Immune profiling identifies CD161⁺CD127⁺CD8⁺ T cells as a predictive biomarker for Anti-PD-L1 therapy response in the SCLC-I subtype

Abstract

Background

Advances in molecular subtype classification have improved the understanding of extensive-stage small cell lung cancer (ES-SCLC). However, immune landscape differences across ES-SCLC subtypes remain poorly defined. This study aimed to characterize ES-SCLC immune profiles and explore their association with response to immunotherapy.

Methods

Tumor samples from 135 patients with ES-SCLC were analyzed for molecular subtyping using immunohistochemical markers. Immune profiling of peripheral blood mononuclear cells was performed using cytometry by time-of-flight (CyTOF) and flow cytometry, and tumor tissues were assessed through multiplex immunofluorescence for immune cell subset characterization and distribution.

Results

Molecular subtyping of the 135 ES-SCLC cases identified 54.1%, 20.0%, 7.4%, and 18.5% as ASCL1-dominant, NEUROD1-dominant, POU2F3-dominant, and inflamed SCLC-I subtypes, respectively. CyTOF indicated a distinct enrichment of CD161⁺CD127⁺CD8⁺T cells in SCLC-I,with flow cytometry validating significantly higher proportions. These cells exhibited elevated cytotoxic markers (GZMB and GNLY) and reduced exhaustion markers (PD-1, TIGIT, and LAG-3) compared with those of other subtypes(P < 0.05). Multiplex immunofluorescence confirmed higher intratumoral infiltration of CD161⁺CD127⁺CD8⁺ T cells in SCLC-I. The intratumoral and peripheral levels of this subset were strongly correlated(r = 0.669, P < 0.0001). Patients with a CD161⁺CD127⁺CD8⁺ T/CD8⁺ T cell ratio of ≥ 2.7% had a significantly prolonged progression-free survival (PFS, 11.0 vs. 7.0 months, P = 0.0196).

Conclusions

The SCLC-I subtype exhibited a distinct immune profile characterized by enrichment of CD161⁺CD127⁺CD8⁺ T cells in both peripheral blood and tumor tissue, which was associated with PFS following anti–PD-L1 therapy. These findings highlight the significance of subtype-specific immune profiling and the potential of CD161⁺CD127⁺CD8⁺ T cells as a predictive biomarker to guide precision immunotherapy in ES-SCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-025-06724-8.

Introduction

Small cell lung cancer (SCLC) is an aggressive malignancy with rapid growth, early metastasis, and a poor prognosis [1–3]. Although traditional treatments, such as chemotherapy and radiotherapy, achieve high initial response rates, most patients experience relapse and resistance, causing limited long-term survival. The recent advent of immune checkpoint inhibitors (ICIs), particularly those targeting the programmed death-ligand 1(PD-L1), offers a new therapeutic approach for extensive-stage SCLC (ES-SCLC) [4–6]. However, the clinical benefit of immunotherapy remains inconsistent across patients, underscoring the need for reliable biomarkers to guide treatment decisions.

SCLC is recognized as a heterogeneous disease comprising distinct molecular subtypes. Advances in transcriptomic and epigenetic profiling have revealed the four primary classes of SCLC subtypes based on key transcription factors, namely ASCL1-driven (SCLC-A), NEUROD1-driven (SCLC-N), POU class 2 homeobox 3 (POU2F3, SCLC-P), and an inflamed subtype (SCLC-I) [7]. SCLC-I is characterized by the absence of NEUROD1, ASCL1, and POU2F3 expression. Each subtype displays unique biological and immunological features, which have significant implications for therapeutic strategies. Given its inflamed microenvironment and immune cell infiltration, the SCLC-I subtype presents as a promising candidate for immunotherapy optimization [7]. In contrast, the SCLC-A and SCLC-N subtypes, which are more neuroendocrine than SCLC-I display lower immune activity, complicating immune-based interventions [8, 9].

Given these subtype-specific immune features, characterizing key immune cell subsets involved in anti-tumor responses becomes critical. CD8⁺ T cells are critical mediators of anti-tumor immunity, capable of direct cytotoxic activity against cancer cells. These cells exist in a range of functional states, from active effector to dysfunctional or exhausted phenotypes, shaped by complex regulatory cues within the tumor and systemic immune environments [10, 11]. Identifying specific subsets of CD8⁺ T cells related to therapeutic outcomes in ES-SCLC is crucial for advancing precision immunotherapy. Among the subsets of CD8⁺ T cells, CD161⁺ T cells have garnered attention for their unique properties. CD161, encoded by KLRB1, is a C-type lectin-like receptor expressed on a CD8⁺ T cell subset, natural killer (NK) cells, and other innate immune cells [12]. CD161⁺ CD8⁺ T cells are usually associated with tissue-resident memory-like properties and a poised effector state, enabling rapid responses to antigens [13]. Similarly, these cells demonstrate robust cytokine production and cytotoxic activity, suggesting their potential role in anti-tumor immunity [14]. CD127, the interleukin-7 receptor alpha chain, further defines a subset of CD8⁺ T cells with enhanced survival, homeostasis, and memory potential [15]. CD161 and CD127 expression may mark a population of CD8⁺ T cells that influence the tumor microenvironment(TME). In a study, we suggested that CD161⁺CD127⁺CD8⁺T cells may serve as crucial indicators of poor prognosis in patients with concurrent non-small cell lung cancer (NSCLC) with diabetes undergoing anti-PD-1 immunotherapy [16]. However, the relevance of this subset in ES-SCLC, particularly within its molecular subtypes, remains poorly understood.

In this study, we sought to comprehensively characterize the immune landscapes of ES-SCLC molecular subtypes using cytometry by time-of-flight (CyTOF), flow cytometry, and multiplex immunofluorescence. We focused on identifying immune signatures associated with therapeutic benefit and discovered a distinct enrichment of CD161⁺CD127⁺CD8⁺T cells in the SCLC-I subtype. Furthermore, we evaluated the prognostic significance of this subset in relation to anti–PD-L1 therapy, highlighting it as a novel biomarker for immunotherapy response in ES-SCLC.

Materials and methods

Patient cohort and study design

This study included patients with ES-SCLC who received treatment at The First Affiliated Hospital of Zhejiang University School of Medicine between February 2020 and September 2023. Patients were included if they were histologically or cytologically diagnosed with ES-SCLC, aged ≥ 18 years, and had received first-line anti-PD-L1 immunotherapy. The American Joint Committee on Cancer defines ES-SCLC as stage IV or T3-4, and most of these patients are unsuitable for curative-intent radiotherapy, as they have multiple large lung nodules, tumors, or lymph nodes [17]. To evaluate treatment effectiveness, demographic and clinical data, treatment details, and progression or survival outcomes were collected. Progression-free survival (PFS) was defined as the date from the start of treatment to until discontinuation due to radiologically confirmed disease progression, intolerable side effects, or death. Overall survival (OS) was defined as the time from treatment initiation to the last follow-up or death. Survival analyses were stratified by molecular subtypes (SCLC-A, SCLC-N, SCLC-P, and SCLC-I).

This study adhered to the Declaration of Helsinki and was approved by the ethics committee of The First Affiliated Hospital of Zhejiang University School of Medicine (Approval No.2022 − 1161).

Immunohistochemistry (IHC) of the ES-SCLC subtypes

A total of 135 clinical ES-SCLC samples were included in the IHC to evaluate the expression of markers defining SCLC subtypes (ASCL1, NEUROD1, and POU2F3). Immunostaining was performed as previously described [18] using the following primary antibodies: ASCL1 (556604, BD Biosciences, 1:100), NEUROD1(ab213725, Abcam, 1:250), POU2F3 (NBP1-83966, Novus Biologicals, 1:400), CD3 (17617-1-AP, Proteintech, 1:200), and CD8 (ab237709, Abcam, 1:200).

Marker expression was quantified using two parameters: the percentage of positive cells (0–100%) and the staining intensity (1 = weak, 2 = moderate, 3 = strong). The histoscore (H-score) was calculated as the product of the positivity rate and the intensity score, resulting in a range of 0 to 300. An H-score ≤ 50 was classified as low, while > 50 was defined as high [19, 20].

ASCL1, NEUROD1, and POU2F3 were considered positive in tumors only when their H-scores exceeded 10. Tumors positive for both ASCL1 and NEUROD1 (H-scores > 10) were classified accordingly. Low expression (H-score < 10) of all three markers was considered as a type of SCLC-I. A marker with the highest H-score was considered the dominant marker [21].

CD3 and CD8 positive cells were counted separately, while H-scores for ASCL1, NEUROD1, and POU2F3 were evaluated independently by two pathologists who were blinded to the clinical subtype and treatment outcomes. All samples were anonymized. Discrepancies were resolved through review and consensus to ensure reliable scoring.

Multiplex Immunofluorescence staining and imaging

Multiplex immunofluorescence staining was performed using an Opal Polaris 7 Color Multiplex IHC kit (NEL861001KT; Akoya Biosciences, Delaware, USA), as conducted in previous studies [16, 22]. The slides were dewaxed, and antigenic repair was performed using EDTA buffer (pH 9.0) or citric acid (pH 6.0). The slides were sealed with blocking/Ab Diluent (Akoya Biosciences).

Primary antibodies against CD3 (17617-1-AP, Proteintech; 1:1,000), CD8 (ab237709, Abcam; 1:2,000), CD161 (ab302564, Abcam; 1:300), CD127 (ab180521, Abcam; 1:50), PD-1 (ab234444, Abcam; 1:100), and CD4 (67786-1-Ig, Proteintech; 1:800) were applied. Subsequently, the slides were incubated in Opal Polymer HPR Ms + Rb (Akoya Biosciences) for 10 min at room temperature and agitated in TBST for three times for 2 min each. Next, samples were subjected to optical fluorophore-conjugated tyramide signal amplification (Akoya Biosciences). The detection dyes for each antibody were Opal 570 (CD3), Opal520 dye (CD8 or CD4), Opal 690 (CD161 or PD-1), and Opal620 dye (CD127). This cycle was repeated until all markers were labeled, and sections were counterstained with 4′,6-diamidino-2-phenylindole. Finally, stained slides were visualized using the Vectra Polaris Quantitative Pathology Imaging System (Akoya Biosciences) and analyzed with InForm software (Akoya Biosciences) for quantitative assessment.

To minimize measurement bias, all multiplex immunofluorescence images were analyzed using InForm software by two independent investigators blinded to the patient’s clinical subtype and outcome. Tumor slides were anonymized and randomly coded prior to image analysis, and marker quantification was performed without reference to clinical data.

CyTOF workflow and data analysis

The antibody panel used in this study consisted of 42 metal-tagged antibodies targeting various immune cell populations, as detailed in Supplementary Table 1. Antibody conjugation with the indicated metal isotopes was performed using the Maxpar Antibody Conjugation Kit (Fluidigm, South San Francisco, CA, USA) according to the manufacturer’s instructions. After viability assessment, cells were first incubated with an Fc receptor blocking solution to reduce nonspecific binding, followed by surface staining with a metal-conjugated antibody cocktail for 30 min on ice. Cells were then washed three times with staining buffer (PBS containing 0.5% bovine serum albumin) and incubated in 100 µL of 250 nM cisplatin (Fluidigm, South San Francisco, CA, USA) for 5 min on ice to label dead cells. After cisplatin staining, cells were fixed and incubated overnight at 4 °C in 200 µL of Maxpar Fix and Perm Buffer containing 250 nM Cell-ID™ Intercalator-Ir (191/193Ir; Fluidigm). The following day, cells were washed with staining buffer, permeabilized using Perm Buffer (eBioscience, San Diego, CA, USA), and stained with an intracellular antibody cocktail for 30 min at room temperature. Finally, samples were washed, resuspended in EQ™ Four Element Calibration Beads (Fluidigm), and acquired on a Helios™ mass cytometer (Fluidigm) according to the manufacturer’s standard protocol.

Raw FCS data were debarcoded using mass-tagged barcodes [23] and standardized across batches through the bead normalization method [24]. After standardizing the data, debris and dead cells were manually removed using FlowJo software, leaving only single live immune cells. The PhenoGraph clustering algorithm [25] was used to cluster all immune cells according to the marker expression levels on single cells. The t-distributed stochastic neighbor embedding (t-SNE) data dimensionality decreasing algorithm [26] was applied to visualize the high-dimensional data in two dimensions, illustrating the relative distribution of each cluster on the two-dimensional graph and the expression of each marker or different simple types. The Student’s t-test was used to analyze the frequency of the annotated cell populations.

Flow cytometry

Peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll-based density gradient centrifugation (Cytiva) following the manufacturer’s protocol, as described previously [22]. For flow cytometry, PBMCs were pretreated with a human IgG blocker (BioLegend, Cat#422302) for 20 min, then washed three times with a FACS buffer (1× PBS + 0.5% BSA) and centrifuged at 500 ×g and 4 °C for 5 min. T cells were stimulated for 2 d with phytohemagglutinin (5 µg/mL, Dakewe, Cat#2030411) to enhance activation.

Cells were stained on ice in the dark with antibody conjugates targeting surface (CD3, CD8, CD127, CD161, TIGIT, KLRG1, LAG3, and PD-1) and intracellular (GZMB and GNLY) markers. All antibodies were purchased from BioLegend (San Diego, CA, USA), including APCCY7-CD3 (Cat#300425), PerCP/Cyanine 5.5-CD8a (Cat#301031), APC-CD127 (Cat#351315), PE/Cyanine7-CD161 (Cat#339917), PE-TIGIT (Cat#372703), FITC-KLRG1 (Cat#367713), PE-LAG3 (Cat#369305), FITC-PD-1 (Cat#329903), FITC-GZMB (Cat#515403), and PE-GNLY (Cat#348003). Surface staining was performed for 30 min at 4 °C. For intracellular markers, permeabilization was conducted using the BioLegend Intracellular Staining Permeabilization Wash Buffer (Cat#426803) according to the manufacturer’s instructions. Following staining, cells were resuspended in 300 µL of FACS buffer and analyzed using a BD FACS Fortessa multicolor flow cytometer (BD Biosciences). Data were processed and analyzed with FlowJo vX.07 software (Tree Star), enabling detailed evaluation of immune subpopulations.

To minimize measurement bias and ensure objective interpretation, all immune profiling analyses (CyTOF and flow cytometry) were conducted by investigators blinded to patients’ clinical subtypes and treatment outcomes. Data files were analyzed using predefined gating strategies and marker thresholds, and clinical group information was not available to analysts during clustering, dimensionality reduction, or immune subset quantification.

Statistical analysis

Data visualization and statistical analysis were performed using GraphPad Prism (version 9.5.1, GraphPad Software, San Diego, California, USA). The Student t-test or Mann–Whitney test (nonparametric) was utilized to resolve the quantitative data, and the categorical data were assessed using Fisher’s exact test/chi-squared test. Survival outcomes, including PFS and OS, were estimated using the Kaplan–Meier method, and comparisons between groups were made using the log-rank test. Hazard ratios and their corresponding 95% confidence intervals (CIs) were calculated using a stratified Cox proportional hazards model. Statistical significance was defined as P < 0.05.

Results

Molecular marker expression and subtype classification

The molecular analysis of the 135 ES-SCLC tumors revealed the expression of ASCL1, NEUROD1, and POU2F3 markers, as summarized in Table 1. ASCL1 was expressed in 60.7% (82 of 135) of tumors, including 61 cases with ASCL1-only expression (45.2%) and 21 co-expressing ASCL1 and NEUROD1 (15.6%, A + N+). NEUROD1 was detected in 28.9% (39 of 135) of tumors, comprising 18 cases with NEUROD1-only expression (13.3%) and 21 co-expressing ASCL1 and NEUROD1(15.6%, A + N+). POU2F3 was expressed in 7.4% (10 of 135) of tumors, while 18.5% (25 of 135) showed low expression of the three markers, corresponding to the SCLC-I subtype. The expression patterns of these markers are illustrated in Fig. 1A, which shows representative IHC staining for each molecular subtype. Additionally, the overall distribution of tumors based on single-marker positivity (ASCL1-only, NEUROD1-only, or POU2F3-only) and co-expression (ASCL1 + NEUROD1+, A + N+) is depicted in Fig. 1B, revealing the molecular marker overlap across the cohort.

Table 1.

Expression of subtype-defining markers in 135 Extensive-SCLC patients

Markers	Total cases with positive expression N(%)	H-score Range N(%)
		>10–50	>50–150	>150
ASCL1(SCLC-A)	82(60.7%)	35(42.7%)	27(32.9%)	20(24.4%)
NEUROD1(SCLC-N)	39(28.9%)	18(46.1%)	12(30.8%)	9(23.1%)
POU2F3(SCLC-P)	10(7.4%)	6(60.0%)	3(30.0%)	1(10.0%)
Inflamed(SCLC-I)	25(18.5%)	-	-	-

SCLC-A: 61 patients expressing only ASCL1 and 21 patients expressing both ASCL1 and NEUROD1; SCLC-N: 18 patients expressing only NEUROD1 and 21 co-expressing ASCL1 and NEUROD1

Fig. 1.

Molecular marker expression, subtype classification, and survival analysis (A): Representative IHC staining images demonstrating molecular marker expression in primary ES-SCLC) tumors. (B) Pie chart illustrating the distribution of ES-SCLC tumors based on molecular marker expression, categorized into subtypes: single, or dual positive. (C-D) Tumor sections showing heterogeneity, with clusters of NEUROD1-positive cells within an ASCL1-dominant tumor (C) and ASCL1-positive cells within a NEUROD1-dominant tumor (D). (E) Distribution of subtype dominance based on marker H-scores. (F-G) Kaplan–Meier survival curves showing PFS(F) and OS (G) stratified by ES-SCLC subtypes under immunotherapy.IHC: Immunohistochemistry; PFS: Progression-free survival; OS: Overall survival

To refine the classification of tumors co-expressing ASCL1 and NEUROD1 (A + N+), intratumoral heterogeneity was further evaluated. Figure 1C demonstrates that clusters of NEUROD1-positive cells were interspersed within regions predominantly expressing ASCL1 and vice versa (Fig. 1C and D). Using the highest H-score as the criterion, the dominant marker was determined for these 21 A + N + tumors, resulting in 12 classified as ASCL1-dominant and 9 as NEUROD1-dominant. Based on this approach, the entire cohort was classified as follows: ASCL1 was the dominant marker in 54.1% (73 of 135) of tumors, NEUROD1 was dominant in 20.0% (27 of 135), POU2F3 in 7.4% (10 of 135), and inflammatory markers (SCLC-I subtype) in 18.5% (25 of 135) (Fig. 1E). This classification provides a framework for understanding molecular heterogeneity in ES-SCLC.

SCLC-I subtype shows significant survival advantage in ES-SCLC

The demographic and clinical characteristics of patients in each subtype, grouped by dominant marker expression (ASCL1, NEUROD1, POU2F3, and inflammatory markers for the SCLC-I subtype), are presented in Table 2. The cohort predominantly comprised males (88.9%) with an average age of 65 years, and 85.2% had a history of smoking. All patients received first-line immunotherapy combined with chemotherapy, with durvalumab being the most common agent (68.2%). Subtype-specific comparisons of clinical features showed slight variations in age, smoking history, and treatment distribution, necessitating further investigation into the clinical implications of these molecular subtypes.

Table 2.

Clinicopathological characteristics of patients at baseline

Characteristics	All patients (n = 135)	Subtypes				P-value
		ASCL1 dominant(n = 73)	NEUROD1 dominant(n = 27)	POU2F3(n = 10)	Inflamed(n = 25)
Sex						0.39
Male	120(88.9%)	65(89.0%)	22(81.5%)	10(100.0%)	23(92.0%)
Female	15(11.1%)	8(11.0%)	5(18.5%)	0(0.0%)	2(8.0%)
Median age, years	65.0(43–87)	65.8(49–87)	63.2(54–74)	72.5(57–87)	62.0(43–75)	0.48
Smoking status						0.32
Never smoker	20(14.8%)	13(17.8%)	5(18.5%)	0(0.0%)	2(8.0%)
Smoker	115(85.2%)	60(82.2%)	22(81.5%)	10(100.0%)	23(92.0%)
Disease stage						0.37
IIIb-IIIc	27(20.0%)	13(17.8%)	5(18.5%)	1(10.0%)	8(32.0%)
IV	108(80.0%)	60(82.2%)	22(81.5%)	9(90.0%)	17(68.0%)
CNS metastases						0.71
No	116(85.9%)	64(87.7%)	24(88.9%)	8(80.0%)	20(80.0%)
Yes	19(14.1%)	9(12.3%)	3(11.1%)	2(20.0%)	5(20.0%)
Liver metastases						0.20
No	104(77.0%)	53(72.6%)	20(74.1%)	10(100.0%)	21(84.0%)
Yes	31(23.0%)	20(27.4%)	7(25.9%)	0(0.0%)	4(16.0%)
Bone metastases						0.32
No	95(70.4%)	52(71.2%)	16(59.3%)	9(90.0%)	18(72.0%)
Yes	40(29.6%)	21(28.8%)	11(40.7%)	1(10.0%)	7(28.0%)
Pleural metastases						0.87
No	107(79.3%)	56(76.7%)	22(81.5%)	8(80.0%)	21(84.0%)
Yes	28(20.7%)	17(23.3%)	5(18.5%)	2(20.0%)	4(16.0%)
Immunotherapy regimens						0.73
Duvaluzuma plus platinum-based chemotherapy	92(68.2%)	50(68.5%)	16(59.3%)	8(80.0%)	18(72.0%)
Atezolizumab plus platinum-based chemotherapy	28(20.7%)	17(23.3%)	6(22.2%)	1(10.0%)	4(16.0%)
Serplulimab plus platinum-based chemotherapy	15(11.1%)	6(8.2%)	5(18.5%)	1(10.0%)	3(12.0%)

CNS: Central nervous system

To further assess the implications of molecular subtypes, PFS and OS for the four subtypes were analyzed, with the last follow-up conducted in March 2024. The PFS for the SCLC-A dominant, SCLC-N dominant, SCLC-P, and SCLC-I subtypes were 8.0, 7.5, 5.0, and 12.0 months, respectively (P = 0.0009) (Fig. 1F). The OS were 15.0, 12.5, 13.0, and 22.0 months, respectively (P = 0.0145) (Fig. 1G). Pairwise comparisons among subgroups (see Supplementary Table 2 for P-values and Benjamini-Hochberg adjusted P-values) revealed that the SCLC-I subtype demonstrated significantly superior PFS compared to other subtypes. This distinctive survival advantage may be attributed to its unique molecular characteristics and immune microenvironment. Univariate and multivariate Cox proportional hazards regression analyses showed that, after adjustment for age, sex, smoking history, and distant organ metastasis, patients with the SCLC-A, SCLC-N, and SCLC-P subtypes had significantly shorter PFS compared to those with the SCLC-I subtype (HR = 2.48, 2.70, and 4.58; p = 0.002, 0.003, and < 0.001; Supplementary Table 3). For OS analysis, the SCLC-N and SCLC-P subtypes were also associated with significantly worse outcomes than the SCLC-I subtype (HR = 2.15 and 4.47; p = 0.046 and 0.001; Supplementary Table 4). These findings suggest that SCLC-I may represent a distinct clinical entity within ES-SCLC, warranting further investigation into its therapeutic potential and underlying biology.

SCLC-I subtype exhibited a distinct immune microenvironment with high CD8⁺ T cell infiltration

The relationship between CD8⁺ T and cancer cells, termed tumor immunophenotype can be classified into three categories, namely “desert,” characterized by low CD8⁺ T cell infiltration within tumors; “excluded,” where CD8⁺ T cells are restricted to the stroma adjacent to the tumor; and “inflamed,” where CD8⁺ T cells infiltrate tumor parenchyma and directly contact tumor cells [27]. In 93 ES-SCLC cases, 53.8% (50 of 93) exhibited the “desert” phenotype, 25.8% (24 of 93) were “excluded,” and 20.4% (19 of 93) were “inflamed” (Fig. 2A and B). Among these, the SCLC-A subtype was predominant in the “desert” (62%) and “excluded” (70.8%) categories, while the SCLC-I subtype accounted for most cases in the “inflamed” category (52.6%) (Fig. 2C). Further analysis revealed significantly higher CD8⁺ T cell infiltration in the SCLC-I subtype than those in the other subtypes. CD8-positive cells were independently evaluated by two pathologists (Fig. 2D). Multiple immunofluorescence staining confirmed increased infiltration of CD3⁺CD8⁺ T cells in SCLC-I tumors compared with those in SCLC-A, SCLC-N, and SCLC-P tumors (Fig. 2E). These findings highlight the unique immune microenvironment of the SCLC-I subtype, characterized by its robust CD8⁺ T cell infiltration, which may underlie its observed association with improved responsiveness to immune-based therapies.

Fig. 2.

SCLC-I Subtype exhibited a distinct immune microenvironment with higher CD8⁺ T cell infiltration. (A) Representative image of three distinct CD8⁺ T cell immune-phenotypes in ES-SCLC. (B) Pie chart showing the percentages of different CD8⁺ T cell immune-phenotypes(N = 93). (C) Comparison of molecular subtype composition across the three CD8⁺T cell immune-phenotypes, with the SCLC-I subtype most frequently associated with the “inflamed” phenotype. (D) SCLC-I subtypes had significantly more CD8⁺ T cells infiltration than the SCLC-A subtype (P = 0.0149). (E) Multiple immunofluorescence staining highlighting increased infiltration of CD3⁺CD8⁺ T cells in the SCLC-I subtype

Immune profiling highlights heterogeneity and CD8⁺ T cell subset expansion in SCLC-I post anti-PD-L1 therapy

To evaluate differences in immune subsets among SCLC subtypes, PBMCs from 24 patients with ES-SCLC who received first-line immunotherapy were analyzed. Detailed clinical characteristics of these patients, including disease stage, treatment regimen, and survival analysis, are provided in Supplementary Table 5. The cohort included 9 patients with SCLC-A, 6 with SCLC-N, 2 with SCLC-P, and 7 with SCLC-I, with PBMCs collected at baseline and 3 weeks after treatment initiation. CyTOF was performed to assess immune cell composition and dynamics changes(Fig. 3A). A total of 32 immune cell clusters were identified based on high-dimensional marker expression. The cluster-by-marker heatmap and overall t-SNE visualization are shown in Supplementary Fig. 1A-C, and the corresponding marker panel is listed in Supplementary Table 6. To simplify interpretation, these clusters were grouped into 12 broad immune cell subsets based on lineage markers, including CD4⁺ T cells (26.38%), CD8⁺ T cells (16.37%), NK cells (19.16%), and monocytes (22.9%) as dominant populations, along with smaller proportions of dendritic, γδ T, and plasma cells (Fig. 3B). The distribution of these subsets was visualized using t-SNE, which indicated 13 distinct clusters, including 12 defined immune subsets (Fig. 3B) and one undefined group, highlighting the diversity of PBMC immune populations (Fig. 3C). Signature markers, such as CD8, CD19, and CD56, mapped the distribution of these subsets, revealing variability among patients (Fig. 3D). The variability in immune profiles among patients was evident, with CD4⁺ T cells, CD8⁺ T cells, and monocytes as dominant subsets, while patient B137 showed the highest NK cell infiltration, underscoring inter-patient heterogeneity in immune profiles (Fig. 3E).

Fig. 3.

Immune profiling highlighted heterogeneity and CD8⁺ T cell subset expansion in SCLC-I post anti-PD-L1 therapy. (A) Workflow diagram illustrating the analysis process. CyTOF data were collected from baseline peripheral blood mononuclear cell (PBMC) samples of 9 patients with SCLC-A, 6 with SCLC-N, 2 with SCLC-P, and 7 with SCLC-I before immunotherapy and 3 weeks after immunotherapy. Multiplex IF was performed on tumor samples collected at baseline before immunotherapy. (B) Heatmap showing the proportions of 12 distinct immune cell subsets across 32 identified clusters. (C) t-SNE plot illustrating the distribution of 13 distinct clusters, including 12 defined immune subsets (Fig. 3B) and one undefined group. (D) Signature markers mapped the distribution of these subsets. (E) The variability in immune profiles among patients was evident, with CD4⁺ T cells, CD8⁺ T cells, and monocytes being the dominant subsets. (F) Box plot comparing the frequencies of CD8⁺ T cells in total cells between baseline and after immunotherapy. (G) Box plot comparing the frequencies of C13 cluster (CD161⁺CD127⁺CD8⁺ T Cells) in total cells between baseline and after immunotherapy in the SCLC-I subtype. IF: Immunofluorescence; t-SNE: t-distributed stochastic neighbor embedding; SCLC: Small-cell lung cancer; CyTOF: Mass cytometry by time-of-flight; FFPE: Formalin-fixed paraffin embedded; DCs: Dendritic cells; DPT: double-positive T cells; DNT: double-negative T cells; NK: natural killer cells; NKT: natural killer T cells; I_B: SCLC-I patients before immunotherapy; I_A: SCLC-I patients after immunotherapy

Analysis of immune subsets across SCLC subtypes revealed distinct subtype-specific patterns and dynamic changes before and after anti-PD-L1 therapy. Supplementary Fig. 2A and 2B illustrates the shifts in major and rare immune cell subsets following treatment. A paired analysis particularly of total cell populations demonstrated a significant increase in CD8⁺ T cell frequency in the SCLC-I subtype after treatment (P < 0.01, Fig. 3F), whereas no notable changes were observed in the other subtypes, as shown in Supplementary Fig. 2C. Additionally, within the total cell populations, the C13 cluster (CD161⁺CD127⁺CD8⁺ T cells) showed a marked increase only in SCLC-I post-treatment (P < 0.01, Fig. 3G). The full cluster-level comparisons for all subtypes are provided in Supplementary Fig. 3A-D, illustrating overall frequency changes (Supplementary Fig. 3A) and paired analyses within each subtype (Supplementary Fig. 3B for SCLC-A, Supplementary Fig. 3C for SCLC-N, and Supplementary Fig. 3D for SCLC-I). These findings indicate that SCLC-I exhibits a distinct immune response to anti-PD-L1 therapy, characterized by the expansion of CD8⁺ T cells and the C13 cluster. This emphasizes the critical role of these immune features in driving the enhanced responsiveness of the SCLC-I subtype, further highlighting its unique immunological profile as a crucial consideration for optimizing immunotherapy strategies in ES-SCLC.

SCLC-I subtype exhibited elevated CD161⁺ CD127⁺ CD8⁺ T cell proportion in CD8⁺ T cells, highlighting their biomarker potential

To explore differences in immune cell subsets among SCLC subtypes, CD8⁺ T cell clusters were analyzed before and after anti-PD-L1 therapy. Twenty-five clusters were identified, including effector, naïve, effect memory, central memory CD8⁺ T, and CD161⁺CD127⁺CD8⁺ T cells (Fig. 4A-C). The full list of clusters and their defining markers is provided in Supplementary Table 7. Figure 4D shows the proportional distributions of different CD8⁺ T cell subsets across various SCLC subtypes before and after anti-PD-L1 therapy. Given the few SCLC-P cases (n = 2), our analysis primarily focused on SCLC-A, SCLC-N, and SCLC-I. Notably, patients with SCLC-I exhibited a significantly higher percentage of CD161⁺CD127⁺CD8⁺ T cells within the CD8⁺ T cell population than did those with SCLC-N, a trend that persisted after treatment (P < 0.05, Fig. 4E). Similarly, the co-expression of CD161 and CD127 on CD8⁺ T cells was significantly higher in SCLC-I than that in the other SCLC subsets (Fig. 4F), with marker density analysis confirming the highest levels in this subtype (Fig. 4G). These findings suggest that CD161⁺CD127⁺CD8⁺ T cells may serve as a biomarker for immunotherapy response in the SCLC-I subtype, highlighting their potential role in shaping the distinct immune profile of this subtype.

Fig. 4.

SCLC-I subtype exhibited elevated CD161⁺ CD127⁺ CD8⁺ T cell proportion in CD8⁺ T cells, highlighting their biomarker potential. (A) Heatmaps displaying the distribution of different CD8⁺ T cell subsets and 25 clusters within the CD8⁺ T cell population. (B-C) t-SNE plots showing the distribution of all 25 clusters. (D) The proportional distributions of different CD8⁺ T cell subsets. (E) CD161⁺CD127⁺CD8⁺ T cells were significantly increased in patients with SCLC-I before immunotherapy, and this trend also existed after anti-PD-L1 treatment(*P<0.05). (F) t-SNE plots showing the distinct distribution and higher expression of CD161 and CD127 in CD8⁺ T cells of the SCLC-I subtype than those of the other subtypes. (G) Marker density analysis reveals elevated expression levels of CD161⁺CD127⁺CD8⁺ T cells of the SCLC-I subtype compared with those of the other subtypes. A_B: SCLC-A patients before immunotherapy; A_A: SCLC-A patients after immunotherapy; N_B: SCLC-N patients before immunotherapy; N_A: SCLC-N patients after immunotherapy; P_B: SCLC-P patients before immunotherapy; P_A: SCLC-P patients after immunotherapy; I_B: SCLC-I patients before immunotherapy; I_A: SCLC-I patients after immunotherapy

Building on these findings of immune heterogeneity in the SCLC-I subtype, we investigated the presence of distinct immune cell profiles in other SCLC subtypes, particularly SCLC-N and SCLC-A. Flow cytometry analysis revealed significantly higher frequencies of CD127⁺PD-1⁺CD4⁺ T cells in SCLC-A compared to SCLC-N, as defined by a standard gating strategy (Supplementary Fig. 4A) and quantified in both pre- and post-treatment samples (Supplementary Fig. 4B, P < 0.05). t-SNE visualization demonstrated spatial co-expression of PD-1 and CD127 on CD4⁺ T cells (Supplementary Fig. 4C), further supported by marker density analysis showing increased PD-1 expression in SCLC-A (Supplementary Fig. 4D-E). However, multiplex immunofluorescence performed on tumor tissues showed no significant difference in CD127⁺PD-1⁺CD4⁺ T cell infiltration between the two subtypes (Supplementary Fig. 4F-G, P = 0.425). These results highlight immune heterogeneity among SCLC subtypes and underscore the need for further studies to clarify these findings.

Co-expression of CD161 and CD127 on CD8⁺ T cells was independently validated in peripheral blood and associated with subtype-specific cytotoxicity

To validate the proportion of CD161⁺CD127⁺CD8⁺ T cells among peripheral CD8⁺ T cells, we prospectively enrolled an independent cohort of 35 patients with ES-SCLC who received first-line immunotherapy, including 20 with the SCLC-N subtype and 15 with the SCLC-I subtype. This validation cohort was recruited during a non-overlapping time frame relative to the CyTOF-based discovery cohort and was analyzed using conventional flow cytometry as a distinct immune profiling platform. There was no overlap in patients, biospecimens, or metadata between the two cohorts, thereby ensuring both temporal and analytical independence. Baseline clinical characteristics of patients in the validation cohort are summarized in Supplementary Table 8. The SCLC-N and SCLC-I groups were generally well balanced in terms of age, sex, smoking status, metastases and first-line treatment regimens, with no statistically significant differences observed. Flow cytometry was performed to analyze the PBMCs collected at baseline (Fig. 5A). Consistent with our discovery cohort, SCLC-I subtype showed a significantly higher CD161⁺CD127⁺CD8⁺ T /CD8⁺ T cell ratio than did the SCLC-N subtype (5.0% vs. 2.1%, P < 0.0001, Fig. 5B). This independent validation supports the robustness of our initial observations and highlights the reproducibility of this biomarker signal across patient cohorts and immune profiling platforms.

Fig. 5.

Co-expression of CD161 and CD127 on CD8⁺ T cells was independently validated in peripheral blood and associated with subtype-specific cytotoxicity. (A) Flow cytometry to analyze PBMCs obtained from the SCLC-I and SCLC-N subtype patients. (B) SCLC-I subtype (n = 15) exhibited a significantly higher ratio of CD161⁺CD127⁺CD8⁺ T cells to total CD8⁺ T cells than SCLC-N subtype (n = 20, P < 0.0001). (C-E) Cytotoxic marker expression, reflecting enhanced effector function, was significantly elevated in SCLC-I subtype compared with that in SCLC-N subtype, as revealed via flow cytometry using unpaired Student’s t-test. (F-H) The expression of immune exhaustion molecules was decreased in SCLC-I subtype PBMCs compared with that in the SCLC-N subtype, as revealed via flow cytometry using unpaired Student’s t-test

To further investigate the functional differences between the two subtypes, we assessed the expression of cytotoxic and exhaustion markers on CD161⁺CD127⁺CD8⁺ T cells. As shown in Fig. 5C-H, the cytotoxicity-associated factors KLRG1, GZMB, and GNLY, as well as the exhaustion markers PD-1, TIGIT, and LAG-3, were confirmed using flow cytometry in patients with SCLC-N and SCLC-I. We found that CD161⁺CD127⁺CD8⁺ T cells in the SCLC-I subtype exhibited significantly higher expression of GZMB (P = 0.004) and GNLY (P = 0.001) compared to those from the SCLC-N subtype (Fig. 5D-E). Conversely, the exhaustion markers TIGIT (P = 0.008), LAG-3 (P = 0.025), and PD-1 (P = 0.0018) were expressed at significantly lower levels in CD161⁺CD127⁺CD8⁺ T cells from patients with SCLC-I compared to those with SCLC-N (Fig. 5F-H). Collectively, these results demonstrate that CD161⁺CD127⁺CD8⁺ T cells in SCLC-I exhibit a more favorable effector phenotype than those in SCLC-N.

CD161⁺ CD127⁺ CD8⁺ T cell infiltration was higher in the SCLC-I subtype and correlated with peripheral blood levels

To further validate the enrichment of CD161⁺CD127⁺CD8⁺ T cells in the SCLC-I subtype at the tissue level, we examined their infiltration in formalin-fixed paraffin-embedded (FFPE) sections from patients with SCLC-N (n = 24) and SCLC-I (n = 25) collected prior to initiation of first-line immunotherapy. Multiplex immunofluorescence analysis showed that CD161 and CD127 were co-expressed in CD8 + T cells (Fig. 6A). Quantitative analysis showed that the infiltration of CD161⁺CD127⁺CD8⁺ T cells, as well as their proportion among total CD8⁺ T cells, was significantly higher in the SCLC-I subtype compared to the SCLC-N subtype (29.3% vs. 20.2%, P = 0.0281, Fig. 6B). Among these 49 patients, 35 had archived FFPE tissues matched with pre-treatment peripheral blood samples (20 with SCLC-N and 15 with SCLC-I). Furthermore, we observed a significant correlation between the frequency of CD161⁺CD127⁺CD8⁺ T cells to CD8⁺ T cells ratio in tumor tissues and that in matched peripheral blood in patients with SCLC-N and SCLC-I (r = 0.669, P < 0.0001, Fig. 6C). Simultaneously, we also conducted correlation analysis for SCLC-N and SCLC-I subtypes. Subtype-specific analysis revealed significant correlations in SCLC-N (r = 0.519, P = 0.019, Fig. 6D) and SCLC-I (r = 0.651, P = 0.009, Fig. 6E). These findings indicate a systemic concordance between tumor-infiltrating and circulating CD161⁺CD127⁺CD8⁺T cells, supporting their potential utility as a non-invasive peripheral blood biomarker for inferring tumor immune status.

Fig. 6.

CD161⁺ CD127⁺ CD8⁺ T cell infiltration was higher in the SCLC-I subtype and correlates with peripheral blood levels. (A) Multiplex IF demonstrating the co-expression of CD161 and CD127 in CD8⁺ T cells within tumor tissues. (B) Patients with the SCLC-I subtype (n = 25) showed significantly higher proportion of CD161⁺CD127⁺CD8⁺ T cells among total infiltrating CD8⁺ T cells than those with the SCLC-N subtype (n = 24, P = 0.0281). (C) A strong positive correlation ratio of CD161⁺CD127⁺CD8⁺ T /CD8⁺ T cells in tumor tissues and matched peripheral blood across combined patients with SCLC-N and SCLC-I (n = 35, r = 0.669, P < 0.0001). (D-E) Analysis revealed a significant positive correlation between the CD161⁺CD127⁺CD8⁺ T/CD8 + T cell ratio in tumor tissues and matched peripheral blood in patients with SCLC-N (n = 20, r = 0.519, P = 0.019) and SCLC-I (n = 15, r = 0.651, P = 0.009)

High CD161⁺ CD127⁺ CD8⁺ T cell to CD8⁺ T cell ratio was associated with prolonged PFS in patients with ES-SCLC

The effects of CD161⁺CD127⁺CD8⁺ T cells in the circulation as predictive biomarkers of the response to anti-PD-L1 therapy in ES-SCLC remain unclear. We speculate that CD161⁺CD127⁺CD8⁺ T cells function as an activated effector phenotype associated with survival benefits in SCLC. Receiver operating characteristic (ROC) curve analysis revealed a.

correlation between the ratio of CD161⁺CD127⁺CD8⁺ T cells to total CD8⁺ T cells and response status, with an area under the curve (AUC) of 0.880 (Fig. 7A). Using a cutoff of 2.7% CD161⁺CD127⁺CD8⁺ T cells within the CD8⁺ T cell population, the model achieved 86.7% sensitivity and 72.0% specificity in predicting response to anti-PD-L1 therapy (Fig. 7A). Based on this threshold, patients were stratified into two groups, namely low (< 2.7%, n = 16) and high (≥ 2.7%, n = 19). As of the last follow-up in March 2024, Kaplan–Meier survival analysis revealed that patients with a CD161⁺CD127⁺CD8⁺ T cell to CD8⁺ T cell ratio of ≥ 2.7% had significantly longer PFS than those with a ratio of < 2.7% (11.0 months vs. 7.0 months, P = 0.0196, Fig. 7B). However, differences in OS between the two groups were not statistically significant, possibly owing to the limited follow-up period (Fig. 7C). CD161⁺CD127⁺CD8⁺ T cells showed promise as predictive biomarkers for anti-PD-L1 therapy in ES-SCLC, particularly in predicting PFS. Further studies are needed to validate these findings and assess their impact on OS.

Fig. 7.

High CD161⁺ CD127⁺ CD8⁺ T/ CD8⁺ T cell ratio was associated with prolonged PFS in patients with ES-SCLC. (A) Receiver operating characteristic (ROC) curve analysis evaluating the ability of the CD161⁺CD127⁺CD8⁺ T/CD8⁺ T cells ratio to predict response to immunotherapy in the SCLC-N and SCLC-I subtypes. Sensitivity indicates the proportion of true positives, while specificity represents the proportion of true negatives. (B) Kaplan–Meier survival analysis showing significantly longer PFS in the high-ratio group (≥ 2.7%, N = 19) than that in the low-ratio group (< 2.7%, N = 16, P = 0.0196). (C) Kaplan–Meier survival analysis showing no significant differences between the high- and low-ratio groups (P = 0.989)

Discussion

This study provides a comprehensive molecular and immunological characterization of ES-SCLC, offering novel insights into its biological heterogeneity and therapeutic implications. By integrating the expression of lineage-specific transcription factors (ASCL1, NEUROD1, and POU2F3), we refined the molecular classification of ES-SCLC into four distinct subtypes, including the inflammation-enriched SCLC-I subtype, which accounted for 18.5% of cases. Unlike classical subtypes defined by neuroendocrine lineage regulators, SCLC-I lacked dominant expression of ASCL1, NEUROD1, or POU2F3, yet exhibited a favorable clinical outcome with significantly prolonged PFS and OS following first-line chemoimmunotherapy. These findings suggest that the molecular definition of SCLC subtypes can inform treatment stratification and reflect intrinsic differences in tumor–immune interactions.

Importantly, the SCLC-I exhibited an inflamed tumor microenvironment with high CD8⁺ T cell infiltration and a significantly better prognosis following anti-PD-L1-based chemoimmunotherapy compared to other molecular subtypes. Notably, SCLC-I demonstrated a dynamic immune response to treatment, characterized by the expansion of CD8⁺ T cells, particularly the CD161⁺CD127⁺CD8⁺ T cell subset, in both peripheral blood and tumor tissue. These cells displayed enhanced cytotoxicity and reduced exhaustion, and their abundance at baseline was associated with prolonged PFS. Together, these findings establish a strong link between ES-SCLC molecular subtyping and differential immunotherapy responsiveness, and they position CD161⁺CD127⁺CD8⁺ T cells as a promising biomarker for guiding treatment decisions in patients with SCLC-I.

CD161⁺CD127⁺CD8⁺ T cells represent a phenotypically and functionally distinct subset with significant implications for anti-tumor immunity. CD161, encoded by KLRB1, is associated with tissue-resident memory-like properties and heightened effector functions [28]. The infiltration of CD161⁺ cytotoxic T lymphocyte cells has been linked to favorable treatment outcomes and prolonged survival, suggesting their potential as indicators of effective anti-tumor immune responses [29]. In the context of SCLC-I, we interpreted the anti-tumor potential of this subset based on our phenotypic observations and supporting evidence from the literature.

Transcriptomic and functional analyses have revealed that CD161⁺CD8⁺ T cells exhibit robust effector activity, characterized by high expression of cytotoxic mediators such as granzyme A, granzyme B, perforin, TNF-α, and IFN-γ [14]. Functionally, these cells rely primarily on granule exocytosis–mediated killing, rather than FasL or TRAIL signaling, indicating a direct cytolytic mechanism [30]. Moreover, compared with CD161⁻ counterparts, CD161⁺CD8⁺ T cells display preprogrammed effector memory features and can rapidly respond to antigenic or inflammatory cues, even in the absence of strong TCR stimulation [31]. These characteristics position them as potent effectors in tumors with active immune infiltration, such as the SCLC-I subtype. Notably, co-expression of CD127, the α-chain of the IL-7 receptor, plays a critical role in maintaining the longevity and functional integrity of CD8⁺ T cells [32, 33]. Upon IL-7 engagement, CD127 initiates downstream STAT5 signaling that promotes the expression of anti-apoptotic molecules such as Bcl-2 and Mcl-1, supporting T cell survival and memory differentiation [34]. In follicular lymphoma, CD127⁺CD8⁺ T cell subsets, particularly those co-expressing KLRG1 or lacking it, are associated with superior proliferative capacity, survival potential, and favorable clinical outcomes. Unlike terminal effector-like CD127⁻ subsets, CD127⁺ cells display memory-like properties that support long-term immune surveillance. Furthermore, CD127⁺CD8⁺ T cell differentiation can be modulated through the PI3K signaling axis, suggesting a therapeutic avenue for promoting beneficial T cell phenotypes [35]. Collectively, these features may explain the cytotoxic activity of CD161⁺CD127⁺CD8⁺ T cells observed in the inflammatory immune landscape of the SCLC-I subtype.

Despite the promising implications of our findings, some limitations should be addressed. First, the limited number of patients in the SCLC-P subgroup (n = 10), although consistent with its reported prevalence of approximately 7-10% in SCLC, substantially reduced the statistical power for subtype-specific analyses. The small sample size also precluded the application of comprehensive CyTOF-based immune profiling and limited its inclusion in immunological comparisons across subtypes. As a result, the identification of SCLC-P-specific immune features remains challenging. Future studies involving larger, multi-center cohorts will be necessary to validate and extend these findings to underrepresented subtypes. Second, although a significant association was observed between CD161⁺CD127⁺CD8⁺ T cell levels and prolonged PFS, no statistically significant difference in OS was detected. This discrepancy may be attributed to the still limited follow-up duration, the confounding effects of post-progression interventions such as heterogeneous second-line therapies and immunotherapy crossover, and the restricted statistical power due to sample size. Continued longitudinal follow-up and future multi-center studies with larger cohorts will be essential to clarify the long-term prognostic relevance of this immune subset. Finally, although we assessed their cytotoxic potential through phenotypic markers such as granzyme B and granulysin, direct functional validation such as cytotoxicity assays or tumor cell killing tests was not performed. This limits our ability to confirm their effector activity in a tumor context, and future studies incorporating functional assays will be necessary to determine their precise role in anti-tumor immunity.

Conclusions

This study highlights the potential utility of CD161⁺CD127⁺CD8⁺ T cells as predictive biomarkers for anti–PD-L1 therapy in the SCLC-I subtype, offering novel insights into the immunological heterogeneity of ES-SCLC. Through comprehensive molecular and immune profiling, we observed that SCLC-I exhibits a unique TME characterized by robust CD8⁺ T cell infiltration and an association with improved responsiveness to immune checkpoint inhibitors. The enrichment and functional superiority of CD161⁺CD127⁺CD8⁺ T cells in this subtype underscore their therapeutic relevance and establish them as a potential biomarker for predicting clinical outcomes. The systemic correlation between intratumoral and circulating levels of these cells further emphasizes their utility as minimally invasive markers for monitoring treatment response. These findings underscore the significance of subtype-specific strategies in advancing precision immunotherapy for ES-SCLC and provide insights for future studies aimed at refining and validating these biomarkers in broader, multi-center cohorts.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Authors’ contributions

J.J.Q. designed and drafted the manuscript. Y.K.L. led the statistical analysis, extended the survival follow-up, and substantially revised the results and discussion sections. J.J.Q., Y.K.L., W.J.S., B.G.W., Q.S., and J.Z. contributed to sample collection and data analysis. Z.W.C. assisted with figure preparation and statistical validation. C.C.X. contributed to the interpretation of immune profiling data and preparation of the response letter. L.X.Y. and B.W. performed pathological assessments. L.J.C. and J.Y.Z. critically revised the manuscript for important intellectual content. J.Y.Z. conceptualized and supervised the study. All authors read and approved the final version of the manuscript.

Funding

This work was supported by grants from the Natural Science Foundation of Zhejiang Province (No. LY23H160020), Key Research and Development Program of Zhejiang Province(No.2025C02094,No.2023C03069 and No.2025C02092 ), Zhejiang Provincial Clinical Research Center for Respiratory Disease (No. 2022E50005).

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. Additional data are available as supplementary material.

Declarations

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the First Affiliated Hospital, Zhejiang University School of Medicine (No. 2022 − 1161).

Competing interests

The authors declare that there are no conflicts of interest.