Single-cell genomics highlight MYC-associated metabolic activation and altered cell interactions in T-prolymphocytic leukemia progression

Domain: chemistry
Published: 2026-03-09
Editorial outcome: major_revision

Abstract
T-prolymphocytic leukemia (T-PLL) typically presents with rapid tumor burden. However, 15–25% of cases are diagnosed at an indolent stage with asymptomatic and stable low-level blood lymphocytosis over up to 2-3 years before advancing to active-stage disease. To define the molecular changes underlying this transition, we perform single-cell RNA sequencing of 28 treatment-naïve samples including 11 longitudinally acquired indolent/active pairs, paralleled by longitudinal whole genome sequencing. This reveals both patient-specific lesions and common global alterations of gene expression. Strong upregulation of MYC-target gene signatures in active T-PLL samples associated with enhanced energy metabolism implicate acquired autonomy from energetic restrictions. Recurrent downregulation of genes of the T-cell-receptor signaling cascade and reduced interactions of the T-PLL cell with non-leukemic cell types further indicate progressive independence from regulatory survival signals and escape from micromilieu-mediated control. This single-cell and disease-stage resolved genomic analysis of T-PLL provides insights into shared mechanisms of tumor evolution, which have to prove their amenability as targetable lesions.

Introduction
T-prolymphocytic leukemia (T-PLL) is a neoplasm of mature T cells. Clinical presentation of the majority of T-PLL patients is characterized by aggressive disease kinetics, such as exponentially rising peripheral blood (PB) lymphocytosis and repressive bone marrow (BM) infiltration with consecutive hematopoietic insufficiency. At this typical stage around first diagnosis, infiltrations of lymphoid and non-lymphoid tissues further contribute to the symptomatology and include splenomegaly, lymph-node enlargements, hepatomegaly, polymorphic skin involvement, and effusions.

Patients with newly diagnosed T-PLL continue to face a disappointing median overall survival of 24 months. Attributing to this persistently poor prognosis is an inherent resistance towards current (chemo)therapeutic options. Despite response rates of ≈90% following first-line therapy with the monoclonal anti-CD52 antibody alemtuzumab (+/− conventional cytostatics), relapses are inevitable and sustained leukemic control can only be accomplished by a consolidating allogeneic stem cell transplantation in eligible patients. Although remarkable improvements have been made over the last two decades in the genomic and functional characterization of T-PLL, disease-specific therapeutic strategies still lag behind the advancing biologic concepts.

Current models of the protracted evolution from a post-thymic pre-leukemic cell propose constitutive activation of T-cell leukemia/lymphoma family molecules through chromosome-14 rearrangements (i.e., TCL1A; or of MTCP1 at chromosome X) as the key initiating lesions. Known oncogenic functions of TCL1A include augmentation of pro-survival kinases such as AKT or ERK resulting in amplification of T-cell receptor (TCR) stimuli. The relevance of TCL1A-driven pathways in overt or progressing T-PLL has not been thoroughly interrogated.

The most frequent ‘secondary’ lesions of T-PLL, likely cooperating with TCL1A, comprise a perturbed DNA damage response caused by mono-allelically deleted and/or mutated ATM upstream of a genomically intact TP53, resulting in arrested repression of p53 and its pivotal functions. MYC copy-number gains and activating mutations of JAK/STAT genes are further recurrent aberrations, but without resolved early- vs late-stage relevance in leukemic development.

Naturally, these data on the molecular make-up of T-PLL almost exclusively derive from cells of the active disease stage. However, a sizable subset of ≈15–25% of T-PLL patients are (often coincidentally) diagnosed with an indolent disease. These patients are mostly asymptomatic, maintaining a stable disease with low-to-moderate tumor load and kinetics. As they virtually all advance to a rapidly progressing T-PLL after ≈2–3 years, it has been accepted that there is no relevant subset of a long-standing indolent T-PLL (hence abandonment of the term T-CLL). It is not fully resolved whether rare cases of longer indolent courses and TCL1A-negative status represent variants of regional prevalence or legitimate T-PLL.

Recognizing this indolent stage rather as an early phase towards an inevitable aggressive disease has implications on the initial treatment strategies (e.g., watch-and-wait vs early intervention), particularly to be (re)considered in light of available therapies. We proposed clinical cutoffs to discern indolent-stage T-PLL from active T-PLL, e.g., PB lymphocytes exceeding 30G/l or their doubling time being ≤6 months, or becoming symptomatic, but these criteria were mainly intended as treatment triggers.

Systematic investigations of the pathobiology of indolent-stage T-PLL would provide informative access to early disease stages and by that overcome the limitations of chronological experimental models, such as Lckpr-hTCL1A tg mice. Consequently, molecular analyses of indolent-phase T-PLL in comparison to active T-PLL (ideally within the same patient) harbor the potential to deduce inherent phase-specific vulnerabilities (and resistances) to guide future therapeutic pathway approaches.

Here, we employed single-cell-resolved transcriptomic analyses of 11 longitudinally-paired and 6 single time-point derived T-PLL samples to unveil the molecular changes underlying the evolution from indolent to active T-PLL. We identified both patient-specific mechanisms of tumor evolution and common patterns of disease progression. Our findings suggest a key role of energy metabolism in the transition from indolent to active disease, accompanied by a strong upregulation of MYC gene expression signatures. Furthermore, reduced cell-cell interactions in active T-PLL implicate increased independence from milieu interactions. This was paralleled by decreased signatures from exogenous growth stimuli such as via TCR and TNFα/NFκB/MAPK signaling pathways, which we established as essential drivers of early T-PLL. Additional longitudinal whole genome sequencing (WGS) further pointed towards an outgrowth of ATM-deficient T-PLL cell clones and progressive accumulation of MYC copy number alterations (CNAs).

Results
As a single-cell transcriptome atlas of sequentially acquired samples during T-PLL evolution

To investigate the mechanisms underlying the transformation from indolent to active T-PLL we performed single-cell RNA sequencing on 11 longitudinally sampled indolent/active pairs, each from the same patient, as well as on 6 single time-point T-PLL cell samples (5 from indolent-stage T-PLL; Fig. 1A; supplementary Data 1; supplemental Table 1). All samples were treatment-naïve and classified as either indolent or active disease, based on consensus guidelines. Indolent samples were acquired at the time point of diagnosis whereas active T-PLL samples were taken after disease progression and immediately before initiation of treatment (median time between paired samples was 415 days (139–1241), Fig. 1B). Both PB leukocyte counts (median 19.73 G/l (9–29 G/l) vs 91.74 G/l (57.6–369.7 G/l) in indolent vs active T-PLL, p<0.0001, Mann–Whitney–Wilcoxon test (MWW), Fig. 1C) and serum LDH (median 237.5 U/l (169-324 U/l) vs 693 U/l (291-1935 U/l), p<0.0001, MWW), markedly differed between both phases. Technical validation of DEGs was performed using qRT-PCR (Pearson’s r=0.95, p=0.0003, Supplementary Fig. 1A; supplemental Table 2). Three of the 17 cases (pair_9, single_indolent_2, single_indolent_3) were TCL1A-negative.

After batch correction (Supplementary Fig. 1B) and dimensionality reduction, we observed a high inter-patient heterogeneity among T-PLL cell clusters in contrast to a cell-type specific clustering of non-tumor PBMCs and healthy-donor derived cells (Fig. 1D–E; Supplementary Fig. 1C–D). T-PLL tumor cells were identified based on their expression of T-PLL marker genes (e.g., CD3D, TCL1A, TRAC), and unsupervised cluster allocation (Fig. 1F). Cell labels of non-T-PLL cells were independently predicted via projection onto a reference dataset of healthy-donor derived PBMCs and presented cell type-specific clustering in the t-SNE representation (Fig. 1G). Investigating the relative frequencies of cell types, we observed a high relative tumor load within all samples (median purity 92.4% of T-PLL cells, Fig. 1H), except for patient pair_6 (indolent: 55.6%; active: 34.5%) and the indolent samples of pair_9 (27.6%) and pair_10 (32.5%). The predicted cell types were confirmed by their expression of well-established lineage markers (Fig. 1I).

Pseudo-bulk differential gene expression analysis, conducted between active time-point derived T-PLL cells (n=12 samples) and healthy-donor T cells (n=10 samples, supplementary Figs. 2A–C), revealed a strong correlation of DEGs when compared to bulk gene expression datasets (rho=0.7074–0.7592, Spearman).

Progression-associated transcriptomic shifts are heterogeneous at the individual gene level, but display shared patterns of pathway deregulation

Pairwise differential gene expression analysis (Fig. 2A) retained a total of 1019 genes that were significantly deregulated in the T-PLL cells of at least one sample pair (absolute log2 FC ≥ 0.5, FDR < 0.05). The overall number of DEGs varied strongly among pairs (range: 1-458 DEGs). Importantly, most (61.92%) of the identified DEGs were patient-specific, although there was a large set of DEGs that appeared in at least 2 sample pairs (38.08%; p<0.0001, permutation test; Supplementary Fig. 3A–B). These included major regulators and components of cell metabolism (TXNIP, COX6C), of TNFα/NFκB signaling (TNFAIP3, NFKBIA), and of cellular senescence (GADD45B, ZFP36L2). Notably, DEG clustering identified two groups of patients revealing opposite patterns of expression changes within some of the most recurrently deregulated genes (Fig. 2B). The strongest differences were observed in TNFα/NFκB signaling-associated genes, with a severe downregulation in the active time-point derived sample in those patients that were characterized by a comparably strong NFκB-signature during the indolent T-PLL stage (Supplementary Fig. 4A–F). Overall, the most uniform shifts of gene expression across all indolent/active sample pairs were detected in GSEA-confirmed functional gene sets associated with cell metabolism and cell-cycle regulation (Supplementary Fig. 5A–C).

Based on the observation of conserved T-PLL cell subclusters within paired samples (Supplementary Fig. 5D), we next investigated differential gene expression between T-PLL cell clusters that expanded and those that retracted over time (Fig. 2C). Interestingly, in contrast to the heterogeneous deregulation of single genes, GSEA indicated a higher degree of homogeneity in deregulated pathways. Most prominent was a strong enrichment of genes sets related to cell cycle (e.g., HALLMARK E2F targets or G2M checkpoint, 8/11 pairs), energy metabolism (e.g., HALLMARK oxidative phosphorylation, 7/11 pairs), and the transcription factor MYC (e.g., HALLMARK MYC targets V1 or V2, 8/11 pairs) in the expanding T-PLL cell clusters (Fig. 2D; Supplementary Fig. 5E–G). Pairwise pseudotime analyses further underlined these patterns (Supplementary Fig. 6A–B).

Clonal expansion of ATM lesions and MYC CN gains in active-stage T-PLL

To identify genomic lesions potentially driving the transcriptional changes observed in active T-PLL, we performed WGS on three longitudinal sample pairs. While the overall mutational burden remained largely unchanged (mean number of short variants: 11.83/MB vs 11.62/MB, indolent vs active-stage T-PLL, p=1, MWW, Fig. 2E; Supplementary Fig. 7A, supplementary Data 2), we detected an enrichment of several variants from indolent to active stage, especially in genes reported to be recurrently mutated in T-PLL (Supplementary Fig. 7B, Fig. 2F). Most notably, we observed a marked increase in the allele frequency of mono-allelic ATM lesions in all three patients (mean AF: 69.76% vs 91.33%, indolent vs active-stage T-PLL, Fig. 2G). In contrast, subclonal mutations of JAK/STAT pathway genes, which were detected at all indolent time points, did not show consistent outgrowth in active T-PLL (Supplementary Fig. 7C).

CN inferences based on gene expression highlighted T-PLL-typical aberrations, i.e., chromosome 8q gains, 11q deletions, and alterations of chromosomes 14 and 17 (Supplementary Fig. 8A). These hallmarks, including the 8q gains, were already present in the indolent time-point derived samples, suggesting an early role in disease evolution. The CN abundance of other chromosomal regions varied between indolent and active disease samples due to the outgrowth of different T-PLL subclones. Across all samples, there was no significant difference in the proportion of the genome affected by CN alterations between indolent and active T-PLL (Supplementary Fig. 8B–D). These findings were corroborated by WGS-based structural variant analysis, which confirmed that several key genomic lesions, such as juxtaposition of TCL1A and TCRAD, were already present at the indolent disease stage (Supplementary Fig. 9A–B). The most prominent genomic alteration associated with progression to the active stage was gain of chromosome 8q, encompassing the MYC locus at 8q24.21 (Fig. 2H). Notably, CN variants (CNVs) identified by WGS showed a strong concordance with those inferred from gene expression profiles (Supplementary Fig. 9C), supporting the reliability of the transcriptome-based CNV calls.

Amplified MYC-signatures as a key feature in the transition from indolent to active-stage T-PLL

Given the identification of prominent MYC gene sets in expanding clones and the observed copy number gains of MYC in active-stage T-PLL, we further interrogated the relevance of MYC-regulated genes in the transition from indolent to active disease. We derived cell-wise expression scores based on the HALLMARK genesets MYC targets V1 and V2. Sample-based analysis indicated a marked difference in their expression not only between healthy-donor derived T cells and active T-PLL, but especially in the transition from indolent to active disease (Fig. 3A; Supplementary Fig. 10A). Importantly, the upregulation of MYC targets was further increased in expanding T-PLL subclusters in 8/11 sample pairs (72.7%, Fig. 3B; Supplementary Fig. 10B). In line with the enhanced expression of MYC target genes was an overall upregulation of MYC mRNA from healthy-donor T cells over indolent to active T-PLL (p<0.0001, MWW, Fig. 3C; Supplementary Fig. 10C). Significant upregulation of MYC mRNA in T-PLL was validated in independent bulk-analyzed data sets of 117 T-PLL (n=69 by expression arrays, n=48 by RNAseq; Fig. 3D).

CN inferences revealed increases in the mean number of MYC alleles within several sample pairs (7/11; Fig. 3E), especially within expanding subclusters, which also correlated with MYC mRNA expression (rho=0.268, p<0.0001, Spearman; Supplementary Fig. 10D–E). Importantly, the increase of MYC mRNA levels translated into upregulation of MYC target genes (r=0.945, p<0.0001, Pearson; Fig. 3F; Supplementary Fig. 10F). Elevated MYC levels in T-PLL (over age-matched healthy-donor CD3+ T cells) were verified at the protein level (p=0.014, MWW). Increases of MYC protein in the intra-individual indolent-to-active comparison were observed in 3/3 analyzed sample pairs (Fig. 3G–H; Supplemental Table 3). Observed protein expression was also positively associated with mRNA expression of MYC target genes (Fig. 3I).

Cluster-based correlation of gene expression scores provided implications of these prominently upregulated MYC expression signatures, i.e., by revealing strong positive associations between MYC-target expression scores and processes of energy metabolism, such as oxidative phosphorylation and glycolysis (for both rho≥0.67, p<0.0001, Spearman), within both shrinking and expanding T-PLL subclusters (Fig. 3J; Supplementary Fig. 10G).

Upregulated energy metabolism and ATP generation characterize the progression to active T-PLL

Having detected recurrent alterations in metabolism-associated genes in conjunction with upregulated MYC (and its networks, e.g., TXNIP, ZFP36, UQCR10, FTH1) and given the central role of MYC as a metabolic regulator in cancer, we examined energy metabolism within T-PLL cells in more detail. Both oxidative phosphorylation and glycolysis gene set expression scores were upregulated in active time-point derived T-PLL (Fig. 4A). Remarkably, T-PLL cells from the indolent time point presented mean expression scores of oxidative phosphorylation similar to those of healthy-donor derived T cells, suggesting that the observed strong upregulation of this gene set in active-stage T-PLL cells is a specific feature of the transition from indolent to active T-PLL. These results were confirmed by cluster-based analyses, in which expanding clusters displayed a significant increase in oxidative phosphorylation or glycolysis scores as compared to shrinking T-PLL cell clusters in 7/11 pairs (Supplementary Fig. 11A–B).

There was also a robust interaction between both energy metabolism pathways (rho=0.398, p<0.0001, Spearman; Fig. 4B), indicating a general metabolic activation and increased ATP synthesis in T-PLL cells during disease progression. Investigating the relative expression of transcripts associated with cell metabolism revealed an increase from indolent to active T-PLL (Fig. 4C; Supplementary Fig. 11C). A significant general upregulation of transcripts encoding for metabolic genes in T-PLL vs healthy-donor derived T cells was further confirmed in two independent bulk gene expression data sets of 117 T-PLL (Fig. 4D).

To assess the functional relevance of the observed transcriptomic metabolic activation, we dynamically recorded energy metabolism in our samples using the Seahorse XFe 96 extracellular flux analyzer. Importantly, indolent T-PLL demonstrated low basal activity of glycolysis and respiration as well as a strongly restricted capacity to upregulate their energy metabolism upon TCR stimulation, when compared to age-matched healthy-donor derived T cells (Fig. 4E–F). In contrast, T-PLL cells of the active time point presented a significant increase of basal bioenergetics and surpassed the metabolic confinement observed in indolent T-PLL cells. In line with these functional data, both ex-vivo respiratory capacity and glycolytic reserve were positively correlated in our samples to the gene expression scores of the HALLMARKs oxidative phosphorylation and glycolysis, respectively (Supplementary Fig. 11D–E).

Active-disease T-PLL cells show attenuated signatures by exogenous growth signals, e.g., TCR stimuli

Our indolent/active comparative setup also allowed to assign central growth-regulating pathways, particularly those affected by TCL1A, i.e., TCR signaling, a stage-specific relevance. Therefore, we investigated the gene expression of TCR-signaling components and their down-stream signatures of transcriptomic responses in our dataset. Using the DoRothEA regulon collection to infer transcription factor activities, we found that transcription factors downstream of TCR signaling were among the most profoundly deregulated when comparing indolent with active T-PLL (Supplementary Fig. 14A). Notably, a strong activation of TCR-signaling signatures was particularly evident within indolent-time point T-PLL cells (Fig. 5A), predominantly observed for the transcription factors ATF2, REL, JUN, and RELB. Consistent with these findings, we confirmed that aberrations of proximal negative regulators of TCR-signaling input that are known to be downregulated in T-PLL, namely CTLA4 and LAG3, were already present in all indolent samples (Fig. 5B), strongly suggesting that these changes might contribute to early T-PLL pathogenesis.

Moreover, highlighting a diminished relevance of TCR activation in the active disease stage, we observed downregulations of TCR-signaling components and of downstream transcription factors during disease progression in multiple sample pairs. The expanding clusters of patient pair_3 showed a uniform downregulation of the TCR complex including all CD3 subunits, TRBC2, and the phosphatase PTPRC (CD45; all p<0.0001; Fig. 5C–D). A strong decrease in IL32 expression (log2FC=−2.062, p<0.0001) further pointed towards an overall decrease in cellular immune activation in this patient.

Tumor cells of patient pair_1 displayed similar aberrations during their transformation to active-stage T-PLL. Here, we observed a marked expansion of a subcluster harboring a complete downregulation of PTPRC (CD45, Fig. 5E). Correlating with this aberration, T-PLL cells acquired marked deregulations of other genes well-associated with cancer, including MAL, LY6E, and COX6C. Functionally, this corresponded to an overall decreased ex-vivo TCR response that specifically affected the CD45-negative subcluster. In fact, CD3/CD28 crosslinking resulted in lower induction of T-cell activation markers on CD45-negative cells and in a reduced phosphorylation of TCR-downstream kinases in active-stage T-PLL (Fig. 5F–G, Supplementary Figs. 12–13). In our cohort, downregulation or loss of PTPRC was observed in 4/11 (36.4%) T-PLL. Overall, these results demonstrate diminished TCR signaling in the indolent-to-active-stage transition of T-PLL.

Underrepresented immune-response pathways and reduction of cell interactions with the tumor-microenvironment implicate further mechanisms of autonomy

We next aimed to characterize the non-neoplastic cells in T-PLL. In the 28 samples analyzed here, the transcriptome profiles set apart distinct subsets of PB cell types (Fig. 6A). Investigating the kinetics in the compositions of non-T-PLL cells over disease progression, we observed most distinct changes in the proportion of monocytes (p=0.056, MWW, Fig. 6B–C) and dendritic cells (p=0.076, MWW, Supplementary Fig. 14B) with their relative increase in PBMCs of active T-PLL. Analyses of PB monocyte counts derived from hematology laboratory parameters available for 31 patients confirmed an elevated absolute number of monocytes in the 22 active T-PLL patients (p=0.0005, MWW, Supplementary Fig. 14C). Further characterization revealed a significant increase in the CD14/CD16 ratio from indolent to active T-PLL patient samples (p<0.0001, chi-squared test; Supplementary Fig. 14D).

To identify transcriptional programs associated with disease progression in the tumor microenvironment, we performed pairwise differential expression analysis between indolent and active-stage PBMC subsets. This revealed recurrent patterns of deregulated gene expression (Fig. 6D). Deregulations of the AP-1 subunits (JUN, JUNB, JUND) were prominent in PBMCs of the active time point. Similar to T-PLL tumor cells, we also detected deregulated TNFα/NFκB-signaling components and interferon-α response associated genes (IFI6, IFI44L, MNDA, NFKBIA) in PB monocytes of active T-PLL samples. Similar results were observed in a more conservative pseudo-bulk approach including all 28 T-PLL samples (Supplementary Fig. 15A).

GSEA revealed significant global alterations at the level of functional pathway clusters, including downregulation of inflammation-associated gene sets among nearly all cell types, alongside recurrent upregulation of interferon-alpha signaling and cell-cycle genes (Fig. 6E). In agreement with these dynamics, we observed reduced cell interactions between T-PLL cells and PBMC cell types in active-stage T-PLL (Fig. 6F; Supplementary Fig. 15B). Contrasting this overall pattern, signaling from T-PLL cells to monocytes was markedly enhanced in most active samples.

To explore key ligands involved in the remodeling of the tumor microenvironment, we performed NicheNet analysis in longitudinal sample pairs (Fig. 6H). Among the most recurrent ligands identified was Annexin A1 (ANXA1), a central player in anti-inflammatory signaling. In our dataset, ANXA1 was of particular importance in the T-PLL-monocyte interaction (Supplementary Fig. 15C–D). In addition, recurrent downregulation of CD48 in active-stage T-PLL cells (9/11 T-PLL pairs) emerged as an important contributor to the transcriptomic changes in the tumor microenvironment.

On a broader scale, we noted a distinct downregulation of antigen presentation pathways across non-malignant immune subsets, particularly affecting multiple HLA class II genes (Fig. 6I). Analysis of genes associated with the widely observed upregulation of interferon-α signaling in immune cells of active-stage T-PLL samples identified prominent upregulation of IFI44 and IFI44L, as well as LY6E (Fig. 6J).

Together, these findings suggest a progressive dampening of anti-tumor immune responses from indolent to active T-PLL, marked by reduced antigen presentation and globally diminished cell-cell interactions. These processes were significantly associated with deregulations of TNFα/NFκB and interferon-α signaling in both, the leukemic and non-neoplastic cell compartments.

Methods
Patient samples
Primary cells of 17 untreated T-prolymphocytic leukemia (T-PLL) patients from six centers were used in this study. Samples were obtained at different time points during the transition from indolent to active disease stage. Eleven longitudinal sample pairs from T-PLL patients were acquired. The diagnosis of T-PLL was established according to the WHO criteria and T-PLL consensus guidelines. Written informed consent was provided by all patients according to the Declaration of Helsinki. The collection and use of samples have been approved for research purposes by the ethics committee of the University Hospital Cologne (#11–319) and the Medical University of Vienna (1957/2020). Clinical information for all samples is provided in supplementary Data 1. Samples were classified as either indolent or active disease based on recently defined criteria. This stratification is based on multiple parameters, including tumor load (lymphocyte count) in peripheral blood, bone marrow suppression, nodal and extranodal involvement, as well as clinical presentation of T-PLL patients at the time point of sampling.

We did not report on gender. Patient’s sex was reported as stated by the patients themselves. We designed the study to maintain a comparable sex-distribution between healthy donors and T-PLL patients. We did not perform analyses stratified by sex because the study was not powered at the sample level to support meaningful sex-based comparisons. Sex is provided as metadata and can be considered in future studies.

Atotal of 28 different samples were available for this study. After sampling, peripheral blood mononuclear cells (PBMCs) were isolated via density gradient centrifugation (Histopaque, Sigma-Aldrich). Cryopreserved PBMCs were transferred to the Cologne Center for Genomics (CCG, Cologne) and CeMM-Biomedical Sequencing Facility (BSF, Vienna) for sequencing. Removal of dead cells was accomplished using a Dead Cell Removal Kit (Miltenyi Biotec) or by FACS sorting of DAPI-negative cells. All primary samples were fully consumed during processing and analysis.

Single-cell RNA-library construction and sequencing
Single-cell suspensions were processed with the Chromium Next GEM Single Cell 3ʹ Kit v3.1 (10x Genomics) with dual indices according to the manufacturer’s instructions, using either one reaction per sample or multiplexing two to three samples with the Cell Multiplexing technology option. In short, cells and the appropriate master mix were loaded on a Chromium Next GEM Chip G and processed on the Chromium Controller to generate Gel Bead-In-Emulsions (GEMs). After the creation of barcoded full-length cDNA by incubation with cell lysate, primers, and a reverse transcription master mix, GEMs were broken, and the pooled fractions were recovered. Silane magnetic beads were used to remove leftover biochemical reagents and primers from the post-GEM reaction mixture. Full-length, barcoded cDNA was then amplified by PCR to generate sufficient mass for library construction. Enzymatic fragmentation and size selection were used to optimize the cDNA amplicon. Library construction was performed, including end repair, A-tailing, adapter ligation, and PCR. The final libraries were quantified (Qubit), pooled, and the pool was quantified using the Peqlab KAPA Library Quantification kit and the Applied Biosystems 7900HT Sequence Detection System. Libraries were sequenced on an Illumina NovaSeq 6000 sequencing instrument with 29+89 bp read length. We retained an average sequencing depth of 28,280 reads per cell. Raw reads were aligned to the GRCh38-2020-A reference genome, filtered, and counted using the 10x Genomics Cell Ranger 6.1.2 pipeline at default parameters. UMI count matrices were then further processed using the R package Seurat v4.0.5 and R version v4.1 (R Foundation for Statistical Computing).

Inclusion of healthy donors
To enable temporal analysis of leukemogenesis in T-PLL and facilitate tumor cell identification, we integrated publicly available single-cell RNA-sequencing data of PBMCs from healthy donors (Vu et al., GEO: GSE214284). Ten donors were selected to closely match the sex and age distribution of our T-PLL cohort (female/male ratio: 1.43 vs 1.5; mean age: 66.9 vs 58 years, T-PLL vs healthy donors). External datasets were generated using compatible technologies (10x Genomics Chromium Single Cell 3ʹ v3 kit) and processed with Cell Ranger v6 (10x Genomics) to obtain raw feature expression matrices.

Quality filtering, data integration, dimensionality reduction
The quality of each sample was reviewed individually. We filtered out poor-quality cells keeping cells with at least 250 and not more than 4500 detected features and less than 20% mitochondrial genes. In total, we retained 204,959 single cells (78.6%, supplemental Table 1). UMI counts were normalized to the number of total counts per cell, scaled by a factor of 10,000, and transformed using log1p. Principal component analysis (PCA) was calculated after scaling to reduce the complexity of data. To account for batch effects between healthy-donor derived cells and cells from T-PLL samples and to facilitate subsequent clustering and cell type prediction, batch correction was performed using Harmony with soft parameters (v1.2.3, theta=0.1). Dimensionality reduction was performed on all single and integrated datasets using Seurat’s implementation of the t-distributed stochastic neighbor embedding algorithm on the top 15 principal components. Subsequent clustering was carried out using the shared nearest neighbor (SNN) modularity optimization of Seurat at a resolution of 1.5.

Cell type identification
To assign cell type labels to all single cells we followed a semi-supervised approach. First, T-PLL cell clusters were manually identified based on the following criteria: (i) expression of common T-PLL cell markers including CD3D, CD5, CD4, and/or CD8A/CD8B, TCL1A, MYC, and absence of CD19 expression, (ii) independent clustering from other TCL1A-negative T cell clusters in the low-dimensional space of the merged and batch-corrected dataset, (iii) expression patterns of TRBC1 and TRBC2 as a surrogate for clonality. Subsequently, non-tumor cells were subjected to an unsupervised prediction of detailed cell labels. For this, we made use of a publicly available CITE-sequencing dataset of 162,000 PBMCs from 8 healthy donors that were pre-annotated with a marker panel of 228 antibodies. Count data of non-tumor cells of each sample were normalized using Seurat’s SCTransform function and projected into the sPCA space of the referenced dataset. Cell-type annotation labels were transferred based on the derived anchor sets as previously described. To validate this unsupervised cell type identification, we manually confirmed the predicted cell annotations based on the expression of well-known marker genes. In the analysis of relative cell type distributions, we included samples with at least 50 non-T-PLL cells.

Differential gene expression analyses
We followed multiple strategies to identify differentially deregulated genes in our dataset. Genes significantly deregulated at an adjusted p-value less than 0.05 and an absolute log2 fold-change (log2FC) greater than 0.5 were considered for further analyses. In analyses involving pooled or pseudo-bulk comparisons across individuals, we evaluated the inclusion of age as an additional covariate. However, given the longitudinal structure of the dataset, sample age was largely matched by design. Incorporating it as an additional covariate in conjunction with the comparably small sample size of our cohort introduced disproportionate variance attributable to age, which masked disease-related transcriptional changes. We therefore opted to control for age through study design rather than statistical adjustment.

Pairwise comparison between indolent and active sample-derived T-PLL cells
Differential gene expression was tested between T-PLL cells of the indolent and the active time point of longitudinal sample pairs. We only considered genes expressed in at least 1% of T-PLL cells among all sample pairs. Filtered normalized expression data were submitted to Seurat’s FindMarkers function using its default Wilcoxon Rank sum test. Overlaps of differentially expressed genes (DEGs) between sample pairs were illustrated using the R package UpSetR (v1.4.0). To determine the significance of the number of overlapping genes we compared against a null distribution derived by calculating 10,000 permutations of gene labels.

Pairwise comparison between shrinking and expanding T-PLL cell clusters
We also investigated gene expression differences between shrinking and expanding T-PLL cell clusters. For this, SNN clustering was performed on a pairwise integrated T-PLL cell dataset using a resolution of 5. Integration anchors were derived using STACAS (v.1.1.0) at default parameters. For each identified cluster, we computed the relative frequency of cells from the indolent and active samples. Clusters were labeled as shrinking or expanding based on whether their relative abundance significantly differed from the expected distribution of cells across time points. Clusters significantly enriched for active-stage T-PLL cells were classified as expanding, while those dominated by indolent-stage T-PLL cells were classified as shrinking. For this, statistical significance was assessed using 10,000 permutations of the observed cell proportions under a null model preserving the overall distribution of indolent and active cells. The analysis was performed using the R package scProportionTest (v0.0.0.9, Robert Policastro) with FDR cutoff of 0.05 and an observed log2 difference in proportion cutoff of 1. This method enabled the identification of clusters significantly enriched in cells from either the indolent or the active time point, reflecting that these clusters were underperforming or overperforming in their growth relative to the other cell clusters. In a second step, normalized gene expression of each cluster was aggregated to create pseudo-bulk expression data. Genes were filtered to features expressed in at least 1% of T-PLL cells of this patient to increase the relevance of identified genes. Differential gene analysis on the generated pseudo-bulks was calculated using the edgeR LRT method implemented in the R package Libra (v1.0.0).

Comparison of non-tumor cell types between indolent and active T-PLL samples
Similar to the analyses in T-PLL cells, differential gene expression of non-tumor cells between the indolent and active time point was investigated pairwise using Seurat’s FindMarkers function on log1p-normalized data. For each cell type and patient, we only considered genes that were expressed in at least 1% of cells.

In addition, we aimed to derive DEGs that were significantly deregulated from indolent to active disease across all patients. In short, normalized single-cell expression values of each cell type and sample were aggregated to build a pseudo-bulk expression dataset. Genes were filtered to features present in more than 1% of cells. These pseudo-bulks were then tested for differential gene expression between indolent and active disease over all samples using Libra’s edgeR LRT implementation at default values.

Comparison of T-PLL cells and whole T-PLL PBMC samples to healthy-donor derived T cells
A similar approach was applied to derive deregulated genes between T-PLL cells and healthy-donor derived T cells as well as between whole T-PLL sample PBMCs and healthy-donor derived T cells. Prior to the calculation of DEGs, features were filtered to (i) those being expressed in at least 1% of all cells and that (ii) were detected with at least one read in both data sets. The filtered feature matrix was then subjected to Libra’s edgeR LRT function using sex as a confounder, as we observed an influence of sex on the first principal components of gene expression data. Mean expression values were calculated on relative count normalized expression values and then transformed using log1p.

Pseudotime inference
Linear pseudotime values of T-PLL cells were inferred for each pair separately with the R package SCORPIUS (v1.0.8). For this analysis, each sample was subsampled to 500 T-PLL cells. We subjected gene expression values of the top 200 differentially expressed genes to the algorithm. Trajectory inference was performed in Spearman-reduced space using k=10. As SCORPIUS operates unsupervised and therefore does not incorporate a predefined starting or end point, the direction of the pseudotime was oriented after inference such that the mean pseudotime of indolent cells was smaller than the mean pseudotime of active-stage T-PLL cells. This resulted in cell-wise pseudotime scores representing each cell’s position on the trajectory from 0 (indolent T-PLL expression profile) to 1 (active T-PLL expression profile). Inferred pseudotime values were then used to calculate gene importance values of the whole feature expression matrix with SCORPIUS.

In addition to patient-specific pseudotime inference, global approaches incorporating T-PLL cells of many patients are of scientific interest to display the overall transcriptomic shifts during disease progression. In our cohort, the degree of inter-patient transcriptional heterogeneity combined with the low number of commonly deregulated single genes prevented robust alignment and yielded trajectories lacking coherent biological interpretation. As a result, we limited our analyses to patient-specific pseudotime trajectories, which better preserved the temporal structure inherent to the longitudinal sampling design.

T-PLL bulk mRNA expression data
Validation of single deregulated genes and correlation analysis of global gene expression patterns between our dataset and independent T-PLL cohorts were performed using preprocessed expression matrices from two publicly available T-PLL bulk gene expression data sets: (i) Gene expression profiling data performed on Illumina Human HT-12 v4 BeadChip arrays, which compared PBMCs of 69 T-PLL cases with CD3+ T cells isolated from 10 healthy donors and (ii) mRNA sequencing data that compared PBMCs from 48 T-PLL cases with CD3+ T cells from 6 healthy donors.

Gene set enrichment analyses (GSEA)
All gene set enrichment analyses (GSEAs) were performed using GSEA v4.1.0 for Linux on different Molecular Signatures Database (MSigDB) gene set collections (HALLMARK gene sets v7.1, Reactome Pathway collection v7.5.1, KEGG pathway gene sets v7.4). To allow for direct comparison of normalized enrichment scores (NES) between different pairs or cell types, input metrics were subsetted to features that were shared by all data sets prior to calculation. Input genes were then ranked by their fold-change (FC) and provided as input to the GSEA-Preranked method at default parameters. For the GSEA of pseudotime-associated gene importance values, we applied the more conservative classic statistic, which does not involve rank weighting, due to the uncertainty about inter-individual comparability of importance measures. To enhance visualization, enriched Reactome pathways were summarized to their top-level node.

Gene ontology analysis
Gene ontology analysis was performed using the R package topGO (v2.44.0, Adrian Alexa, Jörg Rahnenführer) on the sub-ontology ‘Biological Process’. Overrepresentation of DEGs significant at an adjusted p-value less than 0.01 was tested against the human genome.

Copy number analyses
Detection of large-scale chromosomal copy number alterations from gene expression data was performed for T-PLL cells of each sample separately. Raw expression matrices were analyzed using the R package inferCNV (v1.9.0, Trinity CTAT Project, Broad Institute of MIT and Harvard). Healthy-donor derived T cells were used as a reference. InferCNV was run on tumor subclusters partitioned by the implemented Leiden algorithm. Predictions were derived with the default Hidden Markov Model (HMMi6). Posterior probabilities were calculated using inferCNV’s Bayesian latent mixture model, retaining only those predicted copy number alterations with a probability higher than 0.5. For improved visualization, we summarized identified alterations to the level of chromosome arms, utilizing centromere positions from the UCSC Genome Browser database.

Pathway expression scores
Pathway expression scores were calculated cell-wise using Seurat’s AddModuleScore function at default parameters comparing the gene expression of candidate genes to that of randomly sampled control genes exhibiting similar gene expression. Scores were calculated on the entire data set, which comprised tumor cells, non-tumor cells, and healthy-donor derived T cells. Comparisons were conducted between T-PLL cells of different conditions. To facilitate visualization and account for variations in sample sizes, we calculated sample and cluster means. Statistical comparison of sample means were performed using a permutation test (B=10,000) to account for the presence of both paired and unpaired samples in the dataset.

Cluster activity scores
Activity scores were calculated on a cluster level using the HALLMARK gene set collection (v7.1) as previously described. To this end, we averaged expression values of the entire disease dataset over SNN-derived clusters (resolution=5) and performed log1p normalization using Seurat. Pseudo-bulk gene expression of each relevant feature was then transformed to percentile ranks comparing all clusters and averaged among gene sets. Finally, scores were shifted by −0.5 to obtain values in the range of [−0.5, 0.5] depicting the relative expression strength of a gene set among all clusters.

Comparison of metabolic transcript expression
Expression of metabolic transcripts was assessed within our single-cell sequencing data set as well as in previously published T-PLL bulk expression data sets. It was defined as the ratio of transcripts that encoded for cell metabolism-associated genes over the expression of all protein-coding transcripts as proposed by Wagner et al. Features were classified as protein-coding based on the Ensembl 98 genome annotation. Normalized expression values were used to calculate ratios.

DoRothEA transcription factor activities
Activities of the DoRothEA regulon collection were calculated for each cell separately using the R package decoupleR (v2.2.0). We used log1p-normalized expression data on a subsampled dataset including 1000 cells per sample. Enrichment values were calculated using decoupleR’s weighted mean implementation at default parameters. Before plotting, summarized enrichment values were z-transformed to illustrate relative differences in activities.

Visualization of deregulated T cell receptor pathway components
To visualize the deregulation of T cell receptor (TCR)-signaling components between active and indolent time point T-PLL cells of patient pair_3 we calculated log2 fold-changes of TCR signaling genes as curated by the KEGG pathway database (HSA04660). Fold-changes were based on log2p1 transformed mean expression values of T-PLL cells.

Inference of cell-cell interactions
Cell-cell interactions between T-PLL cells and cells of the tumor microenvironment were assessed using the CellChat R package (v2.1.2) and NicheNet (v2.2.0) as previously described. In short, we separated non-tumor cells of the indolent and active time points and applied downsampling in order to ensure a comparable number of cells for each cell type, patient, and time point. Immune subsets with fewer than 10 cells were discarded from further analysis. Inference of receptor-ligand interactions between T-PLL tumor cells of each sample and PBMC cells of the respective disease stage was then performed separately using CellChat’s human interaction database at default parameters. Next, interactions were filtered to only keep incoming and outgoing signals of T-PLL cells. Differential interactions between indolent and active T-PLL cells of each patient were derived after merging of CellChat objects. Significance of the differential total interaction counts and weights was evaluated using paired Wilcoxon tests. Finally, differential interaction counts and weights were visualized using CellChat’s netVisual_heatmap function.

To identify candidate ligands driving the transcriptional programs observed in T-PLL cells and non-malignant immune cells, we applied NicheNet (v2.2.0) separately to each longitudinal T-PLL sample pair, using default parameters. We conducted distinct analyses to (i) identify ligands in T-PLL cells potentially mediating the gene expression changes between indolent and active non-tumor cells, and (ii) uncover ligands in T-PLL cells contributing to monocyte-specific transcriptional deregulation. Input gene sets for both analyses were derived using Seurat’s FindMarkers function, applying thresholds of adjusted p≤0.05 and absolute log₂ fold-change ≥0.25. Ligand activity was ranked based on the corrected area under the precision-recall curve (AUPR) as computed by NicheNet.

To improve interpretability and reduce false positive signals in downstream visualizations, we excluded ligands with very low expression (defined as mean log2(UMI+1) < 0.1). In addition, only ligands expressed in sender populations and for which target genes were sufficiently expressed in receiver populations were considered.

Whole genome sequencing
A total of 6 T-PLL samples (3 longitudinal sample pairs) were subjected to whole genome sequencing (WGS). DNA was extracted from isolated PBMCs as described in the section on sample processing. Libraries were prepared with the DNA tagmentation-based library preparation kit (Illumina) without PCR, starting with 500 ng gDNA input. Library preparation was followed by cleanup and size selection using SPRI beads (Beckman Coulter Genomics). After fluorometric quantification (Qubit, Life Technologies) the libraries were pooled and the pool quantified by real-time PCR using the Peqlab KAPA Library Quantification Kit and the Applied Biosystems 7900HT Sequence Detection System. The library pool was sequenced on an Illumina NovaSeq 6000 sequencing instrument with a paired-end 2x150bp protocol. Samples were sequenced to an average depth of 44.99x. Raw sequencing data were processed following the Genome Analysis Toolkit 4 (GATK4, Broad Institute) best practices for data preprocessing and somatic short variant discovery in tumor-only samples. All tools were run with default parameters using the GATK V4.5.0.0 Docker container. Reference files were obtained from the GATK hg38 resource bundle (v0, Broad Institute).

Reads were aligned to the GRCh38 reference genome using BWA-MEM and preprocessed using Picard’s MergeBamAlignment and MarkDuplicates. After base recalibration, raw variant candidates were called with Mutect2 (GATK, Broad Institute) and filtered based on standard parameters using GATK’s FilterMutectCalls.

Variant annotation was performed using Funcotator (V4.5.0.0) with the following databases: Gencode 43 CANONICAL, Achilles 110303, ClinVar_VCF20230717_hg38, Cosmic v98, Cosmic Fusion v98, Cosmic Tissue v98, Familial_Cancer_Genes 20110905, Gencode_X HGNC 110_38, Gencode_X RefSeq 110_38, HGNC Jun28 2023, Oreganno 20160119, Simple_Uniprot 2014_12, dbSNP 9606_b151.

Short variants were retained if they met the following criteria: (i) variant allele frequency (VAF) ≥ 5%, (ii) depth ≥ 10 reads and ≥ 3 reads supporting the variant, (iii) population allele frequency of ≤ 0.01, dbSNP MAF≤0.05, and TOPmed MAF≤0.05. Predicted functional consequences were derived from Ensembl’s VEP release 113. Functional coding variants are provided in supplementary Data 2.

Structural variants and copy numbers were derived from aligned whole genome sequencing data using ClinSV (v1.1.0) at default parameters. High-confidence variants were kept for further analysis.

Cell culture
Cells were cultured in RPMI-1640 medium including L-glutamine supplemented with 10% FBS and 1% penicillin/streptomycin (100 U/0.1 M) (all Gibco). For TCR crosslinking, 2×106 PBMC/mL were stimulated using plate-bound anti-CD3 (1µg/mL) and anti-CD28 (2µg/mL) antibodies (OKT3/CD28.2, BioLegend).

qRT-PCR
RNA was isolated from PBMCs using Promega ReliaPrep RNA MiniPrep System according to the manufacturer’s recommendations. Reverse transcription of total RNA was conducted using the SuperScript VILO cDNA Synthesis Kit (Invitrogen) according to the manual. qRT-PCR was performed on an ABI 7500 Fast Real-Time PCR System using the Power SYBR Green PCR Master Mix (both Applied Biosystems). Primers specific to human β-actin were used as a reference for quantification via the 2(-ΔΔCT) method. Primers are listed in the supplements (supplemental Table 2).

Flow cytometry
To analyze surface expression of T-cell activation markers, 2×105 PBMCs were stained 1:200 with fluorochrome-conjugated antibodies (Supplemental Table 3). Surface expression was measured on a Gallios flow cytometer and analyzed using the Kaluza software (both Beckman Coulter).

Immunoblots
For immunoblots, 10µg of whole protein lysates of 1×107 PBMCs were separated via SDS-PAGE and semi-dry transferred to NC membranes using Trans-Blot Turbo Transfer System (BioRad) according to standard techniques. Blocked membranes were stained with primary antibodies and HRP-conjugated secondary antibodies. Antibodies and working concentrations are listed in supplements (Supplemental Table 3). Signals were visualized by Western Bright ECL (Advansta), recorded on autoradiography films (Santa Cruz Biotechnology) using the CAWOMAT 2000 IR (CAWO Solutions), or detected via the ECL Chemostar Imager (Intas). Chemiluminescence was quantified via densitometry using the ImageJ software.

Glycolysis and oxidative phosphorylation assays
Bioenergetics of glycolysis and mitochondrial respiration were analyzed on the extracellular flux analyzer Seahorse XFe 96 (Agilent, Santa Clara, USA), as previously described. Briefly, one day prior to measurements, Seahorse XFe 96 culture plates were coated with Corning™ Cell-Tak Cell and Tissue Adhesive (BD, Franklin Lakes, USA) according to the manufacturer’s recommendations. A Seahorse XFe 96 cartridge was loaded with XF Calibrant solution and incubated overnight in a CO2-free atmosphere. Also, healthy-donor derived, magnetically isolated T cells as well as patient-derived PBMCs were seeded at a density of 106/mL in RPMI-1640 + 10% FCS + 2 mM L-glutamine + penicillin/streptomycin and cultured in absence/presence of anti-CD2/CD3/CD28 coated activation beads (Miltenyi Biotec, Bergisch-Gladbach, Germany) at a bead-to-cell ratio of 1:2. The next day, cells were harvested from the culture, washed in assay-specific medium according to the manufacturer’s recommendations and viable cells were counted. The cells were seeded at a density of 2×105 T cells in 175µL per well in at least 3 technical replicates. The ports of the Seahorse cartridge were loaded with 25µL each of 80 mM glucose, 9µM oligomycin, and 1M 2DG for the glycolysis stress test and 25µL of 12.5µM oligomycin, 13.5µM FCCP, and of 30µM antimycin A/rotenone for the mitochondrial stress test. After sensor calibration, assays were run as detailed in the manufacturer’s manual by recording ECAR (extracellular acidification rate) and OCR (oxygen consumption rate). Metabolic parameters were obtained from the XF Wave software, calculated using Microsoft Excel, and plotted using R’s ggplot package (v3.4.4).

Statistics and reproducibility
We performed two-tailed statistical tests unless otherwise stated. No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is an interesting study comparing the single-cell profile of indolent and active T-prolymphocytic leukemia (T-PLL). The concept of the study is nice but results in limited novel insights due to the small sample size (19 samples), superficial analysis, lack of proper control and validation sets. The conclusions of the study are very confusing as Fig 1D shows that T-PLL is quite homogenous, but it seems heterogeneous based on analysis shown in Fig 2A therefore it seems that authors just implemented a single cell analytical approach without solid rationale behind. The author need to increase sample size and perform validation on independent single-cell data as well as add healthy subjects for reliable tumor cell identification. Here are major concerns:\na) The tSNE clustering (Fig1D) of the active and indolent and active samples after Log Normalization and Integration anchor-based batch correction depict over clustering with minimal segregation of T-PLL. It is not justified why batch correction using the Integration anchor approach was performed. From visualization, data seems to be over-corrected losing intra and interpatient heterogeneity. Authors need to show that batch correction is needed and not cause over-clustering. It seems samples are heterogeneous as Fig2A & B depict large variations in the differentially expressed genes. Please re-evaluate the analysis\nb) In figures 1D & E not clear how many clusters identified, are these clusters patient-specific, Please color UMAP/tSNE with clusters and show contribution of each patient in clusters\nc) Raw data is not available, please make Fastq files available for public access to allow further data analysis. Further, all analytical codes should be also made publicly available to enhance the reproducibility of analysis.\nd) To annotate tumor cells, the study needs to be supported by whole genome or exome analysis for variant analysis, the current CNV analysis is not convincing. The CNV analysis should show the tumor cells have CNV but not normal T cells from the same patients. The analysis needs to be performed through down-sampling of tumor cells due to their significantly higher number than normal T cells.\ne) Further for reliable tumor cell annotation, authors need to perform single-cell analysis on age and gender-matched healthy cells and perform merged analysis for reliable tumor cell annotation.\nf) None of the analyses take into consideration Age, Gender for comparing the proposition cells or gene expression, these confounders need to be adjusted in the analysis.\ng) Not clear if all analysis in Pathways or Differential expression was performed on batch-corrected or non-batch-corrected data, if data has a batch effect then batch-corrected data should be used for all analysis using Limma with confounders\nh) The study has Indolent and Active samples from 7 patients, it will be great to perform trajectory analysis to see which cells clonally expand from Indolent to Active disease transition. How the origin cluster for Pseudotime analysis shown in Figure 2F is decided ?\ni) The approach for identifying shrinking and expanding clusters is not clear (Fig 2D). The clusters with significant change (mean and SD) from indolent to active phases in each patients should be used for defining shrinking and expanding clusters for downstream analysis.\nj) Cellular Communication analysis on tumor cells that change from indolent to active disease with immune cells might provide some interesting insight into key molecules critical for disease progression. The analysis is lacking in the current study.\nk) Most of the gene targets, pathways, and gene set boxplots shown in the manuscript are not able to achieve significance this might be due to the limited sample size. The significance testing outcomes should be added to each figure Fig 3A, 4A, 4D,6F, 6G etc. A higher sample size is needed to make sure that results are not false positive. The authors should also add another single cell dataset to validate the findings from the preliminary analysis.\nl) The authors show that single-cell analysis results related to MYC targets, oxidative phosphorating, and glycolysis seem similar to results from previous bulk RNA studies, so it is not evident what novel insights single-cell profiling is providing. Therefore, further deeper analysis of larger single-cell data might provide novel insight into cellular interaction/ communication that is involved in the progression of indolent to active disease. The current findings seem very generalized and associated with the progression of any cancer not only T-PLL.", "reviewer_2": "In this study, the authors investigated the potential mechanisms underlying the progression of T-PLL from an indolent phase to an active phase. They identified distinctive features in active-stage T-PLL, including upregulated MYC pathways, increased metabolic activity, downregulation of immune-response pathways and decreased interaction with cells in microenvironment. The study is valuable, well designed and the results are supportive of the conclusions. Couple comments:\n1, The authors meticulously detailed the alterations at the RNA level. Have the authors had an opportunity to examine genetic changes at the mutational and karyotyping levels? Specifically, are there any additional mutations and/or cytogenetic changes identified in association with the progression of the disease from an indolent to an active state?\n2, JAK/STAT pathway plays an important role in T-PLL. Is there any change in this pathway (additional mutation involving this pathway or overexpression) during disease progression from indolent to active?\n3, In the discussion, there is an opportunity for the authors to further elaborate on their novel findings and explore potential therapeutic implications, particularly considering the poor prognosis associated with T-PLL. The authors mentioned MYC as potential therapeutic target, how about potential targets in metabolic pathways? This study showed downregulation of immune-related signaling pathways. Any potential role of immunotherapy? The findings of tumor-associated monocytes are also interesting, and any potential treatment targeting tumor associated monocytes/macrophage and other cells in the microenvironment?\n4. The discovery of downregulation of PTPRC (CD45) is intriguing. In my daily practice, I have encountered several cases of T-PLL exhibiting decreased to negative CD45 expression as assessed by flow cytometry.\n5. From Figure 3G, we can see that MYC is active and its expression is increased in indolent phase. The author may want to briefly mention and discuss it.\n6. Can the author comment on ATM mutation in T-PLL? Can this be a potential therapeutic target? ATM mutations in T-PLL are very common. In contrast, they are extremely rare in other T-cell lymphomas/leukemias. How is the authors’ experience? How often do you see ATM mutations in other T cell neoplasms?\n7. I may miss this information. Did all T-PLL cases in this study carry TCL1 rearrangement? Any cases with t(X;14)?", "reviewer_3": "General comments:\nThe authors performed single-cell RNA-seq of 7 longitudinally paired and 5 single time-point derived T-PLL samples to study the molecular changes underlying the evolution from indolent to active T-PLL. Based on the fact that T-PLL is a rare cancer, the study of 7 paired cases is of interest, but has limitations. Overall, my feeling is that the study is performed very well, technically there are no concerns and data analysis seems very robust, but due to the low number of cases and the variability from case to case, there are no highly novel conclusions: it is well known that TCL1A is implicated in the pathogenesis of T-PLL and also that increased MYC expression caused by MYC amplification is a common feature of T-PLL.\nThe analysis of differentially expressed genes (DEGs) in the paired samples is not very informative and seems to indicate that each case has a rather unique set of DEGs.\nSince MYC is very important in the pathogenesis of T-PLL, it is not surprising that the most uniform shifts of gene expression across all indolent/active sample pairs were detected in GSEA-confirmed functional gene sets associated with cell metabolism and cell-cycle regulation, which are very much controlled by MYC, as previously illustrated for T-ALL and other T-cell malignancies.\nInterestingly, the authors investigated differential gene expression between T-PLL cell clusters that expanded compared to those that retracted over time and identified in this way a strong enrichment of gene sets related to cell cycle, energy metabolism, and the transcription factor MYC in the expanding T-PLL cell clusters. This is an interesting analysis, and one of the strongest parts of the study. However, I am not sure how this was performed as there were no clear clusters shown in the malignant cells based on the analysis of the single-cell data shown in figure 1. How were these clusters identified? I could not find a figure clearly illustrating these clusters.\nSpecific major comments:\nFigure 1:\nOn figure 1D/E the single cell data has been integrated removing all patient specific information, but at the same time also removing some of the indolent-active information. Indeed, on figure 1E we see a gradient from indolent to active, but one might expect a clearer separation between the two phases. It would be interesting to see the data before this data integration where the single cells cluster according to the patient and the timepoint. The normal cells of all patients still should cluster together in this plot.\nWhy does the patient-specific data needs to be removed in this analysis? All subsequent comparisons are on patient level, which means that comparing the indolent with the active samples of the same patient already filters out patient specific information.\nIn figure 1G the different clusters are annotated. For me it seems weird that the CD4 T-cells cluster more closely to the B-cells than to the CD8 T-cells. Could this be due to the data integration?\nOn line 185 it is stated that a high intra- and inter-sample heterogeneity is observed. This is not clear from this figure as all samples cluster together on the t-SNE plot. In supplementary figure 1B, we see clustering based on the patients, which would indicate that there is not a lot of intra-tumor heterogeneity. Please elaborate.\nIn the differential analyses the authors used data on normal cells from an independent study. Why was it not possible to use the normal cells that were sequenced in the current study? How did the authors deal with possible batch effects? Please elaborate.\nFigure 2:\nIn figure 2G a GSEA is performed on the pseudotime correlated genes. How were these genes determined? How many genes are included ? Is the number large enough to perform GSEA?\nDoes the pseudotime correlate with the indolent/active timepoints? Maybe add a tSNE/UMAP plot with the pseudotime (in the supplementary)?\nWhat is the added value of this analysis in comparison to the analyses on the indolent-vs-active and the expanding-vs-shrinking? Please explain.\nFigure 5:\nThis is well presented, but does not provide a lot of new information as most of the data shown here has been reported previously. Data on CTLA4 were reported in Tian, S. et al. Sci Rep 11, 8318 (2021).\nFigure 6:\nIn figure 6G/H the cell-cell interactions are investigated. However, only the number of interactions are reported here. It would be interesting to see what kind of interactions are happening (and disappearing).\nWhile the changes in cell-cell interactions determined by single-cell RNA-seq analysis are of interest, these remain predicted (and not confirmed) and is not very convincing. Conclusions are limited.\nMinor comments:\n- In figure 5A the heatmap is clustered both on the gene expression and the samples. As this figure wants to indicate a difference between the indolent and the active timepoints (line 328) it would be interesting to see the samples clustered according to their timepoint.\n- In figure 6D, there is a character ‘|’ missing in the legend.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "point-by-point replies to reviewers:\nReviewer #1\nThis is an interesting study comparing the single-cell profile of indolent and active T-prolymphocytic leukemia (T-PLL). The concept of the study is nice but results in limited novel insights due to the small sample size (19 samples), superficial analysis, lack of proper control and validation sets. The conclusions of the study are very confusing as Fig 1D shows that T-PLL is quite homogenous, but it seems heterogeneous based on analysis shown in Fig 2A therefore it seems that authors just implemented a single cell analytical approach without solid rationale behind. The author need to increase sample size and perform validation on independent single-cell data as well as add healthy subjects for reliable tumor cell identification.\nREPLY:\nWe thank Reviewer #1 for the thoughtful and constructive evaluation of our manuscript. We value the reviewer’s expertise and the detailed consideration of our work, and we are grateful for the opportunity to address the concerns raised. Our rationale was to decipher molecular changes during T-PLL evolution, which the exceptionally rare samples from inactive disease stages and their pairing with active phases uniquely allow, and for which a single-cell approach is ideally suited.\nT-PLL itself is an exceptionally rare leukemia, with an incidence of ~1 per million individuals per year. Indolent cases, the focus of our study, constitute only about 20% of all diagnoses, and longitudinally paired, treatment-naïve samples of indolent and active phases are even more scarce. To put this into perspective: since 2008, a dedicated mono-centric collection in Cologne combined with a national registry effort has led to the banking of 496 samples from 252 T-PLL patients, among which only very few inactive cases and an even smaller number of longitudinally matched, untreated, pairs could be identified. The cohort we assembled therefore represents an unparalleled collection that has required years of concerted effort.\nAt the international level, we reached out to major centers and reference collections to compile the cohort underlying the first submission of this work. In our revision, we have gone further: by contacting more than 15 additional research facilities worldwide over the past 18 months, we were able to expand our dataset with four additional indolent/active pairs and one more indolent case. Together with new analyses and the inclusion of additional age- and gender-matched healthy controls, this substantially strengthens the robustness and impact of our findings.\nImplementing a single-cell approach allowed us to resolve dynamic tumor subpopulations and to characterize the tumor microenvironment, both of which would be obscured in bulk RNA-seq due to signal averaging and the low abundance of non-tumor cells. To our knowledge single-cell resolved genomic analyses of T-PLL have not been performed to date. Our longitudinal sampling further enabled a chronologically resolved analysis of both malignant and non-malignant compartments, providing novel insight into disease progression. Below, we provide a point-by-point response to each of the reviewer’s comments.\nHere are major concerns:\n1) The tSNE clustering (Fig1D) of the active and indolent samples after Log Normalization and Integration anchor-based batch correction depict over clustering with minimal segregation of T-PLL. It is not justified why batch correction using the Integration anchor approach was performed. From visualization, data seems to be over-corrected losing intra and interpatient heterogeneity. Authors need to show that batch correction is needed and not cause over-clustering. It seems samples are heterogeneous as Fig2A & B depict large variations in the differentially expressed genes. Please re-evaluate the analysis\nREPLY: We thank the reviewer for this comment. We agree that the original approach leveraging anchor-based integration for the purpose of clustering and cell type identification (Fig. 1D-G) may have inadvertently conveyed a misleading impression of homogeneity among T-PLL samples, and we appreciate the opportunity to clarify our rationale and improve this aspect of the analysis.\nIn the revised manuscript, we have restructured our approach of data integration and visualization. Specifically, we removed anchor-based integration in the initial visualization step as we did not observe substantial batch effects across our T-PLL patients (rebuttal Figure 1). Importantly, non-tumor cells from all T-PLL patients clustered homogenously and in a cell-type specific manner, whereas T-PLL cells clustered separately and in a patient-specific manner. This pattern suggests that the observed variability is driven by biological differences of T-PLL cells rather than by technical confounders. However, as we did identify small, but persistent, batch effects between the non-tumor compartment of T-PLL patients and PBCMs of independently sequenced healthy controls,1 we decided to apply the Harmony2 algorithm with soft parameters (theta = 0.1) to account for this confounder without overriding patient-specific expression signatures. In line with the reviewer’s suggestion, we now provide tSNE clustering performed on merged normalized datasets and believe that these better represent the heterogeneity among T-PLL cells observed throughout our study.\nRebuttal Figure 1. Overview on non-batch corrected gene expression data and possible confounders\ntSNE representation of T-PLL samples from both indolent and active disease and external healthy controls prior to batch correction using Harmony. Subpanels illustrate the distribution of different biological and technical parameters. Within T-PLL samples, malignant cells cluster in a strongly patient-specific manner, while non-tumor cells group by cell type. Before batch correction, non-tumor cells from T-PLL patients are distinct from healthy-donor PBMCs. For example, B cells (top row, blue) form two separate clusters, one comprising B cells from T-PLL patients and the other from healthy donors.\nTo increase transparency for the reader regarding potential batch effects within our data and the performed batch correction, we included a new supplementary figure (supplemental Figure 1B) comparing different potential confounders in tSNE embeddings without batch correction. We demonstrate that our revised approach retains inter-patient heterogeneity with regard to T-PLL cell gene expression while still minimizing confounding batch effects between T-PLL samples and healthy-donor derived cells.\nImportantly, consistent with the first version of the manuscript, all downstream analyses, including differential expression, pathway analysis, and pseudo-time inference, were performed on non-integrated, normalized expression matrices, ensuring that no biological signal was lost due to batch correction.\n2) In figures 1D & E not clear how many clusters identified, are these clusters patient-specific, Please color UMAP/tSNE with clusters and show contribution of each patient in clusters\nREPLY: We acknowledge the need for greater clarity in the presentation of our clustering results. In the revised manuscript, we now provide a supplementary figure (supplemental Figure 1D, rebuttal Figure 2) showing all identified clusters used for cell type identification and included a figure panel quantifying the contribution of each patient to each cluster.\nRebuttal Figure 2. Identified clusters and distribution of clusters among patients\nLeft panel: Identified clusters as used for cell type identification. Clusters were derived using Seurat’s shared nearest neighbor (SNN) modularity optimization at a resolution of 1.5 on batch-corrected PCA space. Right panel: Stacked bar plot representing the relative contribution of T-PLL patients and healthy controls (HC) to each identified cell cluster (cluster # at x-axis). Bars are colored by individual patients (see color legend), with some clusters displaying patient-specific enrichment, while clusters corresponding to non-tumor cell populations include contributions from multiple patients. Clusters are ordered by cellular composition diversity, from those with highly mixed contributions across individuals (left) to those dominated by a single patient (right).\n3) Raw data is not available, please make Fastq files available for public access to allow further data analysis. Further, all analytical codes should be also made publicly available to enhance the reproducibility of analysis.\nREPLY: We fully support the principles of reproducibility and transparency. In response, we have deposited all raw scRNA-seq data to GEO under accession number GSE238130 (Reviewer access token: yzclceigzdmzzmt). The new whole-genome sequencing (WGS) data will be made available upon reasonable request to us and in accordance with German regulations of data protection. All scripts, workflows, and pre-processing steps used in the analysis are now available via a publicly accessible GitHub repository (https://github.com/linus235/Single-cell-genomics-in-T-PLL-progression) to facilitate reanalysis.\n4) To annotate tumor cells, the study needs to be supported by whole genome or exome analysis for variant analysis, the current CNV analysis is not convincing. The CNV analysis should show the tumor cells have CNV but not normal T cells from the same patients. The analysis needs to be performed through down-sampling of tumor cells due to their significantly higher number than normal T cells.\nREPLY: We thank the reviewer for this important point. We fully acknowledge the limitations of expression-based copy-number inference and appreciate the recommendation to validate our findings using orthogonal genomic data. In response, we have now incorporated new matched WGS data from six samples (three T-PLL patients, each with paired indolent and active-stage samples). This WGS was performed on bulk PBMCs, which predominantly consisted of leukemic T-PLL cells (median tumor fraction: 92.97%), with only minimal contamination by residual non-malignant T cells (median: 1.7%).\nWe also understand and agree with the reviewer’s suggestion that using patient-matched normal T cells would be preferable for defining tumor-specific CNV profiles. In line with this, we attempted to use pooled non-malignant T cells from within the T-PLL samples as an internal reference for transcriptome-based CNV inference. However, due to their very limited number and variable transcriptomic quality, this approach produced low concordance with the WGS-derived CNV profiles, particularly in key lesions (see rebuttal Figure 3).\nGiven these technical limitations, we retained the use of healthy-donor T cells as the reference population for CNV inference in the main manuscript. This strategy now benefits from the independent validation through the newly generated WGS data, which substantially strengthens the reliability of our CNV analyses.\nRebuttal Figure 3. Copy number prediction leveraging health-donor derived T cells as a control shows high correlation to WGS-based copy number inference.\nDot plots showing the correlation between predicted copy numbers for T-PLL key genomic alterations comparing different technical approaches. Top: WGS-derived copy numbers for MYC and ATM are compared to copy numbers inferred from scRNA-expression data using healthy-donor T cells as controls. Predicted copy numbers show a high correlation between both methods (MYC: r=0.9816, p=0.0005; ATM: r=0.9817, p=0.0005, Pearson correlation) affirming the validity of the approach of utilizing healthy-donor T cells. Bottom: Copy numbers for MYC and ATM as predicted from WGS data are compared to scRNA-expression derived copy numbers using non-tumor T cells of T-PLL patients as controls. Here, correlation between both methods appears weaker as compared to the approach using healthy-donor T cells as controls (MYC: r=0.8284, p=0.0416; ATM: r=0.7419, p=0.0913, Pearson correlation).\n5) Further for reliable tumor cell annotation, authors need to perform single-cell analysis on age and gender-matched healthy cells and perform merged analysis for reliable tumor cell annotation.\nREPLY: We fully agree that matching for age and sex is crucial to control for potential confounders in scRNA-seq data. In response to this valuable comment, we have now included 10 additional PBMC samples from healthy donors, carefully matched to the T-PLL cohort in terms of age and sex (female/male ratio: 1.43 vs. 1.5; mean age: 66.9 vs. 58 years, T-PLL vs. healthy donors).1 These controls were incorporated into the tumor cell annotation pipeline and used as a reference for CNV detection, thereby enhancing biological validity. Importantly, in contrast to many other malignancies, tumor cell identification in T-PLL is comparatively straightforward due to the high tumor cell burden, strong genomic and transcriptomic signatures, including the frequent distinguishing overexpression of TCL1A, a well-established oncogene present in the majority (>90%) of T-PLL and absent in non-malignant T cells.3\n6) None of the analyses take into consideration Age, Gender for comparing the proposition cells or gene expression, these confounders need to be adjusted in the analysis.\nREPLY: Thank you for raising this important point. Because the core of our study involves intra-individual comparisons of indolent and active samples, age and sex are inherently matched for the majority of our analyses. In gene expression analyses of pseudo-bulk samples, we adjusted for sex as suggested by the reviewer (supplemental Figure 2).\nNotably, we did not observe any dominant age‑related patterns in our gene expression data (rebuttal Figure 4). Given our naturally small cohort size, including multiple covariates in statistical models would be statistically impractical, risking overfitting, and substantially reducing power to detect true biological signals. We, therefore, believe that controlling for age at the design level, rather than adjusting for it post hoc, is the most appropriate approach in this context. We have added a detailed explanation of this decision to the revised Supplemental Methods section.\nRebuttal Figure 4. PCA of gene expression values comparing active-stage T-PLL cells and healthy-donor derived T cells\n(A) Dot plot showing the first two principal components from PCA of gene expression in active-stage T-PLL cells and healthy-donor derived T cells. Colors indicate the sex of the respective sample. Among T-PLL samples there is a relevant association of patient’s sex and PC1. (B) PCA as performed in A. Text and point colors represent the age [years] of patients and healthy controls at the timepoint of sampling. There is no substantial effect of age on the first two PCA components.\n7) Not clear if all analysis in Pathways or Differential expression was performed on batch-corrected or non-batch-corrected data, if data has a batch effect then batch-corrected data should be used for all analysis using Limma with confounders\nREPLY: We thank the reviewer for this request for clarification. We confirm that all differential gene expression and pathway enrichment analyses were conducted on normalized, non-integrated count matrices as commonly suggested. Batch correction was applied for the purposes of visualization, clustering, and cell type identification, to facilitate interpretation. This approach ensures that the underlying biological variation is preserved in statistical testing and that technical artifacts do not bias our conclusions.\n8) The study has Indolent and Active samples from 7 patients, it will be great to perform trajectory analysis to see which cells clonally expand from Indolent to Active disease transition. How the origin cluster for Pseudotime analysis shown in Figure 2F is decided ?\nREPLY: We agree that the question of clonal evolution and its modeling in pseudotime is of central importance. In this study, we employed the SCORPIUS4 algorithm to infer pseudotime separately for each patient. SCORPIUS does not require a predefined origin cluster, but does independently predict a linear undirected pseudotime across all cells. For all patients, the trajectory was later oriented such that indolent cells had lower pseudotime values. This approach yielded robust and interpretable trajectories across individuals.\nWe also attempted to derive a unified pseudotime across all samples using Monocle35 and Slingshot.6 However, due to substantial inter-patient heterogeneity and the absence of consistently differentially expressed genes across patients, these global models failed to produce biologically meaningful and stable trajectories. The resulting outputs were highly sensitive to the choice of hyperparameters. We now clarify in the Supplementary Methods why we did not include a shared pseudotime analysis. We agree that the development of a pan-patient pseudotime model of disease evolution remains an important goal for future studies (however, being limited by the need of larger cohorts). In response, we chose to move the pairwise pseudotime analyses to the supplemental data.\n9) The approach for identifying shrinking and expanding clusters is not clear (Fig 2D). The clusters with significant change (mean and SD) from indolent to active phases in each patients should be used for defining shrinking and expanding clusters for downstream analysis.\nREPLY: We apologize for the lack of detail in our original description. Expanding and shrinking clusters were indeed defined as suggested by the reviewer for each patient separately. Specifically, we calculated the relative contribution of T-PLL cells from each time point to each cluster and retained those clusters that were significantly enriched for cells from a particular time point. We expanded the corresponding passages in the Supplemental Methods as well as in the figure legends (Figure 2) to enhance clarity and to improve understanding. Additionally, all analysis codes have now been made publicly available within the GitHub repository.\n10) Cellular Communication analysis on tumor cells that change from indolent to active disease with immune cells might provide some interesting insight into key molecules critical for disease progression. The analysis is lacking in the current study.\nREPLY: We thank the reviewer for this excellent suggestion. As originally included in our manuscript, we had already performed cell-cell communication modeling using CellChat,7 which revealed a global reduction in intercellular signaling in active T-PLL compared to the indolent stage. In line with the reviewer’s recommendation, we have now expanded and refined this analysis to better capture cell-cell interactions relevant to disease progression.\nIn addition to the CellChat framework, we applied NicheNet8 to further dissect ligand-receptor dynamics and their downstream effects on gene expression in non-malignant immune cells (rebuttal Figure 5). Despite considerable inter-patient variability, this extended analysis revealed relevant and recurring patterns across samples. Specifically, we analyzed ligand expression profiles on the T-PLL cells themselves to better understand how leukemic T cells may actively shape their immune surroundings and to explain the transcriptomic dynamics observed within the tumor microenvironment.\nIn this context, two ligands stood out as consistently deregulated in the active disease stage: Annexin A1 (ANXA1) and CD48. First, ANXA1 was upregulated in T-PLL cells during disease progression. As an endogenous anti-inflammatory mediator with known immunomodulatory properties, ANXA1 can suppress dendritic cell maturation and promote the resolution of immune responses.9,10 Its increased expression in active-stage T-PLL is, therefore, consistent with an actively immunosuppressive phenotype, possibly contributing to reduced immunogenicity in the surrounding tumor microenvironment (immune escape). Second, we observed homogenous downregulation of CD48 in T-PLL cells of the active stage. CD48 is a co-stimulatory ligand for 2B4 (CD244), which is expressed on NK cells and cytotoxic T lymphocytes. This interaction typically promotes immune synapse formation and cytolytic activity.11 Loss of CD48 expression has been previously described as a mechanism of NK-cell immune evasion in various hematologic malignancies.12–14 Its downregulation in T-PLL cells may, therefore, reduce their recognition and killing by NK cells, further supporting that active mechanisms of immune escape are at play in active T-PLL.\nFurthermore, building on our initial identification of genes deregulated in T-PLL cells that influence the tumor microenvironment, we have now extended our analyses to the reverse direction: we systematically investigated genes deregulated within microenvironmental cell populations that may, in turn, shape T-PLL biology. These analyses revealed candidate pathways contributing to the diminished cell-cell interactions and reduced immune responses we had observed.\nHere, we recurrently identified progressive downregulation of genes associated with antigen processing and presentation among all analyzed immune cell subsets during disease progression. While T-PLL tumor cells presented mostly downregulation of HLA class I genes, non-tumor immune cells revealed reduced expression of HLA class II genes. This global pattern might potentially contribute to a reduction of antigen cross presentation ultimately reducing the anti-tumor immune response. Similar patterns have been identified in other entities.15–17 In addition, we observed strong upregulation of IFI44, IFI44L and LY6E in immune cells of active-stage T-PLL samples conveying the general upregulation of interferon-alpha signaling during disease progression observed in our data. While interferon signaling has traditionally been attributed tumor suppressive functions, recent studies have increasingly demonstrated its role also in the negative regulation of anti-tumor immunity, mainly through induction of immunosuppressive cytokines and promotion of tumor associated macrophages.18 Interferon-inducible LY6E has widely been accepted as a central regulator of the anti-tumor immune response associated with CD8+ T cell exclusion and resistance to immunotherapy.19,20\nWe have integrated these new findings into Figure 6 and added a new supplementary figure (supplemental Figure 13) summarizing the most relevant ligands and patterns of transcriptional reprogramming across (anti-tumor) immune cells. In the revised Results, Supplementary Methods, and Discussion sections, we now provide a more comprehensive overview of how shifts in cell-cell communication may contribute to the evolving immune microenvironment in T-PLL. We certainly acknowledge in that respect the technical limitations inherent to scRNA-seq data, particularly with regard to low-abundance cytokines and immune-signaling molecules, and the necessity of subsequent in vitro and in vivo functional studies outside the scope of this study.\nRebuttal Figure 5. Recurrently deregulated ligands in the (immunosuppressive) T-PLL-immune cell interaction\n(A) Gene expression of ligands predicted to modulate the immune microenvironment during T-PLL disease progression. Ligands were inferred using NicheNet based on transcriptomic changes detected in non-tumor immune cells. Colors represent differential gene expression (log2 fold change) in T-PLL cells comparing active and indolent stages (red: upregulated in active-stage T-PLL cells; white: no change in gene expression; blue: downregulated in active-stage T-PLL cells). LGALS3BP, ANXA1, and CD48 were among the most recurrently identified and deregulated effectors. (B) Differential gene expression of most deregulated genes associated with antigen processing and presentation in non-tumor immune cells. There was a strong downregulation of HLA class II genes across PBMC subtypes in active-stage T-PLL while genes coding for heat shock proteins were uniformly upregulated. Color indicates the log2 fold-change between active and indolent-stage non-tumor cells (red: upregulated in active-stage T-PLL; white: no change in gene expression; blue: downregulated in active-stage T-PLL). (C) Gene expression shifts of interferon-alpha response-associated genes during disease activation in non-tumor PBMCs. There was a strong upregulation of type I interferon signaling among all immune subsets. Color indicates the log2 fold-change between active and indolent-stage non-tumor cells (red: upregulated in active-stage T-PLL; white: no change in gene expression; blue: downregulated in active-stage T-PLL).\n11) Most of the gene targets, pathways, and gene set boxplots shown in the manuscript are not able to achieve significance this might be due to the limited sample size. The significance testing outcomes should be added to each figure Fig 3A, 4A, 4D,6F, 6G etc. A higher sample size is needed to make sure that results are not false positive. The authors should also add another single cell dataset to validate the findings from the preliminary analysis.\nREPLY: Thank you for this valuable point. We acknowledge that sample size is a limitation. T-PLL is an exceptionally rare disease, with an incidence of approximately 1 per million individuals per year. Indolent cases, on which our analysis is focused, constitute only around 20% of all T-PLL diagnoses. Furthermore, our study specifically sought longitudinally paired and treatment-naïve primary samples, which significantly restricts availability, but simultaneously underscores the uniqueness and value of our cohort. To address this concern, we have significantly expanded our cohort by contacting more than 15 research facilities all over the world. This allowed us to include four new indolent/active sample pairs and one additional indolent case. In addition, we added previously missing formal statistical testing for all key analyses, as requested by the reviewer. We further strengthened our data as our key findings proved to be consistent and statistically significant also in this expanded cohort. This consistence was very assuring in terms of robustness of our findings. A representative exemplary comparison of key figure panels, both prior and post inclusion of the additional samples (for these revisions), can be derived from rebuttal Figure 6.\nWhile no comparable single-cell RNA-seq dataset currently exists for T-PLL, and to our knowledge, no prior gene expression analyses have included annotated indolent-stage T-PLL, we did perform validation of single deregulated genes and pathways, where possible, using independent bulk RNA-seq data from two publicly available T-PLL cohorts21,22 (e.g., see Figure 3D and Figure 4D). Although these datasets do not capture the early indolent phase, these analyses revealed strong concordance with our scRNA results for these individual genes and pathways.\nRebuttal Figure 6. Comparison of key analyses prior to (original submission) and after expansion (revision) of the T-PLL cohort size.\n(A) Gene set enrichment analysis (GSEA) calculated on differentially expressed genes between expanding and shrinking clusters of T-PLL cells. Left: Heatmap showing the most recurrently enriched HALLMARK pathways23 and their respective normalized enrichment scores (NES) in a cohort of 7 longitudinal pairs of T-PLL samples. Right: Heatmap of most enriched pathways after inclusion of an additional 4 sample pairs (n=11 total T-PLL pairs). Pathways involved in cell cycle, MYC signaling, and oxidative phosphorylation are among the most recurrently enriched in expanding T-PLL clusters in both cohorts (consistency confirming robustness). (B) Box-whisker-charted mean expression scores of the HALLMARK MYC targets V123 gene set comparing the original cohort including 19 T-PLL samples to the extended cohort of 28 T-PLL samples and ten age- and sex-matched healthy donors.24 The overall pattern of a progressive and significant upregulation of MYC target V1 genes from healthy T cells over indolent to active T-PLL cells is preserved and further validated after inclusion of an additional set of 9 T-PLL samples (permutation test, B=10,000). (C) Mean expression scores of the HALLMARK oxidative phosphorylation23 gene set prior and after expansion of our T-PLL cohort from 19 to 28 T-PLL samples and the inclusion of age and sex-matched healthy donors. Oxidative phosphorylation proves to be significantly upregulated in active stage T-PLL cells while indolent T-PLL cells did not show a significant difference compared to healthy-donor derived T cells (permutation test, B=10,000).\n12) The authors show that single-cell analysis results related to MYC targets, oxidative phosphorating, and glycolysis seem similar to results from previous bulk RNA studies, so it is not evident what novel insights single-cell profiling is providing. Therefore, further deeper analysis of larger single-cell data might provide novel insight into cellular interaction/ communication that is involved in the progression of indolent to active disease. The current findings seem very generalized and associated with the progression of any cancer not only T-PLL.\nREPLY: We thank the reviewer for raising this important point. While chromosomal aberrations of chromosome 8q involving the MYC locus are indeed established findings in T-PLL,25–27 research on the specific role of MYC and its deregulation in T-PLL, to our awareness, has not been conducted. Upregulation of MYC mRNA and protein expression in the context of active-stage T-PLL was first identified by our group in 201822 and was later validated in smaller cohorts by two other studies.28,29 Johannson et al. further observed a downregulation of both mRNA and protein expression of MYC in T-PLL upon ex vivo treatment with a CDK9 inhibitor, suggesting MYC to play a central role in cell cycle regulation in T-PLL. In 2021, Tian et al. identified increased levels of H3K4me3 and H3K27ac in a region covering the promoter and gene body of MYC in a genome-wide explorative ChIP-sequencing, potentially contributing to the upregulation of MYC in T-PLL besides copy number amplifications. In line with this, we observed a progressive increase in MYC mRNA expression from indolent to active disease even in longitudinal sample pairs without further copy number gains (e.g. pair 2, pair 8, pair 11). This finding provides novel support for epigenetic or transcriptional regulatory mechanisms driving MYC activation in T-PLL. Importantly, studies involving downstream effector functions of MYC in T-PLL have, to our knowledge, not been performed. Furthermore, we are not aware of analyses investigating metabolic deregulations of T-PLL cells. We are certain that our new findings of sub-clonal copy number increases of MYC, the clear pattern of progressive upregulation of MYC mRNA associated with T-PLL tumor progression, and MYC’s strong correlation to effector pathways, such as significant upregulation of oxidative phosphorylation, provide significant advances in the understanding of MYC’s central role in T-PLL. It also underscores its suitability as a uniform target pathway.\nAt a more general level, the “novelty” to us is already inherent to the discovery (and our first description) that MYC associated gene signatures become highly prominent upon intra- and cross-individual disease progression of T-PLL. We deliberately focused on this new and main finding based on relevance, rather than limiting the reporting to biologically secondary findings that could have been picked based on their “unknown” nature (“novel”) in the literature, that is biased to active-stage T-PLL and their bulk-analysis. In fact, the unique experimental design of our study provides a relevant value. To date, bulk mRNA sequencing studies in T-PLL have exclusively focused on comparisons between active-stage T-PLL cells and healthy donor T cells. The clinically distinct indolent stage has not previously been captured at the transcriptomic level. Our study is the first to characterize indolent T-PLL cells, allowing us to investigate the emergence and timing of key molecular lesions such as downregulation of the checkpoint molecules CTLA4 and LAG3, which we could establish as early occurring lesions (Figure 5B). Further, we identified clonal expansion of ATM-deficient T-PLL cells over disease progression and a strong MYC target gene upregulation (rebuttal Figure 7C, Figure 3). This temporal resolution, achieved through longitudinal sampling and intra-individual comparisons, is critical for understanding how T-PLL transitions from an indolent to an aggressive phenotype and cannot be obtained from cross-sectional bulk studies.\nIn addition, based on the reviewer's suggestion, we expanded our analysis of the non-tumor cell compartments. To our knowledge, this represents the first single-cell characterization of the T-PLL microenvironment. This enabled us to uncover a progressive reduction in cell-cell communication of T-PLL tumor cells and immune cells of the tumor microenvironment. We observed a significant downregulation of genes related to immune signaling and antigen presentation among the non-tumor cells of our samples (e.g. multiple HLA class II genes, rebuttal Figure 5C), potentially contributing to immune evasion. These mechanisms would be masked in bulk RNA-seq due to signal averaging and a very small fraction of non-tumor cells.\nFinally, the ability to resolve dynamic tumor subpopulations within individual patients allowed us to describe patterns of clonal evolution and shared transcriptional programs and lesions during disease progression, such as subcluster-specific downregulation of CD45 in 4 out of 11 patients. These insights extend beyond general cancer hallmarks and contribute to a more refined understanding of T-PLL pathogenesis, providing a framework for identifying stage-specific vulnerabilities and therapeutic opportunities, such as MYC inhibition and modulations of anti-tumor immune responses.\nReviewer #2:\nIn this study, the authors investigated the potential mechanisms underlying the progression of T-PLL from an indolent phase to an active phase. They identified distinctive features in active-stage T-PLL, including upregulated MYC pathways, increased metabolic activity, downregulation of immune-response pathways and decreased interaction with cells in microenvironment. The study is valuable, well designed and the results are supportive of the conclusions. Couple comments:\nREPLY: We thank the reviewer for the thoughtful and constructive comments, and for acknowledging the value and quality of our study. Below, we respond to each point in detail.\n1) The authors meticulously detailed the alterations at the RNA level. Have the authors had an opportunity to examine genetic changes at the mutational and karyotyping levels? Specifically, are there any additional mutations and/or cytogenetic changes identified in association with the progression of the disease from an indolent to an active state?\nREPLY: We thank the reviewer for this suggestion. In response, we performed whole genome sequencing (WGS) on three matched pairs of indolent and active T-PLL samples (six samples in total). We observed a high concordance between the inferred CNV profiles from the scRNA-seq and those derived from the WGS analysis, particularly for hallmark T-PLL lesions such as MYC amplifications and ATM deletions (see rebuttal Figure 3).\nRebuttal Figure 7. Whole genome sequencing reveals enrichment of ATM lesions and MYC amplifications in disease progression\n(A) Total number and predicted type of short variants passing quality filtering in three longitudinal sample pairs as derived from whole genome sequencing (WGS). There was no substantial and homogenous change in mutational burden from indolent to active disease. (B) Bar chart depicting allele frequencies of detected functional coding variants in genes reported to be recurrently mutated in T-PLL.22 All three patients harbored missense mutations of ATM as well as of JAK/STAT genes already at the indolent timepoint. (C) Dot plot comparing allele frequencies of ATM mutations and copy number alterations between indolent and active-stage T-PLL samples. Data were derived from WGS in three longitudinal sample pairs. Mutations of ATM with concurring deletions of the remaining allele were detected in all three patients. The variant allele fractions (VAFs) of ATM mutations consistently increased in the active timepoint to a near 100% penetrance. (D) Copy number alterations of MYC in three sequential T-PLL sample pairs analyzed with WGS. The prevalence of MYC-amplifications increased from indolent to active disease in all analyzed patients. (E) Overview on most relevant emerging genomic lesions during disease progression. Dot plots show the VAFs and predicted copy numbers of affected genes. The top 10 functional coding variants with the highest deltas within their VAFs from indolent to active stage T-PLL are reported for each of the patients. (blue dot: indolent-stage T-PLL; red dot: active-stage T-PLL). Among the top lesions are variants affecting ATM (pair_1), FASTK (a strong inducer of lymphocyte apoptosis30; pair_2), and STAT5B (pair_3).\nThese analyses revealed several key insights (rebuttal Figure 7): Although we did not observe a consistent change in large-scale structural variants or in overall mutation burden during disease progression (rebuttal Figure 7A), we detected mutations in genes known to be recurrently affected in T-PLL (rebuttal Figure 7B). All three pairs harbored subclonal monoallelic ATM mutations at the indolent stage in conjunction with deletion of the remaining allele. The VAFs of these ATM mutations consistently increased in the active timepoint to a near 100% penetrance (rebuttal Figure 7C, new Figure 2G). This suggests that increasingly dysfunctional ATM confers a selective advantage during disease progression. Furthermore, the finding of progressive MYC amplifications during disease activation, which we had initially inferred from single-cell data, was independently validated by WGS, supporting the biological relevance (rebuttal Figure 3 and 7D, new supplemental Figure 9C). When investigating emerging variants enriched in active-stage T-PLL, we observed isolated mutations in genes involved in energy metabolism, such as FASTK and SLC2A14. These may contribute to the metabolic rewiring observed in active T-PLL; however, their functional impact remains unclear and will have to be addressed in future studies (rebuttal Figure 7E, new supplemental Figure 7B).\n2) JAK/STAT pathway plays an important role in T-PLL. Is there any change in this pathway (additional mutation involving this pathway or overexpression) during disease progression from indolent to active?\nREPLY: We agree with the reviewer that JAK/STAT signaling is a critical pathway in T-PLL’s biology, as also suggested by our own previous data.22,31 To investigate its potential role in disease progression, we examined both transcriptomic and genomic alterations in this pathway using our scRNA-seq and WGS data.\nImportantly, we did not observe consistent transcriptional upregulation of JAK/STAT pathway associated gene sets (Hallmark IL2 STAT5 Signaling and Hallmark IL6 JAK STAT3 Signaling)23 during progression from indolent to active disease, suggesting that the pathway’s activation status may not shift dramatically at the level of gene expression. At the genomic level, we identified subclonal mutations in JAK3 and/or STAT5B in all three WGS-analyzed patients (rebuttal Figure 8, new supplemental Figure 7C). In line with our observation regarding JAK/STAT pathway gene expression, these mutations did not show a consistent pattern of enrichment during progression. For example, the activating STAT5B N642H mutation was present only at the indolent stage in one patient, suggesting that while such lesions may confer a proliferative or survival advantage in experimental systems,32 they do not necessarily drive clonal outgrowth in human T-PLL. This observation is consistent with earlier reports showing that such mutations often remain subclonal and exhibit low VAFs in active-stage T-PLL.31 Fittingly, JAK inhibitors did not show major clinical efficacy, while newly developed STAT-inhibiting compounds, acting more down-stream, have shown promising ex vivo efficacy, irrespective of the presence of JAK or STAT gene mutations.33\nRebuttal Figure 8. JAK and STAT gene mutations during disease activation\nDot plots displaying the temporal dynamics of identified variants within the JAK/STAT signaling pathway. VAFs are displayed for the indolent and active timepoint. An increase in VAFs was observed for JAK3 p.L875H (pair_1) and STAT5B p.T628S (pair_3) during progression, whereas STAT5B p.N642H (pair_2) and STAT5B p.S635T (pair_3) showed a decline, suggesting reduced clonal relevance over time.\n3) In the discussion, there is an opportunity for the authors to further elaborate on their novel findings and explore potential therapeutic implications, particularly considering the poor prognosis associated with T-PLL. The authors mentioned MYC as potential therapeutic target, how about potential targets in metabolic pathways? This study showed downregulation of immune-related signaling pathways. Any potential role of immunotherapy? The findings of tumor-associated monocytes are also interesting, and any potential treatment targeting tumor associated monocytes/macrophage and other cells in the microenvironment?\nREPLY: We thank the reviewer for this valuable suggestion. We have expanded the discussion to further explore potential therapeutic implications. We now elaborate more profoundly on the role of MYC as a potential therapeutic target. While MYC activation appears to be an early event, we observe a progressive enhancement of MYC-driven transcriptional programs during disease progression, highlighting MYC and its downstream networks as attractive interventional targets in active T-PLL. Although direct MYC inhibition has historically been considered unfeasible,34 this perception is changing with the development of new MYC inhibitors, one of which has recently entered clinical trials.35 We also discuss the MYC-dependent efficacy of CDK inhibitors in T-PLL,28,36 as well as the potential of targeting MYC regulators and effectors.29,37 Further, we elaborate on the downregulation of immune signaling pathways, reduced TCR signaling components and MHC expression in T-PLL cells and non-tumor immune cells, as well as the general detachment from the immune microenvironment that we observed in our data. These findings suggest immune evasion mechanisms that could be therapeutically targeted. We discuss why conventional immune checkpoint blockade may be unsuitable for T-PLL and propose alternative immunotherapeutic strategies, including modulation of the CD47/SIRPα axis. We further highlight allogeneic CAR-based cell therapy approaches, which may be particularly effective in the context of an exhausted TME, as they are less susceptible to local immunosuppression.38 These points are now addressed in the revised discussion.\n4) The discovery of downregulation of PTPRC (CD45) is intriguing. In my daily practice, I have encountered several cases of T-PLL exhibiting decreased to negative CD45 expression as assessed by flow cytometry.\nREPLY: We thank the reviewer for sharing this clinically relevant observation. By expanding our cohort during the revisions of this manuscript, we were able to identify two additional patients with downregulation of PTPRC during tumor progression (pair_8, pair_9). The recurrent downregulation of CD45 (PTPRC), consistent with data from flow cytometry, provides an interesting feature and should be further interrogated in future research, for example by a systematic assessment of CD45 downregulation in our prospective German T-PLL registry.\n5) From Figure 3G, we can see that MYC is active and its expression is increased in indolent phase. The author may want to briefly mention and discuss it.\nREPLY: We agree with the reviewer that MYC is already highly expressed at the indolent stage, both at mRNA and protein levels. We have now added a brief statement to clarify this in the text, emphasizing that MYC activity appears to be an early event in T-PLL pathogenesis, but with further amplification and stronger effects on downstream signaling during T-PLL progression.\n6) Can the author comment on ATM mutation in T-PLL? Can this be a potential therapeutic target? ATM mutations in T-PLL are very common. In contrast, they are extremely rare in other T-cell lymphomas/leukemias. How is the authors’ experience? How often do you see ATM mutations in other T cell neoplasms?\nREPLY: As noted above, our WGS data confirmed the presence of ATM mutations and deletions in all three assessed patient pairs, with increased clonality observed at the active stage (rebuttal Figure 7B-C). These findings support the view that ATM inactivation is a central event in early T-PLL pathogenesis as well as in subsequent progression. ATM mutations and deletions are indeed frequent in T-PLL, affecting approximately 81% of patients, while being rare in other T-cell malignancies.22 However, time-resolved data comparing (paired) inactive and active stages of T-PLL had been missing. Our novel data would suggest that ATMs contribution to T-PLL’s pathogenesis shows a two-step pattern, with its early role conferred by its initial lesion (mono-allelic CN loss or mutation) followed by its relevance in advanced stages associated with bi-allelic lesions and/or clonal selection. Broader genomic studies report ATM mutations in fewer than 10% of PTCL-NOS,39,40 with similarly low incidences in T-LGLL, T-ALL, and AITL.41 This relative specificity to T-PLL, combined with the clear clonal selection we observed, underscores dysfunctional ATM signaling as a key driver of initial T-PLL leukemogenesis and progression. As we previously demonstrated, ATM deficiency in T-PLL disrupts canonical DNA damage signaling and repair mechanisms, promoting genomic instability and impairing apoptotic responses.22\nThis represents a targetable vulnerability. In T-PLL, the functionally hypomorphic ATM is not only depleted in its DNA repair capacity, but is also deficient in its capacity to downstream signal upon (endogeneous or therapeutic) DNA insults. This means that an apoptotic p53 response is insufficiently evoked via ATMs intermediate CHEK2. This inability of conferring a proper ATM-CHEK2 mediated activation of p53 upon DNA damage is thought to confer the notorious chemo-resistance in T-PLL. While TP53 itself is genomically intact in T-PLL (TP53 deletions and mutations are rare22,42) p53 is locked in an inactive hypophosphorylated and deacetylated state, bound to its negative regulator MDM2. Therefore, ATM itself is not considered an ideal therapeutic target since its loss-of-function nature can not be reversed pharmacologically; in fact ATM inhibitors showed low in-vitro activity in our hands.22 Consequently, we have been building therapeutic strategies aimed at reactivating p53, either via direct inhibition of MDM2 or by reversing HDAC-mediated epigenetic silencing.42 Recent ex vivo drug screens have provided functional evidence supporting this rationale in T-PLL.36,43\n7) I may miss this information. Did all T-PLL cases in this study carry TCL1 rearrangement? Any cases with t(X;14)?\nREPLY: We thank the reviewer for this request for clarification. Not all cases in our cohort carried a TCL1 rearrangement or showed TCL1 overexpression. Specifically, three cases (pair_9, single_indolent_2, single_indolent_3) were TCL1A-negative T-PLL. In one of these cases, we observed a t(X;14) aberration (pair_9). We have now included this information in the revised Supplemental Methods section for clarity.\nReviewer #3; co-reviewed with Reviewer #4:\nGeneral comments:\nThe authors performed single-cell RNA-seq of 7 longitudinally paired and 5 single time-point derived T-PLL samples to study the molecular changes underlying the evolution from indolent to active T-PLL. Based on the fact that T-PLL is a rare cancer, the study of 7 paired cases is of interest, but has limitations. Overall, my feeling is that the study is performed very well, technically there are no concerns and data analysis seems very robust, but due to the low number of cases and the variability from case to case, there are no highly novel conclusions: it is well known that TCL1A is implicated in the pathogenesis of T-PLL and also that increased MYC expression caused by MYC amplification is a common feature of T-PLL.\nThe analysis of differentially expressed genes (DEGs) in the paired samples is not very informative and seems to indicate that each case has a rather unique set of DEGs.\nSince MYC is very important in the pathogenesis of T-PLL, it is not surprising that the most uniform shifts of gene expression across all indolent/active sample pairs were detected in GSEA-confirmed functional gene sets associated with cell metabolism and cell-cycle regulation, which are very much controlled by MYC, as previously illustrated for T-ALL and other T-cell malignancies.\nInterestingly, the authors investigated differential gene expression between T-PLL cell clusters that expanded compared to those that retracted over time and identified in this way a strong enrichment of gene sets related to cell cycle, energy metabolism, and the transcription factor MYC in the expanding T-PLL cell clusters. This is an interesting analysis, and one of the strongest parts of the study. However, I am not sure how this was performed as there were no clear clusters shown in the malignant cells based on the analysis of the single-cell data shown in figure 1. How were these clusters identified? I could not find a figure clearly illustrating these clusters.\nREPLY:\nWe appreciate the reviewer’s detailed comments and the constructive feedback provided. We acknowledge the limitations posed by sample size. T-PLL is a very rare leukemia with an incidence of approximately 1 per million individuals per year. Beyond that, indolent cases, which represent the focus of our study, constitute only about 20% of all diagnoses. Longitudinally paired, treatment-naïve samples of indolent and active phases are even more scarce. To put this into perspective: since 2008, a dedicated mono-centric collection in Cologne, complemented by a national registry initiative, has facilitated the systematic banking of 496 samples from 252 T-PLL patients. Within this extensive resource, only a very limited number of inactive cases and an even smaller subset of longitudinally matched pairs could be identified. The cohort assembled for the present study therefore represents an exceptional and essentially unique collection, established through more than a decade of sustained and coordinated effort.\nAt the international level, the initial submission was based on collaborations with major centers and reference collections. For the revision, these efforts were substantially expanded: over the past 18 months, outreach to more than 15 additional research facilities worldwide enabled the inclusion of four further indolent/active pairs and one additional indolent case. As a result, the dataset now comprises 11 T-PLL patients with paired samples, in addition to 5 further inactive and one additional active sample. Together with new analyses and the incorporation of age- and sex-matched healthy controls, this expansion considerably strengthens the robustness, generalizability, and impact of our findings.\nOverall, despite inter-patient variability, the initial key findings, such as MYC network upregulation, surpassing of metabolic confinement, and shifts in cell-cell communication were consistent and statistically supported across patients (see rebuttal Figure 6). This is very assuring with respect to the robustness of the data and their biological relevance.\nRegarding the novelty of MYC-related findings in T-PLL, we agree that MYC alteration is a known feature of T-PLL, primarily based on cytogenetic observations involving chromosome 8q. However, mechanistic insights into MYC’s functional role in T-PLL biology remain scarce. In 2018 our group first described MYC mRNA and protein upregulation in active stage T-PLL, which has since been validated in two smaller cohorts.22,28,29 Yet, to our knowledge, no previous studies have systematically investigated MYC expression dynamics over disease progression or analyzed MYC-associated metabolic pathway activation in T-PLL. Our data reveal a progressive, clonal upregulation of MYC mRNA, frequent subclonal MYC copy number gains, and a consistent enrichment of MYC target genes in expanding T-PLL subpopulations. Notably, this was accompanied by strong and statistically significant upregulation of pathways such as oxidative phosphorylation and cell cycle regulation - hallmark MYC effector programs. Thus, while MYC is not a novel gene in T-PLL, our study provides the first integrative, functional characterization of MYC’s downstream impact on T-PLL subclone fitness and links it to a specific transcriptional and metabolic phenotype. In this respect, the “novelty” to us is already inherent to the discovery (and our first description) that MYC associated gene signatures become highly prominent upon intra- and cross-individual disease progression of T-PLL. We deliberately focused on this new and main finding based on relevance, rather than limiting the reporting to biologically secondary findings that could have been picked as a focus based on their “unknown” nature (“novel”) in the literature, which is biased to active-stage T-PLL and their bulk-analysis\nLastly, we thank the reviewer for the interest in the definition of shrinking and expanding clusters. These were derived for each of the T-PLL sample pairs separately. For this, SNN clustering was performed on pairwise integrated T-PLL cell data sets. For each identified cluster, we computed the relative frequency of cells from the indolent and active-stage samples. Clusters were labeled as “shrinking” or “expanding” based on whether their relative abundance significantly differed from the expected distribution of cells across timepoints in a permutation test. Clusters are visualized in supplemental Figure 5D.\nBelow are our responses to each of the specific points raised.\nSpecific major comments:\n1) Figure 1:\nOn figure 1D/E the single cell data has been integrated removing all patient specific information, but at the same time also removing some of the indolent-active information. Indeed, on figure 1E we see a gradient from indolent to active, but one might expect a clearer separation between the two phases. It would be interesting to see the data before this data integration where the single cells cluster according to the patient and the timepoint. The normal cells of all patients still should cluster together in this plot.\nWhy does the patient-specific data needs to be removed in this analysis? All subsequent comparisons are on patient level, which means that comparing the indolent with the active samples of the same patient already filters out patient specific information.\nREPLY: We thank the reviewer for raising these important questions. In the original version of the manuscript, anchor-based integration was used primarily to facilitate clustering and cell type identification by removing patient-specific technical variation. As commented by the reviewer, we agree that this approach might have inadvertently suggested greater homogeneity among T-PLL samples than is truly present. In response to this and related comments (including those from Reviewer 1), we revised our integration strategy to more accurately reflect the underlying biological heterogeneity among T-PLL samples. Specifically, we removed anchor-based integration between T-PLL samples entirely, as we did not observe substantial batch effects between T-PLL patient datasets. This is supported by our revised analysis (rebuttal Figure 1, supplemental Figure 1B), which demonstrates that while non-tumor cells from all patients cluster coherently by cell type, T-PLL cells retain their patient-specific expression profiles. This indicates that observed differences among T-PLL cells from different patients are primarily biological rather than technical.\nHowever, we did detect minor, but consistent, batch effects between the non-tumor cell compartment of T-PLL patients and peripheral blood mononuclear cells (PBMCs) from independently processed healthy donors. To address this, we applied a targeted batch correction using the Harmony algorithm with soft parameters (theta = 0.1), limited to this specific comparison. This approach was chosen to reduce technical confounding without overriding meaningful biological differences, especially those between indolent and active T-PLL states.\nTo increase transparency, we now provide tSNE visualizations of merged, uncorrected data alongside batch-corrected embeddings (supplemental Figure 1B, Figure 1D-G).\n2) In figure 1G the different clusters are annotated. For me it seems weird that the CD4 T-cells cluster more closely to the B-cells than to the CD8 T-cells. Could this be due to the data integration?\nREPLY: This observation was indeed related to the earlier use of strongly integrated data, which may have influenced the clustering of cell types. After revising the analysis to avoid over-integration, we now observe that non T-PLL CD4 T cells from both T-PLL patients and healthy individuals cluster closer to CD8 T cells, as expected. We have updated the figure and the figure legend to clarify this point (new Figure 1D-G, rebuttal Figure 9).\nRebuttal Figure 9. Predicted cell types in the combined PBMC dataset of T-PLL patients and healthy donors.\nt-SNE projection of unsupervised-predicted cell type labels in the PBMC dataset. Colors represent the assigned major cell types. Non-T-PLL cells show homogenous clustering and separation from tumor cells.\n3) On line 185 it is stated that a high intra- and inter-sample heterogeneity is observed. This is not clear from this figure as all samples cluster together on the t-SNE plot. In supplementary figure 1B, we see clustering based on the patients, which would indicate that there is not a lot of intra-tumor heterogeneity. Please elaborate.\nREPLY: We appreciate this point and agree that the t-SNE representation as displayed in Figure 1D-G did not clearly demonstrate intra-tumoral heterogeneity. This was caused by our original approach to use anchor-based integration to facilitate clustering and cell type identification. In the revised version of our manuscript, as outlined above, tSNE representation now clearly depicts inter-patient differences in line with the data presented in supplemental Figure 1C (former supplemental Figure 1B).\n4) In the differential analyses the authors used data on normal cells from an independent study. Why was it not possible to use the normal cells that were sequenced in the current study? How did the authors deal with possible batch effects? Please elaborate.\nREPLY:\nIn the present study, we sequenced samples only from primary T-PLL cases, which naturally contain an extremely small non-malignant T-cell compartment with typically fewer than 150 non-tumor T cells per sample contrasted by over 5,000 malignant T-PLL cells. Comparing these two subsets for differential analysis would lead to strongly biased results. Moreover, these residual non-tumor T cells could themselves be subject to disease-related or microenvironmental influences, including cytokine-mediated signaling, leading to altered transcriptional states. As our dataset demonstrates, the tumor microenvironment in T-PLL exhibits substantial gene expression deregulations affecting antigen presentation and cell-cell interactions. This further limits the validity of using local non-tumor T cells as a control population, at least for questions on tumor-to-normal comparisons.\nTherefore, we chose to leverage an external reference consisting of publicly available PBMC datasets from age- and sex-matched healthy donors, processed with comparable single-cell protocols. To address potential batch effects between these datasets, we applied the Harmony algorithm2 with conservative parameter settings, allowing us to retain true biological variation while minimizing technical noise. We have revised the Methods section accordingly to clarify this analytical strategy.\n5) Figure2:\nIn figure 2G a GSEA is performed on the pseudotime correlated genes. How were these genes determined? How many genes are included ? Is the number large enough to perform GSEA?\nDoes the pseudotime correlate with the indolent/active timepoints? Maybe add a tSNE/UMAP plot with the pseudotime (in the supplementary)?\nWhat is the added value of this analysis in comparison to the analyses on the indolent-vs-active and the expanding-vs-shrinking? Please explain.\nREPLY: We thank the reviewer for this important remark. The pseudotime-correlated genes (gene importance values) were identified using the SCORPIUS pipeline4 at default parameters on the full feature matrix (~20,000 genes). Gene set enrichment analysis (GSEA) was performed on the ranked list of gene importance values, ensuring that the analysis remained unbiased and comprehensive. We believe this approach is well-suited for detecting pathway-level changes, and we have updated the Supplemental Methods to clarify the gene selection process.\nAs for the relationship between pseudotime and disease progression, we confirm that the inferred pseudotime trajectory correlates with the transition from indolent to active disease, as shown in supplemental Figure 6A.\nWe further recognize the reviewer’s interest in the added value of this analysis compared to our dichotomous comparisons (indolent vs. active and expanding vs. shrinking clusters). While those analyses capture discrete differences between disease stages or cell populations, pseudotime analysis complements them by modeling the continuous progression of gene expression changes within tumor cells. This dynamic perspective helps to contextualize the intermediate transcriptional states observed in our dataset. However, due to the high degree of patient-specific variation in pseudotime structure and the lack of a meaningful common pseudotime trajectory depicting the overall evolution from indolent to active T-PLL over all samples, we agree that the additional biological insight is limited. To reflect this, we have moved the pairwise pseudotime analysis to the Supplementary Materials.\n6) Figure 5:\nThis is well presented, but does not provide a lot of new information as most of the data shown here has been reported previously. Data on CTLA4 were reported in Tian, S. et al. Sci Rep 11, 8318 (2021).\nREPLY: We agree with the reviewer that CTLA4 downregulation has been previously reported.22,44 However, we believe our study adds novel insights by delineating the timing of this downregulation during the course of T-PLL leukemogenesis, an aspect that has not been systematically explored before.16,36\nBeyond the downregulation of checkpoint molecules shown in Figure Panel 5B, we are not aware of prior reports addressing the other findings presented in Figure 5 in the context of T-PLL. Notably, our observation that TCR activation appears to loose its relevance in advanced stages of T-PLL offers an important refinement of existing disease models, which have emphasized TCR signaling as a key driver of T-PLL pathogenesis.22,44 Our data indicate that while indolent T-PLL indeed shows strong TCR pathway activation signatures, these signatures progressively diminish during disease progression.\nFurthermore, we identified individual cases displaying downregulation or even loss of TCR signaling components. Of particular interest, we found recurrent downregulation of CD45 (PTPRC) in 4 out of 11 T-PLL cases, which, together with its demonstrated functional relevance for reduced cellular activation upon TCR stimulation (Figure 5E-G), expands upon previous single-case observations of CD45 loss in relapsed T-PLL.45,46\n7) Figure 6:\nIn figure 6G/H the cell-cell interactions are investigated. However, only the number of interactions are reported here. It would be interesting to see what kind of interactions are happening (and disappearing).\nWhile the changes in cell-cell interactions determined by single-cell RNA-seq analysis are of interest, these remain predicted (and not confirmed) and is not very convincing. Conclusions are limited.\nREPLY: We thank the reviewer for this valuable suggestion. In addition to reporting the overall number of inferred cell-cell interactions, we now provide more detailed information regarding the types of interactions that are altered between indolent and active T-PLL in the revised Figure 6 H-J and the new supplemental Figure 13. Using complementary cell-cell communication frameworks (CellChat7 and NicheNet8), we identified specific alterations in ligand-receptor interactions and associated downstream effects on transcriptional programs in non-malignant immune cells. Overall, these strongly suggest the pronounced presence of immune-escape mechanisms in active-stage T-PLL.\nSpecifically, we analyzed potential ligands expressed by T-PLL tumor cells that may contribute to the transcriptomic dynamics observed within the tumor microenvironment during disease activation (rebuttal Figure 5A). Among the most consistent and repeatedly identified changes were two genes with well-established relevance in the regulation of the tumor micromilieu, namely upregulation of ANXA1 (Annexin A1) and downregulation of CD48. ANXA1 is an anti-inflammatory mediator that modulates dendritic cell maturation, monocyte polarization, and T cell activation. Its increased expression in active-stage T-PLL may thus contribute to immunosuppressive reprogramming of surrounding immune cells, promoting a tolerogenic environment. In addition, CD48, a co-stimulatory molecule that enhances NK and T cell cytotoxicity via its receptor 2B4 (CD244), was recurrently downregulated. This reduction may impair immune synapse formation and facilitate immune evasion, a mechanism previously described in other hematologic malignancies.12–14\nNotably, these ligand changes were paralleled by widespread transcriptional reprogramming in the tumor microenvironment (rebuttal Figure 5B-C). We observed downregulation of genes involved in antigen processing and presentation across multiple non-malignant immune subsets, particularly in HLA class II genes within non-tumor T cells, dendritic cells, and B cells. In T-PLL cells, we also noted reduced HLA class I expression. These patterns suggest diminished antigen cross-presentation capacity, which could impair cytotoxic immune responses. In addition, we observed upregulation of IFI44, IFI44L, and LY6E in several immune populations, reflecting an increase in interferon-alpha signaling during disease activation. While traditionally considered tumor-suppressive, recent work has highlighted the role of chronic type I interferon signaling in promoting immunosuppressive circuits, including induction of regulatory cytokines and recruitment of tumor-associated macrophages. Notably, LY6E has been linked to CD8+ T-cell exclusion and resistance to immunotherapy, reinforcing its relevance in shaping an immune-evading microenvironment.\nImportantly, we want to emphasize the inherent limitations of interpreting individual ligand-receptor interactions in single-cell RNA-seq datasets, especially in the context of cytokine signaling. Due to the typically low expression levels of many cytokines, compounded by the sparse and shallow nature of scRNA-seq data, such predictions can be highly variable and prone to noise. This challenge is further exacerbated in our study by the relatively small number of non-malignant immune cells and the high degree of inter-patient heterogeneity observed in T-PLL. Given these limitations, we intentionally focused our primary interpretation on the global pattern of cell-cell communication, as reflected by the total number of interactions and pathway-level changes. We believe this provides a more conservative and robust framework for identifying biologically meaningful trends in the tumor microenvironment. Functional validations of these findings in subsequent efforts are definitely needed.\nMinor comments:\n8) In figure 5A the heatmap is clustered both on the gene expression and the samples. As this figure wants to indicate a difference between the indolent and the active timepoints (line 328) it would be interesting to see the samples clustered according to their timepoint.\nREPLY: As requested by the reviewer we performed clustering of TCR-response transcription factor activities based on the timepoint of samples in addition to the original clustering based on mean enrichment (rebuttal Figure 10). While we would generally prefer the unsupervised approach to avoid introducing potential bias, we leave it to the reviewer’s discretion which version should be included in the manuscript to best highlight the differences between indolent and active-stage samples.\nRebuttal Figure 10. Signatures of TCR-response transcription factors\nHeatmap of scaled TCR-pathway transcription factor signatures in indolent and active-stage T-PLL cells. Enrichment of transcription factor target genes was derived for transcription factors downstream of TCR-signaling using the DoRothEA gene regulatory network. T-PLL samples were arranged in ascending order based on their mean enrichment within indolent and active samples, separately.\n9) In figure 6D, there is a character ‘|’ missing in the legend.\nREPLY: Thank you for pointing this out. We have corrected this issue in the revised figure."}, {"round": 2, "reviewer_comments": {"reviewer_2": "All of my comments are addressed thoroughly and appropriately.", "reviewer_3": "The authors have responded to all comments and questions. I have no further remarks.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter