Back

How the prospect of a clinical trial impacts decision-making for predictive genetic testing in amyotrophic lateral sclerosis

Fontaine, M.; Horowitz, K.; Anoja, N.; Genge, A.; Salmon, K.

2024-10-02 genetic and genomic medicine
10.1101/2024.09.30.24314632 medRxiv
Show abstract

ObjectiveGenetic testing practices are rapidly evolving for people living with, or at-risk for, amyotrophic lateral sclerosis (ALS), due to emerging genotype-driven therapies. This study explored how individuals at-risk for familial ALS (fALS) perceive the opportunity to participate in a clinical trial, and to better understand how that may influence the decision-making process for predictive genetic testing. MethodsThis study used both quantitative and qualitative data analyses. Data were collected through an online questionnaire, followed by semi-structured interviews conducted with twelve (n=12) individuals at-risk for either SOD1- or C9orf72-ALS who had predictive testing prior to study participation. Interview data were analyzed using reflexive thematic analysis. ResultsThree overarching themes were conceptualized from the data: i) the psychosocial impact of fALS; ii) perspectives of at-risk individuals on research involvement; and iii) predictive genetic counselling and testing considerations. These results contribute perspectives of the lived experience to inform predictive genetic counselling and testing practices for individuals at-risk for fALS. ConclusionIndividuals at-risk for fALS view potential participation in a presymptomatic clinical trial as an actionable measure that may increase their desire for predictive genetic testing. Genetic counselling was identified as a critical component of the predictive testing process given the life-changing implications associated with a positive result. Increased access to genetic counselling, and in a timely manner, is a significant need in the ALS population given potential access to gene-specific therapies in the presymptomatic stage.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.