GENOMED4ALL: Improving MDS Classification and Prognosis by AI

NCT04889729 | Unknown

• 1 other identifier: GENOMED4ALL: MDS
• Study Type: observational
• Target: 13,284
• Locations: 1 country
• Sites: 1

Brief Summary

Myelodysplastic syndromes (MDS) typically occur in elderly people. Current disese classifcation system and prognostic scores (International Prognostic Scoring System, IPSS) present limitations and in most cases fail to capture reliable prognostic information at individual level. Study of MDS has been rapidly transformed by genome characterization and there is increasing evidence that mutation screening may add significant information to currently available prognostic scores. The project will aim to develop artificial intelligence (AI)-based solutions to improve MDS classification and prognostication, through the implementation of a personalized medicine approach. In close collaboration with the European Reference Network on Rare Hematological Diseases (ERN-EuroBloodNet, FPA 739541), 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 (ENROL, GA 947670).

Conditions

Myelodysplastic Syndromes

Keywords

ARTIFICIAL INTELLIGENCE; HEMATOLOGICAL DISEASE; GENOMICS; PROGNOSIS; DISEASE CLASSIFICATION

Outcome Measures

Primary Outcomes (3)

• Improving MDS classification

  To improve classification of MDS 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

• Prediction of probability of overall survival (months between diagnosis and death or end of follow up) for patients with MDS

  Overall survival (OS) will be defined as the time (expressed in months) between diagnosis and death (as a result of all causes) or end of follow-up (censored observations). New prognostic scores will be defined including the following features: age expressed in years; sex (male or female); neutrophils count (number of neutrophils\*10\^6/L), platelets count (number of plateles 10\^6/L), hemoglobin concentration (g/dl), cytogenetics (stratified according to IPPS-R criteria, Blood 2012 120: 2454-2465), percentage of bone marrrow blasts and presence of gene mutations (presence versus absence). Different statistical methods will be used to measure prediction accuracy (measured by concordance index, C-index): Cox proporsional-hazard methods, random survival forests, neural networks, continous individualized risk index (CIRI), times series analysis and Markov modeling for stochastic trajectories prediction

  through study completion, an average of 2 years

• Prediction of probability of leukemia free surivival (months from diagnosis to progression to acute leukemia or end of follow up) for patients with MDS

  Leukemia will be defined as the time (expressed in months) between diagnosis and progression to acute leukemia or end of follow-up. New prognostic scores will be defined including the following features: age expressed in years; sex (male or female); neutrophils count (number of neutrophils\*10\^6/L), platelets count (number of plateles 10\^6/L), hemoglobin concentration (g/dl), cytogenetics (stratified according to IPPS-R criteria, Blood 2012 120: 2454-2465), percentage of bone marrrow blasts and presence of gene mutations (presence versus absence). Different statistical methods will be used to measure prediction accuracy (measured by concordance index, C-index): Cox proporsional-hazard methods, random survival forests, neural networks, continous individualized risk index (CIRI), times series analysis and Markov modeling for stochastic trajectories prediction

  through study completion, an average of 2 years

Study Arms (1)

GENOMED4ALL - MDS patients

Information on targeted mutation screening (NGS including 60 genes related to MDS) from 13284 MDS patients

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients affected by MDS. 13284 patients with clinical and genomic information availability

You may qualify if:

• Patients affected by MDS according WHO criteria \> 18 years old
• Avaliability of clinical and hematological information
• Availability of information on targeted mutation screening

You may not qualify if:

• none of the above

Sponsors & Collaborators

• Istituto Clinico Humanitas: lead

Study Sites (1)

Istituto Clinico Humanitas

Milan, Italy

Location

Related Publications (33)

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  PMID: 36930857 DERIVED

• GenoMed4All consortium. A sex-informed approach to improve the personalised decision making process in myelodysplastic syndromes: a multicentre, observational cohort study. Lancet Haematol. 2023 Feb;10(2):e117-e128. doi: 10.1016/S2352-3026(22)00323-4. Epub 2022 Nov 24.

  PMID: 36436542 DERIVED

MeSH Terms

Conditions

Myelodysplastic Syndromes; Hematologic Diseases

Condition Hierarchy (Ancestors)

Bone Marrow Diseases; Hemic and Lymphatic Diseases

Study Officials

• Federico Alvarez

  UNIVERSIDAD POLITECNICA DE MADRID SPAIN

  PRINCIPAL INVESTIGATOR

• Lucia Comnes

  DATAWIZARD SRL ITALY

  PRINCIPAL INVESTIGATOR

• Mar Manu Pereira

  FUNDACIO HOSPITAL UNIVERSITARI VALL D'HEBRON - INSTITUT DE RECERCA SPAIN

  PRINCIPAL INVESTIGATOR

• Pierre Fenaux

  ASSISTANCE PUBLIQUE HOPITAUX DE PARIS FRANCE

  PRINCIPAL INVESTIGATOR

• Torsten Haferlach

  MLL MUNCHNER LEUKAMIELABOR GMBH GERMANY

  PRINCIPAL INVESTIGATOR

• Maria Diez Campelo

  Instituto de investigacion biomedica de Salamanca, IBSAL SPAIN

  PRINCIPAL INVESTIGATOR

• Uwe Platzbecker

  UNIVERSITAET LEIPZIG GERMANY

  PRINCIPAL INVESTIGATOR

• Gastone Castellani

  ALMA MATER STUDIORUM - UNIVERSITA DI BOLOGNA ITALY

  PRINCIPAL INVESTIGATOR

• Andres Krogh

  KOBENHAVNS UNIVERSITET DENMARK

  PRINCIPAL INVESTIGATOR

• Babita Singh

  FUNDACIO CENTRE DE REGULACIO GENOMICA SPAIN

  PRINCIPAL INVESTIGATOR

• Piero Fariselli

  UNIVERSITA DEGLI STUDI DI TORINO ITALY

  PRINCIPAL INVESTIGATOR

• Kostantinos Marias

  IDRYMA TECHNOLOGIAS KAI EREVNAS GREECE

  PRINCIPAL INVESTIGATOR

• Mar Mañu Pereira

  European Reference Network on Rare Hematological Diseases (ERN-EuroBloodNet)

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 5, 2021
• First Posted: May 17, 2021
• Study Start: March 15, 2021
• Primary Completion: December 15, 2022
• Study Completion: December 31, 2024
• Last Updated: September 9, 2022

Record last verified: 2022-09

Locations

IT (1)