Up regulation in gene expression of chromatin remodelling factors in cervical intraepithelial neoplasia

<p>Abstract</p> <p>Background</p> <p>The highest rates of cervical cancer are found in developing countries. Frontline monitoring has reduced these rates in developed countries and present day screening programs primarily identify precancerous lesions termed cervical intraepithelial neoplasias (CIN). CIN lesions described as mild dysplasia (CIN I) are likely to spontaneously regress while CIN III lesions (severe dysplasia) are likely to progress if untreated. Thoughtful consideration of gene expression changes paralleling the progressive pre invasive neoplastic development will yield insight into the key casual events involved in cervical cancer development.</p> <p>Results</p> <p>In this study, we have identified gene expression changes across 16 cervical cases (CIN I, CIN II, CIN III and normal cervical epithelium) using the unbiased long serial analysis of gene expression (L-SAGE) method. The 16 L-SAGE libraries were sequenced to the level of 2,481,387 tags, creating the largest SAGE data collection for cervical tissue worldwide. We have identified 222 genes differentially expressed between normal cervical tissue and CIN III. Many of these genes influence biological functions characteristic of cancer, such as cell death, cell growth/proliferation and cellular movement. Evaluation of these genes through network interactions identified multiple candidates that influence regulation of cellular transcription through chromatin remodelling (<it>SMARCC1</it>, <it>NCOR1</it>, <it>MRFAP1 </it>and <it>MORF4L2</it>). Further, these expression events are focused at the critical junction in disease development of moderate dysplasia (CIN II) indicating a role for chromatin remodelling as part of cervical cancer development.</p> <p>Conclusion</p> <p>We have created a valuable publically available resource for the study of gene expression in precancerous cervical lesions. Our results indicate deregulation of the chromatin remodelling complex components and its influencing factors occur in the development of CIN lesions. The increase in SWI/SNF stabilizing molecule <it>SMARCC1 </it>and other novel genes has not been previously illustrated as events in the early stages of dysplasia development and thus not only provides novel candidate markers for screening but a biological function for targeting treatment.</p

Exploratory analysis of quantitative histopathology of cervical intraepithelial neoplasia: objectivity, reproducibility, malignancy-associated changes, and human papillomavirus

Background: Background: As part of a project to evaluate emerging optical technologies for cervical neoplasia, our group is performing quantitative histopathological analyses of biopsy specimens from 1,190 patients. Objectives in the interim analysis are (a) quantitatively assessing progression of the neoplastic process of cervical intraepithelial neoplasia (CIN)/squamous intraepithelial lesions (SIL), (b) detecting malignancy-associated changes (MACs), and (c) phenotypically measuring human papillomavirus (HPV) detected by DNA testing. Methods: The diagnostic region of interest (ROI) from immediately adjacent sections were imaged, and the basal lamina and surface of the superficial layer were delimited. Nonoverlapping quantitatively stained nuclei were selected from 1,190 samples with histopathological characteristics of normal (929), koilocytosis (130), CIN 1 (40), CIN 2 (23), and CIN 3/carcinoma in situ (CIS) (68). A fully automatic procedure located and recorded the center of every nucleus in the region of interest (ROI). We used linear discriminant analysis to assess the changes between normal and CIN 3/CIS

Cooperation and competition in the dynamics of tissue architecture during homeostasis and tumorigenesis

The construction of a network of cell-to-cell contacts makes it possible to characterize the patterns and spatial organisation of tissues. Such networks are highly dynamic, depending on the changes of the tissue architecture caused by cell division, death and migration. Local competitive and cooperative cell-to-cell interactions influence the choices cells make. We review the literature on quantitative data of epithelial tissue topology and present a dynamical network model that can be used to explore the evolutionary dynamics of a two dimensional tissue architecture with arbitrary cell-to-cell interactions. In particular, we show that various forms of experimentally observed types of interactions can be modelled using game theory. We discuss a model of cooperative and non-cooperative cell-to-cell communication that can capture the interplay between cellular competition and tissue dynamics. We conclude with an outlook on the possible uses of this approach in modelling tumorigenesis and tissue homeostasis.Comment: 11 pages, 5 figures; Seminars in Cancer Biology (2013

Identification of azimuthal light scattering signatures to selectively track changes in subnuclear refractive index profile of epithelial cell models

We construct stochastically inhomogeneous epithelial cell models via simulation of Gaussian random fields; the extent and correlation length of subnuclear refractive index fluctuations are based on values quantified from high-resolution images of cervical tissue. We then employ the finite-difference time-domain method to simulate azimuth-resolved light scattering patterns of the constructed models. We process these two-dimensional patterns and calculate a series of Haralick features with the ultimate goal of identifying signatures that directly point to changes in subnuclear refractive index profile. Our results show that azimuthal contrast calculated over specific angular ranges is highly sensitive to the extent and correlation length of refractive index fluctuations. This metric is insensitive to changes in the overall size and mean refractive index of the constructed models, thereby allowing for selective tracking of changes in subnuclear refractive index variations

Prediction using hierarchical data: Applications for automated detection of cervical cancer

Although the Papanicolaou smear has been successful in decreasing cervical cancer incidence in the developed world, there exist many challenges for implementation in the developing world. Quantitative cytology, a semi-automated method that quantifies cellular image features, is a promising screening test candidate. The nested structure of its data (measurements of multiple cells within a patient) provides challenges to the usual classification problem. Here we perform a comparative study of three main approaches for problems with this general data structure: (i) extract patient-level features from the cell-level data, (ii) use a statistical model that accounts for the hierarchical data structure, and (iii) classify at the cellular level and use an ad hoc approach to classify at the patient level. We apply these methods to a dataset of 1728 patients, with an average of 2600 cells collected per patient and 133 features measured per cell, predicting whether a patient had a positive biopsy result. The best approach we found was to classify at the cellular level and count the number of cells that had a posterior probability greater than a threshold value, with estimated 61% sensitivity and 89% specificity on independent data. Recent statistical learning developments allowed us to achieve high accuracy