Circulating microRNAs in cellular and antibody-mediated heart transplant rejection.
Circulating microRNAs in cellular and antibody-mediated heart transplant rejection.
复制标题
DOI：
10.1016/j.healun.2022.06.019
复制
发表时间：
2022-10
影响因子：
8.9
通讯作者：
Investigators, GRAfT
中科院分区：
文献类型：
--
作者：
Shah, Palak;Agbor-Enoh, Sean;Bagchi, Pramita;deFilippi, Christopher R.;Mercado, Angela;Diao, Gouqing;Morales, Dave J. P.;Shah, Keyur B.;Najjar, Samer S.;Feller, Erika;Hsu, Steven;Rodrigo, Maria E.;Lewsey, Sabra C.;Jang, Moon Kyoo;Marboe, Charles;Berry, Gerald J.;Khush, Kiran K.;Valantine, Hannah A.;Investigators, GRAfT
非侵入性监测同种异体心脏移植物的健康状况对于改善临床结果非常重要。 MicroRNA (miR) 是有前途的心血管疾病生物标志物，有限的研究表明它们可用于非侵入性诊断急性心脏移植排斥反应。移植基因组研究联盟 (GRAfT) 是一项多中心前瞻性队列研究，对来自大西洋中部 5 个中心的心脏移植患者进行表型分析。将移植后无排斥史的患者与患有急性细胞排斥（ACR）或抗体介导的排斥（AMR）的患者进行比较。对心内膜心肌活检时收集的血浆样本进行小 RNA 测序。通过调整临床协变量进行差异 miR 表达。使用回归来开发对 ACR 和 AMR 具有高诊断准确性的 miR panel。然后，这些面板在 GRAfT 和斯坦福大学的独立样本中进行了验证。生成受试者工作特征曲线并计算曲线下面积 (AUC) 统计数据。开发了不同的 ACR 和 AMR 临床评分来转化 miR 表达数据以供临床使用。 GRAfT 队列的中位年龄为 52 岁，其中 35% 为女性，45% 为黑人患者。在 GRAfT 和斯坦福大学之间，我们纳入了 157 名心脏移植患者：108 名对照患者和 49 名排斥反应患者（50 次 ACR 和 38 次 AMR）。经过差异 miR 表达和回归分析，我们鉴定了 12 个能够准确区分 ACR 的 miR 和 17 个 AMR 中的 miR。 GRAfT 内 miR 组的独立验证得出 ACR AUC 0.92 (95% CI: 0.86–0.98) 和 AMR AUC 0.82 (95% CI: 0.74–0.90)。外部验证的 ACR AUC 为 0.72（95% CI：0.59-0.82）。我们对 ACR 和 AMR 制定了不同的 miR 临床评分（范围 0-100），评分≥ 65，确定 ACR 的敏感性为 86%，特异性为 76%，阴性预测值为 98%，AMR 评分表现分别为 82%、84% 和 97%。我们发现了新型 miR，其在非侵入性诊断心脏移植后急性排斥反应方面具有出色的性能。一旦经过严格验证，独特的临床 ACR 和 AMR 评分将迎来一个时代，基因组生物标志物可用于筛查和诊断排斥亚型。这些新型生物标志物可能会减轻心内膜心肌活检的需要，同时促进基于 ACR 或 AMR 非侵入性诊断的靶向治疗的启动。
Non-invasive monitoring of heart allograft health is important to improve clinical outcomes. MicroRNAs (miRs) are promising biomarkers of cardiovascular disease and limited studies suggest they can be used to non-invasively diagnose acute heart transplant rejection. The Genomic Research Alliance for Transplantation (GRAfT) is a multicenter prospective cohort study that phenotyped heart transplant patients from 5 mid-Atlantic centers. Patients who had no history of rejection after transplant were compared to patients with acute cellular rejection (ACR) or antibody-mediated rejection (AMR). Small RNA sequencing was performed on plasma samples collected at the time of an endomyocardial biopsy. Differential miR expression was performed with adjustment for clinical covariates. Regression was used to develop miR panels with high diagnostic accuracy for ACR and AMR. These panels were then validated in independent samples from GRAfT and Stanford University. Receiver operating characteristic curves were generated and area under the curve (AUC) statistics calculated. Distinct ACR and AMR clinical scores were developed to translate miR expression data for clinical use. The GRAfT cohort had a median age of 52 years, with 35% females and 45% Black patients. Between GRAfT and Stanford, we included 157 heart transplant patients: 108 controls and 49 with rejection (50 ACR and 38 AMR episodes). After differential miR expression and regression analysis, we identified 12 miRs that accurately discriminate ACR and 17 miRs in AMR. Independent validation of the miR panels within GRAfT led to an ACR AUC 0.92 (95%CI: 0.86–0.98) and AMR AUC 0.82 (95%CI: 0.74–0.90). The externally validated ACR AUC was 0.72 (95% CI: 0.59–0.82). We developed distinct miR clinical scores for ACR and AMR (range 0–100), a score ≥ 65, identified ACR with 86% sensitivity, 76% specificity, and 98% negative predictive value, for AMR score performance was 82%, 84% and 97%, respectively. We identified novel miRs that had excellent performance to non-invasively diagnose acute rejection after heart transplantation. Once rigorously validated, the unique clinical ACR and AMR scores usher in an era whereby genomic biomarkers can be used to screen and diagnose the subtype of rejection. These novel biomarkers may potentially alleviate the need for an endomyocardial biopsy while facilitating the initiation of targeted therapy based on the non-invasive diagnosis of ACR or AMR.
登录
查看更多内容
影响因子：
37.8
作者：
Agbor-Enoh S;Shah P;Tunc I;Hsu S;Russell S;Feller E;Shah K;Rodrigo ME;Najjar SS;Kong H;Pirooznia M;Fideli U;Bikineyeva A;Marishta A;Bhatti K;Yang Y;Mutebi C;Yu K;Kyoo Jang M;Marboe C;Berry GJ;Valantine HA;GRAfT Investigators
通讯作者：
GRAfT Investigators
影响因子：
2.3
作者：
Lewsey SC;Breathett K
通讯作者：
Breathett K
影响因子：
17.1
作者：
De Vlaminck I;Valantine HA;Snyder TM;Strehl C;Cohen G;Luikart H;Neff NF;Okamoto J;Bernstein D;Weisshaar D;Quake SR;Khush KK
通讯作者：
Khush KK
影响因子：
8.8
作者：
Chih, S.;Tinckam, K. J.;Ross, H. J.
通讯作者：
Ross, H. J.
影响因子：
8.9
作者：
Cole, Robert Townsend;Gandhi, Jonathan;Morris, Alanna A.
通讯作者：
Morris, Alanna A.