ml; Sigma-Aldrich) for 45 min at 37°C with intermittent vortexing. Tissues were processed through a 70-μm filter, washed, and put through a 30/70% Percoll (GE Healthcare) gradient. Cells were spun at 800g for 30 min at room temperature. Single cells were removed from the interface, washed, and used for subsequent analysis. For cytokine analyses, cells were stimulated with MOG35–55 peptide (10 μg/ml) for 20 hours with the last 4 hours in the presence of BFA.
In vivo metalloprotease inhibition assays
Osmotic pumps were placed subcutaneously on the back of the mice. After surgery, infusion lasted for 14 days at a release rate of 0.25 μl/hour. Plasma was collected by bleeding from the submandibular vein after implantation of subcutaneous Alzet pumps delivering DMSO or GI245023X (Tocris Bioscience) inhibitor (20 mg/kg per day). sLAG3 levels were determined in plasma samples by ELISA.
In vitro assays
Pure populations of splenocytes or lymphocytes were isolated from nontumor-bearing Foxp3YFP-Cre, Lag3NC.L/L, Lag3NC.L/LCD4Cre, or Lag3NC.L/LE8ICre.GFP mice, stimulated with plate-bound anti-CD3 (145-2C11; 5 μg/ml) and soluble anti-CD28 (37.51; 5 μg/ml). For metalloprotease inhibition assays, cells were incubated with varying concentrations of GI245023X (Tocris Bioscience) inhibitor or DMSO as a control. Supernatant was collected and assayed for sLAG3 by ELISA.
sLAG3 ELISA
For murine sLAG3 quantification by ELISA, C9B7W (5 μg/ml) monoclonal antibody was coated on a 96-well flat-bottomed microtiter plate (Thermo Fisher Scientific) in carbonate buffer [50 mM Na2CO3 (pH 10.4)] at 37°C for 1 hour. The plates were washed three times with PBS Tween 20 (0.05%) and then blocked with 1% FBS in carbonate buffer at 4°C overnight. The plates were washed, and supernatant or serum was added for 1-hour incubation at 37°C; the plates were washed and incubated with rabbit anti–LAG3-D1 antisera (1:200 dilution) at 37°C for 1 hour. This was followed by three washes and a 1-hour incubation with horseradish peroxidase–conjugated, anti-rabbit IgG secondary antibody (1:2000 dilution; GE Healthcare). Plates were developed with 3,3ʹ,5,5ʹ-tetramethylbenzidine (TMB) substrate solution (Thermo Fisher Scientific), and the reaction was stopped by adding 50 μl of 1 N H2SO4. Absorbance was measured by an Epoch microplate spectrophotometer (BioTek Instruments). sLAG3 concentration was calculated using a purified sLAG3 standard curve, as previously described (11). For human sLAG3 quantification, the human LAG-3 DuoSet ELISA kit (R&D Systems) was used, following the manufacturer’s instructions.
Quantitative real-time PCR
Total RNA was extracted from sorted T cell populations isolated from tumor or peripheral cells of B16-F10 tumor-bearing Foxp3Cre-YFP mice using RNeasy Plus Micro Kit (QIAGEN), according to the manufacturer’s instructions. RNA was reversed-transcribed into cDNA via the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems), according to the manufacturer’s instructions. qPCR was performed using Bullseye EvaGreen qPCR MasterMix in a total volume of 20 μl and detected using a LightCycler 96 (Roche) instrument. The following primer sequences were used for Adam10 (forward primer, 5′-GCAAAGGAAGGGATATGCAA; reverse primer, 5′-ATAGAACCTGCACATTGGCC) and Hprt (forward primer, 5′-CAGTACAGCCCCAAAATGGTTA; reverse primer, 5′-AGTCTGGCCTGTATCCAACA).
Single-cell RNA-seq
Generation of single-cell RNA-seq libraries
Live CD4+/CD8α+/CD8β+ T cells were sorted from MC38 tumors of three mice and pooled for each experimental condition. Single-cell libraries were generated from sorted cells using the Chromium Single Cell 3′ Reagent (v2 Chemistry) as previously described (33). Briefly, sorted cells were resuspended in PBS with 0.04% bovine serum albumin (Sigma-Aldrich) and were loaded into parallel lanes for droplet generation in the 10× controller targeting a recovery of 2000 cells per sample. After partitioning into droplets, cells were lysed, and reverse transcription was performed within droplets. After reverse transcription, cDNA was isolated and amplified in bulk with 12 cycles of PCR. Amplified cDNA was then selected by SPRIselect beads, fragmented, and adaptors were ligated. Sample indices were then added by PCR, and samples were once again selected using SPRIselect beads. Concentration of libraries was then determined by PCR using KAPA DNA Quantification.
Sequencing of single-cell libraries
After generation and quantification of single-cell libraries, samples were diluted to 2 nM and pooled for downstream sequencing on a NextSeq500 at the Health Sciences Sequencing Core at the UPMC Children’s Hospital of Pittsburgh. Samples were run using NextSeq 500/550 high-output v2 kits (150 cycles) with the following parameters: Read 1: 26 cycles; i7 Index: 8 cycles, Read 2: 98 cycles.
Demultiplexing, alignment, and generation of gene/barcode matrices
Data from the sequencing runs were processed through the CellRanger pipeline (10x Genomics) for demultiplexing and alignment using the mouse reference genome mm10, followed by generation of filtered gene/barcode matrices. Gene/barcode matrices were then further filtered at both the cell and gene level. For cell-level filtering, cell barcodes with fewer than 200 or greater than 10,000 unique molecular identifiers (UMIs) were removed. For gene-level filtering, genes were filtered such that only those expressed at level of at least 1 UMI counts in 1% of cells were retained. These cell- and gene-level filtered gene/barcode matrices were then used for downstream analysis.
Integration of data across samples
To perform a unified analysis of single-cell RNA-seq datasets, we used a data integration workflow recently implemented in Seurat v3 (34). Briefly, this approach aligns datasets by first performing canonical correlation analysis (35) to embed cells in a shared space, followed by mutual nearest neighbor analysis (36) to identify similar cells across datasets. These “anchors” are then used to align datasets. We used this approach to aligning each genotype into a unified analysis.
Visualization, clustering, and identification of immune lineages
After integrating our datasets, we scaled the combined aligned data and performed principal components analysis, heuristically selecting significant principal components for downstream analysis. To visualize our data, we used an FItSNE (19) to embed cells in two dimensions. Clustering was performed using our recently described deterministic annealing Gaussian mixture model clustering (DRAGON) algorithm (37). T cell subpopulations were identified on the basis of expression of canonical markers (i.e., CD4+ Tconv: Cd3d+, Cd4+, Cd8a−, and Foxp3−; CD4+ Treg: Cd3d+, Cd4+, Cd8a−, and Foxp3+; CD8+ T cells: Cd3d+, Cd8a+, Cd4−, and Foxp3−) in clusters.
Quantifying differences in immune populations between conditions
We next quantified the magnitude of the differences in each immune population between genotypes and treatment conditions. To accomplish this, we assessed the BD between subsets of cells from each sample unique sample (i.e., genotype and treatment) as recently described (37). Briefly, we compared the BD between subsets of 100 randomly selected cells from each sample for pair of genotype/treatment combinations for each cell type 50 times. We repeatedly subsample cells to empirically determine the distribution of cells with a sample. We also compared the BD between two sets of 100 randomly selected cells for each cell type from the entire dataset to generate a background distribution for comparison.
Clustering of subpopulations
To characterize more subtle differences in each T cell subpopulation, we bioinformatically isolated and reclustered each T cell subset using the data integration approach described above. DRAGON was once again used to identify clusters within the independent analysis of each of the T cell subsets.
Determining sample enrichment across clusters
To determine the relationship between samples and clusters, we sought to evaluate the frequency of cells from a given genotype/phenotype present in each cluster while controlling for different numbers of cells present from each sample type and within each cluster. We did this by first dividing the number of cells within a cluster from a given sample by the total number of cells in the dataset from that given sample. We then scaled these values within each cluster by subtracting the mean and dividing by the standard deviation for each cluster, yielding normalized and scaled frequencies of samples in each cluster.
Differential gene expression analysis
Differentially expressed genes were determined by comparing the fold change in expression in a cluster versus all other clusters using a Wilcoxon rank sum test, as implemented in Seurat v3. (34).
Gene set enrichment analysis
To determine whether there was statistically significant enrichment of known gene sets across clusters, we used a gene set enrichment analysis based on the competitive gene set enrichment test CAMERA (38), implemented in our recently described R package “singleseqgset” (37). The curated Reactome gene sets (C2:CP:REACTOME) for Mus musculus were downloaded from the Molecular Signatures Database using the “msigdbr” R package. P < 0.05 was considered statistically significant.
Statistical methods
Statistical analyses were conducted using Prism version 7 (GraphPad). Tumor growth and EAE clinical score over time was analyzed using two-way analysis of variance (ANOVA) with multiple comparisons [Figs. 1F and 3F and figs. S2 (A and B), S3 (A, C, and D), S8A, S9B, and S10A]. Event-free survival was calculated with the log-rank (Mantel-Cox) test applied to Kaplan-Meier survival function estimates to determine statistical significance [Figs. 1 (D to F), 3B, 4B, and 5F and figs. S9A and S16E). Comparisons of independent samples were conducted using the Mann-Whitney test [Figs. 3 (C to E) and 4 (C to E) and figs. S8 (B to E, G, and H), S9 (D to G), S10 (B to G), and S11B]. Comparisons of independent samples were conducted using unpaired t test [Figs. 3 (G and H) and 5, (A and D) and figs. S1 (F and G). S2 (C to M), S12 (A to F), S13 (A to L), S14 (D and E), S15 (A to I and M to O), and S16 (A and B)]. Comparisons of paired samples were conducted using Wilcoxon matched pair test (Fig. 5C and fig. S14, A to C, F to H, K, and L). Optimal cut points of survival predictors (Fig. 5F and fig. S16E) were selected by recursive partitioning with a minimum group size of 5, using the rpart() package for R version 3.3.1 (R Foundation for Statistical Computing, Vienna, Austria). “n” represents the number of mice or human individuals used in an experiment, with number of individual experiments listed in the legend. Samples are shown with the mean with or without error bars indicating SEM. Significance was defined as P = 0.05.
SUPPLEMENTARY MATERIALS
immunology.sciencemag.org/cgi/content/full/5/49/eabc2728/DC1
Fig. S1. Validation of the LAG3NC conditional knock-in mouse.
Fig. S2. Tumor growth and analysis of TILs isolated from Lag3NC.L/L and Lag3NC.L/L CD4Cre mice.
Fig. S3. Restriction of LAG3NC to T cells affects anti-PD1–mediated tumor regression.
Fig. S4. Single-cell RNA-seq analysis of MC38 tumor-infiltrating T cells isolated from Lag3NC.L/L and Lag3NC.L/L CD4Cre mice.
Fig. S5. Transcriptomic analysis of LAG3NC conventional CD4+ T cells in the context of PD1 blockade.
Fig. S6. Transcriptomic analysis of LAG3NC regulatory CD4+ T cells in the context of PD1 blockade.
Fig. S7. Transcriptomic analysis of LAG3NC CD8+ T cells in the context of PD1 blockade.
Fig. S8. Tumor growth of Lag3NC.L/L ThPOKCreERT2 mice and analysis of tumor-infiltrating conventional CD4+ T cells isolated from Lag3NC.L/L and Lag3NC.L/L CD4Cre mice in the context of PD1 blockade.
Fig. S9. Tumor growth of Lag3NC.L/L Foxp3CreERT2 mice and analysis of tumor-infiltrating regulatory CD4+ T cells isolated from Lag3NC.L/L and Lag3NC.L/L CD4Cre mice in the context of PD1 blockade.
Fig. S10. Tumor growth of Lag3NC.L/L E8ICre.GFP mice and analysis of tumor-infiltrating CD8+ T cells isolated from Lag3NC.L/L, Lag3NC.L/L CD4Cre, and Lag3NC.L/L E8ICre.GFP mice in the context of PD1 blockade.
Fig. S11. Adoptive transfer of CD8+ pmel into B16-gp100 tumor-bearing Lag3NC.L/L and Lag3NC.L/L CD4Cre mice.
Fig. S12. Inhibition of ADAM10-mediated LAG3 shedding in vitro and in vivo.
Fig. S13. LAG3 and ADAM10 expression on PBL and TIL isolated from patients with metastatic melanoma (cohort A).
Fig. S14. LAG3 and ADAM10 expression on PBL isolated from patients with advanced skin cancer (cohort B).
Fig. S15. LAG3 and ADAM10 expression on PBL and TIL isolated from patients with HNSCC (cohort C).
Fig. S16. LAG3 and ADAM10 expression on PBL isolated from treatment-naïve patients with HNSCC (cohort D).
Table S1. Fold change and corrected P value for BD between random and sample conditions.
Table S2. Cohort A clinical information of patient with metastatic melanoma.
Table S3. Patient cohort B clinical information.
Table S4. Cohort C clinical information of patient with HNSCC patient.
Table S5. Cohort D clinical information of treatment-naïve patient with HNSCC.
Table S6. Raw data file.
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REFERENCES AND NOTES
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