- Research article
- Open Access
CD45RA+CCR7− CD8 T cells lacking co-stimulatory receptors demonstrate enhanced frequency in peripheral blood of NSCLC patients responding to nivolumab
- Andre Kunert†1, 4Email author,
- Edwin A. Basak†2,
- Daan P. Hurkmans2,
- Hayri E. Balcioglu1,
- Yarne Klaver1,
- Mandy van Brakel1,
- Astrid A. M. Oostvogels1,
- Cor H. J. Lamers1,
- Sander Bins2,
- Stijn L. W. Koolen2,
- Astrid A. M. van der Veldt3,
- Stefan Sleijfer3,
- Ron H. J. Mathijssen2,
- Joachim G. J. V. Aerts†4 and
- Reno Debets†1Email author
© The Author(s). 2019
- Received: 19 September 2018
- Accepted: 2 May 2019
- Published: 8 June 2019
Checkpoint inhibitors have become standard care of treatment for non-small cell lung cancer (NSCLC), yet only a limited fraction of patients experiences durable clinical benefit, highlighting the need for markers to stratify patient populations.
To prospectively identify patients showing response to therapy, we have stained peripheral blood samples of NSCLC patients treated with 2nd line nivolumab (n = 71), as well as healthy controls, with multiplex flow cytometry. By doing so, we enumerated 18 immune cell subsets and assessed expression for 28 T cell markers, which was followed by dimensionality reduction as well as rationale-based analyses.
In patients with a partial response (PR), representing best overall response (BOR) according to RECIST v1.1, the number of CD8 T cells at baseline and during treatment is similar to those of healthy controls, but 2-fold higher than in patients with progressive and stable disease (PD and SD). CD8 T cell populations in PR patients show enhanced frequencies of T effector memory re-expressing CD45RA (TEMRA) cells, as well as T cells that express markers of terminal differentiation (CD95+) and egression from tumor tissue (CD69-). In PR patients, the fraction of CD8 T cells that lacks co-stimulatory receptors (CD28, ICOS, CD40L, 4-1BB, OX40) correlates significantly with the total numbers and differentiated phenotype of CD8 T cells.
This study demonstrates that high numbers of peripheral CD8 T cells expressing differentiation markers and lacking co-stimulatory receptors at baseline are associated with response to nivolumab in NSCLC patients.
- T cells
- Co-stimulatory receptors
The onset of T cell activation and differentiation, generally a consequence of the T cell receptor (TCR) recognizing its cognate antigen, is usually accompanied by up-regulated expression of co-inhibitory receptors such as programmed-death 1 (PD-1), proving a negative feedback mechanism to keep T cell activity ‘in check’ [1, 2]. Many types of cancer exploit this adaptive immunity and demonstrate high expression levels of co-inhibitory ligands such as PD-L1 to resist anti-tumor T cell responses. Clinical use of nivolumab, a monoclonal antibody targeting PD-1, showed promising results in metastatic melanoma , NSCLC [4, 5] as well as various other types of cancer . Collectively, however, study results reveal that only a limited subset of patients experiences durable clinical benefit . This highlights the need for markers that would identify patients prone to responding to treatment at an early time point and select these patients for extended treatment, thereby avoiding further exposure of patients with limited benefit to a potentially toxic and costly treatment.
Initial searches for predictive markers focused on the expression of PD-L1 [8, 9], but despite FDA approval for patient stratification based on PD-L1 expression in primary tumor tissue of NSCLC patients, interpretation of such immune stainings with respect to cell type and optimal cut off percentage remains challenging [10, 11]. Similarly, investigations assessing tumor mutational burden (TMB), mismatch repair deficiency (dMMR) and microsatellite instability (MSI) reveal that a high score on each of these markers correlates with enhanced responsiveness to anti-PD-1 therapy [12, 13], but on their own these markers may not be sufficiently discriminative to predict clinical response. Also, CD8 T cell density within tumor biopsies has been shown to predict anti-PD-1 response in patients diagnosed with advanced melanoma . Interestingly, local CD8 T cell immunity is affected by escape mechanisms , and profiles based on multiple immune parameters, such as the presence of effector cells, MHC molecules, suppressor cells, as well as immune and metabolic checkpoints provide predictive value exceeding that of single markers such as PD-L1 or mutational load [16–18]. However, limited availability of biopsy tissue and its invasiveness, especially in case of visceral tumors, often limits in situ determination of such markers. Multi-parameter analysis of immune cell subsets in blood is an easily employable screening method anticipated to reveal surrogate markers for clinical responses. Indeed, the absolute number of lymphocytes in blood samples correlates with clinical outcome in melanoma patients treated with ipilimumab, a monoclonal antibody targeting the co-inhibitory receptor CTLA-4 . And more recently, Ki67 expression in a subset of PD-1+ CD8+ T cells has been reported as a measure of effector T cell invigoration in patients with advanced melanoma and NSCLC who were treated with antibodies targeting the PD-1/PD-L1 axis [20–22].
In the current study, we have enumerated 18 immune cell populations and performed both cluster and selected analyses to assess differential frequencies of multiple T cell subsets using 28 markers of T cell activation, maturation, co-signaling and chemotaxis in NSCLC patients treated with 2nd line nivolumab in order to obtain prospective immune markers identifying those patients showing a clear response to therapy.
The MULTOMAB study (local ethics board study number MEC16–011) was originally designed by the Laboratory of Translational Pharmacology, Dept Medical Oncology at the Erasmus MC Cancer Institute (PIs: R. Mathijssen; J. Aerts and R. Debets). Patients asked to participate in the reported analysis are suffering from NSCLC and receiving treatment in the form of nivolumab (BMS936558, Opdivo®). Written informed consent was obtained from all participants prior to inclusion into the study.
Patients and collection of specimens
Data was prospectively collected from NSCLC patients treated with 3 mg/kg of nivolumab (intravenously every 2 weeks) between May 5th 2016 and November 1st 2017, with a minimum follow-up of three months. Patient characteristics are provided in Additional file 1: Table S1. Blood was drawn at 3 time points (pre-treatment (“baseline”) and prior to 2nd and 3rd administration of nivolumab (visits (V) 1 and 2). For an overview of patient treatment and sample acquisition, see Additional file 1: Figure S1. Freshly obtained, whole blood was used to enumerate immune cell populations, whereas PBMCs were isolated using ficoll gradient and stored using standard protocols and thawed at later time points to assess frequencies of T cell subsets. Healthy control samples were obtained from 15 donors that were matched with patients for age and gender-distribution (median age: 65 years (60–69); 6 female (40%) and 9 male (60%) donors) (Sanquin, Amsterdam, The Netherlands).
Assessment of tumor volume and clinical response
Baseline tumor burden was defined as the sum of the longest diameter of all target lesions. Best overall response (BOR) was assessed according to RECIST v1.1. Partial response (PR) was defined as a minimal decrease of 30% in the sum of diameters of the target lesions, taking as reference the sum of diameters at baseline, while progressive disease (PD) was defined as a minimal increase of 20% in the sum of diameters of the target lesions, taking as reference the smallest sum of diameters while on study and a minimal absolute increase of 5 mm. Stable disease (SD) was defined as insufficient change in tumor sizes to qualify for either PR or PD and if duration of SD was 90 days or more. Patients with non-measurable lesions were excluded from analysis. All three BOR response groups displayed similar medians and ranges with regard to age, sex and histology of primary lung tumor.
Whole blood was stained and after lysis of red blood cells analyzed by multi-color FCM on a BD 3-laser Celesta flow cytometer using FACSDIVA 8.x software. Absolute cell counts were determined using Flow-Count Fluorospheres (Beckman Coulter). Cryopreserved PBMC samples were thawed and stained with a master mix of antibodies. Please refer to Additional file 1: Table S2 for an overview of staining panels and utilized markers; all panels were optimized, compensated using Fluorescence minus one (FMO) controls and measurements were corrected for background fluorescence; a detailed list of antibodies is available upon request. Data were gated and analyzed using FlowJo software (Tree Star). Please refer to Additional file 1: Table S3 for an overview of our data analysis work scheme, in which dimensionality reduction analysis (tSNE, see below) preceded two-dimension (2D) analysis of selected markers. The latter analysis of large datasets was conducted using R.
T-distributed stochastic neighbor embedding (tSNE) analysis
tSNE analysis was performed using the Cytosplore software, with an interactive graphical user interface. CD8 T cell populations were extracted as individual .fcs files and imported into Cytosplore , where they were down-sampled to at most 1000 cells per sample, and tSNE analysis was performed on these 211,000 ± 6000 data points (cells from 71 patients, 3 time points each). Clustering was carried out with gradients of density plots, where first a threshold (sigma) of 26 was used, which provided 22 ± 8 clusters per combination of markers (see Additional file 1: Table S2, panels 2–6). This threshold was iteratively increased to a lower number of clusters in such a way that differential marker intensities were not compromised, providing a total of 12 ± 4 clusters per combination of markers. A total of 58 clusters was identified across all markers. The marker intensity profiles and contributions of individual BORs in these clusters were extracted from Cytosplore to excel sheets (Microsoft) for visualization.
tSNE-identified clusters were tested for differential abundance among BOR groups and time points using the Student’s T-test of the scipy stats package in python, while 2D analysis of selected markers was conducted using the Kruskal Wallis test. Descriptive statistics included median, standard deviation and range for continuous variables. For comparison of median differences between individual BOR groups the Mann–Whitney U test was used. For normally distributed data, significant changes of median cell numbers or frequencies within BOR groups over time were determined using two-sided, paired Student’s T-test. Correlations between continuous variables were determined by Pearson’s r coefficient. Differences were considered significant with a p-value below 0.05.
In this discovery study, experiments were not randomized and the investigators were not blinded to patient sample allocation during experiments and outcome assessment.
NSCLC patients with PR to nivolumab harbor normal, non-decreased numbers of CD8 T cell numbers in blood in contrast to PD and SD
PR patients show enriched frequencies of CD8 T cells with a phenotype that corresponds to enhanced T cell differentiation
When assessing CD8 T cell frequencies according to markers of proliferation and regulatory T cells in an identical manner (Additional file 1: Figures S3B and S4), we identified clusters with significant, albeit low intensity differences between BOR groups. However, neither frequencies of CD8 T cells expressing individual markers nor those expressing combinations of markers, such as CD25 and FOXP3, were differently present among BOR groups. Notably, frequency of CD4 regulatory T cells showed no difference between BOR groups (data not shown). Frequencies of CD8 T cells expressing the proliferation marker Ki67 either as a single marker or in combination with PD-1 did not show significant differences between BOR groups either. It is noteworthy, however, that we did observe a significant increase in frequency of Ki67+ CD8 T cells expressing PD-1 after onset of therapy in all BOR groups (Additional file 1: Figure S5) and that there was a positive correlation between frequency of Ki67+ within PD1+CD8 T cells and pre-treatment tumor volume of target lesions in NSCLC patient. This correlation, however, was not predictive of response to therapy.
PR patients show decreased frequencies of CD28+CD40L+ and CD28+ICOS+ CD8 T cells
In PR patients, the CD8 T cell differentiation phenotype coincides with a complete lack of co-stimulatory receptors
In this explorative study, we set out to discover potential immune markers in NSCLC patients that correspond with response to nivolumab therapy. The distribution of BOR in this prospective study of 71 patients is reflective of clinical outcome in large clinical trials with NSCLC patients [4, 5] with about 20% of treated patients showing response. Using our prospectively collected cohort of patients, we have enumerated immune cell populations and assessed clusters of T cell markers and frequencies of T cells subsets in blood samples drawn prior to and during therapy, using reference values from age- and gender-matched healthy controls.
Most studies evaluating systemic immune profiles generally rely on frozen PBMC samples, resulting in a bias towards immune cell populations that show high stability throughout the freeze/thaw procedure . To address this issue, we have determined numbers of 18 different immune cell populations in freshly obtained blood. Amongst the significant differences in numbers of major immune cell populations between the three BOR groups, we detected a general increase in numbers of eosinophils during nivolumab therapy. Such an increase in peripheral eosinophils has previously been identified as a prognostic marker for survival in metastatic melanoma patients treated with various types of immune therapy . However, increase in eosinophils was not associated with BOR in our NSCLC cohort as this increase occurred irrespective of BOR. At baseline, only immature neutrophils and T cells, in particularly CD8 T cells, showed differences among BOR groups. The increased number of immature neutrophils in SD patients is interpreted with caution since this finding may have been the result of exclusion of several outliers in this particular BOR group at baseline, part of our downstream analysis, which may have reduced the spread in this immune cell subset. The reduced number of CD8 T cells in SD and PD patients prior to therapy on the other hand shows a relatively low spread and is consistent over time. The latter observation may explain the lack of responsiveness to therapy and is supported by previous findings of reduced numbers of T cells (CD45+CD3+) during immune checkpoint inhibition . Besides therapy-induced changes, we also observed changed numbers of immune cell populations at baseline when compared with healthy controls. Increased numbers of mature neutrophils and monocytes correspond with an inflamed tumor microenvironment that may drive the proliferation of these cells and their detection in the periphery . Also, our finding of increased numbers of M-MDSCs is in line with multiple reports, and may be of interest since these cells have been described as main suppressors of immune responses [27, 28]. The role of activated NK cells (expressing MIP-1β and CD69) in the context of anti PD-1 therapy of melanoma patients has recently been highlighted by Hodi and colleagues . These authors observed increased frequencies of these cells as well as NK cells in patients showing response to therapy. Important to note that numbers of neutrophils, M-MDSCs, B or NK cells, neither by themselves nor in combination with other immune cell populations, did correlate with BOR in the present study, indicating that immune response in NSCLC patients may be mostly driven by T cells, rather than NK, B or other effector cells.
To follow-up on the different CD8 T cell numbers, we conducted a dimensionality reduction as well as 2D analyses to identify marker combinations and T cell subsets. Notably, we observed that reduced numbers of CD8 T cells in SD and PD patients were not due to changed frequencies of CD8 regulatory T cells nor a general lack of T cell proliferation (Additional file 1: Figure S4). Although the presence of CD4 Treg cells within the tumor microenvironment has been described as a potential driver of tumor immune escape (reviewed in ), peripheral frequencies of this subset may not be sufficiently reflective of local conditions. An increase in the frequency of PD-1+ CD8 T cells and an enhanced frequency of PD-1+ CD8 T cells that express Ki67 has previously been observed in NSCLC patients undergoing anti-PD-1/anti-PD-L1 therapy [20, 31]. Similar to this study, we found an increase in PD-1+ CD8 T cells expressing Ki67, yet no correlation between their frequencies after onset of therapy and the clinical response according to RECIST1.1 (see Additional file 1: Figure S5). Huang and colleagues demonstrated that the ratio between Ki67+PD-1+ CD8 T cells and pre-therapy tumor burden was indicative of a clinical response of melanoma patients to pembrolizumab . While we observed a similar correlation between 1D tumor measurements and frequencies of Ki67+PD-1+ CD8 T cells, albeit to a lower degree (see Additional file 1: Figure S5C), we were unable to demonstrate this ratio to be of discriminatory value among BORs in our NSCLC patient cohort. Although we cannot exclude that increased frequencies of Ki67+PD-1+CD8 T cells depend on tumor type, mutational load and/or certain patient subgroups, our findings do argue that further studies are required to better define how the Ki67 marker relates to clinical response to checkpoint inhibition. When conducting similar tSNE and 2D analysis of chemo-attractant receptors, we observed that the frequency of CD8 T cells expressing such receptors did not yield differences between BOR groups or time points (Additional file 1: Figure S6).
When looking into maturation states of T cells, we detected significantly higher frequencies of CD45RA+CCR7− CD8 T cells, a phenotype often related to terminal T cell differentiation , in PR patients compared to PD patients at baseline and during treatment. Moreover, in PR patients we observed higher frequencies of CD95+CD69− CD8 T cells. While CD95 has been recognized for FAS-mediated apoptosis, there is evidence for FAS-mediated T cell proliferation and differentiation as well . High numbers of CD95+ CD8+ tumor infiltrating lymphocytes have previously been demonstrated to have predictive value in breast cancer patients  and an enhanced frequency of CD95+ T cells in blood of stage IV melanoma patients has been reported to associate with clinical response upon anti-PD-1 treatment . CD69 is an early activation marker that shows a rapid and transient upregulated expression upon TCR-mediated activation of CD8 T cells. Additionally, CD69 has been described as a tissue retention marker, indicating that down-regulated expression of CD69 coincides with egress of T cells into the blood flow . Therefore, the observed changes, with respect to both CD45RA+CCR7− and CD95+CD69− CD8 T cell phenotypes, may be a consequence of local antigen encounter, T cell differentiation, and tissue egression of CD8 T cells in PR patients. Interestingly, these findings are nicely in line with recent observations by Gide and colleagues showing that differentiated effector memory T cells are more abundant in melanoma patients who respond to PD1 and CTLA-4 antibody treatment . Further evidence for enhanced T cell differentiation in PR patients comes from the observation that the frequency of the mentioned phenotypes highly correlates with the frequency of CD8 T cells expressing CD57, another marker of terminal exhaustion upon antigen encounter . Lastly, other CD8 T cell phenotypes that have been reported to relate to late T cell differentiation, such as lack of the co-stimulatory receptor CD28 and co-expression of PD-1 and TIM3, also show enhanced frequencies in PR patients (discussed below). Analysis of co-signaling receptors revealed that clear differences between BOR groups are particularly related to a CD8 T cell subset lacking the co-stimulatory receptors CD28, ICOS, CD40L, 4-1BB and OX40. Interestingly, PR patients show an increased frequency of CD8 T cells lacking co-stimulatory receptors, in particular CD28 and CD40L or CD28 and ICOS. Moreover, the frequency of CD28+ CD8 T cells showed a high and inverse correlation with the frequency of CD8 T cells lacking co-stimulatory receptors (Fig. 6). While expression of CD28 is a pre-requisite for proper activation of T cells, the absence of this receptor has been described as part of a negative feedback loop following long-term antigen stimulation , and fits the above-described phenotype of antigen-exposed and differentiated CD8 T cells. Further substantiating the premise that a higher frequency of CD8 T cells in PR patients have encountered antigen, is our observation that these patients contain higher frequencies of PD-1+TIM3+ CD8 T cells at baseline (see Fig. 5c). The combination of these two receptors has been well described as a sign of activation-mediated T cell differentiation and potentially exhaustion [39–41]. Moreover, in patients with squamous cell carcinoma of the head and neck, recent studies showed that PD-1+TIM3+ CD8 T cells that lack CD28 and CD27 were able to suppress proliferation of autologous peripheral blood T cells ex vivo . Of interest, the presence of intra-tumoral PD-1+ CD8 T cells expressing the transcription factor Tcf has been related to tumor control in response to immunotherapy [43, 44] and these T cells may harbor stemness and yield T cells that are more differentiated. Since PD-1 primarily intervenes with CD28 co-signaling, rather than TCR signaling itself [20, 45], we cannot exclude that the frequency of CD28+ T cells that co-express Ki67 and PD-1 becomes enhanced upon treatment with checkpoint inhibitor. Along these lines, it is striking that the frequency of CD8 T cells devoid of multiple co-stimulatory receptors is highest in PR patients at baseline and throughout therapy, and correlates with the total number of CD8 T cells as well as frequencies of CD8 T cells showing a CD45RA+CCR7− phenotype.
In conclusion, we found that NSCLC patients with a PR upon treatment with nivolumab demonstrate enhanced numbers of CD8 T cells and a phenotype corresponding with late differentiation at baseline. Collectively, our findings argue that a large fraction of CD8 T cells in PR patients has been exposed to tumor antigen and subsequently matured and egressed into the bloodstream. This enhanced CD8 T cell differentiation was accompanied by a higher frequency of PD-1 and TIM3 and a complete loss of co-stimulatory receptors. We propose that a panel comprising the markers CD45RA, CCR7, CD95, CD69, CD57, PD-1 as well as CD28, CD40L, and ICOS should be validated in larger cohorts of patients and used to develop a model aiding in the identification of NSCLC patients prone to show tumor regression upon anti-PD-1 therapy. While novel approaches are emerging that include assessment of tumor material with regard to T cell exclusion and exhaustion , to our knowledge this is the first description of peripheral immune markers able to identify NSCLC patients showing response to nivolumab treatment prior to onset of therapy (see Additional file 1: Figure S7 for a schematic overview of our findings).
This research was entirely funded by the Departments of Medical Oncology and Pulmonary Diseases, Erasmus MC, Rotterdam, The Netherlands.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the senior authors on reasonable request.
A.K. was involved in design of study, conduction of experiments, acquisition of data, analysis of data, interpretation of data and writing of manuscript. E.B. was involved in providing patient samples, conduction of experiments, acquisition of data, analysis of data, interpretation of data and writing of manuscript. D.H. was involved in providing patient samples, acquisition of data, analysis of data and writing of manuscript. E.H.B. was involved in dimensionality reduction analysis of data and writing of manuscript. Y.K. was involved in acquisition of data, analysis of data and interpretation of data. M.B. was involved in conduction of experiments, acquisition of data and analysis of data. A.O. was involved in conduction of experiments, acquisition of data and analysis of data. C.L. was involved in design of study, interpretation of data and writing of manuscript. S.B. and S. K were involved in design and setting up of study logistics. A.V. was involved in recruitment of patients, interpretation of data and writing of manuscript. S.S. was involved in interpretation of data and writing of manuscript. R.M. was involved in design of study, financing of project, interpretation of data and writing of manuscript. J.A. was involved in design of study, recruitment of patients, financing of project, interpretation of data and writing of manuscript. R.D. was involved in design of study, financing of project, interpretation of data and writing of the manuscript, and supervision of the onco-immunology part of this study. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The MULTOMAB trial that forms the basis of the research presented here was approved by the Erasmus MC Ethics Committee and is internally registered under EMC16–011.
Consent for publication
The authors declare no potential conflicts of interest.
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