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Updated: May 28, 2026
Use of Hematopoietic Stem Cell Transplantation to Assess the Origin of Myelodysplastic Syndrome
Published on: October 3, 2018
Prognostic Score for Myelodysplastic Syndromes Based on Molecular Evolution
Ivan Civettini1,2,3, Federica Malighetti1, Matteo Villa1
1Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
Background:
Myelodysplastic syndromes are clonal hematopoietic stem cell disorders characterized by multistep molecular evolution and a variable risk of leukemic transformation. Given this prognostic heterogeneity, accurate risk stratification is essential for clinical decision-making. We developed ProgEvo, a proprietary framework that infers molecular evolutionary trajectories and integrates them with clinical data to improve prognostic accuracy.
Methods:
ProgEvo was trained on 2519 patients in cBioPortal (https://www.cbioportal.org) and validated using two external cohorts: Genomed4All (2043 patients) and a Moffitt Cancer Center (MCC) cohort (2157 patients). Directional evolutionary routes were inferred and selected for prognostic modeling if they were consistently associated with leukemia-free survival. A multivariable feature selection strategy was applied to integrate evolution-consistent variables into the existing IPSS-M model.
Results:
ProgEvo identified 1765 gene co-occurrences aggregated into 45 directional evolutionary routes. Of these, 18 were validated in the Genomed4All cohort. Five evolution-informed variables, two directional routes (Additional Sex Combs-Like 1 [ASXL1]→KRAS Proto-Oncogene [KRAS] and Serine and Arginine-Rich Splicing Factor 2 [SRSF2]→NRAS Proto-Oncogene [NRAS]), one co-occurrence (NRAS/RUNX Family Transcription Factor 1 [RUNX1]), and two early mutations (ATRX [ATRX Chromatin Remodeler] and Janus Kinase 2 [JAK2]) were integrated into IPSS-M to generate IPSS-M-Evo. The model with "-Evo" improved discrimination for both leukemia-free survival and overall survival, with over 40% of patients restratified in the Genomed4All data. The performance of the model was further confirmed in the MCC cohort.
Conclusions:
ProgEvo enabled inference of a molecular evolution model and integration of evolution-informed covariates into clinical prognostic frameworks, supporting the development of the IPSS-M-Evo model. A free web-based tool allows clinicians to calculate the IPSS-M-Evo score and match individual mutational profiles to cohort-derived evolutionary trajectories (https://evoclin.unimib.it/tools/evolution-graphs.html and https://evoclin.unimib.it/tools/ipssmevo.html). (Funded by the European Union and others.).
