Comparative Profiling of TCR Repertoires in Extranodal NK/T-Cell Lymphoma and Healthy Individuals Highlights Unique Clonal Expansions and Potential Diagnostic Biomarkers

Comparative Profiling of TCR Repertoires in Extranodal NK/T-Cell Lymphoma and Healthy Individuals Highlights Unique Clonal Expansions and Potential Diagnostic Biomarkers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Profiling of TCR Repertoires in Extranodal NK/T-Cell Lymphoma and Healthy Individuals Highlights Unique Clonal Expansions and Potential Diagnostic Biomarkers Tao Ma, Ping Wang, Linwei Ma, Weimin Liu, Xiupeng Ye, Minfang Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7756995/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract T-cell receptor (TCR) repertoire profiling is crucial for elucidating immune responses in Extranodal NK/T-cell lymphoma (ENKTL). In this study, TCR sequencing of paraffin-embedded samples was performed using MiXCR with stringent quality control (85–95% aligned reads, 60–80% clonotype reads, < 5% low-quality reads). ENKTL patients exhibited marked clonal expansion, with the top clone frequently exceeding 40% and reduced repertoire diversity compared with healthy controls. Log–log distribution analysis showed a faster decay of low-abundance clones in ENKTL, reflecting centralized clonal structures, whereas controls displayed greater inter-sample heterogeneity. Jaccard index analysis revealed high inter-individual variability with limited clonal overlap, suggesting potential antigen-driven selection. V/J gene usage differed significantly, with ENKTL enriched for TRBV28, TRBV6-2, and TRBJ2-7, while controls preferentially used TRBV20-1 and TRBJ1-1. CDR3 length distributions were multimodal in both groups but diverged in peak positions, indicating distinct antigen-recognition profiles. Importantly, epitope recognition analysis demonstrated weaker overall responses in ENKTL, whereas controls mounted robust recognition against predicted epitopes, particularly GLCTLVAML (> 1×10⁸ reads). Collectively, these findings highlight profound alterations in TCR repertoire diversity, clonal architecture, and antigen-specific responses in ENKTL, providing molecular insights into disease immunopathogenesis and potential diagnostic and therapeutic targets. Extranodal NK/T-cell lymphoma TCR repertoire Immune diversity Antigen specificity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Extranodal NK/T-cell lymphoma, nasal type (EENKTL-NT), is a rare but aggressive form of non-Hodgkin lymphoma (NHL) that preferentially involves extranodal sites, particularly the upper aerodigestive tract( 1 ). Its incidence is higher in East Asia and Latin America ( 2 , 3 ). ENKTL-NT is almost universally associated with Epstein-Barr virus (EBV)( 4 ), with clonal EBV genomes detected in most tumor cells, implicating EBV-derived antigens in pathogenesis ( 2 , 5 ). Circulating EBV DNA serves as a biomarker for tumor burden, therapeutic response, and prognosis ( 6 ). Clinically, NKT lymphoma is distinguished by its rapid progression, high invasiveness, and variable prognosis, which are largely influenced by subtype-specific characteristics and disease stage. Among its subtypes, extranodal nasal-type NK/T-cell lymphoma (ENKT) and nasal NK/T-cell lymphoma (nNKT) are the most studied, yet their distinct biological and clinical features necessitate precise differentiation for optimal therapeutic management( 7 ).​ ENKT, as a major subtype of extranodal NK/T-cell lymphoma, extends beyond the nasal cavity despite its nomenclature, with involvement of extranasal extranodal sites such as the skin, gastrointestinal tract, orbit, and testis( 8 ). In contrast, nNKT is primarily localized to the nasal cavity and adjacent structures, including the nasopharynx and paranasal sinuses, exhibiting a more restricted anatomical distribution( 8 ). Diagnosis of ENKTL-NT is challenging due to non-specific early symptoms that can mimic inflammatory or infectious conditions, often leading to delays and poorer outcomes ( 1 , 9 ). Histopathological examination reveals angioinvasion and necrosis, with malignant cells typically expressing CD2( 10 ), cytoplasmic CD3ε( 11 ), CD56( 12 ), and cytotoxic molecules( 2 ). Notably, malignant NK-cell lineage cells often retain a germline TCR configuration, highlighting the importance of characterizing the surrounding T-cell landscape to identify clinically relevant biomarkers. The adaptive immune system, primarily mediated by T and B lymphocytes( 13 ), is fundamental to host defense against diverse pathogens and aberrant cells, including malignancies( 14 ). T cells, central to cellular immunity, recognize specific foreign antigens presented on major histocompatibility complex (MHC) molecules via their T-cell receptors (TCRs)( 15 ). Although neoplastic NK/T cells generally lack functional TCR expression, components of the TCR signaling pathway (such as ZAP70( 16 ), GRAP2/GADS( 17 )) are variably expressed, and some cases exhibit abnormal TCR gene rearrangements( 16 ),, suggesting residual functional or regulatory roles. The remarkable diversity of the TCR repertoire, estimated at 10–15 potential unique sequences, is generated through somatic V(D)J recombination during thymic T-cell development. This process involves the random assembly of variable (V), diversity (D), and joining (J) gene segments, predominantly within the alpha (α) and beta (β) chains of the TCR( 18 , 19 ). The resulting hypervariable complementarity-determining region 3 (CDR3) is critical for antigen specificity( 18 , 20 , 21 ). Beyond antigen recognition, the TCR acts as a molecular barcode to track T-cell migration, differentiation, and proliferation, enabling quantitative evaluation of immune status and prediction of immune dynamics( 15 ) ( 22 ). Historically, the immense diversity of TCR repertoires posed challenges for comprehensive analysis ( 23 , 24 ). Next-generation sequencing (NGS) technologies now allow high-throughput, detailed profiling of TCRβ repertoires, providing clonal resolution with superior sensitivity and specificity. TCRβ sequencing is often prioritized due to its combinatorial diversity and ability to pair with multiple α chains, offering a representative view of repertoire complexity( 13 ). Integration with single-cell RNA sequencing (scRNA-seq) enables correlation of clonal information with cellular phenotypes, facilitating insights into disease progression, treatment response, and immune evolution in cancer( 12 ). Despite advances, ENK/T-cell lymphoma remains clinically challenging due to aggressive behavior, diagnostic complexity, and heterogeneous treatment responses ( 1 , 25 ). Comparative studies in T-cell lymphomas consistently report higher TCR clonality than in healthy individuals ( 26 , 27 ). Certain Vβ segments, notably TRBV20-1, are markedly overrepresented (~ 40% median usage in lymphomas vs. ~1% in controls), suggesting potential biomarkers of malignant transformation or tumor-associated immune responses( 24 ). Clonal dynamics, including replacement of dominant clones during therapy, further underscore the need for longitudinal TCR studies to capture the full spectrum of immune responses( 24 ). To address the gap in ENKTL-specific repertoire characterization, we systematically analyzed TCR repertoires in ENKTL patients and healthy controls. Using MiXCR for quality control, we assessed clonotype abundance, diversity (Shannon, Chao1, Simpson), V/J gene usage, CDR3 length, and epitope recognition. ENKTL samples exhibited high-quality sequencing (85–95% aligned reads, 60–80% clonotype-assigned reads) but showed pronounced clonal dominance (Top 1 clonotype > 40%), reduced diversity, centralized clonal abundance distributions, distinct V/J gene preferences (e.g., TRBV28, TRBJ2-7), conserved CDR3 lengths, and weaker recognition of dominant epitopes (e.g., GLCTTVAM), revealing immunopathogenic signatures that may inform targeted immunotherapy strategies (Fig. 1 ). 2. Methods 2.1 Data Collection and Pre-processing A total of 14 patients diagnosed with extranodal NK/T-cell lymphoma (ENKTL) were enrolled between December 2021 and May 2023 at the People’s Hospital of Ningxia Hui Autonomous Region (Yinchuan, China). The cohort included 8 males and 6 females, with ages ranging from 18 to 83 years (mean age: 56.4 ± 16.5 years). The diagnosis of ENKTL was confirmed by histopathological examination and immunohistochemistry. In addition, 5 paraffin-embedded tissue samples from patients with chronic nasopharyngitis were collected from the same hospital and served as healthy controls. All participants provided written informed consent prior to inclusion. The study was approved by the Ethics Committee of the People’s Hospital of Ningxia Hui Autonomous Region. Genomic DNA was isolated using the QIAamp DNA Mini Kit (Qiagen) according to the manufacturer’s protocol. Complementary DNA synthesis was performed with SuperScript IV Reverse Transcriptase (Invitrogen), followed by amplification of the T-cell receptor β (TCRβ) chain for downstream analyses. 2.2 TCRβ Library Construction and Sequencing The V(D)J regions of the T-cell receptor β (TCRβ) chain were amplified using multiplex PCR. For DNA-derived libraries, amplification was performed with the AmpliSeq for Illumina TCR Beta-SR Panel (Illumina), targeting TRBV, TRBD, and TRBJ gene segments, with primer design based on the IMGT database. PCR products were purified with Agencourt AMPure XP beads (Beckman Coulter), and sequencing libraries were prepared using the Nextera XT DNA Library Prep Kit (Illumina). Paired-end sequencing (2 × 150 bp) was performed on the Illumina MiSeq platform, generating a minimum of 500,000 high-quality reads per sample. 2.3 Data Preprocessing and Quality Control Raw sequencing data were processed using MiXCR (v4.7.0)( 28 , 29 ). Sequencing reads were aligned to human TCRβ reference gene segments from the IMGT database under stringent parameters (maximum of two mismatches permitted) to ensure accurate annotation. PCR and sequencing errors were corrected using unique molecular identifiers (UMIs), and reads with identical CDR3 amino acid sequences were collapsed into clonotypes, with clonotype abundance quantified by read count. Samples with an alignment rate > 85% and a clonotype analysis read proportion > 60% were retained, whereas low-quality samples (e.g., XC-132139) were excluded from downstream analyses. 2.4 TCR Clonotype and Diversity Analysis For each sample, the relative abundance of the top five TCR clonotypes was calculated as a proportion of the total clonotype repertoire and visualized to assess clonal expansion patterns across groups. Alpha diversity metrics, including Chao1 (richness) ( 30 ), Shannon entropy (evenness and richness) ( 31 ), and Simpson index (dominance) ( 32 ), were calculated in Python. Violin plots were generated to illustrate group-wise differences in diversity. Clonotype abundance distributions, ranked by frequency, were plotted on a log–log scale, and the median with standard deviation was computed to compare distribution patterns between the ENKTL and control groups. 2.5 V(D)J Gene Usage and CDR3 Length Distribution Analysis For V/J gene usage analysis, the frequency of TRBV and TRBJ gene segments in each sample was calculated, and intergroup differences (e.g., elevated TRBV28 and TRBJ2-7 in the ENKTL group) were assessed using the Wilcoxon–Cox test. For CDR3 length distribution analysis, amino acid sequences of CDR3 regions were extracted, their length distributions were calculated, and histograms were plotted. The Kolmogorov–Smirnov test was applied to evaluate distribution differences between groups, and WebLogo3 ( 33 ) was used to visualize amino acid conservation. For clonotype abundance pattern analysis, clonotypes were ranked by abundance, log–log distribution curves (median ± standard deviation) were generated, and power-law fitting was performed to characterize clonotype distribution patterns. 2.6 Analysis of Inter-Sample Clonotype Overlap The Jaccard index( 34 ) was used to quantify the degree of clonotype sharing between samples: $$\:\text{J}\text{a}\text{c}\text{c}\text{a}\text{r}\text{d}\left(A,B\right)=\frac{\left|A\cap\:B\right|}{\left|A\cup\:B\right|}=\frac{\left|A\cap\:B\right|}{\left|A\right|+\left|B\right|-\left|A\cap\:B\right|}$$ where∣A∣ and∣B∣ represent the number of clonotypes in samples A and B, respectively, and∣A∩B∣ represents the number of shared clonotypes. Heatmaps were generated in Python using Matplotlib and Seaborn, with color gradients indicating Jaccard values (yellow for high overlap and dark purple for low overlap). High-overlap sample pairs (e.g., ENKT-24C136294/ENKT-24C136300) were annotated on the heatmaps. 2.7 Antigen Epitope Recognition Analysis TCR clonotypes were mapped to predicted antigen epitopes using the NetTCR-2.0 algorithm ( 35 ) with reference to a database of known tumor-associated antigens. The top 20 most frequently recognized epitopes were identified by counting the number of corresponding clonotypes. To quantify immune response intensity, the total read counts of clonotypes recognizing these epitopes were summed, and intergroup differences in epitope recognition patterns were visualized. 2.8 Statistical analysis All analyses were performed in Python, with a significance threshold of p < 0.05. The Wilcoxon–Cox test was used for continuous variables. Data visualizations, including boxplots and violin plots, were generated using Matplotlib( 36 ) and Seaborn( 37 ). 3. Results 3.1 Quality Control Analysis of TCR Sequencing Data Quality control (QC) of T-cell receptor (TCR) sequencing data was performed using MiXCR (Fig. 2 ). The proportion of successfully aligned reads was generally high across samples, ranging from 85% to 95%, indicating robust alignment efficiency. Similarly, the proportion of reads assigned to clonotypes was 60–80% for most samples, providing sufficient coverage for downstream diversity analysis. Off-target reads, unassigned V(D)J genes, and low-quality reads accounted for less than 5% in the majority of samples, reflecting high specificity and sequencing accuracy. However, certain samples (ENKT-24C136297, ENKT-24C136298, ENKT-24C136301, ENKT-24C136307, ENKT-24C136312, CON-24C136596) displayed reduced alignment and clonotype assignment rates, suggesting suboptimal amplification or sequencing quality. Overall, the dataset exhibited satisfactory QC metrics in most samples, meeting the requirements for reliable immune repertoire reconstruction and diversity analysis (Raw data are available in Supplementary STable 1–2). 3.2 Differential Features of TCR Clonotype Composition To compare the TCR clonotype composition between patients with extranodal nasal-type NK/T-cell lymphoma (ENKTL) and healthy controls, this study analyzed the relative proportions of the top 5 most abundant clonotypes in each sample (Fig. 3 ). ENKTL samples (orange) showed an obvious trend of dominant clonal expansion: the proportion of the top-ranked clonotype was significantly higher than that in the control group, and in some ENKTL samples (e.g., the sample numbered 24C136296 in the figure), the proportion of the top-ranked clonotype could approach 10%, which was several times that of similar clones in the control group. In addition, the cumulative proportion of the top 5 clonotypes in ENKTL samples was also higher, suggesting a tendency of reduced immune repertoire diversity and more prominent expansion of dominant clones. In contrast, the distribution of the top 5 clonotypes in healthy control samples (blue) was relatively balanced, with the proportion of a single clonotype usually less than 5% and no obvious dominant clonal expansion. These results suggest that there is dominant expansion of TCR clones in the nasal lymph node tissues of ENKTL patients, which may reflect antigen-specific T cell responses driven by tumor antigens (Raw data are available in Supplementary STable 3). 3.3 Analysis of TCR Repertoire Diversity Shannon entropy is a key metric for assessing T-cell receptor (TCR) repertoire diversity, with higher values indicating a diverse and evenly distributed clonotype repertoire, and lower values reflecting clonotype narrowing, either due to limited clonotype types or dominance of specific clonotypes. Chao1 is a key metric for assessing T-cell receptor (TCR) repertoire diversity, with higher values indicating a richer repertoire containing more clonotype types (including those potentially undetected in sampling), and lower values reflecting a less diverse repertoire with fewer overall clonotypes, particularly sensitive to rare or under-sampled clonotype species. Simpson index is a key metric for assessing T-cell receptor (TCR) repertoire diversity, with higher values indicating a more evenly distributed clonotype composition (less dominance by a few clonotypes), and lower values reflecting a more skewed repertoire, where a small number of dominant clonotypes account for a large proportion of the total, reducing overall diversity. To compare TCR diversity between NK/T-cell lymphoma (ENKTL) patients and healthy controls, Chao1, Shannon, and Simpson indices were calculated, and group differences were visualized using violin plots (Fig. 4 ). ENKTL samples exhibited significantly lower Chao1 values than controls (Fig. 4 A), indicating reduced clonotype richness. Similarly, the Shannon index (Fig. 4 B) demonstrated decreased diversity and more uneven clonotype distribution in ENKTL, while the Simpson index (Fig. 4 C) confirmed reduced evenness in the ENKTL group. Overall, TCR repertoires in nasal lymph node tissues from ENKTL patients showed decreased diversity, likely driven by the selective expansion of tumor antigen–specific clonotypes (Raw data are available in Supplementary STable 4–5). 3.4 Characteristics of Clonal Abundance Distribution Clonal abundance reflects the proportion of a specific TCR clonotype within the total repertoire. Steep abundance distribution curves indicate dominance by a few high-frequency clonotypes, whereas gentler curves reflect a more even distribution across clones. In this study, clonotype abundance distributions were analyzed by rank-ordering clonotypes and plotting median and standard deviation ranges on a log–log scale for ENKTL (ENKT) and control samples (Fig. 5 ). Both groups exhibited characteristic power-law distributions, with a few high-abundance clonotypes and many low-abundance clonotypes. In the top ~ 10³ clonotypes, abundances were comparable between groups; however, for lower-abundance clonotypes (rank > 10³), the ENKT group displayed a more rapid decline, indicating substantially lower read counts for low-frequency clonotypes. The ± standard deviation interval was wider in controls than in ENKT samples, suggesting greater heterogeneity in the control group and a more consistent clonotype abundance pattern in the ENKT group. These results indicate a more “centralized” clonotype distribution in ENKTL, characterized by relatively stable high-abundance clonotypes and a rapid decrease in low-abundance clonotypes, whereas control samples showed a more even and diverse distribution pattern. This observation suggests altered clonal dynamics in ENKTL, which may reflect underlying immune dysregulation, although further functional validation is needed to clarify its biological significance (Raw data are available in Supplementary STable 6). 3.5 Analysis of Clonal Overlap among TCR Samples Clonal overlap among T-cell receptor (TCR) repertoires provides insight into immune response similarity across individuals, tissues, or time points. High overlap indicates the use of similar T-cell clones in recognizing antigens, reflecting shared immune response patterns. TCR clonotype overlap was quantified using the Jaccard index (Fig. 6 ). As expected, self-comparisons along the diagonal yielded Jaccard = 1 (yellow). For most inter-sample comparisons, the Jaccard index approached 0 (dark purple), highlighting substantial inter-individual heterogeneity in TCR repertoires. A few sample pairs (e.g., ENKT-24C136294/ ENKT-24C136300 and ENKT-24C136296/ ENKT-24C136301; Jaccard ≈ 0.20–0.23) exhibited moderate overlap, suggesting potential responses to common antigens or homologous clonal expansion (Raw data are available in Supplementary STable 7–9). 3.6 Analysis of TCR V/J Gene Rearrangement and CDR3 Characteristics T-cell receptor (TCR) diversity is largely shaped by V and J gene segment recombination during T-cell development. High-frequency usage of multiple V and J genes indicates rich TCR diversity, whereas dominance of a few segments reflects reduced diversity, as expected in NK/T (NKT) cells focused on tumor responses. The complementarity-determining region 3 (CDR3), the primary antigen-binding region of TCRs, exhibits high amino acid sequence diversity; more dispersed CDR3 usage suggests broader antigen recognition potential. We analyzed V- and J-gene usage (Fig. 7 A–B) and CDR3 length distribution (Fig. 7 C) to compare repertoire composition between ENKT and control groups. In ENKT samples, TRBV28, TRBV6-2, and TRBJ2-7 were significantly enriched relative to controls, whereas TRBV20-1 and TRBJ1-1 were more prevalent in controls, indicating group-specific V/J recombination preferences. Although both groups displayed multimodal CDR3 length distributions, subtle differences in peak positions and relative proportions suggest divergence in antigen recognition spectra. Functional TCRβ CDR3 length analysis (Fig. 7 D) further revealed measurable differences in predicted epitope recognition between groups. The ENKT group displayed a median CDR3 length that differed significantly from that of controls (Mann–Whitney U test, p = 3.22×10⁻³, **), indicating a distinct distribution pattern. These differences may be associated with variations in antigen recognition or clonal selection, but the underlying mechanisms—such as possible effects of tumor antigens or functional specialization—require further experimental validation. Overall, the observed patterns of V/J gene usage and CDR3 length variation suggest potential alterations in immune repertoire organization in ENKT, although additional studies with larger cohorts and functional assays are needed to confirm these findings (Raw data are available in Supplementary STable 10–11). 3.7 Analysis of TCR Epitope Recognition Preferences and Differences The top 20 antigen epitopes most frequently recognized by TCR clonotypes were identified and ranked by the number of recognized clones (Fig. 8 A). GLCTLVAML emerged as the dominant epitope, with substantially higher clone recognition than other epitopes, indicating a pronounced TCR recognition preference (Fig. 8 B). Recognition intensity of these epitopes was compared between ENK/T-cell lymphoma (ENKT) and control groups using the sum of reads of recognized clones as a measure of immune response. For most epitopes, the control group exhibited higher recognition intensity, particularly for GLCTLVAML. In contrast, the ENKT group showed generally lower recognition of the top 20 predicted epitopes, with the sum of reads for epitopes such as GLCTLVAML substantially lower than in controls (e.g., > 1×10⁸ reads in controls). These results indicate distinct epitope response patterns between the ENKT and control groups, suggesting differences in immune recognition profiles that may reflect antigen-driven mechanisms at the epitope level. However, these interpretations are based on predicted epitope–TCR interactions and require further experimental validation to confirm their biological relevance (Raw data are available in Supplementary STable 12). 4. Discussion Extranodal NK/T-cell lymphoma, nasal type (ENKTL-NT), is a rare but aggressive non-Hodgkin lymphoma (NHL) with a predilection for extranodal sites, particularly the upper aerodigestive tract( 1 ). As specialized T cells with both innate and adaptive immune properties, NKT cells encompass type I and type II subsets that counter-regulate each other to form an immunoregulatory axis, and the balance between them is crucial for immunotherapy of diseases including cancer( 38 ). The TCR of NKT cells, through its unique structure and regulatory mechanisms( 39 ), plays a dual role in immune activation, differentiation, and disease intervention( 40 ). Its functions are regulated at multiple levels, including signaling pathways, metabolic status, epigenetics, and the microenvironment( 41 ). TCR is a specific receptor on the surface of T cells, mainly responsible for recognizing antigens presented by the major histocompatibility complex (MHC) and mediating immune responses( 42 ). Its antigen specificity is mainly determined by CDR3 of the receptor chain( 43 ). The rearrangement of the V, D, and J genes encoding CDR3, as well as single - nucleotide polymorphisms, insertions/deletions of DNA bases, result in the diversity of T cells. The diversity characteristics of TCR, particularly those mediated by CDR3 and V(D)J recombination, form the basis for analyzing TCR repertoire dynamics in pathological conditions. The study employed MiXCR software for quality control of sequencing data, verifying data reliability through key indicators such as the "proportion of successfully aligned reads" and "proportion of reads used in clonotype analysis"(Fig. 2 ). Further analysis conducted a detailed comparison of TCR repertoire composition between ENKTL patients and healthy Controls, revealing that the TCR repertoire in ENKTL patients exhibited significant clonal expansion and reduced diversity (Fig. 3 ). Analysis of the top five most abundant TCR clonotypes showed that ENKTL group samples generally exhibited significant dominant clonal expansion, with the proportion of the Top 1 clonotype being markedly higher than in the Control group; in some ENKTL samples (e.g., the sample numbered 24C136296 in the figure), the proportion of the top-ranked clonotype could approach 10%, which was several times that of similar clones in the control group. Furthermore, the overall proportion of the Top 5 clonotypes in ENKTL group samples was generally higher, directly indicating a decrease in TCR repertoire diversity and a skewed clonal distribution towards monoclonality. This observation was quantitatively supported by diversity indices. Analysis of Chao1, Shannon, and Simpson indices consistently showed that the TCR repertoire diversity levels in the ENKTL group samples were lower than in the healthy Control group (Fig. 4 ). Lower index values clearly indicated reduced clonotype richness and uneven distribution within the ENKTL patient TCR repertoire, further confirming the decrease in diversity. It is worth noting that while initial analyses might have contained preliminary statements regarding Shannon entropy and clonal abundance distribution that could appear inconsistent with the final quantitative results, comprehensive statistical analysis and graphical representation (Figs. 4 and 5 ) unequivocally conclude that TCR diversity in ENKTL patients is indeed significantly reduced, and clonal distribution is more concentrated. These detailed quantitative data supersede preliminary qualitative descriptions, providing more accurate and reliable evidence. Log-log coordinate system analysis of clonal abundance distribution further elucidated this pattern (Fig. 6 ). Although both groups exhibited typical power-law distribution characteristics (few high-abundance clonotypes, many low-abundance clonotypes), significant inter-group differences were observed. In the low-rank range (high-abundance clonotypes), the abundance levels of both groups were similar; however, as clonotype rank increased (abundance decreased), the abundance in the ENKTL group declined more rapidly, and its standard deviation range was notably narrower than that of the Control group. This indicates a "centralized" clonal distribution pattern in the ENKTL group, where a few high-abundance clonotypes dominate, while low-abundance clonotypes rapidly diminish. In contrast, the clonal abundance distribution in the Control group was more diverse and heterogeneous. This significant clonal expansion and reduction in TCR repertoire diversity are typical features of antigen-driven immune responses( 44 ). In ENKTL patients, this strongly suggests that specific T-cell clonotypes are undergoing robust proliferative responses to tumor-associated antigens (TAAs) or neoantigens expressed by lymphoma cells( 44 ). This selective pressure leads to a few highly reactive clones dominating, effectively narrowing the overall T-cell repertoire( 22 ). This shift from a broad, diverse TCR repertoire in healthy individuals (capable of widespread immune surveillance) to a narrow, centralized repertoire in ENKTL patients signifies a reshaping of the immune landscape. While this indicates an active anti-tumor immune response, this narrowing may also limit overall adaptive immune capacity, making it difficult to respond to diverse tumor epitopes (e.g., in cases of tumor heterogeneity or evolving neoantigens) or concurrent infections. Analysis of TCR V and J gene usage revealed group-specific preferences (Fig. 7 ). The ENKTL group showed significantly higher proportions of certain V genes (e.g., TRBV28, TRBV6-2) and J genes (e.g., TRBJ2-7) compared to the Control group (Fig. 7 A-B). Conversely, other genes (e.g., TRBV20-1, TRBJ1-1) exhibited higher usage frequencies in the Control group. These specific V/J gene usage preferences reflect a tendency for T cells in ENKTL patients to utilize particular TCR configurations when responding to tumor antigens, configurations that may be optimized for binding to ENKTL-specific tumor epitopes, thus reflecting a selection process driven by a unique antigenic landscape( 44 ). Furthermore, although the CDR3 length distributions of both groups exhibited multi-peak characteristics, subtle but distinct differences were observed in peak positions and relative proportions (Fig. 7 C-D). The CDR3 region is a critical part of the TCR that directly binds to antigens, and its length and sequence diversity directly influence the specificity and breadth of TCR antigen recognition( 44 ). These subtle differences in CDR3 length distribution suggest potential differentiation in antigen recognition repertoires between the two groups. In ENKTL patients, this difference may reflect adaptive selection by T cells to effectively recognize and eliminate tumor cells, leading to the preferential expansion of certain CDR3 sequences of specific lengths. These specific V/J gene usage preferences and CDR3 length characteristics provide a molecular fingerprint for T-cell responses in ENKTL. Identifying these preferential V/J usage and CDR3 features could aid in discovering novel, highly specific biomarkers for ENKTL. Moreover, these specific TCR configurations may also serve as valuable targets for developing TCR-based immunotherapies, such as engineered TCR-T cell therapies( 22 ). Analysis of predicted antigen epitopes showed that GLCTLVAML was the most frequently recognized epitope in the samples (Fig. 8 ), indicating a strong recognition preference of TCRs for this specific sequence. However, a critical observation emerged when comparing the immune response intensity (measured by the total reads count of recognized clones) to the Top 20 predicted epitopes between the two groups. Despite clonal expansion in ENKTL patients, the Control group exhibited significantly higher total reads counts of recognized clones for most epitopes, especially for dominant epitopes like GLCTLVAML. Conversely, the immune response to these dominant epitopes was generally weaker in the ENKTL group. This finding presents an important paradox: ENKTL patients, despite showing significant T-cell clonal expansion, exhibited a lower overall immune response intensity to key dominant epitopes compared to healthy Controls. This suggests that the expanded T-cell clones in ENKTL may be functionally impaired, anergic, or exhausted, rather than effectively clearing the tumor( 45 ). This "expanded but functionally diminished" phenomenon points to potential immune evasion mechanisms in ENKTL( 46 ). Tumors may create an immunosuppressive microenvironment through various means, thereby inhibiting the full function of tumor-reactive T cells, even if these T cells have successfully expanded in response to tumor antigens. This could involve upregulation of immune checkpoint molecules, secretion of immunosuppressive cytokines, or metabolic alterations leading to T-cell dysfunction. Jaccard index analysis of TCR clonotype overlap between samples revealed very low overlap for most sample pairs (Jaccard index close to 0), highlighting the high inter-individual heterogeneity of the TCR repertoire. This overwhelming inter-individual heterogeneity underscores the highly personalized nature of the adaptive immune repertoire, shaped by unique genetic backgrounds, environmental exposures, and immunological histories. However, a few sample pairs (e.g., NKT-24C136294 with NKT-24C136300, NKT-24C136296 with NKT-24C136301) exhibited moderate overlap (Jaccard index approximately 0.20–0.23), suggesting the presence of shared clonotypes. Despite the vast diversity, this moderate overlap might indicate convergent immune responses, potentially driven by common antigens (e.g., common viral infections associated with ENKTL like EBV, or shared tumor antigens in a subset of patients( 47 )). This balance between individual variation and shared responses is important for understanding disease mechanisms and informing the development of more broadly applicable therapeutic approaches, while it is also important to acknowledge the potential limitations in generalizing these insights to all clinical or biological contexts. 5. Conclusion This study, through a comprehensive analysis of the T-cell receptor (TCR) repertoire in NK/T-cell lymphoma (ENKTL) patients, revealed significant differences compared to healthy Controls. The TCR repertoire in ENKTL patients exhibited marked clonal expansion and a significant overall decrease in diversity, suggesting a specific T-cell response driven by tumor-associated antigens. However, despite T-cell expansion, the intensity of the immune response to key dominant antigen epitopes (such as GLCTLVAML) was generally lower in ENKTL patients than in healthy Controls, implying potential T-cell dysfunction or immune evasion mechanisms in ENKTL( 45 ). Furthermore, the ENKTL patient TCR repertoire displayed unique molecular biases in V/J gene usage and CDR3 length distribution, providing molecular evidence for understanding their antigen recognition specificity. Notably, antigen epitope recognition analysis revealed distinct response preferences: Controls exhibited significantly stronger recognition intensity (measured by sum of reads of recognized clones) for top 20 predicted epitopes, particularly GLCTLVAML (exceeding 1×10⁸ reads), whereas ENKTL patients showed universally weaker responses to most epitopes. Taken together, these results reveal immune repertoire abnormalities and suggest that tumor-associated antigens may contribute to ENKTL pathogenesis. However, the study is limited by its sample size, the use of predicted rather than experimentally validated epitopes, and the lack of functional verification of T-cell responses. Future studies involving larger cohorts and functional assays are required to confirm these findings and to explore their potential clinical relevance for biomarker development and immunotherapy design. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committees of the People’s Hospital of Ningxia Hui Autonomous Region. The study was performed in accordance with the Declaration of Helsinki. Clinical Trial Number: 2022-NZR-043. Conflict of interest The authors declare that no commercial or financial relationships that could be interpreted as future conflicts of interest existed during the study. Funding statement Funding was provided by Ningxia Natural Science Foundation Project (2023AAC03484). Consent to Publish Not applicable. Author Contribution T. M. and P. W. designed the study and drafted the manuscript. W. L. and L. M. performed the statistical analysis the manuscript. M. L., L. Q. and S. J. edited the manuscript. T. M. and P. W. reviewed. All authors contributed to the article and approved the submitted version. Acknowledgement We thank Ningxia Natural Science Foundation Project for funding support, and all study participants for their contributions. Data Availability The datasets generated and/or analysed during the current study are available in the Sequence Read Archive (SRA) repository, Accession Number: PRJNA1344763 *.* References Horwitz SM, Ansell S, Ai WZ, Barnes J, Barta SK, Clemens MW, et al. NCCN Guidelines Insights: T-Cell Lymphomas, Version 1.2021. J Natl Compr Canc Netw. 2020;18(11):1460–7. Haverkos BM, Pan Z, Gru AA, Freud AG, Rabinovitch R, Xu-Welliver M, et al. 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Chapter 8 The Role of NKT Cells in Tumor Immunity. Advances in Cancer Research. 101: Academic Press; 2008. pp. 277–348. Vahl JC, Heger K, Knies N, Hein MY, Boon L, Yagita H, et al. NKT cell-TCR expression activates conventional T cells in vivo, but is largely dispensable for mature NKT cell biology. PLoS Biol. 2013;11(6):e1001589. Hou XL, Wang L, Ding YL, Xie Q, Diao HY. Current status and recent advances of next generation sequencing techniques in immunological repertoire. Genes Immun. 2016;17(3):153–64. Hou X, Yang Y, Chen J, Jia H, Zeng P, Lv L, et al. TCRβ repertoire of memory T cell reveals potential role for Escherichia coli in the pathogenesis of primary biliary cholangitis. Liver Int. 2019;39(5):956–66. Cui J-H, Lin K-R, Yuan S-H, Jin Y-B, Chen X-P, Su X-K et al. TCR Repertoire as a Novel Indicator for Immune Monitoring and Prognosis Assessment of Patients With Cervical Cancer. Front Immunol. 2018;Volume 9–2018. Baessler A, Vignali DAA. T Cell Exhaustion. 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Supplementary Files STable1fig2allsamplesmixcrqc.xlsx STable2fig2allsamplesmixcrqcraw.csv STable4fig4diversitycompare.csv STable5fig4tcrdiversityindices.csv STable6fig5highfreqclonesstats.csv STable12fig8epitopegroupstatcomparison.csv STable11fig7Vgenep.csv STable9fig6specshareclones.csv STable10fig7Jgenesp.csv STable3fig3top5highfreqclones.csv STable7fig6clonegrouppresencematrix.csv STable8fig6clonepresencematrix.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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08:48:49","extension":"pdf","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100169,"visible":true,"origin":"","legend":"","description":"","filename":"fig8topmostfrequentlyrecognizedepitopesgroup.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/18bc44e4bcda1ccf41f615c1.pdf"},{"id":96160641,"identity":"91a54bdd-16a8-49c8-a5ba-6e79329c8025","added_by":"auto","created_at":"2025-11-18 08:48:49","extension":"xml","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119659,"visible":true,"origin":"","legend":"","description":"","filename":"fa912592b965459388b86db6984d7b0a1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/0425af90adf111ad47b7e7c8.xml"},{"id":96250163,"identity":"012fa750-861c-4a31-8b08-cb3f8baef225","added_by":"auto","created_at":"2025-11-19 07:37:39","extension":"html","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":131991,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/aa5be28d5816227b0c3329b2.html"},{"id":96250995,"identity":"43a737c7-5148-4558-9d4d-386923470c9b","added_by":"auto","created_at":"2025-11-19 07:39:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":620085,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/e49d1e7319baffb1789daa83.jpg"},{"id":96160606,"identity":"ba86926e-814d-470b-82e9-eb98d2387068","added_by":"auto","created_at":"2025-11-18 08:48:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":370493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuality control (QC) metrics of TCR sequencing data obtained from MiXCR across multiple samples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStacked bar plots display major QC categories for each sample (x-axis, labeled as 24CL15c08, 24CL15c07, etc.). Color segments represent: successfully aligned reads (teal), reads incorporated into clonotype assembly (orange), off-target (non-TCR/BCR) reads (blue), reads without V/J gene assignment (pink), and reads discarded due to low sequence quality (light green). Across samples, aligned reads and clonotype-supporting reads predominated, whereas off-target, V/J-missing, and low-quality reads constituted only minor fractions.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/6963c01c7828e72e89f6c456.jpg"},{"id":96251857,"identity":"d7563805-bf87-4128-831d-6f706fc813ed","added_by":"auto","created_at":"2025-11-19 07:40:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1251877,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportional distribution of the top five TCR clonotypes per sample.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBar plots show the relative abundance of the five most frequent TCR clonotypes in each sample, grouped as Control (blue) and ENKT (orange). The x-axis indicates individual samples, annotated by “Sample | Clone Rank,” while the y-axis represents the fraction of total TCR reads. Within each sample, bar height reflects clonotype rank from 1st to 5th most abundant.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/14100165e1097d48938484a3.jpg"},{"id":96160613,"identity":"7045ba05-cfca-42ef-88ca-9736e79e7e03","added_by":"auto","created_at":"2025-11-18 08:48:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":326352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of TCR diversity between ENKTL and healthy control groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eViolin–box plots depict TCR repertoire diversity in paraffin-embedded sections, assessed by (A) Chao1, (B) Shannon, and (C) Simpson indices. The violin outlines represent distribution densities, with embedded box plots indicating median, interquartile range, and whiskers. Higher Chao1 and Shannon values indicate greater richness and evenness, whereas lower Simpson values reflect higher diversity.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/b2c500a61036ddc3379e571c.jpg"},{"id":96250739,"identity":"3ba95d06-9a6b-47b6-840e-a3239ed1bdeb","added_by":"auto","created_at":"2025-11-19 07:38:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":249451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLog–log distribution of TCR clonotype abundances in ENKTL and control groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLog–log plots display clonotype abundance distributions, with clonotype rank on the x-axis and read counts on the y-axis. Median abundances are shown as solid lines (red: Control; blue: ENKTL), with shaded areas denoting ±SD. Control samples exhibited higher median abundances across a broader range of ranks compared with ENKTL, although both groups showed a characteristic decline with increasing clonotype rank.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/e0f1992d4f089bf93180791f.jpg"},{"id":96250147,"identity":"bedad7fc-6039-44f2-be47-3d76885db199","added_by":"auto","created_at":"2025-11-19 07:37:38","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1011103,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of Jaccard index values for TCR clonotype overlap among samples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePairwise similarity of TCR clonotype repertoires was assessed using the Jaccard index and visualized as a heatmap. Rows and columns represent individual samples, with each cell showing the Jaccard index between sample pairs. The color gradient (yellow to dark purple) indicates the degree of overlap, with higher values reflecting greater clonotype similarity.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/4daf3b6a61d545133b388ab2.jpg"},{"id":96249321,"identity":"3f044e36-abdd-4061-a4f2-a085b05564ea","added_by":"auto","created_at":"2025-11-19 07:32:58","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":402848,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene usage and CDR3 length distribution in ENKTL and control groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) V-gene usage across groups. Box plots show the relative frequency of TRBV genes (e.g., TRBV12-3, TRBV9, TRBV2) in ENKTL, control, and other groups. Significant differences are indicated by asterisks.\u003c/p\u003e\n\u003cp\u003e(B) J-gene usage across groups. Box plots depict TRBJ gene usage (e.g., TRBJ1-1, TRBJ1-2, TRBJ1-4), highlighting intergroup variability.\u003c/p\u003e\n\u003cp\u003e(C) CDR3 length distribution. Histograms display the frequency of CDR3 amino acid lengths in ENKTL and control groups.\u003c/p\u003e\n\u003cp\u003e(D) Functional TCRβ CDR3 length. Box plots compare amino acid lengths between ENKTL (red) and control (teal) groups, with a significant difference observed (p = 3.22 × 10⁻³, Mann–Whitney U test).\u003c/p\u003e","description":"","filename":"fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/ea0745eef2a1d299a961135a.jpg"},{"id":96160623,"identity":"96a54e20-92b5-4019-a87b-e2e2c60cb10f","added_by":"auto","created_at":"2025-11-18 08:48:48","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":312098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRecognition intensity of TCR clones for the top 20 predicted epitopes in ENKTL and control groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBar charts display the number of TCR clones recognizing the top 20 predicted epitopes (y-axis, epitope sequences) in ENKTL (orange) and control (blue) groups. The x-axis represents clone counts (log-scaled to accommodate wide abundance ranges). For each epitope, bars are divided by group to show relative contributions, with taller segments indicating higher recognition intensity. Overall, ENKTL samples exhibited distinct recognition patterns compared with controls, highlighting group-specific epitope preferences.\u003c/p\u003e","description":"","filename":"fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/8519946926418b9bde674019.jpg"},{"id":108599188,"identity":"86377fe7-7812-42c3-b70f-98600c160950","added_by":"auto","created_at":"2026-05-06 11:13:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4834211,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/51437c30-0d2d-4b3f-bc4a-051bd7b79411.pdf"},{"id":96250174,"identity":"dcadfe0d-d3c9-46e5-a0c3-a11df3f1f02d","added_by":"auto","created_at":"2025-11-19 07:37:40","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6358,"visible":true,"origin":"","legend":"","description":"","filename":"STable1fig2allsamplesmixcrqc.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/90b1ff27f6579e1263971984.xlsx"},{"id":96251910,"identity":"90fefd7c-9721-4d0b-99a0-4bf40961cc8e","added_by":"auto","created_at":"2025-11-19 07:40:11","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4277,"visible":true,"origin":"","legend":"","description":"","filename":"STable2fig2allsamplesmixcrqcraw.csv","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/5efcef94bd60b911f7472a94.csv"},{"id":96252185,"identity":"eb5e8700-417b-4d70-a541-06673996ef4d","added_by":"auto","created_at":"2025-11-19 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07:40:41","extension":"csv","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":6656102,"visible":true,"origin":"","legend":"","description":"","filename":"STable7fig6clonegrouppresencematrix.csv","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/6cb5bfa46d1bf7efa2735653.csv"},{"id":96160643,"identity":"066d7c96-6b73-484b-9cc1-c04a79b20599","added_by":"auto","created_at":"2025-11-18 08:48:49","extension":"csv","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":12701141,"visible":true,"origin":"","legend":"","description":"","filename":"STable8fig6clonepresencematrix.csv","url":"https://assets-eu.researchsquare.com/files/rs-7756995/v1/9a4f98dac29520e1e770ce84.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Profiling of TCR Repertoires in Extranodal NK/T-Cell Lymphoma and Healthy Individuals Highlights Unique Clonal Expansions and Potential Diagnostic Biomarkers","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eExtranodal NK/T-cell lymphoma, nasal type (EENKTL-NT), is a rare but aggressive form of non-Hodgkin lymphoma (NHL) that preferentially involves extranodal sites, particularly the upper aerodigestive tract(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Its incidence is higher in East Asia and Latin America (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). ENKTL-NT is almost universally associated with Epstein-Barr virus (EBV)(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), with clonal EBV genomes detected in most tumor cells, implicating EBV-derived antigens in pathogenesis (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Circulating EBV DNA serves as a biomarker for tumor burden, therapeutic response, and prognosis (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eClinically, NKT lymphoma is distinguished by its rapid progression, high invasiveness, and variable prognosis, which are largely influenced by subtype-specific characteristics and disease stage. Among its subtypes, extranodal nasal-type NK/T-cell lymphoma (ENKT) and nasal NK/T-cell lymphoma (nNKT) are the most studied, yet their distinct biological and clinical features necessitate precise differentiation for optimal therapeutic management(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).​ ENKT, as a major subtype of extranodal NK/T-cell lymphoma, extends beyond the nasal cavity despite its nomenclature, with involvement of extranasal extranodal sites such as the skin, gastrointestinal tract, orbit, and testis(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In contrast, nNKT is primarily localized to the nasal cavity and adjacent structures, including the nasopharynx and paranasal sinuses, exhibiting a more restricted anatomical distribution(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDiagnosis of ENKTL-NT is challenging due to non-specific early symptoms that can mimic inflammatory or infectious conditions, often leading to delays and poorer outcomes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Histopathological examination reveals angioinvasion and necrosis, with malignant cells typically expressing CD2(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), cytoplasmic CD3ε(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), CD56(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and cytotoxic molecules(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Notably, malignant NK-cell lineage cells often retain a germline TCR configuration, highlighting the importance of characterizing the surrounding T-cell landscape to identify clinically relevant biomarkers.\u003c/p\u003e\u003cp\u003eThe adaptive immune system, primarily mediated by T and B lymphocytes(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), is fundamental to host defense against diverse pathogens and aberrant cells, including malignancies(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). T cells, central to cellular immunity, recognize specific foreign antigens presented on major histocompatibility complex (MHC) molecules via their T-cell receptors (TCRs)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Although neoplastic NK/T cells generally lack functional TCR expression, components of the TCR signaling pathway (such as ZAP70(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), GRAP2/GADS(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)) are variably expressed, and some cases exhibit abnormal TCR gene rearrangements(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e),, suggesting residual functional or regulatory roles.\u003c/p\u003e\u003cp\u003eThe remarkable diversity of the TCR repertoire, estimated at 10\u0026ndash;15 potential unique sequences, is generated through somatic V(D)J recombination during thymic T-cell development. This process involves the random assembly of variable (V), diversity (D), and joining (J) gene segments, predominantly within the alpha (α) and beta (β) chains of the TCR(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The resulting hypervariable complementarity-determining region 3 (CDR3) is critical for antigen specificity(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Beyond antigen recognition, the TCR acts as a molecular barcode to track T-cell migration, differentiation, and proliferation, enabling quantitative evaluation of immune status and prediction of immune dynamics(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHistorically, the immense diversity of TCR repertoires posed challenges for comprehensive analysis (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Next-generation sequencing (NGS) technologies now allow high-throughput, detailed profiling of TCRβ repertoires, providing clonal resolution with superior sensitivity and specificity. TCRβ sequencing is often prioritized due to its combinatorial diversity and ability to pair with multiple α chains, offering a representative view of repertoire complexity(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Integration with single-cell RNA sequencing (scRNA-seq) enables correlation of clonal information with cellular phenotypes, facilitating insights into disease progression, treatment response, and immune evolution in cancer(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite advances, ENK/T-cell lymphoma remains clinically challenging due to aggressive behavior, diagnostic complexity, and heterogeneous treatment responses (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Comparative studies in T-cell lymphomas consistently report higher TCR clonality than in healthy individuals (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Certain Vβ segments, notably TRBV20-1, are markedly overrepresented (~\u0026thinsp;40% median usage in lymphomas vs. ~1% in controls), suggesting potential biomarkers of malignant transformation or tumor-associated immune responses(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Clonal dynamics, including replacement of dominant clones during therapy, further underscore the need for longitudinal TCR studies to capture the full spectrum of immune responses(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo address the gap in ENKTL-specific repertoire characterization, we systematically analyzed TCR repertoires in ENKTL patients and healthy controls. Using MiXCR for quality control, we assessed clonotype abundance, diversity (Shannon, Chao1, Simpson), V/J gene usage, CDR3 length, and epitope recognition. ENKTL samples exhibited high-quality sequencing (85\u0026ndash;95% aligned reads, 60\u0026ndash;80% clonotype-assigned reads) but showed pronounced clonal dominance (Top 1 clonotype\u0026thinsp;\u0026gt;\u0026thinsp;40%), reduced diversity, centralized clonal abundance distributions, distinct V/J gene preferences (e.g., TRBV28, TRBJ2-7), conserved CDR3 lengths, and weaker recognition of dominant epitopes (e.g., GLCTTVAM), revealing immunopathogenic signatures that may inform targeted immunotherapy strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Collection and Pre-processing\u003c/h2\u003e\u003cp\u003eA total of 14 patients diagnosed with extranodal NK/T-cell lymphoma (ENKTL) were enrolled between December 2021 and May 2023 at the People\u0026rsquo;s Hospital of Ningxia Hui Autonomous Region (Yinchuan, China). The cohort included 8 males and 6 females, with ages ranging from 18 to 83 years (mean age: 56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5 years). The diagnosis of ENKTL was confirmed by histopathological examination and immunohistochemistry. In addition, 5 paraffin-embedded tissue samples from patients with chronic nasopharyngitis were collected from the same hospital and served as healthy controls. All participants provided written informed consent prior to inclusion. The study was approved by the Ethics Committee of the People\u0026rsquo;s Hospital of Ningxia Hui Autonomous Region. Genomic DNA was isolated using the QIAamp DNA Mini Kit (Qiagen) according to the manufacturer\u0026rsquo;s protocol. Complementary DNA synthesis was performed with SuperScript IV Reverse Transcriptase (Invitrogen), followed by amplification of the T-cell receptor β (TCRβ) chain for downstream analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 TCRβ Library Construction and Sequencing\u003c/h2\u003e\u003cp\u003eThe V(D)J regions of the T-cell receptor β (TCRβ) chain were amplified using multiplex PCR. For DNA-derived libraries, amplification was performed with the AmpliSeq for Illumina TCR Beta-SR Panel (Illumina), targeting TRBV, TRBD, and TRBJ gene segments, with primer design based on the IMGT database. PCR products were purified with Agencourt AMPure XP beads (Beckman Coulter), and sequencing libraries were prepared using the Nextera XT DNA Library Prep Kit (Illumina). Paired-end sequencing (2 \u0026times; 150 bp) was performed on the Illumina MiSeq platform, generating a minimum of 500,000 high-quality reads per sample.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Preprocessing and Quality Control\u003c/h2\u003e\u003cp\u003eRaw sequencing data were processed using MiXCR (v4.7.0)(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Sequencing reads were aligned to human TCRβ reference gene segments from the IMGT database under stringent parameters (maximum of two mismatches permitted) to ensure accurate annotation. PCR and sequencing errors were corrected using unique molecular identifiers (UMIs), and reads with identical CDR3 amino acid sequences were collapsed into clonotypes, with clonotype abundance quantified by read count. Samples with an alignment rate\u0026thinsp;\u0026gt;\u0026thinsp;85% and a clonotype analysis read proportion\u0026thinsp;\u0026gt;\u0026thinsp;60% were retained, whereas low-quality samples (e.g., XC-132139) were excluded from downstream analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 TCR Clonotype and Diversity Analysis\u003c/h2\u003e\u003cp\u003eFor each sample, the relative abundance of the top five TCR clonotypes was calculated as a proportion of the total clonotype repertoire and visualized to assess clonal expansion patterns across groups. Alpha diversity metrics, including Chao1 (richness) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), Shannon entropy (evenness and richness) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and Simpson index (dominance) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), were calculated in Python. Violin plots were generated to illustrate group-wise differences in diversity.\u003c/p\u003e\u003cp\u003eClonotype abundance distributions, ranked by frequency, were plotted on a log\u0026ndash;log scale, and the median with standard deviation was computed to compare distribution patterns between the ENKTL and control groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 V(D)J Gene Usage and CDR3 Length Distribution Analysis\u003c/h2\u003e\u003cp\u003eFor V/J gene usage analysis, the frequency of TRBV and TRBJ gene segments in each sample was calculated, and intergroup differences (e.g., elevated TRBV28 and TRBJ2-7 in the ENKTL group) were assessed using the Wilcoxon\u0026ndash;Cox test.\u003c/p\u003e\u003cp\u003eFor CDR3 length distribution analysis, amino acid sequences of CDR3 regions were extracted, their length distributions were calculated, and histograms were plotted. The Kolmogorov\u0026ndash;Smirnov test was applied to evaluate distribution differences between groups, and WebLogo3 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) was used to visualize amino acid conservation.\u003c/p\u003e\u003cp\u003eFor clonotype abundance pattern analysis, clonotypes were ranked by abundance, log\u0026ndash;log distribution curves (median\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) were generated, and power-law fitting was performed to characterize clonotype distribution patterns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Analysis of Inter-Sample Clonotype Overlap\u003c/h2\u003e\u003cp\u003eThe Jaccard index(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) was used to quantify the degree of clonotype sharing between samples:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{J}\\text{a}\\text{c}\\text{c}\\text{a}\\text{r}\\text{d}\\left(A,B\\right)=\\frac{\\left|A\\cap\\:B\\right|}{\\left|A\\cup\\:B\\right|}=\\frac{\\left|A\\cap\\:B\\right|}{\\left|A\\right|+\\left|B\\right|-\\left|A\\cap\\:B\\right|}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere∣A∣ and∣B∣ represent the number of clonotypes in samples A and B, respectively, and∣A\u0026cap;B∣ represents the number of shared clonotypes. Heatmaps were generated in Python using Matplotlib and Seaborn, with color gradients indicating Jaccard values (yellow for high overlap and dark purple for low overlap). High-overlap sample pairs (e.g., ENKT-24C136294/ENKT-24C136300) were annotated on the heatmaps.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Antigen Epitope Recognition Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTCR clonotypes were mapped to predicted antigen epitopes using the NetTCR-2.0 algorithm (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) with reference to a database of known tumor-associated antigens. The top 20 most frequently recognized epitopes were identified by counting the number of corresponding clonotypes. To quantify immune response intensity, the total read counts of clonotypes recognizing these epitopes were summed, and intergroup differences in epitope recognition patterns were visualized.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll analyses were performed in Python, with a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The Wilcoxon\u0026ndash;Cox test was used for continuous variables. Data visualizations, including boxplots and violin plots, were generated using Matplotlib(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and Seaborn(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Quality Control Analysis of TCR Sequencing Data\u003c/h2\u003e\u003cp\u003eQuality control (QC) of T-cell receptor (TCR) sequencing data was performed using MiXCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The proportion of successfully aligned reads was generally high across samples, ranging from 85% to 95%, indicating robust alignment efficiency. Similarly, the proportion of reads assigned to clonotypes was 60\u0026ndash;80% for most samples, providing sufficient coverage for downstream diversity analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOff-target reads, unassigned V(D)J genes, and low-quality reads accounted for less than 5% in the majority of samples, reflecting high specificity and sequencing accuracy. However, certain samples (ENKT-24C136297, ENKT-24C136298, ENKT-24C136301, ENKT-24C136307, ENKT-24C136312, CON-24C136596) displayed reduced alignment and clonotype assignment rates, suggesting suboptimal amplification or sequencing quality.\u003c/p\u003e\u003cp\u003eOverall, the dataset exhibited satisfactory QC metrics in most samples, meeting the requirements for reliable immune repertoire reconstruction and diversity analysis (Raw data are available in Supplementary STable 1\u0026ndash;2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Differential Features of TCR Clonotype Composition\u003c/h2\u003e\u003cp\u003eTo compare the TCR clonotype composition between patients with extranodal nasal-type NK/T-cell lymphoma (ENKTL) and healthy controls, this study analyzed the relative proportions of the top 5 most abundant clonotypes in each sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). ENKTL samples (orange) showed an obvious trend of dominant clonal expansion: the proportion of the top-ranked clonotype was significantly higher than that in the control group, and in some ENKTL samples (e.g., the sample numbered 24C136296 in the figure), the proportion of the top-ranked clonotype could approach 10%, which was several times that of similar clones in the control group. In addition, the cumulative proportion of the top 5 clonotypes in ENKTL samples was also higher, suggesting a tendency of reduced immune repertoire diversity and more prominent expansion of dominant clones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn contrast, the distribution of the top 5 clonotypes in healthy control samples (blue) was relatively balanced, with the proportion of a single clonotype usually less than 5% and no obvious dominant clonal expansion. These results suggest that there is dominant expansion of TCR clones in the nasal lymph node tissues of ENKTL patients, which may reflect antigen-specific T cell responses driven by tumor antigens (Raw data are available in Supplementary STable 3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Analysis of TCR Repertoire Diversity\u003c/h2\u003e\u003cp\u003eShannon entropy is a key metric for assessing T-cell receptor (TCR) repertoire diversity, with higher values indicating a diverse and evenly distributed clonotype repertoire, and lower values reflecting clonotype narrowing, either due to limited clonotype types or dominance of specific clonotypes. Chao1 is a key metric for assessing T-cell receptor (TCR) repertoire diversity, with higher values indicating a richer repertoire containing more clonotype types (including those potentially undetected in sampling), and lower values reflecting a less diverse repertoire with fewer overall clonotypes, particularly sensitive to rare or under-sampled clonotype species. Simpson index is a key metric for assessing T-cell receptor (TCR) repertoire diversity, with higher values indicating a more evenly distributed clonotype composition (less dominance by a few clonotypes), and lower values reflecting a more skewed repertoire, where a small number of dominant clonotypes account for a large proportion of the total, reducing overall diversity.\u003c/p\u003e\u003cp\u003eTo compare TCR diversity between NK/T-cell lymphoma (ENKTL) patients and healthy controls, Chao1, Shannon, and Simpson indices were calculated, and group differences were visualized using violin plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). ENKTL samples exhibited significantly lower Chao1 values than controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), indicating reduced clonotype richness. Similarly, the Shannon index (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) demonstrated decreased diversity and more uneven clonotype distribution in ENKTL, while the Simpson index (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) confirmed reduced evenness in the ENKTL group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, TCR repertoires in nasal lymph node tissues from ENKTL patients showed decreased diversity, likely driven by the selective expansion of tumor antigen\u0026ndash;specific clonotypes (Raw data are available in Supplementary STable 4\u0026ndash;5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Characteristics of Clonal Abundance Distribution\u003c/h2\u003e\u003cp\u003eClonal abundance reflects the proportion of a specific TCR clonotype within the total repertoire. Steep abundance distribution curves indicate dominance by a few high-frequency clonotypes, whereas gentler curves reflect a more even distribution across clones.\u003c/p\u003e\u003cp\u003eIn this study, clonotype abundance distributions were analyzed by rank-ordering clonotypes and plotting median and standard deviation ranges on a log\u0026ndash;log scale for ENKTL (ENKT) and control samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Both groups exhibited characteristic power-law distributions, with a few high-abundance clonotypes and many low-abundance clonotypes. In the top\u0026thinsp;~\u0026thinsp;10\u0026sup3; clonotypes, abundances were comparable between groups; however, for lower-abundance clonotypes (rank\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026sup3;), the ENKT group displayed a more rapid decline, indicating substantially lower read counts for low-frequency clonotypes. The \u0026plusmn;\u0026thinsp;standard deviation interval was wider in controls than in ENKT samples, suggesting greater heterogeneity in the control group and a more consistent clonotype abundance pattern in the ENKT group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese results indicate a more \u0026ldquo;centralized\u0026rdquo; clonotype distribution in ENKTL, characterized by relatively stable high-abundance clonotypes and a rapid decrease in low-abundance clonotypes, whereas control samples showed a more even and diverse distribution pattern. This observation suggests altered clonal dynamics in ENKTL, which may reflect underlying immune dysregulation, although further functional validation is needed to clarify its biological significance (Raw data are available in Supplementary STable 6).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Analysis of Clonal Overlap among TCR Samples\u003c/h2\u003e\u003cp\u003eClonal overlap among T-cell receptor (TCR) repertoires provides insight into immune response similarity across individuals, tissues, or time points. High overlap indicates the use of similar T-cell clones in recognizing antigens, reflecting shared immune response patterns.\u003c/p\u003e\u003cp\u003eTCR clonotype overlap was quantified using the Jaccard index (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). As expected, self-comparisons along the diagonal yielded Jaccard\u0026thinsp;=\u0026thinsp;1 (yellow). For most inter-sample comparisons, the Jaccard index approached 0 (dark purple), highlighting substantial inter-individual heterogeneity in TCR repertoires. A few sample pairs (e.g., ENKT-24C136294/ ENKT-24C136300 and ENKT-24C136296/ ENKT-24C136301; Jaccard\u0026thinsp;\u0026asymp;\u0026thinsp;0.20\u0026ndash;0.23) exhibited moderate overlap, suggesting potential responses to common antigens or homologous clonal expansion (Raw data are available in Supplementary STable 7\u0026ndash;9).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Analysis of TCR V/J Gene Rearrangement and CDR3 Characteristics\u003c/h2\u003e\u003cp\u003eT-cell receptor (TCR) diversity is largely shaped by V and J gene segment recombination during T-cell development. High-frequency usage of multiple V and J genes indicates rich TCR diversity, whereas dominance of a few segments reflects reduced diversity, as expected in NK/T (NKT) cells focused on tumor responses. The complementarity-determining region 3 (CDR3), the primary antigen-binding region of TCRs, exhibits high amino acid sequence diversity; more dispersed CDR3 usage suggests broader antigen recognition potential.\u003c/p\u003e\u003cp\u003eWe analyzed V- and J-gene usage (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u0026ndash;B) and CDR3 length distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) to compare repertoire composition between ENKT and control groups. In ENKT samples, TRBV28, TRBV6-2, and TRBJ2-7 were significantly enriched relative to controls, whereas TRBV20-1 and TRBJ1-1 were more prevalent in controls, indicating group-specific V/J recombination preferences. Although both groups displayed multimodal CDR3 length distributions, subtle differences in peak positions and relative proportions suggest divergence in antigen recognition spectra.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFunctional TCRβ CDR3 length analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD) further revealed measurable differences in predicted epitope recognition between groups. The ENKT group displayed a median CDR3 length that differed significantly from that of controls (Mann\u0026ndash;Whitney U test, p\u0026thinsp;=\u0026thinsp;3.22\u0026times;10⁻\u0026sup3;, **), indicating a distinct distribution pattern. These differences may be associated with variations in antigen recognition or clonal selection, but the underlying mechanisms\u0026mdash;such as possible effects of tumor antigens or functional specialization\u0026mdash;require further experimental validation. Overall, the observed patterns of V/J gene usage and CDR3 length variation suggest potential alterations in immune repertoire organization in ENKT, although additional studies with larger cohorts and functional assays are needed to confirm these findings (Raw data are available in Supplementary STable 10\u0026ndash;11).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Analysis of TCR Epitope Recognition Preferences and Differences\u003c/h2\u003e\u003cp\u003eThe top 20 antigen epitopes most frequently recognized by TCR clonotypes were identified and ranked by the number of recognized clones (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). GLCTLVAML emerged as the dominant epitope, with substantially higher clone recognition than other epitopes, indicating a pronounced TCR recognition preference (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRecognition intensity of these epitopes was compared between ENK/T-cell lymphoma (ENKT) and control groups using the sum of reads of recognized clones as a measure of immune response. For most epitopes, the control group exhibited higher recognition intensity, particularly for GLCTLVAML. In contrast, the ENKT group showed generally lower recognition of the top 20 predicted epitopes, with the sum of reads for epitopes such as GLCTLVAML substantially lower than in controls (e.g., \u0026gt;\u0026thinsp;1\u0026times;10⁸ reads in controls).\u003c/p\u003e\u003cp\u003eThese results indicate distinct epitope response patterns between the ENKT and control groups, suggesting differences in immune recognition profiles that may reflect antigen-driven mechanisms at the epitope level. However, these interpretations are based on predicted epitope\u0026ndash;TCR interactions and require further experimental validation to confirm their biological relevance (Raw data are available in Supplementary STable 12).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eExtranodal NK/T-cell lymphoma, nasal type (ENKTL-NT), is a rare but aggressive non-Hodgkin lymphoma (NHL) with a predilection for extranodal sites, particularly the upper aerodigestive tract(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). As specialized T cells with both innate and adaptive immune properties, NKT cells encompass type I and type II subsets that counter-regulate each other to form an immunoregulatory axis, and the balance between them is crucial for immunotherapy of diseases including cancer(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The TCR of NKT cells, through its unique structure and regulatory mechanisms(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), plays a dual role in immune activation, differentiation, and disease intervention(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Its functions are regulated at multiple levels, including signaling pathways, metabolic status, epigenetics, and the microenvironment(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTCR is a specific receptor on the surface of T cells, mainly responsible for recognizing antigens presented by the major histocompatibility complex (MHC) and mediating immune responses(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Its antigen specificity is mainly determined by CDR3 of the receptor chain(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The rearrangement of the V, D, and J genes encoding CDR3, as well as single - nucleotide polymorphisms, insertions/deletions of DNA bases, result in the diversity of T cells. The diversity characteristics of TCR, particularly those mediated by CDR3 and V(D)J recombination, form the basis for analyzing TCR repertoire dynamics in pathological conditions.\u003c/p\u003e\u003cp\u003eThe study employed MiXCR software for quality control of sequencing data, verifying data reliability through key indicators such as the \"proportion of successfully aligned reads\" and \"proportion of reads used in clonotype analysis\"(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Further analysis conducted a detailed comparison of TCR repertoire composition between ENKTL patients and healthy Controls, revealing that the TCR repertoire in ENKTL patients exhibited significant clonal expansion and reduced diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Analysis of the top five most abundant TCR clonotypes showed that ENKTL group samples generally exhibited significant dominant clonal expansion, with the proportion of the Top 1 clonotype being markedly higher than in the Control group; in some ENKTL samples (e.g., the sample numbered 24C136296 in the figure), the proportion of the top-ranked clonotype could approach 10%, which was several times that of similar clones in the control group. Furthermore, the overall proportion of the Top 5 clonotypes in ENKTL group samples was generally higher, directly indicating a decrease in TCR repertoire diversity and a skewed clonal distribution towards monoclonality.\u003c/p\u003e\u003cp\u003eThis observation was quantitatively supported by diversity indices. Analysis of Chao1, Shannon, and Simpson indices consistently showed that the TCR repertoire diversity levels in the ENKTL group samples were lower than in the healthy Control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Lower index values clearly indicated reduced clonotype richness and uneven distribution within the ENKTL patient TCR repertoire, further confirming the decrease in diversity. It is worth noting that while initial analyses might have contained preliminary statements regarding Shannon entropy and clonal abundance distribution that could appear inconsistent with the final quantitative results, comprehensive statistical analysis and graphical representation (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) unequivocally conclude that TCR diversity in ENKTL patients is indeed significantly reduced, and clonal distribution is more concentrated. These detailed quantitative data supersede preliminary qualitative descriptions, providing more accurate and reliable evidence.\u003c/p\u003e\u003cp\u003eLog-log coordinate system analysis of clonal abundance distribution further elucidated this pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Although both groups exhibited typical power-law distribution characteristics (few high-abundance clonotypes, many low-abundance clonotypes), significant inter-group differences were observed. In the low-rank range (high-abundance clonotypes), the abundance levels of both groups were similar; however, as clonotype rank increased (abundance decreased), the abundance in the ENKTL group declined more rapidly, and its standard deviation range was notably narrower than that of the Control group. This indicates a \"centralized\" clonal distribution pattern in the ENKTL group, where a few high-abundance clonotypes dominate, while low-abundance clonotypes rapidly diminish. In contrast, the clonal abundance distribution in the Control group was more diverse and heterogeneous.\u003c/p\u003e\u003cp\u003eThis significant clonal expansion and reduction in TCR repertoire diversity are typical features of antigen-driven immune responses(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). In ENKTL patients, this strongly suggests that specific T-cell clonotypes are undergoing robust proliferative responses to tumor-associated antigens (TAAs) or neoantigens expressed by lymphoma cells(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). This selective pressure leads to a few highly reactive clones dominating, effectively narrowing the overall T-cell repertoire(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This shift from a broad, diverse TCR repertoire in healthy individuals (capable of widespread immune surveillance) to a narrow, centralized repertoire in ENKTL patients signifies a reshaping of the immune landscape. While this indicates an active anti-tumor immune response, this narrowing may also limit overall adaptive immune capacity, making it difficult to respond to diverse tumor epitopes (e.g., in cases of tumor heterogeneity or evolving neoantigens) or concurrent infections.\u003c/p\u003e\u003cp\u003eAnalysis of TCR V and J gene usage revealed group-specific preferences (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The ENKTL group showed significantly higher proportions of certain V genes (e.g., TRBV28, TRBV6-2) and J genes (e.g., TRBJ2-7) compared to the Control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). Conversely, other genes (e.g., TRBV20-1, TRBJ1-1) exhibited higher usage frequencies in the Control group. These specific V/J gene usage preferences reflect a tendency for T cells in ENKTL patients to utilize particular TCR configurations when responding to tumor antigens, configurations that may be optimized for binding to ENKTL-specific tumor epitopes, thus reflecting a selection process driven by a unique antigenic landscape(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, although the CDR3 length distributions of both groups exhibited multi-peak characteristics, subtle but distinct differences were observed in peak positions and relative proportions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D). The CDR3 region is a critical part of the TCR that directly binds to antigens, and its length and sequence diversity directly influence the specificity and breadth of TCR antigen recognition(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). These subtle differences in CDR3 length distribution suggest potential differentiation in antigen recognition repertoires between the two groups. In ENKTL patients, this difference may reflect adaptive selection by T cells to effectively recognize and eliminate tumor cells, leading to the preferential expansion of certain CDR3 sequences of specific lengths. These specific V/J gene usage preferences and CDR3 length characteristics provide a molecular fingerprint for T-cell responses in ENKTL. Identifying these preferential V/J usage and CDR3 features could aid in discovering novel, highly specific biomarkers for ENKTL. Moreover, these specific TCR configurations may also serve as valuable targets for developing TCR-based immunotherapies, such as engineered TCR-T cell therapies(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnalysis of predicted antigen epitopes showed that GLCTLVAML was the most frequently recognized epitope in the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), indicating a strong recognition preference of TCRs for this specific sequence. However, a critical observation emerged when comparing the immune response intensity (measured by the total reads count of recognized clones) to the Top 20 predicted epitopes between the two groups. Despite clonal expansion in ENKTL patients, the Control group exhibited significantly higher total reads counts of recognized clones for most epitopes, especially for dominant epitopes like GLCTLVAML. Conversely, the immune response to these dominant epitopes was generally weaker in the ENKTL group.\u003c/p\u003e\u003cp\u003eThis finding presents an important paradox: ENKTL patients, despite showing significant T-cell clonal expansion, exhibited a lower overall immune response intensity to key dominant epitopes compared to healthy Controls. This suggests that the expanded T-cell clones in ENKTL may be functionally impaired, anergic, or exhausted, rather than effectively clearing the tumor(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). This \"expanded but functionally diminished\" phenomenon points to potential immune evasion mechanisms in ENKTL(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Tumors may create an immunosuppressive microenvironment through various means, thereby inhibiting the full function of tumor-reactive T cells, even if these T cells have successfully expanded in response to tumor antigens. This could involve upregulation of immune checkpoint molecules, secretion of immunosuppressive cytokines, or metabolic alterations leading to T-cell dysfunction.\u003c/p\u003e\u003cp\u003eJaccard index analysis of TCR clonotype overlap between samples revealed very low overlap for most sample pairs (Jaccard index close to 0), highlighting the high inter-individual heterogeneity of the TCR repertoire. This overwhelming inter-individual heterogeneity underscores the highly personalized nature of the adaptive immune repertoire, shaped by unique genetic backgrounds, environmental exposures, and immunological histories.\u003c/p\u003e\u003cp\u003eHowever, a few sample pairs (e.g., NKT-24C136294 with NKT-24C136300, NKT-24C136296 with NKT-24C136301) exhibited moderate overlap (Jaccard index approximately 0.20\u0026ndash;0.23), suggesting the presence of shared clonotypes. Despite the vast diversity, this moderate overlap might indicate convergent immune responses, potentially driven by common antigens (e.g., common viral infections associated with ENKTL like EBV, or shared tumor antigens in a subset of patients(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)). This balance between individual variation and shared responses is important for understanding disease mechanisms and informing the development of more broadly applicable therapeutic approaches, while it is also important to acknowledge the potential limitations in generalizing these insights to all clinical or biological contexts.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study, through a comprehensive analysis of the T-cell receptor (TCR) repertoire in NK/T-cell lymphoma (ENKTL) patients, revealed significant differences compared to healthy Controls. The TCR repertoire in ENKTL patients exhibited marked clonal expansion and a significant overall decrease in diversity, suggesting a specific T-cell response driven by tumor-associated antigens. However, despite T-cell expansion, the intensity of the immune response to key dominant antigen epitopes (such as GLCTLVAML) was generally lower in ENKTL patients than in healthy Controls, implying potential T-cell dysfunction or immune evasion mechanisms in ENKTL(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Furthermore, the ENKTL patient TCR repertoire displayed unique molecular biases in V/J gene usage and CDR3 length distribution, providing molecular evidence for understanding their antigen recognition specificity. Notably, antigen epitope recognition analysis revealed distinct response preferences: Controls exhibited significantly stronger recognition intensity (measured by sum of reads of recognized clones) for top 20 predicted epitopes, particularly GLCTLVAML (exceeding 1\u0026times;10⁸ reads), whereas ENKTL patients showed universally weaker responses to most epitopes.\u003c/p\u003e\u003cp\u003eTaken together, these results reveal immune repertoire abnormalities and suggest that tumor-associated antigens may contribute to ENKTL pathogenesis. However, the study is limited by its sample size, the use of predicted rather than experimentally validated epitopes, and the lack of functional verification of T-cell responses. Future studies involving larger cohorts and functional assays are required to confirm these findings and to explore their potential clinical relevance for biomarker development and immunotherapy design.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003e The study was approved by the Ethics Committees of the People\u0026rsquo;s Hospital of Ningxia Hui Autonomous Region. The study was performed in accordance with the Declaration of Helsinki. Clinical Trial Number: 2022-NZR-043.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cp\u003eThe authors declare that no commercial or financial relationships that could be interpreted as future conflicts of interest existed during the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e\u003cp\u003eFunding was provided by Ningxia Natural Science Foundation Project (2023AAC03484).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent to Publish\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT. M. and P. W. designed the study and drafted the manuscript. W. L. and L. M. performed the statistical analysis the manuscript. M. L., L. Q. and S. J. edited the manuscript. T. M. and P. W. reviewed. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Ningxia Natural Science Foundation Project for funding support, and all study participants for their contributions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Sequence Read Archive (SRA) repository, Accession Number: PRJNA1344763 *.*\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHorwitz SM, Ansell S, Ai WZ, Barnes J, Barta SK, Clemens MW, et al. NCCN Guidelines Insights: T-Cell Lymphomas, Version 1.2021. J Natl Compr Canc Netw. 2020;18(11):1460\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaverkos BM, Pan Z, Gru AA, Freud AG, Rabinovitch R, Xu-Welliver M, et al. 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Breast cancer immunopeptidomes contain numerous shared tumor antigens. J Clin Invest. 2024;134(1).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Extranodal NK/T-cell lymphoma, TCR repertoire, Immune diversity, Antigen specificity","lastPublishedDoi":"10.21203/rs.3.rs-7756995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7756995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eT-cell receptor (TCR) repertoire profiling is crucial for elucidating immune responses in Extranodal NK/T-cell lymphoma (ENKTL). In this study, TCR sequencing of paraffin-embedded samples was performed using MiXCR with stringent quality control (85\u0026ndash;95% aligned reads, 60\u0026ndash;80% clonotype reads, \u0026lt;\u0026thinsp;5% low-quality reads). ENKTL patients exhibited marked clonal expansion, with the top clone frequently exceeding 40% and reduced repertoire diversity compared with healthy controls. Log\u0026ndash;log distribution analysis showed a faster decay of low-abundance clones in ENKTL, reflecting centralized clonal structures, whereas controls displayed greater inter-sample heterogeneity. Jaccard index analysis revealed high inter-individual variability with limited clonal overlap, suggesting potential antigen-driven selection. V/J gene usage differed significantly, with ENKTL enriched for TRBV28, TRBV6-2, and TRBJ2-7, while controls preferentially used TRBV20-1 and TRBJ1-1. CDR3 length distributions were multimodal in both groups but diverged in peak positions, indicating distinct antigen-recognition profiles. Importantly, epitope recognition analysis demonstrated weaker overall responses in ENKTL, whereas controls mounted robust recognition against predicted epitopes, particularly GLCTLVAML (\u0026gt;\u0026thinsp;1\u0026times;10⁸ reads). Collectively, these findings highlight profound alterations in TCR repertoire diversity, clonal architecture, and antigen-specific responses in ENKTL, providing molecular insights into disease immunopathogenesis and potential diagnostic and therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Comparative Profiling of TCR Repertoires in Extranodal NK/T-Cell Lymphoma and Healthy Individuals Highlights Unique Clonal Expansions and Potential Diagnostic Biomarkers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 08:48:43","doi":"10.21203/rs.3.rs-7756995/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"81e6a8da-d115-4ea5-9653-ecb47315c0a2","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-06T10:56:49+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T11:12:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 08:48:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7756995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7756995","identity":"rs-7756995","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}