Intratumor heterogeneity and T cell exhaustion in primary CNS lymphoma

The “Whiskey Method”: a simple tool for accelerated detection and characterization of PCNSL

Little material is available from CNS biopsies, and it is required for diagnostic confirmation of suspected PCNSL by histology and immunohistochemistry. Here, we identified a simple, but efficient way to obtain suspended PCNSL cells that were abundantly released into the surrounding liquid while briefly swirling the transferred biopsy material in saline. Due to the swirling movement, we denominated this approach the “Whiskey Method” (Fig. 1A). The method did not compromise the quality of the histopathology of the biopsy (Additional file 4: Fig. S4A,B). In total, we obtained biopsy-derived cells from sixteen patients and immediately performed flow cytometry for diagnostic purposes and evaluation of the TME (Additional file 1: Table S1). None of those patients had received corticosteroids or chemotherapy before collecting biopsy-derived cells (Additional file 1: Table S1). FACS analyses of cells released from the biopsy revealed that the mean percentage and number of CD45+ leukocytes was >10 times higher in samples from patients subsequently histologically diagnosed as PCNSL (n = 8, M = 31.3%, SEM = 9.8%; M = 366,094 cells, SEM = 94,908 cells) than in glioblastoma patients (n = 8, M = 3.1%, SEM = 1.7%; M = 24,353 cells, SEM = 5758 cells) (Fig. 1B). Tumor cells detach from the tumor bulk more easily in PCNSL than in glioblastoma patients, likely because of the discohesive growth pattern in PCNSL and less branched morphology compared to glioma cells (Additional file 4: Fig. S4C,D). In addition, the majority of biopsy-released CD45+ cells within the PCNSL samples consisted of CD19+CD20+ B cells (Bc) (M = 67.3%, SEM = 7.0%), while Bc proportions in blood (M = 7.0%, SEM = 1.7%) were in the expected range (Fig. 1C, Additional file 4: Fig. S1).

Extended flow cytometry showed that CD19+CD20+ Bc obtained from PCNSL biopsies were CD10 negative, showed variable expression of CD5 and high CD27 expression (Fig. 1C, Additional file 4: Fig. S1). Both CD38+ and CD38+CD27+ Bcs were significantly elevated in biopsy-derived Bcs when compared to blood, while CD20+CD138+ plasma cells were not increased. Immunoglobulin (Ig) light chain restriction (>80% expression of Kappa (κ) or Lambda (λ) Ig) was detected on all biopsy-derived Bcs. In peripheral blood, κ/λ ratios were mostly within the expected range (M = 1.57) [31]. The “Whiskey Method” thus facilitates the detection and further characterization of PCNSL cells within a few hours.

Distinct immune cell alterations in the PCNSL microenvironment

Next, we aimed to characterize the TME by evaluating immune cells infiltrating PCNSLs by flow cytometry (Fig. 1D, Additional file 4: Fig. S2). Biopsy material contained lower proportions of CD4+ T cells (Tc) than blood. Similar percentages of CD8+ Tc were found in peripheral blood and biopsies, leading to an increase in the CD8/CD4 ratio at the tumor site. Tumor-infiltrating CD4+ and CD8+ Tc displayed an effector memory phenotype (Fig. 1D). Moreover, we identified elevated proportions of CD4+CD25+IL7R− regulatory Tc (Tregs) within the biopsy material. CD3−CD56+ NK cells infiltrated the tumor at low frequencies with a predominance of CD56brightCD16dim NK cells, whereas peripheral blood was dominated by CD56dimCD16+ NK cells. Finally, we detected an induction of the immune checkpoint molecule PD-1 on CD4+ and CD8+ Tc in the biopsy. Collectively, we identified a distinct cellular composition of the TME in PCNSL, featuring signs of T cell exhaustion.

Single-cell transcriptomics reveals heterogeneity of malignant B cells in PCNSL

We sought to better characterize PCNSL by combining the “Whiskey Method” with single-cell RNA sequencing (scRNA-seq). We applied scRNA-seq to five samples from two patients with PCNSL (patients 1 and 2, Additional file 1: Table S1). We performed scRNA-seq and single-cell B cell receptor sequencing (scBCR) of cells from biopsy and peripheral blood (Bc-enriched using anti-CD20 microbeads) at the time of stereotactic biopsy from both patients (Additional file 4: Fig. S5). In addition, we collected CSF in patient 1 at relapse and also performed CITE-seq in blood and biopsy material from this patient (Additional file 1: Table S1).

We merged the data from all samples with batch correction and thereby obtained 73,896 total single-cell transcriptomes (biopsy = 36,266; blood = 33,342; CSF = 4288) after removing low-quality cells and doublets (Additional file 5: Table S3, Methods). We annotated the main clusters based on the expression of marker genes (Fig. 2A,B). Two large clusters expressed Bc markers and were tentatively named non-malignant Bc (nmBc) and malignant Bc (mBc) (CD19, MS4A1/CD20, CD79B) (Fig. 2B, Additional file 6: Table S4). In accordance with flow cytometry, CD27 and CD38 expressions were increased in mBc and SDC1/CD138 was absent, as it is well known in PCNSL [32] (Fig. 2B). Furthermore, we identified two myeloid clusters with monocyte and granulocyte markers (myeloid1-2: LYZ, S100A12, CD14, LYVE1, MRC1) and a cluster exhibiting mDC1 markers (mDC1: CLEC9A, XCR1, BATF3). Furthermore, we detected a T/NK cell cluster (Tc: CD3E, TRAC, IL7R, NKG7), a platelet cell cluster (PLT: CLU, GNG11, PPBP, GP9), and an oligodendrocyte cluster (oligo: PLP1, MBP, MAG). We confirmed the identity of the main cell populations on the protein level by using CITE-seq in one patient (Fig. 2C).

Fig. 2figure 2

Single-cell transcriptomic reveals heterogeneous malignant B cell phenotypes in PCNSL. A UMAP plot of 73,896 total single-cell transcriptomes aggregated from five samples (patient 1: biopsy, blood, CSF; patient2: biopsy, blood). B Gene and protein (C) cell markers of the clusters identified by single-cell RNA sequencing (scRNA-seq) and CITE-seq (biopsy and blood from patient 1). Color encodes average gene/protein expression, and dot size represents the percentage of cells expressing the gene. The threshold of percentage of cells expressing the gene/protein was set to 15% in B and 90% in C. D UMAP plot of 45,890 reclustered B cells (mBc and nmBc cluster from A). E Analysis of copy number variations of downsampled B cell clusters. The nmBc1-2 clusters were used as reference cells and mBc1-4 as observations. The amplification of chromosomal regions is colored in red and the deletion of chromosomal regions in blue. F Feature plots of chromosomal gains and losses with the UMAP embeddings of D. Color encodes the proportion of chromosomal aberration. G Top ten differentially expressed genes of each B cell cluster shown in a heatmap. Selected genes are highlighted. Gene expression values were scaled gene-wise. H Proportions of B cells split by sample and colored by cluster name. I–J Gene expression heatmap of known PCNSL- associated genes (I) and of chemokines and their receptors (J) in B cell clusters, scaled gene-wise. Gene name - alias: MS4A1 - CD20; SDC1 - CD138; SELL - CD62L; IRF4 - MUM1. Abbreviations: mBc - malignant B cells; nmBc - non-malignant B cells; mDC1 - myeloid dendritic cells type 1; oligo - oligodendrocytes; Tc - T cells; PLT -platelets; p1 - patient 1; p2 - patient 2; CSF - cerebrospinal fluid

To better understand the intratumor heterogeneity of PCNSL, we investigated the B cell clusters in more detail by subclustering all cells in the mBc and nmBc clusters (Fig. 2D). We identified four clusters annotated as malignant clusters (mBc1-4) that showed chromosomal aberrations commonly found in PCNSL [33], including gains in chromosome 1, 12, and 22, and losses in chromosome 6 (Fig. 2D–F). This does not imply that all cells of the respective clusters are necessarily malignant. We visualized the most differentially expressed genes between the Bc clusters (Fig. 2G; Additional file 7: Table S5). The mBc1 cluster expressed the pre-B cell receptor-associated molecule VPREB3, the B cell activation marker CD83, and genes associated with cell metabolism, cellular growth, and tumor progression (DDX54, PRDX6, GRHPR). We found an immature, dedifferentiated phenotype with a distinct expression of cell cycle (TOP2A, HMGB2, TUBA1B) and proliferation genes (MKI67) in mBc2 (Fig. 2G). The mBc3 cluster was characterized by a more mature phenotype with signs of class-switching (JCHAIN, MZB1) (Fig. 2G; Additional file 7: Table S5). We found expression of genes involved in cancer proliferation (PARP14, VMP1, APOE) in the mBc4 cluster (Fig. 2G; Additional file 7: Table S5). In contrast, nmBc resembled naive mature B cells (CD52, SCIMP, BANK1) as expected for blood-derived Bc (Fig. 2G; Additional file 7: Table S5). In accordance, mBc1-4 were nearly exclusively found in biopsy- and CSF-derived leukocytes, while nmBc mainly originated from blood (Fig. 2H). CSF mirrored the relative cluster abundance of the biopsy, while blood Bc featured distinct cluster proportions (Fig. 2H). The relative abundance of malignant Bc clusters was surprisingly similar across both patients in blood- and biopsy-derived leukocytes (Fig. 2H). Compared to nmBc, mBc1-4 expressed transcripts previously commonly detected in PCNSL [5, 34], lending further support to the assumed neoplastic identity of those clusters (Fig. 2I). Of note, most of those genes were differentially expressed in two of the malignant clusters (e.g., mBc2: BUB3, KRAS, TP53; mBc3: XBP1, BTG2; mBc4: CXCL13, BCL6, IL10). We thus detected a surprisingly pronounced intratumor heterogeneity in PCNSL. Based on the chromosomal aberrations, tissue origin, transcriptional profile, and presence/absence of a hyperexpanded clone (see below), we annotated mBc1-4 as malignant and nmBc as non-malignant Bc clusters.

Differential expression of chemokines in malignant B cell clusters

Gene expression analysis of chemokines and their receptors further supported intratumoral heterogeneity of PCNSL (Fig. 2J). We observed increased expression of CCL17, CXCL17, and CX3CL1 in mBc1, CXCL1 in mBc2, CCL1, CCL3, CCL25, and CCL26 in mBc3, and CCL2, CCL5, CCL19, CCL27, CXCL8, CXCL12, and CXCL13 among others in mBc4 (Fig. 2J). This is in line with previous studies demonstrating that CXCL13 is highly specific for PCNSL [5, 35]. The chemokines expressed in mBc1-4 have the potential to attract a range of immune cells, including regulatory T cells (Tregs), macrophages, neutrophils, myeloid-derived suppressor cells (MDSC), different T helper, and DC subsets (see Additional file 8: Table S6 for details) [36]. In contrast, nmBc1-2 expressed a different set of chemokines and chemokine receptors than mBc1-4 including CXCL3, CXCL5, CCR3, CCR7, CCR9, CXCR1, CXCR4, and CXCR5. Since the CXCL13–CXCR5 axis is pivotal in recruiting Bc [37], non-malignant Bc might have been attracted from the periphery to the tumor by malignant Bc during lymphoma progression.

Malignant B cell clusters show a phenotypic gradient with multiple developmental trajectories

We next aimed to better understand the developmental relationship between the individual biopsy-derived Bc clusters. mBc2 displayed high expression of cell cycle genes, suggesting an immature, proliferating phenotype (Fig. 3A). We performed RNA velocity of single cells, which is based on the ratio of spliced/unspliced mRNA, with scVelo [26], a likelihood-based dynamical model, and with pseudotime (Fig. 3B,C). The resulting streamlines delineated developmental paths from mBc2 to mBc4 and from mBc2 over mBc1 to mBc3 (Fig. 3C). This suggests multiple developmental trajectories within malignant Bc, potentially differentiating from cycling and immature Bc into later Bc stages. Because of limited applicability of RNA velocity in cancer, such as absence of ancestral cells and aberrant splicing caused by mutations [38], the results should be interpreted with caution. Combining the results of the chromosomal aberration (Fig. 2E, F) with the developmental paths (Fig. 3B, C), it is noticeable that some chromosomal aberrations are not shared between different stages of the same development path (e.g., gain of chromosome 1 is present in mBc3, but not in the presumed progenitor cells of mBc1) (Figs. 2E,F and 3B,C). This might be caused by a clonal evolution of the malignant cells with the emergence of subclones with distinct chromosomal aberrations [39]. Collectively, we provide evidence for developmental intratumoral heterogeneity of PCNSL.

Fig. 3figure 3

Biopsy- and CSF-derived cells but not blood cells share hyperexpanded B cell clones. A Geneset feature plot of G2/M and S phase with the UMAP embeddings of C. Color encodes gene expression. B Feature plots of two lineages of pseudotime analysis. Pseudotime is color-coded and the UMAP embeddings refer to C. C RNA velocity analysis of mBc1-4 clusters (Fig. 2A). Streamlines represent vector velocity fields, which show the developmental pathways. D UMAP plot of all B cell clusters (45,890 cells) from 5 samples (patient 1: biopsy, blood, CSF; patient 2: biopsy, blood) with color-coded frequency of B cell clones. Cells colored in transparent grey represent cells with missing BCR information. E, F Proportions of B cells with BCR information split by sample (E) or cluster (F) and colored by frequency. Frequency was defined by the number of B cells expressing a unique clone (paired BCR heavy and light chains). G Alluvial plot shows the origin tissue and cluster of the hyperexpanded clones. H, J Volcano plots of the differentially expressed (DE) genes of the hyperexpanded clones versus all remaining clones (H) and DE genes of the hyperexpanded clone of patient 1 after treatment at relapse (CSF-derived) versus before treatment (biopsy-derived) (I). The threshold for the log2 fold change was set to 1 and the threshold for the negative log10p-value to 30. I Significantly enriched terms of DE genes based on the NCI-Nature Pathway Interaction Database corresponding to H. Size encodes the significance and color encodes whether the term was enriched in genes with elevated or reduced gene expression. K Correlation coefficients between gene expression of mBc1-4 clusters (row) and different lymphomas (column) from Roider et al. [15] including four follicular lymphoma (FL1-4), four GC-derived DLBCLs, of which two were transformed from FLs (DLBCL1, DLBCL2, tFL1, and tFL2) and one non-GC-derived DLBCL (DLBCL3), visualized in a heatmap. High correlation coefficients, colored in yellow, indicate a high transcriptional overlap. Abbreviations: mBc - malignant B cells; nmBc - non-malignant B cells; p1 - patient 1; p2 - patient 2; Bc - B cells; CSF - cerebrospinal fluid; NS - not significant; FC - fold change; p val - p-value; DLBCL - diffuse large B cell lymphoma; FL - follicular lymphoma (FL); tFL - transformed FL

Hyperexpanded B cell clones are shared between biopsy- and CSF- but not blood-derived cells

To further study clonal relationships between tissues, we extracted single-cell B cell receptor (scBCR) information from the V(D)J-supplemented scRNA-seq (“Methods”) and identified 4259 cells with a heavy and a corresponding light chain that could be matched to scRNA-seq. Most cells in the malignant clusters mBc1-4 were hyperexpanded clones, while the non-malignant cluster nmBc1-2 predominantly harbored unexpanded cells (single clones) (Fig. 3D–F). Of note, the hyperexpanded clones are spread across all malignant Bc clusters (Fig. 3D–F). The biopsy material showed hyperexpansion of a single malignant clone in each patient (p1_biopsy: ~98% of all cells; p2_biopsy: ~82% of all cells) (Fig. 3G, Additional file 9: Table S7). The CDR3 sequences of the hyperexpanded clones were not related between both patients (Additional file 9: Table S7). All other expanded clones in biopsy were closely related to the hyperexpanded clone with single-nucleotide substitutions within each patient (Additional file 9: Table S7). Notably, we could identify the same hyperexpanded clone in the CSF of patient 1 approximately 1 year after the biopsy during relapse (Fig. 3G, Additional file 9: Table S7). In contrast, we could not identify the hyperexpanded clone in the blood in both patients and there was no relevant clonal expansion in the blood (Fig. 3E,G, Additional file 9: Table S7). We detected a single non-expanded clonotype, located in mBc3, that was shared between the blood and the biopsy material in patient 2 (Additional file 9: Table S7). This might represent a malignant B cell that emigrated from the CNS into the peripheral blood compartment but did not expand. Altogether, prominent hyperexpansion of malignant B cells was restricted to the brain and the CSF. We thus provide evidence that malignant clones are shared between the brain and the CSF, but not between the brain and peripheral blood in PCNSL.

Hyperexpanded B cell clones show a loss of maturity

Differential expression analysis of the hyperexpanded clones compared to all other clones revealed an elevated expression of tumor promoting factors/oncogenes (HSPA5, PDIA4, MANF, PPIB) and a gene associated with malignant B cell clones (CD63) [40]. In contrast, BCR activation and signal transduction genes (BANK1, CD37) and maturity genes (CD52, IGHD, MS4A1) were reduced in the hyperexpanded clones compared to all other clones. CD37, whose expression is related to improved patient survival in peripheral DLBCL, while its loss is a risk factor for therapy resistance with rituximab [41], was also reduced in the hyperexpanded clones (Fig. 3H, Additional file 10: Table S8). Enrichment analysis showed that BCR, RAC1, CXCR4, and ErbB1 signaling pathways were enriched in the genes downregulated in the hyperexpanded clones, while tumor- and cell proliferation-associated pathways (c-Myc, c-Myb, Aurora A, nuclear estrogen receptor alpha pathways) were enriched in upregulated genes of the malignant clones (Fig. 3I, Additional file 11: Table S9). This suggests loss of mature B cell features and increased cell proliferation of the hyperexpanded clones.

Signs of altered migration in the relapsed clone

To characterize transcriptional changes between the hyperexpanded clone at initial diagnosis and relapse in patient 1 (after high-dose chemotherapy and autologous stem cell transplantation), we performed differential expression analysis (Fig. 3J, Additional file 12: Table S10). The relapsed clone showed an upregulation of S100A4, a driver of tumor cell invasion and metastasis [42] and enhanced expression of CD81, a tetraspanin molecule, which is crucial for the formation and activation of the B cell coreceptor (CD19–CD21–CD81) complex and has recently been proposed as a novel therapeutic target in B cell lymphomas [43]. We also observed increased expression of CCL5, associated with tumor recurrence [44], and an increase of CCR7, which controls migration of lymphoma cells into niches [45] (Fig. 3J). As CCL19, the ligand of CCR7, promotes the development of PCNSL through the retention of CCR7 expressing lymphoma cells in the brain [46], the CCR7-CCL19 axis might also play a role in the evasion of malignant B cells from the brain to the CSF. Moreover, we observed a reduced expression of HLA class II molecules (CD74, HLA-DRA, HLA-DRB1, HLA-DMA), which might affect the number and function of CD4+ T lymphocytes in the tumor microenvironment [47] (Fig. 3J). In summary, the differentially expressed transcripts of the malignant relapsed clone indicated altered migration promoting malignancy compared to the clone before therapy.

Transcriptional similarity of PCNSL with peripheral B cell lymphomas

We next systematically compared the PCNSL transcriptome with available single-cell data from peripheral Bc lymphomas [15]. We found higher transcriptional correlation between malignant Bc clusters in our dataset (mBc1-4) and published DLBCLs (GC: DLBCL1, DLBCL2; Non-GC: DLBCL3) and GC DLBCL transformed from follicular lymphomas (tFL1, tFL2) and lower correlation between mBc1-4 and follicular lymphomas (FL) (Fig. 3K). In line with previous microarray data [48], this provides evidence for substantial transcriptional overlap between peripheral and central DLBCL. In addition, we systematically compared the chemokine expression between DLBCL and PCNSL (Additional file 4: Fig. S6). We observed that chemokine expression varies considerably between DLBCL and PCNSL, but also between DLBCL samples (GC-derived and non-GC-derived DLBCLs) and within our clusters. Therefore, we could not identify a clear common chemokine pattern that is shared between all PCNSL or all DLBCL and that likely determines the tropism and site specificity of these cells.

Broad expression of immune checkpoints in the PCNSL microenvironment

Based on our FACS data with upregulated PD-1 expression on biopsy-derived Tc, we aimed to further evaluate the expression of immune checkpoints in our scRNA-seq data set. We observed that Tc formed gradients with overlapping signatures rather than distinct subclusters, as we had previously reported [49]. We identified seven sub-clusters (Fig. 4A): NK cells (NK: KLRF1, CD160, NCAM1), CD8+ Tc with a naive- and memory-like phenotype (naive/memCD8: CD8A, KLRG1, CD44, CD69), proliferating Tc (prolTc: MKI67, TOP2A), Tc with an activated phenotype with an interferon signature (IFNG, IFI27, STAT1), Tc with an exhausted phenotype (CD27, PDCD1, LAG3, TNFRSF9), CD4+ Tc with a naive- and memory-like phenotype (naive/memCD4: CD4, CCR7, SELL, CD44, CD69), and regulatory CD4+ Tc (TregCD4: CD4, IL2RA, FOXP3, CTLA4) that also expressed markers of T cell exhaustion (TIGIT) (Fig. 4B, Additional file 13: Table S11). Biopsy-derived cells featured an increase of prolCD8, actTc, and exhTc and a reduction of NK and naive/memCD4/CD8 compared to blood-derived cells (Fig. 4C,D). This indicated an increase of T cells with proliferating, activated, and exhaustive phenotype in biopsy-derived leukocytes. In line with flow cytometry, nearly all canonical exhaustion molecules, including TIGIT, HAVCR2/TIM-3, LAG3, CTLA4, and PDCD1/PD-1, were expressed at higher levels in biopsy- or CSF- than in blood-derived cells (Fig. 4E, Additional file 4: Fig. S3). Interestingly, the expression of most markers was divergent between CSF and biopsy, suggesting site specificity in the milieu induced by the tumor cells. By evaluating the expression of several published exhaustion signatures [50,51,52], we confirmed that biopsy-derived cells showed a higher exhaustion score than blood-derived cells (Fig. 4F, Additional file 14: Table S12). In accordance with flow cytometry, biopsy- and CSF-derived cells also exhibited a stronger regulatory Tc phenotype (FOXP3, IL2RA, CTLA4, IRF4) than blood-derived cells (Fig. 4G). We identified several corresponding immune checkpoint ligands in our malignant Bc clusters (e.g., NECTIN2 and NECTIN4 bind TIGIT; CEACAM1 binds HAVCR2/TIM-3; FGL1 binds LAG3; CD80 binds CTLA4; CD274/PD-L1 and PDCD1LG2 bind PDCD1/PD-1) (Fig. 4H). Of note, most of these ligands were expressed highest in the mBc4 cluster, indicating that mBc4 induces a particularly immunosuppressive TME.

Fig. 4figure 4

Increased expression of regulatory and T cell exhaustion molecules in the PCNSL microenvironment. A UMAP plot of all T cell subclusters, including 17,175 single-cell transcriptomes from 5 samples (patient 1: biopsy, blood, CSF; patient 2: biopsy, blood). B Dot plot of T cell markers in T cell subclusters. Color encodes average gene expression and dot size shows the percentage of cells expressing the gene with the threshold of percentage of cells expressing the gene set to 10%. C Proportion of T cells split by sample and colorized by cluster name. D Comparison of T cell subcluster abundances between biopsy and blood by plotting the log2 fold change. E,F Gene expression heatmaps of canonical immune checkpoints (D), exhaustion profiles from Singer et al. [50], Tirosh et al. [51], Chihara et al. [52] (F), and regulatory T cell markers (G) in T subclusters. Values were scaled row-wise and color encodes gene expression. Exhaustion signatures are listed in Additional file 14: Table S12. H Gene expression heatmap of immune checkpoint ligands in B cell clusters, scaled gene-wise, color encodes gene expression. I,J Biopsy-derived T cells (I) and blood-derived T cells (J) were projected on the latent space of a reference T cell dataset [24]. Gene name - alias: HAVCR2 - TIM3; PDCD1 - PD1; IRF4 - MUM1; PDCD1LG2 - PD-L2; LGALS9 - Galectin9. Abbreviations: NK - natural killer cells; memCD4/CD8 - memory-like CD4+/CD8+ T cells; naiveCD4/CD8 - naive-like CD4+/CD8+ T cells; actTc - activated T cells; exhTc - exhausted T cells; TregCD4 - regulatory CD4+ T cells; p1 - patient1; p2 - patient2; CSF - cerebrospinal fluid; mBc - malignant B cells; nmBc - non-malignant B cells; Tex -terminally-exhausted; Tpex - precursor-exhausted; Tfh - CD4+ follicular-helper cells

When projecting biopsy-derived T cells and blood-derived T cells on a recent reference atlas of tumor-infiltrating T cells [24], we observed a large overlap of biopsy-derived T cells with exhausted CD8 T cells (CD8_Tex) (Fig. 4I), which was absent in blood-derived T cells (Fig. 4J). Collectively, we confirmed and extended our flow cytometry findings that showed elevated expression of immune checkpoints in the TME of PCNSL. This suggests a potential of checkpoint inhibitors (CPI) in the treatment of PCNSL and suggests TIGIT, TIM-3, PD-1, CTLA-4, and LAG-3 as promising targets.

Cellular interactions between PCNSL and its microenvironment reveal immune evasion signaling

To better understand signaling pathways within the tumor micro-milieu, we predicted ligand-receptor pair expression from transcriptome data of biopsy-derived malignant Bc to Tc and myeloid cells (Fig. 5). We identified significant predicted interactions between malignant Bc and immune cells of the TME, e.g., molecules associated with angiogenesis and invasion, including interaction of NRP1 to VEGFA and VEGFB between myeloid1 and malignant Bc clusters. Signaling between malignant Bc clusters and their microenvironment also included cell adhesion interactions (e.g., CD6-ALCAM, ICAM1-ITGAL, PECAM1-CD38, and CEACAM1-CD209). We identified several immunomodulatory signaling pathways. CD47 (mBc1-4) and SIRPA (myeloid1, mDC1) showed significant interaction, indicating a potential mechanism that protects tumor cells from phagocytosis [53]. Further immunosuppressive signaling between mBc1/3 and myeloid1/mDC1 clusters included interactions between LILRB2 and HLA-G. Blocking of LILRB2 promotes anti-tumor immunity of myeloid cells [54]. We also identified several known immune checkpoint signaling molecules between Tc and mBc1-4 clusters including TIGIT-NECTIN2, CTLA4-CD80, and HAVCR2/TIM-3-LGALS9. Moreover, we observed significant interactions between KLRB1 (Tc) and CLEC2D (mBc1-4), which has recently been described to inhibit killing of glioma cells by T cells [55]. In summary, cellular crosstalk could potentially prevent immune cells from attacking the tumor, thus allowing its immune evasion.

Fig. 5

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