EuroClonality/BIOMED-2 guidelines for interpretation and reporting of Ig/TCR clonality testing in suspected lymphoproliferations

Key to the concept of neoplasia is the presence of a clonal cell population. Identification of such a clonal cell population (also referred to as clonality testing or clonality assessment) is dependent on the presence of markers that under normal conditions are highly polymorphic in the cell type of interest. For leukemias and lymphomas, collectively termed neoplastic lymphoid proliferations, such highly polymorphic markers exist in the form of the antigen receptor genes that have to rearrange in a cell-specific way to encode the immunoglobulin (IG) or T-cell receptor (TCR) molecule. In a reactive cell population the repertoire of rearranged IG/TCR genes is thus highly diverse, whereas in a neoplastic context the IG/TCR repertoire is to some or to a large extent identical; of note, these are like the 2 ends of a spectrum with variants in between (Fig. 1). Still, clonality testing of rearranged IG/TCR genes forms a useful tool in the diagnostic armamentarium of leukemias and lymphomas. In this issue Keller et al review the state-of-the-art in clonality testing in veterinary medicine, highlighting the main aspects of all 3 phases of the clonality testing work flow, that is the preanalytical, analytical, and postanalytical phases. Next to that, the authors provide a rationale for harmonization of clonality assessment in veterinary medicine, building on what has been developed and achieved in human medicine for that matter. Given the current differences between veterinary laboratories with respect to laboratory practices and interpretation guidelines, harmonization of terminology, protocols and interpretation is a good step toward improving the accuracy of clonality testing assays worldwide. The glossary of terms as provided by Keller et al would help to avoid Babylonian misunderstandings for a clinical test that has been designated ‘‘molecular morphology’’ referring to the somewhat subjective description of profiles (see also Fig. 1). Furthermore, the detailed discussion of interpretation pitfalls in the review by Keller et al is most useful to create awareness amongst pathologists and molecular biologists active in the veterinary field. However, the veterinary field would strongly profit from making more steps, or better from making a big leap, by striving toward standardization, as has been done in human medicine. Standardization goes 1 step further than harmonization and implies much more attuned, evidence-based optimization to deliver an approach that is robust enough in a multicenter context. What would be the required actions and instruments for that purpose? A first thing to do would be to create a (small) consortium that starts to directly compare existing IG/TCR assays on the very same samples in multiple veterinary laboratories. The best performing assays for the different species would be the winners and could become the gold standards, unless it is clear that no single assay has a clinical sensitivity that is acceptable. In that case, novel multiplex PCR assays should be considered with newly designed primers, taking advantage of additional information that comes from high-throughput sequencing efforts (both on topology and expressed repertoires). In this respect the already existing IMGT veterinary page (www.imgt.org/IMGT veterinary) could be further exploited as a central platform for submitting sequence data in order to better annotate genes, identify polymorphic variants, and denote pseudogenes. Another essential step would be to design a quality assessment scheme for parallel testing of selected samples in multiple laboratories. Such a scheme would be instrumental to evaluate and improve performance of individual laboratories, especially when accompanied by some kind of feedback on the results through educational lectures and/or reports. Finally, it might be worthwhile investing in additional targets to increase the rate of clonality detection. The fact that many of the species are predominantly IG lambda expressing, would make the somatic hyper mutation-insensitive IGK Kde rearrangements an interesting target to consider. The same holds for incomplete IGH D-J rearrangements. That being said, setting up new assays is not an easy task in the veterinary field, as every species would require its own primer set, whereas the lack of complete genome sequence information might hamper design of primers in particular cases. Even though currently the available sequence information might not be sufficient, it might be worth investing in this. The current developments in next

Mono/Oligoclonal T and NK Cells Are Common in Philadelphia Chromosome Positive (Ph+) Leukemia Patients at Diagnosis and Expand During Successful Tyrosine Kinase Inhibitor Therapy.

Introduction: Durvalumab immunotherapy has rapidly emerged as standard treatment for stage 3 NSCLC patients following definitive chemoradiotherapy (CRT). Multiple novel immunotherapeutic strategies are in development to enhance the chance of cure in this setting as well. There exists a critical need to identify blood-based biomarkers capable of predicting clinical benefit from adjuvant immunotherapy as well selecting patients at high-risk of relapse for further drug development. Cell-free DNA (cfDNA)-based analysis of both minimal residual disease (MRD) and T-cell receptor (TCR) clonality have immense potential to predict and monitor response to adjuvant immunotherapy. In this study, we have combined innovative cfDNA measures of MRD (CAPPseq), TCR clonality (CapTCR-seq) and methylation (cfMeDIPseq) as potential predictive biomarkers of disease progression in stage 3 NSCLC patients treated with CRT and durvalumab. Methods: Stage 3 NSCLC patients undergoing CRT and durvalumab were recruited prospectively to undergo serial blood collections at baseline, pre- and post- durvalumab. CAPPseq and cfMeDIPseq were performed as measures of MRD. TCR repertoire analysis (CapTCR-seq) was performed on cfDNA using hybrid-capture TCR sequencing and TCR diversity/clonality was estimated using the Shannon's index. Correlations between MRD, TCR clonality, response and progression-free survival (PFS) were examined using logistic/cox regression. Results: 79 stage 3 NSCLC patients have been prospectively recruited and undergone serial blood collection. CAPPseq, cfMeDIPseq and capTCR-seq have been completed in 22 patients (5 primary progression on CRT, 17 received durvalumab). Tumor cfDNA was detectable by CAPPseq at baseline in 14 patients. High correlation between tumor cfDNA detected by CAPPseq and cfMeDIPseq was found (R=0.68, p<0.0001). Failure to clear MRD with CRT plus durvalumab was associated with significantly increased risk of recurrence with a median PFS of 5.0 vs 15.0 months (p<0.0001). Lower TCR clonality measured pre-durvalumab trended with lower likelihood of response (OR 0.82, p=0.09) and worse PFS (HR 1.16 P=0.10). Importantly, a decrease in TCR clonality compared to baseline, signaling the lack of clonal expansion on treatment, was significantly associated with a worse PFS (p=0.05). A decrease in TCR clonality of 50% after CRT was associated with a worse PFS (HR 3.5, p=0.14). CAPPseq, cfMeDIPseq and capTCR-seq analyses are ongoing in the full cohort. Conclusions: Failure to clear MRD and decreasing TCR clonality as assessed by cfDNA was highly correlated with increased risk of recurrence and reduced PFS with consolidation durvalumab. This innovative approach has significant potential to define a new biomarker for the use and development of adjuvant immunotherapy. Citation Format: Sally CM Lau, Shirin Soleimani, Jinfeng Zou, Justin Burgener, Shelley Kuang, Stephanie WY Wong, Malcolm Ryan, Ben X. Wang, Stephanie Pedersen, Devalben Patel, Penelope A. Bradbury, Geoffrey Liu, Natasha Leighl, Ming S. Tsao, Pamela S. Ohashi, Scott V. Bratman, Trevor Pugh, Frances A. Shepherd, Adrian G. Sacher. cfDNA-based analysis of minimal residual disease and T-cell receptor clonality as predictors of relapse in stage 3 NSCLC treated with chemoradiotherapy and durvalumab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 563.

A Novel High-Throughput Method for Identifying T-Cell Receptor: Minor Histocompatibility Antigen Interactions in Mouse Models of Graft-Vs-Host Disease

Based on necropsy and histopathology records from the Pathology Department of the Federal Rural University of Rio de Janeiro spanning the years 1990 to 2020, this study analyzed the factors influencing diagnostic conclusions. Cases with unclear or inconclusive results were compiled and categorized according to the pre-analytical (prior to necropsy), analytical (during necropsy), and post-analytical (histopathological analysis and ancillary testing following necropsy) phases. The data were categorized into production animals, companion animals, and wildlife, and subsequently subjected to statistical analysis. Over the study period, a total of 37,057 examinations were conducted, of which 3.9% resulted in inconclusive diagnoses. Among these inconclusive cases, 26.4% were attributed to failures in the pre-analytical phase, 34.8% in the analytical phase, and 38.8% in the post-analytical phase. Pre-analytical failures were identified in 37.17% of companion animal cases, 21.22% of production animal cases, and 18.15% of wildlife cases. In the post-analytical phase, failures were identified in 24.73% of companion animal cases, 59.18% of production animal cases, and 66.02% of wildlife cases. The findings indicate that companion animals had the highest prevalence of inconclusive diagnoses, primarily due to the higher volume of cases in this category at SAP. This was followed by wildlife animals, which frequently arrive at the facility in advanced stages of autolysis for necropsy. The lowest prevalence of inconclusive diagnoses was observed in production animals, owing to the smaller number of such cases and the limited availability of complementary diagnostic tests.

Background Patient safety during the clinical care process is considered a priority in healthcare. Failure Mode and Effects Analysis (FMEA) is a proactive and systematic method of process evaluation and is used to identifying where and how each phase of the analytical process could fail allowing the detection of the causes and the effects they produce on the quality of the results that are issued. A regular use of this tool makes possible to identify the most critical steps, propose improvement strategies and evaluate their impact. The aim of the study is to evaluate applicability of the FMEA tool to identify errors in the pre analytical, analytical, and post analytical phase of a clinical laboratory as well as the main causes and effects, to reduce them by proposing improvement strategies Methods For each phase into which the laboratory activity is divided, the frequency and type of errors that are made and their final consequences on the patient are evaluated. A risk index (NPR=OxDxS) is calculated for each error considering the frequency with which it occurs (O), the ease with which it is detected (D) and the severity of the consequences on the patient (S). Values goes from 1 (rarely) to 10 (often). Improvement actions are proposed for all errors with a risk index greater than 100 and this index is re-evaluated after the implementation of that improvement measures. This same analysis is performed for three times over the last 10 years (in 2014, 2019 and 2023). Results In FMEA analysis carried out in 2014, a total of 40 errors were identified in the pre-analytical phase (63.5% of the total), 12 in the analytical phase and 11 in the post-analytical phase. 50% of the total errors detected required improvement strategies because they had an NPR≥100 value. In the 2023 evaluation, a total of 27 errors were identified in the pre-analytical phase, 10 in the analytical phase, and 5 in the post-analytical phase but only 6 required improvement strategies, which represents an 80% reduction in the most critical errors for patient safety. However, the pre-analytical phase continues to be the most critical, as it continues to account for 64% of the total errors identified, including the lack of identification of the patient or sample and the inadequate prior preparation of the patient. In analytical and post-analytical phases, the NPR values obtained in 2023 decrease significantly over analysis carried out in 2014 and its not needed to apply improvement strategies. Automation and the IT tools significantly reduce the number of errors with high NPR values, but it is undoubtedly the involvement of service professionals that makes the difference over the years. Conclusions The use of the FMEA methodological tool allows the design of appropriate indicators to detect and reduce errors in the clinical laboratory. Its application as part of a good quality control allows a continuous improvement in the laboratory process and consequently a guarantee of quality for the patient

Influenza virus is a significant pathogen in humans and animals with the ability to cause extensive morbidity and mortality. Exuberant immune responses associated with immune cell migration/activation and cytokine/chemokine release, can be induced after infections. Recent studies have painted a complex picture of viral clearance and tissue repair in adults. The immune responses in neonatal influenza infections and the potential methods to engineer antigen-specific T cells for immunotherapy against cancers and infections will be the focus of this dissertation." "Compared to adults, infants suffer higher rates of hospitalization, severe clinical complications, and mortality due to influenza infection. We found that T cells protected neonatal mice against mortality during influenza infection. T cell deficiency did not alter viral clearance or interferon- production. Instead, neonatal influenza infection induced the accumulation of interleukin-17A (IL-17A)-producing T cells, which was associated with IL-33 production by lung epithelial cells. Neonates lacking IL-17A-expressing T cells or Il33 had higher mortality upon influenza infection. T cells and IL-33 promoted lung infiltration of group 2 innate lymphoid cells and regulatory T cells, resulting in increased amphiregulin secretion and tissue repair. In influenza-infected children, IL-17A, IL-33, and amphiregulin expression were positively correlated, and increased IL-17A levels in nasal aspirates were associated with better clinical outcomes. Our results indicate that T cells are required in influenza-infected neonates to initiate protective immunity and mediate lung homeostasis." "In addition, transgenic expression of antigen-specific T cell receptor (TCR) genes is a promising approach for immunotherapy against infectious diseases and cancers. A key to the efficient application of this approach is the rapid and specific isolation and cloning of TCRs. Current methods are often labor-intensive, nonspecific, and/or relatively slow. Here, we describe an efficient system for antigen-specific TCR cloning and CDR3 substitution. We demonstrate the capability of cloning influenza-specific TCRs within 10 days using single-cell polymerase chain reaction (PCR) and Gibson Assembly techniques. This process can be accelerated to 5 days by generating receptor libraries, requiring only the exchange of the antigen-specific CDR3 region into an existing backbone. We describe the construction of this library for human TCRs and report the cloning and expression of a TRGV9/ TRDV2 receptor that is activated by zoledronic acid. The functional activity of these and TCRs can be characterized in a novel reporter cell line (Nur77-GFP Jurkat 76 TCR-null) for screening of TCR specificity and avidity. In summary, we provide a rapid method for the cloning, expression, and functional characterization of human and mouse TCRs that can assist in the development of TCR-mediated therapeutics." "Overall, this dissertation addresses the protective roles of T cells in murine and human neonatal influenza infections, and establishes the rapid system for TCR characterization, cloning and expression, which may shed light on T cell adoptive transfer immunotherapy for viral infections.

Revisiting lymphocyte clonality testing in feline B-cell lymphoma

Introduction: Quality indicators (QIs) are important for the monitoring and evaluation of laboratory performance at pre-analytical, analytical, and post-analytical phases. Errors in the laboratory arise more frequently before and after the analysis of samples. The total testing process (TTP) therefore needs to be evaluated for good laboratory performance. A good quality indicator should give information about the performance of a process, thereby governing the quality of services.Objective: To analyse TTP and QIs for a period of six years of screening of laboratory performance.Methods: This retrospective analysis was conducted for the period 2017 to 2022. QIs at pre-analytical (12), analytical (04) and post-analytical (03) phases were defined and analysed on a monthly basis. The laboratory followed the guidelines of the International Organization for Standardization (ISO) 15189:2012 to identify errors in all sections.Results: A total of 259,694 samples were received during the study period. A total of 628,817 tests were performed. The overall error rate was 1.25/1000 tests. Error in the pre-analytic phase was 0.96/1000 tests, the commonest being haemolysed samples. Error in the analytical phase was 0.33/1000 tests. The error of postanalytical phase was 0.48/1000 tests with turn round time (TAT) outliers being the commonest.Conclusion: Amongst all phases of QIs, errors were minimum in the analytical phase. Continuous monitoring and analysis of QIs helps in identification of common errors in the laboratory. Corrective measures will help to improve quality of the laboratory services, and hence the patient outcome. Regular training, evaluation of technical staff, regular preventive maintenance and calibration of all analytical instruments helps in reducing the errors.

Minimal residual disease (MRD) is the most important independent prognostic factor in acute lymphoblastic leukemia (ALL). Since it has been implemented into in treatment stratification strategies, cure rates have improved significantly for all age groups. Real time quantitative (RQ)-PCR of clonal immunoglobulin and T-cell receptor gene rearrangements using allele-specific primers is currently regarded as the gold standard for MRD analysis in ALL, as it is not only highly sensitive and specific but also provides accurate MRD quantification. Following recent advances in next-generation sequencing (NGS), much attention has been devoted to the development of NGS-based MRD assays. This new technique can enhance sensitivity provided that sufficient numbers of cells are analyzed. Recent reports have shown that NGS-MRD also tends to be more specific for relapse prediction than RQ-PCR. In addition, NGS provides information on the physiological B- and T-cell repertoire during and after treatment, which has been shown to be prognostically relevant. However, before implementation of NGS-MRD detection in clinical practice, several issues must be addressed and the whole workflow needs to be standardized, including not only the analytical phase (spike-in calibrators, quality controls) but also the pre-analytical (e.g. sample preparation) and the post-analytical phases (e.g. bioinformatics pipeline, guidelines for correct data interpretation). These topics are currently addressed by a European network, the EuroClonality-NGS Consortium. In conclusion, NGS is a promising tool for MRD detection with the potential to overcome most of the limitations of RQ-PCR and to become the new gold standard for MRD detection in ALL.

Purpose: To explore the role of the immune system in early-stage HER2+ breast cancer (HER2+ BC), focusing on how T-cell receptor (TCR) dynamics relate to the prognostic 14 B-cell gene/IgG immune signature (IGG) included in the clinically available HER2DX genomic test. This study aims to clarify how TCR diversity and targeting of tumor-associated antigens (TAA) contribute to patient outcomes and could inform potential therapeutic strategies. Experimental design: TCR/BCR clones were identified by PCR amplification and deep sequencing (ImmunoSEQ) in 41 early-stage HER2+ BC samples. CDR3 sequences were cross-referenced with the VDJ database, excluding inconclusive matches (VDJdb score 0-1). Protein expression was analyzed by digital spatial profiling (GeoMx) in 23 samples. In 6 samples, TCR identification was performed through single-cell RNA sequencing (scRNAseq; Chromium). IGG expression in each sample was evaluated using the HER2DX assay and correlated with bulk RNA data from TCGA and MTBC datasets. Spearman’s correlation and Wilcoxon tests were used for statistical analysis (R software). Results: Of the 12,575 TCR clones with predicted targets, 759 (5.9%) showed reliable matches (score 2-3). 53 of them (7%) recognized TAA, primarily MART1 (18.9%), followed by gp100, ABCD3, MAGEA6, KRAS, NY-ESO1, p53, TERT, and others. Most non-human epitopes belonged to common viruses such as Influenza A (31.2%), EBV (30.7%), and CMV (20%). IGG expression was correlated with the number of TCR templates (Cor: 0.47, p<0.01), TCR entropy (Cor: 0.60, p<0.001), and shared TCR clonotypes between samples (Cor: 0.48, p<0.01). IGG was higher in samples with TCR clones against TAA (p50 75.6 vs. 61.1, p=0.044). A positive correlation was observed between the number of clones targeting human and viral epitopes (Cor: 0.44, p=0.013). The scRNAseq data confirmed that IGG-high samples exhibit greater TCR polyclonality and a higher fraction of cytotoxic CD8+ T cells (p<0.05), with upregulated perforin and granzyme A/B expression. IGG correlated with CD27 (Cor: 0.47, p=0.025) and CD3 (Cor: 0.52, p=0.011) protein levels, as well as the IFN-γ signature in TCGA (Cor: 0.56, p<0.01) and MTBC (Cor: 0.71, p<0.01) data. Fibronectin correlated with PD1 (Cor: 0.61, p<0.01) and CTLA4 (Cor: 0.81, p<0.001) levels, and inversely with IGG (Cor: -0.50, p=0.017), indicating that IGG-low tumors might have a denser stroma and a more exhausted, less active immune infiltrate. Conclusions: Early-stage HER2+ BC with high IGG expression is characterized by a robust, polyclonal immune response, with TCR clones targeting both tumor and viral antigens. These findings suggest enhanced immune fitness and may explain IGG favorable prognostic value. The discovery of shared TCR clones across tumors may help identify immunogenic targets, providing new opportunities for developing immune-based treatments or engineered T-cell therapies. Citation Format: Víctor Albarrán-Fernández, Carlota Rubio-Pérez, Patricia Galván, Oleguer Castillo, Paula Blasco, Esther Sanfeliu, Anabel Martínez-Romero, Mercedes Marín, Patricia Villagrasa, Francisco Pardo, Laia Paré, Isabel García-Fructuoso, Raquel Gómez, Elia Seguí, Bárbara Adamo, Benjamin Walbaum, Olga Martínez-Saez, Tomás Pascual, María Vidal, Montserrat Muñoz, Sònia Guedan, Fara Brasó, Laura Angelats, Aleix Prat. Uncovering T-cell receptor clones and immunogenic targets in HER2DX-defined HER2-positive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5867.

TCR sequencing and cloning methods for repertoire analysis and isolation of tumor-reactive TCRs

BackgroundT-cell activation and clonal expansion are essential to effective immunotherapy responses in non-small cell lung cancer (NSCLC). The distribution of T-cell clones may offer insights into immunogenic mechanisms and imply potential prognostic and predictive information.MethodsWe analyzed α/β T-cell receptor (TCR) clonality using RNA-sequencing of bulk frozen tumor tissue from 182 patients with NSCLC. The data was integrated with molecular and clinical characteristics, extensive in situ imaging, and spatial sequencing of the tumor immune microenvironment. TCR clonality was also determined in an independent cohort of nine patients with immune checkpoint-treated NSCLC.ResultsTCR clonality (Gini index) patterns ranged from high T-cell clone diversity with high evenness (low Gini index) to clonal dominance with low evenness (high Gini index). Generally, TCR clonality in cancer was lower than in matched normal lung parenchyma distant from the tumor (p=0.021). The TCR clonality distribution between adenocarcinoma and squamous cell carcinoma was similar; however, smokers showed a higher Gini index. While in the operated patient with NSCLC cohort, TCR clonality was not prognostic, in an immune checkpoint inhibitor-treated cohort, high TCR clonality was associated with better therapy response (p=0.016) and prolonged survival (p=0.003, median survival 13.8 vs 2.9 months). On the genomic level, a higher Gini index correlated strongly with a lower frequency of epidermal growth factor receptor (EGFR) and adenomatous polypsis coli (APC) gene mutations, but a higher frequency of P53 mutations, and a higher tumor mutation burden. In-depth characterization of the tumor tissue revealed that high TCR clonality was associated with an activated, inflamed tumor phenotype (PRF1, GZMA, GZMB, INFG) with exhaustion signatures (LAG3, TIGIT, IDO1, PD-1, PD-L1). Correspondingly, PD-1+, CD3+, CD8A+, CD163+, and CD138+immune cells infiltrated cancer tissue with high TCR clonality. In situ sequencing recovered single dominant T-cell clones within the patient tumor tissue, which were predominantly of the CD8 subtype and localized closer to tumor cells.ConclusionOur robust analysis pipeline characterized diverse TCR repertoires linked to distinct genotypes and immunologic tumor phenotypes. The spatial clustering of expanded T-cell clones and their association with immunological activation underscores a functional, clinically relevant immune response, particularly in patients with NSCLC treated with checkpoint inhibitors.

Primary lung mucosa-associated lymphoid tissue lymphoma accompanied by multiple sclerosis: case report and molecular diagnosis.

ALL-312 Ig/T-Cell Receptor Clonality Testing as a Method for Minimal Residual Disease Monitoring in T-Cell Acute Lymphoblastic Leukemia Patients