Histo-genomics: digital pathology at the forefront of precision medicine.

The toughest challenge OMICs face is that they provide extremely high molecular resolution but poor spatial information. Understanding the cellular/histological context of the overwhelming genetic data is critical for a full understanding of the clinical behavior of a malignant tumor. Digital pathology can add an extra layer of information to help visualize in a spatial and microenvironmental context the molecular information of cancer. Thus, histo-genomics provide a unique chance for data integration. In the era of a precision medicine, a four-dimensional (4D) (temporal/spatial) analysis of cancer aided by digital pathology can be a critical step to understand the evolution/progression of different cancers and consequently tailor individual treatment plans. For instance, the integration of molecular biomarkers expression into a three-dimensional (3D) image of a digitally scanned tumor can offer a better understanding of its subtype, behavior, host immune response and prognosis. Using advanced digital image analysis, a larger spectrum of parameters can be analyzed as potential predictors of clinical behavior. Correlation between morphological features and host immune response can be also performed with therapeutic implications. Radio-histomics, or the interface of radiological images and histology is another emerging exciting field which encompasses the integration of radiological imaging with digital pathological images, genomics, and clinical data to portray a more holistic approach to understating and treating disease. These advances in digital slide scanning are not without technical challenges, which will be addressed carefully in this review with quick peek at its future.

Droplet digital PCR improves urinary exosomal miRNA detection compared to real-time PCR.

OBJECT: Quantification of urinary miRNAs can be challenging especially for low abundance miRNAs. We aimed to optimize the quantification of urinary exosomal miRNAs and compare the performance efficiency between droplet digital PCR (ddPCR) and real-time quantitative PCR (qPCR). METHODS: We optimized a number of parameters for ddPCR such as annealing temperatures, annealing time and PCR cycle number. We also compared the performance of ddPCR and qPCR. RESULTS: By comparing the fluorescence amplification separation, the optimal annealing temperature was 59 °C, optimal annealing time was 60s and optimal cycle number was 45 for measuring urinary exosomal miRNAs. ddPCR had much higher technical sensitivity compared to qPCR. The minimal detectable concentration of miR-29a was <50 copies/µL by ddPCR compared to 6473 copies/µL for qPCR. Also, ddPCR generated more consistent results for serially diluted samples compared to qPCR. ddPCR generated smaller within-run variations than qPCR though this did not reach statistical significance. It also resulted in better reproducibility with smaller between-run variations. CONCLUSIONS: Optimization of urinary exosomal miRNA ddPCR assay is dependent on assessing key variables including experimental annealing temperature and time as well as the number of PCR cycles. ddPCR has a higher sensitivity, reproducibility, and accuracy in comparison to qPCR.

Integrated Phenotypic/Genotypic Analysis of Papillary Renal Cell Carcinoma Subtypes: Identification of Prognostic Markers, Cancer-related Pathways, and Implications for Therapy.

BACKGROUND: Two histologic subtypes are recognized for papillary renal cell carcinoma (PRCC). Studies have shown that the subtypes differ in characteristic genetic alterations and clinical behavior. Clinically, the subtypes are managed similarly. OBJECTIVES: To analyze the biological differences between the two PRCC histological subtypes, in order to further guide their clinical management. DESIGN, SETTING, AND PARTICIPANTS: PRCC cohort consisting of 317 patients from the Cancer Genome Atlas database and our institution. Patients were stratified according to histologic criteria as type 1, type 2, or not otherwise specified (NOS). Gene and miRNA expression data for the cohort were examined via unsupervised and supervised clustering. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Significant molecular signatures for each subtype were used to unravel the implicated molecular pathways via bioinformatics analysis. Survival was compared between the subtypes. Newly discovered biomarkers were used to further stratify survival of patients in the NOS category. RESULTS AND LIMITATIONS: Tumor genotyping revealed two distinct PRCC subtypes. The top molecular pathways enriched in PRCC1 were WNT, Hedgehog, and Notch signaling (p=0.001-0.01); highlighting an embryonic developmental theme to the pathogenesis of this subtype. PRCC2 showed enrichment in the mTOR, VEGF (p=7.49E-09) and HIF (p=7.63E-05) signaling pathways. Overall survival and disease-free survival significantly differed between the types. ABCC2 expression was identified as a significant prognostic biomarker for the NOS group in univariate (log rank p<0.0001; hazard ratio [HR] >11.63) and multivariate analysis (p=0.003; HR >2.12). ABCC2 expression and its effect on survival should be further validated at the protein level. CONCLUSIONS: The classical PRCC types 1 and 2 have two distinct genotypes. We unraveled pathways that indicate that the two types could potentially respond differently to current therapies. We also identified biomarkers that stratify tumors within the PRCC NOS category into prognostic subgroups. Our findings highlight the need for molecular markers to accurately subtype PRCC and guide clinical management. PATIENT SUMMARY: The two types of papillary renal cancer are treated similarly. We show that the two types have a different genetic makeup, and hence they should be considered two different tumors. There is a different biology underlying each tumor type that can potentially affect the way they respond to treatment. We uncovered genes that can be tested for to guide therapy in some problematic cases for which it hard to define the tumor type.

S100A11 is a potential prognostic marker for clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is one of few cancers with rising incidence in North America. The prognosis of ccRCC is variable and difficult to predict. Stratification of patients according to disease aggressiveness can significantly improve patient management. We investigated the expression of the S100A11 protein in 385 patients with primary ccRCC using immunohistochemistry on tissue microarrays. We compared its expression with clinicopathologic parameters and patients' survival. We also validated our results at the mRNA level on an independent set from The Cancer Genome Atlas. As a dichotomous variable (low vs. high expression), there was a significant association between S100A11 expression and tumor grade, with higher expression associated with higher tumor grades (p < 0.001). High expression was also significantly more frequently seen in higher versus lower stages (56 vs. 28 %). In the univariate analysis, high S100A11 expression was associated with significantly shorter disease-free survival (DFS) (HR = 2.28; p = 0.001). This was maintained in the multivariate analysis (HR = 1.69; p = 0.042). Expression was not associated with overall survival (OS) (p = 0.10). Comparable results were obtained when S100A11 expression was analyzed as a trichotomous variable (low, moderate, or high expression). The Kaplan­Meier survival analyses showed that higher S100A11 expression was associated with statistically significant decrease in DFS (p < 0.001), but not OS (p = 0.1).