T/myeloid mixed phenotype acute leukaemia harbouring TLX3::BCL11B with TLX3 activation.

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.

Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry-key tools and progress.

There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of accuracy, uncover patterns in high-dimensional cytometric data that are undetectable by human analysis, facilitate the discovery of cell subpopulations, perform semi-automated immune cell profiling, and demonstrate potential to automate aspects of clinical multiparameter flow cytometric (MFC) diagnostic workflow. Utilizing AI in the analysis of cytometry samples can reduce subjective variability and assist in breakthroughs in understanding diseases. Here we review the diverse types of AI that are being applied to clinical cytometry data and how AI is driving advances in data analysis to improve diagnostic sensitivity and accuracy. We review supervised and unsupervised clustering algorithms for cell population identification, various dimensionality reduction techniques, and their utilities in visualization and machine learning pipelines, and supervised learning approaches for classifying entire cytometry samples.Understanding the AI landscape will enable pathologists to better utilize open source and commercially available tools, plan exploratory research projects to characterize diseases, and work with machine learning and data scientists to implement clinical data analysis pipelines.

Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology.

Digital pathology has a crucial role in diagnostic pathology and is increasingly a technological requirement in the field. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond the microscopic slide and enable true integration of knowledge and expertise. There is clear potential for artificial intelligence (AI) breakthroughs in pathology and hematopathology. In this review article, we discuss the approach of using machine learning in the diagnosis, classification, and treatment guidelines of hematolymphoid disease, as well as recent progress of artificial intelligence in flow cytometric analysis of hematolymphoid diseases. We review these topics specifically through the potential clinical applications of CellaVision, an automated digital image analyzer of peripheral blood, and Morphogo, a novel artificial intelligence-based bone marrow analyzing system. Adoption of these new technologies will allow pathologists to streamline workflow and achieve faster turnaround time in diagnosing hematological disease.

Mixed Phenotype Acute Leukemia, B/Myeloid (Bilineal and Biphenotypic), With t(2;22)(q35;q12);EWSR1-FEV.

BACKGROUND: Ewing sarcoma breakpoint region 1 gene (EWSR1) rearrangements are largely associated with the Ewing sarcoma family of tumors. OBSERVATIONS: We report the first case of infantile, mixed phenotype acute leukemia, B/myeloid (bilineal and biphenotypic [B-lymphoid and B-lymphoid/myeloid]), with a t(2;22)(q35;q12). The EWSR1-fifth Ewing variant gene fusion and nonsense mutation in STAG2 were detected by next-generation sequencing and markedly high expression of fifth Ewing sarcoma variant mRNA detected by quantitative reverse transcription polymerase chain reaction. The patient was treated with a combined myeloid/lymphoid leukemia regimen followed by allogeneic stem cell transplant and was in complete remission at 3.8-year follow-up. CONCLUSIONS: Our case study underscores the importance of a comprehensive evaluation of acute leukemia and provides insights into the phenotype of EWSR1 rearranged neoplasms in the context of partner genes and cell type.

Application of flow cytometry in the analysis of lymphoid disease in the lung and pleural space.

Various types of lymphoid neoplasms can occur in the lung. Lung parenchyma, the pleura or the pleural cavity can be the primary site of a lymphoid neoplasm or can be involved secondarily as a result of systemic dissemination from a separate primary site. Recognition of pulmonary lymphoid neoplasms (PLN) has increased secondary to technological advances in the medical field. Multiparameter flow cytometry (FC) is a one of the diagnostic tools that serves an essential role in the detecting and categorizing PLNs. FC allows for rapid identification and immunophenotypic characterization of PLN. In this article, we discuss the role of FC in the diagnosis of the most commonly encountered PLNs as well as their basic clinicopathologic features. We briefly discuss the role of FC in identifying non-hematolymphoid neoplasms in lung specimens as well.

Automated prediction of acute promyelocytic leukemia from flow cytometry data using a graph neural network pipeline.

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.

RNASeq Analysis for Accurate Identification of Fusion Partners in Tumor Specific Translocations Detected by Standard FISH Probes in Hematologic Malignancies.

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.

Clinicopathologic features of relapsed CD19(-) B-ALL in CD19-targeted immunotherapy: Biological insights into relapse and LILRB1 as a novel B-cell marker for CD19(-) B lymphoblasts.

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.

Genomic heterogeneity within B/T mixed phenotype acute leukemia in a context of an immunophenotype.

B/T mixed phenotype acute leukemia (MPAL) is a rare aggressive leukemia. Three cases of B/T MPAL were identified with comprehensive immunophenotypic, cytogenetic, and molecular studies. T-lineage predominant B/T MPAL shares a genetic signature with T-ALL whereas B/T lineage co-dominant B/T MPAL lacks such a T-ALL signature. All three patients were treated with lineage-matched-ALL therapy and alive at the last follow-up. Our study is the first to demonstrate molecular heterogeneity within B/T MPAL in a context of an immunophenotype of T-lineage versus B-lineage predominance. The implication of such a phenotype-genotype association on diagnostic classification is briefly discussed.

Adult mixed phenotype acute leukemia (MPAL): B/myeloid MPALisoMPO is distinct from other MPAL subtypes.

INTRODUCTION: Myeloperoxidase (MPO) is considered a specific marker of myeloid/non-monocytic lineage in the diagnosis of mixed phenotype acute leukemia (MPAL). However, the clinical significance of isolated dim MPO expression in otherwise typical B lymphoblastic leukemia (B-ALL; referred to as B/myeloid MPALisoMPO ) in adult patients is unknown. METHODS: We compared flow cytometric immunophenotype and clinicopathological findings among cases of B/myeloid MPALisoMPO (n = 13), other MPAL subtypes (n = 10, B/myeloid and T/myeloid MPAL), B-ALL (n = 64), and acute myeloid leukemia (AML, n = 58), using the 2016 WHO classification. For MPAL cases, MPO was reported as the percent of MPO positive blasts and its intensity (dim or moderate/strong). The pattern of heterogenous antigen expression (inversely coordinated expression between myeloid and lymphoid markers and cell size) was assessed. RESULTS: Cases of B/myeloid MPALisoMPO showed a fairly homogenous single B-lineage blast population with dim MPO expression whereas cases of other MPAL subtypes displayed heterogeneous antigen expression and moderate/strong MPO expression. The percent of MPO positive blasts in these two groups was similar. Expressions of CD15, CD117, and monocytic markers were more common in other MPAL than in B/myeloid MPALisoMPO . B/myeloid MPALisoMPO patients had similar overall and leukemia free survivals as B-ALL patients and better than other MPAL patients. CONCLUSION: This is the first study to investigate the clinical significance of adult B/myeloid MPALisoMPO using the 2016 WHO classification. Our results suggest that B/myeloid MPALisoMPO clinically behaves more similarly to B-ALL than to other MPAL subtypes, supporting the 2016 WHO classification to segregate this entity from other MPAL subtypes.

Distinct immunophenotype of early T-cell progenitors in T lymphoblastic leukemia/lymphoma may predict FMS-like tyrosine kinase 3 mutations.

FMS-like tyrosine kinase 3 (FLT3) mutation in T lymphoblastic leukemia/lymphoma (T-LL) is rare (∼4%) and reported only in cases with CD117 expression. This study aimed to identify the immunophenotypic features that may predict FLT3 mutations. We report 3 (43%) of 7 CD117(+) T-LL cases harboring FLT3-internal tandem duplication mutation. Compared with 4 FLT3-unmutated cases, all 3 FLT3-mutated cases had a distinct immunophenotype (CD1a(-)/CD2(+)/CD7(+)/CD34(+)/CD117(uniform+)/Tdt(+)) corresponding to the stage of earliest thymic T-cell progenitors possessing myeloid lineage potential. Indeed, all FLT3-mutated T-LL cases expressed myeloperoxidase on a very small subset of blasts and, thus, may be further considered a mixed phenotype acute leukemia, T/myeloid, by the 2008 World Health Organization classification scheme. We conclude that this unique immunophenotype (CD1a(-)/CD2(+)/CD7(+)/CD34(+)/CD117(+)/Tdt(+)) is a better predictor of FLT3 mutation than sole CD117 expression.

CSF3R T618I mutated chronic myelomonocytic leukemia: A proliferative subtype with a distinct mutational profile.

Chronic myelomonocytic leukemia (CMML) is a clonal myeloid neoplasm characterized by sustained monocytosis and mutations in TET2, ASXL1, SRSF2, SETBP1, NRAS, and KRAS. We describe a rare case of CSF3R T618I mutated CMML that has a proliferative phenotype, myelodysplasia, and additional mutations in ASXL1, SETBP1, KRAS, and PTPN11. Comparing the clinicopathologic features of this case to previously reported cases of CSF3R T618I mutated CMML and CSF3R non-T618I mutated CMML, CSF3R T618I seems to define a unique proliferative subtype of CMML with a distinct mutational profile. The diagnostic challenges and molecular pathogenesis associated with this case are also briefly discussed.

A t(11;14)(q13;q32)/CCND1::IGH carrying progenitor germinal B-cell with subsequent cytogenetic aberrations contributes to the development of classic Hodgkin lymphoma.

Classic Hodgkin lymphoma (cHL) is characterized by the presence of Hodgkin Reed-Sternberg (HRS) cells. Although HRS cells express PAX5, cHL frequently lacks other B-cell markers. There is now evidence that HRS cells are monoclonal and are derived from germinal center B-cells. In terms of genetic aberrations, cHL frequently exhibit activated NF-kB signaling pathway. In this study, we present a case of cHL harboring a t(11;14) (q13;q32)/CCND1::IGH, identified by chromosome and fluorescence in situ hybridization analysis and with CCND1 expression in HRS cells. We also analyzed recurrent cytogenetic aberrations in t(11;14) positive mantle cell lymphoma (MCL) and those found in cHL from the literature to assess genetic overlap, clonal evolution, and to identify potential signaling pathways in cHL with CCND1::IGH. This analysis suggests the development of t(11;14)+ cHL and MCL from a transformed precursor cell with t(11;14) through genetic evolution and consequent deregulated pathways, including the NF-κB and NOTCH1 signaling.