Faculty

My research interests mainly concern myeloid neoplasms (MN, including myelodysplastic syndromes, MDS; acute myeloid leukemias AML; and myeloproliferative neoplasms, MPN)

These investigations led to the identification of recurrent gene mutations in myelodysplastic syndromes. In particular, in 2010, I joined the International Cancer Genome Consortium Chronic Myeloid Disorders Working Group. This collaboration led to the identification of somatic mutations of SF3B1, a gene encoding a core component of RNA splicing machinery, in myelodysplasia with ring sideroblasts [N Engl J Med. 2011;365(15):1384-95]

I extensively studied the genotype-phenotype correlations of recurrent gene mutations in MN, and we found a close association between ring sideroblasts and SF3B1 mutations, which is consistent with a causal relationship. Moreover, we found that SF3B1 mutations were independent predictors of favourable prognosis, providing the first evidence that the identification of the mutant gene responsible for the initial clone in MN is relevant to clinical outcome. [Blood. 2011;118(24):6239-46, J Clin Oncol. 2013;31(28):3557-64; Blood. 2013 ;122(22):3616-27 Blood. 2015 Jul 9;126(2):233-41, Leukemia. 2015;29(1):66-75].

According to these progresses in defining the genomic landscape of MN, in recent years there was a shift from clinical and morphologic classification/prognostication schemes to those that are based on genomics which are closer to the disease biology and better capture clinical-pathological entities. In this context, by using advanced statistical models, we developed a proof of concept for the definition of a molecular classification of myelodysplastic syndromes [Nat Med. 2020;26(10):1549-1556; J Clin Oncol. 2021;39(11):1223-1233], thus providing evidence that MDS with SF3B1 and TP53 mutations are distinct disease entities.

In addition, my research activity showed that gene mutations may influence survival and risk of disease progression in MN, and that the evaluation of the mutation status may add significant information to currently used prognostic scores. In this context, we contributed to the definition of the new molecular prognostic score for myelodysplastic syndromes (Molecular International Prognostic Scoring System, IPSS-M) including both clinical and genomic features [NEJM Evid 2022;1(7)DOI:https://doi.org/10.1056/  EVIDoa2200008] and we provided an extensive validation of its prognostic value. [J Clin Oncol, 2023 in press]

In last years, as scientific and clinical coordinator of EU-funded initiatives (GenoMed4all – www.genomed4all.eu and SYNTHEMA – www.synthema.eu), I approached the study of Artificial Intelligence as a toll to improve personalized medicine in hematology.  We developed solutions to improve clinical decision making process; in particular, we build decision models to better define candidate patients and optimal timing of stem cell transplantation, the only curative treatment for MN [J Clin Oncol. 2016;34(30):3627-3637; Leukemia. 2017;31(11):2449-2457; Lancet Haematol. 2023;10(2):117-28]

Overall, my research activity contributed to the development and clinical implementation of personalized medicine programs in MN, including improved disease diagnosis and classification, definition of optimal treatment and prediction of future outcomes.