The 2021 WHO Classification of Tumors of the Central Nervous System includes several tumor types and subtypes for which the diagnosis is at least partially reliant on utilization of whole genome methylation profiling. The current approach to array DNA methylation profiling utilizes a reference library of tumor DNA methylation data, and a machine learning-based tumor classifier. This approach was pioneered and popularized by the German Cancer Research Network (DKFZ) and University Hospital Heidelberg. This research group has kindly made their classifier for central nervous system tumors freely available as a research tool via a web-based portal. However, their classifier is not maintained in a clinical testing environment. Therefore, the Northwestern Medicine (NM) classifier was developed and validated. The NM classifier was validated using the same training and validation data sets as the DKFZ group. Using the DKFZ validation data set, the NM classifier's performance showed high concordance (92%) and comparable accuracy (specificity 94.0% versus 84.9% for DKFZ, sensitivity 88.6% versus 94.7% for DKFZ). Receiver-operator characteristic curves showed areas under the curve of 0.964 versus 0.966 for NM and DKFZ classifiers, respectively. In addition, in-house validation was performed and performance was compared using both classifiers. The NM classifier performed comparably well and is currently offered for clinical testing.

Background: We present a yet to be described association of SARS-CoV-2 infection with Kikuchi-Fujimoto disease. Case presentation: A 32-year-old physician with history of SARS-CoV-2 infection presented to the emergency department with 2 weeks of fever, chills, and right sided cervical lymphadenopathy. He was treated empirically for presumed folliculitis with worsening of symptoms leading to repeat presentation to the emergency department. Extensive workup was unrevealing of an infectious cause and needle biopsy of the lesion was unrevealing. An excisional lymph node biopsy revealed follicular hyperplasia with necrotic foci showing abundance of histiocytes at the edge of necrosis with CD8 predominance of T-cells. Final diagnosis was deemed to be Kikuchi-Fujimoto disease. Antibiotic therapy was discontinued, and the patient’s symptoms resolved with steroid therapy and expectant management. Conclusions: This is the first report of a patient developing Kikuchi-Fujimoto disease following SARS-CoV-2 infection. Clinicians should be aware of Kikuchi-Fujimoto disease as a possibility when approaching patients with hyper-inflammatory states who present with cervical lymphadenopathy.

Chronic myeloid leukemia (CML) is associated with several breakpoint regions that result in different BCR::ABL1 fusion transcripts. These include the major breakpoint region (M‐BCR), minor breakpoint region (m‐BCR), and mu breakpoint region (u‐BCR) corresponding to p210, p190, and p230 fusion transcripts, respectively. This patient is a 38‐year‐old female with a new diagnosis of CML in chronic phase. A novel p210 fusion transcript splice variant was detected with qualitative reverse transcription PCR and capillary electrophoresis. Subsequent FISH study was performed, which revealed 86.5% positive for the BCR::ABL1 fusion. Quantitative real‐time polymerase chain reaction (PCR) showed a negative result for the p210 fusion transcript. The variant was further characterized by Sanger sequencing. This variant is in‐frame and predicted to be functional. This case illustrates the need for a combination of different testing techniques to fully characterize the rare BCR::ABL1 fusion transcripts.