The role of neurofilament light in genetic frontotemporal lobar degeneration.

Genetic frontotemporal lobar degeneration caused by autosomal dominant gene mutations provides an opportunity for targeted drug development in a highly complex and clinically heterogeneous dementia. These neurodegenerative disorders can affect adults in their middle years, progress quickly relative to other dementias, are uniformly fatal and have no approved disease-modifying treatments. Frontotemporal dementia, caused by mutations in the GRN gene which encodes the protein progranulin, is an active area of interventional drug trials that are testing multiple strategies to restore progranulin protein deficiency. These and other trials are also examining neurofilament light as a potential biomarker of disease activity and disease progression and as a therapeutic endpoint based on the assumption that cerebrospinal fluid and blood neurofilament light levels are a surrogate for neuroaxonal damage. Reports from genetic frontotemporal dementia longitudinal studies indicate that elevated concentrations of blood neurofilament light reflect disease severity and are associated with faster brain atrophy. To better inform patient stratification and treatment response in current and upcoming clinical trials, a more nuanced interpretation of neurofilament light as a biomarker of neurodegeneration is now required, one that takes into account its relationship to other pathophysiological and topographic biomarkers of disease progression from early presymptomatic to later clinically symptomatic stages.

Structural Variants May Be a Source of Missing Heritability in sALS.

The underlying genetic and molecular mechanisms that drive amyotrophic lateral sclerosis (ALS) remain poorly understood. Structural variants within the genome can play a significant role in neurodegenerative disease risk, such as the repeat expansion in C9orf72 and the tri-nucleotide repeat in ATXN2, both of which are associated with familial and sporadic ALS. Many such structural variants reside in uncharacterized regions of the human genome, and have been under studied. Therefore, characterization of structural variants located in and around genes associated with ALS could provide insight into disease pathogenesis, and lead to the discovery of highly informative genetic tools for stratification in clinical trials. Such genomic variants may provide a deeper understanding of how gene expression can affect disease etiology, disease severity and trajectory, patient response to treatment, and may hold the key to understanding the genetics of sporadic ALS. This article outlines the current understanding of amyotrophic lateral sclerosis genetics and how structural variations may underpin some of the missing heritability of this disease.

Improved stratification of ALS clinical trials using predicted survival.

INTRODUCTION: In small trials, randomization can fail, leading to differences in patient characteristics across treatment arms, a risk that can be reduced by stratifying using key confounders. In ALS trials, riluzole use (RU) and bulbar onset (BO) have been used for stratification. We hypothesized that randomization could be improved by using a multifactorial prognostic score of predicted survival as a single stratifier. METHODS: We defined a randomization failure as a significant difference between treatment arms on a characteristic. We compared randomization failure rates when stratifying for RU and BO ("traditional stratification") to failure rates when stratifying for predicted survival using a predictive algorithm. We simulated virtual trials using the PRO-ACT database without application of a treatment effect to assess balance between cohorts. We performed 100 randomizations using each stratification method - traditional and algorithmic. We applied these stratification schemes to a randomization simulation with a treatment effect using survival as the endpoint and evaluated sample size and power. RESULTS: Stratification by predicted survival met with fewer failures than traditional stratification. Stratifying predicted survival into tertiles performed best. Stratification by predicted survival was validated with an external dataset, the placebo arm from the BENEFIT-ALS trial. Importantly, we demonstrated a substantial decrease in sample size required to reach statistical power. CONCLUSIONS: Stratifying randomization based on predicted survival using a machine learning algorithm is more likely to maintain balance between trial arms than traditional stratification methods. The methodology described here can translate to smaller, more efficient clinical trials for numerous neurological diseases.