Combining Gene Mutation with Transcriptomic Data Improves Outcome Prediction in Myelodysplastic Syndromes

• Published in: Blood 2023-11, Vol.142 (Supplement 1), p.1863-1863
• Main Authors: Sauta, Elisabetta, Zampini, Matteo, Dall'Olio, Daniele, Sala, Claudia, Todisco, Gabriele, Travaglino, Erica, Lanino, Luca, Tentori, Cristina Astrid, Maggioni, Giulia, D'Amico, Saverio, Asti, Gianluca, Dall'Olio, Lorenzo, Mosca, Ettore, Ubezio, Marta, Campagna, Alessia, Riva, Elena, Bicchieri, Marilena, Savevski, Victor, Santoro, Armando, Kordasti, Shahram, Santini, Valeria, Diez-Campelo, Maria, Kubasch, Anne Sophie, Platzbecker, Uwe, Fenaux, Pierre, Zhao, Lin Pierre, Zeidan, Amer M., Haferlach, Torsten, Castellani, Gastone, Della Porta, Matteo Giovanni
• Format: Article
• Language: English
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood-2023-186222

Background and Aim. Myelodysplastic syndromes (MDS) are myeloid neoplasms characterized by peripheral blood cytopenias and risk of progression to acute myeloid leukemia (AML). Disease management is challenged by heterogeneity in clinical courses and survival probability. Recently, the genomic screening integration (by Molecular International Prognostic Scoring System, IPSS-M) into patient's assessment has resulted into a significant improvement in predicting clinical outcomes compared to the conventional prognostic score (Revised IPSS, IPSS-R). Many of the consequences of genetic and cytogenetic alterations will affect gene expression by means of transcriptional and epigenetic instability and altered microenviromental signaling. The aim of this project conducted by GenoMed4All and Synthema EU consortia is to link genomic information with transcriptomic data for possibly improving the prediction of clinical outcomes in MDS patients. Patients and Methods.Clinical, cytogenetic, genomic (somatic mutations screening of 31 target genes) and transcriptomic (bulk RNA-seq of CD34 + bone marrow cells) data were collected at diagnosis in 389 MDS patients. Transcriptomic and genomic profiles were processed and the former were normalized before Principal Component Analysis (PCA) dimensionality reduction to mine the interdependency of expression-wide perturbation and recurrent genomic alterations. The prognostic impacts of genetic, cytogenetic, transcriptomic, clinical and demographic features were assessed with a penalized Cox's proportional hazards model [Gerstung M et al, Nat Commun. 2015. 6, 5901] considering the Overall Survival (OS) as primary end point. A 5-fold cross-validating (CV) scheme was exploited to control bias in risk estimation. Model accuracy was assessed using Harrell's concordance index (C-index). An independent validation of the results on 202 patients was planned. Results.We first processed each data layer assessing data robustness, removed not informative variables and scaled quantitative ones. We considered recurrent genomic and cytogenetic lesions (present in ≥5 patients), platelets, hemoglobin and bone marrow blasts (%), age and sex as covariates. To explore the main patterns of expression changes, PCA was performed to reduce multidimensional correlated expression features (20 PCs was selected, explaining 42% of the total transcriptomic variability). To evaluate the prognostic power of each data layer we grouped all available features into fiv