Non‐Down‐syndrome‐related acute megakaryoblastic leukemia (non‐DS‐AMKL) is a rare form of leukemia that can present with a variety of initial symptoms, including fever, rash, bruising, bleeding, or other more clinically challenging symptoms. Herein, we describe a 19‐month‐old female patient who presented with left lower extremity pain and language regression who was diagnosed with AMKL, not otherwise specified (NOS), on the basis of peripheral blood and bone marrow analysis, as well as cytogenetic and molecular diagnostic phenotyping. Of note, in addition to this patient's karyotype showing trisomy 3, a fusion between CBFA2T3 (core‐binding factor, alpha subunit 2, translocated to, 3) on chromosome 16 and GLIS2 (GLIS family zinc finger protein 2), also on chromosome 16, was observed. Patients with AMKL who have trisomy 3 with CBFA2T3::GLIS2 fusions are rare, and it is not known if the co‐occurrence of these abnormalities is coincidental or biologically related. This highlights the continued need for further expansion of genetic testing in individuals with rare disease to establish the groundwork for identifying additional commonalities that could potentially be used to identify therapeutic targets or improve prognostication.

1537Background: Renal metanephric adenoma (MA) is a very rare benign renal tumor, which is frequently misclassified when microscopic features alone are applied. Despite the classification of adenoma as a benign tumor, it is difficult to differentiate from other renal carcinomas such as malignant papillary renal cell carcinomas and in children it can be mistaken with Wilms tumor. The correct classification of a renal tumor is critical for diagnostic, prognostic, and therapeutic purposes. Despite the advancements in cancer genomics, there is limited data available regarding the genetic alterations critical to the metanephric adenoma development. Recent data suggest that 90% of MA have BRAFV600Emutations; the genetics of the remaining 10 % are unclear. Methods: This study was conducted on 13 FFPE specimens from patients who were diagnosed with renal metanephric adenoma. H&E stained slides from all cases were reviewed by study pathologist, and representative tissue blocks were further selected for BRAFV600E s...

The relationship between single nucleotide polymorphisms (SNPs) and phenotypes is noisy and cryptic due to the abundance of genetic factors and the influence of environmental factors on complex traits, which makes the idea of applying artificial neural networks (ANNs) as universal approximates of complex functions promising. In this study, we compared different ANN architectures and input parameters to predict the adult length of Pacific lampreys, which is the primary indicator of their total migratory distance. Feedforward and simple recurrent network architectures with a different range of input parameters and different sizes of hidden layers were compared. Results indicate that the highest performing ANN had an accuracy of 67.5% in discriminating between long and short specimens. Sensitivity and specificity were 62.16% and 70.73%, respectively. Our results imply that feedforward ANN architecture with a single hidden neuron is enough to solve the problem of specimen classification. Nonetheless, while ANNs are useful at approximating functions with unknown relationships in the case of SNP data, additional work needs to be performed to ensure that the chosen SNP markers are related to functional regions related to the examined trait, as the use of non-specific markers will result in the introduction of noise into the dataset.