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.

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.