Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes. Issue 11 (10th April 2021)

• Record Type: Journal Article
• Authors: Bersanelli, Matteo; Travaglino, Erica; Meggendorfer, Manja; Matteuzzi, Tommaso; Sala, Claudia; Mosca, Ettore; Chiereghin, Chiara; Di Nanni, Noemi; Gnocchi, Matteo; Zampini, Matteo; Rossi, Marianna; Maggioni, Giulia; Termanini, Alberto; Angelucci, Emanuele; Bernardi, Massimo; Borin, Lorenza; Bruno, Benedetto; Bonifazi, Francesca; Santini, Valeria; Bacigalupo, Andrea; Voso, Maria Teresa; Oliva, Esther; Riva, Marta; Ubezio, Marta; Morabito, Lucio; Campagna, Alessia; Saitta, Claudia; Savevski, Victor; Giampieri, Enrico; Remondini, Daniel; Passamonti, Francesco; Ciceri, Fabio; Bolli, Niccolò; Rambaldi, Alessandro; Kern, Wolfgang; Kordasti, Shahram; Sole, Francesc; Palomo, Laura; Sanz, Guillermo; Santoro, Armando; Platzbecker, Uwe; Fenaux, Pierre; Milanesi, Luciano; Haferlach, Torsten; Castellani, Gastone; Della Porta, Matteo G.;
• Is Part Of: Journal of clinical oncology. Volume 39:Issue 11(2021)
• Page Start: 1223
• Page End: 1233
• Publication Date: 2021-04-10
• Subjects: Oncology -- Periodicals; Cancer -- Periodicals; Oncology; Medical Oncology; Cancérologie -- Périodiques; Cancer -- Périodiques; Cancérologie; Cancer; Oncology; Oncologia; Càncer; Periodicals; 616.994
• DOI: 10.1200/JCO.20.01659
• Languages: English
• ISSNs: 0732-183X

Abstract

Abstract : PURPOSE: Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. METHODS: We retrospectively enrolled 2, 043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed. RESULTS: We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations ( SF3B1, SRSF2, and U2AF1 ) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1 - and SRSF2 -related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia–like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features. CONCLUSION: Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.