Introduction

Pancreatic ductal adenocarcinoma (PDAC) ranks as the fourth-leading cause of cancer-related deaths, characterized by an exceedingly poor prognosis, with only a 12% 5-year survival rate.1 Histologically, PDAC exhibits distinctive characteristics with an abundance of stromal compartments including, immune cells, fibroblasts, extracellular matrix, nerves, and endothelial cells, often occupying a substantial portion of the tumor mass. These components profoundly influence disease biology.2 3 Therefore, a comprehensive understanding of the PDAC tumor microenvironment (TME) composition and its effects on cancer cell biology is critical. The accumulative evidence demonstrates substantial heterogeneity within the TME, necessitating meticulous investigation into the clinical significance and immune features of its distinct cellular subpopulations.

Traditionally considered a homogeneous group, CD8+ T cells are pivotal in antitumor immunity, primarily through IFN-γ and granzyme B secretion. However, recent advances in understanding the TME have unveiled considerable heterogeneity within CD8+ T cells. In PDAC, these cell subsets differ in their degree of infiltration, localization, and phenotypic expression.4 For instance, Carstens et al investigated the heterogeneity spatial distribution of CD8+ T cells in PDAC, noting that patients with CD8+ T cells closer to malignant cells had better prognoses.5 Additionally, Picard et al reported that IL-17-producing CD8+ T cells are associated with an adverse prognosis due to their influence on tumor-associated fibroblasts.6 These findings underscore the importance of further investigating the distinct roles of CD8+ T cell subtypes in PDAC and highlight the necessity to explore and identify specific markers that can delineate their functional diversity.

CD161, a homodimeric C-type lectin, was initially recognized as a marker of natural killer (NK) cells.7 Subsequent research has shown its expression in various T lymphocyte lineages, including CD4+, CD8+, and TCRγδ+ T cells, and is further categorized within CD8+ T cells based on CD161 expression intensity. The majority of CD161hiCD8+ T cells are composed of mucosal-associated invariant T (MAIT) cells in healthy adult blood.8 MAIT cells can detect diverse microbial entities via vitamin B metabolite recognition presented by Major Histocompatibility Complex (MHC)-Related Protein 1.9 Non-MAIT CD161+CD8+ T cells are characterized by their viral antigen specificity, polyclonality, and stem cell-like memory phenotypes.10 Despite this, the distribution of MAIT cells versus conventional CD8+ T cells within the CD161+CD8+ T cell population in the TME remains unexplored. Moreover, the proportion of CD161+CD8+ T cells within the overall CD8+ T cell cohort fluctuates based on the physiological and pathological milieu. In a healthy immune system, these cells represent a smaller fraction of the total CD8+ T cell population, distinguished by their unique functional attributes, especially in mucosal immunity and in response to certain infections and inflammations. This percentage varies among individuals and across different pathological conditions. Particularly in oncological contexts such as non-small cell lung cancer, the frequency of CD161+ cells within CD8+ T cells is increased in tumors,11 highlighting the importance of their investigation in disease-specific scenarios. Additionally, the innate-like response of CD161+CD8+ T cells to cytokine stimulation, which occurs independently of TCR (T-cell receptor) engagement and is particularly notable given the low expression of MHC class I molecules in pancreatic cancer, underscores their potential to circumvent the limitations associated with TCR-mediated reactivity.8 12 Therefore, the distinctive characteristics of these cells warrant further investigation as promising candidates in the realm of antitumor therapeutics.

Nevertheless, the prognostic implications of CD161+CD8+ T cells have shown variability across different cancer types.13–16 In early-stage liver cancer, their presence correlates with increased secondary recurrence rates and a poorer prognosis.15 Whereas in human papillomavirus (HPV)-related oropharyngeal cancer, they are indicative of more favorable outcomes and a more robust immune response.13 Moreover, single-cell RNA (scRNA) sequencing studies across multiple tumors revealed a complex picture, with CD161+CD8+ T cells playing a significant role in chemotherapeutic resistance, recurrence, and immune evasion. However, these findings have been subject to considerable debate.13–16 These collective observations suggest that although CD161+CD8+ T cells hold promise as prognostic markers, their functional and molecular characteristics require further elucidation. This warrants more comprehensive investigations to understand their role within the immune microenvironment of PDAC. Therefore, this study aims to elucidate the prognostic value of CD161+CD8+ T cells, focusing on their molecular characteristics to better recognize their immunological role.

MethodsPatient selection and tissue microarrays

This study used tumor tissues from 192 patients diagnosed with PDAC who underwent radical resection at Zhongshan Hospital, Fudan University between March 2015 and July 2018. From these samples, we created tissue microarrays (TMAs). The inclusion criteria included (1) complete R0 resection confirmed by pathological examination and (2) comprehensive clinical and follow-up data. The exclusion criteria included (1) prior neoadjuvant therapy; (2) history of other malignancies; (3) postoperative participation in clinical trials for immune or targeted therapies; and (4) evidence of distant metastases or undetermined origins. The tumor tissues were formalin-fixed and paraffin-embedded (FFPE). Experienced pathologists selected representative tumor tissue areas for extraction using 2.0 mm diameter cylindrical cuts. The collected clinicopathological data included sex, age, surgical procedure, tumor differentiation, T stage, N stage, TNM stage, preoperative serum CA19-9 levels, microvascular and perineural invasion, history of transfusion, adjuvant radiotherapy (ART), adjuvant chemotherapy (ACT), and follow-up information up to June 2023. These data were collected by one surgeon and cross-verified by another. The TNM staging, as well as T and N classifications, adhered to the American Joint Committee on Cancer eighth edition criteria.17 Six patients were excluded because of the detachment of certain spots during staining, potentially attributable to the insufficient adhesion of tissue sections to the microarray slides. Thus, it leaves 186 patients for further analysis. An additional 19 patients had unknown chemotherapy status but were included. Overall survival (OS) and recurrence-free survival (RFS) were calculated from the date of surgery to the date of death or relapse, or last follow-up, respectively, as previously described.18 19

Immunofluorescence staining and evaluation

Immunofluorescence staining was performed on the FFPE tissue specimens of the TMAs, as previously described.19 During this process, anti-CD161 (Abcam, ab259916) and anti-CD8 (zsbio, ZA-0508) antibodies were applied, and the slides were incubated overnight at 4℃, then further incubated with Goat Anti-Rabbit IgG H&L (Abcam, ab205718), iFluor 488 tyramide (aatbio, 11060), and Alex Fluor594 donkey anti-rabbit IgG (Life, A32754). Subsequently, the slides were treated for 20 min with DAPI (Solarbio, #C0060). The slides were scanned using a 3D HISTECH Pannoramic SCAN fluorescence scanner, and immune cells expressing both CD8 and CD161 were identified. The three most representative and independent high-power fields (HPF) were captured at ×200 magnification (0.305 mm2 per field) for each tumor region in all specimens. Then, two independent pathologists, blinded to the patients’ clinical data, assessed each selected field to determine the number of CD8+ cells and CD161+CD8+ cells. In cases of disagreement, the images were re-reviewed, and a consensus was reached by the two observers. Finally, the three fields were averaged to determine the ultimate count of each sample.

Flow cytometry

Fresh PDAC tissues were collected promptly after tumor resection during surgery. These tissues were then dissociated into single cells using the Tumor Dissociation Kit (Miltenyi Biotec, 130-095-929) according to the manufacturer’s instructions. The isolated cells were stained for surface markers (CD45, CD8a, CD161, TCR Vα7.2, PD-1, CTLA-4, TIM-3, and TIGIT) at 4℃ for 30 min and subsequently fixed with IC Fixation Buffer (eBioscience, 00-8222-49). After three washes in PBS buffer, the cells were permeabilized with Permeabilization Buffer (eBioscience, 00-8333-56) and stained for intracellular markers (TNF-α, IFN-γ, Granzyme B, and Perforin).

Data acquisition was performed using the BD FACS Arial Flow Cytometer (BD Biosciences, USA), and analysis was conducted with FlowJo V.10 software (BD Biosciences, USA). The antibodies used in this study included PerCP/Cyanine 5.5 anti-human CD45 (Biolegend, 304027), FITC anti-human CD8a (Biolegend, 301006), Brilliant Violet 421 anti-human CD161 (Biolegend, 339913), PE anti-human TCR Va7.2 (Biolegend, 331210), APC anti-human PD-1 (Biolegend, 329907), PE anti-human CTLA-4 (Biolegend, 349905), Brilliant Violet 510 anti-human TIM-3 (Biolegend, 345029), PE/Cyanine7 anti-human TIGIT (Biolegend, 372713), Brilliant Violet 510 anti-human TNF-α (Biolegend, 502949), APC anti-human IFN-γ (Biolegend, 502511), PE anti-human/mouse Granzyme B (Biolegend, 372207), and PE/Cyanine7 anti-human Perforin (Biolegend, 353315).

Bioinformatics

The processed dataset of human scRNA data for tumor-infiltrating lymphocytes in PDAC was accessed from the NIH GEO database (Accession # GSE211644). We used Seurat V.4.3.0 for data analysis, employing graph-based clustering of principal components to categorize cell types. This categorization was based on commonly recognized marker genes, as previously detailed.20 Differentially expressed genes (DEGs) between CD161+CD8+ T cells (KLRB1 mRNA>0) and CD161−CD8+ T cells (KLRB1 mRNA=0) were identified using Seurat’s FindMarkers function. P values for these DEGs were calculated using a Wilcoxon test. Marker genes for both CD161+CD8+ T and CD161−CD8+ T cells were ranked by log2 fold change (log2FC) and used in Gene Set Enrichment Analysis (GSEA) to explore the GO Biological Process. The CD161+CD8+ T cell signature was defined based on the CD8A and CD8B genes and DEGs that met the criteria of log2FC>0.25, and an adjusted p<0.05. To further validate the characteristics of CD161+CD8+ T cells, another processed human dataset (GSE155698)21 was downloaded from the NIH GEO database. Then, the same processing and analysis as described above was performed.

Transcriptomic and survival data for PDAC were obtained from the Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/), International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/), and ArrayExpress database. We used TCGA-PAAD (TPM), ICGC-AU-Array, and E-MTAB-6134 cohorts to assess the prognostic significance of the CD161+CD8+ T cell signature. The signature score was determined as the geometric mean of normalized gene expression.

Statistical analysis

Statistical analyses were conducted using R software (V.4.3.0). Continuous variables, including CA19-9, tumor-infiltrating CD161+CD8+ T cells, and CD8+ T cells, were classified into high or low groups based on the optimal threshold set by the “surv_cutpoint” function from the “survminer” R package. The χ2 or Fisher’s exact tests were used to assess the associations between tumor-infiltrating CD8+ T cells, CD161+CD8+ T cells, and clinicopathological features. Scatter diagrams were created using GraphPad Prism V.9 (GraphPad Software) to represent CD161+CD8+ T cell levels in specific groups, and analyzed using Student’s t-test or Mann-Whitney U test, as appropriate. Survival distributions were evaluated with the log-rank test and the “survdiff” function from the “survival” package in R. The univariate and multivariate Cox proportional hazard analyses were used to identify independent prognostic factors for PDAC, which were reported as regression p values, HRs, and 95% CIs. The interactions between indicators were examined using the Cox proportional hazards method. The Wilcoxon matched-pairs test was employed to identify statistically significant functional variations between the CD161+CD8+ T cells and CD161−CD8+ T cells, as determined by flow cytometry. All p values less than 0.05 were deemed statistically significant.

ResultsDemographical and clinicopathological characteristics

In total, 186 PDAC patients were included. The median follow-up duration was 70.1 (95% CI 67.1 to 72.1) months. Of these, 102 (54.8%) were 65 years or younger and the group was composed of 115 males (61.8%) and 71 females (38.2%). A majority, 81.4%, underwent ACT. Disease staging revealed 94 patients (50.5%) at stage I, 79 (42.5%) at stage II, and 13 (7.0%) at stage III. The median level of CA19-9 was 152.2 (IQR: 42.4–374.0) U/mL. Further clinicopathological features can be found in online supplemental table 1.

Identification and correlations of tumor-infiltrating CD161+CD8+ T cells with clinicopathological characteristics

The presence of CD161+CD8+ T cells in PDAC microenvironment was identified by dual-color immunofluorescence staining on TMA slices (online supplemental figure 1A,B) and flow cytometry of fresh tumor tissues (online supplemental figure 1C). The median counts of tumor-infiltrating CD161+CD8+ T cells, and CD8+ T cells were 19.0 (IQR: 8.7–39.6) /HPF and 46.0 (IQR: 23.0–72.5) /HPF, respectively (online supplemental table 1).

The optimal cut-off values were determined to be 231.4 U/mL for CA19-9, 35.3/HPF for tumor-infiltrating CD161+CD8+ T cells, and 59.7/HPF for tumor-infiltrating CD8+ T cells via the “surv_cutpoint” function from the “survminer” R package. Using these thresholds, patients were divided into low (133 patients) and high (53 patients) CD161+CD8+ T cell groups. Representative images of these groups can be seen in figure 1A. Table 1 reveals a significant correlation of CD161+CD8+ T cell infiltration with tumor differentiation (p=0.024), perineural invasion (p=0.019), and serum CA19-9 levels (p=0.043), but not with other factors. Tumors with poor differentiation or perineural invasion displayed notably lower CD161+CD8+ T cell infiltration (p<0.05, figure 1B,C). Additionally, patients with CA19-9 levels less than 231.4 U/L had significantly higher CD161+CD8+ T cell infiltration than those with levels above this threshold (p<0.01, figure 1D).

Table 1

The relationships between tumor-infiltrating CD8+ T cells and CD161+CD8+ T cells and clinicopathological characteristics

Figure 1

The correlation and prognostic value of tumor-infiltrating CD161+CD8+ T cells in the Zhongshan cohort. (A) Representative images of immunofluorescence staining of high and low infiltration levels of CD161+CD8+ T cells. (B–D) A comparison of the numbers of tumor-infiltrating CD161+CD8+ T cells according to different tumor differentiation, perineural invasion, and CA19-9 subgroups. *p<0.05, **p<0.01. Data are presented as median with IQR. (E, F) Kaplan-Meier plots for overall survival and recurrence-free survival according to the different densities of tumor-infiltrating CD161+CD8+ T cells via the log-rank test.

Prognostic value of tumor-infiltrating CD161+CD8+ T cells in PDAC patients

In our study, PDAC patients who underwent radical resection had a median OS and RFS of 27.3 and 17.0 months, respectively. The OS rates were 79.6%, 40.3%, and 26.8% at 1, 3, and 5 years, respectively. Meanwhile, the RFS rates were 60.8%, 25.4%, and 15.8%. Significant differences were observed in median OS and RFS between the high and low tumor-infiltrating CD8+ T cell groups (OS: 45.3 vs 22.3 months, p<0.001, HR=0.529, 95% CI 0.379 to 0.739, (online supplemental figure 2A); RFS: 25.3 vs 15.1 months, p=0.015, HR=0.665, 95% CI 0.483 to 0.915 (online supplemental figure 2B), which was consistent with our previous studies.19 22 Notably, the high tumor-infiltrating CD161+CD8+ T cell group showed significantly longer median OS and RFS compared with the low group (OS: 81.3 vs 19.2 months, p<0.001, HR=0.259, 95% CI 0.185 to 0.361, figure 1E; RFS: 41.4 vs 11.6 months, p<0.001, HR=0.357, 95% CI 0.259 to 0.490, figure 1F).

The univariate Cox analysis revealed that tumor differentiation (p=0.005 and p<0.001, respectively), N stage (p<0.001 for both), TNM stage (p<0.001 for both), CA19-9 (p<0.001 for both), ACT (p=0.024 and p<0.001, respectively), perineural invasion (p=0.002 and p=0.022, respectively), tumor-infiltrating CD8+ T cells (p<0.001 and p=0.016, respectively) and tumor-infiltrating CD161+CD8+ T cells (p<0.001 for both) were all significantly associated with OS and RFS (table 2). The subsequent multivariate Cox analysis further identified CA19-9 (p=0.018, HR=1.531, 95% CI 1.076 to 2.177 and p=0.002, HR=1.693, 95% CI 1.214 to 2.362, respectively), ACT (p<0.001, HR=0.328, 95% CI 0.211 to 0.510 and p=0.002, HR=0.506, 95% CI 0.331 to 0.773, respectively), and tumor-infiltrating CD161+CD8+ T cells (p<0.001, HR=0.209, 95% CI 0.115 to 0.382 and p<0.001, HR=0.243, 95% CI 0.137 to 0.429, respectively) as the independent prognostic indicators for both OS and RFS. In addition, tumor differentiation (p=0.028, HR=1.463, 95% CI 1.041 to 2.054) was also verified as an independent prognostic indicator only for RFS, while N stage (p=0.013, HR=2.118, 95% CI 1.171 to 3.830) and perineural invasion (p=0.015, HR=1.782, 95% CI 1.120 to 2.835) were independent risk factors for OS alone (table 2).

Table 2

Univariate and multivariate analysis of prognostic factors associated with overall survival and recurrence-free survival

Survival predictive value in combination of tumor-infiltrating CD161+CD8+ T cells and CA19-9

CA19-9 traditionally functions as a biomarker and predictor for PDAC but also contributes to malignant progression through mechanisms such as protein glycosylation, E-selectin binding, angiogenesis enhancement, and immune response modulation.23 24 The above analyses have shown that both CA19-9 and tumor-infiltrating CD161+CD8+ T cells were independent prognostic indicators for OS and RFS, which indicated that tumor-infiltrating CD161+CD8+ T cells may be another vital biomarker for malignant behavior. Thus, the combination of these two biological natural features may more accurately distinguish PDAC patients’ prognoses. As expected, a combination of tumor-infiltrating CD161+CD8+ T cells and CA19-9 proved to be a more robust survival stratification in PDAC patients (p<0.001, figure 2A,B). The median OS of the CD161+CD8high and CA19-9low, CD161+CD8high and CA19-9high, CD161+CD8low and CA19-9low and CD161+CD8low and CA19-9high subgroups were 91.2, 70.2, 25.8 and 16.5 months, respectively. The HRs for OS of CD161+CD8high and CA19-9high, CD161+CD8low and CA19-9low, and CD161+CD8low and CA19-9high subgroups by reference to CD161+CD8high and CA19-9low subgroup by univariate analysis were 1.55, 3.62, and 5.70, respectively. The median RFS of the CD161+CD8high and CA19-9low, CD161+CD8high and CA19-9high, CD161+CD8low and CA19-9low, and CD161+CD8low and CA19-9high subgroups were 48.6, 26.8, 15.5 and 9.7 months, respectively. The HRs for RFS of CD161+CD8high and CA19-9high, CD161+CD8low and CA19-9low, and CD161+CD8low and CA19-9high subgroups by reference to CD161+CD8high and CA19-9low subgroup by univariate analysis were 1.93, 2.69, and 4.53, respectively. These results showed a superior stratification in the combination of tumor-infiltrating CD161+CD8+ T cells and CA19-9. Additionally, the area under curves of the combination in 1-year, 3-year, and 5-year for OS prediction were 0.709, 0.783, and 0.782, respectively, which were higher than those of tumor-infiltrating CD161+CD8+ T cells or CA19-9 alone (figure 2C). On decision curve analysis for OS, the combination showed higher total net benefit with a wider range of threshold probability compared with tumor-infiltrating CD161+CD8+ T cells or CA19-9 alone (online supplemental figure 3A). Consistent results were obtained in the RFS analysis (figure 2D and online supplemental figure 3B). Collectively, the CD161+CD8+ Tlow and CA19-9high group exhibited the worst survival, emphasizing the predictive value of these biomarkers when used in conjunction.

Figure 2

Survival predictive value in combination of tumor-infiltrating CD161+CD8+ T cells with CA19-9. (A, B) Kaplan-Meier plots for overall survival (OS) and recurrence-free survival (RFS) according to the different densities of tumor-infiltrating CD161+ CD8+ T cells and CA19-9 levels via the log-rank test. (C, D) The 1-year, 3-year, and 5-year OS and RFS receiver operating characteristic curve (ROC) curves of CD161+CD8+ T cells, CA19-9, and the combination of them.

Therapeutic response to ACT in PDAC patients with different levels of tumor-infiltrating CD161+CD8+ T cells

Numerous studies have indicated that the TME significantly influences response to chemotherapy. Within the TME, immune cells can both augment and impede treatment efficacy, with their activation status being subject to alteration.2 25 Therefore, the impact of ACT on clinical outcomes was evaluated in both low and high tumor-infiltrating CD161+CD8+ T cell subgroups. In the CD161+CD8+ T cellLow subgroup, patients who received ACT demonstrated significantly improved OS and RFS compared with those who did not receive ACT (OS: 25.8 vs 10.8 months, p<0.001, HR=0.369, 95% CI 0.179 to 0.692, figure 3A; RFS: p<0.001, 13.7 vs 6.4 months, HR=0.479, 95% CI 0.271 to 0.845, figure 3B). Conversely, in the CD161+CD8+ T cellHigh subgroup, ACT did not significantly improve OS and RFS (OS: not reached vs 40.4 months, p=0.227, HR=0.42, 95% CI 0.080 to 1.460, figure 3C; RFS: 39.9 vs 30.6 months, p=0.499, HR=0.723, 95% CI 0.247 to 2.116, figure 3D). These data suggest that lower levels of tumor-infiltrating CD161+CD8+ T cells may indicate a superior response to ACT in PDAC.

Figure 3

Survival analysis based on tumor-infiltrating CD161+ CD8+ T cell infiltration and chemotherapy. (A, B) Kaplan-Meier curves of overall survival and recurrence-free survival in patients with low tumor-infiltrating CD161+CD8+ T cells according to adjuvant chemotherapy (ACT). (C, D) Kaplan-Meier curves of overall survival and recurrence-free survival in patients with high tumor-infiltrating CD161+CD8+ T cells according to ACT. Log-rank test was applied to Kaplan-Meier curves.

Tumor-infiltrating CD161+CD8+ T cells as a responsive T cell phenotype in PDAC

Given that MAIT CD8 T cells also represent a subset of CD8+ T cells characterized by high CD161 expression, we employed TCR Vα7.2 staining to accurately identify MAIT cells within the CD161+CD8+ T cell population in five PDAC tissues. Our findings indicated that MAIT CD8 T cells constituted only 1.86%±0.64% of the CD161+CD8+ T cells, thereby suggesting that the observed effects in this study are predominantly attributable to conventional CD161+CD8+ T cells. Given the established clinical relevance of CD161+CD8+ T cells and their role in predicting survival and response to ACT in PDAC, we examined whether this T cell subset correlates with the cytotoxic or exhausted phenotype of CD8+ T cells. We conducted flow cytometry analysis with dissociated fresh tissues from sixteen PDAC patients. CD161+CD8+ T cells constituted 30.9%±13.5% of the total CD8+ T cell population. As delineated in online supplemental figure 4, we compared the expression of cytotoxic and immunoregulatory markers between CD161+CD8+ and CD161−CD8+ T cells. Notably, CD161+CD8+ T cells showed significantly elevated levels of cytotoxic cytokines, including TNF-α, IFN-γ, granzyme B, and perforin, compared with the CD161−CD8+ subset (figure 4A). Intriguingly, CD161+CD8+ T cells also demonstrated higher levels of immune checkpoint molecules, such as TIM3 and TIGIT, while PD-1 and CTLA4 levels were similar between the two CD8+ T cell subsets (figure 4B).

Figure 4

Tumor-infiltrating CD161+CD8+ T cells as a responsive T cell phenotype in PDAC. Flow cytometry to compare the levels of (A) cytotoxic cytokines and (B) immune-checkpoint molecules between CD161+CD8+ T cells and CD161−CD8+ T cells via Wilcoxon matched-pairs test. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. ns, not significant; PDAC, pancreatic ductal adenocarcinoma.

Comparing CD161+ and CD161− cells at the transcriptomic level using scRNA-seq dataset (GSE211644), through KLRB1 expression, CD161+CD8+ T cells, compared with CD161−CD8+ T cells, showed a significant increase in cytotoxic granule-related molecules (GZMB, PRF1, and GNLY) and NK-related genes (KLRC1, KLRF1, and KLRD1). Alternatively, there was a notable decrease in the expression of genes related to antigen presentation (HLA-DQB1, HLA-DPA1, HLA-DPB1, HLA-DRB1, and CD74) in CD161+CD8+ T cells (figure 5A). GSEA revealed significant enrichment in CD161+CD8+ T cells for processes such as reactive oxygen species metabolism, extracellular matrix disassembly, innate immune cell activation, signal transduction, and regulation of T cell immune response and migration. In contrast, processes such as thymic T cell selection, glucocorticoid metabolic process, regulatory T cell differentiation, and leukocyte cell adhesion were significantly enriched in CD161−CD8+ T cells (figure 5B). To mitigate bias from reliance on a single dataset, we corroborated the findings using another independent single-cell sequencing dataset (GSE155698), which yielded consistent results (online supplemental figure 5). Thus, CD161+CD8+ T cells exhibit an innate-like functional state characterized by increased cytotoxicity and immunoregulatory phenotypes.

Figure 5

Validation of the function and prognostic value of CD161+CD8+ T cells via bioinformatics. (A) Volcano plot revealed the differentially expressed genes in CD161+CD8+ T cells compared with CD161-CD8+C T cells. (B) Gene Set Enrichment Analysis (Go biological process) on genes ranked by log2 fold change between CD161+CD8+ and CD161-CD8+ T cells. (C) Survival curves for CD161+CD8+ T cells signature (low and high) for overall survival in the TCGA, ICGC and E-MTAB-6134. ICGC, International Cancer Genome Consortium; TCGA, The Cancer Genome Atlas.

Subsequently, cellular signatures, normalized against the significantly upregulated genes in CD161+CD8+ T cells shown in online supplemental table 2, were used to categorize patients, from TCGA (146 PDAC samples), ICGC (260 PDAC samples), and E-MTAB-6134 (288 PDAC samples), into high or low groups based on bulk RNA-seq data. Survival analysis of the TCGA cohort (20.9 vs 17.0 months, p=0.066, HR=0.647, 95% CI 0.417 to 1.002, figure 5C), ICGC cohort (25.2 vs 16.9 months, p=0.036, HR=0.645, 95% CI 0.448 to 0.930, figure 5D), and E-MTAB-6134 cohort (29.4 vs 20.3 months, p=0.037, HR=0.702, 95% CI 0.484 to 1.019, figure 5E) suggested that a high CD161+CD8+ T cell signature correlates with improved clinical outcomes in PDAC patients.

Discussion

PDAC is characterized by a unique fibrotic TME, which profoundly influences tumor progression and therapeutic resistance.2 Our study has identified CD161+CD8+ T cells as a novel subtype, independently predictive of PDAC prognosis. These cells exhibited increased expression of cytotoxic cytokines and immune checkpoint molecules, indicating a tumor-responsive phenotype. The study further elucidated the prognostic value and immunological characteristics of CD161+CD8+ T cells, underscoring their potential as a valuable biomarker and potential target for immunotherapy.

Originally identified in the context of microbial infection defense, CD161+CD8+ T cells have been found to delay contraction, offering early resistance to reinvasion by the same pathogen.26 Due to their stem cell-like capacity, antiviral specificity, and tissue-homing properties when combating infections, Konduri et al confirmed the enhanced tumor-killing capacity of CD161+CD8+ T cells in peripheral blood.27 This led to their utilization as a superior platform for chimeric antigen receptor (CAR) T cell therapy. CD161+CD8+ CAR T cells displayed improved cytotoxic kinetics, aligning with our findings. Surprisingly, subsequent analyses using Cytometry by Time-Of-Flight and scRNA sequencing have simultaneously highlighted the immunosuppressive role of CD161+CD8+ T cells in various cancer contexts. Mathewson et al investigated the role of CD161 in cytotoxic CD8+ T cells and reported that CD161 functions as an inhibitory receptor on these cells in glioma.14 Silencing the KLRB1 gene, which encodes CD161, or blocking CD161, has been found to enhance the efficacy of activated T cells against glioma. In early-relapse hepatocellular carcinoma, increased infiltration of CD161+CD8+ T cells is paradoxically associated with diminished cytotoxicity, limited clonal expansion, and low expression of T cell recruitment chemokines, contributing to an immunosuppressive microenvironment.15 Likewise, Lao et al reported high infiltration of these cells in chemotherapy-resistant breast cancer, also characterized by reduced cytotoxicity.16 They noted that lectin-like transcript 1 (LLT1), the ligand for CD161, was highly expressed in breast cancer. The interaction between LLT1 and CD161 activates acid sphingomyelinase (aSMase) connected to CD161’s cytoplasmic tail, leading to ceramide production. These ceramides inhibit calcium influx, thereby impairing the cytotoxic function of the cells. The varied outcomes observed can be attributed to several factors. First, LLT1 is typically found only on the surfaces of activated lymphocytes and myeloid cells and has been detected only in a range of cancers, including hematological, prostate, breast, colorectal, and glioma. Notably, LLT1 does not undergo extracellular secretion.28 Therefore, the expression and spatial distribution of LLT1 in different cancers and their microenvironments likely influence the phenotype of tumor-infiltrating CD161+CD8+ T cells. Second, several studies have shown that CD161+CD8+ T cells exhibit innate-like and memory T cell phenotypes.8 11 15 In early-relapse liver cancer, which is predominantly driven by minor subclones from the primary cancer,15 29 increased levels of CD161+CD8+ T cells may not effectively recognize neoantigens to kill cancer cells.15 Lastly, CD161 activation, leading to ceramide production, can have a range of downstream effects.30 For instance, in melanoma, increased ceramide levels within CD8+ T cells have been linked to enhanced cytotoxicity.31

Although immune checkpoint inhibitors have shown lasting clinical benefits in various malignancies, a similar efficacy in PDAC has not been observed.32 Many studies have demonstrated that tumor-infiltrating lymphocytes (TILs) in responsive tumors often express elevated levels of immune checkpoint molecules like PD-1 and CD39, possibly due to prolonged chronic antigen stimulation leading to T cell exhaustion.33–35 In our study, CD161+CD8+ T cells exhibited elevated expression of exhaustion markers while maintaining cytotoxic cytokine expression, consistent with the findings in HPV-positive OPSCC.13 These results imply that despite exhibiting immune checkpoint features, CD161+CD8+ T cells in PDAC may not be functionally exhausted. Furthermore, these cells appear to exhibit a responsive phenotype in PDAC, clarifying their role in predicting a favorable prognosis and highlighting their potential as targets for immunotherapeutic strategies.

In our study, patients with low infiltration of CD161+CD8+ T cells were observed to have an increased incidence of peripheral nerve invasion. This observation is consistent with emerging research on the interplay between nerves and immune cells, particularly as peripheral nerve invasion in PDAC is associated with immune suppression.36 For example, acetylcholine, a neurotransmitter, has been shown to inhibit the recruitment of CD8+ T cells by PDAC cells and suppress IFN-γ production.37 Additionally, our analysis revealed a correlation between CD161+CD8+T cell infiltration levels and serum CA19-9 levels. CA19-9, recognized as a pivotal biomarker in PDAC, is instrumental in assessing tumor burden, prognosis, tumor recurrence, and treatment response.24 Additionally, CA19-9 is not only a biomarker but also plays a role in the development of pancreatitis and pancreatic cancer, potentially exerting a significant impact on the immune microenvironment in PDAC.23 The protein interacting with CA19-9 was predominantly involved in immune-related pathways like cytokine-cytokine receptor interaction and B cell receptor signaling pathway. This highlights the possible role of CA19-9 in modulating the immune microenvironment, although the specific mechanisms involved warrant further investigation.

Treatment options for PDAC are notably limited, with only 15%–20% of patients eligible for surgery. The majority undergo ACT or neoadjuvant chemotherapy. Although some patients derive significant benefits from these treatments, chemotherapy resistance remains an issue.38 Identifying predictive factors for chemotherapy response through clinical and biomarker evaluation is crucial for optimizing treatment strategies. Our study revealed that patients with low CD161+CD8+ T cell infiltration levels had a superior response to ACT. This could be partially explained by the observed link between TCR diversity and enhanced responsiveness to chemoradiotherapy, considering that CD161+CD8+ T cells exhibit an innate-like phenotype with limited clonal expansion.39

Nonetheless, limitations in this study should be acknowledged. First, the retrospective, single-center design potentially limits the generalizability of our findings. A large, prospective, and multicenter cohort is needed to validate the robustness of these results. Next, the study was limited to patients amenable to resection and was composed of mostly of patients with stages I and II disease. Part of stage III patients who received neoadjuvant therapy for PDAC were excluded to avoid potential impact on the TME. Stage IV patients were ineligible for surgical intervention and hence lacked available specimens. The distribution of patients across stages in our cohort is consistent with other studies.5 40–42 Even so, we acknowledge that our study was limited to early-stage patients with pancreatic cancer. Further research is required to establish the applicability of these findings to patients with late-stage PDAC. Moreover, our study focused exclusively on the tumor-responsive phenotype of CD161+CD8+ T cells. Further investigation is necessary to understand the role of the CD161 ligand LLT1 in PDAC and its implications for CD161+CD8+ T cells, along with its downstream effects. Lastly, the implications of CD161+CD8+ T cells in peripheral circulation and their relationship with tumor-infiltrating CD161+CD8+T cells warrant further exploration. Investigating the potential of targeting these cells as a novel approach for immunotherapy is a promising avenue.