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Summary: T/myeloid mixed phenotype acute leukaemia (MPAL) is a rare aggressive acute leukaemia with poorly understood pathogenesis. Herein, we report two cases of T/myeloid MPAL harbouring BCL11B‐associated structural variants that activate TLX3 (TLX3::BCL11B‐TLX3‐activation) by genome sequencing and transcriptomic analyses. Both patients were young males with extramedullary involvement. Cooperative gene alterations characteristic of T/myeloid MPAL and T‐lymphoblastic leukaemia (T‐ALL) were detected. Both patients achieved initial remission following lineage‐matched ALL‐based therapy with one patient requiring a lineage‐switched myeloid‐based therapy. Our study is the first to demonstrate the clinicopathological and genomic features of TLX3::BCL11B‐TLX3‐activated T/myeloid MPAL and provide insights into leukaemogenesis. [ABSTRACT FROM AUTHOR]

Introduction: The mechanism of relapsed CD19(−) B‐ALL after anti‐CD19 immunotherapy (Kymriah [CART‐19] and blinatumomab) is under active investigation. Our study aims to assess LILRB1 as a novel B‐cell marker for detecting CD19(−) B‐lymphoblasts and to analyze the clinicopathologic/genetic features of such disease to provide biological insight into relapse. Methods: Six patients (3 males/3 females, median age of 14 years) with relapsed CD19(−) B‐ALL were analyzed for cytogenetic/genetic profile and immunophenotype. Results: CD19(−) B‐ALL emerged after an interval of 5.8 months following anti‐CD19 therapy. Five of six patients had B‐cell aplasia, indicative of a persistent effect of CART or blinatumomab at relapse. Importantly, LILRB1 was variably expressed on CD19(−) and CD19(+) B lymphoblasts, strong on CD34(+) lymphoblasts and dim/partial on CD34(−) lymphoblasts. Three of six patients with paired B‐ALL samples (pre‐ and post‐anti‐CD19 therapy) carried complex and different cytogenetic abnormalities, either as completely different or sharing a subset of cytogenetic abnormalities. Conclusion: LILRB1 can be used as a novel B‐cell marker to identify CD19(−) B lymphoblasts. The emergence of CD19(−) B‐ALL appears to be associated with complex cytogenetic evolutions. The mechanism of CD19(−) B‐ALL relapse under anti‐CD19 immune pressure remains to be explored by comprehensive molecular studies. [ABSTRACT FROM AUTHOR]

The article explores a specific type of paroxysmal nocturnal haemoglobinuria (PNH) known as ahemolytic PNH or white cell PNH. Unlike typical PNH, this subtype does not exhibit signs of hemolysis but is characterized by bone marrow failure and thrombophilia. The document suggests that this subtype may be caused by a PIGA mutation in a committed progenitor cell rather than a stem cell. The authors recommend considering screening for ahemolytic PNH in cases of thromboembolic events and bone marrow failure without hemolysis. [Extracted from the article]

Objectives Our study aimed to develop a machine learning (ML) model to accurately classify acute promyelocytic leukemia (APL) from other types of acute myeloid leukemia (other AML) using multicolor flow cytometry (MFC) data. Multicolor flow cytometry is used to determine immunophenotypes that serve as disease signatures for diagnosis. Methods We used a data set of MFC files from 27 patients with APL and 41 patients with other AML, including those with uncommon immunophenotypes. Our ML pipeline involved training a graph neural network (GNN) to output graph-level labels and identifying the most crucial MFC parameters and cells for predictions using an input perturbation method. Results The top-performing GNN achieved 100% accuracy on the training/validation and test sets on classifying APL from other AML and used MFC parameters similarly to expert pathologists. Pipeline performance is amenable to use in a clinical decision support system, and our deep learning architecture readily enables prediction explanations. Conclusions Our ML pipeline shows robust performance on predicting APL and could be used to screen for APL using MFC data. It also allowed for intuitive interrogation of the model's predictions by clinicians. [ABSTRACT FROM AUTHOR]

Background: Fluorescence labeled DNA probes and in situ hybridization methods had shorter turn round time for results revolutionized their clinical application. Signals obtained from these probes are highly specific, yet they can produce fusion signals not necessarily representing fusion of actual genes due to other genes included in the probe design. In this study we evaluated discordance between cytogenetic, FISH and RNAseq results in 3 different patients with hematologic malignancies and illustrated the need to perform next generation sequencing (NGS) or RNASeq to accurately interpret FISH results. Methods: Bone marrow or peripheral blood karyotypes and FISH were performed to detect recurring translocations associated with hematologic malignancies in clinical samples routinely referred to our clinical cytogenetics laboratory. When required, NGS was performed on DNA and RNA libraries to detect somatic alterations and gene fusions in some of these specimens. Discordance in results between these methods is further evaluated. Results: For a patient with plasma cell leukemia standard FGFR3 / IGH dual fusion FISH assay detected fusion that was interpreted as FGFR3 -positive leukemia, whereas NGS/RNASeq detected NSD2::IGH. For a pediatric acute lymphoblastic leukemia patient, a genetic diagnosis of PDGFRB -positive ALL was rendered because the PDGFRB break-apart probe detected clonal rearrangement, whereas NGS detected MEF2D::CSF1R. A MYC -positive B-prolymphocytic leukemia was rendered for another patient with a cytogenetically identified t(8;14) and MYC::IGH by FISH, whereas NGS detected a novel PVT1::RCOR1 not previously reported. Conclusions: These are 3 cases in a series of several other concordant results, nevertheless, elucidate limitations when interpreting FISH results in clinical applications, particularly when other genes are included in probe design. In addition, when the observed FISH signals are atypical, this study illustrates the necessity to perform complementary laboratory assays, such as NGS and/or RNASeq, to accurately identify fusion genes in tumorigenic translocations. [ABSTRACT FROM AUTHOR]