GenoMed4ALL: Improving SCD Classification and Prognosis by AI (GenoMed4ALL)

April 11, 2024 updated by: Hospital Universitari Vall d'Hebron Research Institute

Genomics and Personalized Medicine for All Though Artificial Intelligence in Haematological Diseases

GenoMed4All 'Genomics and Personalized Medicine for all though Artificial Intelligence in Haematological Diseases' aims to advance on individual SCD patients' disease characterisation and to improve the monitoring of patients' health status, optimise clinical therapy guidance and ultimately improved health outcomes by the identification of biomarkers and the development of individual (risk) models in SCD. Genomed4All supports the pooling of genomic, clinical data and other "-omics" health through a secure and privacy respectful data sharing platform based on the novel Federated Learning scheme, to advance research in personalised medicine in haematological diseases thanks to advanced Artificial Intelligence (AI) models and standardised interoperable sharing of cross-border data, without needing to directly share any sensitive clinical patients' data. The SCD Use case will gather multi-modal clinical and -OMICs data from 1,000 SCD patients in 4 EU-MS: France, Italy, Spain and The Netherlands.

In close collaboration with the European Reference Network on Rare Hematological Diseases (ERN-EuroBloodNet, GA101157011), GENOMED4ALL involves multiple clinical partners from the network, while leveraging on healthcare information and repositories that will be gathered incorporating interoperability standards as promoted by ERN-EuroBloodNet central registry, the European Rare Blood Disorders Platform.

Study Overview

Status

Active, not recruiting

Conditions

Detailed Description

SCD is a chronic life-threatening multisystem disorder, autosomal recessively inherited, caused by the presence of abnormal hemoglobin S (HbS) resulting from the sickle mutation in the HBB gene. In spite of being a single gene mutation disorder, SCD presents extreme phenotypic variability that is incompletely understood. Several genetic and environmental factors are supposed to have an impact on disease phenotype, clinical manifestations, progression of organ damage and quality of life throughout the lifespan.

Although significant progress has been made over the past few decades in the highly complex pathophysiology of SCD, the possibility of personalised medicine is still in its infancy. There is a lack of markers of disease severity, prognosis, and response to treatment. In particular, the heterogeneity of clinical expression of the disease along with long-term chronic complications due to the increased lifespan of patients should be addressed by innovative and personalised treatments. Furthermore, assessing the role of the novel treatments both in regards of long-term efficacy and safety but also of cost/efficacy ratio are required. The scarcity and fragmentation of SCD data prevent researchers from reaching the critical numbers needed for basic and clinical research. Research and data-driven solutions are therefore essential to improve the care of SCD patients and their quality of life.

The availability of numerous treatment options as well as the high disease heterogeneity highlight the need to address patients' severity profiles and offer the best care for each affected individual. Developing the GENOMED4ALL AI algorithms for SCD will be of great importance for the in-depth characterization and prediction of the diverse complications of SCD. The primary endpoints of interest include:

  • Improving SCD classification
  • Develop a probability score to predict various patterns recognized by Artificial Intelligence (AI) based analyzing brain magnetic resonance imaging (Radiomics)
  • To develop predictive risk scores for the occurrence of most prevalent and severe clinical outcomes
  • To develop predictive risk scores over time for the appearance of most prevalent and severe clinical outcomes.

RADeep will be used for standardization of existing clinical and laboratory data. A CRF was developed, including just over 250 data elements. The GenoMed4All CRF builds on previous work performed by RADeep and includes the "set of common data elements for rare disease registration", which was released in December 2017 as result of a dedicated working group facilitated by the Joint Research Centre (JRC). This approach will ensure interoperability with other similar initiatives in Europe and will also enable the collected data to be reused for future research studies.

Genome-wide Association Studies (GWAS) extends the concept of association studies to assay hundreds of thousands of single-nucleotide polymorphisms (SNPs) simultaneously and provide a cost-effective way to explore genetic variants across the whole genome. But despite considerable interest in identifying genetic modifiers in SCD, the majority of previous GWAS searched for genetic linkage and association with HbF levels, an established ameliorating factor of disease severity. Addintionally, the utilization of data science and artificial intelligence (AI) has been limited in SCD research. Therefore, the generation of GWAS data combined with the use of the most recent imputation panel for imputation offers an opportunity for the development of novel AI techniques and for novel discoveries in SCD.

Silent Cerebral Infarcts (SCIs) are a significant cause of morbidity in SCD: they affect 25% of children by the age of 6 and 40% by the age of 18 with consequences on cognition, schooling, working capacity and quality of life. Hence, one of the aims of the SCD clinical case in GENOMED4ALL is the use of radiomics - quantitative method for the evaluation and interpretation of medical images- and AI firstly to develop an automatic and uniform identification and characterization of SCI on MRIs, secondly, to correlate imaging data with other types of OMICS data in order to predict risk of occurrence and recurrence.

The deformability of red blood cells (RBC) from individuals with SCD is markedly abnormal, regardless of genotype. Several studies reported some associations between the degree of impairment of RBC deformability measured at steady state in SCD patients and the presence of chronic complications, such as priapism, leg ulcers, glomerulopathy, etc. The recently developed technique of oxygen gradient ektacytometry allows for a more comprehensive functional characterization and rheological behavior of SCD RBCs over a range of oxygen tensions to test whether the rheological changes could reflect clinical severity/complications.Data on rheological characteristics of RBC on all patients in steady state is going to be obtained through Laser Optical Rotational Red Cell Analyzer (Lorrca) ektacytometer (RR Mechatronics).

By combining a large amount standardized multimodal (clinical, multi-omics, and imaging) datasets, the investigators hypothesize that AI will allow to understand better SCD biology and classification, enhance prognostic/predictive capacity of currently available tools and apply treatments in a more targeted way, thus facilitating the implementation of personalized medicine program across EU.

Study Type

Observational

Enrollment (Estimated)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • France
      • Créteil, France, 94000
        • APHP Henri Mondor
      • Paris, France, 75015
        • APHP Necker
  • Italy
      • Padova, Italy, 35121
        • Azienda Ospedale Universita Padova
  • Netherlands
      • Utrecht, Netherlands, 3584
        • UMC Utrecht
  • Spain
      • Barcelona, Spain, 08035
        • Hospital Universitari Vall d'Hebron Research Institute

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients affected by SCD

Description

Inclusion Criteria:

  • Patients older than 1 year, diagnosed with SCD, all genotypes.

Exclusion Criteria:

  • Patients treated with stem cell transplant or gene therapy.
  • Patients younger than 1 year old.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
GENOMED4ALL - SCD patients
Non transplanted SCD patients aged over 1 year.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Improving SCD classification
Time Frame: through study completion, an average of 2 years
To improve classification of SCD by integrating clinical and hematological information with genomic features. To address this issue, different methods of statistical learning (Dirichlet processes (DP), Bayesian networks (BN)) and machine learning (deep learning physics informed neural network, constrained regression and deep models) will be compared in order to define specific genotype-phenotype correlations and to develop a new disease classification.
through study completion, an average of 2 years
Improve diagnosis of cerebrovascular complications.
Time Frame: through study completion, an average of 2 years
Develop an artificial intelligence algorithm for early diagnosis of silent infarcts by analyzing brain magnetic resonance imaging (Radiomics).
through study completion, an average of 2 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Hospital Universitari Vall d'Hebron Research Institute

Investigators

  • Principal Investigator: Gastone Castellani, University of Bologna
  • Principal Investigator: Raffaella Colombatti, University of Padova
  • Principal Investigator: Eduard van Beers, UMC Utrecht
  • Principal Investigator: Marianne de Montalembert, APHP Necker
  • Principal Investigator: Pablo Bartolucci, APHP Henri Mondor
  • Principal Investigator: Tiziana Sanavia, University of Torino
  • Principal Investigator: Petros Kountouris, Cyprus Institute of Neurology and Genetics
  • Principal Investigator: Matteo Della Porta, Istituto Clinico Humanitas
  • Principal Investigator: Maria del Mar Mañú Pereira, Hospital Universitari Vall d'Hebron Research Institute
  • Principal Investigator: Federico Alvarez, Universidad Politécnica de Madrid

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2021

Primary Completion (Actual)

September 30, 2023

Study Completion (Estimated)

December 31, 2024

Study Registration Dates

First Submitted

June 15, 2023

First Submitted That Met QC Criteria

August 24, 2023

First Posted (Actual)

August 31, 2023

Study Record Updates

Last Update Posted (Actual)

April 12, 2024

Last Update Submitted That Met QC Criteria

April 11, 2024

Last Verified

June 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 928338

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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