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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.

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 Kaplan–Meier survival analyses showed that higher S100A11 expression was associated with statistically significant decrease in DFS (p < 0.001), but not OS (p = 0.1).

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