Functional systemic CD 4 immunity is required for clinical responses to PD ‐L1/ PD ‐1 blockade therapy
2019; Springer Nature; Volume: 11; Issue: 7 Linguagem: Inglês
10.15252/emmm.201910293
ISSN1757-4684
AutoresMiren Zuazo, Hugo Arasanz, G. Fernández-Hinojal, María Jesús García-Granda, María Gato, Ana Bocanegra, Maite Martínez, Berta Hernández, Lucía Teijeira, Idoia Morilla, María José Lecumberri, Ángela Fernández de Lascoiti, Ruth Vera, Grazyna Kochan, David Escors,
Tópico(s)Immunotherapy and Immune Responses
ResumoArticle6 June 2019Open Access Source DataTransparent process Functional systemic CD4 immunity is required for clinical responses to PD-L1/PD-1 blockade therapy Miren Zuazo Miren Zuazo Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author Hugo Arasanz Hugo Arasanz Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author Gonzalo Fernández-Hinojal Gonzalo Fernández-Hinojal Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Maria Jesus García-Granda Maria Jesus García-Granda Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author María Gato María Gato Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author Ana Bocanegra Ana Bocanegra Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author Maite Martínez Maite Martínez Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Berta Hernández Berta Hernández Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Lucía Teijeira Lucía Teijeira Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Idoia Morilla Idoia Morilla Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Maria Jose Lecumberri Maria Jose Lecumberri Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Angela Fernández de Lascoiti Angela Fernández de Lascoiti Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Ruth Vera Corresponding Author Ruth Vera [email protected] orcid.org/0000-0003-1524-3147 Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Grazyna Kochan Corresponding Author Grazyna Kochan [email protected] orcid.org/0000-0002-0534-9661 Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author David Escors Corresponding Author David Escors [email protected] orcid.org/0000-0003-2828-4458 Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Division of Infection and Immunity, University College London, London, UK Search for more papers by this author Miren Zuazo Miren Zuazo Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author Hugo Arasanz Hugo Arasanz Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author Gonzalo Fernández-Hinojal Gonzalo Fernández-Hinojal Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Maria Jesus García-Granda Maria Jesus García-Granda Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author María Gato María Gato Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author Ana Bocanegra Ana Bocanegra Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author Maite Martínez Maite Martínez Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Berta Hernández Berta Hernández Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Lucía Teijeira Lucía Teijeira Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Idoia Morilla Idoia Morilla Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Maria Jose Lecumberri Maria Jose Lecumberri Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Angela Fernández de Lascoiti Angela Fernández de Lascoiti Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Ruth Vera Corresponding Author Ruth Vera [email protected] orcid.org/0000-0003-1524-3147 Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain Search for more papers by this author Grazyna Kochan Corresponding Author Grazyna Kochan [email protected] orcid.org/0000-0002-0534-9661 Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Search for more papers by this author David Escors Corresponding Author David Escors [email protected] orcid.org/0000-0003-2828-4458 Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain Division of Infection and Immunity, University College London, London, UK Search for more papers by this author Author Information Miren Zuazo1,‡, Hugo Arasanz1,‡, Gonzalo Fernández-Hinojal2,‡, Maria Jesus García-Granda1,‡, María Gato1,‡, Ana Bocanegra1, Maite Martínez2, Berta Hernández2, Lucía Teijeira2, Idoia Morilla2, Maria Jose Lecumberri2, Angela Fernández de Lascoiti2, Ruth Vera *,2, Grazyna Kochan *,1 and David Escors *,1,3 1Immunomodulation Group, Biomedical Research Center of Navarre-Navarrabiomed, Fundación Miguel Servet, IdISNA, Pamplona, Spain 2Department of Oncology, Hospital Complex of Navarre, IdISNA, Pamplona, Spain 3Division of Infection and Immunity, University College London, London, UK ‡These authors contributed equally to this work *Corresponding author. Tel: +34 848 422162; E-mail: [email protected] *Corresponding author. Tel: +34 848 425742; E-mail: [email protected] *Corresponding author. Tel: +34 848 425742; E-mails: [email protected]; [email protected] EMBO Mol Med (2019)11:e10293https://doi.org/10.15252/emmm.201910293 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The majority of lung cancer patients progressing from conventional therapies are refractory to PD-L1/PD-1 blockade monotherapy. Here, we show that baseline systemic CD4 immunity is a differential factor for clinical responses. Patients with functional systemic CD4 T cells included all objective responders and could be identified before the start of therapy by having a high proportion of memory CD4 T cells. In these patients, CD4 T cells possessed significant proliferative capacities, low co-expression of PD-1/LAG-3 and were responsive to PD-1 blockade ex vivo and in vivo. In contrast, patients with dysfunctional systemic CD4 immunity did not respond even though they had lung cancer-specific T cells. Although proficient in cytokine production, CD4 T cells in these patients proliferated very poorly, strongly co-upregulated PD-1/LAG-3, and were largely refractory to PD-1 monoblockade. CD8 immunity only recovered in patients with functional CD4 immunity. T-cell proliferative dysfunctionality could be reverted by PD-1/LAG-3 co-blockade. Patients with functional CD4 immunity and PD-L1 tumor positivity exhibited response rates of 70%, highlighting the contribution of CD4 immunity for efficacious PD-L1/PD-1 blockade therapy. Synopsis Lung cancer patients are often refractory to PD-L1/PD-1 blockade therapy. This study shows that patients progressing from conventional therapies that have functional CD4 T cells respond to PD-L1/PD-1 blockade immunotherapy, while patients with proliferative dysfunctional CD4 T cells do not respond. Functional systemic CD4 immunity is required for objective clinical responses to PD-L1/PD-1 blockade therapy in human lung cancer patients. Systemic memory CD4 T cells identify intrinsic non-responder from potentially responder patients. 70% of patients with high baseline percentages of memory CD4 T cells and PD-L1-positive tumors respond to therapy. Proliferative CD4 dysfunctionality in non-responder patients can be overcome by PD-1/LAG-3 co-blockade. Introduction PD-L1/PD-1 blockade is demonstrating remarkable clinical outcomes since its first clinical application in human therapy (Brahmer et al, 2012; Topalian et al, 2012). These therapies interfere with immunosuppressive PD-L1/PD-1 interactions by systemic administration of blocking antibodies. PD-L1 is overexpressed by many tumor types and generally correlates with progression and resistance to pro-apoptotic stimuli (Azuma et al, 2008; Gato-Canas et al, 2017; Juneja et al, 2017). PD-1 is expressed in antigen-experienced T cells and interferes with T-cell activation when engaged with PD-L1 (Chemnitz et al, 2004; Karwacz et al, 2011). The majority of advanced non-small-cell lung cancer (NSCLC) patients progressing from conventional cytotoxic therapies who receive PD-L1/PD-1 blockade therapy do not respond. The causes for these distinct clinical outcomes are a subject for intense research (Topalian et al, 2016). Emerging studies indicate that PD-L1/PD-1 blockade therapy does not only affect the tumor microenvironment, but also alters the systemic dynamics of immune cell populations (Hui et al, 2017; Kamphorst et al, 2017a,b; Krieg et al, 2018). Some of these changes do correlate with responses and could be used for real-time monitoring of therapeutic efficacy. For example, PD-1+ CD8 T cells expand systemically after PD-1 blockade therapy in lung cancer patients (Kamphorst et al, 2017a). As CD8 T cells are the main direct effectors of responses through cytotoxicity over cancer cells, these changes are thought to be the consequence of efficacious anti-tumor immunity. Indeed, CD8 T-cell infiltration of tumors correlates with good outcomes (Daud et al, 2016). However, the role of CD4 immunity in patients undergoing PD-L1/PD-1 blockade therapy remains poorly understood although extensive pre-clinical data link CD4 responses to anti-tumor immunity. Hence, CD4 T cells recognizing tumor neoepitopes contribute significantly to the efficacy of several types of immunotherapies in murine models and in cancer patients (Kreiter et al, 2015; Knocke et al, 2016; Sahin et al, 2017). Human T cells undergo a natural differentiation process following the initial antigen recognition, characterized by the progressive loss of CD27 and CD28 surface expression, and acquisition of memory and effector functions (Lanna et al, 2014, 2017). Hence, human T cells can be classified according to their CD27/CD28 expression profiles into poorly differentiated (CD27+ CD28+), intermediately differentiated (CD27negative CD28+), and highly differentiated (CD27negative CD28low/negative, THD) subsets (Lanna et al, 2014). Highly differentiated T cells in humans are composed of memory, effector, and senescent T cells, all of which could modulate anti-cancer immunity in patients and alter susceptibility to immune checkpoint inhibitors. To understand the impact of systemic CD4 and CD8 T-cell immunity before the start of immunotherapies, we carried out a discovery study in a cohort sample of 51 NSCLC patients undergoing PD-1/PD-L1 immune checkpoint blockade therapy after progression to platinum-based chemotherapy. Our results indicate that baseline functional systemic CD4 immunity is required for objective clinical responses to PD-L1/PD-1 blockade therapies. Results The baseline percentage of systemic CD4 THD cells within CD4 cells separates NSCLC patients into two groups with distinct clinical outcomes To study whether there was a correlation between specific systemic T-cell subsets and responses to anti-PD-L1/PD-1 immunotherapy in NSCLC patients, a prospective study was carried out in a cohort of 51 patients treated with PD-L1/PD-1 inhibitors (Table EV1). These patients had all progressed to conventional cytotoxic therapies and received immunotherapies as part of their treatments. 78.4% presented an ECOG of 0–1, 70.6% with at least three affected organs, and 25.5% with liver metastases (Table EV1). First, the percentages of CD4 T-cell differentiation subsets according to CD27/CD28 expression profiles were quantified within total CD4 cells in patients before the start of immunotherapies (baseline) from fresh peripheral blood samples and compared to healthy age-matched donors. Overall, cancer patients showed a significantly higher baseline percentage of CD4 THD cells than healthy controls (P < 0.001; Fig 1A). Furthermore, patients were separated into two groups by an approximate cut-off value of 40% CD4 THD cells (Fig 1A); we thus denominated "G1 cohort" to patients with more than 40% THD cells (63.25 ± 13.5%, N = 23) and "G2 cohort" to patients with less than 40% (27.05 ± 10.6%, N = 28). Differences between G1 and G2 cohorts were also highly significant (Fig 1A). Figure 1. Baseline profiling of CD4 T-cell differentiation subsets stratifies clinical responses to PD-L1/PD-1 blockade Percentage of circulating highly differentiated CD4 T cells within CD4 cells in age-matched healthy donors (N = 40) or NSCLC patients (N = 51) or NSCLC patients before undergoing immunotherapies. G1 and G2, groups of patients classified according to high THD cells (G1, > 40% CD4 THD cells) and low THD cells (G2, < 40% CD4 THD cells). Relevant statistical comparisons are shown by the test of Mann–Whitney. In green, objective responders (OR). In red, no OR. Below the graph, correlation of objective responses to G1 and G2 groups by Fisher's exact test. Waterfall plot of change in lesion size in patients with measurable disease classified as having a G1 (blue) or G2 (red) profile. Dotted lines represent the limit to define significant progression (upper line) or significant regression (lower line). ROC analysis of baseline CD4 THD quantification as a function of objective clinical responses. Kaplan–Meier plot for PFS in patients treated with immunotherapies stratified only by G1 (green) and G2 (red) CD4 T-cell profiles. Patients starting therapy with a G2 profile had an overall response rate (ORR) of 0 and 82% of them experienced progression or death by week 9. ORR was 44.8% for G1 patients, and the 12-week PFS was 50.2%. Source data are available online for this figure. Source Data for Figure 1 [emmm201910293-sup-0006-SDataFig1.pdf] Download figure Download PowerPoint Objective responders were found only within the G1 cohort (P = 0.0001), which included all patients that showed significant tumor regression (Fig 1A and B). Accordingly, ROC analysis demonstrated a highly significant association of the CD4 THD cell baseline percentage with objective responses (P = 0.0003) and confirmed the cut-off value of > 40% to identify objective responders with 100% specificity and 70% sensitivity (Fig 1C). A validation dataset from 32 patients was performed by parallel independent double-blind sample handling, staining, data collection, and analyses (Fig EV1). While in the discovery cohort T cells were directly analyzed from peripheral blood samples within the same day, validation samples were processed very differently. Briefly, an overnight depletion step of myeloid cells by adherence to plastic was included before T-cell analyses from non-adherent cells. Hence, relative percentages of CD4 THD cells varied between the discovery and validation cohorts. Even so, there was a significant agreement between the two datasets on patient classification as demonstrated by Cohen's kappa coefficient (κ = 0.932). The highly significant association between G1 patients and objective responses in the validation set was confirmed (P = 0.0006), albeit with a cut-off value of 20% in the validation dataset which was corroborated by ROC analysis (Fig EV1). Click here to expand this figure. Figure EV1. Validation dataset Distribution of circulating CD4 THD cells within CD4+ CD14negative cells in healthy donors (N = 14) and in NSCLC patients constituting the validation set (N = 32). G1 and G2 groups are indicated and separated by the mean (horizontal line). The means ± standard deviations of CD4 THD cells in G1 and G2 groups are shown on the right, as well as the association between G1 profiles and objective responses by Fisher's test. Differences between healthy donors and NSCLC patients were tested with the Mann–Whitney U test. ROC analysis of CD4 THD quantification in the validation dataset and objective responses. The cut-off value for identification of responses is shown in the graph. Download figure Download PowerPoint In agreement with these results, the G1 patient cohort had a significantly longer progression-free survival (PFS) compared to the G2 cohort. The median PFS (mPFS) of G2 patients was only 6.1 weeks (95% C.I., 5.7–6.6) compared to 23.7 weeks for G1 patients (95% C.I., 0–51.7; P = 0.001; Fig 1D). A comparison of G2 versus G1 baseline profiles showed hazard ratios for disease progression or death that favored the latter [3.1 (1.5–6.4; 95% C.I.) P = 0.002]. To assess whether CD4 T-cell profiling had prognostic value, the time elapsed from diagnosis to the start of immunotherapies was compared between G1 and G2 patient cohorts, as described (Le et al, 2015). No significant differences were observed, indicating that G1/G2 classification did not have prognostic value (Fig EV2). This was supported by no association between G1/G2 patient cohorts and baseline ECOG score (P = 0.6), with liver metastases (P = 0.88), with tumor load (P = 0.19), or with the Gustave-Roussy immune score (GRIm; P = 0.14, Table EV2; Bigot et al, 2017). The hazard ratio for progression or death of G2 patients maintained its statistical significance by multivariate analyses (HR 9.739; 95% CI 2.501–37.929) when adjusted for tumor histology, age, gender, smoking habit, liver metastases, number of organs affected, PD-L1 tumor expression, NLR, serum LDH, and albumin. Click here to expand this figure. Figure EV2. CD4 T-cell profiling does not have significant prognostic valueKaplan–Meier plot of relative time elapsed from diagnosis to the start of immunotherapy for G1 (blue) and G2 patient cohorts (red), as indicated. No significant differences were found. Source data are available online for this figure. Download figure Download PowerPoint Functionality of systemic CD4 immunity defines clinical outcomes and susceptibility to PD-L1/PD-1 blockade We hypothesized that the relative percentage of CD4 THD cells was a biomarker for functional differences in systemic CD4 immunity between the two cohorts before the start of immunotherapy. To find out whether this was the case, we first evaluated PD-1 expression in unstimulated CD4 T cells. However, no differences were observed between G1 and G2 patient cohorts or even with healthy age-matched donors (not shown). We then tested whether there were differences in PD-1 upregulation after ex vivo stimulation with lung cancer cells. To this end, we engineered a T-cell stimulator cell line by expressing a membrane-bound anti-CD3 single-chain antibody in A549 human lung adenocarcinoma cells (A549-SC3 cells). This cell line stimulated T cells in co-cultures with the same affinity and specificity while preserving other inhibitory interactions such as PD-L1/PD-1 or MHC II-LAG-3 (Fig EV3A and B). This ensured the same standard assay for cancer cell T-cell recognition for each patient (Fig EV3B–D). CD4 T cells from NSCLC patients significantly upregulated PD-1 compared to cells from age-matched healthy donors after incubation with A549-SC3 cells (P < 0.001; Figs EV3C and 2A). However, no differences were found between G1 and G2 patient cohorts. Coexpression of PD-1 and LAG-3 has been suggested to identify dysfunctional tumor-infiltrating lymphocytes in NSCLC (He et al, 2017). Interestingly, G2 donors presented a significantly higher percentage of CD4 T cells co-expressing both markers than G1 donors after stimulation (Fig 2B). To test whether there were also differences in proliferation, the percentage of Ki67+ cells was compared (Fig 2C and D). Accordingly, CD4 T cells from G2 patients were remarkably impaired in proliferation after ex vivo activation with A549-SC3 cells compared to T cells from G1 patients. As we had observed that G1 and G2 patient cohorts differed in baseline percentages of CD4 THD cells (Fig 1A), we tested whether this subset was responsive to activation by A549-SC3 cells (Fig 2D). Interestingly, CD4 THD cells strongly proliferated in all patients, although they constituted a minority in the G2 patient cohort. Click here to expand this figure. Figure EV3. Ex vivo human lung adenocarcinoma T-cell recognition system A. Top, lentivector co-expressing an anti-CD3 single-chain antibody gene (SC3) and blasticidin resistance for selection. SFFVp, spleen focus-forming virus promoter; UBIp, human ubiquitin promoter; LTR, long terminal repeat; and SIN, U3-deleted LTR leading to a self-inactivating lentivector. Bottom, molecular structure of the SC3 molecule, which is anchored to the cell membrane by a transmembrane domain as indicated. OKT3 VL, variable region of the light chain from the anti-CD3 antibody OKT3; VH, variable region of the heavy chain from the anti-CD3 antibody OKT3. B. Scheme of the cell-to-cell interactions mediated by the lentivector-modified A549 cell and T cells including SC3/CD3, PD-L1/PD-1, and MHCII/LAG-3 interactions as indicated. C, D. Representative flow cytometry density plots with the upregulation of PD-1 expression in CD4 (C) and CD8 T cells (D) from NSCLC patients following co-incubation with A549-SC3 cell as indicated (right graph), or with unmodified A549 control (left graph). Percentages of PD-1+ T cells are shown within the graphs. Download figure Download PowerPoint Figure 2. Differential systemic CD4 immunity and responses to PD-1/PD-L1 blockade in NSCLC patients The scatter plot shows PD-1 expression after co-culture of CD4 T cells from healthy donors (n = 9) or NSCLC patients (n = 14), as indicated, with A459-SC3 lung cancer cells. Relevant statistical comparisons with the test of Mann–Whitney are indicated. Upper graphs, flow cytometry density plots of PD-1 and LAG-3 co-expression in CD4 T cells from healthy donors, a G1 responder (G1 R), a G1 non-responder (G1 NR), and a G2 non-responder as indicated, following stimulation with A549-SC3 cells. Percentage of expressing cells are indicated within each quadrant. Below, same as in the upper graphs but as a scatter plot of the percentage of CD4 T cells that simultaneously co-express PD-1 and LAG-3 that simultaneously co-express PD-1 and LAG-3 in G1 healthy donors (n = 10), G1 (n = 10) and G2 (n = 10) patients. Relevant statistical comparisons are shown with the test of Mann–Whitney. Upper flow cytometry histograms of Ki67 expression in CD4 T cells from the representative subjects as indicated on the right, after stimulation with A549-SC3 cells. Vertical dotted line indicates the cut-off value of positive versus negative Ki67 expression. The percentage of Ki67-expressing CD4 T cells is shown within the histograms. Below, same data represented as a scatter plot from a sample of G1 and G2 donors as indicated, with relevant statistical comparisons with the test of Mann–Whitney (n = 7–10). Proliferation of CD4 T cells stimulated by A549-SC3 cells from the indicated patient groups. CD28 expression is shown together with the proliferation marker Ki67. Percentages of cells within each quadrant are shown. Same as in (D) but in the presence of an isotype control antibody or an anti-PD-1 antibody with the equivalent sequence to pembrolizumab. The effects on CD4 T cells from a G1 and a G2 patient are shown, divided into CD28 high or low/negative subsets as indicated. Relevant statistical comparisons are shown with paired Student's t-test. Top, flow cytometry density plots of Ki67 expression in CD4 T cells from representative G1 or G2 patients after three cycles of therapy, activated by incubation with A549-SC3 cells. Below, same as above but as a dot-plot graph (n = 7–10). A comparison between proliferating CD4 T cells before and after therapy is shown in unpaired patient samples. G1 R, G1 objective responder patient. G2 NR, G2 patient with no objective responses; green, objective responders (OR) and red, no OR; Iso, treatment with an isotype antibody control; and α-PD-1, treatment with anti-PD-1 antibody. Statistical comparisons were performed with the test of Mann–Whitney. Download figure Download PowerPoint The strong proliferative capacities of CD4 THD cells indicated that these were not exhausted, anergic, or senescent subsets, but probably highly differentiated memory subsets. To test this, their baseline phenotype according to CD62L/CD45RA surface expression was assessed in a sample of patients (Fig EV4A). The majority of CD4 THD cells were central-memory (CD45RAnegative CD62L+) and effector-memory (CD45RAnegative CD62Lnegative) cells, without differences between G1 and G2 cohorts. Increased genotoxic damage is strongly associated with T-cell senescence and can be evaluated by H2AX expression (Lanna et al, 2017). Interestingly, NSCLC CD4 T cells exhibited extensive genotoxic damage in both THD and non-THD subsets without differences between G1 and G2 patient cohorts, unlike T cells from age-matched healthy donors (Fig EV4B). Therefore, genotoxic damage did not identify senescent T cells in patients that had been treated with conventional therapies. Then, the expression of the replicative senescence marker CD57 was used to identify bona fide senescent T cells, which accounted to 30% of THD cells in healthy age-matched donors, and about 10% in NSCLC patients (Fig EV4C). Our results strongly suggested that circulating CD4 THD cells in our cohort of NSCLC patients mostly corresponded to non-senescent, non-exhausted memory subsets. Click here to expand this figure. Figure EV4. CD4 THD cells in NSCLC patients are mainly non-senescent memory subsets A. Scatter plot graphs of the percentage of memory phenotypes in baseline CD4 THD cells according to CD62L-CD45RA expression (% CD45RAnegative CD62Lpositive central-memory + % CD45RAnegative CD62Lnegative effector-memory cells) in a sample of healthy donors (n = 18), G1 (n = 12) and G2 (n = 19) patients. Relevant statistical comparisons are shown by one-way ANOVA followed by Tukey's test. B, C. Expression of the genotoxic damage makers H2AX (B) and CD57 (C) by flow cytometry in CD4 T-cell subsets from an aged-matched healthy donor, and NSCLC G1 and G2 patients as indicated. Percentage of positivity and mean fluorescent intensities are indicated for each population. Top, histogram analysis within CD27+ CD28+ CD4 T cells, and bottom, CD27negative CD28low/negative counterparts as indicated. US, unstained control. Download figure Download PowerPoint CD4 T cells of G2 patients strongly co-upregulated PD-1/LAG-3 after stimulation. We wondered if lack of clinical responses in G2 patients could be explained by resistance to single blockade of PD-1. Hence, proliferation of CD4 T cells activated with A549-SC3 in the presence of an anti-PD-1 antibody equivalent to pembrolizumab was assessed (Scapin et al, 2015; Fig 2E). As expected, PD-1 blockade increased proliferation of THD and non-THD CD4 T cells in patients from the G1 cohort. In contrast, their G2 counterparts were largely refractory. To find out whether CD4 T cells from G2 patients remained unresponsive to PD-1 blockade in vivo, cells were obtained from patients after at least three cycles of therapy and tested for their proliferative capacities (Fig 2F). Systemic CD4 T cells from G2 patients remained poorly proliferative during immunotherapy. Absence of cancer-specific CD4 T cells or systemic T-cell exhaustion is not behind the lack of objective clinical responses to PD-L1/PD-1 blockade therapies Then, we thought that G2 patients could be refractory to anti-PD-1 immunotherapy by not having systemic cancer-specific CD4 T cells. To this end, we quantified CD4 T cells reactive to lung adenocarcinoma antigens using IFN-γ-activated autologous monocyte-derived DCs as antigen presenting cells, as described (Escors et al, 2008). DCs were loaded with A549 cell lysate, as these cells contain numerous common lung adenocarcinoma antigens (Madoz-Gurpide et al, 2008). We used this approach as we lacked sufficient biopsy material to get tumor antigens or tumor-infiltrating T cells. CD4 T cells reactive to A549 cell antigens were identified by IFN-γ upregulation. Interestingly, lung cancer-specific CD4 T cells were present at varying proportions before the start of immunotherapy in both G1 and G2 patients (Fig 3A). Indeed, although the average percentages of circulating lung cancer-specific CD4 T cells were low
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