NEJM evidence · 2026
Prognostic Score for Myelodysplastic Syndromes Based on Molecular Evolution.
СтатьяНазвание и аннотация: Перевод в очереди
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Civettini Ivan, Malighetti Federica, Villa Matteo, Crippa Valentina, Aroldi Andrea, Cavalca Fabrizio, Graudenzi Alex, Lanino Luca, Maggioni Giulia, Haferlach Torsten, Fenaux Pierre, Platzbecker Uwe, Díez-Campelo María, Borin Lorenza Maria, Fumagalli Monica, Zappaterra Arianna, Manghisi Beatrice, Al Ali Najla H, Sallman David A, Padron Eric, Xie Zhuoer, Chan Onyee, Zeidan Amer M, Mologni Luca, Piazza Rocco, Komrokji Rami S, Della Porta Matteo Giovanni, Gambacorti-Passerini Carlo, Ramazzotti Daniele
Аннотация
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.).
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.).