Summary Ruxolitinib is a potent Janus kinase (JAK) 1/JAK2 inhibitor approved for the treatment of myelofibrosis (MF). Ruxolitinib was assessed in JUMP, a large (N = 2233), phase 3b, expanded‐access study in MF in countries without access to ruxolitinib outside a clinical trial, which included patients with low platelet counts (<100 × 109/l) and patients without splenomegaly – populations that have not been extensively studied. The most common adverse events (AEs) were anaemia and thrombocytopenia, but they rarely led to discontinuation (overall, 5·4%; low‐platelet cohort, 12·3%). As expected, rates of worsening thrombocytopenia were higher in the low‐platelet cohort (all grades, 73·2% vs. 53·5% overall); rates of anaemia were similar (all grades, 52·9% vs. 59·5%). Non‐haematologic AEs, including infections, were mainly grade 1/2. Overall, ruxolitinib led to meaningful reductions in spleen length and symptoms, including in patients with low platelet counts, and symptom improvements in patients without splenomegaly. In this trial, the largest study of ruxolitinib in patients with MF to date, the safety profile was consistent with previous reports, with no new safety concerns identified. This study confirms findings from the COMFORT studies and supports the use of ruxolitinib in patients with platelet counts of 50–100 × 109/l. (ClinicalTrials.gov identifier NCT01493414).

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Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification.

Failure of second-generation tyrosine kinase inhibitors (2GTKI) is a challenging situation in patients with chronic myeloid leukemia (CML). Asciminib, recently approved by the US Federal Drug Administration, has demonstrated in clinical trials a good efficacy and safety profile after failure of 2GTKI. However, no study has specifically addressed response rates to asciminib in ponatinib pretreated patients (PPT). Here, we present data on responses to asciminib from 52 patients in clinical practice, 20 of them (38%) with prior ponatinib exposure. We analyzed retrospectively responses and toxicities under asciminib and compared results between PPT and non-PPT patients.After a median follow-up of 30 months, 34 patients (65%) switched to asciminib due to intolerance and 18 (35%) due to resistance to prior TKIs. Forty-six patients (88%) had received at least 3 prior TKIs. Regarding responses, complete cytogenetic response was achieved or maintained in 74% and 53% for non-PPT and PPT patients, respectively. Deeper responses such as major molecular response and molecular response 4.5 were achieved in 65% and 19% in non-PPT versus 32% and 11% in PPT, respectively. Two patients (4%) harbored the T315I mutation, both PPT.In terms of toxicities, non-PPT displayed 22% grade 3–4 TEAE versus 20% in PPT. Four patients (20% of PPT) suffered from cross-intolerance with asciminib as they did under ponatinib.Our data supports asciminib as a promising alternative in resistant and intolerant non-PPT patients, as well as in intolerant PPT patients; the resistant PPT subset remains as a challenging group in need of further therapeutic options.

Multiparameter flow cytometry (MFC)-based clonality assessment is a powerful method of diagnosis and follow-up in monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma (MM). However, the relevance of intraclonal heterogeneity in immunophenotypic studies remains poorly understood. The main objective of this work was to characterize the different immunophenotypic subclones in MGUS and MM patients and to investigate their correlation with disease stages. An 8-color MFC protocol with 17 markers was used to identify the subclones within the neoplastic compartment of 56 MGUS subjects, 151 newly diagnosed MM patients, 30 MM subjects in complete remission with detectable minimal residual disease, and 36 relapsed/refractory MM patients. Two or more clusters were observed in > 85% of MGUS subjects, 75% of stage I MM patients, and < 15% in stage III. Likewise, a significant correlation between the dominant subclone size, secondary cytogenetic features, and changes in the expression of CD27, CD44, and CD81 was detected. The loss of intraclonal equilibrium may be an important factor related with kinetics and risk of progression not well considered to date in MFC studies. The MFC strategy used in this work can provide useful biomarkers in MGUS and MM.