Resampling reveals sample-level differential expression in clinical genome-wide studies

Gene expression profiles separate endometriosis lesion subtypes and indicate a sensitivity of endometrioma to estrogen suppressive treatments through elevated ESR2 expression

BACKGROUND

Endometriosis is a common, gynaecological disease characterised by the presence of endometrial-like cells growing outside the uterus. Lesions appear at multiple locations, present with variation in appearance, size and depth of invasion. Despite hormones being the recommended first-line treatment, their efficacy, success and side effects vary widely amongst study populations. Current, hormonal medication for endometriosis is designed to suppress systemic oestrogen. Whether these hormones can influence the lesions themselves is not yet clear. Evidence of hormone receptor expression in endometriotic lesions and their ability to respond is conflicting. A variation in their expression, activation of transcriptional co-regulators and the potential to respond may contribute to their variation in patient outcomes. Identifying patients who would benefit from hormonal treatments remain an important goal in endometriosis research.

METHODS

Using gene expression data from endometriosis lesions including endometrioma (OMA, n = 28), superficial peritoneal lesions (SUP, n = 72) and deeply infiltrating lesions (DIE, n = 78), we performed principal component analysis, differential gene expression and gene correlation analyses to assess the impact of menstrual stage, lesion subtype and hormonal treatment on the gene expression.

RESULTS

The gene expression profiles did not vary based on menstrual stage, but could distinguish lesion subtypes with OMA significantly differentiating from both SUP and DIE. Additionally, the effect of oestrogen suppression medication altered the gene expression profile in OMA, while such effect was not observed in SUP or DIE. Analysis of the target receptors for hormonal medication indicated ESR2 was differentially expressed in OMA and that genes that correlated with ESR2 varied significantly between medicated and non-medicated OMA samples.

CONCLUSIONS

Our results demonstrate of the different lesion types OMA present with strongest response to hormonal treatment directly through ESR2. The data suggests that there may be the potential to target treatment options to individual patients based on pre-surgical diagnoses.

Evaluation of drug-targetable genes by defining modes of abnormality in gene expression

In the post-genomic era, many researchers have taken a systematic approach to identifying abnormal genes associated with various diseases. However, the gold standard has not been established, and most of these abnormalities are difficult to be rehabilitated in real clinical settings. In addition to identifying abnormal genes, for a practical purpose, it is necessary to investigate abnormality diversity. In this context, this study is aimed to demonstrate simply restorable genes as useful drug targets. We devised the concept of “drug targetability” to evaluate several different modes of abnormal genes by predicting events after drug treatment. As a representative example, we applied our method to breast cancer. Computationally, PTPRF, PRKAR2B, MAP4K3, and RICTOR were calculated as highly drug-targetable genes for breast cancer. After knockdown of these top-ranked genes (i.e., high drug targetability) using siRNA, our predictions were validated by cell death and migration assays. Moreover, inhibition of RICTOR or PTPRF was expected to prolong lifespan of breast cancer patients according to patient information annotated in microarray data. We anticipate that our method can be widely applied to elaborate selection of novel drug targets, and, ultimately, to improve the efficacy of disease treatment.

Ovarian endometriosis signatures established through discovery and directed mass spectrometry analysis

New molecular information on potential therapeutic targets or tools for noninvasive diagnosis for endometriosis are important for patient care and treatment. However, surprisingly few efforts have described endometriosis at the protein level. In this work we enumerate the proteins in patient endometrium and ovarian endometrioma by extensive and comprehensive analysis of minute amounts of cryosectioned tissues in a three-tiered mass spectrometric approach. Quantitative comparison of the tissues revealed 214 differentially expressed proteins in ovarian endometrioma and endometrium. These proteins are reported here as a resource of SRM (selected reaction monitoring) assays that are unique, standardized, and openly available. Pathway analysis of the proteome measurements revealed a potential role for Transforming growth factor β-1 in ovarian endometriosis development. Subsequent mRNA microarray analysis further revealed clear ovarian endometrioma specificity for a subset of these proteins, which was also supported by further in silico studies. In this process two important proteins emerged, Calponin-1 and EMILIN-1, that were additionally confirmed in ovarian endometrioma tissues by immunohistochemistry and Western blotting. This study provides the most comprehensive molecular description of ovarian endometriosis to date and researchers with new molecular methods and tools for high throughput patient screening using the SRM assays.

Endometrial and endometriotic concentrations of estrone and estradiol are determined by local metabolism rather than circulating levels

CONTEXT

Aberrant estrogen synthesis and metabolism have been suggested to increase local estradiol (E2) concentration in endometriosis and thus to promote the growth of the lesions. However, tissue estrogen concentrations within the endometrium and different types of endometriosis lesions have not been described.

OBJECTIVE

The aim of the study was to evaluate local E2 and estrone (E1) concentrations in the endometrium and different types of endometriosis lesions, and to correlate them with the expression of estrogen-metabolizing enzymes.

PATIENTS

Patients with endometriosis (n = 60) and healthy controls (n = 16) participated in the study.

MAIN OUTCOME MEASURES

We measured serum and tissue concentrations of E2 and E1 as well as mRNA expression of the estrogen-metabolizing enzymes.

RESULTS

Endometrial or endometriotic intratissue E2 concentrations did not reflect the corresponding serum levels. In the proliferative phase, endometrial E2 concentration was five to eight times higher than in the serum, whereas in the secretory phase the E2 concentration was about half of that in the serum. Accordingly, a markedly higher E2/E1 ratio was observed in the endometrium at the proliferative phase compared with the secretory phase. In the endometriosis lesions, E2 levels were predominating over those of E1 throughout the menstrual cycle. Among the hydroxysteroid (17β) dehydrogenase (HSD17B) enzymes analyzed, HSD17B2 negatively correlated with the E2 concentration in the endometrium, and HSD17B6 was strongly expressed, especially in the deep lesions.

CONCLUSIONS

Endometrial or endometriotic tissue E2 concentrations are actively regulated by local estrogen metabolism in the tissue. Thus, the inhibition of local E2 synthesis is a valid, novel approach to reduce local E2-dependent growth of endometriotic tissue.

State of the art in silico tools for the study of signaling pathways in cancer

In the last several years, researchers have exhibited an intense interest in the evolutionarily conserved signaling pathways that have crucial roles during embryonic development. Interestingly, the malfunctioning of these signaling pathways leads to several human diseases, including cancer. The chemical and biophysical events that occur during cellular signaling, as well as the number of interactions within a signaling pathway, make these systems complex to study. In silico resources are tools used to aid the understanding of cellular signaling pathways. Systems approaches have provided a deeper knowledge of diverse biochemical processes, including individual metabolic pathways, signaling networks and genome-scale metabolic networks. In the future, these tools will be enormously valuable, if they continue to be developed in parallel with growing biological knowledge. In this study, an overview of the bioinformatics resources that are currently available for the analysis of biological networks is provided.

Genetic variants and their interactions in the prediction of increased pre-clinical carotid atherosclerosis: the cardiovascular risk in young Finns study

The relative contribution of genetic risk factors to the progression of subclinical atherosclerosis is poorly understood. It is likely that multiple variants are implicated in the development of atherosclerosis, but the subtle genotypic and phenotypic differences are beyond the reach of the conventional case-control designs and the statistical significance testing procedures being used in most association studies. Our objective here was to investigate whether an alternative approach--in which common disorders are treated as quantitative phenotypes that are continuously distributed over a population--can reveal predictive insights into the early atherosclerosis, as assessed using ultrasound imaging-based quantitative measurement of carotid artery intima-media thickness (IMT). Using our population-based follow-up study of atherosclerosis precursors as a basis for sampling subjects with gradually increasing IMT levels, we searched for such subsets of genetic variants and their interactions that are the most predictive of the various risk classes, rather than using exclusively those variants meeting a stringent level of statistical significance. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive value of the variants, and cross-validation was used to assess how well the predictive models will generalize to other subsets of subjects. By means of our predictive modeling framework with machine learning-based SNP selection, we could improve the prediction of the extreme classes of atherosclerosis risk and progression over a 6-year period (average AUC 0.844 and 0.761), compared to that of using conventional cardiovascular risk factors alone (average AUC 0.741 and 0.629), or when combined with the statistically significant variants (average AUC 0.762 and 0.651). The predictive accuracy remained relatively high in an independent validation set of subjects (average decrease of 0.043). These results demonstrate that the modeling framework can utilize the "gray zone" of genetic variation in the classification of subjects with different degrees of risk of developing atherosclerosis.