Anti-programmed death-1 (anti-PD-1) immunotherapy reinvigorates CD8 T cell responses in patients with cancer but PD-1 is also expressed by other immune cells, including follicular helper CD4 T cells (Tfh) which are involved in germinal centre responses. Little is known, however, about the effects of anti-PD-1 immunotherapy on noncancer immune responses in humans. To investigate this question, we examined the impact of anti-PD-1 immunotherapy on the Tfh–B cell axis responding to unrelated viral antigens. Following influenza vaccination, a subset of adults receiving anti-PD-1 had more robust circulating Tfh responses than adults not receiving immunotherapy. PD-1 pathway blockade resulted in transcriptional signatures of increased cellular proliferation in circulating Tfh and responding B cells compared with controls. These latter observations suggest an underlying change in the Tfh–B cell and germinal centre axis in a subset of immunotherapy patients. Together, these results demonstrate dynamic effects of anti-PD-1 therapy on influenza vaccine responses and highlight analytical vaccination as an approach that may reveal underlying immune predisposition to adverse events.
Cancer immunotherapy has revolutionized our ability to target and redirect immune system activity to treat malignancies. PD-1 (CD279) expressed on T cells during activation and exhaustion is the most common target of cancer immunotherapy1. Blockade of PD-1 signaling using anti-PD-1 or anti-Programmed Death-Ligand1 (anti-PD-L1) immunotherapy has been associated with improved survival or cure in several different types of malignancies, in part due to the reinvigoration of exhausted CD8 T cells2. However, PD-1 is expressed by other immune cells including Tfh that provide help to B cells in the germinal centre (GC), thereby enabling affinity-matured, long-lived antibody responses3,4. Indeed, Tfh cells in secondary lymphoid tissue have high PD-1 expression in humans3,5,6,7. Little is known, however, about the effects of anti-PD-1 immunotherapy on Tfh or on related events including the outcomes of Tfh–B cell interactions and GC-dependent immune responses. Moreover, approximately 50% of patients on anti-PD-1 immunotherapy develop immune-related adverse events (irAEs)8,9,10,11. At least some of these irAEs have been postulated to be linked to autoreactive humoral responses12,13,14, but precise mechanisms remain poorly defined. Thus, there is a need to better understand the immunological effects of anti-PD-1 on parts of the immune system not directly related to the on-tumor effects.
The main goal of immunotherapy for cancer or other diseases is to treat the tumour or affected tissue or system. As a result, the impact of immunotherapies on immune responses unrelated to disease have received less attention. For example, the Tfh–B cell axis is central for vaccine responses, yet the effects of anti-PD-1 therapy on vaccine-induced Tfh and humoral responses have not been extensively studied in humans. Indeed, examining immune responses to vaccine antigens in the setting of PD-1 blockade may provide insights into the function of PD-1 in the ‘normal’ immune system and potentially reveal new opportunities to improve vaccination or understand the role of the PD-1 pathway in other diseases. A small number of studies have evaluated influenza vaccines, mainly for safety or serology in the setting of anti-PD-1 immunotherapy15,16,17,18,19,20. However, no information exists on how perturbing the PD-1 pathway in humans impacts Tfh responses to vaccination or the Tfh–B cell axis responsible for generating high-quality antibodies.
To examine these questions, we used influenza vaccination in human patients with cancer to directly investigate the effects of anti-PD-1 immunotherapy on Tfh and B cell responses to noncancer antigens. Specifically, we examined responses of the circulating subset of PD-1+ CXCR5+ CD4 cells, termed circulating Tfh (cTfh), which share phenotypic and functional characteristics of lymphoid Tfh5,21,22,23,24,25. These studies demonstrate that anti-PD-1 treatment was associated with more robust vaccine-induced cTfh responses and increased proliferation of responding cTfh and B cells including antibody secreting cells (ASCs). Mechanistically, this increased cTfh and ASC proliferation was coupled to reduced transcriptional signatures of cytokine signaling including IL-2/STAT5 in cTfh and TNF/NFkB in ASCs. These anti-PD-1-induced changes in vaccine-responding cTfh and B cells were associated with greater increase in titer but lower affinity and lower sialylation of neutralizing influenza-specific antibodies compared with those not receiving immunotherapy. Increases in serum CXCL13 following vaccination in the presence of anti-PD-1 implicated altered GC activity. Finally, although vaccination does not provoke immunotherapy-associated complications17, a subset of participants with robust changes in cTfh were enriched for previous (or future) irAEs associated with immunotherapy, suggesting an underlying change in Tfh–B cell, humoral and/or GC activity. This latter observation points to a potential common signature or baseline immune state altered by PD-1 that impacts vaccination and autoreactivity. Moreover, these studies highlight the potential to use analytical vaccination to detect these changes, possibly identifying patients with higher likelihood for irAEs. Together, these data demonstrate that anti-PD-1 immunotherapy has broad effects on the Tfh–B cell axis with major implications for vaccination, humoral responses and changes in baseline immune health.
Anti-PD-1 is associated with increased cellular responses to vaccination
Anti-PD-1 immunotherapy with nivolumab or pembrolizumab is given over months or even years1 with the intention of enhancing the immune response to cancer. The effects of long-term disruption of the PD-1 pathway on immune responses to other antigens including humoral responses to vaccines that involve PD-1-expressing Tfh remain poorly understood. To address this question, we enrolled adults with renal cell carcinoma or urothelial carcinoma who were receiving immunotherapy (Cohort 1; Supplementary Table 1) and were due to receive seasonal inactivated influenza vaccine. Participants were divided into two groups: non-anti-PD-1-based therapy (n = 10, median age = 65) or anti-PD-1-based therapy (n = 29, median age = 71.5). Given the importance of Programmed Death-Ligand 2 signaling in the humoral response26,27, we considered participants who received anti-Programmed Death-Ligand 1 as members of the non-anti-PD-1 group. In a second, independently generated cohort at a different institution, we enrolled adults with melanoma receiving immunotherapy (n = 30, median age = 61.5) and healthy adults not receiving immunotherapy (n = 27, median age = 33) (Cohort 2; Supplementary Table 2). Participants received influenza vaccination on the same day as immunotherapy infusion (median of cycle 12 of anti-PD-1 for Cohort 1 and cycle 7 for Cohort 2; Extended Data Fig. 1a,b). Blood was drawn on the day of vaccination (baseline), 1 week later and again 3–6 weeks after vaccination (late) (Extended Data Fig. 1a), as has been done previously21,22,25,28.
In healthy adults, CXCR5+ PD-1+ cTfh expressing the activation markers Inducible Costimulator (ICOS, CD278) and CD38 expand 1 week after influenza vaccination and this population contains influenza-specific CD4 T cells25. We therefore first asked whether anti-PD-1 immunotherapy alters the cTfh response to the vaccine. To avoid bias of using PD-1 itself in detection of cTfh due to treatment with therapeutic anti-PD-1 antibodies, we used a broader definition of non-naive CXCR5+ CD4 T cells that coexpressed ICOS and CD38 (ICOS+CD38+ cTfh) to identify responding cTfh in this study (Extended Data Fig. 1c–e). Following vaccination in Cohort 1, there was a 2.6-fold-induction of ICOS+CD38+ cTfh in patients with cancer receiving anti-PD-1 compared with a 1.1-fold-change in patients with cancer on therapies other than anti-PD-1 (P = 0.013, Wilcoxon test; Fig. 1b), demonstrating that anti-PD-1 therapy is associated with augmented cTfh responses to influenza vaccination.
We examined an independent cohort of patients with melanoma receiving anti-PD-1 immunotherapy (Cohort 2; Supplementary Table 1). Again, anti-PD-1 therapy was associated with more robust increases in cTfh responses compared with healthy control participants 1 week after vaccination (P = 0.05, Wilcoxon test), with responses returning to baseline by the late time point (Fig. 1c,d and Extended Data Fig. 1f–h). We also confirmed that using PD-1 in the definition of cTfh resulted in similar observations (Extended Data Fig. 1i–k). The increase in the cTfh response was not associated with more recent initiation of anti-PD-1 therapy (Extended Data Fig. 1l) and was unlikely to be due to age-related differences between participants (Extended Data Fig. 1m,n). Further subsetting based on CCR6 and CXCR3, as described previously23, did not identify differences between treatment groups (Extended Data Fig. 1o). Finally, we considered other regulators of the B cell response. In particular, T follicular regulatory cells (Tfr) phenotypically resemble Tfh but express Foxp3 and can regulate Tfh–B cell interactions29. There was a greater decrease in the frequency of circulating Tfr (cTfr) in the anti-PD-1-treated participants than in the healthy adults in Cohort 2 (Extended Data Fig. 1p–r). However, this change could reflect dilution of cTfr by the ICOS+CD38+ CXCR5+ cTfh or perhaps be due to altered kinetics. Given the structure of the study, it was not possible to assess cTfh and cTfr responses at different time points. Nonetheless, these data indicate that anti-PD-1 treatment was associated with an augmented numerical response of cTfh in a subset of patients 1 week after influenza vaccination.
We next asked if the B cell response to vaccination was affected by anti-PD-1 therapy. Although the plasmablast frequency was not higher in the anti-PD-1 group following vaccination, there was a subset of anti-PD-1 participants with robust induction of plasmablasts 1 week after vaccination, (Fig. 1e and Extended Data Fig. 2a–d). Correlations have been observed previously between plasmablast and cTfh responses to influenza vaccination22,25. Here, the plasmablast response correlated with ICOS+CD38+ cTfh responses 1 week after vaccination in the anti-PD-1 group in both cohorts (Fig. 1f). These data suggest that anti-PD-1 therapy alters Tfh–B cell interactions, likely in secondary lymphoid tissues, resulting in higher frequencies of both cTfh and plasmablasts in the peripheral blood following vaccination.
One biomarker of GC activity in lymphoid tissue is plasma CXCL13 (ref. 30). We observed weak induction of plasma CXCL13 in healthy adults 1 week after influenza vaccination (Fig. 1g), consistent with previous studies showing that influenza vaccination induced only modest changes in this chemokine detectable in blood thought to be related to the relatively weak immune response to this vaccine30. In contrast, however, anti-PD-1-treated participants had substantial induction of plasma CXCL13 1 week after vaccine compared with baseline in both cohorts (Fig. 1g,h and Extended Data Fig. 2e–g). Although this CXCL13 in the blood did not correlate strongly with the cTfh or plasmablast response, there was a correlation with later antibody responses (Extended Data Fig. 2h). Collectively, these data reveal a robust enhancement of vaccine-induced cTfh, plasmablast and likely GC activity in the presence of anti-PD-1 therapy.
Anti-PD-1 altered antibody responses
Humoral responses to influenza vaccination are typically assessed by hemagglutinin inhibitory (HAI) antibody titers, which are a correlate of protection following vaccination31. Thus, we investigated whether anti-PD-1 therapy impacted the induction of HAI titers. We focused on Cohort 2 because of the larger number of individuals available for analysis.
We first examined the fold-change in the HAI antibody titer at the 3–6-week time point compared with the baseline titer. Indeed, across all three strains of influenza included in the vaccine, neutralizing antibody titers increased by a median of 4-fold in the presence of anti-PD-1 compared with 2-fold in the absence of anti-PD-1 (Fig. 2a and Extended Data Fig. 3a). Most anti-PD-1-treated adults had antibody titers of >1:40 following vaccination and were thus seroprotected, similar to healthy adults, indicating no clinical deficiency in the outcome of vaccination in this setting (Fig. 2b). The absolute HAI titer was similar between the anti-PD-1 and non-anti-PD-1 at the late time point, and the difference in fold-induction reflected slightly lower baseline HAI titers in the anti-PD-1 group than in the non-anti-PD-1 group (Fig. 2c and Extended Data Fig. 3b). These data are consistent with a previous study showing increases in influenza-specific antibodies in patients vaccinated while on anti-PD-1 therapy15. In Cohort 1, there were similar trends, but these differences in HAI titers in Cohort 1 did not reach statistical significance, perhaps because of a smaller number of participants, more varied treatment regimens or the effects of previous cancer therapy (Extended Data Fig. 3c,d). In this setting, correlations between cTfh or B cell responses and HAI titers were not apparent (Extended Data Fig. 3e). The differences in antibody observed in Cohort 2 were unlikely to be due to the differences in age between the two groups, since independent analyses did not reveal age-associated differences in influenza-specific neutralizing antibodies at baseline (Extended Data Fig. 3f). Together, these data show that the increases in cTfh, plasmablast and CXCL13 responses were associated with quantitatively increased antibody responses in the setting of anti-PD-1 compared with participants not receiving anti-PD-1 therapy.
Antibodies provide protection by being present at sufficient quantities and by undergoing qualitative improvements during the T cell-dependent phase of induction in lymphoid tissues. However, because the data above revealed a quantitative impact of anti-PD-1 on induction of antibody following vaccination, but endpoint HAI titers were similar, we next wanted to investigate potential qualitative effects of anti-PD-1 treatment on antibody responses. For example, antibody effector function is dictated by two important components of the antibody Fc fragment, both the amino acid sequence (for example, IgG subclass IgG1–4) and the composition of the conserved N-linked glycan at asparagine position 297 (ref. 32). We previously demonstrated that changes to anti-hemagglutinin (HA) glycoforms, in particular increased total sialylation of influenza-specific IgG, drove B cell affinity selection and determined the efficacy of influenza vaccination33. We therefore investigated how disruption of the PD-1 pathway impacted antibody glycosylation and subsequent affinity. We focused on the antibody response to hemagglutinin (H1), the primary target of the antibody response in vaccinated individuals34. IgG subclass and glycan distribution of antigen-specific IgG were evaluated using mass spectrometry33. Overall, anti-H1 antibodies were enriched for the IgG1 subclass, with less IgG2 and IgG3/4 in the setting of anti-PD-1 compared with controls (Extended Data Fig. 4a; P = 9 × 10−3, two-way analysis of variance (ANOVA) with Sidak’s post-test). We next investigated how anti-PD-1 treatment impacted antibody glycosylation. Afucosylation of antibodies results in enhanced antibody-dependent cytotoxicity32, but was not affected by anti-PD-1 treatment (Extended Data Fig. 4b). Sialylation and galactosylation are two additional Fc glycosylation events that can regulate antibody function. Sialylated antibodies conferred improved protection to influenza in vivo33 and galactosylation is a prerequisite for sialylation32. Indeed, sialylation and galactosylation of anti-H1 antibodies were correlated for both anti-PD-1-treated and nontreated groups (Extended Data Fig. 4c). However, the anti-PD-1-treated patients had lower total galactosylation (Fig. 2d,e) and sialylation (Fig. 2f,g) of anti-H1 antibodies than non-anti-PD-1-treated participants at baseline and after vaccination, and these observations were consistent across all immunoglobulin subclasses (Extended Data Fig. 4d–f). In Cohort 1, anti-PD-1-treated participants had lower galactosylation and sialylation of anti-H1 antibodies at baseline (Extended Data Fig. 4g–j). In previous studies, we found similar changes in glycosylation between antigen-specific and total antibodies33,35,36. We therefore focused here on anti-H1 antibodies because these are a major mechanism of vaccine-induced protection. However, we observed similar changes in glycosylation patterns on anti-H1 antibodies of all antibody subclasses examined, suggesting a broad impact on antibody glycosylation (Extended Data Fig. 4d,f). Altogether, these data identify an effect of anti-PD-1 therapy on sialylation of anti-H1 antibodies at baseline and after vaccination, effects that might be predicted to impact the quality of influenza-specific antibodies.
To test whether altered antibody glycosylation in the setting of anti-PD-1 treatment had functional consequences, we next examined antibody affinity. Sialylation of antigen-specific IgG and immune complexes contributes to affinity maturation through FcγRIIb-mediated modulation of GC B cell selection favoring higher affinity clones33. Thus, lower sialylation in the context of anti-PD-1 was predicted to impact subsequent antibody affinity. Indeed, baseline antibody affinity was lower in patients on anti-PD-1 compared with healthy controls (Fig. 2h and Extended Data Fig. 4k). Using a second assay that estimates affinity based on binding to low- versus high-density antigen33, we confirmed that anti-PD-1-treated participants had lower affinity IgG1 at baseline (Extended Data Fig. 4l–n). Collectively, these data identify both quantitative and qualitative changes in antibody responses to influenza vaccine associated with blockade of PD-1. Because some of these changes were present before yearly vaccination, these data suggest perhaps an underlying impact of anti-PD-1 therapy on influenza-specific immune memory, in addition to the effects on the responses provoked by the vaccination studied here.
Given these changes in antibody, we next investigated whether anti-PD-1 treatment was associated with changes in B cell subsets that responded to vaccination, including ASCs (gated as CD19+IgD−CD71+CD20lo) and activated B cells (ABCs; gated as CD19+IgD−CD71+CD20hi)37. Even before vaccination, anti-PD-1 therapy was associated with a higher frequency of circulating ASCs and reciprocally reduced ABC frequencies (Extended Data Fig. 4o,p). However, whereas ABCs only weakly correlated with ICOS+CD38+ cTfh, at baseline and after vaccination, ASC frequencies were positively correlated with ICOS+CD38+ Tfh in anti-PD-1 patients before and after vaccination (Extended Data Fig. 4q,r). Taken together, these data indicate that anti-PD-1 treatment is associated with alterations in B cell subsets at baseline and in vaccine-induced antibody quantity and quality. Moreover, these alterations before vaccination suggest that treatment with anti-PD-1 may alter the baseline Tfh–B cell and humoral immune set point in these individuals.
Anti-PD-1 is associated with dynamic transcriptional effects
To further interrogate the effects of anti-PD-1 on immune responses to influenza vaccination, we performed transcriptional profiling on sorted ICOS+CD38+ cTfh, ABCs, ASCs or naïve B cells (gated as CD19+IgD+CD27lo) from participants in Cohort 2. Sample distribution in t-distributed stochastic neighbor embedding (t-SNE) space was driven primarily by cell subset and minimally by treatment or timing relative to vaccination (Extended Data Fig. 5a–e). However, at 1 week post vaccination, transcriptional differences were readily apparent in ICOS+CD38+ cTfh from anti-PD-1-treated adults compared with ICOS+CD38+ cTfh from healthy adults including upregulation of genes such as MKI67 and ESPL1, indicating recent proliferation38, and lower expression of genes including IFI44L, OASL and TNFRSF1B (Fig. 3a and Supplementary Table 5), suggesting reduced response to interferon signaling39,40. A similar upregulation of ESPL1, MKI67 and other cell cycle genes was observed in anti-PD-1-induced ‘burned-out’ CD8 T cells in patients with cancer41. Gene ontology (GO) term enrichment highlighted proliferation and cell cycle in the transcriptional signatures of ICOS+CD38+ cTfh from anti-PD-1-treated participants following vaccination compared with leukocyte activation, migration, as well as cytokine production and signaling in the control group (Fig. 3b and Extended Data Fig. 5f). Indeed, Ki67 protein expression 1 week after vaccination correlated with the fold-change in the ICOS+CD38+ cTfh (Extended Data Fig. 5g), supporting the notion that anti-PD-1 therapy was associated with an enhanced proliferative cTfh response after immunization.
Similarly, ASCs from vaccinated anti-PD-1-treated participants upregulated genes such as AURKB, BUB1 and ESPL1, consistent with greater proliferation relative to ASCs from healthy adults (Fig. 3c and Extended Data Fig. 5h), and had terms for ‘cell cycle’ and ‘mitotic cell division’ by GO (Fig. 3d and Extended Data Fig. 5i). ASCs from the anti-PD-1 group also had higher expression of CCL2 and IL18RAP, but lower expression of IFNGR2, DUSP1 and KCNA3. Moreover, upregulation of ARID1A in ICOS+CD38+ cTfh and SMARCD1 in ASCs from the anti-PD-1 group also suggested a potential impact on SWI/SNF complex use in these cells responding to vaccination in the absence of normal PD-1 signaling. These proliferation-related genes were not observed before vaccination (Extended Data Fig. 5i,j).
We next performed gene set enrichment analysis (GSEA)42 for both cTfh and ASCs to identify pathways altered due to anti-PD-1 at 1 week after vaccination. For the ICOS+CD38+ cTfh, we identified four gene sets that were enriched in the anti-PD-1 participants, all of which were associated with proliferation, whereas 27 gene sets were enriched in the controls (Fig. 3e). Indeed, enrichments of gene sets associated with proliferation were also identified in the ASC and ABC subsets of anti-PD-1 participants 1 week after vaccination (Fig. 3f–h and Extended Data Fig. 6a,b). These data indicate that anti-PD-1 drove greater activation and proliferation of cTfh and B cell populations during the vaccine response.
In addition to the genes and pathways upregulated in the presence of anti-PD-1, genes and pathways downregulated or that failed to be induced following influenza vaccination in anti-PD-1 patients were also of interest. Indeed, anti-PD-1 was associated with relative reduction in several pathways of direct relevance for cTfh biology in vaccine-responding ICOS+CD38+ cTfh. First, the STAT5/Blimp-1 axis is a negative regulator of Tfh differentiation and function, in part through antagonism of the Tfh-driving transcription factor Bcl6 (refs. 43,44). Here, anti-PD-1 treatment was associated with downregulation of IL-2/STAT5 signaling in activated cTfh (Fig. 3i) consistent with the enhanced cTfh response in a subset of anti-PD-1 patients. Moreover, enrichment for the IL-2/STAT5 gene set was negatively correlated with ICOS+CD38+ cTfh frequency in anti-PD-1 patients but not healthy controls, consistent with a regulatory effect of this pathway on Tfh activation and/or expansion that is revealed by anti-PD-1 treatment (Extended Data Fig. 6c). Other Tfh-regulating pathways, such as TGF-β signaling and apoptosis, were also downregulated in the setting of anti-PD-1 treatment (Fig. 3e). Downregulation of regulatory signaling pathways such as IL-2/STAT5 signaling that normally impede Tfh activity in the setting of anti-PD-1 may help explain the greater influenza vaccine-induced cTfh and antibody responses in the setting of anti-PD-1 (Fig. 2).
A second relevant transcriptional imprint of anti-PD-1 treatment was downregulation of genes involved in the TNF/NFkB pathway. These changes were of interest at least in part because TNF and NFkB signaling are regulators of cTfh biology45. In the setting of anti-PD-1 treatment, signatures of TNF/NFkB-related genes in ICOS+CD38+ cTfh were highly biased to the control participants (Fig. 3e), suggesting that anti-PD-1 treatment impaired efficient use of TNF/NFkB signaling in vaccine-responding activated cTfh. A similar picture emerged for ASCs (Fig. 3f), suggesting a coordinated set of biological pathways impacted by anti-PD-1 therapy for both cTfh and ASCs. Thus, these data indicate that anti-PD-1 may alter the humoral, cTfh and likely GC responses by dysregulating control of proliferation and altered sensing of key cytokine circuits needed for proper Tfh control of humoral immunity.
cTfh responses are predictive of irAEs
A major limitation of current checkpoint blockade in cancer immunotherapy is the development of irAEs12,13. Although the underlying mechanisms have been unclear, one possibility is dysregulated GC-dependent immune responses including CD4 T cells (for example, Tfh) and antibody responses46,47. Thus, we hypothesized that differences in Tfh biology during anti-PD-1 therapy may offer insights into the mechanisms of irAE.
To test this idea, we focused on participants in Cohort 2 where anti-PD-1 was predominantly used as monotherapy, and considered irAEs irrespective of timing to influenza vaccination (Extended Data Fig. 7a and Supplementary Table 6). We considered whether messenger RNA profiling of ICOS+CD38+ cTfh at baseline could identify characteristics of those who had an irAE versus those who did not have any irAEs. Patients with irAEs had slightly more transcripts for genes associated with cellular activation, such as PDCD1 and ICOS, relative to patients who did not have irAEs, although these differences were not statistically significant (Extended Data Fig. 7b,c). However, we observed higher expression of ICOS protein and PD-1 protein, using αIgG4 as a proxy for PD-1 (ref. 2), in anti-PD-1-treated adults with irAEs compared with anti-PD-1-treated adults who did not have irAEs (Extended Data Fig. 7d–f). Transcriptional analysis of ICOS+CD38+ cTfh revealed 18 genes upregulated in activated cTfh from patients with irAEs, including KIF2C, MKI67 and BIRC5, compared with patients without irAEs (Fig. 4a, Extended Data Fig. 7g and Supplementary Table 7). Consistent with these proliferation-associated genes, GO analysis also revealed enrichment for terms related to cell cycle and cellular activation in patients with irAEs (Extended Data Fig. 7h). Other genes of potential interest were also revealed, including the cytokine IL32, the splicing factor XAB2 and CD52, the target of alemtuzumab used to treat chronic lymphocytic leukeamia and multiple sclerosis48, which were all more highly expressed in activated cTfh from anti-PD-1-treated adults with irAEs. In contrast, higher ITGA5, CD82 and FOS were found in cTfh from anti-PD-1-treated adults without irAEs. These data indicated that, before influenza vaccination, ICOS+CD38+ cTfh in anti-PD-1-treated adults with irAEs were more activated than in anti-PD-1-treated adults without irAEs and anti-PD-1 therapy may engage additional biologically relevant pathways.
GSEA identified enrichment of G2M checkpoint and E2F targets in ICOS+CD38+ cTfh at baseline from participants with irAEs compared with those without irAEs (Fig. 4b), further supporting the idea that ICOS+CD38+ cTfh from patients with irAEs were more activated, even before influenza vaccination, compared with patients without irAEs. This observation is consistent with other reports that identified T cell activation as a predictor of irAEs49. In addition to proliferation-associated pathways, multiple other irAE-associated biologic pathway changes in cTfh were revealed, including downregulation of IL-2/STAT5, TNF/NFkB, IL6/JAK/STAT, IFN-γ and TGF-β signaling, consistent with the results above. Taken together, these data support the notion of an anti-PD-1-mediated rewiring of cTfh biology in patients with irAEs that includes increased activation and proliferation concomitant with blunting of cytokine pathway signaling.
These observations provoked the hypothesis that the post-influenza vaccine cTfh response might distinguish anti-PD-1 patients with irAEs from those without irAEs because of an underlying change in the global set point of Tfh regulation. To test this idea, we examined the magnitude of the ICOS+CD38+ cTfh increase 1 week following influenza vaccination in patients who did, or did not, have irAEs. Indeed, in patients with irAEs, influenza vaccination had a more robust ICOS+CD38+ cTfh response compared with patients without irAEs (~2.4-fold compared with 1.3-fold; Fig. 4c). A similar trend was seen in patients in Cohort 1 who were receiving anti-PD-1 monotherapy (Extended Data Fig. 7i). No difference was observed in the frequency of ICOS+CD38+ cTfh before vaccination (Extended Data Fig. 7j), suggesting a key role for the vaccine-induced perturbation in revealing this biology. Moreover, we did not observe increases in titers of anti-nuclear or anti-double-stranded DNA (anti-dsDNA) autoantibodies with anti-PD-1 therapy or in the setting of irAEs (Extended Data Fig. 7k,l). Thus, these observations suggest that anti-PD-1 therapy alters Tfh–B cell and likely underlying GC biology, at least in a subset of patients, and that these changes can alter the magnitude and quality of the response to vaccination. The alteration in this Tfh–B cell and GC axis correlates with propensity for anti-PD-1-associated irAEs and perhaps points to underlying rewiring of Tfh and GC biology contributing to these outcomes which may be more sensitive than general autoantibody measurements such as anti-nuclear or anti-dsDNA antibodies. Thus, using influenza vaccination as an ‘analytical probe’ of underlying changes in immune function revealed a proliferatively enhanced, but biologically altered, response to vaccination which also identified participants predisposed to irAEs.
Checkpoint blockade therapies including anti-PD-1 antibodies are now a major tool in the arsenal for the treatment of cancer. Substantial effort has focused on the in vivo effects of anti-PD-1 therapy on the CD8 T cell response1. However, as evidenced by the global effects of PD-1 deficiency26 and the immune-related complications of anti-PD-1 therapy, better understanding of the mechanisms of anti-PD-1 therapy on additional immune cell types is needed. Here, we exploited influenza vaccine-induced immune responses to evaluate the effects of anti-PD-1 on the Tfh–B cell axis. We identified evidence of increased early germinal centre activation which was associated with proliferation in cTfh and responding B cells. Moreover, transcriptional profiling of ICOS+CD38+ cTfh following vaccination identified key altered signaling pathways including reduced IL-2/STAT5 signaling in the setting of anti-PD-1, and these changes were associated with quantitatively robust but qualitatively impaired antibody responses to vaccination. Finally, we demonstrated that cTfh from participants with irAEs had evidence of hyper-responsiveness compared with those without irAEs.
Mechanistic studies of the in vivo effects of immunomodulators such as anti-PD-1 are challenging given limits on our ability to study immune mechanisms in humans. In previous studies, we established ICOS+CD38+ cTfh as a circulating cellular biosensor in the setting of aging45, given that many cTfh are recent emigrants from lymphoid tissue50,51. Thus, platforms that employ an immunologic stimulus, particularly one widely used in routine clinical care, offer a window of opportunity to elucidate the in vivo effects of therapeutics on the Tfh–B cell axis. Indeed, little is known about the protective effects of vaccines used clinically in patients with cancer receiving immunologically relevant therapies. Moreover, influenza vaccination in the presence of anti-PD-1 augmented the induction of antibody, although antibody levels achieved by the late time point were similar to healthy adults. However, the antibodies generated in anti-PD-1-treated patients had reduced sialylation, a change that has implications for regulation of germinal centre responses and antibody affinity maturation33. Although it is currently assumed that influenza vaccination offers similar efficacy in patients receiving anti-PD-1 compared with patients not receiving checkpoint inhibitors, further studies will be needed to determine whether alternative vaccination strategies, such as use of adjuvants, high-dose vaccine or perhaps mRNA-based influenza vaccines, are needed to optimally protect patients.
One distinct aspect of using influenza vaccine for these studies is that the response to influenza vaccination likely mainly involves a recall response of previously primed memory CD4 T cells and B cells, with perhaps a smaller newly primed response depending on the year and strains of influenza virus in the vaccine52. Indeed, we previously observed recall responses in cTfh following successive annual vaccination25. It is worth noting that the impact of anti-PD-1 may differ for primary versus secondary responses53 and deconvolving the primary versus recall responses in humans is complex, especially at 1 week following vaccination when both primary and recall responses would both already be clonally expanded. Thus, future studies, perhaps using antigens not previously encountered (for example, SARS-CoV-2 mRNA vaccines) and high-resolution multi-modal profiling using Cellular Indexing of Transcriptoms and Epitopes by Sequencing (CITE-seq54), should help further dissect the role of anti-PD-1 on pre-existing versus newly primed CD4 T cell and B cell responses.
A major gap in our knowledge is the inability to predict cancer treatment success or failure at the time of initiation of immunotherapy. Accurately predicting these responses requires better understanding of the mechanisms by which immunotherapies work. We identified increased Tfh and B cell responses to vaccination in the setting of anti-PD-1, and our findings are consistent with other reports of anti-PD-1 inducing Tfh proliferation55. Although evidence supports a major role for anti-PD-1 augmenting antitumour immunity via CD8 T cells2, recent studies have also suggested a link between the Tfh–B cell axis and efficacy of immunotherapy56,57,58,59. Here, we enrolled participants who were receiving immunotherapy in the form of single-agent anti-PD-1 therapy (for example, pembrolizumab or nivolumab). This strategy has two implications. First, the immune response to initiation of anti-PD-1 therapy is dynamic and typically occurs within the first one to two cycles2. Thus, the patients studied here were biased towards those in whom loss of PD-1 signaling was more established, allowing the setting of treatment-induced steady-state PD-1 deficiency to be assessed. Second, because these patients on average had received seven or more cycles of immunotherapy, corresponding to 4–6 months since initiation, they were likely enriched for participants who were having a successful clinical response. This latter effect was advantageous in allowing us to study reasonably healthy patients, but is also worth noting since perhaps not all patients with cancer receiving anti-PD-1 therapy will respond the same way to vaccines. Whether use of such analytical vaccination as a measure of immune fitness will aid in determining outcomes of cancer therapy is unclear, but these data suggest that immuno-monitoring of the Tfh–B cell axis during cancer therapy could provide additional insights about overall immune health.
IrAEs lead to morbidity and premature discontinuation of therapy but are not well understood. Here, we found that patients with irAEs had evidence of greater sensitivity to anti-PD-1 in ICOS+CD38+ cTfh, compared with patients without irAEs, all of whom were receiving anti-PD-1. We also found that patients with irAEs had more robust influenza vaccine-induced cTfh responses relative to patients without irAEs. Indeed, there is additional evidence from other studies for enhanced T cell activation in the setting of irAEs49. Patients with irAEs following immune checkpoint blockade often have better anti-cancer responses and better survival60,61,62, suggesting broad rewiring of immunity by anti-PD-1 immunotherapy that impacts responses as diverse as those targeting tumour and self-antigens. Although the mechanisms for the increased sensitivity of cTfh and the germinal centre-dependent immune responses to anti-PD-1 identified here are not fully understood, these data are consistent with previous work identifying cTfh as sensitive readouts of underlying changes in overall immune system status45. Indeed, in the setting of immunotherapy, we hypothesize that coordination of germinal centre-dependent vaccine responses may serve as a proxy for understanding broader immune response regulation in the context of anti-PD-1 therapy. The coordinated response provoked by vaccination, however, may allow a temporally synchronized immune response and provide a high-resolution window into the anti-PD-1-dependent changes in underlying immune biology. Thus, the use of influenza vaccine as an analytical probe of broader immune function on anti-PD-1 therapy revealed insights into underlying immune set point, specifically for immune features and pathways that have a role in germinal centre-dependent immune responses which may include both responses to vaccination and autoimmunity.
In summary, these studies not only reveal a role for treatment with anti-PD-1 to reset the immune landscape beyond anti-cancer responses in some patients, but also suggest the potential of PD-1 pathway manipulation in anti-vaccine responses, and point to activated cTfh including in the context of an analytical vaccine to identify patients at risk of irAEs. The data presented here demonstrate the utility of immunoprofiling studies during a vaccine-induced perturbation as a means of uncovering mechanisms of immunomodulator therapy, which will have major implications for design and use of immunomodulator therapy.
Participants were recruited and underwent informed consent between 2015 and 2019 at the Memorial Sloan Kettering Cancer Center (Cohort 1) and at the University of Pennsylvania (Cohort 2), in accordance with the Institutional Review Boards of each institution, also described in NCT03346772 and NCT03315975. Participants were eligible if they required influenza vaccine for routine care and had not received influenza vaccine in the previous 6 months; they were excluded if they had contraindications to influenza vaccine, or needed immunosuppressive medications (that is, corticosteroids). Participants received influenza vaccine on the same day as their scheduled dose of anti-PD-1. In Cohort 1, participants under age 65 received Flulaval quadrivalent vaccine (GlaxoSmithKline), whereas participants over age 65 received Fluzone High-Dose quadrivalent vaccine (Sanofi-Pasteur). In Cohort 2, healthy adults received Fluzone quadrivalent vaccine (GlaxoSmithKline) and anti-PD-1 adults received quadrivalent vaccine (manufacturer information unavailable). Participants were compensated for their participation for visits not linked to usual care.
Peripheral venous blood was drawn at baseline (day 0), at 1 week (days 7–10) and late (21–28 d) after seasonal inactivated influenza vaccination. Blood was collected into heparinized tubes. Plasma was isolated by centrifugation, and peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll-Paque PLUS (GE Healthcare) or Sepmate (Stem Cell) tubes. Participants in influenza vaccine studies in the setting of aging were described previously21.
PBMCs were stained for surface proteins for 20 min at 25 °C. Permeabilization was performed using the Intracellular Fixation/Permeabilization Concentrate and Diluent kit (ThermoFisher) for 20 min at 25 °C. Intracellular staining was performed for 60 min at 25 °C. Antibodies and clones are described in Supplementary Table 3. Cells were resuspended in 1% para-formaldehyde until acquisition on a BD Symphony A5 cytometer. Fluorescence-minus-one controls were performed in pilot studies.
PBMCs were sorted on a BD Aria II cell sorter using FACSDiva (v.8.0), followed by total RNA extraction by RNeasy Micro Plus kit (Qiagen) into DNA LoBind tubes (Eppendorf) in randomly allocated small batches. Samples were randomly distributed throughout a 96-well skirted twin.tec PCR plate (Eppendorf), followed by polyA amplification with SMART-Seq HT kit (Clontech) according to manufacturer instructions. Libraries were prepared using the Nextera XT Library Preparation kit (Illumina) using the protocol modification recommended by the Clontech kit. Libraries were indexed with a unique dual indexing strategy (IDT for Illumina Nextera DNA Set A, Illumina) to account for index hopping. Sequencing was performed on an Illumina Novaseq 6000, using 51 + 10 + 10 + 51 run geometry. FASTQ files were trimmed with Trimmomatic (v.0.32) and assessed for quality with FastQC. Libraries were aligned using STAR (v.2.5.2a) and normalized by PORT (https://github.com/itmat/Normalization/, v.0.8.5) against the GRCh38 reference assembly of the human genome. One of the 89 resultant libraries was excluded from further analysis due to poor sequencing (tenfold fewer reads than next least sample).
Variance-stabilizing transformation and differential expression were performed using DESeq2 (v.1.28.1) based on genes with at least 20 counts in at least 25% of all libraries, using the R environment (v.4.0.2). Plots were made by ggplot2 (v.3.3.2). Heatmap colouring was based on the ‘inferno’ colour-scheme from the viridis library. t-SNE maps were produced using Rtsne (v.0.15) from the variance-stabilized counts data. GO was performed using Metascape (https://metascape.com). GSEAs were performed with at least 10,000 permutations of pre-ranked GSEA (http://software.broadinstitute.org/gsea/downloads.jsp), and gene set variation analysis63 was performed with the GSVA library (v.1.36.2).
The two influenza A vaccine strains of the 2014/2015 seasonal influenza vaccine, A/California/7/2009 (H1N1) pdm09-like virus and A/Victoria/361/2011 (H3N2)-like virus, were obtained from the Centers for Disease Control and Prevention (Atlanta, GA, USA). Influenza B strain B/Brisbane/33/2008 was obtained from BEI Bioresources (NR-42006). Assays to detect hemagglutinin inhibition (HAI) antibody titers were performed as previously described33. Infectious virus was used for neutralizing antibody assays or inactivated by β-propiolactone for H1N1/California-, H3N2/Victoria- or B/Victoria-specific binding antibody ELISA assays. Nunc MaxiSorp plates were coated with 10 mg ml−1 influenza A/H1N1/California, A/H3N2/Victoria or B/Brisbane/33/2008 virus along with isotype standards for IgG (Athens Research and Technology) in bicarbonate buffer overnight at 4 °C. Plates were blocked with 3% BSA in PBS and incubated with heat-inactivated sera of young and elderly adults. Antibodies were detected using alkaline phosphatase-conjugated mouse anti-human IgG (Southern Biotechnology).
HA ELISAs were performed as described previously33. Negative control sera values were subtracted from readings given by test samples. Serum antibody 50% effective concentration (EC50) levels were determined by nonlinear regression analysis of the serially diluted serum optical density (OD) values as calculated by GraphPad Prism. The EC50 takes into account both the concentration (maximum binding) and the affinity of antibodies (for example, the slope of the binding curve) and is an accurate measure of total binding which generally correlates with the endpoint titer64. For hi-lo affinity ELISAs, sera diluted 1:200 were incubated on plates coated with 1 μg ml−1 HA protein (low density) or 6 mg ml−1 HA (high density). The affinity of HA-specific IgG was expressed as a ratio of binding to low-density/high-density HA-coated plates33.
Anti-nuclear antibody and dsDNA screen
Anti-nuclear antibodies were detected by ELISA (BioVision). Here, diluted patient serum (1:21) was added to the ELISA plate coated with purified nuclear antigens. The enzyme conjugate was then added to all wells and incubated for 20 min at 25 °C. Following removal and washing three times with wash buffer, 100 µl of TMB substrate was added and allowed to incubate for 10 min before adding 100 µl of stop solution. The OD of the plates was measured at 450 nm. Antibody indices were calculated per manufacturer recommendations and values >0.9 were considered positive. Similarly, anti-dsDNA antibodies were detected by quantitative ELISA (Eagle Biosciences). Briefly, the antibodies of the calibrators and control and diluted patient samples were incubated with dsDNA immobilized on the solid phase of microtiter plates. Following an incubation period of 60 min at 25 °C, unbound sample components were removed, and plates were washed three times. The bound IgG antibody complexes were detected using anti-human-IgG conjugated to horseradish peroxidase (HRP) for 30 min at 25 °C. Then, 100 µl of TMB solution was added to each of the wells and allowed to incubate for 10 min at 25 °C before the addition of 100 µl of stop solution. The OD of the solution was measured at 450 nm. A standard curve was established by plotting the antibody concentrations of the calibrators (x axis) and their corresponding OD values (y axis) measured. The antibody concentration of each specimen was then interpolated from the standard curve.
Fc glycan analysis
IgGs were isolated from serum by protein G purification. HA-specific IgGs were isolated on agarose resin (Pierce) coupled to HA protein. Total anti-H1 HA IgGs were captured on Cal/09-coupled resin. IgG Fc-associated glycans were analysed by mass spectrometry following tryptic digestion of purified IgG or on-bead IgG.
Protein samples (10 µg) enriched by Protein G beads either in-solution or on-beads in 50 mM Tris buffer pH 8.0 were denatured with 6 M guanidine HCl, reduced with 10 mM dithiothreitol at 56 °C for 45 min and alkylated with 60 mM iodoacetamide for 1 h at 25 °C in the dark. Excess iodoacetamide was quenched with 20 mM dithiothreitol for 30 min at 25 °C. The samples were diluted to a final volume of 200 µl with 50 mM ammonium bicarbonate to reduce the guanidine HCl concentration to less than 1 M. The samples were digested by adding trypsin (0.5–1 µg, Promega) at a 1:20 ratio (w/w) and incubated at 37 °C for 16 h. All samples were desalted using solid phase extraction on Sep-Pak Cartridges (Waters) and the eluted tryptic peptides were evaporated to dryness in a Speedvac SC110 (Thermo Savant) before analysis.
Liquid chromatography–tandem mass spectrometry (MS/MS) analysis for characterization of glycosylation sites was performed on an UltiMate3000 RSLCnano (Dionex) coupled with a hybrid triple quadrupole linear ion trap mass spectrometer, the 4000 Q Trap (AB SCIEX). The digested samples were reconstituted in 50 µl of 0.2% formic acid (FA), of which 2–3 µl was injected with an autosampler onto a PepMap C18 trap column (5 µm, 300 µm × 5 mm, Dionex) with 0.1% FA at 20 ml min−1 for 1 min, then separated on a PepMap C18 RP nano column (3 µm, 75 µm × 15 cm, Dionex) using a 60-min gradient of 10% to 35% acetonitrile (ACN) in 0.1% FA at 300 nl min−1, followed by a 3-min ramp to 95% ACN–0.1% FA and a 5-min hold at 95% ACN–0.1% FA.
Mass spectrometry data acquisition was performed using Analyst 1.4.2 software (Applied Biosystems) for precursor ion scan-triggered information-dependent acquisition (IDA) analysis and enhanced mass spectrometry-based IDA analysis65,66. The precursor ion scan of the oxonium ion (HexNAc+ at m/z 204.08) was monitored at a step size of 0.2 Da cross a mass range of m/z 400 to 1,600 for detecting glycopeptides containing N-acetylhexosamine unit. The nanospray voltage was 1.9 kV, and was used in positive ion mode for all experiments. The declustering potential was set at 50 eV and nitrogen as collision gas. In IDA analysis, after each precursor ion scan or enhanced mass spectrometry scan, and enhanced resolution scan, the two to three highest intensity ions with multiple charge states were selected for MS/MS with rolling collision energy applied for detected ions based on different charge states and m/z values. All acquired MS/MS spectra from enhanced mass spectrometry-IDA were subjected to Mascot database search. All acquired MS/MS spectra for detected glycopeptide ions by precursor ion scanning were manually inspected and interpreted with Analyst 1.4.2 and BioAnalysis 1.4 software (Applied Biosystems). The peak areas of detected precursor ions were determined by extracted chromatogram at each specific m/z representing glycopeptide isoforms. The relative quantitations of the sugar glycan isoforms of N-linked peptide ions were carried out based on precursor ion peak areas under the assumption that all sugar glycan isoforms linked to the same core peptide have identical or similar ionization efficiency.
No statistical methods were used to predetermine sample sizes but our sample sizes are similar to those reported in previous publications25,45. Randomization was not performed in these observational studies. Data collection and analysis were not performed blind to the conditions of the experiments. Statistical analyses were performed with R (v.4.0.2) using the rstatix library (v.0.6.0) or Prism 9 (GraphPad). Data were compared using Student’s t-test, paired t-test, Wilcoxon test, one-way or two-way ANOVA with Tukey post hoc analysis, or nonparametric ANOVA with Friedman’s or Sidak’s post-test, as indicated. Nonparametric tests were used unless data were distributed normally based on Shapiro’s test of normality. All tests throughout these studies were performed as two-tailed tests with α = 0.05. All available data were used, and no outlier analyses were performed.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Transcriptional profiling data reported here are available at the Gene Expression Omnibus (GEO accession GSE179487). The publicly available MSigDB Hallmark database was used for pathway analysis. Data from GSE68245 were used to validate B cell transcriptional profiles. Source data are provided with this paper.
The minimum dataset and R scripts used in analyses and figure generation are available on Zenodo (https://doi.org/10.5281/zenodo.6665441).
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We thank the participants who volunteered for our studies, as well as members of the Wherry and Ravetch laboratories who provided critical feedback in this project. This work was supported by National Institutes of Health (NIH) grants: no. AI114852 (R.S.H.), no. AI082630 (R.S.H.), no. AI158617 (R.S.H.) and no. AG047773 (R.S.H.); the Pediatric Infectious Diseases Society Fellowship Award (L.A.V.); National Center for Advancing Translational Sciences (NCATS) award no. KL2TR001879 (L.A.V.); grant no. CA230157 (A.C.H.) and the Parker Bridge Scholar Award (A.C.H.); grant nos. AI155577 (E.J.W.), AI108545 (E.J.W.), AI112521 (E.J.W.), AI082630 (E.J.W.), AI149680 (E.J.W.) and AI117950 (E.J.W.); the US Broad Agency Announcement grant no. HHSN272201100018C (to H.C.J.E. and E.J.W.), NIH contract number 75N93021C00015 (E.J.W.) and the Allen Institute for Immunology (E.J.W.); and grant nos. U19-AI142737 (J.V.R.), R35-CA196620 (J.V.R.), K08-CA248966 (D.A.K.), R01-AI145870 (J.V.R.) and P30-CA008748. Blood collection was supported by SPORE in Skin Cancer grant nos. P50-CA174523 and P50-CA221745. We thank K. Schmader and S. Doyle for their help in influenza vaccine studies. Thanks to J. Pippin and S. Grant (Children’s Hospital of Philadelphia) for assistance with sequencing. We also thank the Flow Cytometry Core and the Human Immunology Core at the University of Pennsylvania for their advice and assistance in these experiments. E.J.W. is also supported by the Parker Institute for Cancer Immunotherapy which supports the cancer immunology program at UPenn. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The project described was also supported in part by grant no. UL1TR001866 from NCATS Clinical and Translational Science Award (CTSA) program. The following reagent was obtained through BEI Resources, NIAID, NIH: Influenza B Virus, B/Brisbane/33/2008 (Victoria Lineage), NR-42006.
E.J.W. is an advisor for Merck, Marengo, Janssen, Related Sciences, Synthekine and Surface Oncology. E.J.W. is a founder of Surface Oncology, Danger Bio and Arsenal Biosciences. E.J.W. has a patent on the PD-1 pathway. R.S.H.’s spouse is an employee of Merck but was not involved in the studies. A.C.H. is a consultant for Immunai and received research funding from BMS. T.C.M. has had advisory roles with Merck, Bristol-Myers Squibb, OncoSec, GigaGen and Instil Bio. D.F.B. reports consulting fees from Bristol-Myers Squibb, Merck, Genentech‐Roche, AstraZeneca and Pfizer, and institutional research support from Merck, Genentech‐Roche, AstraZeneca, Novartis and Bristol‐Myers Squibb. S.A.F. reports consulting fees from Merck, institutional research support from AstraZeneca and Genentech/Roche, and has stock and other ownership interest in Urogen, Allogene Therapeutics, Neogene Therapeutics, Kronos Bio, Iconovir and Vida Ventures. C.-H.L. served in a consultancy or advisory role to Amgen, Bristol-Myer Squibb, Exelixis, Merck, Pfizer, EMD Serono and Eisai, and also received research funding from Bristol-Myers Squibb, Calithera, Eisai, Exelixis, Eli Lilly, Merck and Pfizer. R.J.M. served in a consultancy or advisory role for Pfizer, Novartis, Merck, Genentech/Roche, Eisai and Exelixis, and received research funding from Bristol-Myers Squibb, Merck, Pfizer, Genentech/Roche, Eisai, Exelixis and Novartis outside of the submitted work. M.H.V. had the following consulting or advisory roles: Alexion Pharmaceuticals, Calithera Biosciences, Exelixis, GlaxoSmithKline, Natera, Novartis, Pfizer; research funding: Bristol-Myers Squibb, Genentech/Roche, Pfizer; personal fees: Novartis, Takeda; travel fees: Novartis, Takeda, Eisai, AstraZeneca; honoraria: Novartis, outside of the submitted work. The remaining authors declare no competing interests.
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A. Study schematic. B. Histogram of cycle of immunotherapy shown for each cohort. C. Gating strategy shown for CD4 T cells. D. anti-PD-1 binding to cells demonstrated by absence of PD-1 staining and presence of α-human IgG4 staining. E. tSNE representation showing gating on ICOS+CD38+ either directly on CXCR5+ CD4 T cells (left) or after first gating on PD-1+ cells (right). F. ICOS+CD38+ cTfh responses over time. Repeat measurements from the same subjects shown as connected lines. G. ICOS+CD38+ responses shown as % cTfh (P = 2×10−4 for anti-PD-1 oneWeek compared to baseline; paired two-way ANOVA with Sidak’s correction). Data are presented as mean values (point) ± SEM (shaded area). H. ICOS+CD38+ cTfh responses shown as % CD4 (P = 1.3×10−3 for anti-PD-1 at one week compared to baseline; paired two-way ANOVA with Sidak’s correction). Data are presented as mean values (point) ± SEM (shaded area). I. PD-1 expression among CXCR5+ CD4 T cells was identified by either direct staining for PD-1 or by indirect staining for α-human IgG4 staining. J. ICOS+CD38+ coexpression shown after gating on CXCR5+PD-1+ as identified in panel I. K. Fold-change in ICOS+CD38+ cTfh shown after gating on PD-1 as in panel J (P = 0.01; two-sided t-test; anti-PD-1 (n = 16) vs Healthy (n = 15)). L. cTfh responses vs cycle of immunotherapy for the melanoma cohort (Kendall t = 0.26; two-sided test; P = 0.15). Shaded area represents SEM by linear smoothing. M. An independent cohort of young and elderly adults without cancer was assessed for cTfh responses following influenza vaccination. N.S., not significant (two-way ANOVA with Tukey’s post-test). N. Kendall correlations between the cellular responses and chronological age shown for Cohort 2. Nominal P values from two-sided test. O. ICOS+CD38+ cTfh were analysed for expression of CXCR3 and CCR6. P-values on summary plots from paired two-sided t-test comparisons. P. T follicular regulatory (Tfr) cells were identified among ICOS+CD38+ cTfh based on expression of Foxp3. Q. Summary plots for Tfr in Cohort 2, gated on ICOS+CD38+ cTfh. P values from paired two-sided Wilcoxon test without correction for multiple comparisons. R. Fold-change in cTfr at one week relative to baseline for Cohort 2 (P = 2.2×10−3; two-sided Wilcoxon test; anti-PD-1 (n = 21) vs Healthy (n = 27)).
A. Gating strategy for B cell analysis. B. Plasmablast fold-change at one week compared to baseline in Cohort 1 (P = 0.18; two-sided t-test; anti-PD-1 (n = 16) vs Healthy (n = 14)). C. Plasmablast responses, gated as CD27++CD38++ nonnaive CD19+ lymphocytes in Cohort 1. D. Plasmablast responses shown as fold-change (P = 0.09, two-sided t-test). E. Plasma CXCL13 for individual subjects in Cohort 2. F. Plasma CXCL13 in Cohort 1. G. Plasma CXCL13 fold-change in Cohort 1 (P = 0.06; two-sided Wilcoxon test; anti-PD-1 (n = 18) vs non-anti-PD-1 (n = 7)). H. Kendall correlations between the fold-change in CXCL13 for Cohort 2 and the T, B, and humoral responses. Nominal P values from two-sided test.
A. Hemagglutinin-inhibitory titers (HAI) for three influenza virus strains for Cohort 2. Connected lines indicate repeated measurements from the same individual over time. B. Direct comparison of HAI titers per strain at the time of influenza vaccination for Cohort 2. P values from two-sided Wilcoxon tests. C. HAI titers over time for Cohort 1. Connected lines indicate repeated measurements from the same individual over time. D. Seroconversion as a fold-change at the late time point relative to baseline for Cohort 1. P values from two-sided Wilcoxon tests. E. Kendall correlations shown for the cellular responses shown versus the fold-change in HAI titer for each viral strain for Cohort 2. Nominal P values from two-sided test. F. HAI titers were determined in an independent cohort of young and elderly adults without cancer for H1N1 (left, Young (n = 28) vs Elderly (n = 35)) and H3N2 (right, Young (n = 28) vs Elderly (n = 35)). N.S., not significant (two-sided Mann-Whitney U-test; Young vs Elderly at day 0) for each strain.
A. Plasma levels of IgG subclasses (P = 9.6×10−3 for IgG1 between Healthy and anti-PD-1; two-way ANOVA with Sidak’s correction). B. Proportion of afucosylated anti-H1 IgG1 antibodies. C. Kendall correlation between sialylated anti-HA IgG1 antibodies and proportion of galactosylated anti-H1 IgG1 antibodies for Cohort 2. Nominal P values from two-sided test. D. Proportion of total (G1 + G2) galactosylated anti-H1 IgG1 antibodies by Ig subclass. E. Galactosylation shown for no galactose residues (left), one galactose residue (middle), or two galactose residues (right). P values from two-sided Wilcoxon test (anti-PD-1 (n = 30) vs Healthy (n = 27). F. Sialylated anti-H1 IgG1 antibodies by subclass. G. Total galactosylation for anti-H1 IgG1 antibodies at baseline in Cohort 1 (P = 0.18; two-sided Wilcoxon; anti-PD-1 (n = 17) vs non-anti-PD-1 (n = 8)). H. Total galactosylation for anti-H1 IgG1 antibodies over time in Cohort 1. N.S., not significant (two-way ANOVA with Tukey’s). Data are presented as mean values (point) ± SEM (shaded area). I. Sialylation of anti-H1 IgG1 antibodies at baseline in Cohort 1 (P = 0.03; two-sided Wilcoxon test; anti-PD-1 (n = 17) vs non-anti-PD-1 (n = 8)). J. Sialylation for anti-H1 IgG1 antibodies over time in Cohort 1. Data are presented as mean values (point) ± SEM (shaded area). N.S., not significant (two-way ANOVA with Tukey’s). K. Anti-HA affinity at baseline (P = 0.04; two-sided t-test; anti-PD-1 (n = 14) vs Healthy (n = 12)). L. Anti-HA affinity for Cohort 2, as determined by ELISA and calculated as a binding ratio of low-density to high-density of plate-bound HA protein (two-way ANOVA with Tukey’s; *P = 0.02; Healthy baseline vs late; **P = 1.7×10−4, anti-PD-1 baseline vs late; ***P = 5.0×10−4, Healthy vs anti-PD-1 at late; ****P = 4.2×10−7, anti-PD-1 vs Healthy at baseline). Data are presented as mean values (point) ± SEM (shaded area). M. Anti-HA affinity over time. N. EC50/Kd affinity at baseline for Cohort 2 for H1N1. P value from two-sided t-test (anti-PD-1 (n = 22) vs non-anti-PD-1 (n = 8)). O. Examples of ASC and ABC at baseline. P. Baseline ASC frequencies (P = 2.8×10−5; t-test) and ABC frequencies by cohort (P = 1.1×10−4; two-sided t-test) for anti-PD-1 (n = 15) vs Healthy (n = 15). Q. Kendall correlations at baseline between ICOS+CD38+ cTfh for ASC (left) or ABC (middle) for Cohort 2. Nominal P values from two-sided test. Bar graph shows Kendall correlation coefficients. R. Kendall correlations at one week after vaccine between ICOS+CD38+ cTfh for ASC (left) or ABC (middle) for Cohort 2. Bar graph shows Kendall correlation coefficients. Nominal P values from two-sided test.
A. tSNE for cellular subsets evaluated by bulk RNAseq. B. tSNE recoloured by treatment. C. tSNE plot recoloured by time point. D. ASC transcriptional profile validated against GSE68245. E. ABC transcriptional profile validated against GSE68245. F. Gene ontology for differentially expressed genes at p < 0.01 for ICOS+CD38+ cTfh for Healthy (top) or anti-PD-1 (bottom). P values from hypergeometric test with Benjamini-Hochberg P-value correction. G. Kendall correlations for the Ki67+ICOS+CD38+ cTfh (% CD4) assessed at one week after vaccination versus the fold-change in ICOS+CD38+ cTfh at oneWeek relative to baseline. Nominal P value from two-sided test. H. Gene ontology for differentially expressed genes at p < 0.01 for ASC for Healthy (top) or anti-PD-1 (bottom). P values from hypergeometric test with Benjamini-Hochberg P-value correction. I. Differential expression analysis of ICOS+CD38+ cTfh at baseline. J. Differential expression analysis of ASC at baseline.
A. GSEA for Hallmark gene sets for ABC for FDR < 0.05. B. GSEA plots for the G2M checkpoints and Mitotic Spindle gene sets. C. Pearson correlation for ICOS+CD38+ cTfh frequency at one week versus the GSVA score for the IL2/STAT5 gene set for ICOS+CD38+ cTfh at one week for the full study (left) and split by treatment group (right). P value from two-sided test.
A. Time to development of irAE in Cohort 2. B-C. Log-transformed counts for PDCD1 (irAE Yes (n = 3) vs No (n = 3)) and ICOS (irAE Yes (n = 3) vs No (n = 3)). P values from two-sided t-test. D. ICOS protein in ICOS+CD38+ cTfh before influenza vaccination in Cohort 2 (P = 0.04; two-sided t-test; irAE Yes (n = 11) vs No (n = 5)). E. αIgG4 staining in ICOS+CD38+ cTfh before influenza vaccination in Cohort 2 (P = 0.02; two-sided t-test; irAE Yes (n = 11) vs No (n = 5)). F. αIgG4 and ICOS protein in total CD4 (left) or ICOS+CD38+ cTfh (right) for participants who had irAE (orange) versus those who did not have irAE (grey). G. Volcano plot of genes differentially expressed in anti-PD-1 cohort with respect to irAE. P values from Wald test with Benjamini-Hochberg correction. Genes labelled where Padj < 0.05. H. Gene ontology for genes differentially expressed at nominal P < 0.05 for ICOS+CD38+ cTfh at baseline in participants in Cohort 2 who developed irAE. P values from hypergeometric test with Benjamini-Hochberg P-value correction. I. Fold-change in ICOS+CD38+ cTfh at one week compared to pre-vaccination baseline in Cohort 1 (P = 0.67; two-sample t-test; irAE Yes (n = 5) vs No (n = 1)). J. Baseline ICOS+CD38+ cTfh frequency among those who developed irAE in Cohort 2 (irAE Yes (n = 13) vs No (n = 6)). K. Plasma levels of antinuclear antibodies (ANA, left, anti-PD-1 (n = 30) vs Healthy (n = 15)) and anti-dsDNA antibodies (right, anti-PD-1 (n = 30) vs Healthy (n = 15)) were assessed at baseline in Cohort 2. L. Plasma ANA (irAE Yes (n = 14) vs No (n = 7)) and anti-dsDNA antibodies (irAE Yes (n = 14) vs No (n = 7)).
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Herati, R.S., Knorr, D.A., Vella, L.A. et al. PD-1 directed immunotherapy alters Tfh and humoral immune responses to seasonal influenza vaccine. Nat Immunol 23, 1183–1192 (2022). https://doi.org/10.1038/s41590-022-01274-3
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