Acquired cystic disease associated renal cell carcinomas (ACD-RCC) are rare and their molecular and histopathological characteristics are still being explored. We therefore investigated the clinicopathologic and molecular characteristics of 31 tumors. The patients were predominantly male (n = 30), with tumors mainly left-sided (n = 17), unifocal (n = 19), and unilateral (n = 29) and a mean tumor size of 25 mm (range, 3-65 mm). Microscopically, several histologic patterns were present, including pure classic sieve-like (n = 4), and varied proportions of mixed classic sieve-like with papillary (n = 23), tubulocystic (n = 9), compact tubular (n = 4) and solid (n = 1) patterns. Calcium-oxalate crystals were seen in all tumors. Molecular analysis of 9 tumors using next generation sequencing showed alterations in SMARCB1 in 3 tumors (1 with frameshift deletion and 2 with copy number loss in chromosome 22 involving SMARCB1 region), however, INI1 stain was retained in all. Nonrecurrent genetic alterations in SETD2, NF1, NOTCH4, BRCA2 and CANT1 genes were also seen. Additionally, MTOR p.Pro351Ser was identified in one tumor. Copy number analysis showed gains in chromosome 16 (n = 5), 17 (n = 2) and 8 (n = 2) as well as loss in chromosome 22 (n = 2). In summary, ACD-RCC is a recognized subtype of kidney tumors, with several histological architectural patterns. Our molecular data identifies genetic alterations in chromatin modifying genes (SMARCB1 and SETD2), which may suggest a role of such genes in ACD-RCC development.

Loss of the polybromo-1 (PBRM1) protein has been expected as a possible biomarker for clear cell renal cell carcinoma (ccRCC). There is little knowledge about how PBRM1 immunohistochemical expression correlates with the histomorphological features of ccRCC and the endothelial expression of tumor vasculature. The present study evaluates the association of architectural patterns with the PBRM1 expression of cancer cells using a cohort of 425 patients with nonmetastatic ccRCC. Furthermore, we separately assessed the PBRM1 expression of the endothelial cells and evaluated the correlation between the expression of cancer cells and endothelial cells. PBRM1 loss in cancer cells was observed in 148 (34.8%) patients. In the correlation analysis between architectural patterns and PBRM1 expression, macrocyst/microcystic, tubular/acinar, and compact/small nested were positively correlated with PBRM1 expression, whereas alveolar/large nested, thick trabecular/insular, papillary/pseudopapillary, solid sheets, and sarcomatoid/rhabdoid were negatively correlated with PBRM1 expression. PBRM1 expression in vascular endothelial cells correlated with the expression of cancer cells (correlation coefficient = 0.834, p < 0.001). PBRM1 loss in both cancer and endothelial cells was associated with a lower recurrence-free survival rate (p < 0.001). Our PBRM1 expression profile indicated that PBRM1 expression in both cancer and endothelial cells may be regulated in an orchestrated manner.

The differential diagnosis of proliferative breast lesions, benign usual ductal hyperplasia (UDH) versus malignant ductal carcinoma in situ (DCIS) is challenging. This involves a pathologist examining histopathologic sections of a biopsy using a light microscope, evaluating tissue structures for their architecture or size, and assessing individual cell nuclei for their morphology. Imposing diagnostic boundaries on features that otherwise exist on a continuum going from benign to atypia to malignant is a challenge. Current computational pathology methods have focused primarily on nuclear atypia in drawing these boundaries. In this paper, we improve on these approaches by encoding for both cellular morphology and spatial architectural patterns. Using a publicly available breast lesion database consisting of UDH and three different grades of DCIS, we improve the classification accuracy by 10% over the state-of-the-art method for discriminating UDH and DCIS. For the four way classification of UDH and the three grades of DCIS, our method improves the results by 6% in accuracy, 8% in micro-AUC, and 19% in macro-AUC.