Gurevich Michael, Zilkha-Falb Rina, Sherman Jia, Usdin Maxime, Raposo Catarina, Craveiro Licinio, Sonis Polina, Magalashvili David, Menascu Shay, Dolev Mark, Achiron Anat
Multiple Sclerosis Center, Sheba Medical Center, Ramat-Gan 5262, Israel.
Sackler School of Medicine, Tel-Aviv University, Tel Aviv 6139601, Israel.
Brain Commun. 2025 Jan 8;7(1):fcae427. doi: 10.1093/braincomms/fcae427. eCollection 2025.
Primary progressive multiple sclerosis (PPMS) affects 10-15% of multiple sclerosis patients and presents significant variability in the rate of disability progression. Identifying key biological features and patients at higher risk for fast progression is crucial to develop and optimize treatment strategies. Peripheral blood cell transcriptome has the potential to provide valuable information to predict patients' outcomes. In this study, we utilized a machine learning framework applied to the baseline blood transcriptional profiles and brain MRI radiological enumerations to develop prognostic models. These models aim to identify PPMS patients likely to experience significant disease progression and who could benefit from early treatment intervention. RNA-sequence analysis was performed on total RNA extracted from peripheral blood mononuclear cells of PPMS patients in the placebo arm of the ORATORIO clinical trial (NCT01412333), using Illumina NovaSeq S2. Cross-validation algorithms from Partek Genome Suite (www.partek.com) were applied to predict disability progression and brain volume loss over 120 weeks. For disability progression prediction, we analysed blood RNA samples from 135 PPMS patients (61 females and 74 males) with a mean ± standard error age of 44.0 ± 0.7 years, disease duration of 5.9 ± 0.32 years and a median baseline Expanded Disability Status Scale (EDSS) score of 4.3 (range 3.5-6.5). Over the 120-week study, 39.3% (53/135) of patients reached the disability progression end-point, with an average EDSS score increase of 1.3 ± 0.16. For brain volume loss prediction, blood RNA samples from 94 PPMS patients (41 females and 53 males), mean ± standard error age of 43.7 ± 0.7 years and a median baseline EDSS of 4.0 (range 3.0-6.5) were used. Sixty-seven per cent (63/94) experienced significant brain volume loss. For the prediction of disability progression, we developed a two-level procedure. In the first level, a 10-gene predictor achieved a classification accuracy of 70.9 ± 4.5% in identifying patients reaching the disability end-point within 120 weeks. In the second level, a four-gene classifier distinguished between fast and slow disability progression with a 506-day cut-off, achieving 74.1 ± 5.2% accuracy. For brain volume loss prediction, a 12-gene classifier reached an accuracy of 70.2 ± 6.7%, which improved to 74.1 ± 5.2% when combined with baseline brain MRI measurements. In conclusion, our study demonstrates that blood transcriptome data, alone or combined with baseline brain MRI metrics, can effectively predict disability progression and brain volume loss in PPMS patients.
原发性进行性多发性硬化症（PPMS）影响10%至15%的多发性硬化症患者，其残疾进展速度存在显著差异。识别关键生物学特征以及进展较快风险较高的患者对于制定和优化治疗策略至关重要。外周血细胞转录组有潜力提供有价值的信息来预测患者的预后。在本研究中，我们利用机器学习框架，将其应用于基线血液转录谱和脑MRI放射学计数，以开发预后模型。这些模型旨在识别可能经历显著疾病进展且能从早期治疗干预中获益的PPMS患者。对ORATORIO临床试验（NCT01412333）安慰剂组中PPMS患者外周血单核细胞提取的总RNA进行了RNA序列分析，使用的是Illumina NovaSeq S2。应用Partek Genome Suite（www.partek.com）的交叉验证算法来预测120周内的残疾进展和脑容量损失。对于残疾进展预测，我们分析了135例PPMS患者（61名女性和74名男性）的血液RNA样本，其平均年龄±标准误为44.0±0.7岁，病程为5.9±0.32年，基线扩展残疾状态量表（EDSS）评分中位数为4.3（范围3.5 - 6.5）。在为期120周的研究中，39.3%（53/135）的患者达到了残疾进展终点，EDSS评分平均增加1.3±0.16。对于脑容量损失预测，使用了94例PPMS患者（41名女性和53名男性）的血液RNA样本，平均年龄±标准误为43.7±0.7岁，基线EDSS中位数为4.0（范围3.0 - 6.5）。67%（63/94）的患者经历了显著的脑容量损失。对于残疾进展的预测，我们开发了一个两级程序。在第一级，一个10基因预测器在识别120周内达到残疾终点的患者时，分类准确率为70.9±4.5%。在第二级，一个4基因分类器以506天为截止值区分快速和缓慢残疾进展，准确率达到74.1±5.2%。对于脑容量损失预测，一个12基因分类器的准确率为70.2±6.7%，与基线脑MRI测量结果相结合时提高到74.1±5.2%。总之，我们的研究表明，血液转录组数据单独或与基线脑MRI指标相结合，可以有效预测PPMS患者的残疾进展和脑容量损失。
