Comparative Study on the Clinicopathologic and Molecular Characteristics of Primary and Secondary Diffuse Large B-cell Lymphoma in the Central Nervous System.

BACKGROUND/AIM: Diffuse large B-cell lymphoma of the central nervous system (CNS-DLBCL) is an aggressive B-cell lymphoma with clinical and molecular heterogeneity. Primary CNS-DLBCL (PCNSL) affects the brain, eyes, leptomeninges, or spinal cord without systemic involvement. Secondary CNS-DLBCL (SCNSL) manifests concurrently with systemic lymphoma or as an isolated CNS relapse with poor prognosis. MATERIALS AND METHODS: Next-generation sequencing (NGS) was used to identify genomic alterations in 32 PCNSL and 9 SCNSL cases. Single nucleotide variants and copy number variations in addition to the clinicopathologic data and proposed risk predictive values were compared to aid in diagnostic differentiation between the two types of lymphomas. RESULTS: The MCD genotype, characterized by mutations in MYD88 and CD79B, is the most common alteration in PCNSL and is associated with lower survival rates. The frequency of MYD88 mutation was significantly higher in PCNSL compared to SCNSL (75.0% vs. 33.3%; p=0.042). Recurrent copy number loss of 6p21 occurred in 56.1% of cases, more often in PCNSL (65.6%) than in SCNSL (22.2%) (p=0.028). Diagnostic positive predictive values (PPV) of MYD88 mutation and 6p21 loss for PCNSL were 89% and 91%, respectively. PPV of both alterations was 93% for the diagnosis of PCNSL. CONCLUSION: MYD88 mutation and 6p21 loss were significantly higher in PCNSL than in SCNSL, and novel risk prediction models based on these distinct genomic profiles can aid in the clinical differentiation of PCNSL and SCNSL.

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.

Next Generation Sequencing Analysis of Fibrin-associated Large B-cell Lymphoma Reveals Pathogenic Single Nucleotide Variants.

BACKGROUND/AIM: Fibrin-associated large B-cell lymphoma (FA-LBCL) is a newly identified subtype of Epstein-Barr virus (EBV)-associated lymphoma. Arising within fibrinous material in confined spaces, FA-LBCL is associated with chronic inflammation. We herein report histopathologic features and molecular alterations of three cases of FA-LBCL to refine this new disease entity. MATERIALS AND METHODS: We performed immunohistochemical staining for CD3, CD20, CD10, Bcl-2, Bcl-6, MUM-1, CD10, and c-Myc and in situ hybridization for EBV-encoded RNA. Additionally, targeted DNA sequencing was conducted using commercially available gene panels. RESULTS: Three cases of FA-LBCL developed underlying lesions of retroperitoneal cyst, cardiac myxoma, and pancreatic cyst. Histopathologic features of these lesions were characterized by aggregates of atypical large cells in a background of fibrinous cellular debris. Atypical lymphoid cells were positive for CD20, Bcl-2, MUM-1, and EBV-in situ hybridization, negative for CD10, and variably positive for Bcl-6 and c-Myc. NGS analysis revealed the presence of pathogenic mutations in BRIP1, SOCS1, and KRAS. CONCLUSION: This is the first report of NGS analysis in FA-LBCL cases. It provides precise clinicopathological and molecular traits and allows its recognition as a new entity.

Familial 4p Interstitial Deletion Provides New Insights and Candidate Genes Underlying This Rare Condition.

Chromosome 4p deletions can lead to two distinct phenotypic outcomes: Wolf--Hirschhorn syndrome (a terminal deletion at 4p16.3) and less frequently reported proximal interstitial deletions (4p11-p16). Proximal 4p interstitial deletions can result in mild to moderate intellectual disability, facial dysmorphisms, and a tall thin body habitus. To date, only 35 cases of proximal 4p interstitial deletions have been reported, and only two of these cases have been familial. The critical region for this syndrome has been narrowed down to 4p15.33-15.2, but the underlying causative genes remain unclear. In this study, we report the case of a 3-year-old female with failure to thrive, developmental and motor delays, and morphological features. The mother also had a 4p15.2-p14 deletion, and the proband was found to have a 13.4-Mb 4p15.2-p14 deletion by chromosome microarray analysis. The deleted region encompasses 16 genes, five of which have a high likelihood of contributing to the phenotype: PPARGC1A, DHX15, RBPJ, STIM2, and PCDH7. These findings suggest that multiple genes are involved in this rare proximal 4p interstitial deletion syndrome. This case highlights the need for healthcare providers to be aware of proximal 4p interstitial deletions and the potential phenotypic manifestations.

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.