Cerebrospinal Fluid Proteomic Changes after Nusinersen in Patients with Spinal Muscular Atrophy

Topic:

Translational Research

Poster Number: S118

Author(s):

Marie Beaudin, MD MSc, Stanford University, Tahereh Kamali, PhD, Stanford University, Whitney Tang, Stanford University, Katharine A. Hagerman, PhD, Stanford University, Sally Dunaway Young, PT, DPT, Stanford University, Lisa Ghiglieri, Stanford University, Dana M. Parker, Stanford University, Benoit Lehallier, PhD, Stanford Univerisity, Ana Carolina Tesi Rocha, MD, Stanford University, Jacinda B. Sampson, MD, PhD, Stanford University, Tina Duong, PT, PhD, Stanford University Medical Center, John Day, MD, PhD, Stanford University Medical Center

Background: Disease-modifying treatments have transformed the natural history of spinal muscular atrophy (SMA), but the cellular pathways altered by SMN restoration remain undefined and biomarkers cannot yet precisely predict treatment response.

Objectives: We performed an exploratory cerebrospinal fluid (CSF) proteomic study in a diverse sample of SMA patients treated with nusinersen to elucidate therapeutic pathways and identify predictors of motor improvement.

Methods: Proteomic analyses were performed on CSF samples collected before treatment (T0) and at 6 months (T6) using an Olink panel to quantify 1113 peptides. A supervised machine learning approach was used to identify proteins that discriminated patients who showed motor improvement from those who did not after 2 years of treatment. The performance of the machine learning model to predict motor improvement at 2 years was assessed by leave-one-out cross-validation.

Results: A total of 49 SMA patients were included (10 type 1, 18 type 2, and 21 type 3), ranging in age from 3 months to 65 years. Most proteins showed a decrease in CSF concentration at T6. The machine learning algorithm identified ARSB, ENTPD2, NEFL, and IFI30 as the proteins most predictive of motor improvement. While the average CSF concentration of these proteins decreased after nusinersen treatment, the decrease appeared to be less in those who demonstrated motor improvement, although this was not statistically significant. Exploration of the effect of age suggested that younger patients appeared to have more decrease in NEFL levels after treatment compared with older patients, but this was not seen for other proteins. The machine learning model was able to predict motor improvement at 2 years with 79.6% accuracy.

Conclusion: The results highlight the potential application of CSF biomarkers to predict motor improvement following SMA treatment. Validation with alternative techniques and larger datasets is needed.