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

NCT06734949 | Enrolling By Invitation

• 1 other identifier: 101080874
• Study Type: observational
• Target: 500
• Locations: 2 countries
• Sites: 3

Brief Summary

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.

Conditions

Neuromuscular; Disorder, Hereditary

Keywords

HNMD; STRATIFICATION; COMPUTATIONAL MODELS; CLUSTERING

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (3)

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

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

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

Interventions

AI-guided patient classification OTHER

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; LMUM - LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN; UNIMORE - University of Modena and Reggio Emilia

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• University of Modena and Reggio Emilia: lead

Study Sites (3)

Ludwig-Maximilians-Universitaet Muenchen

Munich, Germany

Location

Fondazione Stella Maris

San Miniato, Pisa, Italy

Location

Azienda Ospedaliero-Universitaria di Modena

Modena, Italy

Location

Related Publications (7)

• 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.

  PMID: 15208196 BACKGROUND

• 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.

  PMID: 27281536 BACKGROUND

• 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.

  PMID: 34930546 BACKGROUND

• 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.

  PMID: 33185984 BACKGROUND

• 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.

  PMID: 15157280 BACKGROUND

• 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/

  PMID: 31820887 BACKGROUND

• 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.

  PMID: 26612235 BACKGROUND

Biospecimen

Retention: SAMPLES WITH DNA

Blood samples, skin/biopsy sample, MRI data

Study Officials

• ROSSELLA G TUPLER

  University of Modena and Reggio Emilia

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Month
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associated Professor

Study Record Dates

• First Submitted: December 5, 2024
• First Posted: December 16, 2024
• Study Start: May 1, 2024
• Primary Completion (Estimated): March 31, 2027
• Study Completion (Estimated): April 30, 2027
• Last Updated: February 5, 2025

Record last verified: 2025-01

Locations

IT (2); DE (1)