Babak Myeloma Group, Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic.
Sci Rep. 2024 Aug 13;14(1):18777. doi: 10.1038/s41598-024-69408-1.
Multiple myeloma (MM) is the second most prevalent hematological malignancy, characterized by infiltration of the bone marrow by malignant plasma cells. Extramedullary disease (EMD) represents a more aggressive condition involving the migration of a subclone of plasma cells to paraskeletal or extraskeletal sites. Liquid biopsies could improve and speed diagnosis, as they can better capture the disease heterogeneity while lowering patients' discomfort due to minimal invasiveness. Recent studies have confirmed alterations in the proteome across various malignancies, suggesting specific changes in protein classes. In this study, we show that MALDI-TOF mass spectrometry fingerprinting of peripheral blood can differentiate between MM and primary EMD patients. We constructed a predictive model using a supervised learning method, partial least squares-discriminant analysis (PLS-DA) and evaluated its generalization performance on a test dataset. The outcome of this analysis is a method that predicts specifically primary EMD with high sensitivity (86.4%), accuracy (78.4%), and specificity (72.4%). Given the simplicity of this approach and its minimally invasive character, this method provides rapid identification of primary EMD and could prove helpful in clinical practice.
多发性骨髓瘤(MM)是第二大常见的血液系统恶性肿瘤,其特征是恶性浆细胞浸润骨髓。髓外疾病(EMD)是一种更具侵袭性的疾病,涉及浆细胞亚克隆迁移到骨骼外或骨骼外部位。液体活检可以改善和加速诊断,因为它们可以更好地捕捉疾病异质性,同时由于微创性降低患者的不适。最近的研究证实了各种恶性肿瘤中蛋白质组的改变,表明蛋白质类别的特定变化。在这项研究中,我们表明,基于 MALDI-TOF 质谱指纹图谱的外周血可以区分 MM 和原发性 EMD 患者。我们使用有监督学习方法(偏最小二乘判别分析,PLS-DA)构建了一个预测模型,并在测试数据集上评估了其泛化性能。该分析的结果是一种具有高灵敏度(86.4%)、准确性(78.4%)和特异性(72.4%)的特异性原发性 EMD 预测方法。鉴于这种方法的简单性和微创性,这种方法可以快速识别原发性 EMD,并在临床实践中可能有所帮助。