Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes.
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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
Аннотация
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.