Dynamic forecasting module for chronic graft-versus-host disease progression based on a disease-specific subpopulation of B cells

Dynamic forecasting module for chronic graft-versus-host disease progression based on a disease-specific subpopulation of B cells | 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 Dynamic forecasting module for chronic graft-versus-host disease progression based on a disease-specific subpopulation of B cells Andy Xiang, Yuanchen Ma, Jieying Chen, Zhiping Fan, Jiahao Shi, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4264249/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Due to its dynamic nature and the absence of reliable real-time monitoring tools, predicting chronic graft-versus-host disease (cGVHD) progression was challenging. This caused a significant investment of both time and financial resources to ensure good management of cGVHD. In response to this challenge, we identified a distinct B-cell subpopulation characterized by CD27 + CD86 + CD20 - , which could precisely distinguish cGVHD from healthy donors. Leveraging this discovery, we developed cGPS, a user-friendly tool based on marker distribution, which demonstrated exceptional efficacy in tracking cGVHD progression. Its validation, conducted through retrospective and prospective studies involving 91 patients (25 non-GVHD and 66 cGVHD cases), confirmed cGPS's predictive prowess. Remarkably, our retrospective analysis revealed an impressive area under the curve (AUC) of 0.9773 for identifying non-GVHD patients at risk of cGVHD and 0.8846 for predicting disease progression in cGVHD patients. Subsequent validation in an independent prospective study yielded equally promising results, with cGPS accurately predicting all instances of cGVHD development or progression within a three-month observation window. With three independent cohorts, cGPS underscores its robust ability for sensitive and dynamic monitoring of cGVHD progression, provides a solution for early diagnosis and assessment of treatment effectiveness for cGVHD. Health sciences/Diseases/Immunological disorders/Graft-versus-host disease Health sciences/Health care/Prognosis/Disease-free survival Health sciences/Health care/Diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Chronic graft-versus-host disease (cGVHD) is a major and serious late complication in patients after allogeneic hematopoietic stem cell transplantation (allo-HSCT). It has become the leading cause of non-relapse mortality in allo-HSCT, exhibiting an incidence of approximately 30–70% 1,2 . The clinical features resemble those of autoimmune diseases and affects multiple organs and tissues 3,4 . Corticosteroids are the mainstay of first-line treatment 5 and are administered either alone or in combination with immunosuppressants. However, the poor clinical outcomes of standard treatments and the significant toxicities induced by prolonged steroid treatment mean that approximately 50–60% of patients require secondary treatment within 2 years 6 . Some second-line treatments have increased the response among patients, but the limited treatment options and lack of real-time disease activity monitoring continue to hinder cGVHD treatment and outcomes 7 . According to the third National Institutes of Health (NIH) Consensus Development Project on Criteria for Clinical Trials in Chronic GVHD 8 , preemptive and individual therapy will be critical components of cGVHD management in the future 6,9,10 . However, it remains challenging to recognize the earliest signs and symptoms of cGVHD 11 . The standardization provided by the 2005 and 2014 NIH consensus projects helped improve the diagnostic accuracy and severity scoring for clinical trials, but the consensus criteria cannot be used to recognize or predict the imminent onset of cGVHD at an early stage, and some patients who do not meet the NIH criteria may still be at risk for developing active cGVHD 12 . The consensus criteria enable clinicians only to evaluate a patient’s current state, not predict a future response or outcome. This leads to a lag in cGVHD therapeutic strategy and necessitates an trial-and-error approach for subsequent treatments 13 . Usually, the therapeutic response should be assessed at 8–12 weeks as it is reported that most therapies take some time to reach therapeutic peak 14 . If a patient exhibits progression of cGVHD after being on a given treatment for 4 or more weeks, a new treatment option should be offered 15,16 . Many patients may fail to engage with a given treatment for enough time to fully assess its efficacy (at least 4 weeks), limiting the ability of clinicians to identify an effective treatment for each patient 17 . The development of a real-time and objective indicator for cGVHD prediction and/or disease staging could be very meaningful in guiding preemptive therapy and/or establishing personalized therapy. B cells play a substantiated role in cGVHD pathogenesis. Numerous studies have revealed that there are B cell homeostasis alterations in cGVHD 18,19 , including the presence of characteristic autoreactive B cells, delayed reconstitution of naive B cells 20 , and alterations of signaling pathway activities, such as increased BAFF signaling and abnormally activated BCR signaling 21–24 . Depletion of B cells by rituximab or selective targeting of B cell signaling pathways can be effective therapeutic approaches in patients with cGVHD 25,26 . Our previous studies revealed that the mesenchymal stem cell (MSC)-based improvement of cGVHD was accompanied by alteration of naïve, memory, and regulatory B cell subsets, modulation of the plasma BAFF level, and the expression of BAFF-R on B lymphocytes 27 . Observations that B cell changes are closely related to cGVHD outcome suggested that a B cell-based strategy might be a useful approach for monitoring cGVHD activity. As an integrative approach to build a cGVHD disease activity evaluation module, we herein used spectral flow cytometry and a machine-learning model to assess B cell alterations in a cGVHD patient, and used retrospective and prospective studies to develop and initially validate an indicator for cGVHD management. Methods Study design and participants Cohort 1: 5 healthy donors and 5 cGVHD patients were studied to identify a cGVHD-specific cell population. Cohort 2: 17 healthy donors, 31 cGVHD patients, and 15 non-GVHD patients were included. This cohort validated the universality of the disease-specific cell population, confirmed the best marker combination and developed the cGPS module for enhanced accessibility. It also used to assess the module's performance in early diagnosis and disease prognosis through retrospective analysis. Cohort 3: Comprised 32 healthy donors, 35 cGVHD patients, and 10 non-GVHD patients. It validated the cGPS module's ability to predict non-GVHD group members' disease development risk and forecast disease progression in cGVHD patients. Patients characteristics The cGVHD patient inclusion criteria were as follows: 1) aged 15–65 years; 2) gender unrestricted; 3) diagnosed with malignant hematologic disease, received allogeneic hematopoietic stem cell transplantation at least 3 months ; and 4) diagnosed with cGVHD according to the NIH criteria of 2005 and 2014. The exclusion criteria were: 1) presence of residual underlying malignant hematologic disease or disease progression; 2) treatment with rituximab (anti-CD20mAb) or implantation, or experience of life-threatening infection; and 3) difficult-to-control hypertension, congestive heart failure, frequent ventricular arrhythmia, angina, or other related symptom. cGVHD was diagnosed and graded at the time of sample collection. Patients who had received HSCT but had not developed cGVHD at the time of sample collection were defined as non-GVHD patients. Peripheral blood samples were collected from all patients and healthy donors, with their informed consent. Grading of cGVHD Organ scoring was conducted according to the 2005 and 2014 NIH consensus criteria for cGVHD 28,29 . The evaluated organs included skin, oral mucosa, eyes, gastrointestinal tract, liver, lung, joints, and genitalia. Organ scoring was independently assessed by at least two clinical hematologists. Peripheral blood mononuclear cell (PBMC) isolation The peripheral blood was diluted with 1×PBS at a ratio of 1:1, and lymphocytes were isolated by Ficoll-Paque density gradient centrifugation. The obtained lymphocytes were washed with PBS to remove debris and platelets, and then resuspended with PBS for further phenotype staining, or resuspended with lymphocyte cryopreservation solution and then frozen and transferred to liquid nitrogen for storage. Antibodies and flow cytometry PBMC from cGVHD patients, non-GVHD patients, or healthy donors were stained for B cell phenotyping with a 20-marker panel that could accurately distinguish B cell subsets and indicate their developmental stage and activation status (Table S2), or with a five-marker panel for detecting specific B cell subsets. Antibodies were detailed in Table S2. Phenotyping data were collected using an Aurora (Cytek, USA) and a CytoFLEX (Beckman Coulter, USA) and analyzed using the FlowJo and CytExpert software packages. Downstream analysis of flow cytometry data The transformed data from FlowJo underwent analysis with Seurat, following the tutorial guidelines( https://satijalab.org/seurat/articles/pbmc3k_tutorial.html ) 30 . Given that the data were previously normalized, quality control and normalization steps were omitted. Parameters were adjusted to accommodate the spectral flow cytometry data structure, specifying 20 markers per cell. Batch effect removal was performed for cohort 1 data using the ScaleData function with the vars.to.regress argument. We utilized random forest machine learning models to compare marker set accuracies for cohort 1. Within the Seurat framework, candidate markers representing a cGVHD-specific cell population were selected. Using Python's built-in combination function, we obtained all possible marker combinations. For each combination, a random forest machine model was constructed using Sklearn (version 0.23.2) 31 . Data were split into training and testing sets using train_test_split, allocating 70% for training and 30% for testing. Model selection criteria We selected the models that could accurately distinguish cGVHD patients and healthy donors both in cohort1 and cohort2. Each model was directly used to distinguish disease samples (label 1) and healthy samples (label 0) without further training. Models with an AUC score > 0.9 32 and the fewest markers in combination (2 or 3) were selected. These models were considered to represent a cGVHD-specific cell population with acceptable accuracy. ROC analysis ROC analysis was performed using pROC (version 1.16.2). The roc curve was first calculated using function roc and plot with pROC build-in function ggroc. The confidence interval for threshold, specificity,sensitivity, accuracy was calculated with a build-in function ci.coords, with parameter best.policy set to random. Statistical analysis Any two categories (e.g., HD vs. cGVHD or disease recovered vs. disease progressed in the cGVHD group) were compared using Fisher’s exact test. All statistical analyses were performed using R (version 4.0.2). Machine learning model selection was performed on the python platform (Python3.6). All codes generated for each analysis are available upon request. Results Cohort characteristics and B cell phenotyping profile We enrolled 150 well-characterized participants into our study and distributed them to the following groups: 1) Control: healthy individuals recruited from volunteers (n = 54); 2) cGVHD: individuals diagnosed using the criteria defined by National Institutes of Health (NIH) Consensus Conference (n = 71); and ( 3 ) non-GVHD: individuals who received HSCT but lacked any apparent clinical feature of cGVHD as defined by the NIH criteria (n = 25). These participants were divided into the three cohorts. In cohort1, five cGVHD patients and five healthy donors were enrolled for an exploratory study. In cohort2, 31 cGVHD patients, 15 non-GVHD patients, and 17 healthy donors were enrolled for a retrospective study. In cohort3, 35 cGVHD patients, 10 non-GVHD patients, and 32 healthy donors were enrolled for a further prospective study. B cell phenotyping profiles of cGVHD patients, non-GVHD patients, and healthy controls were generated by spectral flow cytometry using a 20-marker panel that could accurately distinguish B cell subsets and indicate the developmental stage and activation status of B cells (Table S1 ). The classical B cell subsets distinguishable by this phenotyping panel included transitional B cells, naïve B cells, memory B cells (including IgM + , marginal zone-like, and switched memory B cells), IgD − CD27 − double-negative B cells, and plasmablasts (Figure S1 ). The B cell phenotyping profiles were further explored to find a cGVHD-related B cell subset, build a cGVHD disease activity evaluation module, and figure out the threshold for cGVHD management through retrospective and prospective studies. Clinical information on the cGVHD and non-GVHD group members are presented in Tables 1 , 2 , and S1. Table 1 Clinical characteristics of participants in Cohort 1 Characteristic cGVHD, n = 5 Age, median (range), y 35 (19–56) Gender, n(%) Male 4 Female 1 Source of graft, no(%) BMT 0 PBSCT 2 BMT + PBSCT 2 PBSCT + UCBT 1 Source of donor, no(%) Sibling 3 Unrelated 0 Parents 0 Children 2 Relatives 0 HLA matching, no(%) Identical matched 1 Non-identical matched 2 Haploid matched 2 GVHD prophylaxis, no(%) CSA + MTX 0 CSA + MTX + ATG 0 CSA + MTX + MMF 1 CSA + MTX + ATG + MMF 4 Post-Cy 0 Others 0 Initial disease, no(%) AML/AML from MDS 3 ALL 1 CML 0 MDS 0 Others 1 Table 2 Clinical characteristics of participants in Cohort 2 cohort2 cGVHD patient profile cohort2 non-GVHD patient profile Characteristic PR/CR/SD cGVHD, n = 26 DP cGVHD, n = 5 P-value Characteristic Stable, n = 11 Active, n = 4 P-value Age, median (range), y 30 (17–58) 27 (17–52) 0.4976 Age, median (range), y 27 (16–61) 39.5 (19–57) 0.5810 Gender, No.(%) > 0.99 Gender, n0(%) 0.1033 Male 21 (81%) 4 (80%) Male 5 (45%) 4 (100%) Female 5 (19%) 1 (20%) Female 6 (55%) 0 Source of graft, No.(%) 0.4129 Source of graft, No.(%) > 0.99 BMT 1 (4%) 0 BMT 1 (4%) 0 PBSCT 18 (69%) 2 (40%) PBSCT 3 (27%) 1 (25%) BMT + PBSCT 7 (27%) 3 (60%) BMT + PBSCT 8 (73%) 3 (75%) PBSCT + UCBT 0 0 PBSCT + UCBT 0 0 Source of donor, No.(%) 0.8704 Source of donor, No.(%) 0.7363 Sibling 13 (50%) 3 (60%) Sibling 8 (73%) 2 (50%) Unrelated 1 (4%) 0 Unrelated 0 0 Parents 5 (19%) 2 (40%) Parents 2 (18%) 1 (25%) Children 2 (7.7%) 0 Children 1 (9%) 1 (25%) Relatives 2 (7.7%) 0 Relatives 0 0 others 3 (11.6%) 0 others 3 (11.5%) 0 HLA matching, No.(%) 0.3892 HLA matching, No.(%) > 0.99 Identical matched 15 (58%) 3 (60%) Identical matched 4 (36.5%) 1 (25%) Non-identical matched 1 (4%) 1 (20%) Non-identical matched 3 (27%) 1 (25%) Haploid matched 10 (38%) 1 (20%) Haploid matched 4 (36.5%) 2 (50%) GVHD prophylaxis, No.(%) 0.8372 GVHD prophylaxis, No.(%) 0.3626 CSA + MTX 3 (11%) 0 CSA + MTX 0 0 CSA + MTX + ATG 1 (4%) 0 CSA + MTX + ATG 0 0 CSA + MTX + MMF 10 (38%) 3 (60) CSA + MTX + MMF 3 (27%) 0 CSA + MTX + ATG + MMF 6 (24%) 1 (20%) CSA + MTX + ATG + MMF 6 (55%) 4 (100%) Post-Cy 2 (8%) 1 (20%) Post-Cy 2 (18%) 0 Others 4 (15%) 1 ( 7 ) Others 0 0 Initial disease, No.(%) 0.3699 Initial disease, No.(%) > 0.99 AML/AML from MDS 14 (54%) 1 (20%) AML/AML from MDS 8 (73%) 3 (75%) ALL 6 (23%) 3 (60%) ALL 3 (27%) 1 (25%) CML 0 0 CML 0 0 MDS 3 (11.5%) 0 MDS 0 0 Others 3 (11.5%) 1 (20%) Others 0 0 Identification of a cGVHD-specific B cell subpopulation Spectral flow cytometry data obtained from cohort1 (10 participants; 5 cGVHD patients and 5 healthy donors) were used to identify a cGVHD-specific B cell population. Uniform Manifold Approximation and Projection (UMAP) and Louvain clustering of cells 33,34 yielded three cell clusters (Clusters 0–2; Fig. 1 A, 1 B). From the cell composition of each cluster, Cluster 2 was found to be a cGVHD-specific cell cluster (Fig. 1 C): cGVHD samples accounted for 82.0% of the Cluster 2 cells (1748 cells), while healthy donor samples accounted for only 18% (383 cells). To phenotypically characterize the cluster 2, we examined the cell surface phenotypes of each cluster. From among the 20 studied markers, CD20, CD268, CD5, CD27, CD38, CD86, CD269, and CD319 were identified as specific markers for Cluster 2 based on their expression levels (Fig. 1 D). We used these eight markers to generate an unbiased random forestry machine learning model and found that these markers could accurately label a disease-specific cell cluster. We then explored whether these eight markers could be further streamlined, with the goal of improving the clinical applicability of our method. We iterated all possible marker combinations and evaluated their accuracy. As presented in Fig. 1 E, the accuracy scores indicated that certain combinations of only two or three markers could substitute for the full eight-marker panel with little loss of accuracy. Optimal marker combination for identifying a cGVHD-specific B cell subpopulation To identify a simple marker combination that could identify a cGVHD-specific B cell subset, we used an independent cohort (48 participants; 31cGVHD patients and 17 healthy donors) and marker combinations with accuracy scores > 0.9 (20 combinations; Fig. 2 A). The three-marker combination of CD20/CD27/CD86 yielded the best AUC score (AUC score = 0.958, Fig. 2 B) and thus appeared to be the optimal option. The results of further cross-validation using flow cytometry indicated that this CD20/CD27/CD86 marker set could precisely identify a cGVHD-specific B cell subpopulation, as the CD27 + CD86 + CD20 − B cells were extremely frequent in cGVHD patients and almost entirely absent from healthy donors (Fig. 2 C). Thus, this three-marker combination appeared to successfully substitute for the original 20-marker panel. To further increase the clinical applicability of this marker set, we utilized its distribution to develop a module known as the cGVHD Progress Score (cGPS). This module aims to provide a single, easily interpretable score, serving as a direct reflection of the disparity between cGVHD and health. Using the formula presented in Fig. 2 D, we calculated the cGPS for each participant in the cohort. The cGPS was significantly different between cGVHD patients and healthy donors ( P = 0.00003, Fig. 2 E). Using a cGPS threshold to predict progression risk among non-GVHD patients Recognizing or predicting the imminent onset of cGVHD at an early stage remains a significant challenge because non-GVHD patients do not present obvious signs (per the NIH consensus criteria) at the early stage of developing cGVHD 35–37 . Hence, we tested whether cGPS could potentially predict progression of non-GVHD patients to cGVHD. Details on the non-GVHD patients enrolled into Cohort-2 are presented in Table 2 . According to their progression (or lack thereof) to cGVHD within a 3-month period observation, the subjects were categorized as stable state (those remaining in non-GVHD status) and active state (patients that developed cGVHD). The patients of the two groups had similar baseline clinical characteristics (Table 2 ). Our statistical analysis indicated that the cGPS values were significantly different between the stable and active groups ( P = 0.0029). As shown in Fig. 3 A, the cGPS of the patients in the stable group were largely 1. This suggested that a threshold cGPS approximate to 1 might potentially be used to distinguish between patients in the stable and active states. Our receiver operating characteristics (ROC) analysis revealed that the precise cGPS threshold for this cohort was 1.150; indeed, 100% of non-GVHD patients scoring below this threshold were in stable state (Fig. 3 B). In addition to being able to distinguish the state of non-GVHD patients (Fig. 3 C), this cGPS threshold could also be used to predict whether non-GVHD patients would become sick: As shown in Fig. 3 D, four out of five (80%) non-GVHD patients with cGPS > 1.150 developed into cGVHD during the evaluation period, whereas all 10 non-GVHD patients with cGPS < 1.150 remained in stable state. Using a cGPS threshold to estimate the treatment efficiency of cGVHD It is difficult to quickly and efficiently estimate whether a cGVHD patient is likely to become disease-progressed 38,39 . As NIH consensus criteria for organ scoring was a follow-up evaluation system, cGVHD real time state monitor was hard to captured. Since the cGPS module was generated from the disease-related B cell subset, we questioned whether it might be able to distinguish disease-progressed cGVHD patients. To address this question, we classified the cGVHD patients of Cohort-2 as having stable disease (SD), partial response (PR), complete response (CR), or disease progression (DP), and collected them into two groups: the DP group and the non-DP group (PR/CR/SD groups). The baseline clinical characteristics of the two groups are shown in Table 2 As shown in Fig. 4 A, the cGPS were significantly different between the DP and non-DP groups ( P = 0.00029), with an apparent threshold around 1.5. Indeed, our ROC analysis revealed that a cGPS of 1.505 could be used to successfully separate the DP and non-DP groups (Fig. 4 B). We further validated this finding by exploring the cGPS distribution between two groups, and found that a cGPS threshold of 1.505 could be used to clearly separate the two groups (Fig. 4 C). No patient with cGPS 1.505 entered the DP state (Fig. 4 D). Using the above-identified cGPS thresholds in a prospective study To validate the potential utility of the cGPS thresholds for cGVHD management in the clinic, we performed a prospective study in a new cohort of 77 participants (Cohort-3: 35 cGVHD patients, 10 non-GVHD patients, and 32 healthy donors). The cGVHD-specific B cell subpopulation of each participant was analyzed and cGPS values were generated (Figure S2). The cGPS thresholds were then used to divide the cGVHD and non-GVHD patients into high-risk and low-risk groups. Their clinical information is summarized in Table S1 . At 3 months after the initial evaluation, the 10 non-GVHD patients were re-evaluated based on the NIH consensus criteria, and the results were compared to those generated by our patient classification strategy. As shown in Fig. 5 B and 5 C, four out of the four non-GVHD patients in high-risk group (with an initial cGPS > 1.150) were diagnosed with cGVHD at the second evaluation, while six of the six cases with cGPS < 1.150 (low-risk group) were still in stable state at the second evaluation. The evaluation accuracy of the cGPS module therefore reached 100% in predicting non-GVHD patients who would become disease-progressed. We then assessed the ability of cGPS to predict the disease progression of cGVHD. As shown in Fig. 5 D and 5 E, all 24 patients in the low-risk group (with an initial cGPS < 1.505) were diagnosed as PR/CR/SD (i.e., non-DP) at the second evaluation. The accuracy of the cGPS module was thus 100% for predicting no disease progression. Of the eight patients in the high-risk group (with cGPS > 1.505), four (50%) were classified into the DP group at the second assessment, one was lost to follow-up, and one died of infection. The results of our prospective study therefore collectively indicated that the cGPS module may be a powerful tool for predicting the future course of disease for both cGVHD and non-GVHD patients in the clinic. This strategy should help doctors obtain timely and effective information that can be leveraged to improve patient care. Overall summary We herein leveraged previous reports that aberrant B-cell homeostasis could influence the progression of cGVHD to develop a new strategy for recognizing and predicting the disease. Using spectral flow cytometry in a small cohort, we successfully identified a cGVHD-specific subpopulation of B cells and delineated the defining markers of this subgroup. By applying machine learning in an independent cohort, we derived a simple marker combination and used it to develop an all-in-one scoring module named cGPS. ROC analysis was used to identify cGPS thresholds that can be used to evaluate disease progression in cGVHD and non-GVHD patients of this cohort. Finally, we performed a prospective study in a new cohort using these thresholds, and verified that our cGPS module could be a powerful tool for predicting the future course of disease among cGVHD and non-GVHD patients in the clinic. Discussion In this study, we identified a cGVHD-specific B cell subpopulation using spectral flow cytometry, this identified cGVHD-specific B cell subpopulation expressed the surface markers CD19 + CD20 − CD27 + CD38 + IgD − . Moreover, CD269 and CD319 were highly expressed, suggesting that the cGVHD-specific B cell subpopulation could correspond to plasmablast-like cells. Plasmablasts are generally expected to play an important role in the etiology of cGVHD 40 . Increased levels of B cell activation factor (BAFF) disrupt the negative selection mechanisms to prime the survival of autoreactive B cells, which leads to altered peripheral B cell compartment including post-germinal center plasmablasts (CD27 + CD38 hi IgD – ) 20 . These abnormal plasmablasts have pathologic functions, including autoantibody production, cross-presentation to T cells, and B cell cytokine production, all of which could promote the development of cGVHD 41 . In addition, results obtained from three independent cohorts confirmed the universality of this subpopulation: the majority of cGVHD patients had a persistent and stable subpopulation of aberrant B cells, and this was far more frequent in patients than in the healthy donor group. We further found that the levels of these aberrant B cells were positively associated with disease progression. This is consistent with recent reports that there is a strong correlation between the development of cGVHD and high levels of plasmablasts 42,43 and that an increase of plasmablasts accompanies cGVHD progression 44 . Given the above, we believed that our findings were rational from an etiological perspective. Recognizing the early signs and symptoms of cGVHD remains a challenge. While the NIH consensus meetings have developed a series of diagnostic strategies, there is still no clear guidance on how to predict a patient’s progression to cGVHD at a very early stage. E. M. Weissinger et al. developed a proteome pattern for early cGVHD diagnosis based on mass spectrometry, the higher cost and 84% sensitivity of this method has hindered its clinical application 45 . We used machine learning to quantify the importance of each marker and streamline the marker set for the cGVHD-associated B cell subset to CD27 + CD86 + CD20 − , then converted the data obtained from these refined markers into a more simplified format called the cGPS. Our cGPS module successfully discerned high-risk patients 3 months before they were diagnosed. Among non-GVHD patients with a cGPS > 1.150, almost everyone developed cGVHD during the 3-month observation period both in the retrospective and prospective studies. However, 100% of non-GVHD patients with cGPS < 1.150 had shown no disease occurs at the 3-month timepoint. Indeed, 50% of these non-GVHD patients were followed for 6 months, and the results suggest that our module can provide an extended and sensitive warning of disease progression. Another clinical challenge is to predict a future response or outcome for a cGVHD patient 16 . This partially reflects our lack of a methos for real-time disease monitoring. “Trial-and-error” remains the only treatment strategy for cGVHD patients 46 . Our cGPS module showed a great capacity for reflecting treatment efficacy in cGVHD patients: 100% patients with cGPS 1.505, the treatment was adjusted in four cases, with the addition of a therapy such as MSC transplantation. Upon the treatment adjustment, the cGPS of these patients decreased to < 1.505 and their disease improved, as found in a follow-up observation (data not shown). This further revealed that our cGPS module could dynamically reflect therapeutic responses and might be an ideal tool for real-time monitoring of cGVHD. Although numerous markers have been identified as being useful for cGVHD diagnosis or prognosis 47–50 , few of the relevant studies have included prospective research. Moreover, some of the identified markers are specific to certain organs, which can restrict their clinical application. Here, we included a prospective study to increase our sample size and improve the strength of our conclusions. We also carefully examined all participants information to ensure that no selective bias was introduced to our study. When calculating the required sample size, we set the power to 0.8, the effect size to 0.5, and the significance level to 0.05. Based on these parameters, we calculated that, for the prospective study, a sample size > 33 would minimize the impacts of type I and type II errors on the final results. For our prospective study, we recruited 35 cGVHD patients, and thus met the required number. We are thus sufficiently confident that cGPS 1.505 was a reliable threshold for judging whether a cGVHD treatment would be effective. Unfortunately, we recruited only 10 patients who could be used to study the ability of cGPS to provide early progression warning for cGVHD, yielding a power of only approximate to 0.3. Although the limited patient number hindered our ability to draw strong conclusions about the use of cGPS 1.150 as a threshold in the non-GVHD group, the combination of retrospective and prospective study designs and the significant difference of the principal effect suggest that this threshold can be used to predict the risk of a non-GVHD patient developing cGVHD. That said, cGPS 1.150 might be an approximation of the most accurate threshold, and future work is needed to clarify this point. Conclusion By combining the power of spectral flow cytometry and machine learning, we successfully invented a dynamic forecasting module, named cGPS, for chronic graft-versus-host disease. This module enables the sensitive and dynamic monitoring of cGVHD progression, and thus may enable the early diagnosis and treatment efficacy assessment for cGVHD. Our findings cater to the clinical needs of cGVHD patients, and we believe that the cGPS module has the potential to become an ideal tool for estimating the progression of cGVHD. Abbreviations AUC area under the curve BAFF B cell activating factor cGVHD Chronic graft-versus-host disease cGPS cGVHD progress score CR complete response DP disease progression HSCT hematopoietic stem cell transplantation mAb monoclonal antibody NIH National Institutes of Health PR partial response ROC curve receiver operating characteristic curve SD stable disease UMAP Uniform Manifold Approximation and Projection Declarations Ethical approval and consent to participate This study was conducted in compliance with the Helsinki declaration. All procedures involving human subjects were approved by the Ethics Committee of Nanfang Hospital, Southern Medical University (Ethical approval No. NCT04692376). All patients gave written informed consent to participate in the study. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests All authors declare they have no conflicting interest. Authors' contributions APX, XC, YM and JC conceptualized and designed the project; APX and XC supervised the research; YM, JC, JS, GL, XL, TW, JL, ZL and XZ performed experiments and/or data analysis; QL, ZF and NX contributed to the provision of the patients and the collection of data. JC, JS, XL and XC performed the spectral flow cytometry data analysis. YM and WH performed the bioinformatic analysis. MY, XC, JC and WH drafted the manuscript. APX, XC and MY revised the manuscript. All authors read and approved the final manuscript. Funding This work was supported by grants from the National Key Research and Development Program of China, Stem Cell and Translational Research (2022YFA1104100, 2022YFA1105000); the National Natural Science Foundation of China (32130046, 82270230, 81970109); Guangdong Basic and Applied Basic Research Foundation (2023B1515020119); Key Scientific and Technological Program of Guangzhou City (2023B01J1002); Pioneering talents project of Guangzhou Development Zone (2021-L029); the Shenzhen Science and Technology Program (KJZD20230923114504008). References D'Souza A, Fretham C, Lee SJ, et al. Current Use of and Trends in Hematopoietic Cell Transplantation in the United States. Biol Blood Marrow Transplant. 2020;26(8):e177-e182. MacDonald KPA, Hill GR, Blazar BR. Chronic graft-versus-host disease: biological insights from preclinical and clinical studies. Blood. 2017;129(1):13–21. Zanin-Zhorov A, Blazar BR. ROCK2, a critical regulator of immune modulation and fibrosis has emerged as a therapeutic target in chronic graft-versus-host disease. Clinical Immunology. 2021;230:108823. Lee SJ. Classification systems for chronic graft-versus-host disease. Blood. 2017;129(1):30–37. Sullivan KM, Witherspoon RP, Storb R, et al. Prednisone and azathioprine compared with prednisone and placebo for treatment of chronic graft-v-host disease: prognostic influence of prolonged thrombocytopenia after allogeneic marrow transplantation. Blood. 1988;72(2):546–554. Flowers ME, Martin PJ. How we treat chronic graft-versus-host disease. Blood. 2015;125(4):606–615. Penack O, Marchetti M, Ruutu T, et al. Prophylaxis and management of graft versus host disease after stem-cell transplantation for haematological malignancies: updated consensus recommendations of the European Society for Blood and Marrow Transplantation. Lancet Haematol. 2020;7(2):e157-e167. Jagasia MH, Greinix HT, Arora M, et al. National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: I. The 2014 Diagnosis and Staging Working Group Report. Biology of Blood and Marrow Transplantation. 2015;21(3):389–401.e381. Wolff D, Hilgendorf I, Wagner-Drouet E, et al. Changes in Immunosuppressive Treatment of Chronic Graft-versus-Host Disease: Comparison of 2 Surveys within Allogeneic Hematopoietic Stem Cell Transplant Centers in Germany, Austria, and Switzerland. Biol Blood Marrow Transplant. 2019;25(7):1450–1455. Saidu NEB, Bonini C, Dickinson A, et al. New Approaches for the Treatment of Chronic Graft-Versus-Host Disease: Current Status and Future Directions. Front Immunol. 2020;11:578314. Wolff D, Greinix H, Lee SJ, et al. Biomarkers in chronic graft-versus-host disease: quo vadis? Bone Marrow Transplantation. 2018;53(7):832–837. Milosevic E, Babic A, Iovino L, Markovic M, Grce M, Greinix H. Use of the NIH consensus criteria in cellular and soluble biomarker research in chronic graft-versus-host disease: A systematic review. Front Immunol. 2022;13:1033263. Peric Z, Schoemans H, Peczynski C, et al. Management of Steroid-Refractory Graft-Versus-Host Disease - a Survey By the Transplant Complications Working Party of the EBMT. Blood. 2021;138:2884. Holtzman NG, Pavletic SZ. The clinical landscape of chronic graft-versus-host disease management in 2021. British Journal of Haematology. 2022;196(4):830–848. Holtzman NG, Pavletic SZ. The clinical landscape of chronic graft-versus-host disease management in 2021. 2022;196(4):830–848. Sarantopoulos S, Cardones AR, Sullivan KM. How I treat refractory chronic graft-versus-host disease. Blood. 2019;133(11):1191–1200. Zeiser R. Novel Approaches to the Treatment of Chronic Graft-Versus-Host Disease. Journal of Clinical Oncology. 2023;41(10):1820–1824. Zeiser R, Sarantopoulos S, Blazar BR. B-cell targeting in chronic graft-versus-host disease. Blood. 2018;131(13):1399–1405. Zeiser R, Blazar BR. Pathophysiology of Chronic Graft-versus-Host Disease and Therapeutic Targets. New England Journal of Medicine. 2017;377(26):2565–2579. Sarantopoulos S, Ritz J. Aberrant B-cell homeostasis in chronic GVHD. Blood. 2015;125(11):1703–1707. Allen JL, Fore MS, Wooten J, et al. B cells from patients with chronic GVHD are activated and primed for survival via BAFF-mediated pathways. Blood. 2012;120(12):2529–2536. Jia W, Poe JC, Su H, et al. BAFF promotes heightened BCR responsiveness and manifestations of chronic GVHD after allogeneic stem cell transplantation. Blood. 2021;137(18):2544–2557. Li X, Gao Q, Feng Y, Zhang X. Developing role of B cells in the pathogenesis and treatment of chronic GVHD. Br J Haematol. 2019;184(3):323–336. Jia W, Poe JC, Su H, et al. Recipient-Derived BAFF and Alloantigen Synergistically Activate B Cells in Murine Chronic Gvhd. Blood. 2016;128(22):498–498. Malard F, Labopin M, Yakoub-Agha I, et al. Rituximab-based first-line treatment of cGVHD after allogeneic SCT: results of a phase 2 study. Blood. 2017;130(20):2186–2195. Solomon SR, Sizemore CA, Ridgeway M, et al. Safety and efficacy of rituximab-based first line treatment of chronic GVHD. Bone Marrow Transplantation. 2019;54(8):1218–1226. Peng Y, Chen X, Liu Q, et al. Alteration of naïve and memory B-cell subset in chronic graft-versus-host disease patients after treatment with mesenchymal stromal cells. Stem Cells Transl Med. 2014;3(9):1023–1031. Jagasia MH, Greinix HT, Arora M, et al. National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: I. The 2014 Diagnosis and Staging Working Group report. Biol Blood Marrow Transplant. 2015;21(3):389–401.e381. Filipovich AH, Weisdorf D, Pavletic S, et al. National Institutes of Health consensus development project on criteria for clinical trials in chronic graft-versus-host disease: I. Diagnosis and staging working group report. Biol Blood Marrow Transplant. 2005;11(12):945–956. Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573–3587.e3529. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine learning in Python. the Journal of machine Learning research. 2011;12:2825–2830. Avni B, Neiman D, Shaked E, et al. Chronic Graft Versus Host Disease Detected By Tissue-Specific Cell-Free DNA Methylation Biomarkers. Blood. 2022;140(Supplement 1):1574–1575. McInnes L, Healy J, Melville J. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:180203426 . 2018. Traag VA, Waltman L, Van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports. 2019;9(1):5233. Kitko CL, Pidala J, Schoemans HM, et al. National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: IIa. The 2020 Clinical Implementation and Early Diagnosis Working Group Report. Transplant Cell Ther. 2021;27(7):545–557. Cuvelier GDE, Nemecek ER, Wahlstrom JT, et al. Benefits and challenges with diagnosing chronic and late acute GVHD in children using the NIH consensus criteria. Blood. 2019;134(3):304–316. Carpenter PA, Logan BR, Lee SJ, et al. A phase II/III randomized, multicenter trial of prednisone/sirolimus versus prednisone/ sirolimus/calcineurin inhibitor for the treatment of chronic graft-versus-host disease: BMT CTN 0801. Haematologica. 2018;103(11):1915–1924. Pusic I, Pavletic SZ. Challenges in Conducting Studies in Chronic Graft-versus-Host Disease. Clin Hematol Int. 2019;1(1):36–44. Schoemans HM, Lee SJ, Ferrara JL, et al. EBMT – NIH – CIBMTR Task Force position statement on standardized terminology & guidance for graft-versus-host disease assessment. Bone Marrow Transplantation. 2018;53(11):1401–1415. MacDonald KP, Hill GR, Blazar BR. Chronic graft-versus-host disease: biological insights from preclinical and clinical studies. Blood. 2017;129(1):13–21. McManigle W, Youssef A, Sarantopoulos S. B cells in chronic graft-versus-host disease. Hum Immunol. 2019;80(6):393–399. Dubovsky JA, Flynn R, Du J, et al. Ibrutinib treatment ameliorates murine chronic graft-versus-host disease. J Clin Invest. 2014;124(11):4867–4876. Flynn R, Du J, Veenstra RG, et al. Increased T follicular helper cells and germinal center B cells are required for cGVHD and bronchiolitis obliterans. Blood. 2014;123(25):3988–3998. Wang KS, Kim HT, Nikiforow S, et al. Antibodies targeting surface membrane antigens in patients with chronic graft-versus-host disease. Blood. 2017;130(26):2889–2899. Weissinger EM, Human C, Metzger J, et al. The proteome pattern cGvHD_MS14 allows early and accurate prediction of chronic GvHD after allogeneic stem cell transplantation. Leukemia. 2017;31(3):654–662. Yalniz FF, Murad MH, Lee SJ, et al. Steroid Refractory Chronic Graft-Versus-Host Disease: Cost-Effectiveness Analysis. Biol Blood Marrow Transplant. 2018;24(9):1920–1927. Ji R, Li Y, Huang R, Xiong J, Wang X, Zhang X. Recent advances and research progress in biomarkers for chronic graft versus host disease. Critical Reviews in Oncology/Hematology. 2023;186:103993. Svegliati S, Olivieri A, Campelli N, et al. Stimulatory autoantibodies to PDGF receptor in patients with extensive chronic graft-versus-host disease. Blood, The Journal of the American Society of Hematology. 2007;110(1):237–241. Giesen N, Schwarzbich MA, Dischinger K, et al. CXCL9 Predicts Severity at the Onset of Chronic Graft-versus-host Disease. Transplantation. 2020;104(11):2354–2359. Levine JE, Braun TM, Harris AC, et al. A prognostic score for acute graft-versus-host disease based on biomarkers: a multicentre study. The Lancet Haematology. 2015;2(1):e21-e29. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Supplementaryfiguresandtables.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4264249","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":291391745,"identity":"c2c2b8b7-53c7-4b6d-9633-4fc325c46afe","order_by":0,"name":"Andy Xiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACZjBpA2QwN8DEDIjRkgZkMBKrBQIOAzGxWgyOMz97+HXH+Wj+dsYGxp9tdYkN7M3bJBhq7uDUItnMZm4se+Z27ozDjA3MvG2HExt4jpVJMBx7hlMLPzODmbRk2+3cBpAWxrYDiQ0SOWYSjA2HcWphY2b/BtRyLnf+YZjD5N/g18LPzGMm+bHtQO4GoBYG3jZmoC08+LVINvOUSTOeSc7dCNRymOfcYeM2nrRii4RjuLUYnD++TfLnDrvceecPH3z4o6xOtp/98MYbH2pwawEBZt4GCOMAIxvQdyBWAl4NwDj82QBj/iGgdBSMglEwCkYkAAAUE1P5VcTlYAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3409-5012","institution":"Sun Yat-sen University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Andy","middleName":"","lastName":"Xiang","suffix":""},{"id":291391746,"identity":"47551eb3-0496-4cb8-bb9e-45846dc7a91d","order_by":1,"name":"Yuanchen Ma","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuanchen","middleName":"","lastName":"Ma","suffix":""},{"id":291391747,"identity":"9dd65494-e402-46b3-8dfc-bcdea7bab802","order_by":2,"name":"Jieying Chen","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jieying","middleName":"","lastName":"Chen","suffix":""},{"id":291391748,"identity":"665d89dd-240a-4863-982d-77e56c9715a0","order_by":3,"name":"Zhiping Fan","email":"","orcid":"","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiping","middleName":"","lastName":"Fan","suffix":""},{"id":291391749,"identity":"3f4db414-16c2-4df9-bb77-8e997d438ddc","order_by":4,"name":"Jiahao Shi","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Shi","suffix":""},{"id":291391750,"identity":"14fe5fd1-36ba-48f8-bd8c-e7446e41cda7","order_by":5,"name":"Gang Li","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Li","suffix":""},{"id":291391751,"identity":"c61f2705-207a-4a78-bda9-45edd8320d84","order_by":6,"name":"Xiaobo Li","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaobo","middleName":"","lastName":"Li","suffix":""},{"id":291391752,"identity":"a4782fa4-be6a-49ef-83ee-eef28cc56cf4","order_by":7,"name":"Tao Wang","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Wang","suffix":""},{"id":291391753,"identity":"a2ef5fa8-3e02-4d40-a506-3a17dd170e40","order_by":8,"name":"Na Xu","email":"","orcid":"","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Xu","suffix":""},{"id":291391754,"identity":"694ddcc2-2e51-4a8e-8b1b-0771dc6ac35c","order_by":9,"name":"Jialing Liu","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jialing","middleName":"","lastName":"Liu","suffix":""},{"id":291391755,"identity":"965d7d22-67ba-4c6d-8fd0-52de325f5797","order_by":10,"name":"Zhishan Li","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhishan","middleName":"","lastName":"Li","suffix":""},{"id":291391756,"identity":"7c831d18-865d-4011-aab5-4103b71cca39","order_by":11,"name":"Xiaoran Zhang","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoran","middleName":"","lastName":"Zhang","suffix":""},{"id":291391757,"identity":"5a1fdb6a-dfd9-4b67-8ecd-a67e76968e95","order_by":12,"name":"Qifa Liu","email":"","orcid":"https://orcid.org/0000-0003-4015-3952","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qifa","middleName":"","lastName":"Liu","suffix":""},{"id":291391758,"identity":"85036689-0d26-4add-a02d-175c16c83ef1","order_by":13,"name":"Weijun Huang","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weijun","middleName":"","lastName":"Huang","suffix":""},{"id":291391759,"identity":"7588c3f3-d00c-49a4-ad9b-200277b5360d","order_by":14,"name":"Xiaoyong Chen","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoyong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-04-14 08:55:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4264249/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4264249/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55112516,"identity":"03e9ea2a-019a-44e2-b370-a4b0491cbf58","added_by":"auto","created_at":"2024-04-22 19:09:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":638584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ecGVHD-specific cell clusters and corresponding markers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) UMAP plot for the B cell samples of 10 participants. Cell coloring indicates the sample source: red, cGVHD patients; gray, healthy donors.\u003c/p\u003e\n\u003cp\u003e(B) UMAP plot for the B cell samples of 10 participants. Cells are colored to indicate participants category(left panel) and cell cluster(right panel).\u003c/p\u003e\n\u003cp\u003e(C) Bar plot of cell proportions (y-axis) contained in each Louvain cell cluster (x-axis). Bar color indicates the sample source: red, cGVHD patients; gray, healthy donors. Bar length represents the percentage of the sample under the corresponding cluster.\u003c/p\u003e\n\u003cp\u003e(D)\u003cstrong\u003e \u003c/strong\u003eViolin plot of the markers selected as potentially defining cGVHD-specific cell clusters. The x-axis in each plot represents the Louvain cell clusters, while the y-axis represents the relative expression level of each marker. The width of each curve corresponds to the approximate frequency of data.\u003c/p\u003e\n\u003cp\u003e(E)\u003cstrong\u003e \u003c/strong\u003eBox plot of machine learning model accuracy scores for each marker combination. The x-axis represents the marker combination category; for example, \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;indicates a combination consisting of two non-repetitive markers randomly picked from all eight markers. The y-axis represents the machine learning model-calculated accuracy score for the ability of each combination to distinguish those with and without cGVHD. Each dot represents a machine learning model built from a distinct marker combination.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4264249/v1/ea886251c64711f5ea4f795c.png"},{"id":55112524,"identity":"c98bcfcf-1186-4081-9f90-d5fb13a53d0e","added_by":"auto","created_at":"2024-04-22 19:09:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":275862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOptimization of marker combinations and building of the cGPS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Scatter plot showing the results obtained using each high-performing model on cohort 2. The x-axis presents the marker combinations; for example, \u003cem\u003eC\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e8\u003c/sub\u003e\u0026nbsp;indicates a combination consisting of two non-repetitive markers randomly picked from all eight markers. The y-axis presents the corresponding AUC scores.\u003c/p\u003e\n\u003cp\u003e(B)\u003cstrong\u003e \u003c/strong\u003eAUC plot showing the performance of the best six-marker combination models. The x-axis presents the false-positive rate and the y-axis presents the true-positive rate. The color of each curve indicates the marker combination.\u003c/p\u003e\n\u003cp\u003e(C)\u003cstrong\u003e \u003c/strong\u003eThe representative flow cytometry plot showing the CD20\u003csup\u003e-\u003c/sup\u003eCD27\u003csup\u003e+\u003c/sup\u003eCD86\u003csup\u003e+\u003c/sup\u003e cell proportions among cGVHD and healthy donor samples.\u003c/p\u003e\n\u003cp\u003e(D) Formula used to transfer the results from a marker combination into a more accessible scoring system.\u003c/p\u003e\n\u003cp\u003e(E)\u003cstrong\u003e \u003c/strong\u003eStatistical analysis of differences in the cGPS among cGVHD and healthy donor samples. The x-axis represents the healthy donors (HD, gray) and cGVHD patients (red). The y-axis presents the corresponding cGPS.\u0026nbsp;\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4264249/v1/b65fa60bd509f568d167fefe.png"},{"id":55112519,"identity":"f1e277fd-a361-4428-86b6-1954074a14bd","added_by":"auto","created_at":"2024-04-22 19:09:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":264104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRetrospective study of cGPS in non-GVHD patients\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e(A) Statistical analysis of cGPS among patients with stable state (those remaining in non-GVHD status) and active state (patients that developed cGVHD). **: \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01. The x-axis presents the two non-GVHD groups. The y-axis presents the cGPS. The red dashed line indicates the approximate cGPS threshold for separating the two groups.\u003c/p\u003e\n\u003cp\u003e(B) ROC analysis identifying the threshold of cGPS that can be used to separate active group on non-GVHD patients. The left panel shows the AUC plot, where the x-axis presents the false-positive rate and the y-axis presents the true-positive rate. The right panel shows various parameters, including the AUC, threshold, specificity, sensitivity, and accuracy values, with 95% confidence intervals (CI).\u003c/p\u003e\n\u003cp\u003e(C)\u003cstrong\u003e \u003c/strong\u003eDensity plot of the two non-GVHD groups. The x-axis presents the cGPS, while the y-axis presents the frequency of each score. The area color indicates the group: gray, Stable groups; and red, Activate groups. The dark blue dashed line represents the threshold cGPS of 1.150.\u003c/p\u003e\n\u003cp\u003e(D)\u003cstrong\u003e \u003c/strong\u003ePie plot of cGPS distribution by group: The left pie plot represents cGPS \u0026lt; 1.150 and the right pie plot represents cGPS \u0026gt;= 1.150; gray indicates patients of the stable group and red indicates those of the active group.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4264249/v1/37475283b75a9910c42a34ca.png"},{"id":55114000,"identity":"6e236b60-9ca0-4be3-8bbc-dca1c7ba8248","added_by":"auto","created_at":"2024-04-22 19:17:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":237868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRetrospective study of cGPS among additional cGVHD patients\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e(A) Statistical analysis of cGPS between the two cGVHD groups: DP (disease progressed) and non-DP (PR: partial response, CR: complete response, SD: stable disease). ***: \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001. The x-axis presents the two cGVHD groups, while the y-axis presents the cGPS score. The red dashed line indicates the approximate cGPS threshold separating the two cGVHD groups.\u003c/p\u003e\n\u003cp\u003e(B) ROC analysis identifying the cGPS threshold that can be used to separate out the DP group of cGVHD patients. The left panel represents the AUC plot; the x-axis presents the false-positive rate and the y-axis presents true-positive rate. The right panel presents various parameters, including the AUC, threshold, specificity, sensitivity, and accuracy values, with 95% CI.\u003c/p\u003e\n\u003cp\u003e(C) Density plot for the two cGVHD groups. The x-axis presents the cGPS, while the y-axis presents the frequency of each score. The area color indicates the group: green, non-DP(PR/CR/SD) groups; and red, DP groups. The blue dashed line indicates the threshold cGPS of 1.505.\u003c/p\u003e\n\u003cp\u003e(D) Pie plot of cGPS distribution by category: The left pie plot presents cGPS \u0026lt; 1.505 and the right pie plot presents cGPS \u0026gt; 1.505; green, non-DP(PR/CR/SD) cGVHD patients; and red, DP cGVHD patients.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4264249/v1/c0cb35074343b4f4bee24997.png"},{"id":55112521,"identity":"91649ab3-2bb0-4f58-a54a-abda1012cd13","added_by":"auto","created_at":"2024-04-22 19:09:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":700231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProspective study of cGPS among cGVHD and non-GVHD patients\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e(A) The workflow of the prospective study. cGVHD patients were separated into high- and low-risk groups based on their cGPS. After 3 months, the states of these patients were re-evaluated based on the NIH consensus criteria, and the accuracy of the cGVHD classification was assessed.\u003c/p\u003e\n\u003cp\u003e(B) Scatter plot of non-GVHD patients at 3 months after the first evaluation. The x-axis presents the cGPS, the y-axis presents the NIH consensus score, the dot color indicates whether the patient had developed cGVHD (red) or remained non-GVHD (gray), and the black dashed line represents the cGPS threshold at 1.150.\u003c/p\u003e\n\u003cp\u003eC) Pie plot of cGPS distribution: The left pie plot presents the low-risk ground (cGPS \u0026lt; 1.150)and the right pie plot represents high-risk ground (cGPS \u0026gt; 1.150); gray indicates stable patients and red indicates active patients.\u003c/p\u003e\n\u003cp\u003e(D) Segment plot of differences in NIH scores between the first evaluation (Day 0) and 3 months later, among patients categorized based on initial cGPS \u0026lt; or \u0026gt; 1.505. The x-axis presents the time point and the y-axis presents the NIH score; red indicates disease progressed (DP), green indicates partial or complete response or stable disease (PR/CR/SD), and gray indicates stable disease (SD).\u003c/p\u003e\n\u003cp\u003e(E) Pie plot of cGPS distribution: The left pie plot presents low-risk groups (cGPS \u0026lt; 1.505)and the right pie plot represents high-risk groups (cGPS \u0026gt; 1.505); green indicates non-DP cGVHD patients and red indicates DP patients, and gray indicates lost contact or dead.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4264249/v1/c001ff64b3a120ea7a1585e0.png"},{"id":56031122,"identity":"29f84317-9da1-4f11-bba1-51223a6973de","added_by":"auto","created_at":"2024-05-07 17:54:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2212914,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4264249/v1/37d732e6-f404-45e5-ae34-d704b1d875ea.pdf"},{"id":55112522,"identity":"fefe8cb3-eb28-4657-bed1-f704e84747c3","added_by":"auto","created_at":"2024-04-22 19:09:44","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":783644,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiguresandtables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4264249/v1/29da759d7154f9b7310c9271.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Dynamic forecasting module for chronic graft-versus-host disease progression based on a disease-specific subpopulation of B cells","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic graft-versus-host disease (cGVHD) is a major and serious late complication in patients after allogeneic hematopoietic stem cell transplantation (allo-HSCT). It has become the leading cause of non-relapse mortality in allo-HSCT, exhibiting an incidence of approximately 30\u0026ndash;70% \u003csup\u003e1,2\u003c/sup\u003e. The clinical features resemble those of autoimmune diseases and affects multiple organs and tissues \u003csup\u003e3,4\u003c/sup\u003e. Corticosteroids are the mainstay of first-line treatment\u003csup\u003e5\u003c/sup\u003e and are administered either alone or in combination with immunosuppressants. However, the poor clinical outcomes of standard treatments and the significant toxicities induced by prolonged steroid treatment mean that approximately 50\u0026ndash;60% of patients require secondary treatment within 2 years\u003csup\u003e6\u003c/sup\u003e. Some second-line treatments have increased the response among patients, but the limited treatment options and lack of real-time disease activity monitoring continue to hinder cGVHD treatment and outcomes\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccording to the third National Institutes of Health (NIH) Consensus Development Project on Criteria for Clinical Trials in Chronic GVHD\u003csup\u003e8\u003c/sup\u003e, preemptive and individual therapy will be critical components of cGVHD management in the future\u003csup\u003e6,9,10\u003c/sup\u003e. However, it remains challenging to recognize the earliest signs and symptoms of cGVHD\u003csup\u003e11\u003c/sup\u003e. The standardization provided by the 2005 and 2014 NIH consensus projects helped improve the diagnostic accuracy and severity scoring for clinical trials, but the consensus criteria cannot be used to recognize or predict the imminent onset of cGVHD at an early stage, and some patients who do not meet the NIH criteria may still be at risk for developing active cGVHD\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe consensus criteria enable clinicians only to evaluate a patient\u0026rsquo;s current state, not predict a future response or outcome. This leads to a lag in cGVHD therapeutic strategy and necessitates an trial-and-error approach for subsequent treatments \u003csup\u003e13\u003c/sup\u003e. Usually, the therapeutic response should be assessed at 8\u0026ndash;12 weeks as it is reported that most therapies take some time to reach therapeutic peak \u003csup\u003e14\u003c/sup\u003e. If a patient exhibits progression of cGVHD after being on a given treatment for 4 or more weeks, a new treatment option should be offered\u003csup\u003e15,16\u003c/sup\u003e. Many patients may fail to engage with a given treatment for enough time to fully assess its efficacy (at least 4 weeks), limiting the ability of clinicians to identify an effective treatment for each patient\u003csup\u003e17\u003c/sup\u003e. The development of a real-time and objective indicator for cGVHD prediction and/or disease staging could be very meaningful in guiding preemptive therapy and/or establishing personalized therapy.\u003c/p\u003e \u003cp\u003eB cells play a substantiated role in cGVHD pathogenesis. Numerous studies have revealed that there are B cell homeostasis alterations in cGVHD\u003csup\u003e18,19\u003c/sup\u003e, including the presence of characteristic autoreactive B cells, delayed reconstitution of naive B cells\u003csup\u003e20\u003c/sup\u003e, and alterations of signaling pathway activities, such as increased BAFF signaling and abnormally activated BCR signaling\u003csup\u003e21\u0026ndash;24\u003c/sup\u003e. Depletion of B cells by rituximab or selective targeting of B cell signaling pathways can be effective therapeutic approaches in patients with cGVHD\u003csup\u003e25,26\u003c/sup\u003e. Our previous studies revealed that the mesenchymal stem cell (MSC)-based improvement of cGVHD was accompanied by alteration of na\u0026iuml;ve, memory, and regulatory B cell subsets, modulation of the plasma BAFF level, and the expression of BAFF-R on B lymphocytes\u003csup\u003e27\u003c/sup\u003e. Observations that B cell changes are closely related to cGVHD outcome suggested that a B cell-based strategy might be a useful approach for monitoring cGVHD activity.\u003c/p\u003e \u003cp\u003eAs an integrative approach to build a cGVHD disease activity evaluation module, we herein used spectral flow cytometry and a machine-learning model to assess B cell alterations in a cGVHD patient, and used retrospective and prospective studies to develop and initially validate an indicator for cGVHD management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eCohort 1: 5 healthy donors and 5 cGVHD patients were studied to identify a cGVHD-specific cell population.\u003c/p\u003e \u003cp\u003eCohort 2: 17 healthy donors, 31 cGVHD patients, and 15 non-GVHD patients were included. This cohort validated the universality of the disease-specific cell population, confirmed the best marker combination and developed the cGPS module for enhanced accessibility. It also used to assess the module's performance in early diagnosis and disease prognosis through retrospective analysis.\u003c/p\u003e \u003cp\u003eCohort 3: Comprised 32 healthy donors, 35 cGVHD patients, and 10 non-GVHD patients. It validated the cGPS module's ability to predict non-GVHD group members' disease development risk and forecast disease progression in cGVHD patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatients characteristics\u003c/h2\u003e \u003cp\u003eThe cGVHD patient inclusion criteria were as follows: 1) aged 15\u0026ndash;65 years; 2) gender unrestricted; 3) diagnosed with malignant hematologic disease, received allogeneic hematopoietic stem cell transplantation at least 3 months ; and 4) diagnosed with cGVHD according to the NIH criteria of 2005 and 2014. The exclusion criteria were: 1) presence of residual underlying malignant hematologic disease or disease progression; 2) treatment with rituximab (anti-CD20mAb) or implantation, or experience of life-threatening infection; and 3) difficult-to-control hypertension, congestive heart failure, frequent ventricular arrhythmia, angina, or other related symptom. cGVHD was diagnosed and graded at the time of sample collection. Patients who had received HSCT but had not developed cGVHD at the time of sample collection were defined as non-GVHD patients. Peripheral blood samples were collected from all patients and healthy donors, with their informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGrading of cGVHD\u003c/h2\u003e \u003cp\u003eOrgan scoring was conducted according to the 2005 and 2014 NIH consensus criteria for cGVHD\u003csup\u003e28,29\u003c/sup\u003e. The evaluated organs included skin, oral mucosa, eyes, gastrointestinal tract, liver, lung, joints, and genitalia. Organ scoring was independently assessed by at least two clinical hematologists.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePeripheral blood mononuclear cell (PBMC) isolation\u003c/h2\u003e \u003cp\u003eThe peripheral blood was diluted with 1\u0026times;PBS at a ratio of 1:1, and lymphocytes were isolated by Ficoll-Paque density gradient centrifugation. The obtained lymphocytes were washed with PBS to remove debris and platelets, and then resuspended with PBS for further phenotype staining, or resuspended with lymphocyte cryopreservation solution and then frozen and transferred to liquid nitrogen for storage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAntibodies and flow cytometry\u003c/h2\u003e \u003cp\u003ePBMC from cGVHD patients, non-GVHD patients, or healthy donors were stained for B cell phenotyping with a 20-marker panel that could accurately distinguish B cell subsets and indicate their developmental stage and activation status (Table S2), or with a five-marker panel for detecting specific B cell subsets. Antibodies were detailed in Table S2. Phenotyping data were collected using an Aurora (Cytek, USA) and a CytoFLEX (Beckman Coulter, USA) and analyzed using the FlowJo and CytExpert software packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDownstream analysis of flow cytometry data\u003c/h2\u003e \u003cp\u003eThe transformed data from FlowJo underwent analysis with Seurat, following the tutorial guidelines(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://satijalab.org/seurat/articles/pbmc3k_tutorial.html\u003c/span\u003e\u003cspan address=\"https://satijalab.org/seurat/articles/pbmc3k_tutorial.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e30\u003c/sup\u003e. Given that the data were previously normalized, quality control and normalization steps were omitted. Parameters were adjusted to accommodate the spectral flow cytometry data structure, specifying 20 markers per cell. Batch effect removal was performed for cohort 1 data using the ScaleData function with the vars.to.regress argument. We utilized random forest machine learning models to compare marker set accuracies for cohort 1. Within the Seurat framework, candidate markers representing a cGVHD-specific cell population were selected. Using Python's built-in combination function, we obtained all possible marker combinations. For each combination, a random forest machine model was constructed using Sklearn (version 0.23.2)\u003csup\u003e31\u003c/sup\u003e. Data were split into training and testing sets using train_test_split, allocating 70% for training and 30% for testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eModel selection criteria\u003c/h2\u003e \u003cp\u003eWe selected the models that could accurately distinguish cGVHD patients and healthy donors both in cohort1 and cohort2. Each model was directly used to distinguish disease samples (label 1) and healthy samples (label 0) without further training. Models with an AUC score\u0026thinsp;\u0026gt;\u0026thinsp;0.9\u003csup\u003e32\u003c/sup\u003e and the fewest markers in combination (2 or 3) were selected. These models were considered to represent a cGVHD-specific cell population with acceptable accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eROC analysis\u003c/h2\u003e \u003cp\u003eROC analysis was performed using pROC (version 1.16.2). The roc curve was first calculated using function roc and plot with pROC build-in function ggroc. The confidence interval for threshold, specificity,sensitivity, accuracy was calculated with a build-in function ci.coords, with parameter best.policy set to random.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAny two categories (e.g., HD vs. cGVHD or disease recovered vs. disease progressed in the cGVHD group) were compared using Fisher\u0026rsquo;s exact test. All statistical analyses were performed using R (version 4.0.2). Machine learning model selection was performed on the python platform (Python3.6). All codes generated for each analysis are available upon request.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCohort characteristics and B cell phenotyping profile\u003c/h2\u003e \u003cp\u003eWe enrolled 150 well-characterized participants into our study and distributed them to the following groups: 1) Control: healthy individuals recruited from volunteers (n\u0026thinsp;=\u0026thinsp;54); 2) cGVHD: individuals diagnosed using the criteria defined by National Institutes of Health (NIH) Consensus Conference (n\u0026thinsp;=\u0026thinsp;71); and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) non-GVHD: individuals who received HSCT but lacked any apparent clinical feature of cGVHD as defined by the NIH criteria (n\u0026thinsp;=\u0026thinsp;25). These participants were divided into the three cohorts. In cohort1, five cGVHD patients and five healthy donors were enrolled for an exploratory study. In cohort2, 31 cGVHD patients, 15 non-GVHD patients, and 17 healthy donors were enrolled for a retrospective study. In cohort3, 35 cGVHD patients, 10 non-GVHD patients, and 32 healthy donors were enrolled for a further prospective study.\u003c/p\u003e \u003cp\u003eB cell phenotyping profiles of cGVHD patients, non-GVHD patients, and healthy controls were generated by spectral flow cytometry using a 20-marker panel that could accurately distinguish B cell subsets and indicate the developmental stage and activation status of B cells (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The classical B cell subsets distinguishable by this phenotyping panel included transitional B cells, na\u0026iuml;ve B cells, memory B cells (including IgM\u003csup\u003e+\u003c/sup\u003e, marginal zone-like, and switched memory B cells), IgD\u003csup\u003e\u0026minus;\u003c/sup\u003eCD27\u003csup\u003e\u0026minus;\u003c/sup\u003e double-negative B cells, and plasmablasts (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The B cell phenotyping profiles were further explored to find a cGVHD-related B cell subset, build a cGVHD disease activity evaluation module, and figure out the threshold for cGVHD management through retrospective and prospective studies. Clinical information on the cGVHD and non-GVHD group members are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and S1.\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\u003eClinical characteristics of participants in Cohort 1\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\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecGVHD, n\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (range), y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (19\u0026ndash;56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSource of graft, no(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMT\u0026thinsp;+\u0026thinsp;PBSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBSCT\u0026thinsp;+\u0026thinsp;UCBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSource of donor, no(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSibling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnrelated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHLA matching, no(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentical matched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-identical matched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaploid matched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGVHD prophylaxis, no(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u0026thinsp;+\u0026thinsp;ATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u0026thinsp;+\u0026thinsp;MMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u0026thinsp;+\u0026thinsp;ATG\u0026thinsp;+\u0026thinsp;MMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-Cy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial disease, no(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML/AML from MDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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 \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\u003eClinical characteristics of participants in Cohort 2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ecohort2 cGVHD patient profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003ecohort2 non-GVHD patient profile\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\u003eCharacteristic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR/CR/SD cGVHD, n\u0026thinsp;=\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDP cGVHD, n\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCharacteristic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStable, n\u0026thinsp;=\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eActive, n\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, median (range), y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (17\u0026ndash;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (17\u0026ndash;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAge, median (range), y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27 (16\u0026ndash;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39.5 (19\u0026ndash;57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, No.(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eGender, n0(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSource of graft, No.(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSource of graft, No.(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePBSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMT\u0026thinsp;+\u0026thinsp;PBSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBMT\u0026thinsp;+\u0026thinsp;PBSCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBSCT\u0026thinsp;+\u0026thinsp;UCBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePBSCT\u0026thinsp;+\u0026thinsp;UCBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSource of donor, No.(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSource of donor, No.(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSibling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSibling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnrelated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnrelated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eParents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHLA matching, No.(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eHLA matching, No.(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentical matched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIdentical matched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-identical matched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-identical matched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaploid matched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHaploid matched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGVHD prophylaxis, No.(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eGVHD prophylaxis, No.(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u0026thinsp;+\u0026thinsp;ATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u0026thinsp;+\u0026thinsp;ATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u0026thinsp;+\u0026thinsp;MMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u0026thinsp;+\u0026thinsp;MMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u0026thinsp;+\u0026thinsp;ATG\u0026thinsp;+\u0026thinsp;MMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSA\u0026thinsp;+\u0026thinsp;MTX\u0026thinsp;+\u0026thinsp;ATG\u0026thinsp;+\u0026thinsp;MMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-Cy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePost-Cy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitial disease, No.(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eInitial disease, No.(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML/AML from MDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAML/AML from MDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of a cGVHD-specific B cell subpopulation\u003c/h2\u003e \u003cp\u003eSpectral flow cytometry data obtained from cohort1 (10 participants; 5 cGVHD patients and 5 healthy donors) were used to identify a cGVHD-specific B cell population. Uniform Manifold Approximation and Projection (UMAP) and Louvain clustering of cells \u003csup\u003e33,34\u003c/sup\u003e yielded three cell clusters (Clusters 0\u0026ndash;2; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). From the cell composition of each cluster, Cluster 2 was found to be a cGVHD-specific cell cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003eC): cGVHD samples accounted for 82.0% of the Cluster 2 cells (1748 cells), while healthy donor samples accounted for only 18% (383 cells). To phenotypically characterize the cluster 2, we examined the cell surface phenotypes of each cluster. From among the 20 studied markers, CD20, CD268, CD5, CD27, CD38, CD86, CD269, and CD319 were identified as specific markers for Cluster 2 based on their expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eWe used these eight markers to generate an unbiased random forestry machine learning model and found that these markers could accurately label a disease-specific cell cluster. We then explored whether these eight markers could be further streamlined, with the goal of improving the clinical applicability of our method. We iterated all possible marker combinations and evaluated their accuracy. As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, the accuracy scores indicated that certain combinations of only two or three markers could substitute for the full eight-marker panel with little loss of accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eOptimal marker combination for identifying a cGVHD-specific B cell subpopulation\u003c/h2\u003e \u003cp\u003eTo identify a simple marker combination that could identify a cGVHD-specific B cell subset, we used an independent cohort (48 participants; 31cGVHD patients and 17 healthy donors) and marker combinations with accuracy scores\u0026thinsp;\u0026gt;\u0026thinsp;0.9 (20 combinations; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The three-marker combination of CD20/CD27/CD86 yielded the best AUC score (AUC score\u0026thinsp;=\u0026thinsp;0.958, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and thus appeared to be the optimal option.\u003c/p\u003e \u003cp\u003eThe results of further cross-validation using flow cytometry indicated that this CD20/CD27/CD86 marker set could precisely identify a cGVHD-specific B cell subpopulation, as the CD27\u003csup\u003e+\u003c/sup\u003eCD86\u003csup\u003e+\u003c/sup\u003eCD20\u003csup\u003e\u0026minus;\u003c/sup\u003e B cells were extremely frequent in cGVHD patients and almost entirely absent from healthy donors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Thus, this three-marker combination appeared to successfully substitute for the original 20-marker panel. To further increase the clinical applicability of this marker set, we utilized its distribution to develop a module known as the cGVHD Progress Score (cGPS). This module aims to provide a single, easily interpretable score, serving as a direct reflection of the disparity between cGVHD and health. Using the formula presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, we calculated the cGPS for each participant in the cohort. The cGPS was significantly different between cGVHD patients and healthy donors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00003, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eUsing a cGPS threshold to predict progression risk among non-GVHD patients\u003c/h2\u003e \u003cp\u003eRecognizing or predicting the imminent onset of cGVHD at an early stage remains a significant challenge because non-GVHD patients do not present obvious signs (per the NIH consensus criteria) at the early stage of developing cGVHD \u003csup\u003e35\u0026ndash;37\u003c/sup\u003e. Hence, we tested whether cGPS could potentially predict progression of non-GVHD patients to cGVHD. Details on the non-GVHD patients enrolled into Cohort-2 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. According to their progression (or lack thereof) to cGVHD within a 3-month period observation, the subjects were categorized as stable state (those remaining in non-GVHD status) and active state (patients that developed cGVHD). The patients of the two groups had similar baseline clinical characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur statistical analysis indicated that the cGPS values were significantly different between the stable and active groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0029). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, the cGPS of the patients in the stable group were largely\u0026thinsp;\u0026lt;\u0026thinsp;1 whereas those of the patients in the active group were \u0026gt;\u0026thinsp;1. This suggested that a threshold cGPS approximate to 1 might potentially be used to distinguish between patients in the stable and active states. Our receiver operating characteristics (ROC) analysis revealed that the precise cGPS threshold for this cohort was 1.150; indeed, 100% of non-GVHD patients scoring below this threshold were in stable state (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In addition to being able to distinguish the state of non-GVHD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), this cGPS threshold could also be used to predict whether non-GVHD patients would become sick: As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, four out of five (80%) non-GVHD patients with cGPS\u0026thinsp;\u0026gt;\u0026thinsp;1.150 developed into cGVHD during the evaluation period, whereas all 10 non-GVHD patients with cGPS\u0026thinsp;\u0026lt;\u0026thinsp;1.150 remained in stable state.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eUsing a cGPS threshold to estimate the treatment efficiency of cGVHD\u003c/h2\u003e \u003cp\u003eIt is difficult to quickly and efficiently estimate whether a cGVHD patient is likely to become disease-progressed\u003csup\u003e38,39\u003c/sup\u003e. As NIH consensus criteria for organ scoring was a follow-up evaluation system, cGVHD real time state monitor was hard to captured. Since the cGPS module was generated from the disease-related B cell subset, we questioned whether it might be able to distinguish disease-progressed cGVHD patients. To address this question, we classified the cGVHD patients of Cohort-2 as having stable disease (SD), partial response (PR), complete response (CR), or disease progression (DP), and collected them into two groups: the DP group and the non-DP group (PR/CR/SD groups). The baseline clinical characteristics of the two groups are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, the cGPS were significantly different between the DP and non-DP groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00029), with an apparent threshold around 1.5. Indeed, our ROC analysis revealed that a cGPS of 1.505 could be used to successfully separate the DP and non-DP groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). We further validated this finding by exploring the cGPS distribution between two groups, and found that a cGPS threshold of 1.505 could be used to clearly separate the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). No patient with cGPS\u0026thinsp;\u0026lt;\u0026thinsp;1.505 entered the DP state, whereas five out of 11 (45.5%) patients with cGPS\u0026thinsp;\u0026gt;\u0026thinsp;1.505 entered the DP state (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eUsing the above-identified cGPS thresholds in a prospective study\u003c/h2\u003e \u003cp\u003e To validate the potential utility of the cGPS thresholds for cGVHD management in the clinic, we performed a prospective study in a new cohort of 77 participants (Cohort-3: 35 cGVHD patients, 10 non-GVHD patients, and 32 healthy donors). The cGVHD-specific B cell subpopulation of each participant was analyzed and cGPS values were generated (Figure S2). The cGPS thresholds were then used to divide the cGVHD and non-GVHD patients into high-risk and low-risk groups. Their clinical information is summarized in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. At 3 months after the initial evaluation, the 10 non-GVHD patients were re-evaluated based on the NIH consensus criteria, and the results were compared to those generated by our patient classification strategy. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, four out of the four non-GVHD patients in high-risk group (with an initial cGPS\u0026thinsp;\u0026gt;\u0026thinsp;1.150) were diagnosed with cGVHD at the second evaluation, while six of the six cases with cGPS\u0026thinsp;\u0026lt;\u0026thinsp;1.150 (low-risk group) were still in stable state at the second evaluation. The evaluation accuracy of the cGPS module therefore reached 100% in predicting non-GVHD patients who would become disease-progressed.\u003c/p\u003e \u003cp\u003eWe then assessed the ability of cGPS to predict the disease progression of cGVHD. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, all 24 patients in the low-risk group (with an initial cGPS\u0026thinsp;\u0026lt;\u0026thinsp;1.505) were diagnosed as PR/CR/SD (i.e., non-DP) at the second evaluation. The accuracy of the cGPS module was thus 100% for predicting no disease progression. Of the eight patients in the high-risk group (with cGPS\u0026thinsp;\u0026gt;\u0026thinsp;1.505), four (50%) were classified into the DP group at the second assessment, one was lost to follow-up, and one died of infection. The results of our prospective study therefore collectively indicated that the cGPS module may be a powerful tool for predicting the future course of disease for both cGVHD and non-GVHD patients in the clinic. This strategy should help doctors obtain timely and effective information that can be leveraged to improve patient care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOverall summary\u003c/h2\u003e \u003cp\u003eWe herein leveraged previous reports that aberrant B-cell homeostasis could influence the progression of cGVHD to develop a new strategy for recognizing and predicting the disease. Using spectral flow cytometry in a small cohort, we successfully identified a cGVHD-specific subpopulation of B cells and delineated the defining markers of this subgroup. By applying machine learning in an independent cohort, we derived a simple marker combination and used it to develop an all-in-one scoring module named cGPS. ROC analysis was used to identify cGPS thresholds that can be used to evaluate disease progression in cGVHD and non-GVHD patients of this cohort. Finally, we performed a prospective study in a new cohort using these thresholds, and verified that our cGPS module could be a powerful tool for predicting the future course of disease among cGVHD and non-GVHD patients in the clinic.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified a cGVHD-specific B cell subpopulation using spectral flow cytometry, this identified cGVHD-specific B cell subpopulation expressed the surface markers CD19\u003csup\u003e+\u003c/sup\u003eCD20\u003csup\u003e\u0026minus;\u003c/sup\u003eCD27\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003eIgD\u003csup\u003e\u0026minus;\u003c/sup\u003e. Moreover, CD269 and CD319 were highly expressed, suggesting that the cGVHD-specific B cell subpopulation could correspond to plasmablast-like cells. Plasmablasts are generally expected to play an important role in the etiology of cGVHD\u003csup\u003e40\u003c/sup\u003e. Increased levels of B cell activation factor (BAFF) disrupt the negative selection mechanisms to prime the survival of autoreactive B cells, which leads to altered peripheral B cell compartment including post-germinal center plasmablasts (CD27\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003ehi\u003c/sup\u003eIgD\u003csup\u003e\u0026ndash;\u003c/sup\u003e) \u003csup\u003e20\u003c/sup\u003e. These abnormal plasmablasts have pathologic functions, including autoantibody production, cross-presentation to T cells, and B cell cytokine production, all of which could promote the development of cGVHD\u003csup\u003e41\u003c/sup\u003e. In addition, results obtained from three independent cohorts confirmed the universality of this subpopulation: the majority of cGVHD patients had a persistent and stable subpopulation of aberrant B cells, and this was far more frequent in patients than in the healthy donor group. We further found that the levels of these aberrant B cells were positively associated with disease progression. This is consistent with recent reports that there is a strong correlation between the development of cGVHD and high levels of plasmablasts\u003csup\u003e42,43\u003c/sup\u003e and that an increase of plasmablasts accompanies cGVHD progression\u003csup\u003e44\u003c/sup\u003e. Given the above, we believed that our findings were rational from an etiological perspective.\u003c/p\u003e \u003cp\u003eRecognizing the early signs and symptoms of cGVHD remains a challenge. While the NIH consensus meetings have developed a series of diagnostic strategies, there is still no clear guidance on how to predict a patient\u0026rsquo;s progression to cGVHD at a very early stage. E. M. Weissinger \u003cem\u003eet al.\u003c/em\u003e developed a proteome pattern for early cGVHD diagnosis based on mass spectrometry, the higher cost and 84% sensitivity of this method has hindered its clinical application \u003csup\u003e45\u003c/sup\u003e. We used machine learning to quantify the importance of each marker and streamline the marker set for the cGVHD-associated B cell subset to CD27\u003csup\u003e+\u003c/sup\u003eCD86\u003csup\u003e+\u003c/sup\u003eCD20\u003csup\u003e\u0026minus;\u003c/sup\u003e, then converted the data obtained from these refined markers into a more simplified format called the cGPS. Our cGPS module successfully discerned high-risk patients 3 months before they were diagnosed. Among non-GVHD patients with a cGPS\u0026thinsp;\u0026gt;\u0026thinsp;1.150, almost everyone developed cGVHD during the 3-month observation period both in the retrospective and prospective studies. However, 100% of non-GVHD patients with cGPS\u0026thinsp;\u0026lt;\u0026thinsp;1.150 had shown no disease occurs at the 3-month timepoint. Indeed, 50% of these non-GVHD patients were followed for 6 months, and the results suggest that our module can provide an extended and sensitive warning of disease progression.\u003c/p\u003e \u003cp\u003eAnother clinical challenge is to predict a future response or outcome for a cGVHD patient \u003csup\u003e16\u003c/sup\u003e. This partially reflects our lack of a methos for real-time disease monitoring. \u0026ldquo;Trial-and-error\u0026rdquo; remains the only treatment strategy for cGVHD patients\u003csup\u003e46\u003c/sup\u003e. Our cGPS module showed a great capacity for reflecting treatment efficacy in cGVHD patients: 100% patients with cGPS\u0026thinsp;\u0026lt;\u0026thinsp;1.505 showed complete or partial responses to treatment. Clinically, this would suggest that there is no need to alter the treatment strategy. Among the patients with cGPS\u0026thinsp;\u0026gt;\u0026thinsp;1.505, the treatment was adjusted in four cases, with the addition of a therapy such as MSC transplantation. Upon the treatment adjustment, the cGPS of these patients decreased to \u0026lt;\u0026thinsp;1.505 and their disease improved, as found in a follow-up observation (data not shown). This further revealed that our cGPS module could dynamically reflect therapeutic responses and might be an ideal tool for real-time monitoring of cGVHD.\u003c/p\u003e \u003cp\u003eAlthough numerous markers have been identified as being useful for cGVHD diagnosis or prognosis\u003csup\u003e47\u0026ndash;50\u003c/sup\u003e, few of the relevant studies have included prospective research. Moreover, some of the identified markers are specific to certain organs, which can restrict their clinical application. Here, we included a prospective study to increase our sample size and improve the strength of our conclusions. We also carefully examined all participants information to ensure that no selective bias was introduced to our study. When calculating the required sample size, we set the power to 0.8, the effect size to 0.5, and the significance level to 0.05. Based on these parameters, we calculated that, for the prospective study, a sample size\u0026thinsp;\u0026gt;\u0026thinsp;33 would minimize the impacts of type I and type II errors on the final results. For our prospective study, we recruited 35 cGVHD patients, and thus met the required number. We are thus sufficiently confident that cGPS 1.505 was a reliable threshold for judging whether a cGVHD treatment would be effective. Unfortunately, we recruited only 10 patients who could be used to study the ability of cGPS to provide early progression warning for cGVHD, yielding a power of only approximate to 0.3. Although the limited patient number hindered our ability to draw strong conclusions about the use of cGPS 1.150 as a threshold in the non-GVHD group, the combination of retrospective and prospective study designs and the significant difference of the principal effect suggest that this threshold can be used to predict the risk of a non-GVHD patient developing cGVHD. That said, cGPS 1.150 might be an approximation of the most accurate threshold, and future work is needed to clarify this point.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBy combining the power of spectral flow cytometry and machine learning, we successfully invented a dynamic forecasting module, named cGPS, for chronic graft-versus-host disease. This module enables the sensitive and dynamic monitoring of cGVHD progression, and thus may enable the early diagnosis and treatment efficacy assessment for cGVHD. Our findings cater to the clinical needs of cGVHD patients, and we believe that the cGPS module has the potential to become an ideal tool for estimating the progression of cGVHD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBAFF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eB cell activating factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecGVHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic graft-versus-host disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecGPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecGVHD progress score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomplete response\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edisease progression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHSCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehematopoietic stem cell transplantation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emAb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emonoclonal antibody\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Institutes of Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epartial response\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC curve\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estable disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniform Manifold Approximation and Projection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in compliance with the Helsinki declaration. All procedures involving human subjects were approved by the Ethics Committee of Nanfang Hospital, Southern Medical University (Ethical approval No. NCT04692376). All patients gave written informed consent to participate in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare they have no conflicting interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAPX, XC, YM and JC conceptualized and designed the project; APX and XC supervised the research; YM, JC, JS, GL, XL, TW, JL, ZL and XZ performed experiments and/or data analysis; QL, ZF and NX contributed to the provision of the patients and the collection of data. JC, JS, XL and XC performed the spectral flow cytometry data analysis. YM and WH performed the bioinformatic analysis. MY, XC, JC and WH drafted the manuscript. APX, XC and MY revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Key Research and Development Program of China, Stem Cell and Translational Research (2022YFA1104100, 2022YFA1105000); the National Natural Science Foundation of China (32130046, 82270230, 81970109); Guangdong Basic and Applied Basic Research Foundation (2023B1515020119); Key Scientific and Technological Program of Guangzhou City (2023B01J1002); Pioneering talents project of Guangzhou Development Zone (2021-L029); the Shenzhen Science and Technology Program (KJZD20230923114504008).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eD'Souza A, Fretham C, Lee SJ, et al. Current Use of and Trends in Hematopoietic Cell Transplantation in the United States. Biol Blood Marrow Transplant. 2020;26(8):e177-e182.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacDonald KPA, Hill GR, Blazar BR. Chronic graft-versus-host disease: biological insights from preclinical and clinical studies. Blood. 2017;129(1):13\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanin-Zhorov A, Blazar BR. ROCK2, a critical regulator of immune modulation and fibrosis has emerged as a therapeutic target in chronic graft-versus-host disease. Clinical Immunology. 2021;230:108823.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee SJ. Classification systems for chronic graft-versus-host disease. Blood. 2017;129(1):30\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSullivan KM, Witherspoon RP, Storb R, et al. Prednisone and azathioprine compared with prednisone and placebo for treatment of chronic graft-v-host disease: prognostic influence of prolonged thrombocytopenia after allogeneic marrow transplantation. Blood. 1988;72(2):546\u0026ndash;554.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlowers ME, Martin PJ. How we treat chronic graft-versus-host disease. Blood. 2015;125(4):606\u0026ndash;615.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePenack O, Marchetti M, Ruutu T, et al. Prophylaxis and management of graft versus host disease after stem-cell transplantation for haematological malignancies: updated consensus recommendations of the European Society for Blood and Marrow Transplantation. Lancet Haematol. 2020;7(2):e157-e167.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJagasia MH, Greinix HT, Arora M, et al. National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: I. The 2014 Diagnosis and Staging Working Group Report. Biology of Blood and Marrow Transplantation. 2015;21(3):389\u0026ndash;401.e381.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolff D, Hilgendorf I, Wagner-Drouet E, et al. Changes in Immunosuppressive Treatment of Chronic Graft-versus-Host Disease: Comparison of 2 Surveys within Allogeneic Hematopoietic Stem Cell Transplant Centers in Germany, Austria, and Switzerland. Biol Blood Marrow Transplant. 2019;25(7):1450\u0026ndash;1455.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaidu NEB, Bonini C, Dickinson A, et al. New Approaches for the Treatment of Chronic Graft-Versus-Host Disease: Current Status and Future Directions. Front Immunol. 2020;11:578314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolff D, Greinix H, Lee SJ, et al. Biomarkers in chronic graft-versus-host disease: quo vadis? Bone Marrow Transplantation. 2018;53(7):832\u0026ndash;837.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilosevic E, Babic A, Iovino L, Markovic M, Grce M, Greinix H. Use of the NIH consensus criteria in cellular and soluble biomarker research in chronic graft-versus-host disease: A systematic review. Front Immunol. 2022;13:1033263.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeric Z, Schoemans H, Peczynski C, et al. Management of Steroid-Refractory Graft-Versus-Host Disease - a Survey By the Transplant Complications Working Party of the EBMT. Blood. 2021;138:2884.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoltzman NG, Pavletic SZ. The clinical landscape of chronic graft-versus-host disease management in 2021. British Journal of Haematology. 2022;196(4):830\u0026ndash;848.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoltzman NG, Pavletic SZ. The clinical landscape of chronic graft-versus-host disease management in 2021. 2022;196(4):830\u0026ndash;848.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarantopoulos S, Cardones AR, Sullivan KM. How I treat refractory chronic graft-versus-host disease. Blood. 2019;133(11):1191\u0026ndash;1200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeiser R. Novel Approaches to the Treatment of Chronic Graft-Versus-Host Disease. Journal of Clinical Oncology. 2023;41(10):1820\u0026ndash;1824.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeiser R, Sarantopoulos S, Blazar BR. B-cell targeting in chronic graft-versus-host disease. Blood. 2018;131(13):1399\u0026ndash;1405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeiser R, Blazar BR. Pathophysiology of Chronic Graft-versus-Host Disease and Therapeutic Targets. New England Journal of Medicine. 2017;377(26):2565\u0026ndash;2579.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarantopoulos S, Ritz J. Aberrant B-cell homeostasis in chronic GVHD. Blood. 2015;125(11):1703\u0026ndash;1707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen JL, Fore MS, Wooten J, et al. B cells from patients with chronic GVHD are activated and primed for survival via BAFF-mediated pathways. Blood. 2012;120(12):2529\u0026ndash;2536.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia W, Poe JC, Su H, et al. BAFF promotes heightened BCR responsiveness and manifestations of chronic GVHD after allogeneic stem cell transplantation. Blood. 2021;137(18):2544\u0026ndash;2557.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Gao Q, Feng Y, Zhang X. Developing role of B cells in the pathogenesis and treatment of chronic GVHD. Br J Haematol. 2019;184(3):323\u0026ndash;336.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia W, Poe JC, Su H, et al. Recipient-Derived BAFF and Alloantigen Synergistically Activate B Cells in Murine Chronic Gvhd. Blood. 2016;128(22):498\u0026ndash;498.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalard F, Labopin M, Yakoub-Agha I, et al. Rituximab-based first-line treatment of cGVHD after allogeneic SCT: results of a phase 2 study. Blood. 2017;130(20):2186\u0026ndash;2195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolomon SR, Sizemore CA, Ridgeway M, et al. Safety and efficacy of rituximab-based first line treatment of chronic GVHD. Bone Marrow Transplantation. 2019;54(8):1218\u0026ndash;1226.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng Y, Chen X, Liu Q, et al. Alteration of na\u0026iuml;ve and memory B-cell subset in chronic graft-versus-host disease patients after treatment with mesenchymal stromal cells. Stem Cells Transl Med. 2014;3(9):1023\u0026ndash;1031.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJagasia MH, Greinix HT, Arora M, et al. National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: I. The 2014 Diagnosis and Staging Working Group report. Biol Blood Marrow Transplant. 2015;21(3):389\u0026ndash;401.e381.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFilipovich AH, Weisdorf D, Pavletic S, et al. National Institutes of Health consensus development project on criteria for clinical trials in chronic graft-versus-host disease: I. Diagnosis and staging working group report. Biol Blood Marrow Transplant. 2005;11(12):945\u0026ndash;956.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573\u0026ndash;3587.e3529.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine learning in Python. the Journal of machine Learning research. 2011;12:2825\u0026ndash;2830.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvni B, Neiman D, Shaked E, et al. Chronic Graft Versus Host Disease Detected By Tissue-Specific Cell-Free DNA Methylation Biomarkers. Blood. 2022;140(Supplement 1):1574\u0026ndash;1575.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcInnes L, Healy J, Melville J. Umap: Uniform manifold approximation and projection for dimension reduction. \u003cem\u003earXiv preprint arXiv:180203426\u003c/em\u003e. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTraag VA, Waltman L, Van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports. 2019;9(1):5233.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitko CL, Pidala J, Schoemans HM, et al. National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: IIa. The 2020 Clinical Implementation and Early Diagnosis Working Group Report. Transplant Cell Ther. 2021;27(7):545\u0026ndash;557.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuvelier GDE, Nemecek ER, Wahlstrom JT, et al. Benefits and challenges with diagnosing chronic and late acute GVHD in children using the NIH consensus criteria. Blood. 2019;134(3):304\u0026ndash;316.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarpenter PA, Logan BR, Lee SJ, et al. A phase II/III randomized, multicenter trial of prednisone/sirolimus versus prednisone/ sirolimus/calcineurin inhibitor for the treatment of chronic graft-versus-host disease: BMT CTN 0801. Haematologica. 2018;103(11):1915\u0026ndash;1924.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePusic I, Pavletic SZ. Challenges in Conducting Studies in Chronic Graft-versus-Host Disease. Clin Hematol Int. 2019;1(1):36\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchoemans HM, Lee SJ, Ferrara JL, et al. EBMT \u0026ndash; NIH \u0026ndash; CIBMTR Task Force position statement on standardized terminology \u0026amp; guidance for graft-versus-host disease assessment. Bone Marrow Transplantation. 2018;53(11):1401\u0026ndash;1415.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacDonald KP, Hill GR, Blazar BR. Chronic graft-versus-host disease: biological insights from preclinical and clinical studies. Blood. 2017;129(1):13\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcManigle W, Youssef A, Sarantopoulos S. B cells in chronic graft-versus-host disease. Hum Immunol. 2019;80(6):393\u0026ndash;399.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDubovsky JA, Flynn R, Du J, et al. Ibrutinib treatment ameliorates murine chronic graft-versus-host disease. J Clin Invest. 2014;124(11):4867\u0026ndash;4876.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlynn R, Du J, Veenstra RG, et al. Increased T follicular helper cells and germinal center B cells are required for cGVHD and bronchiolitis obliterans. Blood. 2014;123(25):3988\u0026ndash;3998.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang KS, Kim HT, Nikiforow S, et al. Antibodies targeting surface membrane antigens in patients with chronic graft-versus-host disease. Blood. 2017;130(26):2889\u0026ndash;2899.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeissinger EM, Human C, Metzger J, et al. The proteome pattern cGvHD_MS14 allows early and accurate prediction of chronic GvHD after allogeneic stem cell transplantation. Leukemia. 2017;31(3):654\u0026ndash;662.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYalniz FF, Murad MH, Lee SJ, et al. Steroid Refractory Chronic Graft-Versus-Host Disease: Cost-Effectiveness Analysis. Biol Blood Marrow Transplant. 2018;24(9):1920\u0026ndash;1927.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi R, Li Y, Huang R, Xiong J, Wang X, Zhang X. Recent advances and research progress in biomarkers for chronic graft versus host disease. Critical Reviews in Oncology/Hematology. 2023;186:103993.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSvegliati S, Olivieri A, Campelli N, et al. Stimulatory autoantibodies to PDGF receptor in patients with extensive chronic graft-versus-host disease. Blood, The Journal of the American Society of Hematology. 2007;110(1):237\u0026ndash;241.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiesen N, Schwarzbich MA, Dischinger K, et al. CXCL9 Predicts Severity at the Onset of Chronic Graft-versus-host Disease. Transplantation. 2020;104(11):2354\u0026ndash;2359.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevine JE, Braun TM, Harris AC, et al. A prognostic score for acute graft-versus-host disease based on biomarkers: a multicentre study. The Lancet Haematology. 2015;2(1):e21-e29.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4264249/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4264249/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to its dynamic nature and the absence of reliable real-time monitoring tools, predicting chronic graft-versus-host disease (cGVHD) progression was challenging. This caused a significant investment of both time and financial resources to ensure good management of cGVHD. In response to this challenge, we identified a distinct B-cell subpopulation characterized by CD27\u003csup\u003e+\u003c/sup\u003eCD86\u003csup\u003e+\u003c/sup\u003eCD20\u003csup\u003e-\u003c/sup\u003e, which could precisely distinguish cGVHD from healthy donors. Leveraging this discovery, we developed cGPS, a user-friendly tool based on marker distribution, which demonstrated exceptional efficacy in tracking cGVHD progression. Its validation, conducted through retrospective and prospective studies involving 91 patients (25 non-GVHD and 66 cGVHD cases), confirmed cGPS's predictive prowess. Remarkably, our retrospective analysis revealed an impressive area under the curve (AUC) of 0.9773 for identifying non-GVHD patients at risk of cGVHD and 0.8846 for predicting disease progression in cGVHD patients. Subsequent validation in an independent prospective study yielded equally promising results, with cGPS accurately predicting all instances of cGVHD development or progression within a three-month observation window. With three independent cohorts, cGPS underscores its robust ability for sensitive and dynamic monitoring of cGVHD progression, provides a solution for early diagnosis and assessment of treatment effectiveness for cGVHD.\u003c/p\u003e","manuscriptTitle":"Dynamic forecasting module for chronic graft-versus-host disease progression based on a disease-specific subpopulation of B cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-22 19:09:38","doi":"10.21203/rs.3.rs-4264249/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b4c22c2-f8cd-4947-885d-08c14433167a","owner":[],"postedDate":"April 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30694049,"name":"Health sciences/Diseases/Immunological disorders/Graft-versus-host disease"},{"id":30694050,"name":"Health sciences/Health care/Prognosis/Disease-free survival"},{"id":30694051,"name":"Health sciences/Health care/Diagnosis"}],"tags":[],"updatedAt":"2024-05-07T16:18:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-22 19:09:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4264249","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4264249","identity":"rs-4264249","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}