Characterization of non-driver mutations and identification of different outcomes and treatment strategies based on NGS results in myelofibrosis patients in different clinical stages

Myelofibrosis (MF), a chronic Ph-negative myeloproliferative neoplasm, is a clinically and genetically heterogeneous disease. Beside driver mutations that represent the hallmark of pathogenesis, the rapid advancements in gene sequencing technology, like Next Generation Sequencing (NGS), have led to discover additional mutations revealing biological insights in MF and possible novel prognostic markers. However, current clinical prognostic risk-stratification models are the most used in clinical practice. The aim of our project is to use and validate NGS technology in transplant-eligible MF patients, prospectively refining a more reliable prognostic risk assessment and risk-adapted treatment strategy, in real-life setting. We enrolled 68 MF patients, consecutively diagnosed and followed at Sapienza University. Twenty-two out of 68 (32%) subjects had secondary MF (SMF), diagnosed post essential thrombocythemia and polycythaemia vera. As for driver mutations, 52%, 28% and 3% of patients, carried JAK2V617F, CALR and MPL mutation, respectively. One patient had double mutation (JAK2V617F/MPL); 10 (15%) patients were identified as triple negative. We found 72 non-driver mutations; 13 out of 68 (19%) patients had a high molecular risk (HMR) profile. The most frequently mutated genes were TET2 (n=14, 20%), DNMT3A (n=7, 10%) and ASXL1 (n=11, 16%). ASXL1 mutated patients carried distinct high-risk clinical features, including higher value of LDH (p<0.001), monocytes (p<0.001), spleen diameter (p=0.035) and symptoms (p=0.042). Focusing on mutational profile, no significant differences were detected comparing PMF and SMF. According to the IPSS survival risk distribution at diagnosis in PMF, 32 patients were classified as low risk (70%), 9 as intermediate-1 (20%), 3 as intermediate-2 (6%) and 2 as high (4%). In SMF, the MYSEC-PM risk distribution identified 7 patients as low risk (32%), 13 (59%) as intermediate-1 and 2 as intermediate-2 (9%). The real-life application of MIPSS70 model identified 22 patients, who were ...