RESUMO
Molecular subtypes of medulloblastoma [Sonic Hedgehog (SHH), Wingless/INT (WNT), Group 3, and Group 4] are defined by common patterns of gene expression. These differential gene expression patterns appear to result in different histomorphology and prognosis. Quantitative histomorphometry is a well-known method of computer-aided pathology image analysis. The hypotheses we sought to examine in this preliminary proof of concept study were whether computer extracted nuclear morphological features of medulloblastomas from digitized tissue slide images could independently: (1) distinguish between molecularly determined subgroups and (2) identify patterns within these subgroups that correspond with clinical outcome. Our dataset was composed of 46 medulloblastoma patients: 16 SHH (5 dead, 11 survived), 3 WNT (0 dead, 3 survived), 12 Group 3 (4 dead, 8 survived), and 15 were Group 4 (5 dead, 10 survived). A watershed-based thresholding scheme was used to automatically identify individual nuclei within digitized whole slide hematoxylin and eosin tissue images. Quantitative histomorphometric features corresponding to the texture (variation in pixel intensity), shape (variations in size, roundness), and architectural rearrangement (distances between, and number of connected neighbors) of nuclei were subsequently extracted. These features were ranked using feature selection schemes and these top-ranked features were then used to train machine-learning classifiers via threefold cross-validation to separate patients based on: (1) molecular subtype and (2) disease-specific outcomes within the individual molecular subtype groups. SHH and WNT tumors were separated from Groups 3 and 4 tumors with a maximum area under the receiver operating characteristic curve (AUC) of 0.7, survival within Group 3 tumors was predicted with an AUC of 0.92, and Group 3 and 4 patients were separated into high- and low-risk groups with p = 0.002. Model prediction was quantitatively compared with age, stage, and histological subtype using univariate and multivariate Cox hazard ratio models. Age was the most statistically significant variable for predicting survival in Group 3 and 4 tumors, but model predictions had the highest hazard ratio value. Quantitative nuclear histomorphometry can be used to study medulloblastoma genetic expression phenotypes as it may distinguish meaningful features of disease pathology.
RESUMO
Significant progress has been made to advance stem cell products as potential therapies for kidney diseases: various kinds of stem cells can restore renal function in preclinical models of acute and chronic kidney injury. Nonetheless this literature contains contradictory results, and for this reason, we focus this review on reasons for apparent discrepancies in the literature, because they contribute to difficulty in translating renal regenerative therapies. Differences in methodologies used to derive and culture stem cells, even those from the same source, in addition to the lack of standardized renal disease animal models (both acute and chronic), are important considerations underlying contradictory results in the literature. We propose that harmonized rigorous protocols for characterization, handling, and delivery of stem cells in vivo could significantly advance the field, and present details of some suggested approaches to foster translation in the field of renal regeneration. Our goal is to encourage coordination of methodologies (standardization) and long-lasting collaborations to improve protocols and models to lead to reproducible, interpretable, high-quality preclinical data. This approach will certainly increase our chance to 1 day offer stem cell therapeutic options for patients with all-too-common renal diseases. Stem Cells Translational Medicine 2019;8:82-92.
