Project 1

Mayo Clinic Rochester, Rochester MN

Investigators

Linked publications & trials

Abstract

ABSTRACT – ALLFTD2: PROJECT 1 Familial (f-) FTLD, which is caused by autosomal dominant mutations that most commonly affect the C9orf72, GRN, and MAPT genes, is a particularly important group for drug development in FTLD because more is known about the biological mechanisms leading to neurodegeneration in these contexts, and the potential to identify high risk individuals in advance of symptom onset allows treatment to begin in the presymptomatic and/or early symptomatic phases. Consequently, most therapeutic development in FTLD is focusing on f-FTLD. In the first cycle of ALLFTD, we made excellent progress developing methods for clinical trials in f-FTLD. Using clinical and biomarker data from asymptomatic and symptomatic f-FTLD participants, we identified MRI-based gray matter volumes (vMRI) and Neurofilament Light Chain (NfL) as important markers of disease evolution, and incorporated these measures into mutation-specific longitudinal multimodal models to estimate an individual’s status on the path toward symptoms. Our analyses indicate that using these models to plan clinical trials in FTLD can improve power, which would lower costs and speed discovery of interventions. We have also identified additional imaging and fluid markers that appear to be useful for predicting future clinical changes, and we have developed smartphone based remote digital assessments that robustly capture early cognitive and functional changes. Despite this progress, significant challenges remain. Our current models of disease progression models are based on annual data collection and are therefore under-sampled relative to the data collection frequency used in clinical trials. The low sampling rate does not provide the temporal precision needed for optimally predicting rate of progression. The value of fluid-based measures beyond NfL, additional imaging approaches, and novel clinical measures for tracking disease in multimodal models has yet to be established. Furthermore, optimization of remote assessment for characterization of participants who might be invited to enroll in clinical trials is a critical goal for expanding opportunities for trial participation. In the coming cycle, we will focus on improving characterization of the period spanning from the late asymptomatic phase through the early symptomatic phase of f-FTLD, as participants in this period are most likely to be recruited for clinical trials. We will remotely collect clinical and plasma biomarker data every three months in this group, and concurrent in- person assessments at a subset of time points that will include MRI and CSF collection. Using these approaches, we will evaluate the utility of additional types of brain imaging, biofluid proteins, and genetic variants for predicting and tracking progression in f-FTLD, expand our Bayesian multimodal models of disease course with additional variables and more frequent measurements, and quantify the value of remotely collected assessments for predicting onset and progression of symptoms in f-FTLD.