After development of a prediction model and creation of a training dataset, machine learning was applied to the deep sequencing of the blood transcriptome data for predicting two endpoints. One was disease progression at 120 weeks defined as a 1 point or more increase in the Expanded Disability Status Scale (EDSS) among patients with confirmed disability progression for at least 12 weeks (12W-CDP).
‘We are looking at larger sample sizes to improve the accuracy and generalizability of our model, but we can use it now to inform treatment decisions.’ for PPMS. Analyses were conducted only on blood samples from those randomized to placebo, who, like those in the active treatment arm, were evaluated at baseline and at 12-week intervals for more than 2 years.
The other was change at 120 weeks in brain morphology defined as a 1% or more reduction in brain volume (120W PBVC). The peripheral blood samples were subjected to RNA sequencing analysis (RNA-Seq) using commercially available analysis techniques. The prediction model for the disability endpoint was based on data generated from the blood transcriptome of 135 patients of which 53 (39%) met the endpoint at 120 weeks. The prediction model for the change in brain morphology was based on the blood transcriptome from 94 patients of which 63 (67%) met the endpoint. headtopics.
Excerpt from from Medscape
