Single-cell characterization of leukemic and non-leukemic immune repertoires in CD8+ T-cell large gr

T cell large granular lymphocytic leukemia (T-LGLL) is a rare lymphoproliferative disorder of mature, clonally expanded T cells, where somatic-activating STAT3 mutations are common. Although T-LGLL has been described as a chronic T cell response to an antigen, the function of the non-leukemic immune system in this response is largely uncharacterized. Here, by utilizing single-cell RNA and T cell receptor profiling (scRNA+TCRαβ-seq), we show that irrespective of STAT3 mutation status, T-LGLL clonotypes are more cytotoxic and exhausted than healthy reactive clonotypes. In addition, T-LGLL clonotypes show more active cell communication than reactive clones with non-leukemic immune cells via costimulatory cell–cell interactions, monocyte-secreted proinflammatory cytokines, and T-LGLL-clone-secreted IFNγ. Besides the leukemic repertoire, the non-leukemic T cell repertoire in T-LGLL is also more mature, cytotoxic, and clonally restricted than in other cancers and autoimmune disorders. Finally, 72% of the leukemic T-LGLL clonotypes share T cell receptor similarities with their non-leukemic repertoire, linking the leukemic and non-leukemic repertoires together via possible common target antigens. Our results provide a rationale to prioritize therapies that target the entire immune repertoire and not only the T-LGLL clonotype.

T cell Large Granular Lymphocytic Leukemia (T-LGLL) is a rare lymphoproliferative disease characterized by the accumulation of abnormal, clonally restricted, and activated effector T cells in the blood, bone marrow, and spleen^1,2. Although immune-mediated cytopenias (most frequently neutropenia, 70–80%) and autoimmune manifestations (most frequently rheumatoid arthritis [RA], 10–18%) are commonly associated with T-LGLL, it usually presents as an indolent disease that is manageable with low-dose immunosuppressive therapies^3,4,5.

The absence of presenting symptoms and shared morphological and phenotypic (CD3⁺CD8⁺CD45RA⁺CD57⁺) features with healthy reactive cytotoxic T cells impose challenges in the diagnosis of CD8⁺ T-LGLL³. Activating somatic mutations in the STAT3 gene, the hallmark of CD8⁺ T-LGLL, occurs in 40–50% of patients^{6,7,8,9,10,11}, where the majority of variants (e.g., Y640F, D661V, and D661Y) are located in the SH2 domain of STAT3. Although the clinical features of patients with mutated and wild-type STAT3 overlap, (severe) neutropenia and autoimmune manifestations are more common in mutated STAT3 cases^{6,10,12,13,14}. Nevertheless, T-LGLL clonotypes show constant STAT3 activation irrespective of the STAT3 mutation status and are resistant to FAS/FAS-L mediated apoptosis, which can be explained by upregulated survival signaling pathways, such as MCL1, NF-κB, and PI3K/AKT^15,16,17.

Although chronic antigenic stimulation has been suggested to drive cytotoxic T cell lymphoproliferation in T-LGLL, little has been reported about the function of non-leukemic populations in driving or aiding T-LGLL pathogenesis. Altered B-cell activities (dyscrasias, hypergammaglobulinemia, enhanced production of immunoglobulins, including autoantibodies)¹⁸, elevated levels of multiple cytokines (e.g., IL-15, TNF, IL-6)^19,20,21,22, and that IL-15 expression by monocytes can initiate T-LGLL in transgenic mice^19,20 suggest the possible function of non-leukemic cells in the disease. As changes in leukemia cell burden cannot be associated with therapy responses^12,23,24, and multiple symptoms can be attributed to elevated cytokine expressions³, a holistic understanding of the total immune repertoire behind T-LGLL is an unmet need.

Here, we use single-cell RNA and TCR sequencing (scRNA+TCRαβ-seq) to separate T-LGLL clonotypes from their non-leukemic repertoire and compare them with healthy controls, other cancers, and autoimmune disorders to identify the position of T-LGLL in the intersection of cancer, autoimmune disorders, and chronic inflammation. We extend our findings with bulk-RNA-seq, TCRβ-seq, flow cytometry, serum protein profiling, and ex vivo validations. Our systems immunology analysis highlights the synergistic function of clonal and non-clonal immune repertoires in the pathogenesis of T-LGLL and suggests that future therapies should be geared toward attenuating the entire immune system and not the T-LGLL clone alone (Fig. 1a).

a Schematic diagram of the study, where the left panel denotes the different cohorts, and the right panel highlights the main findings. b Uniform Manifold Approximation and Projection (UMAP) representation of CD45⁺ sorted cells from T-LGLL (n = 11) and healthy donor (n = 6) samples profiled with scRNA+TCRαβ-seq. Different colors indicate clusters, and cells with detected TCR are highlighted in red. c UMAP representation of the reclustered T cells and their phenotypes (left). Cells with detected TCR (right) were divided into singletons (TCR detected once), expanded (TCR detected ≥2 times), and hyperexpanded (TCR detected ≥10 times) clonotypes. d Proportion of the cells from hyperexpanded (TCR detected ≥10 times) clonotypes as compared between T-LGLL (n = 11) and healthy (n = 6). The definition of box plot visualization is stated in the Methods section Data visualization. P-value was calculated with two-sided Mann-Whitney test. e Focused UMAP of the cells with hyperexpanded TCRs from panel c without reclustering (left). Distribution of the cells from patients with T-LGLL and healthy controls are shown separately (right). f Differentially expressed genes (P_adj < 0.05, calculated with a Bonferroni corrected t-test) between hyperexpanded T-LGLL and healthy clonotypes. Top 30 differentially expressed genes from T-LGLL and top 10 from healthy are labelled. X-axis denotes the average log2 fold-change between the two conditions and Y-axis denotes the P_adj-value in a negative log10. Dashed line denotes P_adj = 0.05. g The scaled expression of the selected top differentially expressed genes highlighted using the same UMAP representation as in panel e. h Top upregulated GO-pathways (P_adj < 0.05, Benjamini-Hochberg corrected Fisher’s one-sided exact test on differentially expressed genes) in T-LGLL clonotypes in comparison to hyperexpanded clonotypes from healthy controls. Colors indicate whether the pathway can be associated to immune function by manual curation. i Protein level expression (mean fluorescence intensity, MFI) of cytotoxic proteins (GMZA, GZMB, and PRF1) from patients with T-LGLL (n = 6) and healthy controls (n = 6) in flow cytometry cohort. P-values were calculated with two-sided Mann-Whitney test. j Protein level expression (log2 fold-change of MFI) of cytokines (TNF and IFNγ) and degranulation markers (CD107a and CD107b) between TCR stimulated and unstimulated conditions. P-values were calculated with two-sided Mann-Whitney test.

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T-LGLL cells show elevated cytotoxicity and exhaustion

To gain an unsupervised view of the immune system in T-LGLL, we analyzed over 150,000 flow cytometry-sorted CD45⁺ blood mononuclear cells (Supplementary Fig. 1a) from 11 T-LGLL samples from nine individuals and six age-matched healthy controls with scRNA+TCRαβ-seq (10X Genomics, Supplementary Data 1). After initial clustering of the entire dataset (Fig. 1b, Supplementary Fig. 2a–d), we focused on cells expressing TCR and reclustered these (Fig. 1c, Supplementary Fig. 3a–d). Despite similarities with the clonally expanded CD8⁺ T cells (defined as at least two cells with identical TCR) from the healthy controls, the clonally expanded CD8⁺ T cells from T-LGLL patients also had unique T-LGLL-specific characteristics, and they were overrepresented in several CD8⁺ T cell clusters (Supplementary Fig. 3e).

As expected, samples from patients with T-LGLL were more clonal and had more expanded cells than those from healthy controls (P < 0.01, Mann-Whitney test) (Fig. 1d). This was invariant to the chosen threshold for expanded clones (Supplementary Fig. 4a–b). To identify transcriptomic differences between T-LGLL cells and reactive cytotoxic clonotypes, we extracted the hyperexpanded clonotypes (defined as at least 10 cells with identical TCR) from patients with T-LGLL and healthy controls and annotated them with the previously calculated clusters (Fig. 1c, e). In healthy controls, the hyperexpanded cells had preferentially CD8⁺ effector memory (CD8⁺ T_EM) phenotype (P < 0.0001, two-sided Fisher’s test), whereas in T-LGLL, the hyperexpanded cells were phenotypically more heterogeneous (Fig. 1e, Supplementary Fig. 5a). In comparison with hyperexpanded reactive cells from the healthy, the top upregulated genes in T-LGLL cells included multiple cytotoxicity-associated transcripts (GZMB, PRF1, KLRB1, KLRD1), where the most significantly upregulated was NKG7, which is essential in the mobilization of cytotoxic vesicles²⁵ (Fig. 1f, Supplementary Data 2). Another top differentially expressed (DE) gene included common T-LGLL markers (CCL4, CCL5 [RANTES], FOS, FCGR3A [CD16], IFNG), anti-apoptotic genes (JUN, DUSP1), and genes associated with T cell exhaustion (LAG3 and TIGIT) (Fig. 1g). The DE genes translated to the top upregulated pathways in T-LGLL being cell killing, T cell activation, and response to IFNγ signaling pathways (Fig. 1h, Supplementary Data 2). In healthy controls, the top DE genes, including other cytotoxic genes (GNLY, LYZ), genes forming calprotectin (S100A8 and S100A9), and CD52, were not enriched to any immune-associated pathway (Fig. 1f, g, Supplementary Fig. 5b).

To validate the higher cytotoxicity profile in T-LGLL in comparison to reactive cells, we performed flow cytometry analysis with six T-LGLL and six healthy control samples (Supplementary Data 1). Putative T-LGLL clonotypes (CD8⁺CD57⁺) were confirmed to express more cytotoxic proteins (GZMA/GZMB P < 0.01, PRF1 P < 0.05, Mann-Whitney test) than the CD8⁺CD57⁺ T cells from healthy controls (Fig. 1i, Supplementary Fig. 6a). In addition, the CD8⁺CD57⁺ T-LGLL cells failed to respond well to anti-CD3/CD28/CD49 antibody-mediated TCR stimulation. Their degranulation responses (CD107a/b) were deficient (P < 0.01), and their cytokine production (TNF/IFNγ) in response to stimulation was diminished (P < 0.01) relative to the healthy CD8⁺CD57⁺ cells (Fig. 1j, Supplementary Fig. 6a). However, basal levels of TNF/IFNγ were higher in T-LGLL (Supplementary Fig. 6b). Since higher levels of TNF/IFNγ have been previously reported in T cells of patients with latent infections like CMV²⁶, we hypothesized that this phenomenon is suggestive of antigen-experienced T cell exhaustion, which is concordant with DE genes.

Phenotypic characterization of T-LGLL clones

The TCR-repertoires of patients with T-LGLL varied from oligoclonal to polyclonal, with the immunodominant clonotype explaining 7–50% of the total TCR repertoire (Fig. 2a). We separated the T-LGLL clonotypes from other CD8⁺ T cells by manually curating data from (1) scTCRαβ-seq, (2) Vβ flow cytometry (presence of immunodominant Vβ family), and (3) STAT3 amplicon sequencing (matching variant allele frequency of STAT3 mutation or wild-type STAT3 with Vβ flow cytometry data, Supplementary Data 1). In total, we identified 18 T-LGLL clonotypes (nine mutated STAT3 and nine wild-type STAT3, Supplementary Data 3), and each individual patient harbored one to four T-LGLL clonotypes (Fig. 2a). Two patients had follow-up samples, and the same T-LGLL clonotypes were observed in both timepoints (four T-LGLL clonotypes in patient 1, and one in patient 2) (Fig. 2a).

a Clonal expansion of clonotypes in T-LGLL (n = 11) and one representative healthy control. Each box denotes a unique T cell clonotype in a sample as detected with scTCRαβ-seq and the size of the box corresponds to its frequency in the repertoire occupancy. b UMAP representation of the transcriptomes of the selected 18 T-LGLL clonotypes highlighted in panel a from 11 T-LGLL samples (n = 9 patients). c Scaled expression of selected differentially expressed genes between T-LGLL clusters highlighted in the same UMAP representation as in panel b. dHeatmap showing scaled expression of differentially expressed genes (P_adj < 0.05, calculated with Bonferroni corrected two-sided t-test) between T-LGLL phenotype clusters. Cluster (cl) numbers referring to panel b are marked on right side of the heatmap. e Proportion of the different patient-specific T-LGLL clonotypes in different clusters. Each bar represents an individual T-LGLL clone. f The imputed and detected STAT3 mutation status presented in the UMAP representation. The inferred STAT3 mutation status was obtained clonotype-wise as in panel a(shown in the panel on the left) and the detected STAT3 mutation status was retrieved from the scRNA-seq data using a variant detection tool Vartrix (shown in the panel on the right). gDifferentially expressed genes between (P_adj < 0.05, calculated with Bonferroni corrected two-sided t-test) the mutated and wild-type STAT3 T-LGLL clones. Top 20 genes from each condition are labeled. X-axis denotes the average log2 fold-change between the two conditions and Y-axis denotes the P_adj-value in a negative log10 transformed scale. h Top upregulated GO-pathways (P_adj < 0.05, Benjamini-Hochberg corrected Fisher’s one-sided exact test on differentially expressed genes) in wild-type STAT3 in comparison to mutated STAT3 clonotypes. iCytotoxicity score of individual cells in wild-type STAT3 clones in comparison to mutated STAT3 clones in scRNA-seq. P-value was calculated with two-sided Mann-Whitney test. j Principal component analysis (PCA) plot from bulk-RNA-sequencing data from 10 mutated and 5 wild-type STAT3 T-LGLL patients’ and 5 healthy donors’ CD8⁺-sorted T cells. k Cytotoxicity score of the wild-type STAT3 patients (n = 5) as compared to the STAT3 mutated (n = 10) patients’ scores in the bulk-RNA-seq validation cohort. P-value was calculated with two-sided Kruskal-Wallis test.

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A reclustering of the cells belonging to the T-LGLL clonotypes identified seven different transcriptomic T-LGLL phenotypes (Fig. 2b–d, Supplementary Fig. 7a–b). Each T-LGLL clonotype was represented in more than one cluster (Fig. 2e). The biggest cluster, which upregulated T-LGLL markers FCGR3A (CD16) and CCL4 and exhaustion markers LAG3, TIGIT, and TOX, was the dominant phenotype in the majority of clonotypes (13/18, 72.2%) (Fig. 2c–e, Supplementary Data 2). Other T-LGLL phenotypes included an effector cluster with upregulated KLRB1, IL32, and BATF expression (cluster 1); a highly cytotoxic cluster (cluster 2); memory-like cluster (cluster 3); and a preferentially cytokine-producing cluster with upregulated IFNG and CCL3 (cluster 4). In pathway analysis, 4/7 (57.14%) clusters (clusters 0, 3, 4, and 6) had upregulated NF-kB activation, whereas clusters 3 and 6 demonstrated a significant response to IFNγ (Fig. 2d, Supplementary Data 2). Clusters 0, 3, and 4 displayed upregulated STAT3 transcription factor regulatory networks in SCENIC²⁷ analysis (Supplementary Fig. 7c–d).

Wild-type STAT3 T-LGLL clones are more cytotoxic than mutated clones

Notably, STAT3 mutated and wild-type clonotypes imputed from scTCRαβ-seq and amplicon sequencing data were partly grouped separately in the dimensionality-reduced space (Fig. 2f). To validate our manual inference of STAT3 status, we analyzed off-target reads from the scRNA-seq data and identified 83 cells that expressed mutated STAT3 and 200 cells that expressed wild-type STAT3 (Fig. 2f, Supplementary Fig. 8a). STAT3 cells expressing Y640F, S614R, and D661Y mutations were enriched in the CD52⁺ memory-like cluster 3 (P < 0.001, two-sided Fisher’s exact test), whereas the wild-type STAT3 cells were enriched in the cytotoxic cluster 2 (P < 0.0001). The largest cluster (cluster 0) contained both mutated and wild-type STAT3 cells.

DE gene and pathway analysis showed that the wild-type STAT3 cells were more activated and displayed increased cytotoxicity compared with the mutated STAT3 cells. The top DE genes in the wild-type STAT3 cells included GNLY, KLRG1, and CD5, and the most upregulated pathways included T cell activation, upregulated TCR signaling, and response to IFNγ (Fig. 2g, h, Supplementary Data 2). Furthermore, the wild-type STAT3 T-LGLL clonotypes also demonstrated a higher cytotoxicity score²⁸ (P < 0.0001, Mann-Whitney test) and a lower exhaustion score (P < 0.0001) than the mutated STAT3 T-LGLL clonotypes (Fig. 2i, Supplementary Fig. 8b). On the contrary, the upregulated genes in the mutated STAT3 clonotypes included genes associated with T cell survival (JUND, KLF2) and cytokine signaling (CCL3, CCL4L2, IL2RG), and the top upregulated pathways were associated with protein translation and response to type I interferons (IFNα, IFNβ) although none were significant after P-value adjustment (Supplementary Fig. 8c).

To validate the differences between mutated and wild-type STAT3 T-LGLLs, we profiled additional patients with mutated STAT3 (n = 10) and wild-type STAT3 (n = 5) CD8⁺ T-LGLL together with CD8⁺T cells from healthy donors (n = 5) with bulk-RNA-seq (Supplementary Data 1). The bulk-RNA-seq data confirmed that the wild-type STAT3 samples were separated from the mutated STAT3 ones in the dimensionality-reduced space by principal component 2 (PC2) which explained 15.85% of the variance (Fig. 2j). The bulk-RNA-seq data also validated the higher cytotoxicity scores of CD8⁺ T cells in the wild-type STAT3 patients compared with the mutated STAT3 patients (P < 0.05, Mann-Whitney test) (Fig. 2k).

Leukemic and non-leukemic TCRs share structural similarities

Although direct evidence is lacking, it is generally hypothesized that the T-LGLL clones originate from antigen-specific immune responses³. As the underlying antigen specificities of the T-LGLL clonotypes remain largely unknown, we combined previously TCRβ-seq profiled T-LGLL clonotypes^24,29 together with our samples profiled with scTCRαβ-seq (n = 11), TCRβ-seq from CD8⁺ sorted samples (n = 8), and TCRαβs inferred from bulk-RNA-seq (n = 15) data (Supplementary Data 1) to form the largest described dataset of 199 T-LGLL clones from 170 patients (Supplementary Data 3). By genotyping or inferring the HLA-types³⁰ from scRNA-seq and bulk-RNA-seq data, we were able to determine the HLA type for 31% of the clonotypes (62/199), and 69% (43/62) were HLA-A*02 + . All T-LGLL clonotypes were restricted to individual patients, and no structural amino acid-level similarities were identified by GLIPH2³¹, even when the analysis was focused only on the 43 HLA-A*02 + T-LGLL clones. This suggests the absence of shared target antigen(s) driving the clonal expansions in T-LGLL.

Next, we hypothesized that despite there being no shared antigen between patients, the non-leukemic clonotypes could target the same eliciting antigen in individual patients, which would be observed as shared TCR motifs between leukemic and non-leukemic clonotypes. Iterative GLIPH2 analysis performed on CD8⁺ (n = 8) and mononuclear cell (MNC) sorted (n = 17) TCRβ-seq patient samples indicated that the leukemic T-LGLL clones indeed shared amino acid-level similarities with their non-leukemic repertoire in 72% of patients with T-LGLL (6/8 CD8⁺ and 12/17 MNC-sorted samples, Fig. 3a, b, Supplementary Fig. 9, Supplementary Data 3). To avoid bias due to differences in the sequencing depth, the samples were subsampled to the same read-depth (30,000 reads per sample) before analysis. Similar results were also obtained after excluding the leukemic clone and subsampling only the non-leukemic TCRs to 30,000 reads per sample (Supplementary Fig. 10a, Supplementary Data 3). These findings denote that the majority of the leukemic T-LGLL clonotypes are likely to target the same antigen as clonotypes in their non-leukemic repertoire, and therefore, we termed them antigen-driven clonotypes.

a Network plots showing antigen-driven clonotypes from three selected patients with T-LGLL. Antigen drive denotes that the T-LGLL clone shares amino acid-level similarities with its non-leukemic repertoire. Each dot (a node) is a TCR clonotype, and clonotypes with shared amino acid-level similarities are connected by a line (an edge). The T-LGLL clones are highlighted with blue and the non-leukemic with red. Below each network plot are shown parts of the GLIPH2 results for the individual patient with the same color coding on the TCRs. Additional T-LGLL cases are shown in the Supplementary Fig. 9. b The presence of antigen drive (i.e., whether the largest clonotypes have shared amino acid-level similarities with the rest of the TCR repertoire) in T-LGLL (mononuclear cell [MNC]-sorted n = 17, CD8⁺-sorted n = 8), metastatic melanoma sampled from blood (SKCM, n = 29), rheumatoid arthritis (RA, n = 32), and healthy controls (HC, MNC-sorted n = 785, CD8⁺-sorted n = 38). T-LGLL patients have more antigen-driven cases than the rest of the conditions (P < 0.05, Fisher’s one-sided exact test). All samples were subsampled to the same read-depth (30,000 reads per sample). Results where the T-LGLL clone was excluded before downsampling and in which the subsampling was only done for the non-leukemic library are shown in the Supplementary Fig. 10a. c The proportion of mutated (n = 6) and wild-type STAT3 patients (n = 6) where antigen-driven or no antigen-driven clonotypes were detected in the MNC-cohort. d The evolution of antigen-driven and non-antigen-driven T-LGLL clonotypes in multiple timepoints. Individual lines correspond to individual T-LGLL clonotypes while the bolded line shows the median. P-value was calculated with two-sided Mann-Whitney test. e Flow cytometry analysis of Vβ repertoires and variant allele frequency (VAF) of STAT3 Y640F clone (located in the shrinking Vβ1 clone) are used to demonstrate T-LGLL clonal dynamics (clonal drift) in patient 1. f UMAP representation of the CD8⁺ T cells from patient 1 from two different timepoints. The left panel highlights the different clusters, and the superimposed lines correspond to predicted maturation trajectories (pseudotime) calculated with a pseudotime algorithm Slingshot. The middle panel illustrates the expanding and shrinking clones. The panel on the right highlights the previously defined cytotoxicity score in T-LGLL clonotypes.

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To understand whether the antigen drive is restricted to T-LGLL, we performed a similar analysis using TCRβ-seq samples from patients with RA³² (n = 45), metastatic melanoma sampled from blood³³ (SKCM, n = 29), and healthy controls (CD8⁺ sorted³², n = 38; MNC-sorted n = 785³⁴) (Supplementary Data 1) with similar subsampling. The antigen drive was the most prevalent in T-LGLL (P < 0.05, two-sided Fisher’s exact test) and the least seen in healthy controls (Fig. 3b, Supplementary Data 4). The antigen-driven clonotypes were detected in both mutated (n = 6) and wild-type STAT3 patients (n = 6) in equal proportions (Fig. 3c).

We next asked whether the antigens causing these polyclonal responses in T-LGLL are caused by commonly encountered antigen epitopes. Less than half (74/199, 37.19%) of the T-LGLL clonotypes were found at least once in the healthy (n = 785) TCRβ repertoires³⁴, and they explained <1% of the healthy repertoire (Supplementary Fig. 10b, c). To control the impact of HLA genotypes, we also restricted our analysis to 43 T-LGLL clonotypes with known HLA-A*02⁺ and searched them only among HLA-A*02⁺ healthy donors (n = 294). 44.2% (19/43) of the clonotypes were found at least once, also in low frequencies (Supplementary Fig. 10d, Supplementary Data 3).

Interestingly, the antigen-driven clonotypes were more frequently observed in the healthy controls’ (n = 785) TCRβ repertoires than the non-antigen-driven clonotypes (P < 0.01, Kruskal-Wallis test, Supplementary Fig. 10e), suggesting that antigen-driven clonotypes could recognize commonly encountered antigens. Therefore, we predicted the antigen specificities for T-LGLL clonotypes with a supervised machine-learning method TCRGP³⁵ against common viral epitopes from CMV, EBV, Influenza A, and HSV2. Only 2 of 199 (1.0 %) T-LGLL clonotypes were predicted to recognize these antigens and both clonotypes were targeting CMV pp65 epitope (Supplementary Data 3). As the TCRGP-models have been trained using data from HLA-A*02 donors, we next focused only on the 43 T-LGLL clonotypes detected in HLA-A*02+ patients. None of the T-LGLL clonotypes from either HLA-A*02+ positive (n = 43) or HLA-A*02 negative (n = 19) patients were predicted to recognize these viruses (Supplementary Data 1 and 3). The two TCRs predicted to target CMV pp65 were from patients from which HLA type was not available. Overall, these results suggest that these four viruses do not contain major driver antigens for T-LGLL.

We next studied a cohort of T-LGLL patients from which follow-up samples were available²⁴ (n = 17, 38 samples). We noted that the same patient could harbor both antigen-driven and non-driven clones, and that the clonotypes with antigen drive were larger than the non-driven clonotypes during follow-up (P < 0.05, Mann-Whitney test, Fig. 3d, Supplementary Fig. 10f) suggesting that antigen drive can potentially provide a growth advantage for the clones.

To further understand the evolution of the antigen-driven and non-driven clones, we studied samples from a patient with both types of clones (patient 1). The analysis was performed using three peripheral blood samples collected seven years apart (2011–2018). Although the patient had no treatment for her T-LGLL disease, the dominant STAT3 mutated clone (78% → 10%) which was not antigen-driven, was replaced by an antigen-driven STAT3 wild-type clone (5% → 24%) carrying a different TCRαβ (Fig. 3e, f, Supplementary Fig. 11a–e). The non-antigen-driven STAT3 mutated and the antigen-driven STAT3 wild-type clones were phenotypically different and possibly two different maturation end-points as suggested by trajectory analysis with Slingshot³⁶ (Fig. 3f). The expanding, antigen-driven wild-type STAT3 clone was more cytotoxic than the shrinking STAT3 mutated clone (Fig. 3f, Supplementary Fig. 11a–e). The top DE genes in the expanded clone included GZMH, GNLY, and FCGR3A (CD16) and the clone had a higher cytotoxicity score than the shrinking one (P < 0.0001, Mann-Whitney test, Fig. 3f, Supplementary Fig. 11d, Supplementary Data 2). Conversely, the STAT3 mutated shrinking clone presented an attenuated CD8⁺ T_EM phenotype marked by the expression of GZMK and upregulated SOCS1 and SOCS3, which are known to inhibit JAK-STAT signaling¹⁹.

To conclude, our results suggest that a majority, but not all, T-LGLL clonotypes could originate from an initial polyclonal antigen-response. Also, the antigen-driven clonotypes are larger and often display a more cytotoxic phenotype than the non-driven T-LGLL clonotypes.

In T-LGLL non-leukemic T cell populations are mature and clonal

After finding the connection that clonal and non-clonal immune cell repertoires are possibly connected via antigen preferences, we sought to investigate the phenotypes of non-leukemic immune cell populations in T-LGLL in detail. As the presence of T-LGLL clonotypes biases the immune repertoire, we removed the clonally expanded T-LGLL cells from scRNA+TCRαβ-seq data and compared it with similar data from solid cancers³⁷ (n = 3), hematologic cancers³⁸ (n = 8), and healthy controls (n = 6) (Supplementary Data 1). After clustering (Fig. 4a, Supplementary Fig. 12a–c), we observed that, in comparison with the other cancers, the proportion of conventional dendritic cells (cDCs) (P < 0.01 and P_adj = 0.052, Benjamini-Hochberg corrected Mann-Whitney test) and naïve B-cells (P < 0.05 and P_adj = 0.16) were reduced, and the proportion of mature CD4⁺ T_EM-cells (clusters 2 and 13, both P < 0.01 and P_adj = 0.052) was increased in T-LGLL (Fig. 4b, Supplementary Fig. 13a–b). When including only patients with hematologic cancers (n = 8), CD4⁺ T_EM cells were still markedly elevated in T-LGLL (Fig. 4b). Similar results were obtained when compared to healthy controls (n = 6, Supplementary Fig. 13c, d).

a UMAP representations of non-leukemic CD45⁺ sorted cells from 11 T-LGLL, 6 healthy, 4 CML, 4 CLL, 2 RCC, and 1 NSCLC samples profiled from peripheral blood with 10X technologies, where different colors indicate clusters. Density estimates showing the overlapping dots for each cohort are presented on the right. b Differentially abundant non-leukemic clusters (from panel a) between patients with T-LGLL (n = 9) and patients with other cancers (n = 11, upper panel) and T-LGLL and with blood cancers (n = 8, lower panel). The horizontal line indicates P = 0.05, as calculated with two-sided Mann-Whitney test. c Percentage of mature effector memory CD4⁺CD57⁺ cells out of CD4⁺ T cells in T-LGLL (n = 6) as compared with healthy controls (n= 6) in the flow cytometry cohort. P-values were calculated with two-sided Mann-Whitney test. d Percentage of proliferating CD4⁺ T cells out of CD4⁺ T cells measured with CFSE dilution after stimulation with either TCR ligation or TLR stimulation in patients with T-LGLL compared with healthy controls in the flow cytometry cohort. P-values were calculated with two-sided Mann-Whitney test. e Left: Clonality index (Gini, higher denotes more clonal) in CD8⁺ sorted populations from T-LGLL (n = 10), rheumatoid arthritis (RA) (n = 32), and healthy controls (n = 38) profiled with TCRβ-seq. Middle: Clonality index in non-leukemic TCR-repertoires of mononuclear cell (MNC) samples in patients with T-LGLL (n = 38) and healthy controls (n = 785). Right: Clonality index in non-leukemic TCR-repertoires of MNC samples in STAT3 mutated (mt) (n = 26) and wild-type (wt) (n = 39) patients. P-values were calculated with two-sided Mann-Whitney test.

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A patient cohort profiled with flow cytometry validated that patients with T-LGLL had a significantly higher percentage of mature terminally differentiated, antigen-experienced CD4⁺CD57⁺ T cells^39,40 when compared with the healthy controls (P < 0.05, Mann-Whitney test, Fig. 4c, Supplementary Fig. 13e–g). To support the maturity of the CD4⁺ T cells in T-LGLL, we noted that the proliferative capacity of the total CD4⁺ T cell compartment, measured as carboxyfluorescein succinimidyl ester (CFSE) dilution upon TCR ligation and TLR stimulation, was reduced in T-LGLL compared with the healthy controls (P < 0.05, Fig. 4d, Supplementary Fig. 13f).

Besides increasing T cell maturity, antigen-driven processes increase T cell repertoire clonality. Therefore, we compared the clonality of the non-leukemic CD8⁺ T cells in T-LGLL to CD8⁺ sorted healthy and RA samples^24,32 and found that the non-leukemic CD8⁺ T cells in patients with T-LGLL had a more restricted TCR repertoire than patients with RA (P < 0.01, Mann-Whitney test) and healthy controls (P < 0.05), latter of which was validated in the MNC-cohort (P < 0.0001, Fig. 4e). In addition, the non-leukemic repertoires of wild-type STAT3 patients were more clonal than those of the mutated STAT3 patients (P < 0.0001) (Fig. 4e).

IFNγ drives activation of the non-leukemic immune cell repertoire

Besides changes in cell abundances, scRNA-seq also showed the activation of different non-leukemic subsets in T-LGLL in comparison with patients with other cancers and healthy controls. For example, the expression of different cytokines (CCL2/3/4/5), co-stimulatory genes (CD27, TNFRSF4 [HVEM], TNFRSF14 [OX40], TNFRSF25 [DR3]), and IFNγ response genes (e.g., B2M, TAP1, HLA molecules) were upregulated in different non-leukemic NK-cells, monocytes, and B-cell clusters in comparison with healthy controls (Fig. 5a), other cancers (Supplementary Fig. 14a), and patients with blood cancers (Supplementary Fig 14b, Supplementary Data 2). Notably, the expression of different cytotoxic genes (GZMA/B/H, PRF1, NKG7) was upregulated in non-leukemic CD8⁺, CD4⁺, and NK-cell clusters. The phenotype of the non-leukemic T cells was validated in the flow cytometry cohort, where CD8⁺CD57⁻ and total CD4⁺ populations in T-LGLL expressed higher levels of GZMA/B (P < 0.01 for CD8⁺CD57⁻ and CD4⁺, Mann-Whitney test) and PRF1 (P < 0.01 for CD8⁺CD57⁻ and CD4⁺) than healthy (Fig. 5b).

a Expression of selected differentially expressed genes (P_adj < 0.05, calculated with Bonferroni corrected two-sided t-test) grouped by their functional pathways between the non-leukemic CD45⁺ sorted cells from patients with T-LGLL (n = 9) and healthy controls (n = 6). Values are presented as log2 fold-change (log2fc). b Left: Protein level expression (MFI mean fluorescence intensity) of cytotoxic proteins GZMA/B and PRF1 in CD8⁺CD57⁻ cells in T-LGLL patients (n = 6) and healthy controls (n = 6). Right: The proportion of GZMA/B and PRF1⁺ CD4⁺ cells in the flow cytometry cohort. P-values calculated with two-sided Mann-Whitney test. c Upregulated HALLMARK-category pathways (P_adj < 0.05, Benjamini-Hochberg corrected Fisher’s one-sided exact test on differentially expressed genes) in non-leukemic cells from T-LGLL (n = 9) in comparison with healthy (n = 6). d Median expression of the IFNγ response module score in different immune subsets in patients with T-LGLL (n = 9), healthy controls (n = 6), and patients with other cancers (n = 11). The T-LGLL samples were enriched in the IFNγ high cluster (P < 0.05, Fisher’s one-sided exact test). Clustering was performed with Ward’s linkage. e Left: Scaled expression of IFNG in leukemic (red) and non-leukemic (green) populations. P-value was calculated with two-sided Mann-Whitney test. Right: Scaled expression of IFNG in different leukemic (red) and non-leukemic populations (green). Cluster numbers refer to Fig. 2b (leukemic clusters) and Fig. 4a (non-leukemic clusters).

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To understand the pathways driving immune activation in T-LGLL, we performed pathway analysis among patients with T-LGLL, patients with other cancers, and healthy subsets. The most upregulated pathways in T-LGLL included IFNγ-response (upregulated in 12/16 subsets vs. other cancers, 16/17 vs. healthy, 9/15 vs other blood cancers), IFNα-response (9/16 vs. other cancers, 15/17 vs. healthy, 8/15 vs other blood cancers), and NFκB (11/16 vs. other cancers, 0/17 vs. healthy, 4/15 vs other blood cancers) pathways (Fig. 5c, Supplementary Fig. 14c, d).

We focused on the IFNγ response, as it was among the most upregulated pathway in all comparisons and quantified its effect by calculating an IFNγ response module score⁴¹ in all immune subsets in individual patients. The strongest IFNγ response was seen in different myeloid subsets (CD16⁺ monocytes, CD16⁻ monocytes, cDCs), NK-cells, and CD8⁺ T_EM cells (Fig. 5d). In unsupervised clustering, the samples were split into two groups, high IFNγ and low IFNγ, based on their IFNγ-response scores. The samples from the T-LGLL group were enriched to the high-IFNγ group (P < 0.05, Fisher’s one-sided exact test), confirming that IFNγ response is more strongly activated in T-LGLL than in other cancers. A similar analysis with the NF-κB pathway did not show enrichment of T-LGLL samples (Supplementary Fig. 14e). Interestingly, T-LGLL cells expressed higher amounts of IFNG than non-leukemic cells (P < 0.0001, Mann-Whitney), where the highest expression was seen in cytokine-secreting T-LGLL cluster 4 (Fig. 5e).

T-LGLL clones show an elevated amount of predicted cell–cell interactions

To further understand the function of cytokines as mediators of immune responses between the leukemic and non-leukemic compartments in T-LGLL, we reanalyzed the plasma cytokine profiles of nine T-LGLL patients and eight healthy controls⁴² (Supplementary Data 1). In patients with T-LGLL, multiple cytokines, including IFNγ-inducible cytokines (CXCL10, CXCL11), JAK-STAT pathway-activating cytokines (IL-6, IL-10, IL-15RA), and inflammatory chemokines (CCL3, CCL4, MCP1), were elevated (Fig. 6a). However, in line with previous publications^20,21, IFNγ levels were not elevated in T-LGLL. The incorporation of plasma cytokine data with scRNA-seq showed that the majority (11/17, 64.7%) of upregulated cytokines were expressed predominantly by monocytes or cDCs (e.g., CCL2/3/7, CXCL10/11, IL15RA) instead of T-LGLL clonotypes (Fig. 6b, Supplementary Fig. 15a). Conversely, 6 of 17 upregulated cytokines (e.g., CCL4, TNFRSF9 [CD137], TNFRSF14 [HVEM]) and IFNGwere preferentially expressed by T-LGLL clones.

a Differentially expressed (unadjusted two-sided Mann-Whitney test) plasma cytokines between patients with T-LGLL (n = 9) and healthy controls (n = 8), where cytokines P < 0.05 (horizontal line) are labeled. b Median expression of differentially expressed cytokines in the scRNA-seq data in non-leukemic and leukemic immune cell subsets in the patients with T-LGLL (cluster numbers refer to Fig. 4a). Heatmap clustering was performed with Ward’s linkage. Values are scaled for each column. c HLA class II module score in T-LGLL in different monocyte clusters (as seen in Fig. 4a) in comparison with healthy controls and other disease cohorts. P-values were calculated with two-sided Kruskal-Wallis test. d Proportion of bead-adhering (fluorescent microspheres) CD16⁺ and CD16⁺ CD14^dim monocytes in patients with T-LGLL (n = 6) in comparison to healthy controls (n = 6). P-values were calculated with Mann-Whitney test. e Number of significant ligand–receptor interactions (P < 0.05, CellPhoneDB permutation test) between T-LGLL clonotypes (as shown in Fig. 2e) or the top expanded hyperexpanded clonotypes (>50 TCRs) from healthy controls and different non-leukemic immune cell subpopulations, calculated CellPhoneDB. Clustering was performed with Ward’s linkage. Color scale from blue to red marks the number of predicted interactions between cell types. f Number of significant ligand–receptor interactions of T-LGLL clonotypes with different immune subpopulation. Shown receptor–ligand pairs are statistically significant interactions that have been attributed as co-stimulatory or inhibitory. The color indicates the number of T-LGLL clonotypes that have the interaction with the cell type. g Left: UMAP representation showing the distribution of the T-LGLL clonotypes with different numbers of interactions with their non-leukemic counterparts. Right: UMAP representation of the transcriptomes of the selected 18 leukemic T-LGLL clonotypes as seen in Fig. 2b.

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In addition to being the most important cytokine producers, monocytes were also the most transcriptionally altered subpopulations between T-LGLL and other conditions in the DE gene analysis (first, CD16⁺ monocytes; fourth, CD16^- monocytes) (Supplementary Fig. 15b–g). Flow cytometry analysis also confirmed that although the total number of monocytes was reduced (P < 0.05, Mann-Whitney test), the distribution of different monocyte subsets was altered, and T-LGLL patients had a bigger proportion of CD16⁺ cells (P < 0.05, Supplementary Fig. 16a, b) out of the CD14⁺monocytes. The upregulated DE genes in monocyte populations included multiple HLA molecules and classical scavenging receptors (e.g., CLEC10A, CD44, CLEC2B, CLEC9A, MRC1), translating into upregulated HLA class II²⁸ (P < 0.0001, Kruskal-Wallis test) and scavenging scores (P < 0.0001, Fig. 6c, Supplementary Fig. 16c, Supplementary Data 2). To analyze the antigen-presenting function of the monocytes, we incubated blood MNCs with fluorescent microspheres and found that the proportions of bead-adhering CD14⁺CD16⁺ and CD14^dimCD16⁺ monocytes were increased in T-LGLL compared with healthy controls (P < 0.05 Mann-Whitney test, Fig. 6d, Supplementary Fig. 16d), which may indicate higher scavenging potential.

Next, we calculated ligand–receptor interactions with CellPhoneDB⁴³ between T-LGLL clonotypes and other immune cells and compared that to the interactome of hyperexpanded clonotypes from healthy controls. The interactome analysis implicated an increased number of interactions between T-LGLL clonotypes and other immune cells in comparison with healthy hyperexpanded clonotypes (Fig. 6e). The majority of the differences could be tracked to T-LGLL–monocyte interactions, and many of the predicted interactions could be attributed as co-stimulatory (e.g., CD2–CD58, CD48–CD244, CLEC2B–KLRF1, TNFSF14–TNFRSF14); while only a few interactions were inhibitory (e.g., LGALS9–HAVCR2) (Fig. 6f). Based on the number of ligand–receptor interactions, T-LGLL clonotypes formed three clusters: (1) strongly interacting (the highest number of interactions), (2) interacting, and (3) immune independent (the lowest number of interactions), which was also evident in the focused clustering of T-LGLL clonotypes (Fig. 6g). Immune independent STAT3 mutated T-LGLL clonotype from patient 2 had the lowest number of interactions, and during the 1-year follow-up, the size and the phenotype of this clone were stable (Supplementary Fig. 17a–e).

The asset of scRNA+TCRαβ-seq in this study is its accuracy in identifying the TCR-sequence-restricted clonal expansions. In other non-T cell-malignancies, similar cell-specific markers are rarely available or require simultaneous DNA sequencing to define clonal cells. With scRNA+TCRαβ-seq, we were able to perform detailed and precise characterizations of the expanded T-LGLL clones from the oligo- and polyclonal CD8⁺ T cell repertoires and show evidence of a strong antigen-driven immune response that shapes the entire immune cell repertoire in T-LGLL.

T-LGLL clonotypes are known to overexpress cytotoxic and T cell activation-associated genes in comparison with their healthy reactive counterparts^44,45. Here, we also found exhaustion-associated genes, such as LAG3 and TIGIT, among the most upregulated genes between T-LGLL and hyperexpanded cells in healthy controls, but not the previously found PDCD1 (PD1) and HAVCR2 (TIM-3)^44,45. Our in vitro validation suggested that TCR ligation fails to trigger normal degranulation and cytokine production in the T-LGLL clonotypes. These findings may partly explain why T-LGLL usually presents only with moderate lymphocytosis and rarely develops into a more aggressive (proliferative) disease despite highly activating STAT3 and STAT5B mutations^46,47.

Our data demonstrate inter and intrapatient heterogeneity, where T-LGLL clones with the same TCR rearrangement can harbor multiple phenotypes. Importantly, the CD16⁺ CCL4⁺ LAG3⁺ TOX⁺phenotype was identified as the dominant phenotype in most clonotypes (13/18, 72.2%) and was seen in patients with either mutated or wild-type STAT3, further unifying these diseases besides the noted shared JAK-STAT activity¹⁹. As this phenotype differs significantly from the effector memory phenotype of hyperexpanded CD8⁺ T cells in healthy controls, it could aid the diagnostic process, particularly in the distinction of wild-type STAT3 cases from reactive processes. However, the finding that wild-type STAT3 clonotypes have higher T cell activity, cytotoxicity and non-leukemic clonality than those with mutated STAT3, which was not seen in a previous publication with flow cytometry¹⁴, proposes that mutated STAT3 T-LGLL, wild-type STAT3 T-LGLL, and reactive processes arise from different pathogeneses.

The eliciting antigen in T-LGLL has remained elusive. We analyzed TCRs in both unsupervised and supervised manners with the current best-practice bioinformatics tools^31,35,48 but found no evidence of common putative, known or unknown antigens, even in individual patients. The obvious limitation in these analyses is that they were done independently of HLA-genotype or involved only T-LGLL clones from patients with HLA-A*02+ background. Unfortunately, HLA information was not available from all patients that were included in the previously published T-LGLL TCR datasets²⁴. Supervised TCRGP tool has shown to outperform other similar methods³⁵, when the genotype of the analyzed TCR repertoire is known. However, as the training data of epitope-specific TCRs is limited, it is probable that the existing TCR analysis tools do not capture the full heterogeneity of antigen-specific repertoire, resulting in false negatives even in the cases of HLA-A*02⁺ patients. Nevertheless, our results imply that the common denominator underlying T-LGLL patients is perhaps not the antigen, but rather the environmental, genetic, and/or immunological factors that support the expansion and persistence of T-LGLL clonotypes. These results are in accordance with Gao et al.⁴⁹, who profiled alemtuzumab treated T-LGLL patients with scRNA+TCRαβ-seq, and no shared T-LGLL clonotypes or T-LGLL clonotypes targeting known antigens were observed.

Our results do not, however, contradict that T-LGLL is driven by an abnormal response to an antigen. On the contrary, in an analysis that is invariant to HLA genotypes, we observed that over half (72%) of the T-LGLL clonotype TCRs share structural similarities with TCRs from the same patients’ non-leukemic repertoires. Our results from the antigen drive support the view that the antigen response in T-LGLL is poly- or oligoclonal, rather than monoclonal. Our results are in line with the previous data suggesting that STAT3 mutation follows the initial clonal expansion and is an event that solidifies the clonal dominance³. The antigen-driven clonotypes in T-LGLL patients were larger, and they could occur concomitantly with non-antigen-driven clones. Interestingly, in one patient with follow-up samples, the mutated STAT3 clone was replaced by a more cytotoxic wild-type STAT3 clone. Further, the non-leukemic CD8⁺ and CD4⁺ T cell repertoires in T-LGLL were more mature, cytotoxic, and clonally restricted than in other cancers, in RA, and in healthy controls, suggesting the strong immune-editing capacity of a driving antigen. The advent of high-throughput epitope-MHC-TCR-screening tools⁵⁰ and their use in T-LGLL will provide invaluable information about the antigen-specific response in general.

Also other findings, besides non-leukemic CD8⁺ and CD4⁺ T cells, further support the idea of an aberrant oligoclonal immune response against a patient-specific antigen as a disease-inducing and evolution-driving trigger in T-LGLL. We noted increased co-stimulatory cell–cell interactions between T-LGLL clonotypes and monocytes and enhanced antigen-presenting cell function of monocytes. The immunological factor driving these differences was a response to IFNγ and it was the most evident in monocyte populations. The IFNG was preferentially expressed by T-LGLL clonotypes, and not by monocytes, linking the leukemic and non-leukemic repertoires into a vicious cycle. With only incidental cases of clonal drift seen in our data, we cannot pinpoint whether the non-leukemic immune repertoire caused the transformation of a T cell clonotype to a T-LGLL clone or vice versa, which needs to be addressed in future studies.

Current therapies in T-LGLL, including corticosteroids, methotrexate, and cyclosporine A, offer unsatisfactory results, as over half of patients eventually relapse¹⁸, posing a need for combined or sequential therapies. Current salvage therapies include T cell depleting anti-CD52 (alemtuzumab) and anti-CD3 (anti-thymocyte globulin) regimens^3,51,52. These approaches also target non-T-LGLL clones which could explain why TCR repertoire does not diversify after alemtuzumab treatment⁴⁹ and why treatment responses do not correlate with the STAT3 mutation status or clonal burden²³. Moreover, treatments that attenuate the entire immune system have shown encouraging results, both as first-line (cyclophosphamide, >70% response rate)⁵³ and salvage therapies (tofacitinib, a JAK3 inhibitor >60% response rate)⁵⁴.

In conclusion, our study highlights how the entire immune cell repertoire, including hyperexpanded CD8⁺ T-LGLL cells, non-leukemic CD8⁺ cells, CD4⁺ cells, and monocytes, contribute to the CD8⁺ T-LGLL disease phenotype. An aberrant antigen-driven immune response shapes the repertoire and maintains the persistence of the hyperexpanded T-LGLL clonotypes. Our results imply that future therapies should not only target the T-LGLL clonotypes but also other immune cell types and their interactions to transform the outcome of patients with T-LGLL.