Pleural mesothelioma in a nine-month-old dog

This paper reports on an unusual case of pleural epitheloid mesothelioma in a nine-month-old male, mixed breed dog. The dog was presented in-extremis and, on post mortem examination, multiple, exophytic, frequently pedunculated, yellowish-red, soft to firm masses ranging from 3 mm to 6 cm in diameter were diffusely distributed over, and attached to, the pericardial and parietal pleural surfaces. Microscopically, these masses consisted of round to partially polygonalshaped, anaplastic cells with minimal cytoplasm and hyperchromatic nuclei covering papillomatous projections or as part of more densely cellular masses. A supporting fibrovascular stroma and mitotic figures were also evident. Constituent tumour cells were labeled positively with antibodies against both vimentin and cytokeratin. In contrast, the same cells exhibited equivocal labeling with an antibody directed against calretinin antigen and did not label with antibodies against carcinoembryonic antigen (CEA) and milk fat globule-related antigen (MFGRA). Such tumours are rare in dogs, particularly in such a young animal

Construction of tissue microarrays from prostate needle biopsy specimens

Needle biopsies are taken as standard diagnostic specimens for many cancers, but no technique exists for the high-throughput analysis of multiple individual immunohistochemical (IHC) markers using these samples. Here we present a simple and highly reliable technique for constructing tissue microarrays (TMAs) from prostatic needle biopsies. Serial sectioning of the TMAs, called ‘Checkerboard TMAs', facilitated expression analysis of multiple proteins using IHC markers. In total, 100% of the analysed biopsies within the TMA both preserved their antigenicity and maintained their morphology. Checkerboard TMAs will allow the use of needle biopsies (i) alongside other tissue specimens (trans-urethral resection of prostates and prostatectomies in the case of prostate cancer) in clinical correlation studies when searching for new prognostic markers, and (ii) in a diagnostic context for assessing expression of multiple proteins in cancers from patients prior to treatment

Expression of high p53 levels in colorectal cancer: a favourable prognostic factor

The expression of p53 protein was examined in a series of 111 colorectal cancer adenocarcinomas with a long follow-up. A quantitative luminometric immunoassay (LIA) was used for the measurement of wild-type and mutant p53 protein in extracts from colorectal tumour cytosols, p53 being detected in 42% of the samples (range 0.0–52 ng mg−1). Using an arbitrary cut-off value of 2.7 ng mg−1, 25% of the tumours were classified as manifesting high p53 levels. There was no association of p53 expression with patient age, sex, serum preoperative carcinoembryonic antigen (CEA) levels, tumour site and size, nodal status or TNM stage. Significant and independent correlation was found to exist between high p53 levels and prolonged disease-free survival (P = 0.05) at a median follow-up of 60 months. This survival advantage was most apparent among stage III cancer patients. The results from this study would suggest that expression of high p53 levels appear to be useful in selecting a group of colorectal cancer patients with a better prognosis. © 1999 Cancer Research Campaig

Automated detection of regions of interest for tissue microarray experiments: an image texture analysis

BACKGROUND: Recent research with tissue microarrays led to a rapid progress toward quantifying the expressions of large sets of biomarkers in normal and diseased tissue. However, standard procedures for sampling tissue for molecular profiling have not yet been established. METHODS: This study presents a high throughput analysis of texture heterogeneity on breast tissue images for the purpose of identifying regions of interest in the tissue for molecular profiling via tissue microarray technology. Image texture of breast histology slides was described in terms of three parameters: the percentage of area occupied in an image block by chromatin (B), percentage occupied by stroma-like regions (P), and a statistical heterogeneity index H commonly used in image analysis. Texture parameters were defined and computed for each of the thousands of image blocks in our dataset using both the gray scale and color segmentation. The image blocks were then classified into three categories using the texture feature parameters in a novel statistical learning algorithm. These categories are as follows: image blocks specific to normal breast tissue, blocks specific to cancerous tissue, and those image blocks that are non-specific to normal and disease states. RESULTS: Gray scale and color segmentation techniques led to identification of same regions in histology slides as cancer-specific. Moreover the image blocks identified as cancer-specific belonged to those cell crowded regions in whole section image slides that were marked by two pathologists as regions of interest for further histological studies. CONCLUSION: These results indicate the high efficiency of our automated method for identifying pathologic regions of interest on histology slides. Automation of critical region identification will help minimize the inter-rater variability among different raters (pathologists) as hundreds of tumors that are used to develop an array have typically been evaluated (graded) by different pathologists. The region of interest information gathered from the whole section images will guide the excision of tissue for constructing tissue microarrays and for high throughput profiling of global gene expression