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Abstract In 2016, the WHO included haemoglobin values within normal ranges as a diagnostic criterion for Polycythaemia Vera (PV). Since then, concerns have arisen that a large number of patients are undergoing unnecessary screening for PV. To address this issue, we estimated the prevalence of JAK2 p.V617F in individuals with elevated haemoglobin or haematocrit and developed and validated a screening algorithm for PV. A total of 15,366 blood counts performed in seven non-consecutive days were reviewed, of which 1001 were selected for subsequent JAK2 p.V617F mutation screening. Eight (0.8%) new JAK2 p.V617F-mutated cases were detected. From ROC curves, a two-step algorithm was developed based on the optimal cut-off for the detection of the JAK2 p.V617F mutation. The algorithm was prospectively validated in an independent cohort of 15,298 blood counts. A total of 1595 (10.4%) cases met the criterion for haemoglobin or haematocrit, of whom 581 passed to step 2 (3.8% of the total). The JAK2 p.V617F mutation was detected in 7 of the 501 patients tested, which accounts for 0.04% of the total cohort and 0.4% of patients with erythrocytosis. In conclusion, this data show that our two-step algorithm improves the selection of candidates for JAK2 p.V617F testing.

Abstract Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease whose prognosis is associated with clinical features, cell-of-origin and genetic aberrations. Recent integrative, multi-omic analyses had led to identifying overlapping genetic DLBCL subtypes. We used targeted massive sequencing to analyze 84 diagnostic samples from a multicenter cohort of patients with DLBCL treated with rituximab-containing therapies and a median follow-up of 6 years. The most frequently mutated genes were IGLL5 (43%), KMT2D (33.3%), CREBBP (28.6%), PIM1 (26.2%), and CARD11 (22.6%). Mutations in CD79B were associated with a higher risk of relapse after treatment, whereas patients with mutations in CD79B, ETS1, and CD58 had a significantly shorter survival. Based on the new genetic DLBCL classifications, we tested and validated a simplified method to classify samples in five genetic subtypes analyzing the mutational status of 26 genes and BCL2 and BCL6 translocations. We propose a two-step genetic DLBCL classifier (2-S), integrating the most significant features from previous algorithms, to classify the samples as N12-S, EZB2-S, MCD2-S, BN22-S, and ST22-S groups. We determined its sensitivity and specificity, compared with the other established algorithms, and evaluated its clinical impact. The results showed that ST22-S is the group with the best clinical outcome and N12-S, the more aggressive one. EZB2-S identified a subgroup with a worse prognosis among GCB-DLBLC cases.