Cervical intraepithelial neoplasia: prognosis by combined LOH analysis of multiple loci

Assessment of the frequency of genetic alterations (LOH/MSI) in patients with intraepithelial cervical lesions with HPV infection: a pilot study

In the present study, we analyzed (1) the type of HPV infection and (2) the frequency of loss of heterozygosity and microsatellite imbalance (LOH/MSI) in normal cytology and cervical intraepithelial neoplasia (CIN1-3). The cytological material included: low-grade squamous intraepithelial lesions (CIN1, n = 11), high-grade lesions (CIN2 and CIN3, n = 13), and cytologically normal cells from non-neoplastic cervical samples (n = 8). HPV genotyping was done using RealLine HPV 16/18 kit. We used 20 microsatellite markers from: 1p31.2, 3p14.3, 3p21.3, 3p22.2, 3p24.2, 3p25.3, 7q32.2, 9p21.3, 11p15.5, 12q23.2, and 16q22.1. LOH/MSI was correlated with clinicopathological parameters. The presence of HPV DNA was revealed in 78.13 % samples, including normal cytology. LOH/MSI was the most frequent for: 3p25.3 (39 %), 3p22.2 (20.83 %), 3p24.2 (20 %), and 3p14.3 (16.67 %). It was demonstrated that D3S1234 (FHIT; 3p14.3), D3S1611 (MLH1; 3p22.2), D3S1583 (RARB; 3p24.2), D3S1317 and D3S3611 (VHL; 3p25.3) could differentiate patients with CIN2/CIN3 versus CIN1, showing significantly higher frequency in CIN2/CIN3. LOH/MSI frequency for other than 3p markers was lower, 10-22.2 %. The simultaneous occurrence of LOH/MSI for several markers (OFAL) was higher in CIN2/CIN3. Significant differences in OFAL were found between samples with versus without HPV infection. In HPV-positive patients, significant differences in OFAL were found between normal cytology, CIN1 and CIN2/CIN3. HPV infection influences the increase in LOH/MSI frequency, especially in tumor suppressor gene loci. Several studied microsatellite markers seem to be useful for CIN grading. Hopefully, the obtained results, if confirmed on larger patient cohort, would allow creating a panel of markers supporting clinical diagnosis in patients with HPV infection.

Progesterone signaling inhibits cervical carcinogenesis in mice

Human papillomavirus is the main cause of cervical cancer, yet other nonviral cofactors are also required for the disease. The uterine cervix is a hormone-responsive tissue, and female hormones have been implicated in cervical carcinogenesis. A transgenic mouse model expressing human papillomavirus oncogenes E6 and/or E7 has proven useful to study a mechanism of hormone actions in the context of this common malignancy. Estrogen and estrogen receptor α are required for the development of cervical cancer in this mouse model. Estrogen receptor α is known to up-regulate expression of the progesterone receptor, which, on activation by its ligands, either promotes or inhibits carcinogenesis, depending on the tissue context. Here, we report that progesterone receptor inhibits cervical and vaginal epithelial cell proliferation in a ligand-dependent manner. We also report that synthetic progestin medroxyprogesterone acetate promotes regression of cancers and precancerous lesions in the female lower reproductive tracts (ie, cervix and vagina) in the human papillomavirus transgenic mouse model. Our results provide the first experimental evidence that supports the hypothesis that progesterone signaling is inhibitory for cervical carcinogenesis in vivo.

Screening of microsatellite markers in penile cancer reveals differences between metastatic and nonmetastatic carcinomas

Penile cancer, observed only rarely in the western world, represents a carcinoma that may be cured by resection of primary lesion and in case of lymph node metastasis by early lymph node dissection. This early inguinal lymphadenectomy bares a significant better survival even in cases of nonpalpable lymph nodes, but carries also a high risk of overtreatment, especially in lower tumor stages. Due to the low incidence, only few data are available on the molecular genetic background of this tumor, especially concerning tumor progression and metastasis. Therefore, we studied 62 microsatellite markers in 28 penile carcinomas searching for markers predicting progression or outcome. LOH in more than 25% of primary tumors was found on six different chromosomes, including 2q, 6p, 8q, 9p, 12q and 17p13. Statistically significant correlations could be established in D6S260 to clinical outcome and in markers from chromosomes 6, 9 and 12 to tumor stage and metastasis. These regions are worthy for further analysis concerning tumor suppressor genes and metastasis suppressor genes.

Gene expression profiles in squamous cell cervical carcinoma using array-based comparative genomic hybridization analysis

Our aim was to identify novel genomic regions of interest and provide highly dynamic range information on correlation between squamous cell cervical carcinoma and its related gene expression patterns by a genome-wide array-based comparative genomic hybridization (array-CGH). We analyzed 15 cases of cervical cancer from KangNam St Mary's Hospital of the Catholic University of Korea. Microdissection assay was performed to obtain DNA samples from paraffin-embedded cervical tissues of cancer as well as of the adjacent normal tissues. The bacterial artificial chromosome (BAC) array used in this study consisted of 1440 human BACs and the space among the clones was 2.08 Mb. All the 15 cases of cervical cancer showed the differential changes of the cervical cancer-associated genetic alterations. The analysis limit of average gains and losses was 53%. A significant positive correlation was found in 8q24.3, 1p36.32, 3q27.1, 7p21.1, 11q13.1, and 3p14.2 changes through the cervical carcinogenesis. The regions of high level of gain were 1p36.33-1p36.32, 8q24.3, 16p13.3, 1p36.33, 3q27.1, and 7p21.1. And the regions of homozygous loss were 2q12.1, 22q11.21, 3p14.2, 6q24.3, 7p15.2, and 11q25. In the high level of gain regions, GSDMDC1, RECQL4, TP73, ABCF3, ALG3, HDAC9, ESRRA, and RPS6KA4 were significantly correlated with cervical cancer. The genes encoded by frequently lost clones were PTPRG, GRM7, ZDHHC3, EXOSC7, LRP1B, and NR3C2. Therefore, array-CGH analyses showed that specific genomic alterations were maintained in cervical cancer that were critical to the malignant phenotype and may give a chance to find out possible target genes present in the gained or lost clones.

Identification of novel prognostic markers in cervical intraepithelial neoplasia using LDMAS (LOH Data Management and Analysis Software)

Background

Detection of Loss of Heterozygosity (LOH) is one of the most common molecular applications in the study of human diseases, in particular cancer. The technique is commonly used to examine whether a known tumour suppressor gene is inactivated or to map unknown tumour suppressor gene(s). However, with the increasing number of samples analysed using different software, no tool is currently available to integrate and facilitate the extensive and efficient data retrieval and analyses, such as correlation of LOH data with various clinical data sets.

Results

An algorithm to identify prognostic disease markers is devised and implemented as novel software called LDMAS. LDMAS is a software suite designed for data retrieval, management and integrated analysis of the clinico-pathological data and molecular results from independent databases. LDMAS is used in stratification of disease stages according to clinical stage or histological features and correlation of various clinico-pathological features with molecular findings to obtain relevant prognostic markers such as those used in predicting the outcome of cervical intraepithelial neoplasia (CIN). This approach lead to the identification of novel prognostic cervical cancer markers and extraction of useful clinical information such as correlation of Human Papilloma Virus (HPV) status with CIN lesions.

Conclusions

A novel software called LDMAS is implemented and used to extract and identify prognostic disease markers. The software is used to successfully identify 4 novel prognostic markers that can be used to predict the outcome of CIN. LDMAS provides an essential platform for the extraction of useful information from large amount of data generated by LOH studies. LDMAS provides three unique and novel features for LOH analysis : (1) automatic extraction of relevant data from patient records and reports (2) correlation of LOH data with clinico-pathological data and (3) storage of complex data in flexible format. The first feature automates the creation of database of clinically relevant information from huge amount of data, the second feature extracts useful biomedical information such as prognostic markers in CIN and the third feature simplifies the statistical analyses of the data and allows non-statisticians to carry out the analysis. Additionally, LDMAS can be used to extract clinically useful markers from other diseases and interface to high throughput genotyping analysis software such as GDAS used to generate LOH data from Affymetrix^® GeneChip Mapping arrays.