HLH and Recurrent EBV Lymphoma as the presenting manifestation of MAGT1 Deficiency: A Systematic Review of the Expanding Disease Spectrum.

Magnesium transporter 1 (MAGT1) gene loss-of-function variants lead to X-linked MAGT1 deficiency with increased susceptibility to EBV infection and N-glycosylation defect (XMEN), a condition with a variety of clinical and immunological effects. In addition, MAGT1 deficiency has been classified as a congenital disorder of glycosylation (CDG) due to its unique role in glycosylation of multiple substrates including NKG2D, necessary for viral protection. Due to the predisposition for EBV, this etiology has been linked with hemophagocytic lymphohistiocytosis (HLH), however only limited literature exists. Here we present a complex case with HLH and EBV-driven classic Hodgkin lymphoma (cHL) as the presenting manifestation of underlying immune defect. However, the patient's underlying immunodeficiency was not identified until his second recurrence of Hodgkin disease, recurrent episodes of Herpes Zoster, and after he had undergone autologous hematopoietic stem cell transplant (HSCT) for refractory Hodgkin lymphoma. This rare presentation of HLH and recurrent lymphomas without some of the classical immune deficiency manifestations of MAGT1 deficiency led us to review the literature for similar presentations and to report the evolving spectrum of disease in published literature. Our systematic review showcased that MAGT1 predisposes to multiple viruses (including EBV) and adds risk of viral-driven neoplasia. The roles of MAGT1 in the immune system and glycosylation were highlighted through the multiple organ dysfunction showcased by the previously validated Immune Deficiency and Dysregulation Activity (IDDA2.1) score and CDG-specific Nijmegen Pediatric CDG Rating Scale (NPCRS) score for the patient cohort in the systematic review.

Siglec-15 Promotes Evasion of Adaptive Immunity in B-cell Acute Lymphoblastic Leukemia.

Siglec-15 (Sig15) has been implicated as an immune checkpoint expressed in solid tumor-infiltrating macrophages and is being targeted in clinical trials with mAbs to normalize the tumor immune microenvironment and stimulate antitumor immunity. However, the role of Sig15 in hematologic malignancies remains undefined. Sig15 mRNA and protein expression levels in hematologic malignancies were determined from publicly available databases, cell lines, and primary patient samples. Human B-cell acute lymphoblastic leukemia (B-ALL) cell lines were used to identify signaling pathways involved in the regulation of Sig15 expression. Secreted/soluble Sig15 and cytokine levels were measured from the plasma of children with leukemia and healthy controls. Knockdown and knockout of Siglec15 in a murine model of B-ALL was used to evaluate the effect of leukemia-derived Sig15 on the immune response to leukemia. We observed pathologic overexpression of Sig15 in a variety of hematologic malignancies, including primary B-ALL samples. This overexpression was driven by NFκB activation, which also increased the surface localization of Sig15. Secreted/soluble Sig15 was found to circulate at elevated levels in the plasma of children with B-ALL and correlated with an immune-suppressive cytokine milieu. Genetic inhibition of Sig15 in murine B-ALL promoted clearance of the leukemia by the immune system and a marked reversal of the immune-privileged leukemia bone marrow niche, including expanded early effector CD8+ T cells and reduction of immunosuppressive cytokines. Thus, Sig15 is a novel, potent immunosuppressive molecule active in leukemia that may be targeted therapeutically to activate T lymphocytes against leukemia cells. Significance: We demonstrate that Sig15 is overexpressed in hematologic malignancies driven by NFκB, is required for immune evasion in a mouse model of leukemia, and, for the first time, that it circulates at high levels in the plasma of children with leukemia.

A single-center retrospective review of pediatric cases of progressive transformation of germinal centers.

BACKGROUND: Progressive transformation of germinal centers (PTGC) is a rare diagnosis characterized by asymptomatic lymph node enlargement. It has previously been associated with lymphoma, autoimmune conditions, and lymphoproliferative diseases in small pediatric case series. PROCEDURES: We conducted a single-center retrospective review of pediatric cases of PTGC diagnosed at our institution by hematopathologists from 2000 to 2020. RESULTS: We identified 57 primary cases and three recurrent cases of PTGC. Laboratory and imaging evaluations were obtained inconsistently. Nine patients (16%) saw a pediatric hematology/oncology (PHO) specialist prior to diagnosis, and 21 (37%) had follow-up with PHO after diagnosis. CONCLUSIONS: Patients with PTGC had similar age and involved lymph node sites to those from previous case series. Fewer patients underwent recurrent lymph node biopsy than previously described. PTGC has been linked to certain types of lymphoma, although never definitively associated with lymphoma. Follow-up with a PHO provider is indicated to ensure that close surveillance is performed.

An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears.

The pathologic diagnosis of bone marrow disorders relies in part on the microscopic analysis of bone marrow aspirate (BMA) smears and the manual counting of marrow nucleated cells to obtain a differential cell count (DCC). This manual process has significant limitations, including the analysis of only a small subset of optimal slide areas and nucleated cells, as well as interobserver variability due to differences in cell selection and classification. To address these shortcomings, we developed an automated machine learning-based pipeline for obtaining 11-component DCCs on whole-slide BMAs. This pipeline uses a sequential process of identifying optimal BMA regions with high proportions of marrow nucleated cells, detecting individual cells within these optimal areas, and classifying these cells into 1 of 11 DCC components. Convolutional neural network models were trained on 396,048 BMA region, 28,914 cell boundary, and 1,510,976 cell class images from manual annotations. The resulting automated pipeline produced 11-component DCCs that demonstrated a high statistical and diagnostic concordance with manual DCCs among a heterogeneous group of testing BMA slides with varying pathologies and cellularities. Additionally, we demonstrated that an automated analysis can reduce the intraslide variance in DCCs by analyzing the whole slide and marrow nucleated cells within all optimal regions. Finally, the pipeline outputs of region classification, cell detection, and cell classification can be visualized using whole-slide image analysis software. This study demonstrates the feasibility of a fully automated pipeline for generating DCCs on scanned whole-slide BMA images, with the potential for improving the current standard of practice for utilizing BMA smears in the laboratory analysis of hematologic disorders.

Mismatched Unrelated Donor Hematopoietic Stem Cell Transplantation Rescues Marrow Failure From Acute Leukemia Therapy in a Patient With Previously Undiagnosed Ligase IV Syndrome.

Patients with DNA double-strand breakage repair disorders are at increased risk of malignancy which is often difficult to treat given underlying sensitivity to chemotherapy and radiotherapy, lending an important role to hematopoietic stem cell transplantation. The choice of conditioning regimen used must balance reducing risk of rejection with minimizing excessive toxicity from myeloablative chemotherapy or ionizing radiation. We describe successful engraftment following a nonmyeloablative hematopoietic stem cell transplantation in a patient with Ligase IV syndrome and numerous pretransplant complications including malignancy, cardiac failure, and secondary hemophagocytic lymphohistiocytosis. Congruent with prior reports, a reduced intensity regimen appears efficacious in Ligase IV syndrome patients.

The Hematological Differential Diagnosis of Mediastinal Masses.

Mediastinal masses commonly present in children and may pose diagnostic challenges, particularly with limited sampling. This article aids the pathologist by reviewing the hematologic differential diagnosis of a pediatric mediastinal mass, along with ancillary testing useful for rendering the correct diagnosis. A review of the more common lymphomas is presented, including classic Hodgkin lymphoma, T-lymphoblastic leukemia/lymphoma, and primary mediastinal (thymic) large B-cell lymphoma, along with brief mentions of less common entities such as gray zone lymphoma and thymoma as well as non-neoplastic conditions such as benign cysts and infections.

Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.

Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable automated counter has yet to be developed, largely due to the inherent complexity of bone marrow specimens. Digital pathology imaging coupled with machine learning algorithms represents a highly promising emerging technology for this purpose. Yet, training datasets for BMA cellular constituents, critical for building and validating machine learning algorithms, are lacking. Herein, we report our experience creating and employing such datasets to develop a machine learning algorithm to detect and classify BMA cells. Utilizing a web-based system that we developed for annotating and managing digital pathology images, over 10,000 cells from scanned whole slide images of BMA smears were manually annotated, including all classes that comprise the standard clinical DCC. We implemented a two-stage, detection and classification approach that allows design flexibility and improved classification accuracy. In a sixfold cross-validation, our algorithms achieved high overall accuracy in detection (0.959 ± 0.008 precision-recall AUC) and classification (0.982 ± 0.03 ROC AUC) using nonneoplastic samples. Testing on a small set of acute myeloid leukemia and multiple myeloma samples demonstrated similar detection and classification performance. In summary, our algorithms showed promising early results and represent an important initial step in the effort to devise a reliable, objective method to automate DCCs. With further development to include formal clinical validation, such a system has the potential to assist in disease diagnosis and prognosis, and significantly impact clinical practice.