Many attempts have been made to predict solid organ transplant outcomes by measuring immunological parameters in peripheral blood.1-3 A consistent and surprising feature of these studies has been the limited predictive value of mechanistically important biomarkers when considered in isolation, such as regulatory T cell frequencies.4,5 One possible explanation is that biologically relevant changes in expression of these biomarkers are dwarfed by natural variation between individuals. In particular, changes in the frequency of donor antigen-reactive lymphocyte subsets may be very small compared with interindividual biological variations. Hence, it may be more informative to investigate changes in biomarker expression over time rather than making “static” case–control comparisons; in other words, to study the individual response of patients to the challenge of allogeneic transplantation. To test this concept, serial samples were prospectively collected from orthotopic liver transplant recipients from 2 separate clinical trials, namely, the RISET-Regensburg Study and the BUiLT Study. Estimates of peripheral blood CD4+ CD25+ CD127low FoxP3+ Treg frequencies were made using flow cytometry. Patients in the RISET-Regensburg Study received conventional triple immunosuppressive treatment comprising calcineurin inhibitor, MMF, and prednisolone. Interestingly, those study patients who did not experience a rejection episode during the first year after transplantation exhibited an early enrichment of Treg within their total CD4+ T cell compartment, which decayed to pretransplant levels within 8 weeks (Figure 1A). In this group, Treg frequency at 1 week after transplantation was significantly elevated compared with pretransplant, “baseline” values but not later time points. By contrast, no relative increase in Treg frequency was observed in patients registering one or more rejection episodes during the first year posttransplant (Figure 1B). Notably, absolute Treg counts were not higher in nonrejecting patients than rejecting patients (Figure 1C). Similar observations were made in the BUiLT study (clinicaltrials.gov: NCT01023542) patients, who received either conventional triple immunosuppression or a “bottom-up” regimen (Supplementary Information, SDC, http://links.lww.com/TP/B261): Nonrejecting recipients showed a peak in Treg frequency at 2 weeks’ posttransplant that subsided by week 4; by comparison, no transient Treg enrichment was observed in patients who experienced a rejection episode during the first year posttransplant (Figure 1D). The kinetic of this response is suggestive of a primary T cell response; therefore, we tentatively propose that the enrichment and subsequent restitution of the peripheral Treg pool is evidence of an immunoregulatory reaction in nonrejecting patients.6 We speculate that the early enrichment represents a polyclonal activation and expansion of Treg, whereas the decay phase might reflect the selection of a subset of donor antigen-reactive clones. Alternatively, changes in Treg frequency might be explained by more efficient suppression of non-Treg in nonrejectors compared with rejectors. “High granularity” immune monitoring studies that capture kinetic information about evolving immune responses in the early posttransplant period may be useful in predicting long-term transplant outcome. Treg frequency in nonrejectors at 1-week posttransplant was significantly higher than in rejectors (Figure 1E); however, treating each time point separately ignores the kinetic information won from serial sampling. Evaluating changes in leukocyte subset frequency over time may require new statistical approaches, including perhaps mathematical modeling of T cell responses in transplant recipients. Considering the size and duration of Treg enrichment by calculating the “area under the curve” may be a superior analytic approach. Quantifying “area” implicitly asserts a model for the response, which is beyond our current dataset and mechanistic insight. Nevertheless, if the area of a simple polygon is calculated for each patient (Figure 1F), recognizing this crude model might underestimate or overestimate the “true” area, then differences in Treg responses between rejectors and nonrejectors are more clearly resolved (Figure 1G). In presenting our preliminary observations, we hope to encourage others to replicate them and, perhaps, to better characterize the clonal selection that we believe is occurring.
1. Bonaccorsi-Riani E, Pennycuick A, Londono MC, et al. Molecular characterization of acute cellular rejection occurring during intentional immunosuppression withdrawal in liver transplantation. Am J Transplant
2. Baron D, Ramstein G, Chesneau M, et al. A common gene signature across multiple studies relate biomarkers and functional regulation in tolerance to renal allograft. Kidney Int
3. Martinez-Llordella M, Puig-Pey I, Orlando G, et al. Multiparameter immune profiling of operational tolerance in liver transplantation. Am J Transplant
4. Ashton-Chess J, Dugast E, Colvin RB, et al. Regulatory, effector, and cytotoxic T cell profiles in long-term kidney transplant patients. J Am Soc Nephrol
5. Martínez-Llordella M, Lozano JJ, Puig-Pey I, et al. Using transcriptional profiling to develop a diagnostic test of operational tolerance in liver transplant recipients. J Clin Invest
6. Taubert R, Danger R, Londoño MC, et al. Hepatic infiltrates in operational tolerant patients after liver transplantation show enrichment of regulatory T cells before proinflammatory genes are down-regulated. Am J Transplant