Circulating T-Cell Immunosenescence in Advanced Non-Small Cell Lung Cancer Patients Treated with Single Agent PD-1/PD-L1 Inhibitors Or Platinum-Based Chemotherapy

Author Manuscript Published OnlineFirst on September 4, 2020; DOI: 10.1158/1078-0432.CCR-20-1420 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Circulating T-cell immunosenescence in advanced non-small cell lung cancer patients treated with single agent PD-1/PD-L1 inhibitors or platinum-based chemotherapy

Roberto Ferrara1,2,3,4 *, Marie Naigeon1,5 *, Edouard Auclin6, Boris Duchemann1, Lydie Cassard1, Jean M. Jouniaux1, Lisa Boselli1, Jonathan Grivel1, Aude Desnoyer1, Laura Mezquita2, Matthieu Texier7, Caroline Caramella8, Lizza Hendriks2,9, David Planchard2, Jordi Remon2, Sabina Sangaletti4, Claudia Proto3, Marina C. Garassino3, Jean-Charles. Soria5, Aurelien Marabelle10, Anne-Laure. Voisin11, Siham Farhane11, Benjamin Besse5,12 ǂ, Nathalie Chaput1,13 ǂ

1Gustave Roussy Cancer Campus, Laboratory of Immunomonitoring in Oncology, CNRS-UMS 3655 and INSERM-US23, Villejuif, F-94805, France

2 Department of Cancer Medicine, Gustave Roussy, Villejuif, France

3 Department of Medical Oncology, Thoracic Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy

4 Department of Research, Molecular Immunology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy

5 University Paris-Saclay, Faculty of Medicine, Le Kremlin Bicêtre, F-94276, France

6 Department of Hepato-Gastroenterology and Gastrointestinal Oncology, Sorbonne Paris-Cité, Paris Descartes University, Hôpital Européen Georges Pompidou, Paris, France.

7 Biostatistics and Epidemiology Department, Gustave Roussy, Villejuif, France

8 Radiology Department, Gustave Roussy, Villejuif, France

9 Department of Pulmonary Diseases GROW - School for oncology and developmental biology, Maastricht University Medical Center, Maastricht, the Netherlands

10 Departement d'Innovation Thérapeutique et d'Essais Précoces, Gustave Roussy, Université Paris-Saclay, Villejuif, F-94805, France

11Gustave Roussy, Université Paris-Saclay, Unité de Pharmacovigilance, Villejuif, F-94805, France.

12 Gustave Roussy, Cancer Campus, Department of Medicine, Villejuif, F-94805, France

13 University Paris-Saclay, Faculté de Pharmacie, Chatenay-Malabry, F-92296, France

*These authors contributed equally to this work.

‡These authors contributed equally as senior and corresponding authors.

*Correspondence to: Prof. Nathalie Chaput, Laboratory of Immunomonitoring in Oncology at Gustave Roussy 114 rue Edouard Vaillant 94805 Villejuif France, Tel (+33)1 42 11 42 11 email 1

Downloaded from clincancerres.aacrjournals.org on September 24, 2021. © 2020 American Association for Cancer Research. Author Manuscript Published OnlineFirst on September 4, 2020; DOI: 10.1158/1078-0432.CCR-20-1420 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

[email protected] ; Prof. Benjamin Besse, Department of Medicine at Gustave Roussy 114 rue Edouard Vaillant 94805 Villejuif France, Tel (+33)1 42 11 42 11 email [email protected]

Keywords: immunosenescence, immune age, PD-1 inhibitors, non-small cell lung cancer, circulating immune phenotype, platinum-based chemotherapy

Running title: Immunosenescence in NSCLC

Translational relevance A senescent immune phenotype (SIP) on circulating T-lymphocytes, defined as high level of circulating CD28-CD57+KLRG1+CD8+ T-cells, was identified at baseline in 28% of advanced NSCLC patients and validated in an independent cohort. Circulating T-cell senescence correlated with progression, hyperprogressive disease and poor survival upon ICI. These findings together with the lack of any predictive and prognostic role of T-cell senescence in a control cohort of NSCLC patients receiving platinum-based chemotherapy suggest that immunosenescence is a reliable and reproducible biomarker of progression specifically for immunotherapy treatment. The correlation of T-cell immunosenescence with specific circulating immunephenotypes (higher circulating terminally differentiated T-cells, T-cytotoxic 1, T-helper 1 and Ox40+ T-regulatory cells), the limited proliferative capacity and the sustained increased inflammation associated with senescent CD8+ T-cells potentially explain the negative impact of immunosenescence on ICI treatment. Our results provide new insights on T-cell immunosenescence as a novel circulating biomarker of progression to single agent ICI in NSCLC patients.

Abstract

Purpose: CD28, CD57 and KLRG1 have been previously identified as markers of T-cell immunosenescence. The impact of immunosenescence on anti-PD(L)-1 (ICI) or platinum-based chemotherapy (PCT) in advanced NSCLC (aNSCLC) patients is unknown. Experimental design: The percentage of CD28-, CD57+, KLRG1+ among CD8+ T-cells (SIP) was assessed by flow cytometry on blood from aNSCLC patients before single-agent ICI (discovery cohort). A SIP cut-off was identified by log-rank maximization method and aNSCLC patients treated with ICI (validation cohort) or PCT were classified accordingly. Proliferation and functional properties of SIP+ CD8+ T-cells were assessed in vitro. Results: In the ICI discovery cohort (N=37), SIP cut-off was 39.5%, 27% of patients were SIP+. In the ICI validation cohort (N=46), SIP+ status was found in 28% of patients and significantly correlated with worse ORR (0% vs 30%, p=0.04), median (m) PFS [1.8 (95% CI 1.3-NR) vs 6.4 (95% CI 2-19) months, p=0.009] and mOS [2.8 (95% CI 2.0-NR) vs 20.8 (95% CI 6.0-NR) months, p=0.02]. SIP+ status was significantly associated with circulating specific immunephenotypes, in- vitro lower CD8+ T-cells proliferation, lower IL-2 and higher TNF and IFN production. In the ICI- pooled population (N=83), SIP+ status did not correlate with any clinical characteristics and it was associated with significantly worse ORR, PFS and OS. In PCT cohort (N=61), 11% of patients were SIP+. SIP status did not correlate with outcomes upon PCT. Conclusion: Circulating T-cell immunosenescence is observed in up to 28% of aNSCLC patients and correlates with lack of benefit from ICI but not from PCT.

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Introduction

Immunosenescence is a global remodeling of immune functions which involves both adaptive and innate immunity and is related to the chronic antigenic stimulation occurring throughout life (1,2). T-cell immunosenescence focuses on the phenotypic characteristics of lymphocytes and refers mainly to a low proliferative activity, whereas, the functions of senescent T- lymphocytes and other immune cells do not necessarily decrease (3). Different markers have been associated with low replicative potential and senescence in T-lymphocytes. The loss of CD28 (4) and the gain of CD57 (5,6) and killer-cell lectin-like receptor (KLRG1) (7) in CD8+ T-lymphocytes correlated with lower proliferation and shorter telomeres, with oligoclonal T-cell receptor (TCR) repertoire and reduced capacity to recognize antigenic diversity (8). T-cell immunosenescence has been described as a multistep process where, under persistent antigenic exposure, T-cells acquired a terminal differentiation status (re-expressing CD45RA) (5) and in some preclinical models also resistance to apoptosis (9). Although both senescent and exhausted T-cells are characterized by low replicative potential and could share some common phenotypic features (expression of PD-1 and/or loss of CD28), they may engage different pathways to induce cell cycle arrest (10). Furthermore, severely exhausted T-cells are mainly dysfunctional (11) while senescent T-lymphocytes retain their cytotoxic potential and ability to secrete high levels of cytokines and soluble factors, mainly TNF and IFN (12,13). The expansion of low replicative, proinflammatory and oligoclonal senescent T-cells occurring upon persistent antigenic stimulation (i.e. due to aging, tumor, chemotherapy, chronic inflammation or infections) may negatively affect treatment outcomes with ICI in advanced cancer patients. To explore if T-cell immunosenescence is associated with age, chemotherapy exposure or patients’ clinical characteristics and to define its impact on efficacy from single agent PD-1/PD- L1 inhibitors (ICI) in advanced non-small cell lung cancer (aNSCLC) patients, we assessed a senescent immune phenotype (SIP) (% CD28-CD57+KLRG1+ among circulating CD8+ T-cells) at baseline in a discovery cohort of ICI treated patients. The SIP cut off generated in the discovery cohort was subsequently tested in a validation cohort at baseline and during treatment with ICI. To investigate if T-cell immunosenescence may also affect chemotherapy outcomes, we assessed SIP in a control cohort of treatment naïve aNSCLC receiving platinum-based chemotherapy (PCT).

Patients and treatments

Data were collected from consecutive aNSCLC patients enrolled in PREMIS (2018-A01257- 48) (ICI discovery cohort), CEC-CTC (NCT02666612) (ICI validation cohort) and MSN (NCT02105168) (PCT cohort) prospective studies. These studies allowed the collection of aNSCLC patients’ clinical information and fresh whole blood samples for research purposes (maximum 10 samples in 3 years for each patient), after signature of informed consent. All these 4

studies were approved by the institutional review board and the ethical committee of Gustave Roussy and were conducted in accordance with ethical principles for medical research involving human subjects reported in the Declaration of Helsinki. In the ICI cohorts, aNSCLC patients were treated with single agent ICI from July 2019 to March 2020 (discovery cohort) or from March 2017 to June 2018 (validation cohort). In the control cohort, treatment naïve aNSCLC patients received PCT from September 2017 to July 2018. To be eligible, patients had to be 18 years or older, with histologically or cytologically confirmed stage III or IV NSCLC and available fresh blood samples right before PCT and/or single agent ICI. All radiological evaluations were centrally reviewed by a senior radiologist. Tumour response was assessed by Response Evaluation Criteria in Solid Tumours (RECIST) v.1.1 (14). Objective response rate (ORR) was defined as the sum of Complete (CR) and partial (PR) response, disease clinical benefit (DCB) as CR/PR and stable disease (SD) lasting at least 6 months. Atypical patterns of progression (PD), such as pseudo-progression, defined as initial PD, followed by CR/PR or SD lasting more than 6 months (15) and hyperprogressive disease (HPD) defined as RECIST v1.1 PD at first CT scan during treatment and delta-Tumor Growth Rate (TGR) (16) (computed as previously reported) ≥ 50% (17), were also assessed. HPD was assessed only for patients with measurable disease on two CT scans before and one during treatment.

Flow cytometry and functional characterization of T-cell senescence

The procedures to perform blood immune phenotyping on fresh whole blood samples, functional experiments in human peripheral blood mononuclear cells (PBMCs) and flow cytometry antibodies and fluorochromes used are described in the Supplementary Methods and Table S1, respectively. SIP was measured as percentage of CD28- CD57+ KLRG1+ among CD8+ circulating lymphocytes. Flow cytometry gating strategy for T-cell immunosenescence is shown in Figure S1. Unsupervised analysis of flow cytometry data was performed using t-distributed stochastic neighbor embedding (t-SNE) algorithm with the online R software (version 3.5.0, cytofkit package). After setting the compensation matrix, CD3+ cells events were extracted and logicle transformation was applied. t-SNE analysis was achieved on 7940 CD3+ cells for each sample, using “T-cell senescence” panel markers. Supervised analysis of flow cytometry data was performed using Kaluza Flow Cytometry Software (Beckman Coulter) and was done by a single operator, blinded to the clinical patients’ information. Percentages of each CD4+ or CD8+ subsets was calculated in total CD8+ or CD4+ T cells. Hierarchical clustering of flow cytometry data was performed by Morpheus software. Unsupervised cell populations analysis was performed by hierarchical clustering using euclidean distance on percentages of immune populations after data adjustment by z-score (Morpheus software, https://software.broadinstitute.org/morpheus). PBMCs cultures were established from aNSCLC patients at baseline to perform functional experiments.

Graphs for flow cytometry data were performed by GraphPad Prism 7.0 or 8.0 (GraphPad Software, San Diego, CA).

Statistical analysis

To better stratify progression free survival (PFS) risk an optimal cut-off for SIP level was chosen on the base of maximization of log-likelihood ratio method as proposed by Hothorn et al. (18) and patients were dichotomized accordingly. Associations between SIP and categorical or continuous variables were performed by logistic regression/Fisher exact test or Mann-Whitney test, respectively. For longitudinal analyses (before and after ICI), Wilcoxon matched-pairs signed rank test was used. Overall survival (OS) and PFS curves were estimated with the Kaplan-Meier method and compared by the log-rank test. Median follow-up (FU) was estimated by inverse Kaplan-Meier method. Cox proportional hazards regression model was used to estimate hazard ratio (HR) and to perform multivariable analysis adjusting for potential cofounding effect of variables associated with outcomes from single agent ICI. All p values were 2 sided, and values less than .05 were considered statistically significant. Statistical analyses were performed using R (free software environment for statistical computing and graphics). Analyses were reported according to REMARK guidelines for prognostic studies (19).

Results

Patients’ characteristics

Overall, SIP analysis was performed at treatment baseline in 144 consecutive aNSCLC patients. Number of patients evaluable for responses and flow cytometry analyses in each cohort are reported in Figure S2. In the single agent ICI discovery cohort (n=37), the median follow-up (FU) was 6.9 (95% CI 5.4-9.2) months, ORR was 22% (8/37) and among 33 patients having a FU longer than 6 months DCB was 18% (6/33). Median PFS and OS were 1.7 (95% CI 1.3-3) months and NR (95% CI 3.2- NR), respectively. In the single agent ICI validation cohort (n=46), median follow-up (FU) was 26.6 (95% CI 24.8-29.9) months. Among 43 patients evaluable for response, the ORR was 21% (9/43), DCB was 37% (16/43). One (2%) of 43 patients had pseudo-progression, 27 (63%) of 43 patients had measurable disease on at least 3 CT scans (2 before and one during ICI) and were eligible for TGR analysis, among them 4 (14.8%) were classified as HPD. Median PFS and OS were 5.2 (95% CI 1.9-8) months, 12.7 (95% CI 5-24.2) months, respectively.

The main patients’ characteristics of ICI discovery and validation cohort are listed in Table S2. Clinical characteristic did not significantly differ between cohorts with the exception of PD-L1 inhibitors administration which was less frequent in the validation compared to the discovery cohort. Median OS and PFS also did not significantly differ between discovery and validation cohorts (Figure S3). Overall, in the pooled ICI cohort (n=83), ORR and DCB occurred in 21% (17/80) and 29% (22/76) of evaluable patients, respectively. At a median FU of 22.1 (95% CI 11-26.6) months, median PFS and median OS were 2.01 (95% CI 1.7-6.2) and 12.3 (95% CI 5.5-22.8). In the control cohort, SIP analysis was performed in 61 consecutive treatment naïve aNSCLC patients eligible for PCT. The main patients’ characteristics are reported in Table S3. Median FU was 26.2 (95% CI 23.9-29.1) months, ORR was 39% (24/61), DCB was 36% (22/61). Median PFS and OS were 5.1 (95% CI 4.2-7.8) months and 15.1 (95% CI 7.9-NR) months, respectively.

SIP cut off and association with patients' outcomes and clinical characteristics

t-SNE algorithm, performed on the first consecutive 4 patients with PD (non-DCB) to ICI and 4 patients with CR/PR or SD lasting at least 6 months (DCB) upon ICI, showed that patients who progressed had lower density of CD4+ and CD8+ T-cells expressing CD28 and higher density of CD8+ and CD4+ T-cells expressing CD57 and KLRG1 at baseline, compared to patients with long lasting PR/CR/SD (Figure 1). Therefore, the assessment of CD28, CD57 and KLRG1 expression on circulating T-cells by supervised analysis of flow cytometry data (Figure S1) was subsequently extended to a larger set of patients in order to validate these preliminary findings. In the ICI discovery cohort (n=37), SIP median value was 23.8% (min 1.4%, max 66.5%) (Figure S4). 39.5% was the cut off computed by log-rank maximization method (Figure S5A). At this value, the HR for PFS overcame the threshold of one and continued to increase with a proportional higher risk for patients to experience progression or death (Figure S5B). The specificity and sensitivity of this cut off were 100% and 35%, respectively. 27% (10/37) patients had >39.5% CD28-CD57+KLRG1+ among CD8+ lymphocytes, being classified as SIP+. SIP+ patients had significantly worse median PFS [1.3 (95% CI 0.9-NR) vs 1.8 (95% CI 1.4-NR) months, p=0.03] compared to SIP- patients (Figure S6 left panel). Median OS did not significantly differ between SIP+ and SIP- patients [3.2 (95% CI 2.2-NR) vs NR (95% CI 5.5-NR), p=0.2] (Figure S6 right panel), probably due to the short median FU [6.9 (95% CI 5.4-9.2) months]. In the validation cohort (n=46), SIP median value was 21.8% (min 2.3%, max 63.7%) (Figure S4). 28% (13/46) of patients were SIP+ according to the 39.5% cut off previously identified in the discovery cohort. SIP+ patients had significantly lower ORR (0% vs 30%, p=0.04) and DCB (8% vs 50%, p=0.01) compared to SIP- patients (Figure S7). Furthermore, 3 out of 4 HPD patients

were SIP+, having from 47.7% to 63.7% of senescent circulating CD8+ T-lymphocytes (Figure S8) and HPD was more frequent in SIP+ compared to SIP- patients (50% vs 4.5%, p=0.02). SIP+ patients had significantly worse median PFS [1.8 (95% CI 1.3-NR) vs 6.4 (95% CI 2-19) months, p=0.009] (Figure S6 left panel) and median OS [2.8 (95% CI 2.0-NR) vs 20.8 (95% CI 6.0-NR) months, p=0.02] compared to SIP- patients (Figure S6 right panel). In the ICI pooled population (n=83), SIP median value was 22.9 % (min 1.4%, max 66.5%) (Figure S4). 28% (23/83) had >39.5% CD28- CD57+ KLRG1+ among CD8+ circulating lymphocytes, being classified as SIP+. SIP was not significantly associated with age, PD-L1 expression on tumor cells, immune related adverse events (irAEs) rate, previous chemotherapy exposure, cytomegalovirus (CMV) antibody positivity or any other clinical characteristics (Table 1). SIP+ patients had significantly lower ORR (0% vs 30%, p=0.002) and DCB (4% vs 40%, p=0.002) (Figure 2A) compared to SIP- patients. SIP+ patients had significantly worse PFS [1.7 (95% CI 1.3-2.8) months vs 3.8 (95% CI 1.8-10.3), HR 2.4 (95% CI 1.4-4.2), p<0.0001,] (Figure 3A, left panel) and OS [3.1 (95% CI 2.4-13.3) vs 20.8 (95% CI 8.0-NR), HR 2.3 (95% CI 1.25-4.2), p=0.007)] (Figure 3A, right panel) compared to SIP- patients. In a multivariate Cox regression model, SIP+ status remained associated with worse OS [HR 2.4 (95% CI 1.2-5.0), p=0.02] (Table S4A) and worse PFS [HR 2.2 (95% CI 1.2-4.1), p=0.01] (Table S4B) after adjustment for several factors (i.e. performance status, number of metastatic sites, lung immune prognostic index, type of ICI, line of ICI and previous chemotherapy exposure). In the PCT cohort, SIP median value was 22.5% (min 0.8%, max 76.5%) (Figure S4), 11% (7/61) of patients had >39.5% CD28-CD57+KLRG1+ among CD8+ lymphocytes being classified as SIP+. As in the ICI pooled population, SIP did not significantly correlate with age or other clinical characteristics (Table S3). SIP+ patients had similar ORR (29% vs 41%, p=0.69), DCB (71% vs 31%, p= 0.09) (Figure 2B), PFS [11.7 months (95% CI 4.8-NR) vs 5 months (95% CI 4.1-6.9), HR 0.39 (95% CI 0.14-1.1), p=0.07] (Figure 3B left panel) and OS [NR (95% CI 11.5-NR) vs 13.9 months (95% CI 7.1-23.7), HR 0.35 (95% CI 0.1-1.4) p=0.15] (Figure 3B right panel) compared to SIP- patients.

T-cell immunosenescence is associated with specific circulating immune phenotypes, low proliferation and functional activation.

At the time of data analysis, polarization panel, activation/T-regulatory (Treg) panel and dynamic evolution of SIP after ICI start were performed in 40 (87%), 22 (48%) and 21 (46%) of 46 ICI-treated patients, respectively (Figure S2). Polarization panel allowed a phenotypic + + characterization of CD4 T-helper (TH) and CD8 T-cytotoxic (TC) cell subsets (TH/C1, TH/C2, TH/C9,

TH/C17, TH/C17.1, TH/C17 double negative, TH/C17 double positive and TH/C22; Table S5). Hierarchical clustering showed that high proportion of terminally differentiated (TEMRA) CD4+ and

+ + + CD8 T-cells, lower naïve CD8 T-cells and higher TH1 and TC1 cells clustered with SIP (Figure 4A). These populations were significantly associated with SIP+ status (Figure 4B). Activation/Treg panel allowed a phenotypic characterization of activation markers on conventional and regulatory T-cells. SIP+ patients had significantly increased activated OX40+ Treg cells compared to SIP- (Figure 4C) [mean 6.16%; standard deviation (SD) (4.5) vs 2.15%; (1.6), p=0.01]. Variable PD-1 expression was observed among SIP+ CD8 T-cells [mean: 39.3%; SD (24.0)] (Figure S9A) and did not significantly differ according to DCB upon ICI (Figure S9B). Pre and post comparison of SIP+ population did not show any significant variation during ICI treatment in 21 patients with available SIP analysis performed between 28 and 65 days (D) after ICI start (Figure S10A). Among these 21 patients, 9 did not progress while 12 had progression as best response to ICI. No statistically significant variation of SIP+ status, assessed as % SIP+ + population at D28/65 minus % SIP population at D0, was found in non-progressing compared to progressing patients. No statistically significant variations of immunosenescence markers, (CD28 absence and/or CD57 expression and/or KLRG1 expression) on CD4 and CD8+ T-cells and no + difference in the variation of circulating TEMRA, naïve or TC1 CD8 T-cells were observed neither in the overall population with available pre and post immunophenotype analyses nor according to DCB upon ICI (Figure S10B,C,D). We next investigated the proliferation capacity and the functional properties of SIP+ CD8+ T- cells, assessing the expression of Ki67 and of IL-2, TNF, IFNrespectively, after PBMCs in vitro activation. PBMCs in vitro cultures were established from 22 treatment naïve aNSCLC patients. Cytokine production was measured in 22 patients, while only 13 patients could be assessed for proliferation. Ki67 expression was significantly lower in CD28- and in CD28-CD57+KLRG1+ CD8+ T- cells compared to CD28+ CD8+ T-cells under all the stimulation conditions (Figure 5A). CD28- CD57+ CD8+ T-cells produced significantly higher levels of IFNand lower level of IL-2 compared to CD28+ CD8+ T-cells, TNF-expression was also higher in CD28- CD57+ compared to CD28+ CD8+ T-cells, although not statistically significant (Figure 5B).

Discussion

T-cell immunosenescence, defined by the loss of CD28 and expression of CD57 and KLRG1 on peripheral CD8+ T-cells, was observed at baseline in 28% and 11% of aNSCLC patients treated with ICI or PCT, respectively. The 39.5% cut-off was initially generated in a discovery cohort of aNSCLC patients and subsequently tested in a larger validation cohort with longer FU. The relatively high threshold and specificity suggest that the 39.5% cut-off is able to detect primary immunotherapy resistance and aNSCLC patients with high probability to progress to single agent ICI.

T-cell immunosenescence did not correlate with age, previous chemotherapy exposure or immune related adverse events (irAEs), while it was associated with worse outcomes only in the ICI cohort. The efficacy and safety of single agent ICI among elderly aNSCLC patients is a debated topic. Although the age cut-offs used in clinical trials were different, one metanalysis (20) and some studies including patients older than 75 years (21–23) suggested an absence of a significant survival benefit of ICI in this subgroup. Furthermore, age ≥65 years was associated with HPD in cancer patients treated with ICI (24). On the other hand, a pooled analysis of three trials comparing pembrolizumab to chemotherapy in NSCLC patients (25), a phase II study of nivolumab including 34% of patients older than 70 years (26) and real-world data (27–30) showed a survival improvement with single agent ICI in the elderly population. The reasons for this discrepancy are unknown, however they might potentially be related to the heterogeneity of the studies, the different age cut- offs used and to the fact that elderly patients are largely unrepresented in clinical trials and results come mainly from subgroup analyses. Alpert et al. (31) recently demonstrated that immune and chronological ages do not overlap and that an IMM-AGE score, based on the variation of multiple genes over an older adult lifetime, exhaustively describes a person’s immune status and efficiently predicts all causes related mortality. As for IMM-AGE score, immunosenescence does not simply reflect patients’ age and may be a more reliable biomarker compared to chronological age to assess efficacy from ICI. Increased senescent CD28-CD57+ (32) or highly differentiated CD8+CD28- T-cells (33) were reported respectively in lung cancer patients compared to healthy volunteers and in NSCLC patients receiving PCT compared to treatment naïve subjects, suggesting that T-cell immunosenescence may be associated with cancer diagnosis or chemotherapy exposure. Contrary to patients in PCT cohort who were chemotherapy naïve, in the ICI pooled population only 14% had not been exposed to chemotherapy. Furthermore, although the median percentage of CD28-CD57+KLRG1+ CD8+ T-cells did not differ between ICI pooled population and PCT cohort (≃23%), SIP+ patients were fewer in PCT cohort compared to ICI pooled population (11% vs 28%), suggesting a possible role of previous chemotherapy treatment in increasing circulating T-cell senescence. Similarly, lack of CD28 and expression of CD57 correlated with chronic viral infections (i.e. CMV) in humans (34). In the present study, no significant association between T-cell immunosenescence and positive CMV status was found, however, only 37 (45%) of 83 ICI treated patients had anti-CMV antibodies assessment and definitive conclusions cannot be drawn. In addition, we did not include a control cohort of healthy volunteers to assess a potential role of cancer itself in inducing T-cell immunosenescence. + + + SIP status is significantly associated with higher TEMRA CD8 and CD4 cells, TH1, TC1 lymphocytes, OX40+ Treg cells and with reduced naïve and effector memory CD8+ lymphocytes. Although we included CD45RA in our T-cell immunosenescence flow cytometry panel, we did not

directly assess the co-expression of such terminal differentiation marker on senescent T- lymphocytes. Interestingly, an overlap between TEMRA and senescent phenotypes (35) and low CD28 expression among circulating TEMRA CD8+ T-cells in NSCLC patients before ICI start (36) have been reported. The association between T-cell immunosenescence and terminal differentiation along with the reduction of circulating naïve CD8+ lymphocytes in senescent patients suggest that the ability of the immune system to react against tumor antigenic diversity and emerging neoantigens might be impaired in T-cell immunosenescence. + Furthermore, the increase in circulating TH1, TC1 lymphocytes and OX40 Treg may reflect a chronic systemic proinflammatory state. In fact, both CD28-CD57+ T-cells (12) and TEMRA CD8+ cells (13) have been described as functionally activated and able to produce high levels of proinflammatory cytokines (i.e. TNF-α and IFN-γ), and extracellular matrix remodelling proteases (35). In line with these studies, we found that senescent T-cells produced more TNF-α and IFN-γ compared to the non-senescent counterparts. Sustained increased levels TNF-α and IFN-γ have been associated with smoldering inflammation (37), tumour development and acquisition of cancer stemness and aggressive features (38). Altogether these factors may promote resistance to anticancer treatments. Of note, blood inflammatory parameters such as derived neutrophils/lymphocytes ratio and high LDH have recently been associated with lack of benefit to ICI (39-41), probably due to the suppression of effective T-cell antitumor responses (42). In the present study, no significant correlation between T-cell immunosenescence and these blood parameters was found, therefore how the chronic proinflammatory status associated with T-cell senescence affects ICI activity remains unclear. We demonstrated that circulating senescent T-cells produced less IL-2 compared to non- senescent CD8+ lymphocytes. Low level of IL-2 preferentially promotes Treg homeostasis due to the high constitutive IL-2R-α expression by these cells (43). In addition, lowering IL-2 levels impairs CD8+ T-effectors development (44), tempering anticancer T-cell response upon ICI. A crosstalk between T-cell immunosenescence and Treg lymphocytes has been recently described. Human Treg cells were shown to suppress effector T-cells and initiate DNA damage response triggered by glucose competition, resulting in senescence and functional changes in T-cells (45). In this regard, we could have expected a lower incidence of irAEs in SIP+ patients due to the higher proportion of OX40+ Treg cells. However, the rate of any grade irAEs did not differ according to SIP status. These data would need further exploration due to the small sample size of patients experiencing irAEs in this study (N=15, 18%) or having an assessment of circulating Treg cells before irAEs occurrence (N=4, 9%). To our knowledge, this study is the first to demonstrate that T-cell immunosenescence is significantly associated with lack of response, low DCB, HPD and poor survival upon ICI in aNSCLC patients.

Two recent manuscripts have reported an association between circulating highly differentiated CD28low/negative CD27negative CD4+ T-cells (46) or CD62low CD4+ T-cells (47) and clinical responses in aNSCLC treated with ICI. In particular, in Zuazo M. et al. highly differentiated CD28low/negative CD27negative CD4+ T-cells were mainly central memory (CD45RA- CD62L+) or effector memory (CD45RA- CD62L-) T-cells, not anergic, not exhausted and not senescent which expressed high level of Ki67 and expanded upon PD-1 blockade (46). Similarly, in Kagamu H. et low + al. CD62L CD4 T-cells belonged to a primed Th1 subpopulation and expressed aurora kinase A, + a gene involved in G2-M mitotic phase (47). Of note, highly differentiated CD4 T-cells were able to recover CD8+ systemic immunity with expansion of CD28+ CD8+ subsets upon PD-1 blockade (46). Although we did not assess these circulating CD4+ populations in relation to T-cell senescence, it is likely that they were lower in SIP+ patients. However, the real implication of these cells in the response to ICI was not definitively proven due to the absence of a control cohort of patients not treated with ICI. In the present study, T-cell senescence did not significantly correlate with response or survival to PCT. Although results in the PCT cohort may be influenced by the low number of SIP+ patients, the numerically longer PFS and the higher disease control observed in SIP+ patients further suggest a truly differential effect of T-cell senescence according to treatment type. Therefore, immune aging could specifically affect functions of T-lymphocytes which are necessary for an effective anticancer immune response upon ICI. It’s likely that the low proliferative potential of senescent T-cells, their reduced capacity of recognizing antigenic diversity and their ability to promote immune tolerance or pro-tumoral chronic systemic inflammation may all play a role in explaining the lack of efficacy from ICI in SIP+ patients. Interestingly, Hui et al. (48) reported that CD28 is the primary target for PD-1 mediated inhibition and is strongly preferred over TCR for dephosphorylation by PD-1 recruited phosphatases. This finding suggests that abundance of CD28- T-cells may correlate with absence of efficacy from anti-PD-1/PD-L1 therapy. In vitro experiments showed that blocking PD-1 had no effect on proliferation or functionality of circulating human TEMRA (CD45RA+ CD27-) T-cells, while both were increased by blocking senescence (13), supporting the hypothesis that senescence and exhaustion may involve distinct pathways and that reversing senescence together with PD-1 blockade may be a potential therapeutic strategy. In the present study, the mean PD-1 expression on SIP+ CD8 T-cells was ~40%, suggesting a relatively limited overlap between exhaustion and senescence. Despite its prospective design and the use of a control cohort of chemotherapy treated patients, this study has some limitations such as the small number of patients tested for the dynamic evolution of SIP or for the association between T-cell senescence and specific circulating immune populations, the absence of available tissue samples to assess senescent markers on tumour infiltrating lymphocytes and the lack of mechanistic insights about how T-cell

immunosenescence may negatively impact ICI outcome. Furthermore, SIP cut off generation method suffers of low sensitivity, therefore a subgroup of SIP- patients may still experience primary progression to ICI, as observed in one out of 4 HPD patients who was classified as SIP-, having 28.03% of circulating CD28- CD57+ KLRG1+ CD8+ T-cells. Although SIP status did not significantly change according to PD-L1 expression, it was not possible to evaluate the correlation between T- cell senescence and other known biomarkers of response (i.e. tumour mutational burden) (49) or progression (i.e. LKB1 and KEAP mutations) (50) to ICI. Finally, this study included mainly pretreated NSCLC patients who received single agent ICI in 2nd or further lines. Considering that chemo-immunotherapy and double immune checkpoint blockade have emerged as effective first line treatment options in NSCLC patients (51), the role of T-cell senescence in the context of immunotherapy combinations need to be determined. A better characterization of the proliferation, the secretory phenotype (“inflamm-aging”) (52), and the activated intracellular signalling pathways (“signal-aging”) (53) of both circulating and tumour infiltrating senescent T-cells is currently ongoing and could further validate immunosenescence as a negative predictive biomarker for ICI, providing also novel immunological mechanisms associated with immunotherapy resistance. In conclusion, circulating T-cell immunosenescence is observed in 28% and 11% of aNSCLC treated with ICI or PCT respectively, is not associated with clinical characteristics and correlates with poor outcome upon ICI but not PCT, being a novel negative predictive biomarker in pretreated aNSCLC patients. T-cell senescence was associated with specific circulating immune phenotypes and it was characterized by low CD8+ T-cells proliferation and increased functional activation. Additional studies are needed to characterize the biological mechanisms of resistance to ICI involving T-cell immunosenescence.

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Figures and Tables

Figure 1. Density t-SNE plots of T-cells from each response group in the ICI discovery cohort. t-

SNE plots of T-cells overlaid with the expression of selected markers (red: higher density, blue: lower density).

Figure 2. Overall response rate and disease clinical benefit according to SIP status in the ICI pooled population (A) and PCT cohort (B)

Figure 3. Progression free survival and overall survival according to SIP status in the ICI pooled population (A) and PCT (B) cohort

Figure 4. Heatmap showing a hierarchical clustering of normalized (-1%/+1.5%) percentages of T- helper and T-cytotoxic cell subsets according to SIP status (ICI validation cohort). Immune populations are identified with numbers as reported in Table S4 (A). Comparison of T-helper and

T-cytotoxic subsets (B) and of OX40+ Treg cells (C) according to SIP status in the ICI validation cohort.

Figure 5. Proliferation (A) and characterization of cytokines production (B) after in vitro activation by CD28-CD57+KLRG1+ CD8+ T-cells or CD28-CD57+ CD8+ T-cells compared to CD28+ and CD28-

CD8+ T-cells (ICI validation cohort)

Table 1. Patients characteristics according to SIP status (ICI pooled population)

Acknowledgments

The preliminary results of this article were presented at the World Conference on Lung

Cancer (WCLC) 2018 in Toronto (Journal of Thoracic Oncology, Vol 13, Issue 10, Supplement,

Pages S466–S467) and awarded as Best Poster at ESMO Annual Congress 2018 in Munich

(Annals of Oncology (2018) 29 (suppl_8): viii493-viii547. 10.1093/annonc/mdy292) and at the

European Lung Cancer Congress (ELCC) 2019 in Geneva (Annals of Oncology (2019) 30

(suppl_2): ii7-ii13. 10.1093/annonc/mdz073).

Funding

This work was supported by a grant from “Fondation Bristol-Myers Squibb pour la recherche en Immunoncologie” and by “SIRIC SOCRATE 2.0 INCa-DGOS-Inserm_12551”.

Disclosures

Dr. Ferrara disclosed no conflicts of interest related to the current manuscript, outside of current manuscript: consulting/advisory role Merck Sharp & Dohme and travel accomodation

Pfizer.

Dr. Hendriks disclosed no conflicts of interests related to current manuscript, outside of current manuscript: research funding Roche, Boehringer Ingelheim, AstraZeneca (institution), advisory board: Boehringer, BMS, (both institution, BMS also self), travel reimbursement: Roche,

BMS (self); mentorship program with key opinion leaders: funded by AstraZeneca; fees for educational webinars: Quadia (self).

Dr. Proto disclosed no conflicts of interest related to the current manuscript, outside of the current manuscript: consulting/advisory role Roche, Speaker’s Bureau Lilly, Honorarium and Travle accommodation Bristol-Myers Squibb, Travel accommodation Merck Sharp & Dohme.

Dr. Garassino disclosed personal financial interests with: AstraZeneca, MSD International

GmbH, BMS, Boehringer Ingelheim Italia S.p.A, Celgene, Eli Lilly, Ignyta, Incyte, Inivata,

MedImmune, Novartis, Pfizer, Roche and Takeda; she also disclosed institutional financial

interests with: Eli Lilly, MSD, Pfizer (MISP), AstraZeneca, MSD International GmbH, BMS,

Boehringer Ingelheim Italia S.p.A, Celgene, Ignyta, Incyte, Inivata, MedImmune, Novartis, Pfizer,

Roche, Takeda, Tiziana and Foundation Medicine; she has received research funding from the following organizations: AIRC, AIFA, ItalianMoh and TRANSCAN.

Dr. Soria disclosed consultancy fees from AstraZeneca, Astex, Clovis, GSK, GamaMabs,

Lilly, MSD, Mission Therapeutics, Merus, Pfizer, PharmaMar, Pierre Fabre, Roche/Genentech,

Sanofi, Servier, Symphogen, and Takeda. Dr Soria has been a full-time employee of AstraZeneca since September 2017. He is a shareholder of AstraZeneca and Gritstone.

Dr. Besse disclosed research funding from GlaxoSmithKline, Roche/Genentech, Clovis

Oncology, Pfizer, Boehringer, Lilly, SERVIER, Onxeo, BMS, MSD, OSE Pharma, and Inivata.

Dr. Naigeon, Auclin, Duchemann, Cassard, Jouniaux, Boselli, Grivel, Desnoyer, Mezquita,

Texier, Caramella, Planchard, Remon, Sangaletti, Chaput disclosed no conflicts of interests.

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Tab 1

+ Total Non-Senescent (SIP-) Senescent (SIP ) (N=83) (N=60) (N=23) p-value No. (%) No. (%) No. (%) Age (median; interquartile range) .34 64 (55;70.5) 61.5 (55; 68) 68 (59; 73.5) Smoking history .20 Current 29 (11%) 24 (40%) 5 (22%) Former 45 (54%) 31 (52%) 14 (61%) Non-smoker 9 (35%) 5 (8%) 4 (17%) Histology .13. Adenocarcinoma 60 (72%) 47 (78%) 13 (57%) NSCLC-othera 9 (11%) 5 (8%) 4 (17%) Squamous 14 (17%) 8 (14%) 6 (26%) Stageb .72 III 11 (13%) 9 (15%) 2 (9%) IV 72 (87%) 51 (85%) 21 (91%) PD-L1 statusc .55 PD-L1 <1% 24 (29%) 14 (30%) 10 (44%) PD-L1 1-49% 16 (19%) 12 (25%) 4 (17%) PD-L1>50% 30 (36%) 21 (45%) 9 (39%) Missing 13 (16%) 13 0 Molecular status .08 KRAS mutation 35 (42%) 28 (64%) 7 (37%) Wild-type d 23 (28%) 14 (4%) 9 (47%) Targetable alterations e 5 (6%) 2 (32%) 3 (16%) Missing 20 (24%) 16 4 No. metastatic sites pre-ICI .43 ≤ 2 49 (59%) 37 (62%) 12 (52%) > 2 34 (41%) 23 (38%) 11 (48%) ICI line .15 ≤2 68 (82%) 50 (83%) 18 (78%) ≥ 2 (range 2-7) 15 (18%) 10 (17%) 5 (22%) Chemotherapy exposure .16 Yes 71 (86%) 49 (82%) 22 (96%) No 12 (14%) 11 (18%) 1 (4%) Radiotherapy before/during ICI .88 No 35 (42%) 25 (42%) 10 (44%) Yes f 48 (58%) 35 (58%) 13 (56%) CMV antibody positivity ICI g .12

Tab 1

+ Total Non-Senescent (SIP-) Senescent (SIP ) (N=83) (N=60) (N=23) p-value No. (%) No. (%) No. (%) Negative 12 (15%) 11 (41%) 1 (10%) Positive 25 (30%) 16 (59%) 9 (90%) Missing 46 (55%) 33 13 Performance status (ECOG) .76 0-1 63 (76%) 45 (75%) 18 (78%) 2-3 20 (24%) 15 (25%) 5 (22%) irAEs (any grade) .54 No 68 (82%) 48 (80%) 20 (87%) Yes h 15 (18%) 12 (20%) 3 (13%) dNLR i .26 ≤ 3 47 (57%) 32 (54%) 15 (68%) > 3 34 (41%) 27 (46%) 7 (32%) Missing 2 (2%) 1 1 LDH .44 ≤ ULN j 35 (42%) 27 (55%) 8 (44%) > ULN 32 (38%) 22 (45%) 10 (56%) Missing 16 (20%) 11 5 LIPI k .62 Low 19 (23%) 15 (31%) 4 (22%) Intermediate 34 (41%) 23 (47%) 11 (61%) High 14 (17%) 11 (22%) 3 (17%) Not available 16 (19%) 11 5

a: large cell non-small cell lung cancer, non-small cell lung cancer non-otherwise specified. b: TNM stage 8th edition; c: analysed on tumour cells; d: absence of EGFR mutations, ALK or ROS1 rearrangements; e: ROS1 rearrangement, HER2 mutations, MET alterations, BRAF mutations; f: radiotherapy (including stereotactic radiotherapy) on any site (including bone or central nervous system); g: anti-Cytomegalovirus IgG or IgM positivity; h: 5 patients with grade 1-2 pneumonitis, 5 patients with grade 1-2 endocrinopathies, 2 patient with grade 2-3 colitis; 1 patient with grade 2 arthritis,1 patient with grade 3 cutaneous toxicity, 1 patients with grade 2 neutropenia, i: dNLR= neutrophil/ (leukocytes -neutrophils); j cut-off for Gustave Roussy= 248 U/L, k: LIPI= lung immune prognostic index. LIPI high: dNLR ≥3 and LDH ≥248 U/L; LIPI intermediate: dNLR<3 and LDH ≥248 U/L or dNLR ≥3 and LDH <248 U/L, LIPI low: dNLR<3 and LDH<248 U/L.

Circulating T-cell immunosenescence in advanced non-small cell lung cancer patients treated with single agent PD-1/PD-L1 inhibitors or platinum-based chemotherapy

Roberto Ferrara, Marie Naigeon, Edouard Auclin, et al.

Clin Cancer Res Published OnlineFirst September 4, 2020.

Updated version Access the most recent version of this article at: doi:10.1158/1078-0432.CCR-20-1420

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