Niemann-Pick type C disease as proof-of-concept for intelligent biomarker panel selection in neurometabolic disorders

Abstract

Aim: Using Niemann-Pick type C disease (NPC) as a paradigm, we aimed to improve biomarker discovery in patients with neurometabolic disorders.

Method: Using a multiplexed liquid chromatography tandem mass spectrometry dried bloodspot assay, we developed a selective intelligent biomarker panel to monitor known biomarkers N-palmitoyl-O-phosphocholineserine and 3β,5α,6β-trihydroxy-cholanoyl-glycine as well as compounds predicted to be affected in NPC pathology. We applied this panel to a clinically relevant paediatric patient cohort (n = 75; 35 males, 40 females; mean age 7 years 6 months, range 4 days-19 years 8 months) presenting with neurodevelopmental and/or neurodegenerative pathology, similar to that observed in NPC.

Results: The panel had a far superior performance compared with individual biomarkers. Namely, NPC-related established biomarkers used individually had 91% to 97% specificity but the combined panel had 100% specificity. Moreover, multivariate analysis revealed long-chain isoforms of glucosylceramide were elevated and very specific for patients with NPC.

Interpretation: Despite advancements in next-generation sequencing and precision medicine, neurological non-enzymatic disorders remain difficult to diagnose and lack robust biomarkers or routine functional testing for genetic variants of unknown significance. Biomarker panels may have better diagnostic accuracy than individual biomarkers in neurometabolic disorders, hence they can facilitate more prompt disease identification and implementation of emerging targeted, disease-specific therapies.

What this paper adds: Intelligent biomarker panel design can help expedite diagnosis in neurometabolic disorders. In Niemann-Pick type C disease, such a panel performed better than individual biomarkers. Biomarker panels are easy to implement and widely applicable to neurometabolic conditions.

FIGURE 1

Clinical characteristics of the patient cohort (n = 75). Left: developmental delay (n = 61), non‐epileptic movement disorders (n = 38), and epilepsy (n = 24) were the most encountered presenting features. Other less frequent clinical manifestations included neurological regression, muscle weakness, liver involvement (including organomegaly and/or deranged function), and gaze palsy. Right: Venn diagram depicting the co‐manifestation of the three most common presenting clinical features encountered in the cohort.

FIGURE 2

Multivariate analysis of multiplex bloodspot assay. (a) Principal component analysis of neurodevelopmental cohort and comparison group with 116 variables (compounds). Score plot for the seven‐component principal component analysis model shows t[2] and t[5] account for 13.6% and 5.9% of the variation respectively. (b) Orthogonal projections to latent structures discriminant analysis of comparison between Niemann–Pick type C disease (NPC) and known mutation samples. (c) Corresponding loading plot of (b) showing compounds elevated (right) and decreased (left) in NPC.

FIGURE 3

Univariate analysis of Niemann–Pick type C disease (NPC) biomarkers from multivariate analysis comparing unaffected comparison individuals (n = 10), confirmed NPC (n = 5), neurodevelopmental patients with known mutations (n = 30), and undiagnosed patients with unknown mutations (n = 40). Significance determined by Kruskal–Wallis analysis of variance with Dunn's post hoc test; **p < 0.01, ***p < 0.001, ****p < 0.0001. Patients with known mutations with biomarker values in the NPC range are indicated on the graphs. (a) Known NPC biomarkers N‐palmitoyl‐O‐phosphocholineserine (PPCS) ratioed to lyso‐sphingomyelin and the bile acid 3β,5α,6β‐trihydroxy‐cholanoyl‐glycine. (b) C24 isoforms of hexosylceramides implicated in orthogonal projections to latent structures discriminant analysis. (c) Ratio of hexosylceramide isoforms to corresponding dihexosylceramide precursor isoforms improves specificity. Abbreviation: Lyso‐SM, lyso‐sphingomyelin.