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.

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.

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.

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.

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.

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.

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.

A Combined Biomarker of Bright CD38 and MYC ≥55% Is Highly Predictive of Double-/Triple-Hit High-Grade B-Cell Lymphoma.

OBJECTIVES: Diagnosis of high-grade B-cell lymphoma with MYC and BCL2 or BCL6 rearrangements (double-/triple-hit lymphoma [DTHL]) appears to mandate fluorescence in situ hybridization (FISH) testing for all large B-cell lymphoma (LBCL). Given the low incidence of DTHL, we aimed to identify flow cytometry (FC) and immunohistochemistry (IHC) features of DTHL that could be used to develop an optimal screening strategy. This combined FC-IHC approach has not yet been studied. METHODS: We compared features of 40 cases of DTHL and 39 cases of diffuse LBCL (DLBCL) without MYC rearrangement. RESULTS: Bright CD38 expression (CD38bright) by FC, high MYC expression (≥55%), and double-expressor phenotype by IHC were significantly associated with DTHL. The biomarker combining FC and IHC, CD38bright and/or MYC ≥55%, was superior to FC and IHC markers alone in predicting DTHL. Restricting FISH testing to approximately 25% of LBCL based on CD38brightand/or MYC ≥55% would detect approximately 95% of DTHL-BCL2 and approximately 75% of DHL-BCL6. CONCLUSIONS: Our study demonstrated that the novel biomarker of CD38bright and/or MYC ≥55% is highly predictive of DTHL. Awareness of the advantages and limitations of this screening strategy would facilitate development of a rational diagnostic workflow to provide high-quality patient care.

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 case of a primary myelofibrosis with progression and related literature review of progression phase genetics.

Philadelphia (BCR-ABL)-negative myeloproliferative neoplasms (MPNs) include polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). MPN can transform into an accelerated or a blast phase, which is associated with poor response to standard therapy and low overall median survival. We present an interesting case of a patient with a history of PMF and progression and summarize the current studies on genetic features of myeloproliferative neoplasms in blast phase (MPN-BP) with an emphasis on PMF. Although MPN-BP show ≥20% blasts in peripheral blood or bone marrow, it is not considered as acute myeloid leukemia (AML) according to the WHO classification. While MPNs-BP typically lack genetic mutations seen in de novo AML, they commonly harbor IDH1/2, SRSF2, ASXL1, and TP53 mutations, similar to the genetic profiles of acute myeloid leukemia with myelodysplasia-related changes (AML-MRC).

LILRB1: A Novel Diagnostic B-Cell Marker to Distinguish Neoplastic B Lymphoblasts From Hematogones.

OBJECTIVES: New B-cell markers are needed for monitoring B lymphoblastic leukemia (B-ALL) in the era of immunotherapies directed against CD19 and CD22. The expression of leukocyte immunoglobulin-like receptor subfamily B member 1 (LILRB1) on hematogones in bone marrow (BM) and neoplastic B lymphoblasts has not yet been systematically investigated. METHODS: We assessed LILRB1 expression pattern on B cells in 19 control BMs and 22 B-ALL cases by flow cytometry. RESULTS: In all cases, mature B cells and hematogones exhibited a consistent pattern of LILRB1 expression with variable intensity over different stages of maturation, including a characteristic V-shaped pattern on hematogones. While neoplastic B lymphoblasts in all cases expressed LILRB1, the pattern of expression was distinctly abnormal relative to hematogones (loss of the dynamic pattern in all cases and abnormal expression levels in 83% of cases). CONCLUSIONS: LILRB1 is a novel diagnostic B-cell marker to aid in distinguishing neoplastic B lymphoblasts from hematogones.

MYC protein expression does not correlate with MYC abnormalities detected by FISH but predicts an unfavorable prognosis in de novo acute myeloid leukemia.

While dysregulation of MYC has been implicated in acute myeloid leukemia (AML), the impact of MYC protein expression in AML is less well understood. We investigated the correlation of MYC protein expression by immunohistochemistry (MYC-IHC) with MYC abnormalities and prognosis in adult de novo AML. MYC-IHC in bone marrow of patients with untreated AML (n = 58) was assessed and scored as MYClow (0-40 % of blasts) or MYChigh (> 40 % of blasts). This was correlated with MYC abnormalities by fluorescence in situ hybridization (FISH) and prognosis in the context of cytogenetic risk stratification. Residual myeloid disease at the end of induction was assessed by flow cytometry. MYClow and MYChigh were detected in 24 (41 %) and 34 cases (59 %), respectively. Extra copies of MYC were present in 12 % of cases and were not correlated with level of MYC-IHC. No cases had MYC translocation or amplification. Compared to MYClow patients, MYChigh patients had a shorter overall survival in all cytogenetic risk groups (68 vs. 21 months, p = 0.006) and in the intermediate risk group (61 vs. 21 months, p = 0.046). MYChigh patients had a tendency towards detected residual disease at the end of induction in all cytogenetic risk and intermediate risk groups. Regardless of the underlying mechanisms of MYC dysregulation, high level of MYC protein is expressed in the majority of AML and correlated to worse prognosis. Further studies on MYC dysregulation in leukemogenesis and therapy targeting MYC aberration are warranted.