A de novo germline RUNX1 variant preceding development of concurrent T-lymphoblastic leukemia and myelodysplastic syndrome.

Germline variants of the RUNX1 gene are associated with RUNX1 Familial Platelet Disorder with Associated Myeloid Malignancies (RUNX1-FPDMM), which is characterized by an increased risk of developing myelodysplastic syndrome (MDS) and/or acute myeloid leukemia. Patients with FPDMM have also been described to develop B- or T-cell acute lymphoblastic leukemia. We present a pediatric patient with RUNX1-FPDMM that evolved into concurrent MDS and T-cell acute lymphoblastic leukemia after a decade of monitoring with serial blood counts. We aim to highlight the treatment challenges and clinical decision-making that may be anticipated in this unique disorder, as well as the potentially curative role for allogenic hematopoietic stem cell transplant in the first complete remission.

An isolated sacral promyelocytic sarcoma in a child: A rare case report with emphasis on cytomorphology.

Myeloid sarcoma (MS) is an uncommon localized extramedullary tumor composed of immature myeloid precursor cells that can affect any organ. Promyelocytic sarcoma (PS), an extremely rare subtype of MS, is characterized by immature myeloid cells with features of acute promyelocytic leukemia (APL). We describe a case of pediatric PS that presented as a solitary sacral mass without any evidence of systemic or bone marrow involvement. The cytopathologic evaluation using touch imprint demonstrated numerous blasts with bilobed nuclei, cytoplasmic hyper-granularity, and aggregates of Auer rods, which are typical cytomorphologic features of APL. Herein, we report an extremely rare case of isolated PS in a child, emphasizing the importance of cytomorphologic evaluation, which is complemented by the findings from a comprehensive work-up.

Spatial metabolomics reveals glycogen as an actionable target for pulmonary fibrosis.

Matrix assisted laser desorption/ionization imaging has greatly improved our understanding of spatial biology, however a robust bioinformatic pipeline for data analysis is lacking. Here, we demonstrate the application of high-dimensionality reduction/spatial clustering and histopathological annotation of matrix assisted laser desorption/ionization imaging datasets to assess tissue metabolic heterogeneity in human lung diseases. Using metabolic features identified from this pipeline, we hypothesize that metabolic channeling between glycogen and N-linked glycans is a critical metabolic process favoring pulmonary fibrosis progression. To test our hypothesis, we induced pulmonary fibrosis in two different mouse models with lysosomal glycogen utilization deficiency. Both mouse models displayed blunted N-linked glycan levels and nearly 90% reduction in endpoint fibrosis when compared to WT animals. Collectively, we provide conclusive evidence that lysosomal utilization of glycogen is required for pulmonary fibrosis progression. In summary, our study provides a roadmap to leverage spatial metabolomics to understand foundational biology in pulmonary diseases.

Lymph Node Metastases in Papillary Thyroid Carcinoma can be Predicted by a Convolutional Neural Network: a Multi-Institution Study.

OBJECTIVES: The presence of nodal metastases in patients with papillary thyroid carcinoma (PTC) has both staging and treatment implications. However, lymph nodes are often not removed during thyroidectomy. Prior work has demonstrated the capability of artificial intelligence (AI) to predict the presence of nodal metastases in PTC based on the primary tumor histopathology alone. This study aimed to replicate these results with multi-institutional data. METHODS: Cases of conventional PTC were identified from the records of 2 large academic institutions. Only patients with complete pathology data, including at least 3 sampled lymph nodes, were included in the study. Tumors were designated "positive" if they had at least 5 positive lymph node metastases. First, algorithms were trained separately on each institution's data and tested independently on the other institution's data. Then, the data sets were combined and new algorithms were developed and tested. The primary tumors were randomized into 2 groups, one to train the algorithm and another to test it. A low level of supervision was used to train the algorithm. Board-certified pathologists annotated the slides. HALO-AI convolutional neural network and image software was used to perform training and testing. Receiver operator characteristic curves and the Youden J statistic were used for primary analysis. RESULTS: There were 420 cases used in analyses, 45% of which were negative. The best performing single institution algorithm had an area under the curve (AUC) of 0.64 with a sensitivity and specificity of 65% and 61% respectively, when tested on the other institution's data. The best performing combined institution algorithm had an AUC of 0.84 with a sensitivity and specificity of 68% and 91% respectively. CONCLUSION: A convolutional neural network can produce an accurate and robust algorithm that is capable of predicting nodal metastases from primary PTC histopathology alone even in the setting of multi-institutional data.

The use of diagnostic patterns for interventional cytopathology during rapid on-site evaluation and final classification.

Pathologist-performed fine-needle aspiration, or interventional cytopathology, is a minimally invasive, highly accurate technique for sampling and diagnosing palpable lesions. Utilizing cytomorphologic patterns during rapid onsite evaluation (ROSE) and final classification is one of many strategies that an interventional cytopathologist can employ to simplify the diagnostic approach. Herein, we provide an overview of the salient cytomorphologic patterns encountered in common specimens obtained by the interventional cytopathologist, including major salivary glands, the thyroid gland, and superficial lymph nodes. The topics covered should provide a primer for those interested in utilizing a site-specific, pattern-based approach to cytopathologic evaluation. In summary, cytomorphologic patterns can be used during ROSE to establish adequacy, build a differential diagnosis, and to appropriately triage the specimen for additional investigation, such as microbiology cultures, a liquid-based preparation, a cell block preparation, flow cytometry, chemical analysis, or molecular diagnostic tests. Finally, this approach can be applied at the time of diagnosis to suggest additional ancillary studies, such as immunohistochemistry, and to inform accurate and definitive classification.