Longitudinal biomarkers in amyotrophic lateral sclerosis

Abstract

Objective: To investigate neurodegenerative and inflammatory biomarkers in people with amyotrophic lateral sclerosis (PALS), evaluate their predictive value for ALS progression rates, and assess their utility as pharmacodynamic biomarkers for monitoring treatment effects.

Methods: De-identified, longitudinal plasma, and cerebrospinal fluid (CSF) samples from PALS (n = 108; 85 with samples from ≥2 visits) and controls without neurological disease (n = 41) were obtained from the Northeast ALS Consortium (NEALS) Biofluid Repository. Seventeen of 108 PALS had familial ALS, of whom 10 had C9orf72 mutations. Additional healthy control CSF samples (n = 35) were obtained from multiple sources. We stratified PALS into fast- and slow-progression subgroups using the ALS Functional Rating Scale-Revised change rate. We compared cytokines/chemokines and neurofilament (NF) levels between PALS and controls, among progression subgroups, and in those with C9orf72 mutations.

Results: We found significant elevations of cytokines, including MCP-1, IL-18, and neurofilaments (NFs), indicators of neurodegeneration, in PALS versus controls. Among PALS, these cytokines and NFs were significantly higher in fast-progression and C9orf72 mutation subgroups versus slow progressors. Analyte levels were generally stable over time, a key feature for monitoring treatment effects. We demonstrated that CSF/plasma neurofilament light chain (NFL) levels may predict disease progression, and stratification by NFL levels can enrich for more homogeneous patient groups.

Interpretation: Longitudinal stability of cytokines and NFs in PALS support their use for monitoring responses to immunomodulatory and neuroprotective treatments. NFs also have prognostic value for fast-progression patients and may be used to select similar patient subsets in clinical trials.

Conflict of interest statement

F.H., Y.Z., J.H‐N., X.T., J.C.D., J.M.H., K.S‐L., and C. H. are employees and stockholders of Denali Therapeutics. M.M.L. has no conflicts to declare. J.D.G. has received funding unrelated to this work from the National Institutes of Health, the ALS Association, and the Muscular Dystrophy Association and has consulting agreements with Biogen, Voyager Therapeutics, Apellis Pharmaceuticals, Apic Bio, Clene Nanomedicine, Aruna Bio, Rapa Therapeutics, and Vida Ventures. R.H.B. has no conflicts relevant to this manuscript. S.S.L. has received consulting fees and honoraria unrelated to this work or ALS from Biogen and Sanofi‐Genzyme. D.L. has no conflicts to declare. R.B. is the founder and President of Iron Horse Diagnostics, Inc., a company commercializing diagnostic and prognostic tests for neurologic diseases. J.D.B. has been a consultant to Denali Therapeutics, Clene Nanomedicine, Biogen, Alexion and Anelixis Therapeutics. He has received research support from Anelixis Therapeutics, Amylyx Therapeutics, Biogen, Brainstorm Cell Therapeutics, Cytokinetics, Genentech, the ALS Association, the Muscular Dystrophy Association, ALS Finding A Cure, Project ALS and the National Institute of Neurological Disorders and Stroke.

© 2020 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.

Figures

Figure 1

(A) Comparison of observed versus modeled progression rates. ALSFRS‐R trajectories over the course of disease for (B) slow‐progression, (C) fast‐progression, and (D) C9orf72 subgroups. Slow and fast progressors were defined as those patients showing a drop in the ALSFRS‐R of less than 0.33/month or greater than 0.8/month, respectively

Figure 2

Inflammatory cytokines and glial cell markers in (A) CSF and (B) plasma with significant differences between PALS and non‐ALS controls. Each dot represents an individual patient visit and box plots indicate median ± interquartile range (IQR).

Figure 3

Cytokine levels in (A) CSF and (B) plasma in disease progression subgroups. Box plots indicate median ± IQR.

Figure 4

Longitudinal analysis of (A) MCP‐1, (B) IL‐18, (C) NFL, and (D) pNFH in CSF and plasma of PALS (n = 85). Solid lines are the average trajectory of log MCP‐1, IL‐18, NFL, and pNFH levels from the multilevel model analysis, and dotted lines represent the analyte trajectories of individual PALS. For better visualization, only subjects in the first 8 years of disease are included. Values within each plot indicate the estimated rates of annual change with 95% confidence intervals (CIs).

Figure 5

NF levels in CSF and plasma in PALS, PALS subgroups, and non‐ALS controls. NFLs in PALS and non‐ALS controls in (A) CSF and (B) plasma. (C) Correlation of CSF and plasma NFL levels. (D) Correlation of CSF NFL and pNFH levels. Values within the plot indicate the Pearson r and 95% CI. NFL levels in (E) CSF and (F) plasma, and (G) pNFH in CSF in the non‐ALS cohort, the slow‐ and fast‐progression PALS, and the C9orf72 group. (H) Observed progression rates versus plasma NFL levels, modeled using an ALSFRS‐R score of 48 at disease onset and an NFL cutoff of 40 pg/mL in 105 PALS. Nonslow progressors were defined by an observed decline of ≥4 points/year in the ALSFRS‐R score. “Others” include PALS with an ALSFRS‐R change in 4–9.6/year and those with single timepoint sampling. Box plots indicate median ± IQR.