Introduction. Angiomyolipoma (AML) is a mesenchymal neoplasm that belongs to the perivascular epithelioid cell tumor family (PEComa). AMLs can be subtyped into several patterns dependent on cell type, morphology, and tissue composition. One of the patterns, oncocytoma-like AML is a rare entity with only three cases published in the literature. Case presentation. We present a case of a previously healthy 29-year-old woman who underwent a left partial nephrectomy secondary to a 4.6 cm heterogeneous renal neoplasm. Gross examination demonstrated a well-circumscribed renal mass. Modified Giemsa stain preparation showed oncocytic cells in syncytial pattern with ample granular cytoplasm and round nuclei with prominent nucleoli. Histology assessment showed an oncocytic neoplasm with interspersed adipose tissue. The tumor exhibited tubular architecture with the tubules lined by eosinophilic epithelioid cells with nuclear atypia and prominent nucleoli. Thick blood vessels with emanating epithelioid cells were present. High-grade histology features were not identified. The tumor cells were positive for HMB-45 and SMA and negative for PAX8, keratins, KIT, and vimentin. A diagnosis of oncocytoma-like AML was rendered. Next-generation sequencing (NGS) and RNA fusion were performed. NGS revealed no pathogenic variants and RNA fusion identified no rearrangements. Chromosomal copy number alterations were present in the long arm of chromosome 1 (1p) and chromosome 22. Conclusions. We describe and discuss the clinical, cytomorphologic, histologic, and molecular findings of oncocytoma-like AML, a rare renal neoplasm, and provide a review of the literature.
Precision medicine in cancer treatment depends on deciphering tumor phenotypes to reveal the underlying biological processes. Molecular profiles, including transcriptomics, provide an information-rich tumor view, but their high-dimensional features and assay costs can be prohibitive for clinical translation at scale. Recent studies have suggested jointly leveraging histology and genomics as a strategy for developing practical clinical biomarkers. Here, we use machine learning techniques to identify de novo latent transcriptional processes in squamous cell carcinomas (SCCs) and to accurately predict their activity levels directly from tumor histology images. In contrast to analyses focusing on pre-specified, individual genes or sample groups, our latent space analysis reveals sets of genes associated with both histologically detectable features and clinically relevant processes, including immune response, collagen remodeling, and fibrosis. The results demonstrate an approach for discovering clinically interpretable histological features that indicate complex, potentially treatment-informing biological processes.
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