Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Alberta Transplant Applied Genomics Center, Edmonton, Alberta, Canada.
J Heart Lung Transplant. 2019 May;38(5):504-513. doi: 10.1016/j.healun.2019.01.1317. Epub 2019 Feb 6.
Improved understanding of lung transplant disease states is essential because failure rates are high, often due to chronic lung allograft dysfunction. However, histologic assessment of lung transplant transbronchial biopsies (TBBs) is difficult and often uninterpretable even with 10 pieces.
We prospectively studied whether microarray assessment of single TBB pieces could identify disease states and reduce the amount of tissue required for diagnosis. By following strategies successful for heart transplants, we used expression of rejection-associated transcripts (annotated in kidney transplant biopsies) in unsupervised machine learning to identify disease states.
All 242 single-piece TBBs produced reliable transcript measurements. Paired TBB pieces available from 12 patients showed significant similarity but also showed some sampling variance. Alveolar content, as estimated by surfactant transcript expression, was a source of sampling variance. To offset sampling variation, for analysis, we selected 152 single-piece TBBs with high surfactant transcripts. Unsupervised archetypal analysis identified 4 idealized phenotypes (archetypes) and scored biopsies for their similarity to each: normal; T-cell‒mediated rejection (TCMR; T-cell transcripts); antibody-mediated rejection (ABMR)-like (endothelial transcripts); and injury (macrophage transcripts). Molecular TCMR correlated with histologic TCMR. The relationship of molecular scores to histologic ABMR could not be assessed because of the paucity of ABMR in this population.
Molecular assessment of single-piece TBBs can be used to classify lung transplant biopsies and correlated with rejection histology. Two or 3 pieces for each TBB will probably be needed to offset sampling variance.
提高对肺移植疾病状态的认识至关重要，因为失败率很高，通常是由于慢性肺移植物功能障碍。然而，即使进行了 10 次活检，对肺移植经支气管活检（TBB）的组织学评估也很困难，而且往往无法解释。
我们前瞻性地研究了单个 TBB 能否通过微阵列评估来识别疾病状态并减少诊断所需的组织量。通过采用心脏移植成功的策略，我们使用排斥相关转录物（在肾移植活检中注释）的无监督机器学习表达来识别疾病状态。
所有 242 个单块 TBB 均产生可靠的转录测量值。12 名患者的配对 TBB 块显示出显著的相似性，但也显示出一些采样差异。肺泡内容物，如表面活性剂转录物的表达，是采样差异的来源。为了抵消采样变化，在分析中，我们选择了 152 个具有高表面活性剂转录物的单个 TBB 进行分析。无监督原型分析确定了 4 种理想化的表型（原型），并对每个表型的相似性进行了评分：正常；T 细胞介导的排斥（TCMR；T 细胞转录物）；抗体介导的排斥（ABMR 样；内皮转录物）；和损伤（巨噬细胞转录物）。分子 TCMR 与组织学 TCMR 相关。由于该人群中 ABMR 较少，因此无法评估分子评分与组织学 ABMR 的关系。
单个 TBB 的分子评估可用于对肺移植活检进行分类，并与排斥组织学相关。每个 TBB 需要 2 或 3 个切片来抵消采样差异。
