Machine learning predicts treatment response to nusinersen in non-sitter spinal muscular atrophy (SMA)
Georgia Stimpson, Emer O’Reilly, Giorgia Coratti, Deborah Ridout, Tapabrata Chakraborti, Robin Mitra, Roberto de Sanctis, Marika Pane, Mariacristina Scoto, Eugenio Mercuri, Francesco Muntoni, Giovanni Baranello,Background
Nusinersen has substantially increased survival and improved disease progression in Spinal Muscular Atrophy (SMA) patients. However, treatment response is heterogeneous, with some patients gaining the ability to sit and walk whilst others display less obvious changes.
Method
From the SMA Reach UK and the Italian Telethon Network, 124 non-sitter SMA patients treated with nusinersen under 4 years were included and randomly allocated to training and testing (80/20%). Tree and regression-based machine learning for survival outcomes were compared, and oblique random forests were selected, with a testing C-Index of 0.74. The features were selected from items of the CHOP-INTEND and HINE-2 motor function assessments, respiratory and swallowing status using mutual information.
Findings
Sixty-two patients (50%) achieved sitting, at a median age of 2.4 years. The predicted median time-to-sitting in those requiring tube feeding at treatment initiation was 4-months later than those without, and this was the most influential factor. Specific motor function features, including the ability to kick in supine, were strongly associated with a higher likelihood of sitting.
Interpretation
This work provides a framework for predicting nusinersen-response and represents the first stage in personalised counselling on treatment plans for SMA.
Funding
Support for SMA Reach is provided by Biogen, Roche and Novartis, and historically the NIHR BRC, SMA Trust, the MRC Translational Research Centre and MD UK. Support for the Italian network is provided by Famiglie SMA, Telethon (GSP 13002), and ASAMSI.