Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland.
Department of Molecular Biochemistry and Pharmacology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Nat Commun. 2021 Jun 10;12(1):3532. doi: 10.1038/s41467-021-23880-9.
In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage, but delayed diagnosis is common because symptoms usually appear only after strong organ involvement. Here we present LICTOR, a machine learning approach predicting LC toxicity in AL, based on the distribution of somatic mutations acquired during clonal selection. LICTOR achieves a specificity and a sensitivity of 0.82 and 0.76, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.87. Tested on an independent set of 12 LCs sequences with known clinical phenotypes, LICTOR achieves a prediction accuracy of 83%. Furthermore, we are able to abolish the toxic phenotype of an LC by in silico reverting two germline-specific somatic mutations identified by LICTOR, and by experimentally assessing the loss of in vivo toxicity in a Caenorhabditis elegans model. Therefore, LICTOR represents a promising strategy for AL diagnosis and reducing high mortality rates in AL.
在系统性轻链淀粉样变 (AL) 中，致病性单克隆免疫球蛋白轻链 (LC) 在靶器官中形成有毒聚集体和淀粉样纤维。及时诊断对于避免永久性器官损伤至关重要，但诊断常常会延迟，因为症状通常仅在强烈的器官受累后才会出现。在这里，我们提出了 LICTOR，这是一种基于克隆选择过程中获得的体细胞突变分布来预测 AL 中 LC 毒性的机器学习方法。LICTOR 的特异性和灵敏度分别为 0.82 和 0.76，接收者操作特征曲线 (AUC) 的面积为 0.87。在一组具有已知临床表型的 12 个 LC 序列的独立测试中，LICTOR 的预测准确率为 83%。此外，我们能够通过反向模拟 LICTOR 鉴定的两个种系特异性体细胞突变来消除 LC 的毒性表型，并通过在秀丽隐杆线虫模型中评估体内毒性丧失的实验来证实。因此，LICTOR 是一种有前途的 AL 诊断策略，可以降低 AL 的高死亡率。
