Does pelvic mesh treated with phosphorylcholine improve outcomes? An early experience.

OBJECTIVES: Implantable devices treated with phosphorylcholine (PC) have been successfully used in cardiac, ophthalmic, and other applications. This surface modification has resulted in a reduction in the host inflammatory responses. This pilot study tested the safety and efficacy of PC treated polypropylene mesh grafts implanted for the treatment of pelvic organ prolapse. STUDY DESIGN: Surgeons from five U.S. sites collected data on subjects implanted with Perigee IntePro Lite+PC. Pre-procedure data collected included demographics and prolapse severity. At follow-up, subjects were assessed for anatomical outcomes (success≤stage I POPQ or Baden Walker), symptomatic improvement, and complications, particularly mesh exposure. RESULTS: A total of 40 subjects were enrolled with 80% (32/40) of them completing at least 5-7 months of follow-up. Mean patient age was 60 years (range 36-78 years) and the mean BMI was 28 (range 20-40). There were no cases of mesh exposure/extrusion or granuloma formation. The anatomical success rate was 100% at 5-7 months (32/32). CONCLUSIONS: This is the first publication on pelvic mesh treated with PC. There were no adverse events attributed to this surface modification. However, as the numbers are small, the results are not statistically significant. PC surface modification of pelvic mesh shows promise in its application for the reduction of mesh related complications.

Adult cystic nephroma and mixed epithelial and stromal tumor of the kidney are the same disease entity: molecular and histologic evidence.

Adult cystic nephroma (CN) and mixed epithelial and stromal tumor of the kidney (MEST) are considered as separate entities in the 2004 World Health Organization classification of renal neoplasms. Recent studies suggested that the two share clinicopathologic features and may represent the same disease process of varying morphology. However, definitive genetic evidence is lacking. We examined their relationship using gene expression profiling and histologic analysis. Gene expression profiles of 3 CN and 3 MEST were analyzed using HGU133 Plus 2.0 microarrays (Affymetrix) and were compared with each other and also with 48 other renal tumors and 13 normal kidneys. Histologic examination of 26 CN and 13 MEST focused on the cystic septal thickness, cyst-to-stroma ratio, stromal cellularity and composition, types of epithelial cells lining cysts and glands, and estrogen and progesterone receptors expression. Patients' age, sex distribution, and tumor size were similar between the two. They also shared many histologic features, including lining epithelium of cysts and glands, stromal cellularity and composition. Unsupervised clustering of mRNA expression profiles demonstrated that they had very similar expression profiles that were distinct from other renal tumors. By microarray analysis, progesterone receptor expression was significantly higher in CN and MEST relative to both normal and other renal tumors, while estrogen receptor expression was not. By immunohistochemistry, expression of both receptors was similar between CN and MEST. This study provides the most convincing molecular evidence that CN and MEST represent different parts of the morphologic spectrum of the same disease.

Robust classification of renal cell carcinoma based on gene expression data and predicted cytogenetic profiles.

Renal cell carcinoma (RCC) is a heterogeneous disease that includes several histologically distinct subtypes. The most common RCC subtypes are clear cell, papillary, and chromophobe, and recent gene expression profiling studies suggest that classification of RCC based on transcriptional signatures could be beneficial. Traditionally, however, patterns of chromosomal alterations have been used to assist in the molecular classification of RCC. The purpose of this study was to determine whether it was possible to develop a classification model for the three major RCC subtypes that utilizes gene expression profiles as the bases for both molecular genetic and cytogenetic classification. Gene expression profiles were first used to build an expression-based RCC classifier. The RCC gene expression profiles were then examined for the presence of regional gene expression biases. Regional expression biases are genetic intervals that contain a disproportionate number of genes that are coordinately up- or down-regulated. The presence of a regional gene expression bias often indicates the presence of a chromosomal abnormality. In this study, we demonstrate an expression-based classifier can distinguish between the three most common RCC subtypes in 99% of cases (n = 73). We also demonstrate that detection of regional expression biases accurately identifies cytogenetic features common to RCC. Additionally, the in silico-derived cytogenetic profiles could be used to classify 81% of cases. Taken together, these data demonstrate that it is possible to construct a robust classification model for RCC using both transcriptional and cytogenetic features derived from a gene expression profile.