Expression of the neural stem cell markers NG2 and L1 in human angiomyolipoma: are angiomyolipomas neoplasms of stem cells?

Angiomyolipomas are benign tumors of the kidney which express phenotypes of smooth muscle, fat, and melanocytes. These tumors appear with increased frequency in the autosomal dominant disorder tuberous sclerosis and are the leading cause of morbidity in adults with tuberous sclerosis. While benign, these tumors are capable of provoking life threatening hemorrhage and replacement of the kidney parenchyma, resulting in renal failure. The histogenesis of these tumors is currently unclear, although currently, we believe these tumors arise from "perivascular epithelioid cells" of which no normal counterpart has been convincingly demonstrated. Recently, stem cell precursors have been recognized that can give rise to smooth muscle and melanocytes. These precursors have been shown to express the neural stem cell marker NG2 and L1. In order to determine whether angiomyolipomas, which exhibit smooth muscle and melanocytic phenotypes, express NG2 and L1, we performed immunocytochemistry on a cell line derived from a human angiomyolipoma, and found that these cells are uniformly positive. Immunohistochemistry of human angiomyolipoma specimens revealed uniform staining of tumor cells, while renal cell carcinomas revealed positivity only of angiogenic vessels. These results support a novel histogenesis of angiomyolipoma as a defect in differentiation of stem cell precursors.

A nonparametric method of comparing the partial areas under two correlated receiver operating characteristic curves

A non-parametric method was developed and tested to compare the partial areas under two correlated Receiver Operating Characteristic curves. Based on the theory of generalized U-statistics the mathematical formulas have been derived for computing ROC area, and the variance and covariance between the portions of two ROC curves. A practical SAS application also has been developed to facilitate the calculations. The accuracy of the non-parametric method was evaluated by comparing it to other methods. By applying our method to the data from a published ROC analysis of CT image, our results are very close to theirs. A hypothetical example was used to demonstrate the effects of two crossed ROC curves. The two ROC areas are the same. However each portion of the area between two ROC curves were found to be significantly different by the partial ROC curve analysis. For computation of ROC curves with large scales, such as a logistic regression model, we applied our method to the breast cancer study with Medicare claims data. It yielded the same ROC area computation as the SAS Logistic procedure. Our method also provides an alternative to the global summary of ROC area comparison by directly comparing the true-positive rates for two regression models and by determining the range of false-positive values where the models differ. ^

Novel Imaging-Based Techniques Reveal a Role for PD-1/PD-L1 in Tumor Immune Surveillance in the Lung

The binding of immune inhibitory receptor Programmed Death 1 (PD-1) on T cells to its ligand PD-L1 has been implicated as a major contributor to tumor induced immune suppression. Clinical trials of PD-L1 blockade have proven effective in unleashing therapeutic anti-tumor immune responses in a subset of patients with advanced melanoma, yet current response rates are low for reasons that remain unclear. Hypothesizing that the PD-1/PD-L1 pathway regulates T cell surveillance within the tumor microenvironment, we employed intravital microscopy to investigate the in vivo impact of PD-L1 blocking antibody upon tumor-associated immune cell migration. However, current analytical methods of intravital dynamic microscopy data lack the ability to identify cellular targets of T cell interactions in vivo, a crucial means for discovering which interactions are modulated by therapeutic intervention. By developing novel imaging techniques that allowed us to better analyze tumor progression and T cell dynamics in the microenvironment; we were able to explore the impact of PD-L1 blockade upon the migratory properties of tumor-associated immune cells, including T cells and antigen presenting cells, in lung tumor progression. Our results demonstrate that early changes in tumor morphology may be indicative of responsiveness to anti-PD-L1 therapy. We show that immune cells in the tumor microenvironment as well as tumors themselves express PD-L1, but immune phenotype alone is not a predictive marker of effective anti-tumor responses. Through a novel method in which we quantify T cell interactions, we show that T cells are largely engaged in interactions with dendritic cells in the tumor microenvironment. Additionally, we show that during PD-L1 blockade, non-activated T cells are recruited in greater numbers into the tumor microenvironment and engage more preferentially with dendritic cells. We further show that during PD-L1 blockade, activated T cells engage in more confined, immune synapse-like interactions with dendritic cells, as opposed to more dynamic, kinapse-like interactions with dendritic cells when PD-L1 is free to bind its receptor. By advancing the contextual analysis of anti-tumor immune surveillance in vivo, this study implicates the interaction between T cells and tumor-associated dendritic cells as a possible modulator in targeting PD-L1 for anti-tumor immunotherapy.