BK virus (BKV) is a nonenveloped, double-stranded DNA virus of the polyomavirus family that primarily affects immunocompromised people. BKV may cause nephropathy in renal transplant recipients receiving immunosuppressive therapy, resulting in renal dysfunction and, possibly, graft loss. Monitoring of BK viral load in urine and blood has been used as a surrogate marker of BKV nephropathy (BKVN). Although real-time polymerase chain reaction (PCR) is the method of choice, currently there is no US Food and Drug Administration-approved or standardized BK viral load assay. Different PCR assays vary significantly in sample types, DNA extraction method, PCR primers and probes, and reference materials used to generate a standard curve. These differences can affect the accuracy, specificity, and dynamic ranges of various real-time PCR assays. These analytic differences cause difficulty in comparing test results, making it impossible to establish universal standardized cutoff values that correlate with clinical manifestations of BKVN. In this review, we summarize real-time PCR assays used for managing BKVN.

Purpose Previous studies have shown that ischemia alters gene expression in normal and malignant tissues. There are no studies that evaluated effects of ischemia in renal tumors. This study examines the impact of ischemia and tissue procurement conditions on RNA integrity and gene expression in renal cell carcinoma. Experimental Design Ten renal tumors were resected without renal hilar clamping from 10 patients with renal clear cell carcinoma. Immediately after tumor resection, a piece of tumor was snap frozen. Remaining tumor samples were stored at 4C, 22C and 37C and frozen at 5, 30, 60, 120, and 240 minutes. Histopathologic evaluation was performed on all tissue samples, and only those with greater than 80% tumor were selected for further analysis. RNA integrity was confirmed by electropherograms and quantitated using RIN index. Altered gene expression was assessed by paired, two-sample t-test between the zero time point and aliquots from various conditions obtained from the same tumor. Results One hundred and forty microarrays were performed. Some RNA degradation was observed 240 mins after resection at 37C. The expression of over 4,000 genes was significantly altered by ischemia times or storage conditions. The greatest gene expression changes were observed with longer ischemia time and warmer tissue procurement conditions. Conclusion RNA from kidney cancer remains intact for up to 4 hours post surgical resection regardless of storage conditions. Despite excellent RNA preservation, time after resection and procurement conditions significantly influence gene expression profiles. Meticulous attention to pre-acquisition variables is of paramount importance for accurate tumor profiling.

Histologic assessment of stromal tumor infiltrating lymphocytes (sTIL) as a surrogate of the host immune response has been shown to be prognostic and potentially chemo-predictive in triplenegative and HER2-positive breast cancers. The current practice of manual assessment is prone to intra-and inter-observer variability. Furthermore, the interplay of sTILs, tumor cells, other microenvironment mediators, their spatial relationships, quantity, and other image-based features have yet to be determined exhaustively and systemically. Towards analysis of these aspects, we developed a deep learning based method for joint region-level and nucleus-level segmentation and classification of breast cancer H&E tissue whole slide images. Our proposed method simultaneously identifies tumor, fibroblast, and lymphocyte nuclei, along with key histologic region compartments including tumor and stroma. We also show how the resultant segmentation masks can be combined with seeding approaches to yield accurate nucleus classifications. Furthermore, we outline a simple workflow for calibrating computational scores to human scores for consistency. The pipeline identifies key compartments with high accuracy (Dice= overall: 0.78, tumor: 0.83, and fibroblasts: 0.77). ROC AUC for nucleus classification is high at 0.89 (microaverage), 0.89 (lymphocytes), 0.90 (tumor), and 0.78 (fibroblasts). Spearman correlation between computational sTIL and pathologist consensus is high (R=0.73, p<0.001) and is higher than interpathologist correlation (R=0.66, p<0.001). Both manual and computational sTIL scores successfully stratify patients by clinical progression outcomes.

The high percentage of PI occurring in reexcision specimens vs. primary excisions may indicate that many of the reported cases of basal cell carcinomas with PI are actually examples of RPI.