Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.

CD123, the α chain of the interleukin 3 receptor, is a cytokine receptor that is overexpressed in multiple hematolymphoid neoplasms, including acute myeloid leukemia, blastic plasmacytoid dendritic cell neoplasm, acute lymphoblastic leukemia, hairy cell leukemia, and systemic mastocytosis. Importantly, CD123 expression is upregulated in leukemic stem cells relative to non-neoplastic hematopoietic stem cells, which makes it a useful diagnostic and therapeutic biomarker in hematologic malignancies. Varying levels of evidence have shown that CD123-targeted therapy represents a promising therapeutic approach in several cancers. Tagraxofusp, an anti-CD123 antibody conjugated to a diphtheria toxin, has been approved for use in patients with blastic plasmacytoid dendritic cell neoplasm. Multiple clinical trials are investigating the use of various CD123-targeting agents, including chimeric antigen receptor-modified T cells (expressing CD123, monoclonal antibodies, combined CD3-CD123 dual-affinity retargeting antibody therapy, recombinant fusion proteins, and CD123-engager T cells. In this review, we provide an overview of laboratory techniques used to evaluate and monitor CD123 expression, describe the strengths and limitations of detecting this biomarker in guiding therapy decisions, and provide an overview of the pharmacologic principles and strategies used in CD123-targeted therapies.

Recurrent pregnancy loss (RPL) is a problem affecting up to 5% of women of childbearing age due to many factors. Studies have shown that RPL and cardiovascular disease (CVD) may have shared risk factors. We compared the prevalence of 12 cardiovascular disease related gene mutations in patients with a history of RPL to normal controls in a major tertiary care center in Lebanon. The CVD StripAssay (ViennaLab, Austria) was used to analyze the CVD genes on 70 women with RPL history as part of the initial routine workup for recurrent miscarriage at the American University of Beirut Medical Center. The obtained results were compared with data of controls from the Lebanese population using Fisher's exact test and Chi square analysis. Two genes of the CVD panel demonstrated a strong relationship with RPL, including, MTHFR (C677T homozygosity, A1298C homozygosity, and compound heterozygosity for C677T and A1298C) and Factor II (heterozygosity for G20210A). Moreover, a protective role of positive APO-E3 isoform was observed. This study is the first in the Lebanese population in associating RPL with a large panel of CVD related genes.

Next generation sequencing (NGS) is routinely used for mutation profiling of acute myeloid leukemia. The extensive application of NGS in hematologic malignancies, and its significant association with the outcomes in multiple large cohorts constituted a proof of concept that AML phenotype is driven by underlying mutational signature and is amenable for targeted therapies. These findings urged incorporation of molecular results into the latest World Health Organization (WHO) sub-classification and integration into risk-stratification and treatment guidelines by the European Leukemia Net. NGS mutation profiling provides a large amount of information that guides diagnosis and management, dependent on the type and number of gene mutations, variant allele frequency and amenability to targeted therapeutics. Hence, molecular mutational profiling is an integral component for work-up of AML and multiple leukemic entities. In addition, there is a vast amount of informative data that can be obtained from routine clinical NGS sequencing beyond diagnosis, prognostication and therapeutic targeting. These include identification of evidence regarding the ontogeny of the disease, underlying germline predisposition and clonal hematopoiesis, serial monitoring to assess the effectiveness of therapy and resistance mutations, which have broader implications for management. In this review, using a few prototypic genes in AML, we will summarize the clinical applications of NGS generated data for optimal AML management, with emphasis on the recently described entities and Food and Drug Administration approved target therapies.

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