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. 2023 Apr 26;9(3):e200060.
doi: 10.1212/NXG.0000000000200060. eCollection 2023 Jun.

Distinguishing Loss-of-Function and Gain-of-Function SCN8A Variants Using a Random Forest Classification Model Trained on Clinical Features

Joshua B Hack  1 , Kyle Horning  1 , Denise M Juroske Short  1 , John M Schreiber  1 , Joseph C Watkins  1 , Michael F Hammer  1
Affiliations

Distinguishing Loss-of-Function and Gain-of-Function SCN8A Variants Using a Random Forest Classification Model Trained on Clinical Features

Joshua B Hack et al. Neurol Genet. .

Abstract

Background and objectives: Pathogenic variants at the voltage-gated sodium channel gene, SCN8A, are associated with a wide spectrum of clinical disease outcomes. A critical challenge for neurologists is to determine whether patients carry gain-of-function (GOF) or loss-of-function (LOF) variants to guide treatment decisions, yet in vitro studies to infer channel function are often not feasible in the clinic. In this study, we develop a predictive modeling approach to classify variants based on clinical features present at initial diagnosis.

Methods: We performed an exhaustive search for individuals deemed to carry SCN8A GOF and LOF variants by means of in vitro studies in heterologous cell systems, or because the variant was classified as truncating, and recorded clinical features. This resulted in a total of 69 LOF variants: 34 missense and 35 truncating variants, including 9 nonsense, 13 frameshift, 6 splice site, 6 indels, and 1 large deletion. We then assembled a truth set of variants with known functional effects, excluding individuals carrying variants at other loci associated with epilepsy. We then trained a predictive model based on random forest using this truth set of 45 LOF variants and 45 GOF variants randomly selected from a set of variants tested by in vitro methods.

Results: Phenotypic categories assigned to individuals correlated strongly with GOF or LOF variants. All patients with GOF variants experienced early-onset seizures (mean age at onset = 4.5 ± 3.1 months) while only 64.4% patients with LOF variants had seizures, most of which were late-onset absence seizures (mean age at onset = 40.0 ± 38.1 months). With high accuracy (95.4%), our model including 5 key clinical features classified individuals with GOF and LOF variants into 2 distinct cohorts differing in age at seizure onset, development of seizures, seizure type, intellectual disability, and developmental and epileptic encephalopathy.

Discussion: The results support the hypothesis that patients with SCN8A GOF and LOF variants represent distinct clinical phenotypes. The clinical model developed in this study has great utility because it provides a rapid and highly accurate platform for predicting the functional class of patient variants during SCN8A diagnosis, which can aid in initial treatment decisions and improve prognosis.

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Conflict of interest statement

J.B. Hack, K. Horning, D.M. Juroske Short, J.M. Schreiber, J.C. Watkins, M.F. Hammer. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/NG.

Figures

Figure 1
Figure 1. Visualization of Truth Set and Feature Importance
(A) Principal component analysis bi-plot with top 11 contributing features from Full Model. GOF (red) and LOF (blue) are shown on first 2 principal components. Feature contribution is represented by the length of the vector: age at onset (29.4%), DEE (7.7%), GE (6.2%), absence seizures (6.0%), missense mutation (5.1%), UE (4.4%), focal seizures (3.9%), severe IDD (3.7%), mild IDD (3.2%), and unknown IDD (2.8%). (B) Feature importance in random forest determined by mean decrease Gini in the full model: missense mutation (6.00), age at onset (4.12), DEE (3.25), focal seizures (2.28), absence seizures (1.54), UE (1.43), development of seizures (1.40), GE (1.32), GTC seizures (1.21), and tonic seizures (0.98). (C) Principal component analysis bi-plot of clinical model. (D) Feature importance in random forest model for the clinical model. Gini coefficient importance rankings were as follows: age at seizure onset (10.29), motor/focal seizures (6.92), absence seizures (3.23), development of seizures (2.38), and severe IDD (0.53). DEE = developmental and epileptic encephalopathy; GOF = gain-of-function; GTC = generalized tonic-clonic; IDD = intellectual and developmental disability; LOF = loss-of-function; UE = unclassified epilepsy.
Figure 2
Figure 2. Distribution of Probability of LOF for Each Individual in (A) the Full Model Truth Set, (B) the Clinical Model Truth Set, and (C) Subsets 1–3 (Green)
Individual's true classification is indicated by color: GOF (red) and LOF (blue). Those with probability LOF less than 0.5 are classified as GOF and those greater than 0.5 are classified as LOF. GOF = gain-of-function; LOF = loss-of-function.
Figure 3
Figure 3. Proposed Model Integrating Phenotypic Subcategories of SCN8A Patients With LOF and GOF Variant Functional Classes
Major division between LOF and GOF is mainly governed by the presence of early-onset motor or focal seizures (GOF) and neurodevelopmental delay (NDD) without seizures or NDD with seizures (e.g., late-onset absence seizures) (LOF). Subphenotypic nomenclature is discussed in the text. The black inner circular line represents variant function, while the outer arrows represent the phenotypic spectrum associated with GOF and LOF variants. The triangle represents a possible third subcategory of patients with LOF variants (e.g., individuals in Table 1 with high probLOF scores and DEE and/or tonic-clonic, tonic, or myoclonic seizures). DEE = developmental and epileptic encephalopathy; GOF = gain-of-function; LOF = loss-of-function; NDD = neurodevelopmental delay without epilepsy.
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