Comprehensive multimodal analysis of patients with neuroimmunological diseases

神经免疫疾病患者的综合多模态分析

• 批准号: 10927912
• 负责人: Bibiana Bielekova
• 金额: $ 134.32万
• 依托单位: NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10927912
• 关键词: Acceleration; Age; Aging; Algorithms; Atrophic; Autopsy; Biological Markers; Blinded; Central Nervous System; Central Nervous System Diseases; Cerebrospinal Fluid; Childhood; Clinical; Clinical Data; Clinical Trials; Copy Number Polymorphism; Data; Data Set; Development; Diagnosis; Disease; Drug Screening; Elderly; Employment; Evaluation; Functional disorder; Future; Genetic Transcription; Heterogeneity; Human; Image Analysis; Immune; Immune system; Individual; Injury; Knowledge; Lesion; Light; Linear Regressions; Literature; Longevity; Longitudinal cohort; Machine Learning; Magnetic Resonance Imaging; Measures; Mediating; Meta-Analysis; Modeling; Molecular; Multiple Sclerosis; Nervous System Trauma; Neurologic Examination; Niemann-Pick Diseases; Outcome; Paper; Pathogenicity; Patient Care; Patients; Performance; Persons; Pharmacodynamics; Phenotype; Physiological; Process; Prognostic Marker; Proteins; Protocols documentation; Publishing; Rare Diseases; Relapsing-Remitting Multiple Sclerosis; Sensitivity and Specificity; Serum; Severities; Specificity; Speed; Spinal Cord; Standardization; Structure; Testing; Therapeutic Agents; Tissues; Training; United States Food and Drug Administration; Validation; Work; biomarker selection; brain magnetic resonance imaging; central nervous system injury; cerebral atrophy; clinical care; cognitive disability; cohort; combinatorial; disability; drug development; effective therapy; exome sequencing; genetic variant; gradient boosting; healthy volunteer; immunomodulatory therapies; immunoregulation; machine learning algorithm; model building; molecular marker; multimodality; multiple sclerosis patient; multiple sclerosis treatment; nervous system disorder; neurofilament; neuroimmunologic disease; neuroprotection; participant enrollment; phase II trial; physically handicapped; predictive modeling; prospective; random forest; research clinical testing; response; rho; screening; severe COVID-19; sex; tissue repair; tool; treatment response; white matter

Neuroimmunological diseases of the central nervous system (CNS) represent a growing spectrum of diagnoses, most of which are considered rare disorders. The pathophysiology of these diseases is poorly understood, and effective therapies are sporadic. The most common immune-mediated CNS disease is multiple sclerosis (MS). The initial stage of MS, relapsing-remitting MS (RRMS) can be effectively treated by immunomodulatory treatments, if these are initiated at young age, before the substantial CNS damage occurred. Although there are currently more than 20 Food and Drug Administration (FDA)-approved treatments of MS, their efficacy on disability progression strongly declines with advancing age of patients, so that after age of 54 years, no efficacy on disability progression is seen on a group level. This protocol is advancing knowledge about disease mechanisms that are not targeted by current FDA-approved treatments and is also developing and validating tools of clinical utility. This review period (October 2022-August 2023) we have generated following results: 1. While autopsy studies identify many abnormalities in the central nervous system (CNS) of subjects dying with neurological diseases, without their quantification in living subjects across the lifespan, pathogenic processes cannot be differentiated from epiphenomena. Using machine learning (ML), we searched for likely pathogenic mechanisms of multiple sclerosis (MS). We aggregated cerebrospinal fluid (CSF) biomarkers from 1,305 proteins, measured blindly in the training dataset of untreated MS patients (N=129), into models that predict past and future speed of disability accumulation across all MS phenotypes. Healthy volunteers (N=24) data differentiated natural aging and sex effects from MS-related mechanisms. Resulting models, validated (Rho 0.40-0.51, p<0.0001) in an independent longitudinal cohort (N=98), and uncovered intra-individual molecular heterogeneity. While candidate pathogenic processes must be validated in successful clinical trials, measuring them in living people will enable screening drugs for desired pharmacodynamic effects. This will facilitate drug development making it hopefully more efficient and successful. 2. Composite MRI scales of central nervous system tissue destruction correlate stronger with clinical outcomes than their individual components in multiple sclerosis (MS) patients. Using machine learning (ML), we previously developed Combinatorial MRI scale (COMRISv1) solely from semi-quantitative (semi-qMRI) biomarkers. Here, we asked how much better COMRISv2 might become with the inclusion of quantitative (qMRI) volumetric features and employment of more powerful ML algorithm. The prospectively acquired MS patients, divided into training (n=172) and validation (n=83) cohorts underwent brain MRI imaging and clinical evaluation. Neurological examination was transcribed to NeurEx App that automatically computes disability scales. qMRI features were computed by lesion-TOADS algorithm. Modified random forest pipeline selected biomarkers for optimal model(s) in the training cohort. COMRISv2 models validated moderate correlation with cognitive disability (Rho = 0.674; Linhs concordance coefficient CCC = 0.458; p<0.001) and strong correlations with physical disability (Spearman Rho = 0.830-0.852; CCC = 0.789-0.823; p<0.001). The NeurEx led to the strongest COMRISv2 model. Addition of qMRI features enhanced performance only of cognitive disability model, likely because semi-qMRI biomarkers measure infratentorial injury with greater accuracy. COMRISv2 models predict most granular clinical scales in MS with remarkable criterion validity, expanding scientific utilization of cohorts with missing clinical data. 3. Both aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as accelerated aging. Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects. Standardized brain MRI and neurological examination were acquired prospectively in 649 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n=160). MS patients were randomly split into training (n=277) and validation (n=132) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales. Confounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperforms published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort. GBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials. 4. Our work this period also contributed to meta-analyses that demonstrated value of serum neurofilament light chain (NFL) biomarker in clinical care of patients with severe COVID19, to paper that describes clinical value of combining exome sequencing with copy number variants genetic evaluation of unknown pediatric disorder and to work that defined value of NFL as biomarker of severity and therapeutic response in Niemann-Pick Disease, Type C1.

中枢神经系统 (CNS) 的神经免疫疾病的诊断范围不断扩大，其中大多数被认为是罕见疾病。人们对这些疾病的病理生理学知之甚少，有效的治疗方法也是零星的。最常见的免疫介导的中枢神经系统疾病是多发性硬化症 (MS)。多发性硬化症的初始阶段，即复发缓解型多发性硬化症 (RRMS)，如果在中枢神经系统严重损伤发生之前在年轻时开始，可以通过免疫调节治疗得到有效治疗。尽管目前有超过 20 种经美国食品和药物管理局 (FDA) 批准的多发性硬化症治疗方法，但它们对残疾进展的疗效随着患者年龄的增长而急剧下降，因此在 54 岁之后，在群体水平上看不到对残疾进展的疗效。该协议正在推进有关当前 FDA 批准的治疗方法未针对的疾病机制的知识，并且还在开发和验证临床实用工具。 本次审查期间（2022年10月至2023年8月）我们得出了以下结果： 1. 虽然尸检研究发现死于神经系统疾病的受试者的中枢神经系统 (CNS) 有许多异常，但没有在整个生命周期中对活着的受试者进行量化，因此无法将致病过程与副现象区分开来。使用机器学习 (ML)，我们寻找多发性硬化症 (MS) 的可能致病机制。我们将来自 1,305 种蛋白质的脑脊液 (CSF) 生物标志物（在未经治疗的多发性硬化症患者 (N=129) 的训练数据集中进行盲目测量）聚集到模型中，以预测所有多发性硬化症表型过去和未来的残疾累积速度。健康志愿者 (N=24) 的数据将自然衰老和性别影响与 MS 相关机制区分开来。由此产生的模型在独立纵向队列 (N=98) 中得到验证（Rho 0.40-0.51，p<0.0001），并揭示了个体内分子异质性。虽然候选致病过程必须在成功的临床试验中得到验证，但在活人中进行测量将能够筛选药物以获得所需的药效作用。这将促进药物开发，使其更有效率、更成功。 2. 在多发性硬化症 (MS) 患者中，中枢神经系统组织破坏的复合 MRI 量表与临床结果的相关性比其单独成分的相关性更强。我们之前使用机器学习 (ML)，仅根据半定量 (semi-qMRI) 生物标志物开发了组合 MRI 量表 (COMRISv1)。在这里，我们询问 COMRISv2 如果包含定量 (qMRI) 体积特征并采用更强大的 ML 算法，可能会变得多么好。前瞻性获得的 MS 患者分为训练组 (n=172) 和验证组 (n=83)，接受脑 MRI 成像和临床评估。神经学检查被转录到 NeurEx App 中，自动计算残疾等级。 qMRI 特征通过 lesion-TOADS 算法计算。修改后的随机森林管道选择了训练队列中最佳模型的生物标志物。 COMRISv2 模型验证了与认知障碍的中度相关性（Rho = 0.674；Linhs 一致性系数 CCC = 0.458；p<0.001）以及与身体残疾的强相关性（Spearman Rho = 0.830-0.852；CCC = 0.789-0.823；p<0.001）。 NeurEx 催生了最强大的 COMRISv2 模型。 qMRI 的添加仅增强了认知障碍模型的性能，可能是因为半 qMRI 生物标志物可以更准确地测量幕下损伤。 COMRISv2 模型以显着的标准有效性预测 MS 中最精细的临床量表，扩大了对缺失临床数据的队列的科学利用。 3. 衰老和多发性硬化症（MS）都会导致中枢神经系统（CNS）萎缩。多发性硬化症中过度的脑萎缩被解释为加速衰老。目前的论文测试了另一种假设：多发性硬化症通过与生理衰老不同的机制导致中枢神经系统萎缩。因此，减去生理混杂因素对中枢神经系统结构的影响将隔离多发性硬化症特异性的影响。 对参加 ClinicalTrials.gov 标识符：NCT00794352 方案的 649 名参与者前瞻性地进行了标准化脑部 MRI 和神经学检查。通过 Lesion-TOADS 算法和 Spinal Cord Toolbox 以盲法回顾性测量 CNS 体积。通过逐步多元线性回归，消除了在 80 名健康志愿者中发现的生理混杂因素。在非 MS 队列中评估了混杂因素调整的 MRI 特征的 MS 特异性 (n=160)。 MS 患者被随机分为训练组 (n=277) 和验证组 (n=132)。梯度增强机 (GBM) 模型是在 MS 训练队列中根据四种残疾量表根据未调整和混杂因素调整的 CNS 体积生成的。混杂因素调整强调了 MS 特异性的中枢神经系统白质逐渐丧失。从训练到交叉验证，再到独立验证队列，GBM 模型的性能大幅下降，但所有模型都以较低的 p 值和效果大小预测了认知和身体残疾，其效果优于基于最近荟萃分析的已发表文献。在验证队列中，根据混杂因素调整的 MRI 预测变量构建的模型优于未调整的预测变量建立的模型。来自混杂因素调整的体积 MRI 特征的 GBM 模型反映了 MS 特异性 CNS 损伤，并且由于与脑萎缩相比，这些模型与临床结果的相关性更强，因此应在未来的 MS 临床试验中探索这些模型。 4. 我们这一时期的工作还促成了荟萃分析，证明了血清神经丝轻链 (NFL) 生物标志物在重症新冠肺炎患者临床护理中的价值，撰写了描述外显子组测序与未知儿科疾病拷贝数变异遗传评估相结合的临床价值的论文，并在 Niemann-Pick 中定义了 NFL 作为严重程度和治疗反应生物标志物的价值 疾病，C1 型。

项目成果

Confounder-adjusted MRI-based predictors of multiple sclerosis disability.

• DOI: 10.3389/fradi.2022.971157
• 发表时间: 2022
• 期刊: Frontiers in radiology
• 作者: Kim, Yujin;Varosanec, Mihael;Kosa, Peter;Bielekova, Bibiana
• 通讯作者: Bielekova, Bibiana

Enhancing the clinical value of serum neurofilament light chain measurement.

增强血清神经丝轻链测量的临床价值。

• DOI: 10.1172/jci.insight.161415
• 发表时间: 2022-08-08
• 期刊: JCI INSIGHT
• 影响因子: 8
• 作者: Kosa, Peter;Masvekar, Ruturaj;Komori, Mika;Phillips, Jonathan;Ramesh, Vighnesh;Varosanec, Mihael;Sandford, Mary;Bielekova, Bibiana
• 通讯作者: Bielekova, Bibiana

Drug library screen identifies inhibitors of toxic astrogliosis.

• DOI: 10.1016/j.msard.2022.103499
• 发表时间: 2022-03
• 期刊: Multiple sclerosis and related disorders
• 影响因子: 4
• 作者: Masvekar R;Kosa P;Barbour C;Milstein JL;Bielekova B
• 通讯作者: Bielekova B

Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms.

• DOI: 10.1038/s41467-022-35357-4
• 发表时间: 2022-12-12
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Kosa, Peter;Barbour, Christopher;Varosanec, Mihael;Wichman, Alison;Sandford, Mary;Greenwood, Mark;Bielekova, Bibiana
• 通讯作者: Bielekova, Bibiana

Neurofilament light chain levels correlate with clinical measures in CLN3 disease.

• DOI: 10.1038/s41436-020-01035-3
• 发表时间: 2021-04
• 期刊: Genetics in medicine : official journal of the American College of Medical Genetics
• 作者: Dang Do AN;Sinaii N;Masvekar RR;Baker EH;Thurm AE;Soldatos AG;Bianconi SE;Bielekova B;Porter FD
• 通讯作者: Porter FD