Among the myeloproliferative diseases, myelofibrosis is a widely heterogeneous entity characterized by a highly variable prognosis. In this context, several prognostic models have been proposed to categorize these patients appropriately. Identifying who deserves more invasive treatments, such as bone marrow transplantation, is a critical clinical need. Age, complete blood count (above all, hemoglobin value), constitutional symptoms, driver mutations, and blast cells have always represented the milestones of the leading models still used worldwide (IPSS, DIPSS, MYSEC-PM). Recently, the advent of new diagnostic techniques (among all, next-generation sequencing) and the extensive use of JAK inhibitor drugs have allowed the development and validation of new models (MIPSS-70 and version 2.0, GIPSS, RR6), which are continuously updated. Finally, the new frontier of artificial intelligence promises to build models capable of drawing an overall survival perspective for each patient. This review aims to collect and summarize the existing standard prognostic models in myelofibrosis and examine the setting where each of these finds its best application.

Hematological emergencies are critical medical conditions that require immediate attention due to their rapid progression and life-threatening nature. As various examples, hypercalcemia, often associated with cancers such as multiple myeloma, can lead to severe neurological and cardiac dysfunction. Hyperleukocytosis, common in acute myeloid leukemias, increases the risk of leukostasis and multiorgan failure. Sickle cell crisis, a common complication in sickle cell disease, results from vaso-occlusion, leading to acute pain and tissue ischemia. Tumor lysis syndrome, reported in cases of rapid destruction of cancer cells, causes electrolyte imbalances and acute kidney injury. Acute transfusion reactions, fundamental in hematological conditions, can range from mild allergic responses to severe hemolysis and shock, requiring prompt management. Disseminated intravascular coagulation, involving excessive coagulation and bleeding, is commonly triggered by hematological malignancies, common in the first phases of acute promyelocytic leukemia. Recently, in the era of bispecific antibodies and chimeric antigen receptor T cells, cytokine release syndrome is a manifestation that must be recognized and promptly treated. Understanding the pathophysiology, recognizing the clinical manifestations, and ensuring adequate diagnostic strategies and management approaches for each condition are central to early intervention in improving patient outcomes and reducing mortality.

Background: Acute myeloid leukemia (AML) is genomically heterogeneous, and translating baseline molecular data into individualized prognosis remains difficult. We assessed real-world outcomes and externally validated the Sanger Institute AML multistage prognostic model. Methods: This single-center, retrospective study included 73 AML patients who underwent targeted NGS profiling. In intensively treated patients, the published, validated Sanger AML multistage prognostic model was compared with observed 12- and 36-month clinical outcomes using quadratic-weighted Cohen’s kappa. Results: Median age was 61 years, and median overall survival was 13 months, with the most significant survival differences driven by treatment intensity. TP53 mutations occurred in 7 patients (9.6%) and were linked to primary refractoriness and extremely poor survival. TP53 was the only independent predictor of death (HR 8.07, 95% CI 2.23–29.13; p = 0.0014). Model concordance was moderate at 12 months (29 evaluable cases; weighted κ = 0.52; alive/dead κ = 0.52) and fair-to-moderate at 36 months (23 cases; weighted κ = 0.46). The tool performed best for predicted death without remission, while most discrepancies involved patients expected to remain in first remission who later relapsed and died. Conclusions: TP53 disruption dominates prognosis in real-world AML. The multistage tool supports early high-risk identification but shows limited long-term calibration, motivating the development of dynamic models integrating contemporary therapies and longitudinal min/serial NGS data.

Background: T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive malignancy of immature T cells, driven by dysregulated transcriptional networks and oncogenic signaling pathways. Here, we present the first comprehensive analysis of the expression and regulation of TSPAN32, a tetraspanin implicated in lymphocyte homeostasis, in T-ALL. Methods: Transcriptomic data from the Leukemia MILE study (GSE13159) were analyzed to assess TSPAN32 expression across leukemic subtypes. Gene Set Enrichment Analysis (GSEA) was performed to explore biological pathways associated with TSPAN32-correlated genes. For mechanistic validation, HPB-ALL cells were used as a model, with NOTCH signaling inhibited by γ-secretase inhibitor (GSI) treatment and TAL1–LMO1 overexpression induced through doxycycline-inducible lentiviral vectors. Gene expression changes were quantified by RT-qPCR. Results: TSPAN32 was frequently downregulated in T-ALL compared to healthy bone marrow, although expression was retained in a subset of cases. GSEA revealed that TSPAN32-correlated genes were inversely associated with cell cycle–related programs, consistent with its established role as a negative regulator of T cell proliferation. Mechanistically, TAL1–LMO1 overexpression strongly induced TSPAN32, while GSI-mediated NOTCH inhibition partially reactivated its expression. Interestingly, GSI treatment also increased TAL1 levels despite downregulating LMO1. Conversely, TAL1–LMO1 overexpression suppressed NOTCH1 and NOTCH3, highlighting a reciprocal regulatory interplay between NOTCH and TAL1/LMO1 oncogenic circuits that shapes TSPAN32 expression dynamics in T-ALL. Conclusions: This study identifies TSPAN32 as a novel transcriptional target under the influence of key leukemogenic pathways and suggests its potential role as a modulator of leukemic T cell proliferation, with implications for therapeutic strategies targeting TAL1 and NOTCH signaling.