In this issue of Haematologica, Link-Rachner et al.1 report their findings on CD8 T-cell receptor-alpha (TRα) chain dynamics in patients after hematopoietic stem cell transplantation (HSCT) using next-generation sequencing (NGS) in relation to different treatment platforms. Their study aimed at unraveling the effect of post-transplant T-cell-depleting immunosuppressive therapy (namely anti-thymocyte globulin, ATG, and post-transplant cyclophosphamide, PTCy) and degree of HLA matching on the TRα diversity of naïve and memory CD8 T-cell repertoires reconstituting the patient’s peripheral blood in the first six months after transplantation. Furthermore, the authors attempted to determine the extent to which the donor’s TRα repertoire influences the post-transplant repertoires in the respective patients.
The TR and its huge variability is one of the pillars of adaptive immunity. Healthy, diverse TR repertoires in normal individuals contain millions of different clones with unique TRs,2 and provide the immune system with an arsenal of highly specific, yet also cross-reactive cells to fight off pathogens and malignant cells. Until recently, this extreme diversity limited a detailed and deep analysis of TR repertoires in healthy and pathological conditions, with most available techniques focusing on broad repertoire alterations, and extensive, cumbersome T-cell cloning required to investigate specific complementarity-determining region 3 (CDR3) variants. The advent of NGS-based high-throughput analysis of TRs has revolutionized the field of immune repertoire analysis.3 TR NGS can now provide qualitative and quantitative information on hundreds of thousands of different T-cell clones directly from a single blood or tissue sample.
Hematopoietic stem cell transplantation is a field in which TR analysis is of extreme interest, both for medical and biological reasons. Patients undergoing allogeneic HSCT see the partial or nearly total elimination of their own hematopoietic system with radio- and/or chemotherapy followed by its replacement with that of a donor. In most cases, HSCT is the only curative therapy for the underlying disease. However, HSCT poses several risks for the patient, many of which derive from the ablation of their bone marrow and the concomitant risk of infection and pathogen reactivation. In addition, the new hematopoietic system can induce graft-versus-host disease (GvHD) associated with tissue and organ damage and, in some cases, death.4 T-cell reconstitution dynamics is central to these post-transplant immune processes and represents an area of intense research in the HSCT field. High-throughput TR NGS has thus quickly attracted researchers eager to use its power to study post-HSCT T- cell clonal dynamics, its relationship to transplant-related factors, and its role in transplant complications.5
The study by Link-Rachner et al. contains a number of noteworthy aspects. Contrary to most previous reports, the researchers focus on the TRα chain. Most published TR analyses have studied the TRb chain, probably because it is considered more diverse due to the added combinatorial potential conferred by the D segment, but also perhaps on account of the fact that TR NGS methodologies for this chain are more extended. However, both the α and the b chains contribute to TR specificity and, because of the order of the recombination events during T-cell maturation and development, one mature T cell can express two different functional α chains, both of which can pair with the cell’s b chain, forming two different TR heterodimers.6 Indeed, this happens in approximately 10% of the T cells in peripheral blood, and TRα CDR3 diversity has actually been observed to be 1.2-2.4 times greater than TRb in T-cell subsets from a single individual.7 Importantly, these ‘dual’ T cells have been associated with increased autoimmune and alloreactive capacities,8 as well as with acute9 and chronic10 GvHD. Hence, analysis of TRα chains in HSCT is of special interest, and this study should encourage researchers to pay more attention to this locus in future analyses.
Furthermore, the authors designed their study in order to perform a comparison of TR dynamics between different clinical platforms. By including in their analysis patients transplanted from HLA-matched and -mismatched unrelated donors treated or not treated with ATG, haploidentical donors treated with PTCy, and related donors with no T-cell depletion (TD), the authors provide valuable insights into how these clinical platforms differ in terms of T-cell and TR dynamics during immune reconstitution at different time points in the first six months after HSCT. Despite the limited sample size, each of the groups was analyzed separately, leading to some interesting observations. First, the data suggest that TD has a stronger effect on the reconstitution of naïve than of memory T cells in terms of numbers and TRα diversity; both were significantly lower in patients treated with TD (Figure 1). Considering that diversity is deemed a hallmark of a ‘healthy’ T-cell repertoire,11 this raises questions as to ways in which one could improve TR diversity in patients undergoing HSCT platforms with in vivo TD, as already suggested in the context of TRαb-depleted grafts.12
In addition, the data from Link-Rachner et al. provide some intriguing new insights into the relative contribution of the donor’s memory and naïve T-cell repertoires to immune reconstitution after HLA-identical sibling HSCT in the absence of TD. The authors find that 60% and 11.5% of the TRα clonotypes in the patients’ memory and naïve T cells at day 180 post transplantation could be traced back to the donors’ memory repertoires, compared to only 30% and 15% to the donors’ naïve repertoires, respectively (Figure 2). These data suggest that the donor’s memory compartment is significantly reflected not only in the memory but also to a certain extent in the naïve CD8 compartment after HLA-identical sibling HSCT. Given the high (>99%) purity of the FACS-sorted naïve and memory T-cell subsets used for the experiments, these intriguing findings are unlikely to have been impacted by sample spill-over. It should be noted that 2 of the 5 donors used for these analyses were cytomegalovirus (CMV) seropositive, while the remaining 3 donors were seronegative. In transplants performed under PTCy regimen, donor CMV seropositivity has been shown to correlate significantly with a predominance of donor CD8 memory T-cell reconstitution post transplantation.13 The data from Link-Rachner et al. suggest that this may hold true also for CMV seronegative donors in the non-TD setting, although a separate analysis of a larger number of seropositive and seronegative donors will be needed to verify this point.
The data also have potential practical implications. If the donor’s memory repertoire plays a leading role in shaping the patient’s repertoires after transplantation, the ‘quality’ of that donor memory repertoire might be assessed before transplant as a factor to be considered in donor selection, or as a prognostic tool for post-transplant complications. This observation becomes relevant also in view of the interest in the depletion of naïve T cells from stem cell grafts in the related donor setting.14 However, further research will be needed to understand if this occurs also on the other HSCT platforms, including cord blood transplantation,15 and what the desired qualities of a donor repertoire are.
The study by Link-Rachner et al. does leave some open questions that warrant further study. First, patients who relapsed were explicitly excluded from the study. However, relapse remains the main cause of treatment failure in HSCT,16 and, similar to GvHD, a central role for T cells in the therapeutic graft-versus-leukemia effect has been well established.17 Hence, NGS-based studies of T-cell repertoire characteristics that might associate with leukemia relapse after transplantation are of the foremost importance. Second, in this study, CD4 T-cell reconstitution and repertoire dynamics were not analyzed, yet they are likely to play a central role in patients after HSCT, especially in clinical contexts where HLA-DPB1 mismatches are frequent (e.g. HSCT with unrelated donors).18 Of note, the well-established permissiveness of a proportion of these HLA-DPB1 mismatches and its relationship with TR repertoire characteristics is also of interest and this is currently under investigation.19 The delayed reconstitution of this T-cell compartment might pose methodological challenges for TR repertoire analyses, but that and its central role in the co-ordination of effective immune, as well as alloreactive responses, warrant special attention. Finally, while Link-Rachner et al. focus their attention on the TRα repertoire post HSCT, it would be interesting to assess whether the TRα and TRb repertoires follow similar, complementary, or independent dynamics after different transplant settings. Parallel analysis of both chains, and potentially even attempting to determine their pairing with recent novel high-throughput approaches,20 is likely to give an even more complete picture of TR immune reconstitution with clinical and translational relevance.
Overall, the study by Link-Rachner et al. illustrates how the power of NGS has revolutionized the assessment of immune repertoires and opened a broad spectrum of possibilities for unprecedented analysis of T-cell dynamics in the field of HSCT. Future studies building on this and other earlier pioneering work in this still developing field are called for to fully embrace this potential to enrich our understanding of T-cell immune reconstitution after HSCT. This knowledge should ultimately serve the objective of promoting the regeneration of a healthy TR repertoire that contributes both to the eradication of the underlying disease and to the control of transplant-related morbidities, maximizing the therapeutic potential of allogeneic HSCT.
Acknowledgments
This contribution was supported by grants from the Deutsche José Carreras Leukämie Stiftung (DJCLS R 15/02 and DJCLS R/2017), Dr. Werner Jackstädt Stiftung, and the Josef Senker Stiftung.
References
- Link-Rachner CS, Eugster A, Rucker-Braun E. T cell receptor alpha repertoire of CD8+ T cells following allogeneic stem cell transplantation using next-generation sequencing. Haematologica. 2019; 104(3):622-631. PubMedhttps://doi.org/10.3324/haematol.2018.199802Google Scholar
- Qi Q, Liu Y, Cheng Y. Diversity and clonal selection in the human T-cell repertoire. Proc Natl Acad Sci U S A. 2014; 111(36):13139-13144. PubMedhttps://doi.org/10.1073/pnas.1409155111Google Scholar
- Rosati E, Dowds CM, Liaskou E, Henriksen EKK, Karlsen TH, Franke A. Overview of methodologies for T-cell receptor repertoire analysis. BMC Biotechnol. 2017; 17(1):61. Google Scholar
- Ferrara JL, Levine JE, Reddy P, Holler E. Graft-versus-host disease. Lancet. 2009; 373(9674):1550-1561. PubMedhttps://doi.org/10.1016/S0140-6736(09)60237-3Google Scholar
- Warren EH, Matsen FA, Chou J. High-throughput sequencing of B- and T-lymphocyte antigen receptors in hematology. Blood. 2013; 122(1):19-22. PubMedhttps://doi.org/10.1182/blood-2013-03-453142Google Scholar
- Padovan E, Casorati G, Dellabona P, Meyer S, Brockhaus M, Lanzavecchia A. Expression of two T cell receptor alpha chains: dual receptor T cells. Science. 1993; 262(5132):422-424. PubMedhttps://doi.org/10.1126/science.8211163Google Scholar
- Wang C, Sanders CM, Yang Q. High throughput sequencing reveals a complex pattern of dynamic interrelationships among human T cell subsets. Proc Natl Acad Sci U S A. 2010; 107(4):1518-1523. PubMedhttps://doi.org/10.1073/pnas.0913939107Google Scholar
- Ni PP, Solomon B, Hsieh CS, Allen PM, Morris GP. The ability to rearrange dual TCRs enhances positive selection, leading to increased Allo- and Autoreactive T cell repertoires. J Immunol. 2014; 193(4):1778-1786. PubMedhttps://doi.org/10.4049/jimmunol.1400532Google Scholar
- Morris GP, Uy GL, Donermeyer D, Dipersio JF, Allen PM. Dual receptor T cells mediate pathologic alloreactivity in patients with acute graft-versus-host disease. Sci Transl Med. 2013; 5(188):188ra174. Google Scholar
- Balakrishnan A, Gloude N, Sasik R, Ball ED, Morris GP. Proinflammatory Dual Receptor T Cells in Chronic Graft-versus-Host Disease. Biol Blood Marrow Transplant. 2017; 23(11):1852-1860. Google Scholar
- Attaf M, Huseby E, Sewell AK. alphabeta T cell receptors as predictors of health and disease. Cell Mol Immunol. 2015; 12(4):391-399. Google Scholar
- Zvyagin IV, Mamedov IZ, Tatarinova OV. Tracking T-cell immune reconstitution after TCRalphabeta/CD19-depleted hematopoietic cells transplantation in children. Leukemia. 2017; 31(5):1145-1153. Google Scholar
- Kanakry CG, Coffey DG, Towlerton AM. Origin and evolution of the T cell repertoire after posttransplantation cyclophosphamide. JCI Insight. 2016; 1(5):e86252. Google Scholar
- Bleakley M, Heimfeld S, Loeb KR. Outcomes of acute leukemia patients transplanted with naive T cell-depleted stem cell grafts. J Clin Invest. 2015; 125(7):2677-2689. PubMedhttps://doi.org/10.1172/JCI81229Google Scholar
- Gkazi AS, Margetts BK, Attenborough T. Clinical T Cell Receptor Repertoire Deep Sequencing and Analysis: An Application to Monitor Immune Reconstitution Following Cord Blood Transplantation. Front Immunol. 2018; 9:2547. Google Scholar
- Horowitz M, Schreiber H, Elder A. Epidemiology and biology of relapse after stem cell transplantation. Bone Marrow Transplant. 2018; 53(11):1379-1389. Google Scholar
- Negrin RS. Graft-versus-host disease versus graft-versus-leukemia. Hematology Am Soc Hematol Educ Program. 2015; 2015:225-230. PubMedhttps://doi.org/10.1182/asheducation-2015.1.225Google Scholar
- Fleischhauer K, Shaw BE. HLA-DP in unrelated hematopoietic cell transplantation revisited: challenges and opportunities. Blood. 2017; 130(9):1089-1096. PubMedhttps://doi.org/10.1182/blood-2017-03-742346Google Scholar
- Arrieta-Bolanos E, Crivello P, Metzing M. Alloreactive T Cell Receptor Diversity against Structurally Similar or Dissimilar HLA-DP Antigens Assessed by Deep Sequencing. Front Immunol. 2018; 9:280. Google Scholar
- Howie B, Sherwood AM, Berkebile AD. High-throughput pairing of T cell receptor alpha and beta sequences. Sci Transl Med. 2015; 7(301):301ra131. PubMedhttps://doi.org/10.1126/scitranslmed.aac5624Google Scholar