Sequential quantification of T-cell receptor excision circles (TRECs) and K-deleting recombination excision circles (KRECs) and overall survival after allogeneic HSCT

Sequential quantification of T-cell receptor excision circles (TRECs) and K-deleting recombination excision circles (KRECs) and overall survival after allogeneic HSCT | 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 Article Sequential quantification of T-cell receptor excision circles (TRECs) and K-deleting recombination excision circles (KRECs) and overall survival after allogeneic HSCT Rafael Duarte, Carlos de Miguel, Rosalía Alonso, Guiomar Bautista, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5284927/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Nov, 2025 Read the published version in Bone Marrow Transplantation → Version 1 posted 11 You are reading this latest preprint version Abstract The use of TRECs/KRECs in allogeneic HSCT (alloHSCT) has been limited by a lack of standard technical platforms to allow comparison and validation of results between centres. We quantified absolute TRECs/KRECs on sequential samples collected prospectively (pretransplant, 1, 3, 6 and 12-months posttransplant) in 374 alloHSCT for haematological malignancies using LightCycler 480/TREC-KREC-ACTB (Roche Diagnostics). Following prompt decrease after transplant, KRECs recover as soon as 3 months posttransplant, while TRECs recovery takes up to 1 year (p < 0.001). KRECs do not associate with outcomes. However, higher pretransplant TRECs strongly associate with reduced NRM and increased OS, and remain independent in multivariate analysis (HR 0.37, p = 0.001, and HR 0.51, p < 0.001, respectively). In addition, faster TRECs recovery measured sequentially at 1, 3, 6 and 12 months after alloHSCT associates with better OS. Furthermore, landmark analyses showed that early survivors with higher TRECs levels at 6 and 12 months after alloHSCT had significantly better subsequent long-term survival, independent from GVHD and other clinical factors in multivariate analysis (HR 0.33, p = 0.016 and HR 0.13, p < 0.001, respectively). TRECs levels pretransplant and at 6 and 12 months posttransplant provide novel biomarker measurable data that associate with alloHSCT long-term outcomes. Multicentre, prospective validation of these results is warranted. Health sciences/Medical research/Translational research Biological sciences/Immunology/Transplant immunology/Allotransplantation Figures Figure 1 Figure 2 Figure 3 Introduction Allogeneic hematopoietic stem cell transplantation (alloHSCT) remains a standard of care for many patients with haematological malignancies [ 1 , 2 ]. Long-term survival after alloHSCT has improved over time, primarily through a better understanding of patient and transplant factors associated with outcomes, and through an improved management of transplant complications [ 3 , 4 ]. Beyond clinical factors and improved management, in the current new era of personalized medicine, there is an increasing interest and need for biomarkers which may have an independent association with alloHSCT recipients’ outcomes and may predict their long-term survival [ 5 , 6 ]. T cell receptor excision circles (TRECs) and kappa-deleting recombination excision circles (KRECs), stable fragments of circularized DNA that do not replicate during mitosis and get diluted in each cell division, measure T and B lymphocyte neogenesis in primary lymphoid organs [ 7 – 11 ]. Primary lymphoid reconstitution is essential for a successful immune recovery after alloHSCT [ 12 – 14 ]. Thus, over the past two decades, many studies have explored the measurement of TRECs and KRECs in alloHSCT [ 15 – 32 ]. Unfortunately, the majority of these studies were carried out in relatively small numbers of patients, with assorted samples collected at different time-points, and quantified with diverse methodologies in the absence of standardized TRECs/KRECs technical platforms. All this limited the comparability among studies, led to inconsistent results, and prevented further development of the role of TRECs/KRECs in alloHSCT. Our objective here is to measure TRECs/KRECs in a relatively large population of alloHSCT recipients using a standardized commercial platform in prospectively collected sequential samples, and to ascertain their value as biomarkers to predict long-term outcomes after alloHSCT. Materials, Subjects and Methods Study Design, population and samples This retrospective study includes all consecutive alloHSCT procedures in patients with haematological malignancies at Hospital Universitario Puerta de Hierro Majadahonda between 1999 and 2017. All patients signed informed consent forms for treatment, samples and data collection. The study was approved by our IRB (PI 66/19). DNA samples from peripheral blood mononuclear cells were collected prospectively and sequentially pre-transplant and at 1, 3, 6 and 12 months (m) after transplant. Samples were preserved under optimal conditions, and the concentration and quality of DNA were confirmed in all samples prior to inclusion and testing in this study. TRECs and KRECs measurement Absolute quantification of TRECs and KRECs was carried out using Roche Diagnostics LightCycler® 480 and TREC-KREC-ACTB Protocol (Version 1.0, 25/09/2018; Roche Diagnostics, Barcelona, ES). Briefly, we used 96-well (semi-skirted) plates containing lyophilized primers and probes for simultaneous qPCR detection of TREC-KREC-ACTB. The primers designed for TRECs use a specific LNA hydrolysis probe marked with FAM (530nm) to amplify and to detect a 100bp fragment in a specific locus of the excision circles of T lymphocytes (VDJ genes). For the detection of KRECs, a 105bp fragment is amplified with a LNA hydrolysis probe labelled with LC610 (610nm) within the excision circle locus of the Kappa elements of B lymphocytes (VDJ genes). Finally, the quality control of genomic DNA and amplification detects an 83bp fragment within a specific locus of the ACTB (β-Actin) gene, with a set of primers and LNA hydrolysis probe labelled with the fluorophore LC670 (660nm, equivalent to Cy5). Results are expressed in TRECs/KRECs units per reaction (U/R; reaction = 5 µL DNA). Endpoints and variables The primary endpoint was alloHSCT recipient overall survival (OS) with a follow-up horizon of five years after transplantation. Non-relapse mortality (NRM), with relapse as a competing event, was also included as a secondary endpoint. Landmark analyses were performed at 6 and 12 months after transplant to explore the impact of TRECs/KRECs levels measured at those time-points in early transplant survivors on their long-term OS. The study CRF included all data referring to patient and transplant characteristics, complications, outcomes, and TRECs/KRECs levels at different timepoints. The original dataset was pre-processed to work with some variables and to identify possible outliers. Eighteen TREC and/or KREC outlier values over the threshold of 10^5 were removed. Statistical analyses Statistical analyses and definitions used in our study are consistent with the EBMT Statistical Guidelines [ 33 ]. This study has only right-censored data, given that all time-to-event variables are available, and we have no truncation effects. Primary and secondary outcomes are considered time-dependent categorical variables, with binary (No = 0, Yes = 1) values and related time-to-event variables expressed in days after transplantation. Other categorical variables were treated as factors, and checked, in a preliminary exploratory analysis, with Chi-squared or Fisher’s exact test (provided by the compareGroups R package) against the outcomes. For the survival analyses, we created Kaplan-Meier curves to check and plot OS along time, using the Mantel-Cox log-rank test (performed with the survival R package) when needed to assess differences between groups. For the secondary outcome NRM we used the Fine-Gray approach for competitive risk analysis [ 34 ], applying the cmprsk R package [ 35 ]. We also used Cox proportional hazard models, with hazard ratios (HR) for all categorical variables of interest, and subdistributed hazard ratios (SHR) using competing risks models. Based on the exploratory analyses and the HR and SHR results, we built several Cox regression models using the log10TRECs and log10KRECs values pre-transplant and at 1, 3, 6 and 12 months post-transplant, and adjusting for significant categorical variables from univariable analyses. From these models, we computed log10TRECs and log10KRECs cut point thresholds to split cases in two categories, high or low TRECs/KRECs values. These cut point thresholds were computed with the cutpointr R package, applying options to maximize classification accuracy. This package provides robust methods to identify optimal cut points and allows the establishment of the direction of the dependency between class and outcome variables. Visual inspection was done to evaluate the cut points using the plot_metric function in addition to statistical results. All analyses were done with The R Project for Statistical Computing ( https://www.r-project.org/ ; version 4.0.5), including the following R packages: compareGroups (v 4.5.1); survival (v 3.2.13); cmprsk (v 2.2–11); cutpointr (v 1.1.1); survminer (v 0.4.9); survMisc (v 0.5.5); ezfun (v 0.1.3); splitstackshape (v 1.4.8) and ggplot2 (v 3.3.5). For the transformation between days and months, in time-to-event variables, a ratio of 30.5 days per month has been used. In patients with more than one alloHSCT, data on prior transplants were censored at the day of second or third alloHSCT infusion. P-values were considered statistically significant if < 0.05. We assumed non-informative censoring, as well as sufficient follow-up time (60 months) and events to ensure adequate statistical power. Other assumptions were assessed for each kind of test, and none was significantly violated. Results Patient and transplant characteristics The study comprises 374 alloHSCT procedures in 350 recipients with haematological malignancies, median age at transplant of 45 years (range 16–68), 59.6% male recipients, including 21 second and three third transplants. The main indication for alloHSCT was acute leukaemia/myelodysplastic syndromes (AL/MDS, 69.8%) and the most frequent donor type were matched siblings (52.9%), with also nearly a third of cord blood transplants (30.5%). Other characteristics are presented in Table 1 . Table 1 Patient and Transplant Characteristics Characteristics (374 alloHSCT in 350 patients) Recipient Age 45 (16–68) Recipient Sex (male) 223 (59.6%) Disease - AL/MDS - CLPD - Myeloma 261 (69.8%) 83 (22.2%) 30 (8%) Donor type - Matched related - Cord blood - Matched unrelated - Haploidentical 198 (52.9%) 114 (30.5%) 49 (13.1%) 13 (3.5%) Conditioning (MAC) 191 (51%) AlloHSCT Number - First - Second - Third 350 (93.6%) 21 (5.6%) 3 (0.8%) AlloHSCT: Allogeneic haematopoietic stem cell transplantation; AL/MDS: Acute Leukaemia / Myelodysplastic Syndrome; CLPD: Chronic Lymphoproliferative Disorder; MAC: myeloablative conditioning; Age is presented as median (range) and all other variables are presented as number of cases (percentage). TRECs and KRECs levels and recovery after transplant We analysed the kinetics of recovery of TRECs and KRECs during the first year after alloHSCT (Fig. 1 ). TRECs levels dropped down from pre-transplant levels in the first three months after transplantation, started recovering thereafter and peaked at one year after transplantation: median U/R and interquartile ranges (IQR) were 20 (5.42–120) pre-transplant, 5.28 (0.04–46.9) at 1m, 4.72 (0.01-39) at 3m, 13 (1.61–137) at 6m, and 38.8 (5.06–402) at 12m (p < 0.001). KRECs levels recovered much faster, reaching high levels above baseline values as soon as 3 months after transplant and remaining stable thereafter: 5.55 (0.01–266) pre-transplant, 9.74 (0.4–106) at 1m, 553.5 (28.85–5690) at 3m, 810 (91.5–4930) at 6m, and 704.5 (51.6–2820) at 12m (p < 0.001). KRECs levels and transplant characteristics and outcome Pre-transplant KRECs levels were associated with age (354 U/R 60 years; p < 0.001) and with the underlying malignancy, being higher in patients with AL/MDS compared with those with multiple myeloma or chronic lymphoproliferative disorders (183 vs 106 vs 44.2 U/R respectively; p = 0.002) (Table 2 ). They did not associate with sex, donor type, intensity of conditioning regimen or other patient or transplant characteristics. Moreover, we did not find any association between KRECs levels, neither pre-transplant nor at any time-point post-transplant, and alloHSCT outcomes. Table 2 Pre-transplant TRECs and KRECs levels and association with transplant characteristics TRECs KRECs U/R, median (IQR) p U/R, median (IQR) p Age (years) - 60 26.4 (2.04–234) 18 (1.92–115) 9.12 (0.62–50.7) 5.06 (0.35–24.2) < 0.001 354 (12.1–3740) 126 (2.95–1258) 58.8 (1.28–1690) 27.1 (0.0-867) < 0.001 Underlying disease - AML/MDS - CLPD - Myeloma 8.89 (0.59–83.1) 21.5 (3.66–174) 19.9 (0.66–75.6) 0.001 183 (4-2060) 44.2 (0.0-873) 106 (6.8–913) 0.002 Donor type - Matched related - Others 17.9 (2.22–142) 7.52 (0.24–66.2) < 0.001 97.4 (3.93–1368) 163 (1.95–2190) 0.353 AL/MDS: Acute Leukaemia / Myelodysplastic Syndrome; CLPD: Chronic Lymphoproliferative Disorder; IQR: interquartile range; n.s.: non-significant; U/R: Units/Reaction. Pre-transplant TRECs levels & patient and transplant characteristics Pre-transplant TRECs levels were higher in younger patients compared with the other age groups and were lowest in the oldest patient group (26.4 U/R 60 years; p < 0.001; Table 2 ). We found that pre-transplant TRECs levels were lower in patients with AL/MDS, compared with those with multiple myeloma or chronic lymphoproliferative disease (8.89 vs 19.9 vs 21.5 U/R respectively, p = 0.001). Also, pre-transplant TRECs appeared to be higher in patients who were to receive matched related transplant compared to other donor types (17.9 vs 7.52 U/R, p < 0.001), but no associations were found between TRECs levels and other characteristics such as sex or type of conditioning. Pre-transplant TRECs levels and transplant outcome Patients with higher pre-transplant TREC levels had a significantly higher OS in the univariate analysis (72.7% vs 38.2% at 2 years and 64.7% vs 35.3% at 5 years, p = 0.005; Table 3 and Fig. 2 - Panel A), in keeping with a lower cumulative incidence of NRM (21.2% vs 44.1%, p = 0.044). Table 3 TRECs levels pre-transplant and at sequential time-points after transplant and association with overall survival Overall Survival TRECs levels At 2 years At 5 years P - Pre-transplant 72.7% vs 38.2% 64.7% vs 35.3% 0.0054 - At 1 month 65.9% vs 44.0% 51.6% vs 30.7% 0.0021 - At 3 months 81.1% vs 48.2% 66.2% vs 41.4% 0.0021 - At 6 months 92.6% vs 52.5% 84.8% vs 42.8% < 0.001 - At 12 months 96.4% vs 58.3% 88.8% vs 52.5% < 0.001 This association between higher pre-transplant TREC levels and higher OS was also confirmed when analyzed separately for transplants from matched related donors (81.7% vs 55.2% at 2 years and 69.2% vs 49.9% at 5 years, p = 0.015; Fig. 2 - Panel B) and from other donor types, including cord blood, haploidentical and unrelated donors (63.6% vs 37.9% at 2 years and 55.4% vs 32.8% at 5 years, p = 0.008; Fig. 2 - Panel C). In multivariate analysis, after adjusting for patient age, underlying disease, type of alloHSCT donor, conditioning regimen and graft-versus-host disease (GVHD), higher pre-transplant TRECs levels remained independently associated with increased OS (HR 0.51 [95% CI 0.35–0.73], p < 0.001) and lower NRM (HR 0.37 [95% CI 0.21–0.66], p = 0.001). Patient age (HR 0.54 [95% CI 0.36–0.79], p = 0.002) and type of donor (HR 0.52 [95% CI 0.31–0.85], p = 0.01) were also independently associated with OS. Recovery of TRECs levels after transplant, patient outcomes and landmark analyses in early survivors Beyond the impact of TRECs levels prior to transplant on OS that we have just described, the prospective collection of sequential samples in our study allows for a more detailed analysis of the impact of the recovery of TRECs after transplant on long-term outcomes. As measured by their levels at 1, 3, 6 and 12 months after alloHSCT, faster TRECs recovery, at every time-point, associates with better OS (Table 3 ). Thus, we decided to carry out landmark analyses at 6 and 12 months to identify the impact of TRECs levels on the long-term outcomes of early survivors at those time-points. Landmark analyses showed that higher TRECs levels in survivors at 6 and 12 months after alloHSCT associated with significantly better long-term survival of these patients. At 6 months, TRECs values above the threshold cut point (thrTRECs ≥ 0.1) associated with better OS of 82.4% vs 56.7%, 73.9% vs 43.7%, and 72.4% vs 38.9%, one, three and five years later, respectively ( p < 0.001). At 12 months, TRECs values above the threshold cut point (thrTRECs ≥ 0.4) also associated with better OS of 93% vs 59.7%, 85.6% vs 47.6%, and 83.4% vs 47.6%, one, three and five years later, respectively (p < 0.001; Fig. 3 - Panel A). Furthermore, this association of TRECs levels and long-term survival remains independent in multivariate analysis both at 6 months (HR 0.33 [95% CI 0.13–0.81], p = 0.016) and 12 months (HR 0.13 [95% CI 0.04–0.42], p < 0.001) after adjusting for other clinical factors including GVHD (Fig. 3 - Panels B and C). Discussion This is one of the first studies, the largest in this field, to sequentially measure TRECs and KRECs in alloHSCT recipients. The study shows that sequential quantification of TRECs in alloHSCT recipients using a standardized commercial platform provides novel biomarker measurable data that associate with patient and transplant characteristics and outcomes. Pre-transplant TRECs levels have an independent association with NRM and OS that may complement and inform decision-making in transplant candidates, currently based only on clinical factors. Furthermore, TRECs levels measured at six and 12 months may provide a novel objective tool to predict long-term outcome of early survivors after alloHSCT. Few studies have explored the role of KRECs in alloHSCT [ 28 , 32 ]. Beyond the description of KRECs recovery early after transplant, similar to our own findings [ 28 ], these studies have failed to show any association of KRECs levels and key transplant outcomes [ 32 ]. KRECs do not appear to be a suitable candidate biomarker to predict alloHSCT survival and patient outcomes. Many prior studies explored the role of TRECs in alloHSCT, primarily focusing on their association with patient and transplant characteristics, with differences among studies and often inconclusive results [ 20 – 31 ]. Our data, in line with most prior studies, show an association between pretransplant TRECs levels and patient age, but other than this, we did not find associations with sex or other relevant factors, and associations found with factors such as the underlying malignancy or the type of transplant are most likely surrogates of other features in these subgroups of patients. Some of these studies did also explore the role of TRECs on hard endpoints and patient outcomes. Clave et al and Sairafi et al identified associations of higher TRECs levels pretransplant and at three months posttransplant, respectively, with improved NRM and OS [ 26 , 27 ]. Others showed associations between TRECs levels pretransplant and posttransplant and the risk of disease relapse for some subgroups of malignancies, but without an overall impact on OS and long-term outcomes [ 29 , 31 ]. As described above, our data show a strong association of TRECs levels, pretransplant and at every time-point of their recovery posttransplant, with long-term patient outcomes. This impact, both for NRM and OS, remains independent from other key factors such as age, sex, type of disease, conditioning, type of transplant or GVHD in multivariate analysis. Beyond differences in patient and transplant characteristics among all these TRECs studies, major limitations thus far are that most of them were carried out in relatively small numbers of patients, with assorted samples at different time-points rather than collected prospectively and sequentially, and with diverse methodologies in the absence of a standardized TRECs/KRECs technical platform. Measurement methods have included semiquantitative PCR [ 7 , 16 ], hybridization probe [ 9 , 23 ], and TaqMan probe [ 11 , 24 , 27 , 28 ]. Cell populations used for DNA extraction for TRECs quantification have also varied among studies, including CD3 [ref. 23,27], CD4/CD8 [ref.7,25], or peripheral blood mononuclear cells [ 16 , 24 ]. So have the units in which measurements have been reported, TRECs/µg of DNA [ 16 , 23 ], TRECs/µL of blood [ 20 ], and TRECs/cell count [ 24 ], further complicating the comparability among studies and the reproducibility of their results. Our use of a broadly available standardised commercial platform such as LightCycler 480/TREC-KREC-ACTB will likely improve previous inconsistencies and will facilitate further development and validation of the role of TRECs as biomarkers to predict long-term outcomes in alloHSCT recipients. Apart from TRECs levels pretransplant, our battery of samples collected prospectively at 1, 3, 6 and 12 months confirms that TRECs measured sequentially after transplant strongly associate with long-term OS. Based on such strong signal of TRECs recovery and patient survival, we carried out landmark analyses of the impact of TRECs levels in early survivors on their long-term outcomes. Only one recent study, by Söderström et al [ 32 ], has reported landmark analyses of TRECs in alloHSCT, showing in a group of 90 alloHSCT recipients for AML an association of OS with TRECs levels measured at 12 months, but not at six months. As presented above, our data strongly shows that early transplant survivors at 6 and 12 months with higher TRECs levels at those time-points had significantly better subsequent long-term OS. In addition, this effect in our series is independent from GVHD and from other clinical factors in the multivariate analyses. This finding is particularly remarkable, as the field of alloHSCT currently lacks any such a tool to estimate survival after the first year posttransplant, and identifies TRECs as a potential biomarker to predict long-term OS in early alloHSCT survivors. Our study has some limitations. Despite the impact of age and intensive chemotherapy on thymic involution [ 18 , 19 ], studies have shown that thymic function is not completely eliminated, and under immunosuppressive stress conditions such as HIV infection and alloHSCT, the remaining thymic tissue can contribute to immune recovery in these patients [ 7 , 16 ]. While these findings support the role of TRECs and their relationship with immune recovery and outcomes after alloHSCT, our study was not designed to assess immune recovery parameters that may have helped drawing a connection between our findings with TRECs values and transplant outcomes. In addition, cut point thresholds of TRECs/KRECs levels used in this study come from the available data in this series, and may not be optimal to be applied in other external data sets. Thus, for the purpose of the work presented here, the log10TRECs and log10KRECs cut points are only intended to improve stratification between high and low TRECs or KRECs values for the survival curves and HR estimations in this series. In summary, our study shows that while KRECs do not seem to be a good candidate biomarker in alloHSCT, TRECs have strong impact on patient outcomes, independent from other clinical factors. Pretransplant, they could potentially improve patient assessment beyond current clinical factors, and posttransplant, at 6 and 12 months, they could provide novel biomarker data to predict long-term survival in early transplant survivors. The use of a standardized commercial platform will improve prior inconsistencies in this field. External, multicentre, prospective validation of these results using the standardized LightCycler 480 / TREC-KREC-ACTB platform is warranted. Declarations Funding: Funded by the Centro para el Desarrollo Tecnológico y la Innovación (CDTI) of the Spanish Ministry of Science, Innovation and Universities (Project # IDI-20180259). Acknowledgements: The authors would like to acknowledge other staff members of the HSCT Program and the Cytogenetics and Molecular Biology Laboratory at our Department for their support in the conduction of this study, Dr. Carlos Vilches (Spanish National Transplant Organization) for his expert supervision of immunological aspects of this work, and the Spanish Centro para el Desarrollo Tecnológico y la Innovación (CDTI) for funding this project. Conflict of Interest Statement: FB, CMP and ASP are employees of Roche Diagnostics (Barcelona, Spain), which LightCycler® 480 / TREC-KREC-ACTB Platform was used for the measurement of TRECs/KRECs in this study. CdM work was partly funded by CDTI’s Project # IDI-20180259. 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Clave E, Rocha V, Talvensaari K, et al. Prognostic value of pretransplantation host thymic function in HLA-identical sibling hematopoietic stem cell transplantation. Blood. 2005;105(6):2608–13. doi: 10.1182/blood-2004-04-1667 . Epub 2004 Nov 16. PMID: 15546951. Jiménez M, Martínez C, Ercilla G, et al. Reduced-intensity conditioning regimen preserves thymic function in the early period after hematopoietic stem cell transplantation. Exp Hematol. 2005;33(10):1240-8. doi: 10.1016/j.exphem.2005.06.016 . PMID: 16219547. Jiménez M, Martínez C, Ercilla G, Carreras E, Urbano-Ispízua A, Aymerich M, Villamor N, Amézaga N, Rovira M, Fernández-Avilés F, Montserrat E. Clinical factors influencing T-cell receptor excision circle (TRECs) counts following allogeneic stem cell transplantation in adults. Transpl Immunol. 2006;16(1):52–9. doi: 10.1016/j.trim.2006.02.006 . Epub 2006 Mar 31. Przybylski G.K, Kreuzer K.A, Siegert W, et al. No recovery of T-cell receptor excision circles (TRECs) after non-myeloablative allogeneic hematopoietic stem cell transplantation is correlated with the onset of GvHD. J. Appl. Genet. 2007;48:397–404. doi: 10.1007/BF03195239 . Sugita J, Iwao N, Tanaka J, et al. T cell receptor excision circle levels in CD94-expressing CD8 T Cells during graft-versus-host disease. Leuk Lymphoma. 2008;49(7):1306–10. doi: 10.1080/10428190802146086 . Clave E, Busson M, Douay C, et al. Acute graft-versus-host disease transiently impairs thymic output in young patients after allogeneic hematopoietic stem cell transplantation. Blood. 2009;113(25):6477–84. doi: 10.1182/blood-2008-09-176594 . Epub 2009 Mar 3. Sairafi D, Mattsson J, Uhlin M, et al. Thymic function after allogeneic stem cell transplantation is dependent on graft source and predictive of long-term survival. Clin Immunol. 2012;142(3):343–50. doi: 10.1016/j.clim.2011.12.001. Epub 2011 Dec 16. Mensen A, Ochs C, Stroux A, et al. Utilization of TREC and KREC quantification for the monitoring of early T- and B-cell neogenesis in adult patients after allogeneic hematopoietic stem cell transplantation. J Transl Med. 2013;11:188. doi: 10.1186/1479-5876-11-188 . Uzunel M, Sairafi D, Remberger M, et al. T-cell receptor excision circle levels after allogeneic stem cell transplantation are predictive of relapse in patients with acute myeloid leukemia and myelodysplastic syndrome. Stem Cells Dev. 2014;23(14):1559–67. doi: 10.1089/scd.2013.0588 . Epub 2014 Apr 16. Gaballa A, Sundin M, Stikvoort A, et al. T Cell Receptor Excision Circle (TREC) Monitoring after Allogeneic Stem Cell Transplantation; a Predictive Marker for Complications and Clinical Outcome. Int J Mol Sci. 2016;17(10):1705. doi: 10.3390/ijms17101705 . Mikhael NL, Elsorady M. Clinical significance of T cell receptor excision circle (TREC) quantitation after allogenic HSCT. Blood Res. 2019;54(4):274–281. doi: 10.5045/br.2019.54.4.274 . Epub 2019 Dec 20. Söderström A, Vonlanthen S, Jönsson-Videsäter K, et al. T cell receptor excision circles are potential predictors of survival in adult allogeneic hematopoietic stem cell transplantation recipients with acute myeloid leukemia. Front Immunol. 2022;13:954716. Iacobelli S; EBMT Statistical Committee. Suggestions on the use of statistical methodologies in studies of the European Group for Blood and Marrow Transplantation. Bone Marrow Transplant. 2013;48 Suppl 1:S1-37. doi: 10.1038/bmt.2012.282 . Fine JP and Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J. Am Stat Assoc. 1999;94:496–509. doi: 10.1080/01621459.1999.10474144 . Austin P, and Fine J. Practical recommendations for reporting Fine-Gray model analyses for competing risk data. Statistics in Medicine. 2017;36(27):4391–4400. doi: 10.1002/sim.7501 . Epub 2017 Sep 15. Additional Declarations The authors have declared there is NO conflict of interest to disclose. Cite Share Download PDF Status: Published Journal Publication published 29 Nov, 2025 Read the published version in Bone Marrow Transplantation → Version 1 posted Editorial decision: revise 15 Jan, 2025 Review # 2 received at journal 24 Dec, 2024 Reviewer # 3 agreed at journal 13 Dec, 2024 Reviewer # 2 agreed at journal 13 Dec, 2024 Review # 1 received at journal 11 Nov, 2024 Reviewer # 1 agreed at journal 31 Oct, 2024 Reviewers invited by journal 23 Oct, 2024 Submission checks completed at journal 21 Oct, 2024 First submitted to journal 18 Oct, 2024 Unknown event 18 Oct, 2024 Editor assigned by journal 17 Oct, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5284927","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":369429714,"identity":"0645a79b-8dce-4da5-ab78-eb316a252d63","order_by":0,"name":"Rafael Duarte","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYFCCBDiLmeEDkGRjJ0oLRBsz4wyQFmZStDDzQCzDD8zbcww/V/6wYTA43vvY2ObXNnk+ZgbGDx9zcGuROfPGWPJMQhqDwZnjxsm5fbcN25gZmCVnbsOtRUIix0CyIeEwg9mNNObDuT23GYFa2Jh58Wsx/tmQ8B+ixbLntj0xWsyAthwAa0lm+HE7kbAWnmdllg1pyTz2Z44xG/Y23E5uY2Zsxu8X9uTNNxts7OQk29uYJX78uW07v7354IePeLTAADhGGBjbwGQDYfUI8IcUxaNgFIyCUTBSAAB8RknmsrX3EAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5240-9815","institution":"Hospital Universitario Puerta de Hierro and Instituto Investigación Sanitaria Puerta de Hierro-Segovia Arana","correspondingAuthor":true,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Duarte","suffix":""},{"id":369429715,"identity":"c57be391-45d1-4513-9dac-b47f8307e031","order_by":1,"name":"Carlos de Miguel","email":"","orcid":"","institution":"Hospital Universitario Puerta de Hierro Majadahonda","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"de Miguel","suffix":""},{"id":369429716,"identity":"33a17daa-dd33-409f-a58d-ee7e13290c5b","order_by":2,"name":"Rosalía Alonso","email":"","orcid":"","institution":"Instituto Investigación Sanitaria Puerta de Hierro-Segovia Arana","correspondingAuthor":false,"prefix":"","firstName":"Rosalía","middleName":"","lastName":"Alonso","suffix":""},{"id":369429717,"identity":"f69f0ec6-36c2-47c9-81a3-1dfc2a3c8e2f","order_by":3,"name":"Guiomar Bautista","email":"","orcid":"","institution":"Hospital Universitario Puerta de Hierro and Instituto Investigación Sanitaria Puerta de Hierro-Segovia Arana","correspondingAuthor":false,"prefix":"","firstName":"Guiomar","middleName":"","lastName":"Bautista","suffix":""},{"id":369429718,"identity":"09f2635a-bc4c-46c0-b0ef-2bbf9c40a224","order_by":4,"name":"Luis Espinosa-Hevia","email":"","orcid":"","institution":"Hospital Universitario Puerta de Hierro and Instituto Investigación Sanitaria Puerta de Hierro-Segovia Arana","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Espinosa-Hevia","suffix":""},{"id":369429719,"identity":"79218730-ed2a-40a9-a5f2-d3ea73a8cfee","order_by":5,"name":"María E. Martínez-Muñoz","email":"","orcid":"https://orcid.org/0000-0002-6829-9710","institution":"Hospital Universitario Puerta de Hierro and Instituto de Investigación Sanitaria Puerta de Hierro - Segovia Arana","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"E.","lastName":"Martínez-Muñoz","suffix":""},{"id":369429720,"identity":"d17e01fe-395c-422a-932b-d212e8630240","order_by":6,"name":"Lucía Núñez","email":"","orcid":"","institution":"Hospital Universitario Puerta de Hierro and Instituto Investigación Sanitaria Puerta de Hierro-Segovia Arana","correspondingAuthor":false,"prefix":"","firstName":"Lucía","middleName":"","lastName":"Núñez","suffix":""},{"id":369429721,"identity":"f9db7fa7-eadb-41b9-a092-19aecf4b8263","order_by":7,"name":"Isabel Salcedo","email":"","orcid":"https://orcid.org/0009-0007-1976-320X","institution":"Hospital Universitario Puerta de Hierro and Instituto Investigación Sanitaria Puerta de Hierro-Segovia Arana","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Salcedo","suffix":""},{"id":369429722,"identity":"2b0a2034-25c7-4391-af3f-ac27c59ac56a","order_by":8,"name":"Ferran Briansò","email":"","orcid":"","institution":"Roche Diagnostics","correspondingAuthor":false,"prefix":"","firstName":"Ferran","middleName":"","lastName":"Briansò","suffix":""},{"id":369429723,"identity":"ad2d3307-e23c-4e02-84a9-fa40ad851b48","order_by":9,"name":"Carlos Manchado-Perdiguero","email":"","orcid":"","institution":"Roche Diagnostics","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Manchado-Perdiguero","suffix":""},{"id":369429724,"identity":"99b819ec-e0b1-4083-b528-31ffc0f73a3f","order_by":10,"name":"Ali Sánchez-Peral","email":"","orcid":"","institution":"Roche Diagnostics","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Sánchez-Peral","suffix":""}],"badges":[],"createdAt":"2024-10-17 19:25:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5284927/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5284927/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41409-025-02744-6","type":"published","date":"2025-11-29T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67498869,"identity":"98fd287d-4f58-453e-bf6b-1cb3d6a7450f","added_by":"auto","created_at":"2024-10-25 16:34:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":819133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTRECs and KRECs recovery after alloHSCT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eViolin plots showing the distribution of TRECs (\u003cstrong\u003ePanel A\u003c/strong\u003e) and KRECs (\u003cstrong\u003ePanel B\u003c/strong\u003e) values pre-transplant and at the various post-transplant sequential time-points of the study. The horizontal black lines mark medians and quartiles, and the red lines mark arithmetic means.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5284927/v1/9096559eee377e89c544b398.png"},{"id":67498866,"identity":"44b56cd7-ec42-44dc-8b9d-3a6757b7d225","added_by":"auto","created_at":"2024-10-25 16:34:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":823811,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival according to pre-transplant TRECs levels and type of donor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier overall survival curves according to pre-transplant log10TRECs values above (in yellow) or below (in red) the threshold cut point (thrTRECs ≥1.4), using the Mantel-Cox log-rank, and presented separately for the whole population (\u003cstrong\u003ePanel A\u003c/strong\u003e), for transplants from matched related donors (\u003cstrong\u003ePanel B\u003c/strong\u003e) andfrom other donor types, including cord blood, haploidentical and unrelated donors (\u003cstrong\u003ePanel C\u003c/strong\u003e).The yellow and red ranges are the 95% confidence interval.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5284927/v1/137161fb45d6419b3459e9d3.png"},{"id":67498868,"identity":"7ec3ead7-2c5b-4b46-aaae-37f2bd733278","added_by":"auto","created_at":"2024-10-25 16:34:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":846579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival landmark analysis in early survivors according to their TRECs levels at 12 months after transplant\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival curves of early survivors at 12 months after transplant (landmark analysis) according to 12-month log10TRECs values above (in yellow) or below (in red) the threshold cut point (thrTRECs ≥0.4), using the Mantel-Cox log-rank, and presented separately for the whole population (\u003cstrong\u003ePanel A\u003c/strong\u003e), and for cases with (\u003cstrong\u003ePanel B\u003c/strong\u003e) and without (\u003cstrong\u003ePanel C\u003c/strong\u003e) graft versus host disease. The yellow and red ranges are the 95% confidence interval.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5284927/v1/edf8e8c6a11e82b7fe911bfc.png"},{"id":97040301,"identity":"4a841a4e-81ca-4c28-9188-915e1ed93f44","added_by":"auto","created_at":"2025-11-29 08:13:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2949886,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5284927/v1/28fff551-742b-46da-935f-1e3ca9378f19.pdf"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Sequential quantification of T-cell receptor excision circles (TRECs) and K-deleting recombination excision circles (KRECs) and overall survival after allogeneic HSCT","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAllogeneic hematopoietic stem cell transplantation (alloHSCT) remains a standard of care for many patients with haematological malignancies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Long-term survival after alloHSCT has improved over time, primarily through a better understanding of patient and transplant factors associated with outcomes, and through an improved management of transplant complications [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Beyond clinical factors and improved management, in the current new era of personalized medicine, there is an increasing interest and need for biomarkers which may have an independent association with alloHSCT recipients\u0026rsquo; outcomes and may predict their long-term survival [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eT cell receptor excision circles (TRECs) and kappa-deleting recombination excision circles (KRECs), stable fragments of circularized DNA that do not replicate during mitosis and get diluted in each cell division, measure T and B lymphocyte neogenesis in primary lymphoid organs [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Primary lymphoid reconstitution is essential for a successful immune recovery after alloHSCT [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Thus, over the past two decades, many studies have explored the measurement of TRECs and KRECs in alloHSCT [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Unfortunately, the majority of these studies were carried out in relatively small numbers of patients, with assorted samples collected at different time-points, and quantified with diverse methodologies in the absence of standardized TRECs/KRECs technical platforms. All this limited the comparability among studies, led to inconsistent results, and prevented further development of the role of TRECs/KRECs in alloHSCT.\u003c/p\u003e \u003cp\u003eOur objective here is to measure TRECs/KRECs in a relatively large population of alloHSCT recipients using a standardized commercial platform in prospectively collected sequential samples, and to ascertain their value as biomarkers to predict long-term outcomes after alloHSCT.\u003c/p\u003e"},{"header":"Materials, Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design, population and samples\u003c/h2\u003e \u003cp\u003eThis retrospective study includes all consecutive alloHSCT procedures in patients with haematological malignancies at Hospital Universitario Puerta de Hierro Majadahonda between 1999 and 2017. All patients signed informed consent forms for treatment, samples and data collection. The study was approved by our IRB (PI 66/19). DNA samples from peripheral blood mononuclear cells were collected prospectively and sequentially pre-transplant and at 1, 3, 6 and 12 months (m) after transplant. Samples were preserved under optimal conditions, and the concentration and quality of DNA were confirmed in all samples prior to inclusion and testing in this study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTRECs and KRECs measurement\u003c/h3\u003e\n\u003cp\u003eAbsolute quantification of TRECs and KRECs was carried out using Roche Diagnostics LightCycler\u0026reg; 480 and TREC-KREC-ACTB Protocol (Version 1.0, 25/09/2018; Roche Diagnostics, Barcelona, ES). Briefly, we used 96-well (semi-skirted) plates containing lyophilized primers and probes for simultaneous qPCR detection of TREC-KREC-ACTB. The primers designed for TRECs use a specific LNA hydrolysis probe marked with FAM (530nm) to amplify and to detect a 100bp fragment in a specific locus of the excision circles of T lymphocytes (VDJ genes). For the detection of KRECs, a 105bp fragment is amplified with a LNA hydrolysis probe labelled with LC610 (610nm) within the excision circle locus of the Kappa elements of B lymphocytes (VDJ genes). Finally, the quality control of genomic DNA and amplification detects an 83bp fragment within a specific locus of the ACTB (β-Actin) gene, with a set of primers and LNA hydrolysis probe labelled with the fluorophore LC670 (660nm, equivalent to Cy5). Results are expressed in TRECs/KRECs units per reaction (U/R; reaction\u0026thinsp;=\u0026thinsp;5 \u0026micro;L DNA).\u003c/p\u003e\n\u003ch3\u003eEndpoints and variables\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint was alloHSCT recipient overall survival (OS) with a follow-up horizon of five years after transplantation. Non-relapse mortality (NRM), with relapse as a competing event, was also included as a secondary endpoint. Landmark analyses were performed at 6 and 12 months after transplant to explore the impact of TRECs/KRECs levels measured at those time-points in early transplant survivors on their long-term OS. The study CRF included all data referring to patient and transplant characteristics, complications, outcomes, and TRECs/KRECs levels at different timepoints. The original dataset was pre-processed to work with some variables and to identify possible outliers. Eighteen TREC and/or KREC outlier values over the threshold of 10^5 were removed.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eStatistical analyses and definitions used in our study are consistent with the EBMT Statistical Guidelines [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This study has only right-censored data, given that all time-to-event variables are available, and we have no truncation effects. Primary and secondary outcomes are considered time-dependent categorical variables, with binary (No\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1) values and related time-to-event variables expressed in days after transplantation. Other categorical variables were treated as factors, and checked, in a preliminary exploratory analysis, with Chi-squared or Fisher\u0026rsquo;s exact test (provided by the \u003cem\u003ecompareGroups\u003c/em\u003e R package) against the outcomes. For the survival analyses, we created Kaplan-Meier curves to check and plot OS along time, using the Mantel-Cox log-rank test (performed with the \u003cem\u003esurvival\u003c/em\u003e R package) when needed to assess differences between groups. For the secondary outcome NRM we used the Fine-Gray approach for competitive risk analysis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], applying the \u003cem\u003ecmprsk\u003c/em\u003e R package [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. We also used Cox proportional hazard models, with hazard ratios (HR) for all categorical variables of interest, and subdistributed hazard ratios (SHR) using competing risks models. Based on the exploratory analyses and the HR and SHR results, we built several Cox regression models using the log10TRECs and log10KRECs values pre-transplant and at 1, 3, 6 and 12 months post-transplant, and adjusting for significant categorical variables from univariable analyses. From these models, we computed log10TRECs and log10KRECs cut point thresholds to split cases in two categories, high or low TRECs/KRECs values. These cut point thresholds were computed with the \u003cem\u003ecutpointr\u003c/em\u003e R package, applying options to maximize classification accuracy. This package provides robust methods to identify optimal cut points and allows the establishment of the direction of the dependency between class and outcome variables. Visual inspection was done to evaluate the cut points using the \u003cem\u003eplot_metric\u003c/em\u003e function in addition to statistical results. All analyses were done with The R Project for Statistical Computing (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; version 4.0.5), including the following R packages: \u003cem\u003ecompareGroups\u003c/em\u003e (v 4.5.1); \u003cem\u003esurvival\u003c/em\u003e (v 3.2.13); \u003cem\u003ecmprsk\u003c/em\u003e (v 2.2\u0026ndash;11); \u003cem\u003ecutpointr\u003c/em\u003e (v 1.1.1); \u003cem\u003esurvminer\u003c/em\u003e (v 0.4.9); \u003cem\u003esurvMisc\u003c/em\u003e (v 0.5.5); \u003cem\u003eezfun\u003c/em\u003e (v 0.1.3); \u003cem\u003esplitstackshape\u003c/em\u003e (v 1.4.8) and \u003cem\u003eggplot2\u003c/em\u003e (v 3.3.5). For the transformation between days and months, in time-to-event variables, a ratio of 30.5 days per month has been used. In patients with more than one alloHSCT, data on prior transplants were censored at the day of second or third alloHSCT infusion. P-values were considered statistically significant if\u0026thinsp;\u0026lt;\u0026thinsp;0.05. We assumed non-informative censoring, as well as sufficient follow-up time (60 months) and events to ensure adequate statistical power. Other assumptions were assessed for each kind of test, and none was significantly violated.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient and transplant characteristics\u003c/h2\u003e \u003cp\u003eThe study comprises 374 alloHSCT procedures in 350 recipients with haematological malignancies, median age at transplant of 45 years (range 16\u0026ndash;68), 59.6% male recipients, including 21 second and three third transplants. The main indication for alloHSCT was acute leukaemia/myelodysplastic syndromes (AL/MDS, 69.8%) and the most frequent donor type were matched siblings (52.9%), with also nearly a third of cord blood transplants (30.5%). Other characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient and Transplant Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristics (374 alloHSCT in 350 patients)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecipient Age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (16\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecipient Sex (male)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223 (59.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- AL/MDS\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- CLPD\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- Myeloma\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e261 (69.8%)\u003c/p\u003e \u003cp\u003e83 (22.2%)\u003c/p\u003e \u003cp\u003e30 (8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor type\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- Matched related\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- Cord blood\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- Matched unrelated\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- Haploidentical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (52.9%)\u003c/p\u003e \u003cp\u003e114 (30.5%)\u003c/p\u003e \u003cp\u003e49 (13.1%)\u003c/p\u003e \u003cp\u003e13 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConditioning (MAC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191 (51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlloHSCT Number\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- First\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- Second\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- Third\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350 (93.6%)\u003c/p\u003e \u003cp\u003e21 (5.6%)\u003c/p\u003e \u003cp\u003e3 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlloHSCT: Allogeneic haematopoietic stem cell transplantation; AL/MDS: Acute Leukaemia / Myelodysplastic Syndrome; CLPD: Chronic Lymphoproliferative Disorder; MAC: myeloablative conditioning; Age is presented as median (range) and all other variables are presented as number of cases (percentage).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTRECs and KRECs levels and recovery after transplant\u003c/h3\u003e\n\u003cp\u003eWe analysed the kinetics of recovery of TRECs and KRECs during the first year after alloHSCT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). TRECs levels dropped down from pre-transplant levels in the first three months after transplantation, started recovering thereafter and peaked at one year after transplantation: median U/R and interquartile ranges (IQR) were 20 (5.42\u0026ndash;120) pre-transplant, 5.28 (0.04\u0026ndash;46.9) at 1m, 4.72 (0.01-39) at 3m, 13 (1.61\u0026ndash;137) at 6m, and 38.8 (5.06\u0026ndash;402) at 12m (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). KRECs levels recovered much faster, reaching high levels above baseline values as soon as 3 months after transplant and remaining stable thereafter: 5.55 (0.01\u0026ndash;266) pre-transplant, 9.74 (0.4\u0026ndash;106) at 1m, 553.5 (28.85\u0026ndash;5690) at 3m, 810 (91.5\u0026ndash;4930) at 6m, and 704.5 (51.6\u0026ndash;2820) at 12m (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eKRECs levels and transplant characteristics and outcome\u003c/h3\u003e\n\u003cp\u003ePre-transplant KRECs levels were associated with age (354 U/R\u0026thinsp;\u0026lt;\u0026thinsp;30 years vs 27.1 U/R\u0026thinsp;\u0026gt;\u0026thinsp;60 years; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and with the underlying malignancy, being higher in patients with AL/MDS compared with those with multiple myeloma or chronic lymphoproliferative disorders (183 vs 106 vs 44.2 U/R respectively; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). They did not associate with sex, donor type, intensity of conditioning regimen or other patient or transplant characteristics. Moreover, we did not find any association between KRECs levels, neither pre-transplant nor at any time-point post-transplant, and alloHSCT outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePre-transplant TRECs and KRECs levels and association with transplant characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTRECs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eKRECs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU/R, median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU/R, median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (years)\u003c/p\u003e \u003cp\u003e\u003cem\u003e- \u0026lt;30\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e\u0026minus;\u0026thinsp;31\u0026ndash;45\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e\u0026minus;\u0026thinsp;46\u0026ndash;60\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- \u0026gt;60\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.4 (2.04\u0026ndash;234)\u003c/p\u003e \u003cp\u003e18 (1.92\u0026ndash;115)\u003c/p\u003e \u003cp\u003e9.12 (0.62\u0026ndash;50.7)\u003c/p\u003e \u003cp\u003e5.06 (0.35\u0026ndash;24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e354 (12.1\u0026ndash;3740)\u003c/p\u003e \u003cp\u003e126 (2.95\u0026ndash;1258)\u003c/p\u003e \u003cp\u003e58.8 (1.28\u0026ndash;1690)\u003c/p\u003e \u003cp\u003e27.1 (0.0-867)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnderlying disease\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- AML/MDS\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- CLPD\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e- Myeloma\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.89 (0.59\u0026ndash;83.1)\u003c/p\u003e \u003cp\u003e21.5 (3.66\u0026ndash;174)\u003c/p\u003e \u003cp\u003e19.9 (0.66\u0026ndash;75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183 (4-2060)\u003c/p\u003e \u003cp\u003e44.2 (0.0-873)\u003c/p\u003e \u003cp\u003e106 (6.8\u0026ndash;913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor type\u003c/b\u003e\u003c/p\u003e \u003cp\u003e- \u003cem\u003eMatched related\u003c/em\u003e\u003c/p\u003e \u003cp\u003e- \u003cem\u003eOthers\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.9 (2.22\u0026ndash;142)\u003c/p\u003e \u003cp\u003e7.52 (0.24\u0026ndash;66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.4 (3.93\u0026ndash;1368)\u003c/p\u003e \u003cp\u003e163 (1.95\u0026ndash;2190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAL/MDS: Acute Leukaemia / Myelodysplastic Syndrome; CLPD: Chronic Lymphoproliferative Disorder; IQR: interquartile range; n.s.: non-significant; U/R: Units/Reaction.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePre-transplant TRECs levels \u0026amp; patient and transplant characteristics\u003c/h2\u003e \u003cp\u003ePre-transplant TRECs levels were higher in younger patients compared with the other age groups and were lowest in the oldest patient group (26.4 U/R\u0026thinsp;\u0026lt;\u0026thinsp;30 years vs 5.06 U/R\u0026thinsp;\u0026gt;\u0026thinsp;60 years; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We found that pre-transplant TRECs levels were lower in patients with AL/MDS, compared with those with multiple myeloma or chronic lymphoproliferative disease (8.89 vs 19.9 vs 21.5 U/R respectively, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Also, pre-transplant TRECs appeared to be higher in patients who were to receive matched related transplant compared to other donor types (17.9 vs 7.52 U/R, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but no associations were found between TRECs levels and other characteristics such as sex or type of conditioning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePre-transplant TRECs levels and transplant outcome\u003c/h2\u003e \u003cp\u003ePatients with higher pre-transplant TREC levels had a significantly higher OS in the univariate analysis (72.7% vs 38.2% at 2 years and 64.7% vs 35.3% at 5 years, p\u0026thinsp;=\u0026thinsp;0.005; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e - Panel A), in keeping with a lower cumulative incidence of NRM (21.2% vs 44.1%, p\u0026thinsp;=\u0026thinsp;0.044).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTRECs levels pre-transplant and at sequential time-points after transplant and association with overall survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOverall Survival\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRECs levels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt 2 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAt 5 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Pre-transplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.7% vs 38.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.7% vs 35.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- At 1 month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.9% vs 44.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.6% vs 30.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- At 3 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.1% vs 48.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.2% vs 41.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- At 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.6% vs 52.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.8% vs 42.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- At 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.4% vs 58.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.8% vs 52.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis association between higher pre-transplant TREC levels and higher OS was also confirmed when analyzed separately for transplants from matched related donors (81.7% vs 55.2% at 2 years and 69.2% vs 49.9% at 5 years, p\u0026thinsp;=\u0026thinsp;0.015; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e - Panel B) and from other donor types, including cord blood, haploidentical and unrelated donors (63.6% vs 37.9% at 2 years and 55.4% vs 32.8% at 5 years, p\u0026thinsp;=\u0026thinsp;0.008; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e - Panel C).\u003c/p\u003e \u003cp\u003eIn multivariate analysis, after adjusting for patient age, underlying disease, type of alloHSCT donor, conditioning regimen and graft-versus-host disease (GVHD), higher pre-transplant TRECs levels remained independently associated with increased OS (HR 0.51 [95% CI 0.35\u0026ndash;0.73], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lower NRM (HR 0.37 [95% CI 0.21\u0026ndash;0.66], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Patient age (HR 0.54 [95% CI 0.36\u0026ndash;0.79], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and type of donor (HR 0.52 [95% CI 0.31\u0026ndash;0.85], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) were also independently associated with OS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRecovery of TRECs levels after transplant, patient outcomes and landmark analyses in early survivors\u003c/h2\u003e \u003cp\u003eBeyond the impact of TRECs levels prior to transplant on OS that we have just described, the prospective collection of sequential samples in our study allows for a more detailed analysis of the impact of the recovery of TRECs after transplant on long-term outcomes. As measured by their levels at 1, 3, 6 and 12 months after alloHSCT, faster TRECs recovery, at every time-point, associates with better OS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Thus, we decided to carry out landmark analyses at 6 and 12 months to identify the impact of TRECs levels on the long-term outcomes of early survivors at those time-points. Landmark analyses showed that higher TRECs levels in survivors at 6 and 12 months after alloHSCT associated with significantly better long-term survival of these patients. At 6 months, TRECs values above the threshold cut point (thrTRECs\u0026thinsp;\u0026ge;\u0026thinsp;0.1) associated with better OS of 82.4% vs 56.7%, 73.9% vs 43.7%, and 72.4% vs 38.9%, one, three and five years later, respectively (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At 12 months, TRECs values above the threshold cut point (thrTRECs\u0026thinsp;\u0026ge;\u0026thinsp;0.4) also associated with better OS of 93% vs 59.7%, 85.6% vs 47.6%, and 83.4% vs 47.6%, one, three and five years later, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e - Panel A). Furthermore, this association of TRECs levels and long-term survival remains independent in multivariate analysis both at 6 months (HR 0.33 [95% CI 0.13\u0026ndash;0.81], p\u0026thinsp;=\u0026thinsp;0.016) and 12 months (HR 0.13 [95% CI 0.04\u0026ndash;0.42], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) after adjusting for other clinical factors including GVHD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e - Panels B and C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is one of the first studies, the largest in this field, to sequentially measure TRECs and KRECs in alloHSCT recipients. The study shows that sequential quantification of TRECs in alloHSCT recipients using a standardized commercial platform provides novel biomarker measurable data that associate with patient and transplant characteristics and outcomes. Pre-transplant TRECs levels have an independent association with NRM and OS that may complement and inform decision-making in transplant candidates, currently based only on clinical factors. Furthermore, TRECs levels measured at six and 12 months may provide a novel objective tool to predict long-term outcome of early survivors after alloHSCT.\u003c/p\u003e \u003cp\u003eFew studies have explored the role of KRECs in alloHSCT [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Beyond the description of KRECs recovery early after transplant, similar to our own findings [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], these studies have failed to show any association of KRECs levels and key transplant outcomes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. KRECs do not appear to be a suitable candidate biomarker to predict alloHSCT survival and patient outcomes.\u003c/p\u003e \u003cp\u003eMany prior studies explored the role of TRECs in alloHSCT, primarily focusing on their association with patient and transplant characteristics, with differences among studies and often inconclusive results [\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our data, in line with most prior studies, show an association between pretransplant TRECs levels and patient age, but other than this, we did not find associations with sex or other relevant factors, and associations found with factors such as the underlying malignancy or the type of transplant are most likely surrogates of other features in these subgroups of patients. Some of these studies did also explore the role of TRECs on hard endpoints and patient outcomes. Clave \u003cem\u003eet al\u003c/em\u003e and Sairafi \u003cem\u003eet al\u003c/em\u003e identified associations of higher TRECs levels pretransplant and at three months posttransplant, respectively, with improved NRM and OS [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Others showed associations between TRECs levels pretransplant and posttransplant and the risk of disease relapse for some subgroups of malignancies, but without an overall impact on OS and long-term outcomes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As described above, our data show a strong association of TRECs levels, pretransplant and at every time-point of their recovery posttransplant, with long-term patient outcomes. This impact, both for NRM and OS, remains independent from other key factors such as age, sex, type of disease, conditioning, type of transplant or GVHD in multivariate analysis.\u003c/p\u003e \u003cp\u003eBeyond differences in patient and transplant characteristics among all these TRECs studies, major limitations thus far are that most of them were carried out in relatively small numbers of patients, with assorted samples at different time-points rather than collected prospectively and sequentially, and with diverse methodologies in the absence of a standardized TRECs/KRECs technical platform. Measurement methods have included semiquantitative PCR [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], hybridization probe [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and TaqMan probe [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Cell populations used for DNA extraction for TRECs quantification have also varied among studies, including CD3 [ref. 23,27], CD4/CD8 [ref.7,25], or peripheral blood mononuclear cells [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. So have the units in which measurements have been reported, TRECs/\u0026micro;g of DNA [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], TRECs/\u0026micro;L of blood [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and TRECs/cell count [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], further complicating the comparability among studies and the reproducibility of their results. Our use of a broadly available standardised commercial platform such as LightCycler 480/TREC-KREC-ACTB will likely improve previous inconsistencies and will facilitate further development and validation of the role of TRECs as biomarkers to predict long-term outcomes in alloHSCT recipients.\u003c/p\u003e \u003cp\u003eApart from TRECs levels pretransplant, our battery of samples collected prospectively at 1, 3, 6 and 12 months confirms that TRECs measured sequentially after transplant strongly associate with long-term OS. Based on such strong signal of TRECs recovery and patient survival, we carried out landmark analyses of the impact of TRECs levels in early survivors on their long-term outcomes. Only one recent study, by S\u0026ouml;derstr\u0026ouml;m \u003cem\u003eet al\u003c/em\u003e [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], has reported landmark analyses of TRECs in alloHSCT, showing in a group of 90 alloHSCT recipients for AML an association of OS with TRECs levels measured at 12 months, but not at six months. As presented above, our data strongly shows that early transplant survivors at 6 and 12 months with higher TRECs levels at those time-points had significantly better subsequent long-term OS. In addition, this effect in our series is independent from GVHD and from other clinical factors in the multivariate analyses. This finding is particularly remarkable, as the field of alloHSCT currently lacks any such a tool to estimate survival after the first year posttransplant, and identifies TRECs as a potential biomarker to predict long-term OS in early alloHSCT survivors.\u003c/p\u003e \u003cp\u003eOur study has some limitations. Despite the impact of age and intensive chemotherapy on thymic involution [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], studies have shown that thymic function is not completely eliminated, and under immunosuppressive stress conditions such as HIV infection and alloHSCT, the remaining thymic tissue can contribute to immune recovery in these patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. While these findings support the role of TRECs and their relationship with immune recovery and outcomes after alloHSCT, our study was not designed to assess immune recovery parameters that may have helped drawing a connection between our findings with TRECs values and transplant outcomes. In addition, cut point thresholds of TRECs/KRECs levels used in this study come from the available data in this series, and may not be optimal to be applied in other external data sets. Thus, for the purpose of the work presented here, the log10TRECs and log10KRECs cut points are only intended to improve stratification between high and low TRECs or KRECs values for the survival curves and HR estimations in this series.\u003c/p\u003e \u003cp\u003eIn summary, our study shows that while KRECs do not seem to be a good candidate biomarker in alloHSCT, TRECs have strong impact on patient outcomes, independent from other clinical factors. Pretransplant, they could potentially improve patient assessment beyond current clinical factors, and posttransplant, at 6 and 12 months, they could provide novel biomarker data to predict long-term survival in early transplant survivors. The use of a standardized commercial platform will improve prior inconsistencies in this field. External, multicentre, prospective validation of these results using the standardized LightCycler 480 / TREC-KREC-ACTB platform is warranted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eFunded by the \u003cem\u003eCentro para el Desarrollo Tecnol\u0026oacute;gico y la Innovaci\u0026oacute;n\u003c/em\u003e (CDTI) of the Spanish Ministry of Science, Innovation and Universities (Project # IDI-20180259).\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThe authors would like to acknowledge other staff members of the HSCT Program and the Cytogenetics and Molecular Biology Laboratory at our Department for their support in the conduction of this study, Dr. Carlos Vilches (Spanish National Transplant Organization) for his expert supervision of immunological aspects of this work, and the Spanish \u003cem\u003eCentro para el Desarrollo Tecnol\u0026oacute;gico y la Innovaci\u0026oacute;n\u003c/em\u003e (CDTI) for funding this project.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of Interest Statement:\u003c/strong\u003e \u003cp\u003eFB, CMP and ASP are employees of Roche Diagnostics (Barcelona, Spain), which LightCycler\u0026reg; 480 / TREC-KREC-ACTB Platform was used for the measurement of TRECs/KRECs in this study. CdM work was partly funded by CDTI\u0026rsquo;s Project # IDI-20180259. No other authors have any relevant conflicts of interest to disclose for the work presented here.\u003c/p\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePassweg JR, Baldomero H, Chabannon C, et al. Hematopoietic cell transplantation and cellular therapy survey of the EBMT: monitoring of activities and trends over 30 years. Bone Marrow Transplant. 2021;56(7):1651\u0026ndash;1664. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41409-021-01227-8\u003c/span\u003e\u003cspan address=\"10.1038/s41409-021-01227-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2021 Feb 23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnowden JA, S\u0026aacute;nchez-Ortega I, Corbacioglu S, et al. 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Epub 2017 Sep 15.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bone-marrow-transplantation","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"bmt","sideBox":"Learn more about [Bone Marrow Transplantation](http://www.nature.com/bmt/)","snPcode":"41409","submissionUrl":"https://mts-bmt.nature.com/cgi-bin/main.plex","title":"Bone Marrow Transplantation","twitterHandle":"@bmtjournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5284927/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5284927/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe use of TRECs/KRECs in allogeneic HSCT (alloHSCT) has been limited by a lack of standard technical platforms to allow comparison and validation of results between centres. We quantified absolute TRECs/KRECs on sequential samples collected prospectively (pretransplant, 1, 3, 6 and 12-months posttransplant) in 374 alloHSCT for haematological malignancies using LightCycler 480/TREC-KREC-ACTB (Roche Diagnostics). Following prompt decrease after transplant, KRECs recover as soon as 3 months posttransplant, while TRECs recovery takes up to 1 year (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). KRECs do not associate with outcomes. However, higher pretransplant TRECs strongly associate with reduced NRM and increased OS, and remain independent in multivariate analysis (HR 0.37, p\u0026thinsp;=\u0026thinsp;0.001, and HR 0.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). In addition, faster TRECs recovery measured sequentially at 1, 3, 6 and 12 months after alloHSCT associates with better OS. Furthermore, landmark analyses showed that early survivors with higher TRECs levels at 6 and 12 months after alloHSCT had significantly better subsequent long-term survival, independent from GVHD and other clinical factors in multivariate analysis (HR 0.33, p\u0026thinsp;=\u0026thinsp;0.016 and HR 0.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). TRECs levels pretransplant and at 6 and 12 months posttransplant provide novel biomarker measurable data that associate with alloHSCT long-term outcomes. Multicentre, prospective validation of these results is warranted.\u003c/p\u003e","manuscriptTitle":"Sequential quantification of T-cell receptor excision circles (TRECs) and K-deleting recombination excision circles (KRECs) and overall survival after allogeneic HSCT","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-25 16:34:41","doi":"10.21203/rs.3.rs-5284927/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-01-15T11:09:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-12-24T19:44:40+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-12-13T17:25:52+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-12-13T15:23:34+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-11-11T13:25:38+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-10-31T07:48:26+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-10-23T08:37:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-21T11:09:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bone Marrow Transplantation","date":"2024-10-18T11:38:37+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2024-10-18T10:30:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-17T19:23:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bone-marrow-transplantation","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"bmt","sideBox":"Learn more about [Bone Marrow Transplantation](http://www.nature.com/bmt/)","snPcode":"41409","submissionUrl":"https://mts-bmt.nature.com/cgi-bin/main.plex","title":"Bone Marrow Transplantation","twitterHandle":"@bmtjournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d2991588-476f-4bbe-bdc3-be5cefc80e3a","owner":[],"postedDate":"October 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39313184,"name":"Health sciences/Medical research/Translational research"},{"id":39313185,"name":"Biological sciences/Immunology/Transplant immunology/Allotransplantation"}],"tags":[],"updatedAt":"2025-11-29T08:12:59+00:00","versionOfRecord":{"articleIdentity":"rs-5284927","link":"https://doi.org/10.1038/s41409-025-02744-6","journal":{"identity":"bone-marrow-transplantation","isVorOnly":false,"title":"Bone Marrow Transplantation"},"publishedOn":"2025-11-29 05:00:00","publishedOnDateReadable":"November 29th, 2025"},"versionCreatedAt":"2024-10-25 16:34:41","video":"","vorDoi":"10.1038/s41409-025-02744-6","vorDoiUrl":"https://doi.org/10.1038/s41409-025-02744-6","workflowStages":[]},"version":"v1","identity":"rs-5284927","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5284927","identity":"rs-5284927","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}