A blood-based immune marker for resistance to pembrolizumab in patients with metastatic urothelial cancer

PD1 inhibition is effective in patients with metastatic urothelial cancer (mUC), yet a large fraction of patients does not respond. In this study, we aimed to identify a blood-based immune marker associated with non-response to facilitate patient selection for anti-PD1. To this end, we quantified 18 immune cell populations using multiplex flow cytometry in blood samples from 71 patients with mUC (as part of a biomarker discovery trial; NCT03263039, registration date 28-08-2017). Patients were classified as responder (ongoing complete or partial response, or stable disease; n = 25) or non-responder (progressive disease; n = 46) according to RECIST v1.1 at 6 months of treatment with pembrolizumab. We observed no differences in numbers of lymphocytes, T-cells, granulocytes, monocytes or their subsets between responders and non-responders at baseline. In contrast, analysis of ratios of immune cell populations revealed that a high mature neutrophil-to-T-cell ratio (MNTR) exclusively identified non-responders. In addition, the survival of patients with high versus low MNTR was poor: median overall survival (OS) 2.2 vs 8.9 months (hazard ratio (HR) 6.6; p < 0.00001), and median progression-free survival (PFS) 1.5 vs 5.2 months (HR 5.6; p < 0.0001). The associations with therapy response, OS, and PFS for the MNTR were stronger than for the classical neutrophil-to-lymphocyte ratio (HR for OS 3.5, and PFS 3) and the PD-L1 combined positivity score (HR for OS 1.9, and PFS 2.1). In conclusion, the MNTR distinctly and uniquely identified non-responders to treatment and may represent a novel pre-treatment blood-based immune metric to select patients with mUC for treatment with pembrolizumab. Supplementary Information The online version contains supplementary material available at 10.1007/s00262-022-03250-0.


Background
The therapeutic landscape of metastatic urothelial cancer (mUC) has changed since immune checkpoint inhibitors (ICI) directed against programmed cell death protein (PD)1 or its ligand (PD-L1) were introduced. ICIs are approved in the first-(1, 2) and second-line setting (3,4) for mUC, as maintenance therapy for patients who had a response to chemotherapy (5), and for treatment of Bacillus Calmette-Guérin-unresponsive carcinoma in situ of the bladder (6).
Overall, response rates of ICIs in mUC are modest, and given the high costs and accompanying toxicities (7) pretreatment selection of patients is critical.
Currently, PD-L1 expression in tumor tissue is the only approved biomarker that is used for selection of cisplatinineligible patients for ICIs in the first-line setting (8,9). In the second-line setting, PD-L1 expression does not have predictive value (3), and no biomarkers are applied yet for patient selection. In an effort to identify new selection markers, previous studies have revealed that numbers of circulating T-cells at baseline, and dynamic changes in particular T-cell subsets during treatment, are associated with response to ICIs (10)(11)(12)(13). Furthermore, a relationship between the neutrophil-to-lymphocyte ratio (NLR) and clinical outcome of patients has been observed in multiple tumor types, including UC (14)(15)(16)(17). However, these studies generally have shortcomings, such as lack of predictive value, use of immune cell fractions rather than numbers, and focus on on-treatment rather than baseline predictors. In the current study, we have enumerated 18 immune cell populations in blood of 71 patients with mUC by multiparameter flow cytometry, to study whether individual immune cell populations or ratios thereof identify patients with mUC who do or do not respond to pembrolizumab.

Patients and assessment of clinical response
Patients with locally advanced or mUC of the bladder or upper urinary tract with an indication for pembrolizumab were included in a phase II prospective biomarker discovery study (NCT03263039), and treated as described previously (pembrolizumab, 200 mg intravenously, 3-weekly (18)). Patients were classified as responder (ongoing complete or partial response, or stable disease) or non-responder (progressive disease) at 6 months after treatment initiation according to response evaluation criteria in solid tumors (RECIST) v1.1. Overall survival (OS) was defined as the time from start of pembrolizumab to date of death; progression-free survival (PFS) was defined as the time from start of pembrolizumab to clinical or radiological disease progression.

Multiplex flow cytometry of blood samples
Peripheral blood was prospectively collected in EDTA tubes at baseline, and weeks 6 and 12 of treatment. Whole blood was stained and analyzed by multiplex flow cytometry to quantify 18 immune cell populations as described previously (19), antibody specifications are listed in Supplementary Table 1. In short, lymphocyte,   T-cell, granulocyte, and monocytes populations were  gated separately in a scatter plot of CD45+ staining versus side scatter. Immune cell populations were further  defined using the following markers for B cells: CD3-CD19+ ; natural killer (NK) cells: CD3-CD56+ CD16± ;  T-cells: CD3+ ; γδ T-cells: CD3+ TCRγδ+ ; CD4 or CD8 T-cells: CD3+ TCRγδ-CD4+ or CD8+ ; eosinophils: CD15+ CD16-; mature neutrophils: CD15 high CD16 high ; immature neutrophils: CD15+ CD16+ ; classical monocytes: CD14+ CD16-; intermediate monocytes: CD14+ CD16+ ; non-classical monocytes: CD14-CD16+ ; dendritic cells (DC): CD14-CD16-CD11c+ ; and myeloid derived suppressor cells (MDSC): CD14+ CD16-CD11b+ HLA-DR low . Besides quantitation of immune cell populations for individual timepoints, we performed normalization of data to more specifically assess longitudinal changes in numbers of immune cell populations. To this end, the measured numbers were normalized per patient by subtracting the patients' mean number for a given population from the individual measurement, followed by addition of the overall mean number of that particular population.

Statistical analysis
Statistical analysis was performed using R version 3.5.1. Use of the Mann-Whitney U, Wilcoxon signed rank, or Fisher's exact test is specified in figure legends. The optimal cutoff level for dichotomous analysis of immune markers was determined using receiver operating characteristic (ROC) curves. OS and PFS were estimated using Kaplan-Meier estimates, patients who were alive or without disease progression were censored at last date the patient was known to be alive, or at last date of tumor assessment. Hazard ratios (HR) were calculated using univariate Cox regression models. Multivariate Cox regression analysis was performed for known risk factors: performance status, hemoglobin concentration, presence of liver metastases, and time since completion of previous treatment. Correction for multiple testing was performed using the Holm-Bonferroni method.

Patient cohort
In this study, 71 patients with mUC received first-(n = 9) or second-line (n = 62) treatment with pembrolizumab. Nonresponders were younger than responders and had a lower albumin concentration in blood (Table 1). For patients who received first-line pembrolizumab a PD-L1 CPS of ≥ 10 was required; five of these patients were responders. In the second-line setting, 55% of responders versus 29% of nonresponders had a positive PD-L1 CPS (Table 1).
No differential numbers of immune cell populations in blood of non-responders versus responders to pembrolizumab at baseline.
Fresh blood samples were available for 71 patients at baseline (n = 26 responders, n = 45 non-responders), for 55 patients at week 6 (n = 21 responders, n = 34 nonresponders), and for 38 patients at week 12 (n = 22 responders, n = 16 non-responders). At baseline, no differences were observed between responders and non-responders in the numbers (number of cells per µl blood) of lymphocytes and their subsets (B cells, NK cells, and CD16+ NK Patients were stratified according to response to pembrolizumab at 6 months of therapy (responders: ongoing complete or partial response, or stable disease; non-responders: progressive disease). 1 Eastern cooperative oncology group performance status, score of 0 or 1 was required. 2 Fig. 1d). Also at weeks 6 and 12 of therapy, the numbers of all 18 immune cell populations in blood remained non-different between responders and non-responders ( Fig. 1a-d). To specifically assess therapy-induced changes, numbers of immune cell populations were normalized per patient (see methods section). We did not observe on-treatment changes in any of the immune cell populations either in responders or non-responders (Supplementary Fig. 1).
The mature neutrophil-to-T-cell ratio at baseline exclusively identifies non-responders to pembrolizumab.
Since numbers of individual immune cell populations were not distinctive between responders and non-responders at baseline, we systematically interrogated ratios of granulocyte, monocyte and lymphocyte subsets, and assessed their association with OS and PFS at their respective optimal cutoff levels (Fig. 2). We assessed the classical NLR, defined as the quotient of the sum of mature and immature neutrophil counts and the sum of total lymphocyte and T-cell counts, and the PD-L1 CPS, as references. For granulocyte subsets, the ratio of mature neutrophils to lymphocytes showed similar associations with OS and PFS as the classical NLR, whereas no associations were observed for immature neutrophils or eosinophils. For monocyte subsets, the ratio of monocytes to lymphocytes was associated with OS and PFS. For lymphocyte subsets, the strongest association with OS and PFS was observed for the ratio of mature neutrophils to T-cells (MNTR), which was mostly attributed to CD4+ and not CD8+ T-cells. The median value of the MNTR was not different between responders and non-responders (Fig. 3a, left graph). When using an optimal cut-off level of 11.5 (Fig. 3a, middle graph), this ratio exclusively identified non-responders (n = 9; Fig. 3a, right graph). The positive predictive value (PPV) of a high MNTR for non-response to therapy was 100%, with a specificity of 100% and sensitivity of 19%. Thereby the MNTR outperforms the classical NLR (PPV 91%, specificity 96%, sensitivity 22%) and the PD-L1 CPS in the total cohort (PPV 50%, specificity 36%, sensitivity 35%). Patients with a high versus low MNTR had a significantly shorter OS (median 2.2 vs 8.9 months; HR 6.6; p = 5.6 × 10 -6 ) and PFS (median 1.5 vs 5.2 months; HR 5.6; p = 2 × 10 -5 ; Fig. 3b). This association with survival was stronger compared to the classical NLR (Fig. 3c) and PD-L1 CPS (Fig. 3d). Finally, multivariate cox regression analysis revealed that MNTR was the strongest factor associated with OS (p < 0.0001) and PFS (p < 0.0001), and a weaker association was observed for presence of liver metastases (p = 0.02 for OS, and not significant for PFS), and a treatment-free interval of less than three months from previous chemotherapy (p = 0.021 for OS, and p = 0.03 for PFS).

Discussion
In this study, we enumerated 18 immune cell populations in prospectively collected fresh blood samples from 71 patients with mUC treated with pembrolizumab and demonstrated that a high MNTR prior to treatment is associated with therapy resistance. These data introduce a new blood-based immune marker that can be measured easily and non-invasively, and that has the potential to identify patients with mUC who will not benefit from pembrolizumab before treatment initiation.
The presented data extend our previous study on the frequency of T-cell subsets in blood samples of 56 out of these 71 patients with mUC (18). In this earlier study we demonstrated that responders harbor higher frequencies of CD4+ T-cells that express PD1 and 4-1BB when compared to non-responders at baseline, and responders showed changes in frequencies of these subsets during treatment. In the current study, we analyzed numbers of 14 additional immune cell populations in blood. We did not identify differences between responders and non-responders for any of the 18 T-cell, lymphocyte, granulocyte or monocyte populations at baseline nor at weeks 6 and 12 of treatment. Moreover, we did not identify longitudinal changes during therapy for any of these immune cell populations in responders or non-responders. In line with previous studies (14)(15)(16)(17), we did show that the classical NLR is related to OS and PFS. Extending our analyses to novel ratios of immune cell populations, revealed that the quotient of mature neutrophils and T-cells outperformed the classical NLR and PD-L1 CPS, and was superior above all ratios regarding its potency to Fig. 1 Numbers of immune cell populations in blood were not different between responders and non-responders to pembrolizumab at baseline and during treatment. Boxplots display the number of cells belonging to subsets of: a lymphocytes; b T-cells; c granulocytes; and d. monocytes per microliter blood. Immune markers per subset are provided in the methods section. Timepoints: baseline (Bl), 6w, 12w (6, 12 weeks of treatment). Differences between responders and non-responders were determined using the Mann-Whitney U test, and differences between timepoints were determined for paired samples using the Wilcoxon signed rank test, p-values were corrected for multiple testing using the Holm-Bonferroni method ◂ discriminate non-responders from responders, and regarding its association with Os and PFS.
From a mechanistic point of view, the negative predictive value of the MNTR may be in line with earlier reports showing that tumor-infiltrating neutrophils form a barrier around tumor cells, and as such prevent adequate contact between tumor cells and T-cells (20). When studying paired tumor biopsies from patients with a high versus low MNTR, however, we did not observe differences in densities of CD4+ T-cells, CD8+ T-cells or CD66b+ neutrophils, nor differences in distances among these cells (Supplementary Fig. 2A-D). In other words, we cannot support a direct relationship between a high MNTR in blood with tumor cell-entrapment by neutrophils in tumor tissue. Previously, we showed that a lack of CD4+ T helper type 1 (Th1) cells in the tumor at baseline, and their inability to cluster with CD8+ T-cells and myeloid cells upon treatment, were associated with resistance to pembrolizumab (18). Also, in case of the MNTR, it appeared that lack of CD4+ rather than CD8+ T-cells was predominantly associated with nonresponse and limited survival. Exclusion of CD8+ T-cells from the MNTR, however, reduced its association with survival, suggesting that involvement of CD8+ T-cells in this metric is not negligible. Future studies in patient blood and tumor specimens are required to identify the underlying mechanism of action.
The MNTR can be measured non-invasively by a commonly used technique (flowcytometry) that comes with a low cost burden. Identification of patients with a high MNTR may prevent patients with mUC from receiving potentially toxic and ineffective treatment with pembrolizumab. The optimal cut-off level for MNTR was determined specifically for this study and may therefore overestimate survival associations. Along this line, maximally selected rank statistics (20) were employed as an alternative approach to determine the optimal cut-off levels, and yielded similar results (data not shown). Our results require validation in an independent cohort of patients with mUC treated with an ICI; however, to the best of our knowledge, a homogeneous cohort of patients with thorough measurements of numbers of immune cell populations in blood, is currently not available.
Ethics approval Patients with advanced or metastatic UC were included in a phase II prospective biomarker discovery study (RESPONDER trial, NCT03263039). The study protocol was approved by the medical ethics review board of the Foundation BEBO (Evaluation of Ethics in Biomedical Research), Assen, The Netherlands, and complies with all relevant national and institutional ethical regulations. All patients provided written informed consent before inclusion in the study.

Consent for publication
All authors listed on the title page have made substantial contributions, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its publication.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.