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利用MAGIC生物标志物预测和推断急性移植物抗宿主病

Prediction and Prognostication of Acute Graft-Versus-Host Disease by MAGIC Biomarkers.

作者信息

Levine John E

机构信息

The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

出版信息

Am J Hematol. 2025 May;100 Suppl 3(Suppl 3):5-13. doi: 10.1002/ajh.27594.

Abstract

Recent advancements in prophylaxis for acute graft-versus-host disease (GVHD) have successfully reduced the incidence of severe cases; however, overall survival rates have not significantly improved, and GVHD continues to be a major cause of mortality. The severity of gastrointestinal (GI) damage is especially critical, as it strongly correlates with treatment failure and non-relapse mortality, but clinical symptoms do not reliably predict peak severity in its early stages. Biomarker-based algorithms, such as the Mount Sinai Acute GVHD International Consortium (MAGIC) algorithm, leverage serum levels of GI GVHD biomarkers (ST2 and REG3α) to quantify intestinal crypt damage, providing more accurate predictions of GVHD outcomes compared to clinical assessments. Clinical trials have investigated the use of biomarkers as entry criteria for treatment, with notable success in guiding treatment de-escalation, which is increasingly important as the presentation of GVHD shifts towards milder forms. The recently developed MAGIC composite scores further enhance prediction accuracy by integrating clinical symptom severity with biomarker assessments. Future clinical trials that employ these composite scores or similar algorithms are anticipated to be more efficient by identifying patients who are most likely to benefit from specific therapies and ultimately improving the management of GVHD.

摘要

急性移植物抗宿主病(GVHD)预防方面的最新进展已成功降低了严重病例的发生率;然而,总体生存率并未显著提高,GVHD仍然是主要的死亡原因。胃肠道(GI)损伤的严重程度尤为关键,因为它与治疗失败和非复发死亡率密切相关,但临床症状在早期并不能可靠地预测峰值严重程度。基于生物标志物的算法,如西奈山急性GVHD国际联盟(MAGIC)算法,利用血清中GI GVHD生物标志物(ST2和REG3α)的水平来量化肠道隐窝损伤,与临床评估相比,能更准确地预测GVHD的结果。临床试验已研究将生物标志物用作治疗的纳入标准,在指导降低治疗强度方面取得了显著成功,随着GVHD的表现向更温和形式转变,这一点变得越来越重要。最近开发的MAGIC综合评分通过将临床症状严重程度与生物标志物评估相结合,进一步提高了预测准确性。预计未来采用这些综合评分或类似算法的临床试验将通过识别最有可能从特定疗法中获益的患者而更有效,最终改善GVHD的管理。

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