GenoMed4ALL: Improving SCD Classification and Prognosis by AI

Sickle Cell Disorders Clinical Trial

— GenoMed4ALL  

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06019208
• Other study ID #: 928338
• Status: Active, not recruiting
• Start date: January 1, 2021
• Est. completion date: December 31, 2024

Study information

• Verified date: June 2023
• Source: Hospital Universitari Vall d'Hebron Research Institute
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 1000
• Est. completion date: December 31, 2024
• Est. primary completion date: September 30, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 1 Year and older

Eligibility

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.

Related Conditions & MeSH terms

• Anemia, Sickle Cell
• Sickle Cell Disorders

Locations

Country	Name	City	State
France	APHP Henri Mondor	Créteil
France	APHP Necker	Paris
Italy	Azienda Ospedale Università Padova	Padova
Netherlands	UMC Utrecht	Utrecht
Spain	Hospital Universitari Vall d'Hebron Research Institute	Barcelona

Sponsors (1)

Lead Sponsor	Collaborator
Hospital Universitari Vall d'Hebron Research Institute

Countries where clinical trial is conducted

France,  Italy,  Netherlands,  Spain, 

References & Publications (11)

Aguilar Martinez P, Angastiniotis M, Eleftheriou A, Gulbis B, Manu Pereira Mdel M, Petrova-Benedict R, Corrons JL. Haemoglobinopathies in Europe: health & migration policy perspectives. Orphanet J Rare Dis. 2014 Jul 1;9:97. doi: 10.1186/1750-1172-9-97. — View Citation

Alapan Y, Fraiwan A, Kucukal E, Hasan MN, Ung R, Kim M, Odame I, Little JA, Gurkan UA. Emerging point-of-care technologies for sickle cell disease screening and monitoring. Expert Rev Med Devices. 2016 Dec;13(12):1073-1093. doi: 10.1080/17434440.2016.1254038. Epub 2016 Nov 22. — View Citation

Bao EL, Lareau CA, Brugnara C, Fulcher IR, Barau C, Moutereau S, Habibi A, Badaoui B, Berkenou J, Bartolucci P, Galacteros F, Platt OS, Mahaney M, Sankaran VG. Heritability of fetal hemoglobin, white cell count, and other clinical traits from a sickle cell disease family cohort. Am J Hematol. 2019 May;94(5):522-527. doi: 10.1002/ajh.25421. Epub 2019 Feb 6. — View Citation

Bunn HF. Pathogenesis and treatment of sickle cell disease. N Engl J Med. 1997 Sep 11;337(11):762-9. doi: 10.1056/NEJM199709113371107. No abstract available. — View Citation

Collado A, Boaro MP, van der Veen S, Idrizovic A, Biemond BJ, Beneitez Pastor D, Ortuno A, Cela E, Ruiz-Llobet A, Bartolucci P, de Montalembert M, Castellani G, Biondi R, Manara R, Sanavia T, Fariselli P, Kountouris P, Kleanthous M, Alvarez F, Zazo S, Colombatti R, van Beers EJ, Manu-Pereira MDM. Challenges and Opportunities of Precision Medicine in Sickle Cell Disease: Novel European Approach by GenoMed4All Consortium and ERN-EuroBloodNet. Hemasphere. 2023 Feb 22;7(3):e844. doi: 10.1097/HS9.0000000000000844. eCollection 2023 Mar. No abstract available. — View Citation

INGRAM VM. Abnormal human haemoglobins. III. The chemical difference between normal and sickle cell haemoglobins. Biochim Biophys Acta. 1959 Dec;36:402-11. doi: 10.1016/0006-3002(59)90183-0. No abstract available. — View Citation

Kato GJ, Piel FB, Reid CD, Gaston MH, Ohene-Frempong K, Krishnamurti L, Smith WR, Panepinto JA, Weatherall DJ, Costa FF, Vichinsky EP. Sickle cell disease. Nat Rev Dis Primers. 2018 Mar 15;4:18010. doi: 10.1038/nrdp.2018.10. — View Citation

Rab MAE, van Oirschot BA, Bos J, Merkx TH, van Wesel ACW, Abdulmalik O, Safo MK, Versluijs BA, Houwing ME, Cnossen MH, Riedl J, Schutgens REG, Pasterkamp G, Bartels M, van Beers EJ, van Wijk R. Rapid and reproducible characterization of sickling during automated deoxygenation in sickle cell disease patients. Am J Hematol. 2019 May;94(5):575-584. doi: 10.1002/ajh.25443. Epub 2019 Mar 8. — View Citation

Steinberg MH, Sebastiani P. Genetic modifiers of sickle cell disease. Am J Hematol. 2012 Aug;87(8):795-803. doi: 10.1002/ajh.23232. Epub 2012 May 28. — View Citation

Thein SL, Menzel S, Peng X, Best S, Jiang J, Close J, Silver N, Gerovasilli A, Ping C, Yamaguchi M, Wahlberg K, Ulug P, Spector TD, Garner C, Matsuda F, Farrall M, Lathrop M. Intergenic variants of HBS1L-MYB are responsible for a major quantitative trait locus on chromosome 6q23 influencing fetal hemoglobin levels in adults. Proc Natl Acad Sci U S A. 2007 Jul 3;104(27):11346-51. doi: 10.1073/pnas.0611393104. Epub 2007 Jun 25. — View Citation

Thein SL. Genetic modifiers of the beta-haemoglobinopathies. Br J Haematol. 2008 May;141(3):357-66. doi: 10.1111/j.1365-2141.2008.07084.x. — View Citation

* Note: There are 11 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Improving SCD classification	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
Primary	Improve diagnosis of cerebrovascular complications.	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