Chirumbolo Gabriella, Dicataldo Michele, Barone Martina, Storci Gianluca, De Matteis Serena, Laprovitera Noemi, Sinigaglia Barbara, Barbato Francesco, Maffini Enrico, Cavo Michele, Bonifazi Francesca, Arpinati Mario
Department of Experimental, Diagnostic, and Specialty Medicine, Università di Bologna, Bologna, Italy.
IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Transplant Cell Ther. 2023 May;29(5):302.e1-302.e8. doi: 10.1016/j.jtct.2023.02.008. Epub 2023 Feb 14.
Chronic GVHD (cGVHD) is the major cause of long-term morbidity after allogeneic hematopoietic stem cell transplantation (HSCT). There are no biomarkers that can consistently predict its occurrence. We aimed to evaluate whether numbers of antigen-presenting cell subsets in peripheral blood (PB) or serum chemokine concentrations are biomarkers of cGVHD occurrence. The study cohort comprised 101 consecutive patients undergoing allogeneic HSCT between January 2007 and 2011. cGVHD was diagnosed by both modified Seattle criteria and National Institutes of Health (NIH) criteria. Multicolor flow cytometry was used to determine the number of PB myeloid dendritic cells (DCs), plasmacytoid DCs, CD16 DCs, and CD16 and CD16 monocytes, as well as CD4 and CD8 T cells, CD56 natural killer cells, and CD19 B cells. Serum concentrations of CXCL8, CXCL10, CCL2, CCL3, CCL4, and CCL5 were measured by a cytometry bead array assay. At a median of 60 days after enrollment, 37 patients had developed cGVHD. Patients with cGVHD and those without cGVHD had comparable clinical characteristics. However, previous acute GVHD (aGVHD) was strongly correlated with later cGVHD (57% versus 24%, respectively; P = .0024). Each potential biomarker was screened for its association with cGVHD using the Mann-Whitney U test. Biomarkers that differed significantly (P < .05) between patients with cGVHD and those without cGVHD were analyzed by receiver operating characteristic (ROC) curve analysis to select the variables predicting cGVHD with an area under the ROC curve (AUC) >.5 and a P value <.05. A multivariate Fine-Gray model identified the following variables as independently associated with the risk of cGVHD: CXCL10 ≥592.650 pg/mL (hazard ratio [HR], 2.655; 95% confidence interval [CI], 1.298 to 5.433; P = .008), pDC ≥2.448/μL (HR, .286; 95% CI, .142 to .577; P < .001) and previous aGVHD (HR, 2.635; 95% CI, 1.298 to 5.347; P = .007). A risk score was derived based on the weighted coefficients of each variable (2 points each), resulting in the identification of 4 cohorts of patients (scores of 0, 2, 4, and 6). In a competing risk analysis to stratify patients at differing risk levels of cGVHD, the cumulative incidence of cGVHD was 9.7%, 34.3%, 57.7%, and 100% in patients with scores of 0, 2, 4, and 6, respectively (P < .0001). The score could nicely stratify the patients based on the risk of extensive cGVHD as well as NIH-based global and moderate to severe cGVHD. Based on ROC analysis, the score could predict the occurrence of cGVHD with an AUC of .791 (95% CI, .703 to .880; P < .001). Finally, a cutoff score ≥4 was identified as the optimal cutoff by Youden J index with a sensitivity of 57.1% and a specificity of 85.0%. A multiparameter score including a history of previous aGVHD, serum CXCL10 concentration, and number of pDCs in the PB at 3 months post-HSCT stratify patients at varying risk levels of cGVHD. However, the score needs to be validated in a much larger independent and possibly multicenter cohort of patients undergoing transplantation from different donor types and with distinct GVHD prophylaxis regimens.
慢性移植物抗宿主病(cGVHD)是异基因造血干细胞移植(HSCT)后长期发病的主要原因。目前尚无能够持续预测其发生的生物标志物。我们旨在评估外周血(PB)中抗原呈递细胞亚群数量或血清趋化因子浓度是否为cGVHD发生的生物标志物。研究队列包括2007年1月至2011年期间连续接受异基因HSCT的101例患者。cGVHD采用改良西雅图标准和美国国立卫生研究院(NIH)标准进行诊断。采用多色流式细胞术测定PB中髓样树突状细胞(DC)、浆细胞样DC、CD16 DC以及CD16和CD16单核细胞的数量,以及CD4和CD8 T细胞、CD56自然杀伤细胞和CD19 B细胞的数量。采用细胞计数珠阵列分析法测定血清CXCL8、CXCL10、CCL2、CCL3、CCL4和CCL5的浓度。入组后中位60天时,37例患者发生了cGVHD。发生cGVHD的患者和未发生cGVHD的患者具有可比的临床特征。然而,既往急性移植物抗宿主病(aGVHD)与后期cGVHD密切相关(分别为57%和24%;P = 0.0024)。使用Mann-Whitney U检验筛选每个潜在生物标志物与cGVHD的相关性。对cGVHD患者和未发生cGVHD的患者之间存在显著差异(P < 0.05)的生物标志物进行受试者工作特征(ROC)曲线分析,以选择预测cGVHD的变量,其ROC曲线下面积(AUC)> 0.5且P值< 0.05。多变量Fine-Gray模型确定以下变量与cGVHD风险独立相关:CXCL10≥592.650 pg/mL(风险比[HR],2.655;95%置信区间[CI],1.298至5.433;P = 0.00),浆细胞样DC≥2.448/μL(HR,0.286;95% CI,0.142至0.577;P < 0.001)以及既往aGVHD(HR,2.635;95% CI,1.298至5.347;P = 0.007)。根据每个变量的加权系数(每个变量2分)得出风险评分,从而确定4组患者(评分分别为0、2、4和6)。在一项用于对不同cGVHD风险水平患者进行分层的竞争风险分析中,评分分别为0、2、4和6的患者中cGVHD的累积发生率分别为9.7%、34.3%、57.7%和100%(P < 0.0001)。该评分能够很好地根据广泛cGVHD以及基于NIH的总体和中度至重度cGVHD风险对患者进行分层。基于ROC分析,该评分能够以AUC为0.791(95% CI,0.703至0.880;P < 0.001)预测cGVHD的发生。最后,通过约登J指数确定截断评分≥4为最佳截断值,灵敏度为57.1%,特异性为85.0%。一个包括既往aGVHD病史、血清CXCL1浓度以及HSCT后3个月时PB中浆细胞样DC数量的多参数评分能够对不同cGVHD风险水平的患者进行分层。然而,该评分需要在一个更大的独立且可能为多中心的队列中进行验证,该队列中的患者接受来自不同供体类型的移植且采用不同的GVHD预防方案。