Grade Prostatic Intraepithelial Neoplasia Lesions Research Articles

Neuroendocrine differentiation in the 12T‐10 transgenic prostate mouse model mimics endocrine differentiation of pancreatic beta cells

Neuroendocrine (NE) prostate cancer develops as an aggressive disease that does not respond to androgen ablation therapy. It has been demonstrated that the paracrine action of NE cells facilitates the progression of androgen dependent adenocarcinoma to an androgen independent state, suggesting a significant role for NE cells during failure of androgen ablation therapy. To investigate the pathways that are involved in NE differentiation of prostate cancer, we have looked at the expression of genes known to be involved in endocrine differentiation of beta-cells in the pancreas. This study has been performed using the NE prostate cancer mouse model (12T-10) and the derivative allograft model (NE-10). Immunohistochemical studies have shown that the neuroendocrine prostate tumors express the transcription factors Foxa2, mouse achaete-scute homolog-1 (mash-1), neurogenin3 (Ngn3) and Nkx2.2. These tumors show a loss of hairy/enhancer of split (Hes-1), a gene that inhibits NE differentiation. Human NE prostate cancers also express Foxa2 and human achaete-scute homolog-1 (HASH-1). These genes are expressed in NE prostate tumors in the similar sequential manner as they appear in a pancreatic beta-cell endocrine differentiation. Foxa2 expression is detected in early prostatic intraepithelial neoplasia (PIN). Mash-1 expression is detected in a few clusters within low grade PIN lesions and Nkx2.2 expression is rarely detected in individual scattered cells within the PIN lesion. Ngn3 and Nkx2.2 frequently appear in the invasive NE cancer. Subsequent NE metastasis to lung and liver show a distinct gene expression pattern. The lung metastasis expresses Ngn3 but does not express Nkx2.2 whereas liver metastases do not express Ngn3 but express Nkx2.2. These results suggest that Ngn3 and Nkx2.2 expression are markers for site-specific metastasis and/or transcriptionally regulated genes that are required for organ-specific metastasis. This study indicates that a pathway similar to pancreatic beta-cell differentiation is involved in NE differentiation of prostate cancer.

TMPRSS2-ERG Fusion Prostate Cancer: An Early Molecular Event Associated With Invasion

Prostate cancer (PCA) is one of the most prevalent cancers and a major leading cause of morbidity and mortality in the Western world. The TMPRSS2-ERG fusion was recently identified as a common recurrent chromosomal aberration in this malignancy. In our study, we interrogated a broad spectrum of benign, precursor, and malignant prostatic lesions to assess the TMPRSS2-ERG fusion status using a multicolor interphase fluorescence in situ hybridization assay. Samples from hospital-based cohorts consisted of 237 clinically localized PCA, 34 hormone naive metastases, 9 hormone refractory metastases, 26 high grade prostatic intraepithelial neoplasia lesions, 15 samples of benign prostatic hyperplasia, 38 of proliferative inflammatory atrophy, and 47 of benign prostatic tissue. The TMPRSS2-ERG fusion was present in 48.5% of clinically localized PCA, 30% of hormone naive metastases, 33% of hormone refractory metastases, and in 19% of high grade prostatic intraepithelial neoplasia lesions in intermingling to cancer foci. Almost all these fusion positive cases show a homogenous distribution of the fusion pattern. In contrast, none of the other samples harbored this genetic aberration. If we consider the high incidence of PCA and the high frequency of this gene fusion, TMPRSS2-ERG is the most common genetic aberration so far described in human malignancies. Furthermore, its clinical application as a biomarker and ancillary diagnostic test is promising given its high specificity.

Telomerase Activity in Touch–Imprint Cell Preparations from Fresh Prostate Needle Biopsy Specimens

Objective: To evaluate whether it is possible to detect telomerase activity in cells exfoliated from prostate biopsies immediately before fixation. Methods: A total of 115 transrectal biopsies of prostate tissue from 49 patients were touch–imprinted on an RNase–free microscope slide and then fixed. Touch imprints were immediately frozen and used to extract telomerase. Telomerase activity was determined by a telomeric repeat amplification protocol (TRAP) using a PCR–ELISA method. Inflammation and epithelial cells in each biopsy were quantitated by image cytometry. Results: A total of 90/115 extracts had a proteic content suitable for analysis. Telomerase activity was detected in 18/26 (70%) carcinomas, 2/9 (22%) low–grade prostatic intraepithelial neoplasia (PIN) lesions, and 1/3 (33%) high–grade PIN lesions. In 4 of 7 patients with telomerase–positive tumors, telomerase activity was also found in a distant site devoid of morphologically detectable cancer cells. Telomerase activity was detected in touch imprints from fragments with less than 1 mm² of epithelial tissue, and was not associated with the extent of inflammation. Conclusions: From the technical stand point, the touch–imprint method may provide a useful adjunct for telomerase detection in prostate biopsies. With this procedure the bioptic fragment is left intact for histological examination. Diagnostically, the presence of telomerase activity in sites distant from the original tumor might suggest the presence of tumor cells that are morphologically undetectable.

Expression of π-class glutathione S-transferase: two populations of high grade prostatic intraepithelial neoplasia with different relations to carcinoma

Background/Aims—Patients with high grade prostatic intraepithelial neoplasia of the transition zone appear to be at increased risk of developing prostatic carcinoma, although not to the same degree as patients with...

Prostatic intraepithelial neoplasia (PIN). Performance of Bayesian belief network for diagnosis and grading.

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology, with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens, subdivided as follows: 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often close to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases: one PINlow was classified as PINhigh and three PINhigh were classified as PINlow. In conclusion, the network can grade PIN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.