Is baseline MRI predictive of response to fingolimod treatment in Multiple
Sclerosis patients?
Camilo Diaz-Cruz, Brian Healy, Taimur Malik, Svetlana Egorova, Mark Anderson,
Shahamat Tauhid, Gloria Kim, Rohit Bakshi, Tanuja Chitnis
OBJECTIVE:
To assess the value of quantitative baseline MRI characteristics as predictors of
response to fingolimod treatment in MS patients.
BACKGROUND:
Baseline MRI parameters may have value in predicting response to specific MS
treatments.
DESIGN/METHODS:
We used data from the Comprehensive Longitudinal Investigation of Multiple Sclerosis
at the Brigham and Women’s Hospital (CLIMB). We identified 54 patients who had a
brain MRI performed within 3 months of starting treatment with fingolimod and treated
for at least 12 months. For each patient, semi-automated brain parenchymal fraction
(BPF), T2 lesion volume (T2LV), and manual T1 hypointensities (“black holes”) lesion
volume (T1BHLV), and number of gadolinium-enhanced lesions were calculated.
Exclusions were corticosteroid infusions within 30 days prior to MRI and no other
disease-modifying therapy at the time of baseline MRI. The predictive effect of baseline
MRI data on treatment response between months 3-24 was evaluated. Treatment
responders were defined as having no relapses or new MRI lesions, and no increase in
EDSS between months 3-24. Non-responders were defined as having at least 1 new
attack or MRI event or having an EDSS increase of 1 from baseline (if baseline EDSS
under 6) or 0.5 (if baseline EDSS was 6 or higher). A logistic regression model was used
to assess associations between each MRI parameter and responder/non-responder status.
RESULTS:
Using univariate analysis, we found that individual baseline MRI features T2LV, BPF,
T1BHLV, and number of gadolinium enhancing lesions were not significantly associated
with the probability of treatment response (p>0.5 for each predictor). When all predictors
were included in the model together, none were significantly associated with responder
status.
CONCLUSIONS:
Baseline quantitative MRI data did not predict response to fingolimod in this real world
cohort. Further studies will examine a second MRI timepoint, as well effects of additional
clinical and biomarkers in predicting treatment response to fingolimod.
STUDY SUPPORT: Novartis
Logistic regression results:
Univariate models:
BH1: estimated coefficient= -0.067, SE = 0.16, p-value= 0.674
BPF1 (percent): estimated coefficient= -0.010, SE = 0.062, p-value= 0.871
T2LV1: estimated coefficient= -0.019, SE = 0.085, p-value= 0.823
Combined model:
BH1: estimated coefficient= -0.224, SE = 0.36, p-value= 0.533
BPF1 (percent): estimated coefficient= -0.034, SE = 0.072, p-value= 0.632
T2LV1: estimated coefficient= 0.078, SE = 0.18, p-value= 0.667