Does an epidemiological comparison support a common cellular lineage for similar subtypes of postmenopausal uterine and ovarian carcinoma? The Norwegian Women and Cancer Study.

Uterine and ovarian carcinomas have the same major histological subtypes, but whether they originate from the same cell types is a matter of ongoing debate. Uterine and ovarian endometrioid and clear cell carcinoma (ECC) and uterine and ovarian serous carcinoma (SC) may originate in the same location, or share a common lineage of differentiation. Epidemiologically, a common cellular lineage should be reflected in similar risk associations, and we explored the similarity of uterine and ovarian ECC and uterine and ovarian SC. We included 146,316 postmenopausal participants from the Norwegian Women and Cancer Study. Exposure information was taken from self-administered questionnaires, and cancer cases were identified through linkage to the Cancer Registry of Norway. Hazard ratios with 95% confidence intervals for uterine and ovarian carcinoma and their subtypes were calculated using multivariable Cox regression models, and a Wald test was used to check for heterogeneity. During 1.6 million person-years, 1,006 uterine and 601 ovarian carcinomas were identified. Parity, total menstrual lifespan, body mass index and smoking were differentially associated with total uterine and total ovarian carcinoma (pheterogeneity  = 0.041, 0.027, <0.001 and 0.001, respectively). The corresponding associations for uterine and ovarian ECC did not differ significantly (pheterogeneity  > 0.05). Smoking was differentially associated with uterine and ovarian SC (pheterogeneity  = 0.021). Our epidemiological analyses do not contradict a common differentiation lineage for uterine and ovarian ECC. Uterine and ovarian SC are less likely to be of a common lineage of differentiation, based on their difference in risk associated with smoking.

Associations of breast cancer related exposures and gene expression profiles in normal breast tissue-The Norwegian Women and Cancer normal breast tissue study.

BACKGROUND: Normal breast tissue is utilized in tissue-based studies of breast carcinogenesis. While gene expression in breast tumor tissue is well explored, our knowledge of transcriptomic signatures in normal breast tissue is still incomplete. The aim of this study was to investigate variability of gene expression in a large sample of normal breast tissue biopsies, according to breast cancer related exposures (obesity, smoking, alcohol, hormone therapy, and parity). METHODS: We analyzed gene expression profiles from 311 normal breast tissue biopsies from cancer-free, post-menopausal women, using Illumina bead chip arrays. Principal component analysis and K-means clustering was used for initial analysis of the dataset. The association of exposures and covariates with gene expression was determined using linear models for microarrays. RESULTS: Heterogeneity of the breast tissue and cell composition had the strongest influence on gene expression profiles. After adjusting for cell composition, obesity, smoking, and alcohol showed the highest numbers of associated genes and pathways, whereas hormone therapy and parity were associated with negligible gene expression differences. CONCLUSION: Our results provide insight into associations between major exposures and gene expression profiles and provide an informative baseline for improved understanding of exposure-related molecular events in normal breast tissue of cancer-free, post-menopausal women.

Transcription factor PAX6 as a novel prognostic factor and putative tumour suppressor in non-small cell lung cancer.

Lung cancer is the leading cause of cancer deaths. Novel predictive biomarkers are needed to improve treatment selection and more accurate prognostication. PAX6 is a transcription factor with a proposed tumour suppressor function. Immunohistochemical staining was performed on tissue microarrays from 335 non-small cell lung cancer (NSCLC) patients for PAX6. Multivariate analyses of clinico-pathological variables and disease-specific survival (DSS) was carried out, and phenotypic changes of two NSCLC cell lines with knockdown of PAX6 were characterized. While PAX6 expression was only associated with a trend of better disease-specific survival (DSS) (p = 0.10), the pN+ subgroup (N = 103) showed significant correlation between high PAX6 expression and longer DSS (p = 0.022). Median survival for pN + patients with high PAX6 expression was 127.4 months, versus 22.9 months for patients with low PAX6 expression. In NCI-H661 cells, knockdown of PAX6 strongly activated serum-stimulated migration. In NCI-H460 cells, PAX6 knockdown activated anchorage-independent growth. We did not observe any significant effect of PAX6 on proliferation in either of cell lines. Our findings strongly support the proposition of PAX6 as a valid and positive prognostic marker in NSCLC in node-positive patients. There is a need for further studies, which should provide mechanistical explanation for the role of PAX6 in NSCLC.

Kvik: three-tier data exploration tools for flexible analysis of genomic data in epidemiological studies.

Kvik is an open-source framework that we developed for explorative analysis of functional genomics data from large epidemiological studies. Creating such studies requires a significant amount of time and resources. It is therefore usual to reuse the data from one study for several research projects. Often each project requires implementing new analysis code, integration with specific knowledge bases, and specific visualizations. Although existing data exploration tools are available for single study data exploration, no tool provides all the required functionality for multistudy data exploration. We have therefore used the Kvik framework to develop Kvik Pathways, an application for exploring gene expression data in the context of biological pathways. We have used Kvik Pathways to explore data from both a cross-sectional study design and a case-control study within the Norwegian Women and Cancer (NOWAC) cohort. Kvik Pathways follows the three-tier architecture in web applications using a powerful back-end for statistical analyses and retrieval of metadata.In this note, we describe how we used the Kvik framework to develop the Kvik Pathways application. Kvik Pathways was used by our team of epidemiologists toexplore gene expression data from healthy women with high and low plasma ratios of essential fatty acids.