ClearCode34: A Prognostic Risk Predictor for Localized Clear Cell Renal Cell Carcinoma

Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting

Spatiotemporally separated antigen uptake by alveolar dendritic cells and airway presentation to T cells in the lung

Asthma pathogenesis is focused around conducting airways. The reasons for this focus have been unclear because it has not been possible to track the sites and timing of antigen uptake or subsequent antigen presentation to effector T cells. In this study, we use two-photon microscopy of the lung parenchyma and note accumulation of CD11b(+) dendritic cells (DCs) around the airway after allergen challenge but very limited access of these airway-adjacent DCs to the contents of the airspace. In contrast, we observed prevalent transepithelial uptake of particulate antigens by alveolar DCs. These distinct sites are temporally linked, as early antigen uptake in alveoli gives rise to DC and antigen retention in the airway-adjacent region. Antigen-specific T cells also accumulate in the airway-adjacent region after allergen challenge and are activated by the accumulated DCs. Thus, we propose that later airway hyperreactivity results from selective retention of allergen-presenting DCs and antigen-specific T cells in airway-adjacent interaction zones, not from variation in the abilities of individual DCs to survey the lung

ClearCode34: A Prognostic Risk Predictor for Localized Clear Cell Renal Cell Carcinoma

BACKGROUND: Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting. OBJECTIVE: To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification. DESIGN, SETTING, AND PARTICIPANTS: A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence. RESULTS AND LIMITATIONS: The subtypes were significantly associated with RFS (p < 0.01), CSS (p < 0.01), and OS (p < 0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms. CONCLUSIONS: The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients. PATIENT SUMMARY: We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes