Ki-67 expression in anti-programmed cell death protein-1 antibody-bound CD8+ T cells as a predictor of clinical benefit

doi:10.21203/rs.3.rs-5283855/v1

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Research Article

Ki-67 expression in anti-programmed cell death protein-1 antibody-bound CD8+ T cells as a predictor of clinical benefit

https://doi.org/10.21203/rs.3.rs-5283855/v1

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Aims

Developing predictive biomarkers for immune checkpoint inhibitors (ICIs) is important. Programmed cell death protein-1 (PD-1) receptor occupancy by anti-PD-1 antibodies on circulating T cells varies among patients. However, the association between the exhaustion of these antibody-bound T cells and the clinical efficacy of ICIs remains unknown. Therefore, the present study was aimed at evaluating this association.

Methods

This prospective cohort study included patients with advanced non-small cell lung cancer (NSCLC) and esophageal squamous cell carcinoma (ESCC) who received pembrolizumab therapy. Peripheral blood samples were collected during the second cycle of chemotherapy. We analyzed the relationship between exhaustion markers in pembrolizumab-bound (PB) T cells and clinical response.

Results

A total of 21 patients were analyzed, including 12 patients with NSCLC and 9 patients with ESCC. The expression of Ki-67 in PB-CD8⁺ T_CM and T_EM was negatively correlated with both clinical response and overall survival.

Conclusion

The expression of Ki-67 of PB-CD8⁺ T_CM and T_EM can serve as a predictive biomarker for the clinical benefit of pembrolizumab therapy. Our study suggests that analyzing antibody-bound T cells could be a novel approach to predict the clinical outcomes of PD-1 blockade therapy.

biomarker

exhaustion

Ki-67

memory T cells

immune checkpoint inhibitor

PD-1/PD-L1 signaling

Immune checkpoint inhibitors (ICIs), including anti-programmed cell death protein 1 (PD-1) antibodies, have significantly improved the survival of patients with cancers [1]. Immune checkpoint therapy is now widely used across various advanced cancers and in adjuvant and neo-adjuvant settings [2–5]. However, the number of patients who benefit from ICIs remains limited, highlighting the need for biomarkers that can distinguish between responders and non-responders.

Currently, biomarkers, such as programmed cell death ligand 1 (PD-L1) expression, microsatellite instability, and tumor mutation burden have been approved for predicting responses to ICIs; however, their predictive power is still insufficient [6–9]. Furthermore, tissue-based analysis is often unfeasible due to the biopsy location, and not ideal for longitudinal monitoring following chemotherapy [10]. In contrast, blood-based analysis of circulating immune cells is simple and minimally invasive and could reflect the dynamic changes in the hosts’ immune status. However, T cell repertoire and single-cell RNA sequencing analysis revealed that only a subset of circulating T cells is involved in anti-tumor immunity, which may explain the potential limitation to predicting the effectiveness of ICI therapy using circulating T cells [11, 12]. Therefore, identification of the specific T cell population that is directly related to anti-tumor response by anti-PD-1 therapy is needed to sophisticate the prediction of ICI efficacy.

Anti-PD-1 antibodies bind to PD-1 molecules on the surface of T cells, blocking the interaction between PD-1 and programmed cell death ligand 1 (PD-L1). This blockade can restore T-cell-mediated anti-tumor immune responses. Previous studies have shown that PD-1 receptor occupancy (RO) by anti-PD-1 antibodies on circulating CD3⁺ T cells varies widely, ranging from 30 to 90%, and is dose-independent [13, 14]. However, the extent of PD-1 RO on each subset of T cells and the relationship between the RO and clinical efficacy of ICIs was unclear. Previously, we demonstrated that PD-1 RO with anti-PD-1 antibodies after the first administration of nivolumab varied across different T-cell subsets. Notably, high PD-1 occupancy on effector regulatory T cells (eTregs) was associated with poor clinical response and survival [15]. Our study demonstrated the association between the PD-1 RO and clinical efficacy of ICIs and suggested that the effect of PD-1 blockade varies depending on the subset of T cells. Additionally, RNA sequencing of circulating T cells in patients receiving anti-PD-1 therapy revealed that genes related to cell division and DNA replication were upregulated in antibody-bound T cells compared to antibody-unbound T cells [16]. Based on these findings, we hypothesized that the phenotype of the circulating T cells bound to anti-PD-1 antibodies might be related to the clinical response to anti-PD-1 therapy and that the analysis of the antibody-bound T cells could be a novel approach to predict the effectiveness of ICIs.

Though anti-PD-1 therapy has been considered to reinvigorate exhausted T cells, recent research suggests that its effectiveness may depend on the specific stage of the exhaustion [17]. Upon repetitive stimulation by the T cell receptor (TCR) or interaction with immune checkpoint proteins on tumor cells, CD8⁺ T cells undergo exhaustion, losing their proliferative capacity and effector functions [18]. T cell exhaustion is typically characterized by increased expression of surface proteins such as PD-1, cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), and T-cell immunoglobulin and mucin domain-3 (TIM-3) [19]. In addition to these checkpoint proteins, Ki-67, widely used as a proliferative marker, was also reported to correlate with T-cell exhaustion. Ki-67^high T cells (Ki-67^hi T cells) were shown to be terminally exhausted and have lost their ability to respond to stimulation [20–22]. Although several clinical studies have examined the relationship between the expression of PD-1, TIM-3, and Ki-67 in circulating T cells and the clinical response to anti-PD-1 therapy, the results have been inconsistent across different patient cohorts [23–26]. However, few studies have explored the relationship between the exhaustion of circulating antibody-bound T cells and the clinical efficacy of ICIs.

In this study, we aimed to evaluate the association between the exhaustion of antibody-bound T cells and the clinical response to ICIs in patients treated with anti-PD-1 antibodies. Our findings show that the expression of Ki-67 in PB-CD8⁺ T_CM and T_EM after the first administration of pembrolizumab was negatively correlated with the clinical response and overall survival. These results suggest that analyzing antibody-bound T cells could serve as a novel approach for predicting the clinical benefit of PD-1 blockade therapy.

2.1. Patients and sample collection

This study included patients diagnosed with advanced or recurrent non-small cell lung cancer (NSCLC) or esophageal squamous cell carcinoma (ESCC) who underwent either pembrolizumab monotherapy or combination with cytotoxic chemotherapy, at Showa University Hospital between 2017 and 2023. Patients who underwent prior immunotherapy, missed their second scheduled pembrolizumab dose, or had autoimmune diseases requiring immunosuppressive therapy were excluded. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Showa University School of Medicine (Approval M2253). Written informed consent was obtained from all participants.

2.2. Clinical outcomes

Tumor response was assessed using computed tomography (CT) in accordance with the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. “Disease control” was defined as achieving complete response, partial response, or stable disease as the best overall response. Progression-free survival (PFS) was measured from the initiation of anti-PD-1 therapy to either the date of disease progression or death from any cause. Overall survival (OS) was defined as the date from the initiation of anti-PD-1 therapy to death from any cause. Clinical information was extracted from medical records, with the last follow-up data collected on June 1, 2023.

2.3. Immunological analysis of clinical samples

Peripheral blood was collected at the second administration of pembrolizumab or pembrolizumab combined with cytotoxic agents. Peripheral blood mononuclear cells (PBMCs) were isolated from each patient using density gradient centrifugation with Ficoll-Paque (Amersham Pharmacia Biotech, Uppsala, Sweden). PBMCs were incubated with Fc-Block (BD Biosciences, Franklin Lakes, NJ, USA) at 20°C for 10 min and stained using, fluorescein isothiocyanate (FITC)-conjugated human CD45RA antibody, phycoerythrin (PE)/Dazzle 594-conjugated human CD3 antibody (Ab), PerCP-Cyanine (Cy)-5.5-conjugated human C-C motif chemokine receptor 7 (CCR7) Ab, Alexa Fluor 647-conjugated human Forkhead box P3 (FoxP3) Ab, Alexa Fluor 700-conjugated human Kiel 67 (Ki-67), allophycocyanin (APC)/Cy7-conjugated human CD4 Ab, BV510-conjugated human CD8 Ab, and BV650-conjugated human T cell immunoglobulin and mucin-3 (TIM-3) Ab (BioLegend, San Diego, California, USA). The cells were sorted into various T cell populations: naïve memory T cells (T_N) (CCR7⁺CD45RA⁺), central memory T cells (T_CM) (CCR7⁺CD45RA^–), effector memory T cells (T_EM) (CCR7^–CD45RA^–), terminally differentiated effector memory T cells re-expressing CD45RA (T_EMRA) (CCR7^–CD45RA⁺), and eTregs (CD4⁺CD45RA^–Foxp3^hi) (Supplementary Figure S1). Staining was performed following the manufacturer’s flow cytometry protocols, including intracellular staining for FoxP3 and Ki-67 using the BD Pharmingen Transcription Factor Buffer Set (BD Biosciences). The stained cells were analyzed using a BD LSRFORTESSA X-2 flow cytometer (BD Biosciences), and the data were processed using FlowJo software, version 10.10.0 (FlowJo, Ashland, OR, USA).

2.4. Detection of pembrolizumab bound to PBMCs and PD-1 RO

PBMCs were preincubated for 30 min at 4°C with a saturating concentration (20 µg/mL) of either unlabeled human IgG4 isotype recombinant (BioLegend) or pembrolizumab, washed extensively, and incubated with mouse anti-human IgG4 Fc secondary antibody, biotin (Life Technologies, Thermo Fisher Scientific, Waltham, MA, USA) for 20 min. They were stained with PE-conjugated streptavidin (BioLegend). PE-positive cells incubated with the human IgG4 isotype were gated as pembrolizumab-bound (PB) cells (Supplementary Figure S2). PD-1 occupancy by pembrolizumab was estimated as the ratio of cells stained after saturation with the isotype control (indicating in vivo pembrolizumab binding) to cells stained after pembrolizumab saturation (indicating total binding sites).

2.5. In vitro assays

PBMCs were harvested from three healthy donors with no history of cancer or autoimmune disease, following the protocol described above. T cells were activated using plate-bound anti-human CD3 (2 µg/mL) and anti-human CD28 (2 µg/mL) antibodies (Biolegend), in the presence of 100 U/mL Interleukin-2 (IL-2). Cells were cultured in 75 cm² flasks with iMediam for T (GC LYMPHOTEC Inc., Tokyo, Japan), supplemented with 10% heat-inactivated fetal bovine serum (Life Technologies) and 1% penicillin-streptomycin-amphotericin B (FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan) and maintained at 37 ℃ in 5% CO₂. After two weeks of incubation and expansion, CD8⁺ T cells were enriched using magnetic positive selection with human CD8 microbeads (Miltenyi Biotec, Bergisch Gladbach, Germany). The CD8⁺ T cells were stained with PE-conjugated PD-1 antibody (Ab), APC-Cy7-conjugated CD39, APC-conjugated TIM-3, and BV421-conjugated human Ki-67 (BD Biosciences). The CD8⁺ T cells were also stained with FITC Annexin V Apoptosis Detection Kit with 7-amino-actinomycin D (7-AAD) (BioLegend), and BV421-conjugated Ki-67 Ab (BD Biosciences). The stained cells were detected using a BD FACSLyric flow cytometer (BD Biosciences). The study adhered to the Declaration of Helsinki and was approved by the Ethics Committee of Showa University School of Medicine (Approval B-2018-022).

2.6. Statistical analysis

Continuous data are expressed as mean ± standard deviation (SD) or as median with interquartile range depending on the distribution. Two-group comparisons were performed using the chi-square test and Student’s t-test for categorical and continuous variables, respectively. Cox proportional hazards regression was conducted to assess the relationship between the proportion of Ki-67⁺ or TIM-3⁺ cells and disease control, adjusting for total lymphocyte count as a potential confounder. PFS and OS were estimated using the Kaplan–Meier method, with group differences analyzed by the log-rank test. Cox regression was used to analyze the association between Ki-67⁺ or TIM-3⁺ cell proportions and survival, including a subgroup analysis focused on patients with NSCLC. Pearson’s correlation coefficients were calculated to assess relationships among variables. The Dunnett test was used to compare multiple groups with a single control group, while paired t-tests compared the means of two matched groups. Statistical significance was defined as a two-tailed p-value of less than 0.05. All analyses were conducted using R software version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria).

3.1. Baseline characteristics of the patients

Overall, 25 patients were enrolled in this study, including 16 with NSCLC and nine with ESCC, all of whom provided informed consent. All were confirmed eligible to participate in the study. Peripheral blood samples could not be collected from three patients with NSCLC and one patient died before sampling (Supplementary Figure S3). The baseline characteristics of these 21 patients are summarized in Table 1. The mean follow-up period was 498 days. Of the patients with NSCLC, nine received pembrolizumab monotherapy, and four were treated with pembrolizumab and cytotoxic agents; two with carboplatin and pemetrexed, one with carboplatin and paclitaxel, and one with carboplatin and nab-paclitaxel. All 9 patients with ESCC were treated with pembrolizumab, fluorouracil, and cisplatin. No significant difference in disease control was observed between patients with NSCLC and ESCC (61.5% in NSCLC vs. 62.5% in ESCC; p = 0.965) nor between patients who received pembrolizumab monotherapy and combination therapy (44.4% in monotherapy vs. 75.0% in combination therapy; p = 0.154).

Table 1

Patients’ baseline characteristics
	Total (n = 21)	NSCLC (n = 13)	ESCC (n = 8)
Age, median (range)	70 (46–86)	69 (54–86)	72.5 (46–80)
Sex, n (%)
Female	3 (14.3)	2 (15.4)	1 (12.5)
Male	18 (85.7)	11 (84.6)	7 (87.5)
Histology, n (%)
Squamous carcinoma	12 (57.1)	4 (30.8)	8 (100)
Adenocarcinoma	9 (42.9)	9 (69.2)	0 (0)
Regimen, n (%)
Monotherapy	9 (42.9)	9 (69.2)	0 (0)
Combined therapy	12 (57.1)	4 (30.8)	8 (100)
Total lymphocyte count, mean	1,420	1,505	1,281
Best overall response, n (%)
Complete response (CR)	0 (0)	0 (0)	0 (0)
Partial response (PR)	8 (38.1)	4 (30.8)	4 (50)
Stable disease (SD)	5 (23.8)	4 (30.8)	1 (12.5)
Progressive disease (PD)	8 (38.1)	5 (38.5)	3 (37.5)
PFS, median	251	224	269
OS, median	720	720	NR
NSCLC, non-small cell lung cancer; ESCC, esophageal squamous cell carcinoma; PFS, progression-free survival; OS, overall survival; NR, not reached; irAE, immune-related adverse events

3.2. T cell subset and PD-1 occupancy

The proportion of each T cell subset is presented in Supplementary Table S1a. PD-1 occupancy rate varied from 41.0–98.2% (mean: 67.0%) in CD8⁺ T cells and from 42.8–94.2% (mean: 64.8%) in CD4⁺ T cells (Supplementary Table S1b).

3.3. Disease control with anti-PD-1 therapy and the exhaustion of PB-T cells

Cox proportional hazard regression analysis revealed significant associations between disease control and the proportion of Ki-67⁺ of PB-CD8⁺ T_CM (adjusted odds ratio, 0.750; 95% confidence interval (CI), 0.581–0.968) and T_EM (adjusted odds ratio, 0.825; 95% CI, 0.694–0.981) both before and after adjusting for total lymphocyte count (Table 2). In contrast, no significant difference was observed in all (including both PB and pembrolizumab-unbound) CD8⁺ T_CM or T_EM (Supplementary Table S2). TIM-3 expression in both PB-T cells or all T cells across subsets was not linked to disease control (Supplementary Table S3). Additionally, there was no significant difference in Ki-67 levels of PB-CD8⁺ T_CM and T_EM between patients who were treated with pembrolizumab monotherapy and those receiving combination therapy (T_CM, 8.04 vs. 8.34, p = 0.907; T_EM, 12.2 vs. 12.8, p = 0.876).

Table 2

Multivariate logistic regression models for the disease control ratio and Ki-67 of PB-T cells
		Univariate analysis		Multivariate analysis
	Ki-67 (%), mean (range)	OR (95% CI)	p	OR (95% CI)	p
PB-CD8⁺
PB-CD8⁺ T_N	1.82 (0–10.6)	0.65 (0.40–1.05)	0.08	0.62 (0.37–1.04)	0.07
PB-CD8⁺ T_CM	8.21 (0.99–21.2)	0.76 (0.60–0.97)	0.03	0.75 (0.58–0.97)	0.03
PB-CD8⁺ T_EM	12.6 (1.89–29.7)	0.83 (0.71–0.98)	0.03	0.83 (0.69–0.98)	0.03
PB-CD8⁺ T_EMRA	3.08 (0–16.3)	0.78 (0.55–1.12)	0.18	0.78 (0.55–1.11)	0.17
PB-CD4⁺
PB-CD4⁺ T_N	0.82 (0–3.37)	0.83 (0.37–1.87)	0.65	0.86 (0.36–2.05)	0.74
PB-CD4⁺ T_CM	8.26 (4.03–15.7)	0.75 (0.52–1.07)	0.11	0.74 (0.51–1.08)	0.12
PB-CD4⁺ T_EM	11.2 (3.36–19.9)	0.82 (0.64–1.05)	0.12	0.82 (0.65–1.05)	0.12
PB-CD4⁺ T_EMRA	3.08 (0–15.4)	0.77 (0.57–1.05)	0.10	0.77 (0.57–1.05)	0.10
PB-eTreg	15.2 (0–35.3)	1.00 (0.92–1.09)	0.98	1.00 (0.92–1.10)	0.95
The odds ratio in the multivariate analysis was adjusted for total lymphocyte count.
OR, odds ratio; CI, confidence interval; PB, pembrolizumab-bound; T_N, naïve memory T cells; T_CM, central memory T cells; T_EM, effector memory T cells; T_EMRA, terminally differentiated effector memory T cells re-expressing CD45RA; eTreg, effector regulatory T cells

3.4. Survival and Ki-67 of PB T cells

We evaluated the relationship between Ki-67 in PB-CD8⁺ T_CM and T_EM and the survival. Cox proportional hazard regression analysis revealed a significant association between Ki-67 of PB-CD8⁺ T_CM and T_EM and both PFS (T_CM: adjusted hazard ratio (HR): 1.19; 95% CI: 1.06–1.34; T_EM: adjusted HR: 1.15; 95% CI: 1.06–1.26) and OS (T_CM: adjusted HR: 1.32; 95% CI: 1.14–1.60; T_EM: adjusted HR: 1.22; 95% CI: 1.10–1.39) (Supplementary Table S4a, b). Subgroup analysis of patients with NSCLC showed similar statistically significant results (Supplementary Table S4c, d). Pearson correlation coefficients confirmed a strong negative correlation between Ki-67 levels in PB-CD8⁺ T_CM and T_EM cells and survival (Fig. 1).

3.5. Cutoff value of Ki-67 of PB-CD8⁺ T_CM/_EM and Kaplan–Meier plot

Using the receiver operating characteristic (ROC) curve for Ki-67 in PB-CD8⁺CD45RA⁻ T cells (T_CM and T_EM) with the disease control as the outcome, the optimal cutoff was found to be 8.49% with Youden index (Supplementary Figure S4). The area under the curve was 0.904, with a specificity and sensitivity of 100% and 84.6%, respectively. The log-rank test showed that patients with Ki-67 levels above 8.49% had significantly poor PFS (p < 0.01) and OS (p < 0.01) (Fig. 2). Median PFS was 1,022 days (95% CI: 165 days to not reached) for the low Ki-67 group versus 122 days (95% CI: 41 to 286 days) for the high Ki-67 group. Median OS was 1,030 days (95% CI: 298 days to not reached) in the low Ki-67 group and 254 days in the high Ki-67 group (95% CI: 48 days to not reached).

3.6. Exhaustion and apoptosis of T cells

We evaluated the relationship between Ki-67 expression and exhaustion markers in CD8⁺ T cells. CD8⁺ T cells from three healthy donors, activated by CD3/ CD28 antibodies and IL-2 were stained for exhaustion markers (PD-1, CD39, TIM-3) and Ki-67. Ki-67 expression increased as the T cells had more exhaustion markers (Fig. 3a). Next, we examined how Ki-67 levels changed with apoptosis since terminally exhausted T cells ultimately undergo apoptosis. Using flow cytometry, activated CD8⁺ T cells stained with Annexin Ab and 7-ADD were sorted into live (Annexin⁻ 7-ADD⁻) and early apoptotic cells (Annexin⁺ 7-ADD⁻) (Supplementary Figure S5). Ki-67 expression was nearly twice as high in early apoptosis as in the live state (Fig. 3b).

In this study, we found that Ki-67 expression in PB-CD8⁺ T_CM and T_EM after the first dose was negatively correlated with the clinical response and survival in patients with NSCLC and ESCC. This result implies that Ki-67 expression in PB-CD8⁺ T_CM and T_EM could serve as a predictive biomarker for the clinical benefit of pembrolizumab therapy. Moreover, analyzing antibody-bound T cells can be a novel platform to predict the clinical benefit of PD-1 blockade therapy.

First, Ki-67 expression in circulating antibody-bound T cells was negatively associated with both the efficacy of ICIs and patient survival. Ki-67 proteins act as surfactants on chromosomes to promote mitosis and are widely used as proliferation markers because Ki-67 is expressed throughout the cell cycle except during the resting G0 phase [27, 28]. Upregulation of Ki-67 in tumor tissue is linked to aggressiveness and poor prognosis of various kinds of cancer [29], Ki-67 has also been used as a marker of T cell proliferation. Studies have shown an increase in the number of circulating Ki-67⁺ CD8⁺ T cells after pembrolizumab therapy in patients with melanoma [30]. However, recent research indicates that T cells with high Ki-67 expression (Ki-67^hi T cells) showed terminally exhausted profiles and have limited capacity for expansion upon stimulation [11, 20–22]. We also evaluated the relationship between Ki-67 expression, T cell exhaustion markers, and apoptosis in CD8⁺ T cells in vitro. We found a positive correlation between Ki-67 levels and T-cell exhaustion. Notably, Ki-67 expression was nearly twice as high in apoptotic T cells, which represent a terminal exhaustion state, compared to live cells. Unlike cancer cells, which can proliferate indefinitely, expanding T cells eventually become exhausted and lose their proliferative capacity. Therefore, Ki-67^hi T cells may be exhausted and lose their ability to expand, which can be equivalent to “terminally exhausted T cells.” While ICIs can activate “progenitor exhausted T cells,” which retain stemness and proliferative capacity, terminally exhausted T cells are known to be resistant to checkpoint blockade [19, 31, 32]. Therefore, it is reasonable to think that high Ki-67 expression in T cells correlated with poor response and prognosis.

Second, we discovered that the exhaustion of circulating PB-CD8⁺ T cells, rather than the entire CD8⁺ T cell population, was strongly associated with ICI response. Circulating T cells are considered ideal biomarkers due to their non-invasive accessibility, and repeatability in comparison with tissue-based analysis [10]. Recent research demonstrated the superiority of analysis of circulating T cells in the prediction of ICI efficacy. The main mechanism of PD-1 blockade is believed to reinvigorate tumor-infiltrating T cells. However, in responding to ICIs, circulating progenitor-exhausted T cells become expandable, infiltrate tumors, and contribute to anti-tumor immunity [19]. That is supported by clinical studies which observed clonal expansion of circulating T cells in patients who respond to anti-PD-1 therapy [9, 33, 34]. To date, many studies have examined circulating T cell to predict the clinical benefit of ICIs. However, these attempts only partly succeeded because T cells involved in anti-tumor immunity are only a subset of circulating T cells. For example, the CD8⁺ ratio of circulating T cells did not fully reflect anti-tumor response [35, 36]. Various markers, including CD39⁺, CD103⁺, PD-1^+, and TIM-3⁺ were investigated with some correlation to clinical outcomes, but the results are not consistent [16, 36–38]. Our study highlights a strong correlation between the exhaustion of circulating antibody-bound CD8⁺ T cells and ICI response, suggesting that focusing on these cells could offer a new approach to predicting ICI efficacy.

The association between PB-T cell exhaustion and clinical benefit was only found in CD8⁺ T_CM and T_EM, and not in CD8⁺ T_N or T_EMRA. Upon stimulation by antigens, naïve T cells are activated, and differentiate into various memory subsets. T_CM cells circulate in the blood and lymph vessels and await secondary antigen presentation [17]. Among circulating CD8⁺ memory T cells, both T_CM and T_EM respond to TCR stimulation in different ways. T_CM cells primarily secrete IL-2 and clonally expand, while T_EM cells exhibit direct effector functions like releasing IFN-γ and perforin. Several animal and human studies have shown that both T_CM and T_EM play important roles in anti-tumor immunity, with the activation of these populations being linked to favorable responses to ICIs [39]. Our findings that the exhaustion of PB-CD8⁺ T_CM and T_EM predicts clinical benefit align with these observations.

This study had certain limitations. First, it included patients who received pembrolizumab monotherapy and combination therapy, therefore, we cannot exclude possible effects of cytotoxic chemotherapy on the clinical response. However, the effects seem minimal, as no significant differences in disease control or Ki-67 expression in PB-CD8⁺ T_CM and T_EM cells were observed between the two groups. Second, the study combined patients with NSCLC and ESCC due to the small cohort size, which could influence survival outcomes. However, a separate analysis for NSCLC alone showed similar correlations between Ki-67 in PB-CD8⁺ T_CM and T_EM and survival, suggesting the findings are robust. Finally, the study was conducted at a single institution and included only Asian patients, limiting generalizability. Future validation in a multicenter cohort with a diverse patient population is needed.

Our study revealed that high Ki-67 expression in PB-CD8⁺ T_CM and T_EM cells after the first pembrolizumab dose was linked to poor clinical response and survival in patients with NSCLC and ESCC. Analyzing antibody-bound T cells can be a novel approach to predicting the efficacy of PD-1 blockade therapy.

Acknowledgments

We thank the current and former members of the Department of Clinical Immuno-Oncology, Clinical Research Institute for Clinical Pharmacology and Therapeutics, Showa University, as well as the Division of Medical Pharmacology, Department of Pharmacology, Showa University School of Medicine, for their support in this project. We also thank the Clinical Medicine Joint Research Laboratory, Showa University, for sharing the flow cytometer.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Ethics approval

This study was approved by the Ethics Committee of Showa University School of Medicine (Approval Nos. M2253 and B-2018-022). Informed consent was obtained from all enrolled patients.

Consent

Informed consent was obtained from all enrolled patients.

Authors’ contributions

Toshiaki Tsurui: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing - Original draft preparation, Visualization. Masahiro Hosonuma: Conceptualization, Methodology, Formal analysis, Writing - Reviewing and editing. Aya Sasaki: Investigation, Data curation. Yuuki Maruyama: Investigation, Data curation. Yasunobu Amari: Investigation, Data curation. Eiji Funayama: Investigation, Data curation. Kohei Tajima: Investigation, Data curation. Hitoshi Toyoda: Investigation, Data curation. Junya Isobe: Investigation, Data curation. Yoshitaka Yamazaki: Investigation, Data curation. Yuta Baba: Investigation, Data curation. Midori Shida: Methodology, Formal analysis. Yuko Udaka: Supervision, Project administration. Emiko Mura: Resources, Data curation. Risako Suzuki: Resources, Data curation. Nana Iriguchi: Resources, Data curation. Hirotsugu Ariizumi: Resources, Data curation. Tomoyuki Ishiguro: Resources, Data curation. Yuya Hirasawa: Resources, Data curation. Ryotaro Ohkuma: Resources, Data curation. Masahiro Shimokawa: Resources, Data curation. Yutaro Kubota: Resources, Data curation. Atsushi Horiike: Resources, Data curation. Satoshi Wada: Resources, Data curation. Atsuo Kuramasu: Writing – Reviewing and editing, Supervision, Project administration. Mayumi Tsuji: Supervision, Project administration. Yuji Kiuchi: Supervision, Project administration. Takuya Tsunoda: Supervision, Project administration. Kiyoshi Yoshimura: Conceptualization, Methodology, Writing – Reviewing and editing, Supervision, Project administration.

Ribas A, Wolchok JD. Cancer immunotherapy using checkpoint blockade. Science. 2018;359:1350–5. doi: 10.1126/science.aar4060
Mok TSK, Wu YL, Kudaba I, et al. Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial. Lancet. 2019;393:1819–30. doi: 10.1016/S0140-6736(18)32409-7
Sun JM, Shen L, Shah MA, et al. Pembrolizumab plus chemotherapy versus chemotherapy alone for first-line treatment of advanced oesophageal cancer (KEYNOTE-590): a randomised, placebo-controlled, phase 3 study. Lancet. 2021;398:759–71. doi: 10.1016/S0140-6736(21)01234-4
Forde PM, Spicer J, Lu S, et al. Neoadjuvant nivolumab plus chemotherapy in resectable lung cancer. N Engl J Med. 2022;386:1973–85. doi: 10.1056/NEJMoa2202170
Felip E, Altorki N, Zhou C, et al. Adjuvant atezolizumab after adjuvant chemotherapy in resected stage IB–IIIA non-small-cell lung cancer (IMpower010): a randomised, multicentre, open-label, phase 3 trial. Lancet. 2021;398:1344–57. doi: 10.1016/S0140-6736(21)02098-5
Le DT, Uram JN, Wang H, et al. PD-1 Blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372:2509–20. doi: 10.1056/NEJMoa1500596
Le DT, Kim TW, Van Cutsem E, et al. Phase II open-label study of pembrolizumab in treatment-refractory, microsatellite instability-high/mismatch repair-deficient metastatic colorectal cancer: KEYNOTE-164. J Clin Oncol. 2020;38:11–9. doi: 10.1200/JCO.19.02107
Marabelle A, Le DT, Ascierto PA, et al. Efficacy of pembrolizumab in patients with noncolorectal high microsatellite instability/mismatch repair-deficient cancer: results from the Phase II KEYNOTE-158 study. J Clin Oncol. 2020;38:1–10. doi: 10.1200/JCO.19.02105
Yamaguchi H, Hsu JM, Sun L, et al. Advances and prospects of biomarkers for immune checkpoint inhibitors. Cell Rep Med. 2024;5:101621. doi: 10.1016/j.xcrm.2024.101621
Holder AM, Dedeilia A, Sierra-Davidson K, et al. Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours. Nat Rev Cancer. 2024;24:498–512. doi: 10.1038/s41568-024-00705-7
Gueguen P, Metoikidou C, Dupic T, et al. Contribution of resident and circulating precursors to tumor-infiltrating CD8+ T cell populations in lung cancer. Sci Immunol. 2021;6:eabd5778. doi: 10.1126/sciimmunol.abd5778
Gros A, Parkhurst MR, Tran E, et al. Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients. Nat Med. 2016;22:433–8. doi: 10.1038/nm.4051
Topalian SL, Hodi FS, Brahmer JR, et al. Safety, activity, and immune correlates of anti–PD-1 antibody in cancer. N Engl J Med. 2012;366:2443–54. doi: 10.1056/NEJMoa1200690
Brahmer JR, Drake CG, Wollner I, et al. Phase I study of single-agent anti-programmed death-1 (MDX-1106) in refractory solid tumors: safety, clinical activity, pharmacodynamics, and immunologic correlates. J Clin Oncol. 2010;28:3167–75. doi: 10.1200/JCO.2009.26.7609
Hosonuma M, Hirasawa Y, Kuramasu A, et al. Nivolumab receptor occupancy on effector regulatory T cells predicts clinical benefit. Cancer Sci. 2024;115:752–62. doi: 10.1111/cas.16061
Osa A, Uenami T, Koyama S, et al. Clinical implications of monitoring nivolumab immunokinetics in non-small cell lung cancer patients. JCI Insight. 2018;3:e59125. doi: 10.1172/jci.insight.59125
Henning AN, Roychoudhuri R, Restifo NP. Epigenetic control of CD8+ T cell differentiation. Nat Rev Immunol. 2018;18:340–56. doi: 10.1038/nri.2017.146
Kallies A, Zehn D, Utzschneider DT. Precursor exhausted T cells: key to successful immunotherapy? Nat Rev Immunol. 2020;20:128–36. doi: 10.1038/s41577-019-0223-7
Chow A, Perica K, Klebanoff CA, et al. Clinical implications of T cell exhaustion for cancer immunotherapy. Nat Rev Clin Oncol. 2022;19:775–90. doi: 10.1038/s41571-022-00689-z
Paley MA, Kroy DC, Odorizzi PM, et al. Progenitor and terminal subsets of CD8+ T cells cooperate to contain chronic viral infection. Science. 2012;338:1220–5. doi: 10.1126/science.1229620
Beltra JC, Manne S, Abdel-Hakeem MS, et al. Developmental relationships of four exhausted CD8+ T cell subsets reveals underlying transcriptional and epigenetic landscape control mechanisms. Immunity. 2020;52:825–41.e8. doi: 10.1016/j.immuni.2020.04.014
You R, Artichoker J, Fries A et al. Active surveillance characterizes human intratumoral T cell exhaustion. J Clin Invest. 2021;131:e144353. doi: 10.1172/JCI144353
Kwon M, An M, Klempner SJ, et al. Determinants of response and intrinsic resistance to PD-1 blockade in microsatellite instability–high gastric cancer. Cancer Discov. 2021;11:2168–85. doi: 10.1158/2159-8290.CD-21-0219
Kato R, Yamasaki M, Urakawa S, et al. Increased Tim-3+ T cells in PBMCs during nivolumab therapy correlate with responses and prognosis of advanced esophageal squamous cell carcinoma patients. Cancer Immunol Immunother. 2018;67:1673–83. doi: 10.1007/s00262-018-2225-x
Kamphorst AO, Pillai RN, Yang S, et al. Proliferation of PD-1+ CD8 T cells in peripheral blood after PD-1-targeted therapy in lung cancer patients. Proc Natl Acad Sci U S A. 2017;114:4993–8. doi: 10.1073/pnas.1705327114
Juliá EP, Mandó P, Rizzo MM, et al. Peripheral changes in immune cell populations and soluble mediators after anti-PD-1 therapy in non-small cell lung cancer and renal cell carcinoma patients. Cancer Immunol Immunother. 2019;68:1585–96. doi: 10.1007/s00262-019-02391-z
Yerushalmi R, Woods R, Ravdin PM, et al. Ki67 in breast cancer: prognostic and predictive potential. Lancet Oncol. 2010;11:174–83. doi: 10.1016/S1470-2045(09)70262-1
Cuylen S, Blaukopf C, Politi AZ, et al. Ki-67 acts as a biological surfactant to disperse mitotic chromosomes. Nature. 2016;535:308–12. doi: 10.1038/nature18610
Whitfield ML, George LK, Grant GD, et al. Common markers of proliferation. Nat Rev Cancer. 2006;6:99–106. doi: 10.1038/nrc1802
Huang AC, Postow MA, Orlowski RJ, et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature. 2017;545:60–5. doi: 10.1038/nature22079
Budimir N, Thomas GD, Dolina JS, et al. Reversing T-cell exhaustion in cancer: lessons learned from PD-1/PD-L1 immune checkpoint blockade. Cancer Immunol Res. 2022;10:146–53. doi: 10.1158/2326-6066.CIR-21-0515
Miller BC, Sen DR, Al Abosy R, et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat Immunol. 2019;20:326–36. doi: 10.1038/s41590-019-0312-6
Wu TD, Madireddi S, de Almeida PE, et al. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature. 2020;579:274–8. doi: 10.1038/s41586-020-2056-8
Liu B, Hu X, Feng K, et al. Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer. Nat Cancer. 2022;3:108–21. doi: 10.1038/s43018-021-00292-8
Nabet BY, Esfahani MS, Moding EJ, et al. Noninvasive early identification of therapeutic benefit from immune checkpoint inhibition. Cell. 2020;183:363–376.e13. doi: 10.1016/j.cell.2020.09.001
An HJ, Chon HJ, Kim C. Peripheral blood-based biomarkers for immune checkpoint inhibitors. Int J Mol Sci. 2021;22:9414. doi: 10.3390/ijms22179414
Simoni Y, Becht E, Fehlings M, et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature. 2018;557:575–9. doi: 10.1038/s41586-018-0130-2
Ge W, Dong Y, Deng Y, et al. Potential biomarkers: identifying powerful tumor specific T cells in adoptive cellular therapy. Front Immunol. 2022;13:1003626. doi: 10.3389/fimmu.2022.1003626
Han J, Khatwani N, Searles TG, et al. Memory CD8+ T cell responses to cancer. Semin Immunol. 2020;49:101435. doi: 10.1016/j.smim.2020.101435

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Ki-67 expression in anti-programmed cell death protein-1 antibody-bound CD8+ T cells as a predictor of clinical benefit

Status:

Version 1

Abstract

Aims

Methods

Results

Conclusion

Figures

1. Introduction

2. Material and methods

2.1. Patients and sample collection

2.2. Clinical outcomes

2.3. Immunological analysis of clinical samples

2.4. Detection of pembrolizumab bound to PBMCs and PD-1 RO

2.5. In vitro assays

2.6. Statistical analysis

3. Results

3.1. Baseline characteristics of the patients

3.2. T cell subset and PD-1 occupancy

3.3. Disease control with anti-PD-1 therapy and the exhaustion of PB-T cells

3.4. Survival and Ki-67 of PB T cells

3.5. Cutoff value of Ki-67 of PB-CD8⁺ T_CM/_EM and Kaplan–Meier plot

3.6. Exhaustion and apoptosis of T cells

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Ki-67 expression in anti-programmed cell death protein-1 antibody-bound CD8+ T cells as a predictor of clinical benefit

Status:

Version 1

Abstract

Aims

Methods

Results

Conclusion

Figures

1. Introduction

2. Material and methods

2.1. Patients and sample collection

2.2. Clinical outcomes

2.3. Immunological analysis of clinical samples

2.4. Detection of pembrolizumab bound to PBMCs and PD-1 RO

2.5. In vitro assays

2.6. Statistical analysis

3. Results

3.1. Baseline characteristics of the patients

3.2. T cell subset and PD-1 occupancy

3.3. Disease control with anti-PD-1 therapy and the exhaustion of PB-T cells

3.4. Survival and Ki-67 of PB T cells

3.5. Cutoff value of Ki-67 of PB-CD8+ TCM/EM and Kaplan–Meier plot

3.6. Exhaustion and apoptosis of T cells

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

3.5. Cutoff value of Ki-67 of PB-CD8⁺ T_CM/_EM and Kaplan–Meier plot