Detection of signature double‐negative T cells is a predictive marker to identify autoimmune lymphoproliferative syndrome associated with <scp>FAS</scp> loss of function
Authors: Nina Eisenhauer, Maurizio Miano, Nora Naumann‐Bartsch, Jörg Leyh, Gianluca Dell'Orso, Michael Aigner, Gisela Fecker, Claas Hinze, Helmut Wittkowski, Heiko Bruns, Jakob Zierk, Markus Metzler, Peter D. Arkwright, Frederik Graw, Andreas Mackensen, Simon Völkl
Journal: American Journal of Hematology (2024)

The autoimmune lymphoproliferative syndrome (ALPS) is caused by defects in the FAS-dependent apoptosis leading to chronic lymphoproliferation, autoimmunity, chronic multilineage cytopenia, an increased risk for developing B cell lymphomas, and a characteristic accumulation of CD4/CD8-negative, T-cell receptor (TCR), αβ-positive T-cells (double-negative, DNT-cells).1, 2 The heterogeneous clinical phenotype of ALPS patients and overlap with similar diseases like CTLA-4 and LRBA deficiencies or STAT3 gain-of-function impedes its diagnosis. Additionally, the genetic analysis for mutations effecting the FAS-dependent apoptosis is cost- and time-consuming and therefore challenging, especially if uncommon alterations like deep intronic FAS mutation or copy number variants are present. Notably, patients may not respond to steroid first-line therapy and often require specific immunosuppressive treatments such as mycophenolate mofetil (MMF) or sirolimus. Particularly, the mTOR (mammalian target of rapamycin) inhibitor sirolimus effectively and specifically reduces the clinical symptoms in ALPS patients, due to the mTOR hyperactivity of ALPS DNT-cells. Thus, it should be considered as standard of treatment and provided as quickly as possible.1 Therefore, a fast and secure tool is needed to filter patients with high probability of an ALPS-related mutation to accelerate diagnosis and specific treatment. As DNT-cells can also be increased in other diseases such as LRBA and CTLA-4 deficiencies or STAT3 gain-of-function, analyzing their percentage alone may not be sufficiently discriminatory.3 We therefore investigated how standard flow-cytometry analysis of DNT-cells and their abnormal expression pattern is efficient to predict ALPS associated with FAS deficiency. Effector-memory cells re-expressing CD45RA (TEMRA) can be defined by the expression of CD45RA+ and the absence of CCR7. TEMRA cells show senescence or terminal differentiation with low mitotic capacity, similar to cells expressing CD57. ALPS DNT-cells however simultaneously express CD57, the TERMA phenotype, and the proliferation marker Ki67.2 The expression of CD38 and CD45RA was recently shown to be characteristic for the aberrant ALPS DNT phenotype.3 Hence, we analyzed the expression of Ki67, CD57, TEMRA, CD38, and CD45RA in DNT-cells. Patients were categorized according to the established diagnostic criteria for ALPS and ALPS-related phenotypes as described in Material and Methods (Supporting Information Data S1). 14 ALPS-FAS/sFAS, 12 suspected-ALPS (susp-ALPS), 22 ALPS-like cases were included in this study (Table S1). Susp-ALPS combines patients fulfilling the criteria for ALPS diagnosis but harboring another underlying mutation, ALPS-like were defined as previously reported.4 Additionally, 16 patients with chronic autoimmune cytopenia (AC), 12 patients with chronic nonmalignant lymphoproliferation (LP), and 25 healthy donors (HD) were analyzed. Common ALPS parameters (DNT-cells, TEMRA, CD57) and novel markers (Ki67, CD38+/CD45RA+) showed significant elevation in ALPS-FAS/sFAS compared to HD, AC, and LP but several overlaps with susp-ALPS and ALPS-like (Figures 1A–E; S1). ROC curves were plotted to compare the predictive value of each marker for ALPS-FAS/sFAS diagnosis (Figure 1F,G). Using Ki67+, CD38+/CD45RA+, and TEMRA+ cells as markers resulted in a high predictive performance to distinguish ALPS-FAS/sFAS and ALPS-like with an AUC of, 1.0 (CI 1.0), 0.98 (CI 94–1.0), and TEMRA+ 0.96 (CI 0.9–1.0) respectively. DNT-cell frequency and CD57+ cells performed less well with an AUC of 0.92 (CI 0.82–1-0) and 0.86 (CI 0.73–1.0). Separating ALPS-FAS/sFAS and susp-ALPS however was more challenging with the best prediction by Ki67+ and CD38+/CD45RA+, producing an AUC of 0.88 (CI 0.73–1.0) and 0.87 (CI 0.73–1.0) respectively. DNT-cell frequency, TEMRA+ and CD57+ distinguished ALPS-FAS/sFAS and susp-ALPS with an AUC of 0.73 (CI 0.52–0.94), 0.75 (0.53–0.97), and 0.71 (CI 0.5–0.92). Furthermore, cutoffs for each marker were calculated based on the individual ROC curves as indicated in the Material and Methods (Supporting Information Data S1). For simplification, ALPS-FAS/sFAS patients were compared to susp-ALPS and ALPS-like patients combined as autoimmune-lymphoproliferative immunodeficiencies (AL-PID/ALPID-U) according to recently reported recommendations.3 (Figure 1H,I). Patients with a DNT-cell population above 3.86% were assigned to the ALPS-FAS/sFAS cohort (F1-score: 0.72). The classification of patients with more than 31.36% Ki67+ T-cells as ALPS-FAS/sFAS lead to the highest separation quality of 0.90. Using the cutoff for CD38+/CD45RA+, CD57+, or TEMRA+ DNT-cells, which were 35.33%, 43.78% and 30.76%, respectively, to separate ALPS-FAS/sFAS from AL-PID/ALPID-U produced an F1-score of 0.81, 0.72, and 0.72. Thus, risk identification by Ki67+ or CD38+/CD45RA+ is comparable to commonly used markers like sFASL, IL10, or vitamin B12, suggested for instance in the landmark study of Caminha et al. (2010).5 Patients with a high probability for a FAS mutation could be quickly identified by a single 6–8 parameter flow-cytometric analysis (Table S2) and forwarded to further genetic analysis. Moreover, patients unlikely to harbor a FAS mutation could be ruled out and provided with alternative diagnostic options. This would be much faster than performing several immunoassays to measure sFASL, IL10, or Vitamin B12 and an additional cytometric analysis of the DNT-cell frequency. Like this, time and cost might be saved by reducing the need for gene analysis and the patient can be supported with the best suiting treatment. Similarly, Palmisani et al. evaluated a flow-cytometry panel comparing the increase of four cell populations in ALPS-FAS/sFAS and ALPS-U (CD3+/TCRαβ+/CD4-/CD8-; CD3+/CD25+; CD3+/HLADR+; TCRαβ+/B220+ and CD19+/CD27+). In contrast to our study, only the concurrent elevation of all four populations were shown to be of value to separate ALPS-FAS/sFAS and ALPS-U.6 Four patients of the susp-ALPS cohort showed a CD38+/CD45RA+ and Ki67 expression in the range of ALPS-FAS/sFAS patients. Although other mutations were found by the reference laboratory, the possibility of a hidden FAS mutation cannot be definitively precluded. Therefore, patients categorized as AL-PID or ALPID-U with a high probability for a FAS mutation using the specific markers should be forwarded to additional sequencing methods to check for a somatic FAS mutation in sorted DNT-cells or copy number variants. Those sequencing methods are often not included in the standard diagnostic procedure, resulting in the possibility to overlook those hidden mutations. Unfortunately, no in vitro functional apoptosis test was performed in these four patients, which is often not feasible without specialized laboratories. Functional apoptosis defects were only detected in three ALPS-U patients, which all do not show high CD38+/CD45RA+ or Ki67 expression. This highlights the unreliability of the apoptosis assay, which is not standardized between centers and often produces false positive or negative results. It was further assessed if a combination of the DNT-cells plus one additional marker would lead to a higher quality separation (Figure 1H). Therefore, logistic-regression was performed to predict ALPS-FAS/sFAS or AL-PID/ALPID-U based on combined marker levels. Although it did not improve separation by Ki67 and CD38/CD45RA as single markers, the combination of DNT-cell percentage plus CD57+ and TEMRA+ increased the separation quality to 0.74 and 0.77 respectively. One AL-PID patient with a high expression of CD57 and TEMRA and a DNT-cell frequency of 25.0%, even exceeding those measured in ALPS-FAS/sFAS patients, is mainly impairing the separation. This patient was diagnosed with a NFκB mutation, again underlining the sensitivity of Ki67 and CD38+/CD45RA+ as markers for ALPS-FAS/sFAS, as their expression is not increased. However it shows that analyzing the DNT phenotype might also be interesting to screen for other similar mutations beside FAS. In conclusion, we propose to perform an initial flow-cytometry staining to analyze the expression of CD38/CD45RA in DNT-cells, as it takes shorter time and is less resource-intensive than the intracellular Ki67 staining. Patients with a high probability of a FAS mutation could be detected with only one standard 6-color flow-cytometry staining (Table S2). Especially for small hospitals with no immediate access to genetic analysis, this may lead to a much faster diagnosis. N.E., M.M., N.N.B., J.L., and S.V. designed the research; M.M., N.N.B., J.L., G.D., C.H., H.W., G.F., J.Z., M.M., and P.D.A. repeatedly referred patients; N.E., M.M., M.A., H.B., P.D. A., and S.V. performed experiments; N.E., M.M., F.G., A.M., and S.V. analyzed and interpreted data; N.E., M.M., N.N.B., A.R.E., A.M., and S.V. wrote the manuscript; and all of the authors edited the manuscript. The authors thank all of the patients and healthy donors who participated in this study. We thank Florentine Schonath (Core Unit Cell Sorting and Immunomonitoring Erlangen), Dorothea Gebhardt, Lina Meretuk, Lena Tarantik, and Luisa Albert for excellent technical assistance. We also thank Anne Rensing-Ehl und Stephan Ehl (Center for Chronic Immunodeficiency, University of Freiburg) for continuous support. This work was supported by the "German Research Foundation (VO1835/3-1, MA1351/12-1)". FG is supported by the Hightech Agenda Bavaria. This work was supported by the German Research Foundation (VO1835/3-1, MA1351/12-1). FG is supported by the Hightech Agenda Bavaria. All authors declare no commercial or financial conflict of interest. Written informed consent was obtained from all patients and controls. The data that support the findings of this study are available from the corresponding author upon reasonable request. Data S1. Supporting Information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.