Background: Genetic variations in ALS may influence the rate of disease progression, survival, and serve as pharmacogenomic biomarkers. We evaluated the relationship of ALS-linked genes and single nucleotide polymorphism (SNP) genetic variants known to influence ALS phenotype and clinical outcomes in a Phase III randomized, placebo-controlled study (NCT03280056).
Objective: ALS participants (n=189) were randomized to receive intrathecal NurOwn or placebo at baseline, Week 8 and Week 16 and followed through 28 weeks of treatment. 124 of 189 reconsented participants (63 NurOwn; 61 placebo) were re-evaluated via NGS PGxome, PCR and Sanger sequencing. Responders were pre-specified (≥1.25 points/month improvement in pre- to post-treatment ALSFRS-R slope). Baseline differences by treatment group and genotype are summarized, and pre-specified baseline disease characteristics from the primary efficacy model are used in statistical analysis of response rates by genotype.
Results: The overall response rate of participants in the sub-study was 41% NurOwn and 26% Placebo (p=0.2). Eight of 124 (6.5%) participants expressed seven different ALS gene mutations (SOD1, TARDBP, PSEN2, FUS, TBK1, OPTN, C9orf72). Four SNPs (UNC13A, CAMTA1, MOBP, ZNF512B) for which there were sufficient representations were analyzed. The UNC13A C allele (rs12608932 SNP) is a risk locus for ALS associated with shorter survival. In participants with one or two copies of UNC13A (CC and AC), we observed a 53% responder rate on NurOwn vs. 27% on placebo (p=.016). Responder rates in UNC13A non-carriers were similar between treatments (NurOwn 29%, Placebo 25%). Differences in responder rates between NurOwn vs. placebo for CAMTA1, MOBP, ZNf512B were not statistically significant.
Conclusion: UNC13A rs12608932 SNP risk allele potentiates the deleterious effects of TDP-43 cellular mislocalization in ALS. Our results suggest that NurOwn treatment may influence disease progression in ALS patients who possess this risk allele and provides a basis for further genetic characterization in clinical trials.