CoMPaSS-NMD - Computational Models for New Patients Stratification Strategies of HNMD (CoMPaSS-NMD)

Computational Models for New Patients Stratification Strategies of Neuromuscular Disorders

The project "Computational Models for new Patients Stratification Strategies of Neuromuscular Disorders" (CoMPaSS-NMD) creates novel and universal tools for the diagnostic stratification of patients suffering from Hereditary Neuromuscular Diseases (HNMDs) aiming at personalised treatments. HNMDs often occur in young people, causing long-term disability and early death; these conditions bring lack of participation in society, need for permanent assistance and may require long-term institutionalisation. Multidimensional HNMD data - clinical, genetic, histopathological and MRI - will be provided by third-level clinical centers in Italy, France, Germany, Finland and the United Kingdom as part of the European Reference Network for Rare Neurological Diseases. Computational tools for high-dimensional clustering will be applied in an unsupervised learning approach using the internal structure of data to define groups of similar patients. Classification model averaging and integration techniques for federated learning-inspired model building and novel HNMD-specific descriptors of histopathological images will be implemented. The adoption of this multidimensional view has the potential to increment the diagnostic rate of HNMDs by 30% and foster effective actions by European national health systems. As main project outcome, the CoMPaSS-NMD Atlas Platform will be AI-based application providing precise clinical characterization of patients. The project will deliver recommendations and guidelines for stratification-based patient management to offer superior standard-of-care for diagnosis and prognosis and assist in planning clinical trials. It will follow a user-centred, co-design methodology with a strong stakeholder engagement and networking with other project consortia. The project engages partners with clinical, biotechnological, ICT, AI, ethical and legal, communication and exploitation competences: six clinical/academic centres, one academic, and four industrial partners.

Study Overview

• Status: Enrolling by invitation
• Conditions: Neuromuscular; Disorder, Hereditary
• Intervention / Treatment: Other: AI-guided patient classification

Detailed Description

Hereditary Neuromuscular Disorders (HNMDs) are inherited genetic diseases that cause progressive muscle weakness and atrophy, significantly impacting mobility and often leading to long-term disability [1, 2]. These conditions dramatically impair social participation, result in loss of independence, and can lead to premature death [3]. Beyond affecting daily activities and social engagement, HNMDs necessitate extensive care from others, imposing substantial personal, social, and economic burdens on patients, their families, and society. HNMDs are a subgroup within the broader category of musculoskeletal disorders, which are the leading cause of years lived with disability. Although individually rare, HNMDs collectively affect over two million people worldwide [4].

Diagnostics for HNMDs often involve multiple costly investigations that can be partial or inconclusive, leaving individual prognoses uncertain. Current genetic studies have identified 16 groups of HNMDs with over 600 causative genes [5]. Classification of HNMDs is now primarily based on factors such as mode of inheritance, genetic mutation, clinical presentation of weakness, histological features, age of onset, rate of progression, and prognosis.

Molecular DNA analysis has become the standard for diagnosing these diseases. However, extensive genetic sequencing has revealed a broad phenotypic spectrum, with different clinical presentations in patients or healthy relatives carrying mutations in the same gene, complicating the correlation with clinical "Gestalt." Patients with HNMDs often exhibit similar features and changes in muscle imaging and histology, yet some may have mutations in different genes, while others may have no obvious detrimental variants. These observations highlight the limitations of current diagnostic approaches and suggest that inherited neuromuscular disorders may result from complex, largely unknown biological interactions.

Consequently, 60% of patients presenting with neuromuscular disease symptoms do not receive an accurate molecular diagnosis, and effective treatments are currently unavailable for most of these diseases [6].

In recent years, Artificial Intelligence (AI), particularly Machine Learning (ML), has matured to a level that supports its application in the life sciences, paving the way for personalized healthcare [7]. ML-based computational tools offer an unprecedented opportunity to expand medical knowledge by providing new insights into the pathogenetic and transmission mechanisms of HNMDs, achieving precise clinical characterization and diagnosis, supporting accurate clinical decisions, and improving future prognosis, which is often challenging to predict in clinical practice.

The CoMPaSS-NMD project aims to develop, apply, and test novel ML tools for the stratification of HNMD patients, with the following four general objectives:

1. Generate robust and reliable datasets to develop computational tools based on validated data.
2. Create clinical data integration solutions for the classification of clinical phenotypes to support patients, healthcare professionals, the scientific community, and individuals potentially affected by HNMDs.
3. Develop evidence-based guidelines to improve patient management based on phenotype stratification, enhancing the current standard of care for healthcare providers.
4. Create an online platform for public data collection, compliant with the European Union (EU) FAIR (Findable, Accessible, Interoperable, Reusable) principles, for patients affected by neuromuscular diseases, known as ATLAS-NMD.

The CoMPaSS study processes both pre-existing and newly obtained data in two parts: (i) a retrospective observational study using genetic, histopathology, and Magnetic Resonance Imaging (MRI) data collected from centers in Great Britain (UNEW), France (CERBM), Finland (SFF), and Italy (FSM); and (ii) a prospective study involving the collection of clinical, genetic, histopathology, and MRI data from 500 previously undiagnosed patients at clinical reference centers in Italy (FSM, UNIMORE) and Germany (LMUM).

Each participant in the prospective study will undergo:

• A standardized clinical evaluation protocol assessing all muscle districts, with data recorded in an electronic clinical record (eCRF).
• Muscle MRI performed based on clinical indications.
• Muscle biopsy of an affected muscle, if clinically indicated.
• Genomic analysis of DNA extracted from peripheral blood lymphocytes. Participation in the study will be granted only after the participant or their legal representative has reviewed the study information sheet, provided informed consent for the processing of sensitive data and privacy, and signed the consent document.

The study employs computational tools for clustering based on unsupervised multidimensional processing, which uses the internal structure of data to define similar groups among patients. Regarding data privacy, data minimization, and data access rights, the study follows a Federated Learning approach. This ensures that models for stratification and classification can be trained collaboratively by several entities while keeping data decentralized at each clinical center.

The study adheres to a patient-centered co-design methodology, involving strong stakeholder participation and networking with other project consortia, while respecting all principles of data protection and management.

Regarding the expected outcomes, the study aims to provide researchers with effective solutions for integrating health data and more precise classification of clinical phenotypes. Based on these findings, evidence-based guidelines will be developed using the stratification of distinctive clinical traits, ultimately offering a superior standard of care for the diagnosis and prognosis of patients with HNMDs.

The study is funded by the European framework program "Horizon Europe" under the call HORIZON-HLTH-2022-TOOL-12-two-stage.

• Study Type: Observational
• Enrollment (Estimated): 500

Contacts and Locations

Study Locations

• Germany
  • Munich, Germany
    • Ludwig-Maximilians-Universitaet Muenchen
• Italy
  • Modena, Italy
    • Azienda Ospedaliero-Universitaria di Modena
  • Pisa
    • San Miniato, Pisa, Italy
      • Fondazione Stella Maris

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study will include European population of patients with HNMD. The clinical referral centers UNIMORE and LMUM will recruit patients over 18 years old, while the FSM center will also recruit minors.

Description

Inclusion Criteria:

• Male and female with Age>18 and minors, aged 1-17 years-the latter with parental/guardian consent-who have been diagnosed with an inherited neuromuscular disease of a nature to be determined.
• Willingness to perform assessments as stated in the protocol at least during baseline visit.
• Willingness to donate biological samples collected through biopsy, MRI and blood analysis.

Exclusion Criteria:

• Unwillingness to perform assessments as stated in the protocol at least during baseline visit
• Unwillingness to donate biological samples collected through biopsy, MRI and blood analysis

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
UNIMORE - University of Modena and Reggio Emilia; Adults patients suffering HNDMs Blood samples, skin/biopsy sample, MRI data collection from adults to classify patient with HNMD through an AI system	Other: AI-guided patient classification; The intervention consist in firstly obtaining clinical, genetic, histopathological, MRI data from total 500 patients coming from the defined Cohorts. The adaptative AI-tool developed, based on data provided, will then identify multi-modal characteristics that will support patients' superclusters and their multi-omics signatures.
FSM - FONDAZIONE STELLA MARIS; Adults and kids patients suffering HNDMs Blood samples, skin/biopsy sample, MRI data collection from minors to classify patient with HNMD through an AI system	Other: AI-guided patient classification; The intervention consist in firstly obtaining clinical, genetic, histopathological, MRI data from total 500 patients coming from the defined Cohorts. The adaptative AI-tool developed, based on data provided, will then identify multi-modal characteristics that will support patients' superclusters and their multi-omics signatures.
LMUM - LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN; Adults patients suffering HNDMs Blood samples, skin/biopsy sample, MRI data collection from adults to classify patient with HNMD through an AI system	Other: AI-guided patient classification; The intervention consist in firstly obtaining clinical, genetic, histopathological, MRI data from total 500 patients coming from the defined Cohorts. The adaptative AI-tool developed, based on data provided, will then identify multi-modal characteristics that will support patients' superclusters and their multi-omics signatures.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Updated database of patients with HNMD that will be used to validate the algorithms and computational models developed in the unsupervised studies of existing genetic, histopathological, and MRI data	number of newly diagnosed HNMD patients assigned to a particular supercluster-based phenotype category (+30%);; number of patients' superclusters and their multi-omics signatures achieved (at least 6);; digital representations of the data from total 500 patients included in the study.	36 months

Collaborators and Investigators

• Sponsor: University of Modena and Reggio Emilia
• Investigators: Principal Investigator: ROSSELLA G TUPLER, University of Modena and Reggio Emilia

Publications and helpful links

General Publications

• Bevan S. Economic impact of musculoskeletal disorders (MSDs) on work in Europe. Best Pract Res Clin Rheumatol. 2015 Jun;29(3):356-73. doi: 10.1016/j.berh.2015.08.002. Epub 2015 Oct 24.
• McCarthy JF, Marx KA, Hoffman PE, Gee AG, O'Neil P, Ujwal ML, Hotchkiss J. Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management. Ann N Y Acad Sci. 2004 May;1020:239-62. doi: 10.1196/annals.1310.020.
• Savarese M, Di Fruscio G, Torella A, Fiorillo C, Magri F, Fanin M, Ruggiero L, Ricci G, Astrea G, Passamano L, Ruggieri A, Ronchi D, Tasca G, D'Amico A, Janssens S, Farina O, Mutarelli M, Marwah VS, Garofalo A, Giugliano T, Sampaolo S, Del Vecchio Blanco F, Esposito G, Piluso G, D'Ambrosio P, Petillo R, Musumeci O, Rodolico C, Messina S, Evila A, Hackman P, Filosto M, Di Iorio G, Siciliano G, Mora M, Maggi L, Minetti C, Sacconi S, Santoro L, Claes K, Vercelli L, Mongini T, Ricci E, Gualandi F, Tupler R, De Bleecker J, Udd B, Toscano A, Moggio M, Pegoraro E, Bertini E, Mercuri E, Angelini C, Santorelli FM, Politano L, Bruno C, Comi GP, Nigro V. The genetic basis of undiagnosed muscular dystrophies and myopathies: Results from 504 patients. Neurology. 2016 Jul 5;87(1):71-6. doi: 10.1212/WNL.0000000000002800. Epub 2016 Jun 8. Erratum In: Neurology. 2018 Jun 5;90(23):1084. doi: 10.1212/WNL.0000000000005192. Neurology. 2019 Aug 20;93(8):371. doi: 10.1212/WNL.0000000000007477.
• Cohen E, Bonne G, Rivier F, Hamroun D. The 2022 version of the gene table of neuromuscular disorders (nuclear genome). Neuromuscul Disord. 2021 Dec;31(12):1313-1357. doi: 10.1016/j.nmd.2021.11.004. No abstract available.
• Muller KI, Ghelue MV, Lund I, Jonsrud C, Arntzen KA. The prevalence of hereditary neuromuscular disorders in Northern Norway. Brain Behav. 2021 Jan;11(1):e01948. doi: 10.1002/brb3.1948. Epub 2020 Nov 13.
• Turner JA, Franklin G, Fulton-Kehoe D, Egan K, Wickizer TM, Lymp JF, Sheppard L, Kaufman JD. Prediction of chronic disability in work-related musculoskeletal disorders: a prospective, population-based study. BMC Musculoskelet Disord. 2004 May 24;5:14. doi: 10.1186/1471-2474-5-14.
• Cylus J, Figueras J, Normand C, authors. Sagan A, Richardson E, North J, White C, editors. Will Population Ageing Spell the End of the Welfare State? A review of evidence and policy options [Internet]. Copenhagen (Denmark): European Observatory on Health Systems and Policies; 2019. Available from http://www.ncbi.nlm.nih.gov/books/NBK550573/

Study record dates

Study Major Dates

• Study Start (Actual): May 1, 2024
• Primary Completion (Estimated): March 31, 2027
• Study Completion (Estimated): April 30, 2027

Study Registration Dates

• First Submitted: December 5, 2024
• First Submitted That Met QC Criteria: December 10, 2024
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: January 31, 2025
• Last Verified: January 1, 2025

More Information

Terms related to this study

• Keywords: HNMD; STRATIFICATION; COMPUTATIONAL MODELS; CLUSTERING
• Other Study ID Numbers: 101080874

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No