Introduction

The advancements in anti-cancer therapy have revolutionized cancer management and have led to improved patient outcomes. However, these therapies can also lead to various adverse effects, including infections due to immune suppression/dysregulation and drug-induced toxicity, with the lungs being a commonly affected organ (Conte et al., 2022)(Morelli et al., 2022)(Skeoch et al., 2018). One of the major opportunistic infections in cancer patients or those receiving immunosuppressive treatment is Pneumocystis jirovecii pneumonia (PCP), which can cause severe respiratory complications and high mortality rates (Thomas and Limper, 2004)(Asai et al., 2022)(Apostolopoulou and Fishman, 2022). However, the precise immune reactions during PCP development and mechanisms of lung injury remain largely unknown.

While both cytotoxic drugs and immune-checkpoint inhibitors (ICIs) can induce interstitial lung diseases (ILDs), the mechanisms underlying these complications may differ between the two classes of drugs. In particular, ICI-associated ILDs are considered a type of immune-related adverse events (irAE)(Postow et al., 2018)(Ando et al., 2021). Nonetheless, the precise mechanisms that cause drug-induced ILDs are predominantly unidentified. Further, despite various diagnostic approaches, including radiology and molecular testing, there is a need for useful biomarkers to distinguish PCP, cytotoxic drug-induced (DI)-ILD, and ICI-ILD.

Mass cytometry, also known as cytometry by time-of-flight (CyTOF), is a cutting-edge technology that uses inductively coupled plasma mass spectrometry to detect metal ions tagged to antibodies that bind to specific cellular proteins, allowing for a detailed and multi-dimensional characterization of cellular composition and function (Spitzer and Nolan, 2016). This technique provides a unique advantage over traditional flow cytometry, which is limited by spectral overlap and the number of parameters that can be analyzed simultaneously. Mass cytometry has been used to study a variety of biological systems, including immune-mediated diseases, such as cancer and autoimmune conditions (Matsubara et al., 2021)(Hata et al., 2023)(Couloume et al., 2021)(Chedid et al., 2022). Through high-throughput analysis of large numbers of single cells, mass cytometry can provide a more comprehensive understanding of the cellular heterogeneity and signaling pathways involved in these diseases, allowing for the identification of potential therapeutic targets and biomarkers.

This study aims to distinguish pulmonary involvement in patients with malignancy undergoing chemotherapy and identify potential biomarkers for DI-ILD, ICI-ILD, and PCP by analyzing bronchoalveolar lavage fluid (BALF) samples with mass cytometry. By characterizing the cellular and molecular changes in BALF from patients with these complications, we aim to improve our understanding of their pathogenesis and identify potential therapeutic targets.

Materials and Methods

Patients

In this retrospective study, patients who were newly diagnosed with PCP, DI-ILD, and ICI-ILD and had undergone BALF collection at Kyushu University Hospital from January 2017 to April 2022 were included. The retrospective study was approved by the Ethics Committee of Kyushu University Hospital (reference number 22117-00). Diagnostic criteria for PCP, DI-ILD, and ICI-ILD were in accordance with those previously described (Skeoch et al., 2018)(Asai et al., 2022)(Apostolopoulou and Fishman, 2022)(Delaunay et al., 2019). A visual representation of the experimental and analytical workflow can be found in Figure 1.

Figure 1.

Mass cytometry

Metal-tagged antibodies were obtained from Standard Biotools or purchased in a purified form (Supplementary Table 1) and then labeled with metals using the Maxpar Antibody Labeling Kit (Standard Biotools) as instructed by the manufacturer. The cell labeling was performed as previously described (Hata et al., 2023). Upon collection, BALF samples were immediately centrifuged at 300 g for 5 minutes to pellet the cells. The resultant cell pellets were then resuspended in Cellbanker 1 cryopreservation solution (Takara, catalog #210409). This suspension was aliquoted into cryovials and gradually frozen to –80°C and stored at –80°C until required for experimental analysis. Cryopreserved BALF cells were thawed in PBS and then stained with Cell-ID Cisplatin-198Pt (Standard Biotools #201198, 1:2000 dilution) in PBS before being incubated with FcR blocking reagent (Myltenyi, #130-059-901) and barcoded with each metal-labeled CD45 antibody (Supplementary Table 1). After washing, CD45-labeled cells were mixed and stained with APC-conjugated FCRL5 antibodies (for panel #2), followed by staining with the antibody cocktail (Panels #1 and #2, see Supplementary Table 1). The antibody amount was determined through preliminary experiments with metal minus one. Cells were then washed, fixed with 1.6% formaldehyde, and resuspended in Cell-ID Intercalator 103Rh (Standard Biotools #201103A) in Fix and Perm buffer (Standard Biotools) at 4L overnight. For acquisition, cells were resuspended in MaxPar Cell Acquisition Solution (Standard Biotools #201240) containing one-fifth EQ Four Element Calibration Beads (Standard Biotools #201078) and acquired at a rate of 200-300 events/second on a Helios mass cytometer (Standard Biotools). Files were converted to FCS, randomized, and normalized for EQ bead intensity using the Helios software. Concatenating fcs files in the same group into one file was conducted by FlowJo v10.8 (BD Biosciences). Cytobank Premium (Cytobank Inc.) was used to perform manual gating, visualization of t-distributed stochastic neighbor embedding (viSNE), Uniform manifold approximation and projection (UMAP) analysis, and Citrus analysis (Bruggner et al., 2014).

Data analysis

Cisplatin-positive cells and doublets were excluded to select live cells, and CD45⁺ cells were further analyzed. For T cells, CD2⁺ CD3⁺ cells were gated and subjected to UMAP and Citrus algorithms. The UMAP analysis included clustering channels for CD4, CD8a, CD27, CD28, CD45RA, CD45RO, Fas, and used the parameters: numbers of neighbors = 15, minimum distance = 0.01. The Citrus algorithm for T cells included clustering channels for CD4, CD5, CD7, CD8a, CD11a, CD16, CD27, CD28, CD44, CD45RA, CD45RO, CD49d, CD57, CD69, CD226, Fas, IL-2R, PD-L1, PD-L2, PD-1, OX40, TIGIT, TIM3, CTLA-4, CD223 (LAG-3), BTLA, ICOS, ST2, CCR7, CXCR3, HLA-DR, and used the parameters: association models = nearest shrunken centroid (PAMR), cluster characterization = abundance, minimum cluster size = 5%, cross validation folds = 5, false discovery rate = 1%. The citrus analysis excluded nivolumab-induced ILD cases due to the apparent loss of PD-1 detection caused by competitive inhibition by nivolumab (Supplementary Figure 1B) (Yanagihara et al., 2020). The viSNE analysis for T cells included clustering channels for CD4, CD5, CD7, CD8a, CD11a, CD16, CD27, CD28, CD44, CD45RA, CD45RO, CD49d, CD57, CD69, CD226, Fas, IL-2R, PD-L1, PD-L2, PD-1, OX40, TIGIT, TIM3, CTLA-4, CD223 (LAG-3), BTLA, ICOS, ST2, CCR7, CXCR3, and HLA-DR, and used the parameters: iterations = 1000, perplexity = 30, theta = 0.5.

For myeloid cells, CD3⁻ CD11b⁺ CD11c⁺ cells were gated, and UMAP and Citrus algorithms were used. The UMAP analysis included clustering channels for CD11b, CD11c, CD64, CD14, CD16, CD206, HLA-DR, and CCR2, and used the parameters: numbers of neighbors = 10, minimum distance = 0.01. The Citrus algorithm included clustering channels for CD11b, CD11c, CD64, CD14, CD16, CD32, CD36, CD38, CD84, CD86, CD163, CD206, CD209, CD223, HLA-DR, CCR2, CCR5, and ST2, and used the parameters: association models = nearest shrunken centroid (PAMR), cluster characterization = abundance, minimum cluster size = 5%, cross validation folds = 5, false discovery rate = 1%. One case of PCP was excluded from the Citrus analysis due to low cell numbers.

B cells and Plasma cells were identified using gating of CD3⁻CD64⁻ and CD19⁺ or CD138⁺ cells. viSNE analysis was performed to cluster B cells using the following markers: CD19, CD38, CD11c, IgA, IgG, CD138, CD21, ST2, CXCR5, CD24, CD27, TIM-1, IgM, HLA-DR, IgD, and FCRL5. The analysis was performed on both individual and concatenated files using the parameters of 1000 iterations, perplexity of 30, and theta of 0.5.

The selection of the dimensionality reduction technique, UMAP or viSNE, was made based on their ability to retain the relationships between global structures and the distances between cell clusters (where UMAP outperformed viSNE) and their ability to present a distinct and non-overlapping portrayal of cell subpopulations, facilitating the identification of inter-group variations (where viSNE performed better than UMAP).

Statistical analysis

We utilized a PAMR association model with a stringent threshold of 1% FDR, as described in the Data Analysis section, for the Citrus algorithm experiment. The Student’s two-tailed unpaired t-test was employed to conduct a comparative analysis between the two groups. To determine the significance of serum beta-D-glucan and KL-6 among three groups, a statistical analysis was performed using Kruskal-Wallis tests. We evaluated the manually gated cell proportions by performing a two-way ANOVA, along with Tukey’s multiple comparison tests. Data were analyzed using GraphPad Prism 9 software. Statistical significance was considered to be achieved when the P-value was less than 0.05.

Analysis of single-cell RNA sequencing data of human BALF cells

For our investigation, we utilized the publicly available dataset GSE145926, which contains single-cell RNA sequencing data from BALF cells of both healthy controls (n = 3) and patients with severe COVID-19 (n = 6) (Liao et al., 2020). We initiated our analysis by normalizing the filtered gene-barcode matrix using the ’NormalizeData’ function in Seurat version 4, applying the default settings. Subsequently, we identified the top 2,000 variable genes utilizing the ’vst’ method available in the ’FindVariableFeatures’ function of Seurat. Principal Component Analysis (PCA) and UMAP were then conducted to reduce dimensionality and visualize the data. To isolate T cells for further analysis, we defined them as cells expressing CD2 and CD3E with expression values greater than 1. We specifically examined the expression of FCGR3A (CD16) by setting an expression threshold of 0.5 to identify relevant cell populations. The resulting expression patterns were illustrated using violin plots and bar plots, which were generated using the ’ggplot’ function in R.

Results

Patient characteristics and clinical parameters

We analyzed 7 cases of PCP, 9 of DI-ILD, and 9 of ICI-ILD (Table 1). One of the PCP cases was fatal. Differential cell counts for BALF revealed lymphocytosis in 7 out of 7 cases (100%) of PCP as well as in 8 out of 9 cases (88.9%) of DI-ILD and 7 out of 9 cases (63.6%) of ICI-ILD when the cut-off for the percentage of lymphocytes was set to >20%. Serum levels of beta-D-glucan were significantly higher in patients with PCP than those with DI-ILD or ICI-ILD (196.5 ± 419.8, 3.7 ± 7.1, 0.6 ± 1.4 pg/mL, respectively) (Figure 2A). The receiver operating characteristic (ROC) curve was constructed to evaluate the diagnostic utility of beta-D-glucan for PCP, with values from the DI-ILD group used as a reference (Figure 2B). The area under the curve was determined to be 0.8929. When the cut-off value was set to 14.70 pg/mL, the sensitivity and specificity of PCP were 85.71% and 87.50%, and the positive likelihood ratio was 6.86. The diagnostic value of beta-D-glucan was consistent with the previous study (Tasaka et al., 2007), where the positive likelihood ratio was 6.57, with a cut-off value of 31.0. The serum levels of KL-6, an indicator of various types of interstitial pneumonitis (Ishikawa et al., 2012), tended to be higher in patients with ICI-ILD than PCP or DI-ILD, though insignificant (Figure 2C). Four out of 7 (57.1%) in PCP, 1 out of 9 (11.1%) in DI-ILD, and 6 out of 9 (66.7%) in ICI-ILD were positive for KL-6 with a cut-off value of 500 U/mL.

Figure 2.

Table 1.

Expansion of CD16⁺T cells in BALF from patients with PCP

First, we investigated whether subsets of T cells (gated CD2⁺ CD3⁺) differentially existed in PCP, DI-ILD, and ICI-ILD with mass cytometry. T-cell lymphocytosis was observed in all groups (PCP: 62.9 ± 17.5%, DI-ILD: 58.3 ± 24.5%, ICI-ILD: 50.4 ± 29.9%) with a higher tendency of CD4/CD8 ratio observed in DI-ILD compared to PCP and ICI-ILD (PCP: 1.13 ± 0.86, DI-ILD: 6.08 ± 6.06, ICI-ILD: 1.9 ± 1.40)(Figure 3A). To visualize T cell differentiation within the affected lungs, we generated UMAP plots (Figure 3B). The majority of T cells in the BALF displayed either memory or effector phenotypes, with a limited population of naïve T cells (Figures 3B and 3C). Specifically, we observed a tendency of a higher abundance of transient memory/ effector memory CD4 T cells and a lower abundance of transient memory/ effector memory CD45RA (EMRA) CD8 T cells in DI-ILD compared to the other two groups (Figure 3C).

Figure 3.

We then utilized the Citrus algorithm to classify T cell subpopulations with varying degrees of abundance in an unsupervised manner by analyzing 31 parameters (Figure 3D, Supplementary Figures 1C and 1D). Our analysis identified 30 clusters of T cells, of which 13 were significantly differentiated between the groups. Interestingly, clusters #26299, #26293, and #26278, characterized by CD8⁺ T cells expressing CD16 (FcγRIIIa), and cluster #26286, characterized by CD4⁺ T cells expressing CD16, were abundant in PCP compared to DI-ILD and ICI-ILD (Figures 3E and 3F). These CD8⁺ T cells expressing CD16 were also positive for HLA-DR and CXCR3 (Figure 3F) but negative for CD14, a marker for monocytes (median CD14 expression of these clusters: 0.0–0.1 vs. 7.4–9.5 in monocyte populations). Cluster #26303, prevalent in ICI-ILD, comprised CD8⁺ CD57⁺ TIGIT⁺ LAG3⁺ but CD16⁻ subpopulation (Figures 3E and 3F). These cells also expressed PD-1 and TIM-3, although the expression was lower than clusters #26299, #26293, and #26278. These CD8⁺ T cells expressed CD45RA but no expression of CD45RO and CCR7, indicating EMRA phenotype. Cluster #26281, prevalent in DI-ILD, was marked by CD4⁺ with low immune checkpoint expression (PD-1, TIM-3, TIGIT, LAG3, PD-L1, PD-L2, and OX40; Figures 3E and 3F).

Myeloid cell subpopulations in the lungs of PCP, DI-ILD, and ICI-ILD

Next, we investigated myeloid cell populations (identified as CD3⁻ CD11b⁺ CD11c⁺). We first conducted UMAP to see the major myeloid cell populations. The UMAP plot categorized four major subtypes: monocytes, CCR2⁺ macrophages, alveolar macrophages, and dendritic cells, with no significant difference in PCP, DI-ILD, and ICI-ILD (Figures 4A and 4B). We next utilized the Citrus algorithm to further investigate differently abundant myeloid cell subpopulations by analyzing 18 parameters. Our analysis identified 31 clusters of myeloid cells, of which 17 were significantly differentiated between the groups (Figure 4C, Supplementary Figure 2). Clusters #8220, #8215, and #8195, prevalent in PCP, were comprised of CD11b^hi CD11c^hi CD64⁺ CD206⁺ alveolar macrophages with HLA-DR^hi (Figures 4D and 4E). Clusters #8219 and #8197, prevalent in ICI-ILD, were characterized by CD14⁺ CCR2⁺ CCR5⁺ monocyte subpopulations (Figures 4D and 4E). Clusters #8227, #8223, and #8208, prevalent in DI-ILD, were marked by CD11b^lo CD11c^lo CD64^lo CCR5⁺ subpopulations (Figures 4D and 4E).

Figure 4.

B cell subpopulations in the lungs of ILDs

In addition to T cells and myeloid cells, we sought to investigate whether there were differential representations of B cells in BALF from PCP, DI-ILD, and ICI-ILD. By utilizing CD45⁺CD3⁻CD64⁻ and CD19⁺ or CD138⁺ as gating parameters, we were able to detect the presence of B cells and plasma cells. The frequency of B cells/ plasma cells was found to be relatively higher in patients with PCP compared to the other two groups, although the proportion of B cells and plasma cells remained low in all groups. (Figure 5A). A t-SNE analysis of 17 parameters among B cells/plasma cells revealed the presence of various B cell subpopulations, including IgD⁺ naïve B cells, IgM⁺ B cells, IgG⁺ B cells, IgA⁺ B cells, plasmablasts, and plasma cells (Figure 5B). Although not significant, higher levels of IgG⁺ B cells were observed in patients with PCP, while individuals with ICI-ILD exhibited a greater presence of plasma cells (Figure 5C). Previous research has suggested that FCRL5⁺ B cells contribute to the pathogenesis of autoimmune disorders (Owczarczyk et al., 2020)(Dement-Brown et al., 2012). Therefore, we investigated the frequency of these cells in our study and found that FCRL5⁺ B cells were more abundant in patients with ICI-ILD compared to those with PCP and DI-ILD, suggesting these FCRL5⁺ B cells may have a role in irAE (8.37 ± 6.74%, 1.37 ± 2.30%, 2.29 ± 3.81%, respectively) (Figure 5D).

Figure 5.

Marked CD16+ T cell expansion in BALF from a fatal case of PCP

One case of PCP that developed during the use of immunosuppressive drugs after living donor liver transplantation had a fatal course despite intensive treatment. Upon admission, chest CT images revealed the emergence of diffuse ground glass opacities and infiltration in the bilateral lungs (Figure 6A). The serum levels of beta-D-glucan and KL-6 were highly elevated (1,146 pg/mL and 1,561 U/mL, respectively). The T cell percentage was 51.3%, and the CD4/CD8 ratio was 0.58, which was not particularly different from other PCP cases (Figure 6B). Strikingly, 97.5% of T cells expressed CD16 in the BALF from the fatal case (Figures 6C and 6D). Further, not only the proportion but the CD16 intensity was the highest in the fatal case among PCP cases (Figure 6E). On the contrary, there did not appear to be any significant differences in myeloid cell fractions, and CD16 expression was not particularly high in the fatal case (Figure 6F). The correlation matrix of clinical parameters and mass cytometry parameters revealed that CD16 intensity in T cells may be correlated with disease severity (r = 0.748, p = 0.053) and beta-D-glucan (r = 0.868, p = 0.011) (Figure 6G). Therefore, CD16 expression in T cells is not only a characteristic of PCP but also a potential indicator of disease severity. For reference, we investigated CD16 expression in BALF T cells from healthy controls along with severe COVID-19 patients using the publicly available single-cell RNA sequencing data (GSE145926). In the BALF of healthy controls, FCGR3A (CD16) expression was observed in 18.7% of CD4⁺ T cells and 7.22% of CD8⁺ T cells in healthy controls. In contrast, severe COVID-19 patients exhibited expression rates of 17.2% in CD4+ T cells and 22.1% in CD8⁺ T cells (Figures 6H and 6I). Notably, CD8⁺ T cells from COVID-19 patients showed significantly higher FCGR3A (CD16) expression compared to those from healthy controls (0.31 ± 0.62 vs 0.12 ± 0.43, p = 2.47e-08), while no significant difference was observed in CD4⁺ T cells between the two groups (0.22 ± 0.53 vs 0.34 ± 0.73, p = 0.407).

Figure 6.

Discussion

Here, we have demonstrated the characteristic immune cell subpopulations present in BALF from patients with PCP, DI-ILD, and ICI-ILD. Our analysis revealed an expansion of CD16⁺ T cells in patients with PCP, an increase in CD57⁺ CD8⁺ T cells expressing immune checkpoints, and FCRL5⁺ B cells in ICI-ILD.

CD16 is a low-affinity Fc receptor (FcγRIIIa) for IgG, commonly expressed on NK cells as well as on neutrophils and monocytes (Ravetch and Bolland, 2001), with only a small fraction of T cells expressing CD16 in healthy peripheral blood (Sandor and Lynch, 1993) and BALF (Figures 6H and 6I). On the contrary, CD16⁺ T cells are induced in specific conditions, such as chronic hepatitis C infection (Björkström et al., 2008) and COVID-19 (Georg et al., 2022). In chronic hepatitis C infection, CD16⁺ CD8 T cells displayed a late-stage effector phenotype with high levels of perforin with a restricted TCR profile (Björkström et al., 2008). Stimulation of CD16 on CD8 T cells evokes a vigorous response such as degranulation and cytokine production (IFN-γ and TNF-α) similar to that of CD16 stimulation in NK cells (Björkström et al., 2008). These CD16⁺ CD8⁺ T cells also express a senescent marker, CD57, which differs from our findings that expanding CD16⁺ CD8 T cells in PCP did not express CD57 (Figure 3F).

Georg et al. recently discovered that COVID-19 severity was associated with highly activated CD16⁺ T cells, which exhibit increased cytotoxic functions (Georg et al., 2022). Although this increase was observed in CD8⁺ T cells and not in CD4⁺ T cells, the augmented population of CD16⁺ T cells in BALF from patients with severe COVID-19 was further substantiated by data mining of scRNA-seq (Figures 6H and 6I). CD16 expression allows T cells to degranulate and exert cytotoxicity in an immune-complex-mediated, TCR-independent manner. Moreover, CD16⁺ T cells from COVID-19 patients caused injury to microvascular endothelial cells and induced the release of neutrophil and monocyte chemoattractants (Georg et al., 2022). Georg et al. also discovered that severe COVID-19 induced increased generation of C3a, which further activates CD16⁺ cytotoxic T cells. The proportions of activated CD16⁺ T cells and plasma levels of complement proteins upstream of C3a were found to be associated with fatal outcomes of COVID-19, proving support for the pathological role of exacerbated cytotoxicity and complement activation in COVID-19 (Georg et al., 2022). Given these findings and the observation of the highest proportion of CD16⁺ T cells in the fatal case of PCP, we hypothesize that these CD16⁺ T cells may possess excessive cytotoxicity toward pulmonary microvascular endothelial cells, contributing to lung injury in PCP, in addition to targeting Pneumocystis jirovecii. Based on the therapeutic efficacy of an anti-complement therapy (anti-C5a antibody) for severe COVID-19 (Campbell, 2020)(Vlaar et al., 2020), anti-complement treatments may have the potential to treat severe PCP. One of the other T cell markers correlated with the severity of PCP was CCR7, CD7, and CD57 (Supplementary Figure 4), although the precise biological significance of the correlation remains to be elucidated.

We discovered an increase of CD57⁺ CD8⁺ T cells expressing immune checkpoints, PD-1⁺ TIGIT⁺ LAG3⁺ TIM-3⁺ in ICI-ILDs, which supports our previous findings of increased proportions of CD8⁺ T cells positive for both PD-1 and TIM-3 or TIGIT in ICI-ILD (Suzuki et al., 2020). In the methods section, the low detection of PD-1 expression on T cells in patients treated with nivolumab was noted; this was due to the competitive nature of the PD-1 detection antibody EH12.2 with nivolumab (Yanagihara et al., 2020). The absence of a metal-conjugated PD-1 antibody with the MIH4 clone presented a limitation in our study. Ideally, we would have conjugated the MIH4 antibody with 155Gd for our analysis, which is a refinement we aim to incorporate in future research. A population of PD-1⁺ TIM-3⁺CD8⁺ cells was detected in the peritumoral pleural effusion and ILD lesions of a cancer patient undergoing ICI treatment (Yanagihara et al., 2017). Through the application of next-generation sequencing technology to the DNA encoding the complementarity-determining region of the T-cell receptor (TCR), identical T-cell clones were identified in both peritumoral pleural effusion and ILD lesions (Tanaka et al., 2018). Similar to ICI-ILD, a recent study identified T cells specific to α-myosin drive ICI-related myocarditis (Axelrod et al., 2022). These results indicate that ICI-activated CD8⁺ T cells, with a phenotype of PD-1⁺TIGIT⁺ LAG3⁺ TIM-3⁺, could potentially trigger ILD by recognizing self-peptides or shared epitopes between tumors and lungs. CD16⁺ CD8⁺ T cells in PCP exhibit high CXCR3 and ST2 expression, and CD57⁺ CD8⁺ T cells expressing immune checkpoints in ICI-ILD express high CXCR3 (consistent with the previous study (Kim et al., 2021)). Therefore, these CD8⁺ T cells could potentially migrate into the lungs via chemokines, such as CXCL4, CXCL9, CXCL10, and IL-33 (Van Raemdonck et al., 2015)(Griesenauer and Paczesny, 2017).

Regarding CD57 expression in T cells, it is generally considered a senescent marker (Fehlings et al., 2022). However, CD57⁺ CD8⁺ T cells in the periphery show clonal expansion with the overlap of the TCR repatoire of tumor-infiltrating T cells with favorable response to ICI in patients with cancer (Fehlings et al., 2022), indicating these CD57⁺ CD8⁺ T cells may have a highly active phenotype in ICI-treated individuals.

FCRL5 is a B cell-restricted member of the Fc receptor-like family encoded by the IRTA2 gene (Rostamzadeh et al., 2018). In both rheumatoid arthritis (Owczarczyk et al., 2011) and granulomatosis with polyangiitis and microscopic polyangiitis (Owczarczyk et al., 2020), increased expression of FCRL5 was indicative of a positive response to rituximab. Additionally, our recent research revealed an increase in FCLR5⁺ B cells in connective-tissue disease-related ILD (Hata et al., 2023). These findings suggest a potential pathological role of FCRL5 in autoimmunity. Given that ICI-ILD is considered a type of irAE, it is reasonable to hypothesize that FCRL5⁺ B cells may be one of the immune cells associated with autoimmunity.

Regarding myeloid cells, we found that CD14⁺ CCR2⁺ CCR5⁺ monocyte subpopulations were prevalent in ICI-ILD. Franken et al. revealed a decrease in anti-inflammatory resident alveolar macrophages and an increase in pro-inflammatory ’M1-like’ monocytes (expressing TNF, IL-1B, IL-6, IL-23A, and GM-CSF receptor CSF2RA, CSF2RB) in BALF from ICI-ILD compared with controls (Franken et al., 2022). Taken together with this report, the CD14⁺ CCR2⁺ CCR5⁺ monocytes we found may be an M1-like monocyte.

Our study has several limitations, including the absence of data from healthy individuals, a relatively small sample size, and a retrospective design that resulted in missing clinical data for certain cases. Selection bias may also be a factor, as only patients who underwent bronchoalveolar lavage were eligible for enrollment in this study. It is important to note that these findings demonstrate a correlation rather than proof of causation.

In summary, our study has shown distinct immune cell phenotypes in PCP, DI-ILD, and ICI-ILD. Specifically, we have identified an expansion of CD16⁺ T cells in PCP, as well as an increase in CD57⁺ CD8⁺ T cells expressing immune checkpoints, FCRL5⁺ B cells, and CCR2⁺ CCR5⁺ monocytes in ICI-ILD, which may play a pathogenic role. Based on these findings, further confirmatory research may lead to the development of diagnostic methods and novel strategies that target these specific cell populations to treat chemotherapy-induced pneumonitis.

Author contributions

Conceptualization: TY; Methodology: KH, TY, KM, KK, and YB; Investigation: KH, TY, KM, KK, KS, KT, DE, HA, and MU; Visualization: KH, TY, KM, KK, KS, KT, DE, HA, MU, SI, and YB; Funding acquisition: TY and YF; Supervision: SI, YB, YF, and IO; Writing-original draft: KH and TY; Writing-review and editing: SI, YB, YF, and IO. All authors contributed to the article and approved the submitted version.

Acknowledgements

We thank Ms. Sanae Sekihara, PhD. from Standard Biotools, for her technical assistance. We also extend our appreciation to the Medical Research Center Initiative for High Depth Omics at Kyushu University.

Funding

This research was supported by the Kakihara Foundation, and Boehringer Ingelheim (T.Y.), and the Japan Agency for Medical Research and Development (Y.F.).