LAURA ANTONACI
Map the SMA protocol: a Machine-learning based Algorithm to Predict THErapeutic response in SMA
Autori
- LAURA ANTONACI (FONADAZIONE POLICLINICO GEMELLI IRCSS – NEUROPSICHIATRIA INFANTILE)
- GIORGIA CORATTI (FONADAZIONE POLICLINICO GEMELLI IRCSS – TNPEE)
- ALBERTO MARINI (FONADAZIONE POLICLINICO GEMELLI IRCSS – BIOLOGIA)
- CARLOTTA MASCIOCCHI (FONADAZIONE POLICLINICO GEMELLI IRCSS – INGENIERIA BIOMEDICA)
Presentatore
LAURA ANTONACI (FONADAZIONE POLICLINICO GEMELLI IRCSS)
Modalità
Poster Session
Abstract
Spinal Muscular Atrophy (SMA) results from the loss of SMN1 gene, causing neuron degeneration. Despite approved treatments, response variability remains. Identifying circular RNAs, molecular patterns, and integrating clinical data could explain differences in treatment response. Artificial Intelligence, specifically Machine Learning (ML), remains unexplored in SMA but offers potential to predict treatment outcomes based on individual factors, revolutionizing results interpretation. The aims of this project are: 1. collect clinical data and patient-reported outcome measures (PROM) from patients treated with nusinersen, risdiplam, onasemnogene abeparvovec, 2. identify novel biomarkers and RNA molecular signature profiling, 3. develop a predictive algorithm using artificial intelligence methodologies based on machine learning, able to integrate clinical outcomes, patients, characteristics, and specific biomarkers.
Clinical, biological, and Patient Reported Outcome Measures (PROMs) data will be collected from patients over a period of two years with the aim to conclude within November 2025. The clinical data will include information about patients; demographics, medical history, neurological assessment, motor function measures and laboratory results. PROMs will be collected using standardized questionnaires to assess patient-reported outcomes, such as the PEDI-CAT, SMA-HI and other. Additionally, the RNA molecular signature profiling will be carried out using Next- Generation Sequencing (NGS) to identify possible novel biomarkers. For risdiplam and onasemnogene abeparvovec (gene therapy) treatments, all eligible patients will be enrolled due to their recent indications. Preliminary results and considerations on a cohort of 143 patients will be discussed.