Genome-wide DNA methylation profiles in progression to in situ and invasive carcinoma of the breast with impact on gene transcription and prognosis

Background

Ductal carcinoma in situ (DCIS) of the breast is a precursor of invasive breast carcinoma. DNA methylation alterations are thought to be an early event in progression of cancer, and may prove valuable as a tool in clinical decision making and for understanding neoplastic development.

Results

We generate genome-wide DNA methylation profiles of 285 breast tissue samples representing progression of cancer, and validate methylation changes between normal and DCIS in an independent dataset of 15 normal and 40 DCIS samples. We also validate a prognostic signature on 583 breast cancer samples from The Cancer Genome Atlas. Our analysis reveals that DNA methylation profiles of DCIS are radically altered compared to normal breast tissue, involving more than 5,000 genes. Changes between DCIS and invasive breast carcinoma involve around 1,000 genes. In tumors, DNA methylation is associated with gene expression of almost 3,000 genes, including both negative and positive correlations. A prognostic signature based on methylation level of 18 CpGs is associated with survival of breast cancer patients with invasive tumors, as well as with survival of patients with DCIS and mixed lesions of DCIS and invasive breast carcinoma.

Conclusions

This work demonstrates that changes in the epigenome occur early in the neoplastic progression, provides evidence for the possible utilization of DNA methylation-based markers of progression in the clinic, and highlights the importance of epigenetic changes in carcinogenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0435-x) contains supplementary material, which is available to authorized users.     

Analysis of allelic differential expression in the human genome using allele-specific serial analysis of gene expression tags

Recent reports have demonstrated that a significant proportion of human genes display allelic differential expression (ADE). ADE is associated with phenotypic variability and may contribute to complex genetic diseases. Here, we present a computational analysis of ADE using allele-specific serial analysis of gene expression (SAGE) tags representing 1295 human genes. We identified 472 genes for which unequal representation (>3-fold) of allele-specific SAGE tags was observed in at least one SAGE library, suggesting the occurrence of ADE. For 235 out of these 472 genes, the difference in the expression level between both allele-specific SAGE tags was statistically significant (p < 0.05). Eleven candidate genes were then subjected to experimental validation and ADE was confirmed for 8 out of these 11 genes. Our results suggest that at least 25% of the human genes display ADE and that allele-specific SAGE tags can be efficiently used for the identification of such genes.     

A seriation approach for visualization-driven discovery of co-expression patterns in Serial Analysis of Gene Expression (SAGE) data

Background

Serial Analysis of Gene Expression (SAGE) is a DNA sequencing-based method for large-scale gene expression profiling that provides an alternative to microarray analysis. Most analyses of SAGE data aimed at identifying co-expressed genes have been accomplished using various versions of clustering approaches that often result in a number of false positives.

Principal Findings

Here we explore the use of seriation, a statistical approach for ordering sets of objects based on their similarity, for large-scale expression pattern discovery in SAGE data. For this specific task we implement a seriation heuristic we term ‘progressive construction of contigs’ that constructs local chains of related elements by sequentially rearranging margins of the correlation matrix. We apply the heuristic to the analysis of simulated and experimental SAGE data and compare our results to those obtained with a clustering algorithm developed specifically for SAGE data. We show using simulations that the performance of seriation compares favorably to that of the clustering algorithm on noisy SAGE data.

Conclusions

We explore the use of a seriation approach for visualization-based pattern discovery in SAGE data. Using both simulations and experimental data, we demonstrate that seriation is able to identify groups of co-expressed genes more accurately than a clustering algorithm developed specifically for SAGE data. Our results suggest that seriation is a useful method for the analysis of gene expression data whose applicability should be further pursued.     

Comparison of microarray and SAGE techniques in gene expression analysis of human glioblastoma

To enhance glioblastoma (GB) marker discovery, we compared gene expression in GB with human normal brain (NB) by accessing the SAGE Genie web site and compared the results with published data. Nine GB and five NB SAGE libraries were analyzed using the Digital Gene Expression Displayer (DGED); the results of DGED were tested by Northern blot analysis and RT-PCR of arbitrarily selected genes. Review of available data from the articles on gene expression profiling by microarray-based hybridization showed as few as 35 overlapped genes with increased expression in GB. Some of them were identified in four articles, but most genes were identified in three or even in two investigations. Some differences were also found between SAGE results of GB analysis. The Digital Gene Expression Displayer approach revealed 676 genes differentially expressed in GB vs. NB with cutoff ratio: twofold change and P ≤ 05. Differential expression of selected genes obtained by DGED was confirmed by Northern analysis and RT-PCR. Altogether, only 105 of 955 genes presented in published investigations were among the genes obtained by DGED. Comparison of the results obtained by microarrays and SAGE is very complicated because the authors present only the most prominent differentially expressed genes. However, even available data give quite poor overlapping of genes revealed by microarrays. Some differences between results obtained by SAGE in different investigations can be explained by high dependence on the statistical methods used. As for now, the best solution to search for molecular tumor markers is to compare all available results and to select only those genes where significant expression in tumors combined with very low expression in normal tissues was reproduced in several articles. One hundred five differentially expressed genes, common to both methods, can be included in the list of candidates for the molecular typing of GBs. Some genes, encoded cell surface or extracellular proteins may be useful for targeting gliomas with antibody-based therapy. The text was submitted by the authors in English.     

Changes in gene expression during the development of mammary tumors in MMTV-Wnt-1 transgenic mice

Background

In human breast cancer normal mammary cells typically develop into hyperplasia, ductal carcinoma in situ, invasive cancer, and metastasis. The changes in gene expression associated with this stepwise progression are unclear. Mice transgenic for mouse mammary tumor virus (MMTV)-Wnt-1 exhibit discrete steps of mammary tumorigenesis, including hyperplasia, invasive ductal carcinoma, and distant metastasis. These mice might therefore be useful models for discovering changes in gene expression during cancer development.

Results

We used cDNA microarrays to determine the expression profiles of five normal mammary glands, seven hyperplastic mammary glands and 23 mammary tumors from MMTV-Wnt-1 transgenic mice, and 12 mammary tumors from MMTV-Neu transgenic mice. Adipose tissues were used to control for fat cells in the vicinity of the mammary glands. In these analyses, we found that the progression of normal virgin mammary glands to hyperplastic tissues and to mammary tumors is accompanied by differences in the expression of several hundred genes at each step. Some of these differences appear to be unique to the effects of Wnt signaling; others seem to be common to tumors induced by both Neu and Wnt-1 oncogenes.

Conclusion

We described gene-expression patterns associated with breast-cancer development in mice, and identified genes that may be significant targets for oncogenic events. The expression data developed provide a resource for illuminating the molecular mechanisms involved in breast cancer development, especially through the identification of genes that are critical in cancer initiation and progression.     

Determination of stromal signatures in breast carcinoma

Many soft tissue tumors recapitulate features of normal connective tissue. We hypothesize that different types of fibroblastic tumors are representative of different populations of fibroblastic cells or different activation states of these cells. We examined two tumors with fibroblastic features, solitary fibrous tumor (SFT) and desmoid-type fibromatosis (DTF), by DNA microarray analysis and found that they have very different expression profiles, including significant differences in their patterns of expression of extracellular matrix genes and growth factors. Using immunohistochemistry and in situ hybridization on a tissue microarray, we found that genes specific for these two tumors have mutually specific expression in the stroma of nonneoplastic tissues. We defined a set of 786 gene spots whose pattern of expression distinguishes SFT from DTF. In an analysis of DNA microarray gene expression data from 295 previously published breast carcinomas, we found that expression of this gene set defined two groups of breast carcinomas with significant differences in overall survival. One of the groups had a favorable outcome and was defined by the expression of DTF genes. The other group of tumors had a poor prognosis and showed variable expression of genes enriched for SFT type. Our findings suggest that the host stromal response varies significantly among carcinomas and that gene expression patterns characteristic of soft tissue tumors can be used to discover new markers for normal connective tissue cells.     

The pattern of gene expression in mouse Gr-1(+) myeloid progenitor cells

To understand the pattern of gene expression in mouse myeloid progenitor cells, we carried out a genome-wide analysis of gene expression in mouse bone marrow Gr-1(+) cells using SAGE and GLGI techniques. We identified 22,033 unique SAGE tags with quantitative information from 73,869 collected SAGE tags. Among these unique tags, 64% match known sequences, including many genes important for myeloid differentiation, and 36% have no matches to known sequences and are likely to represent novel genes. We compared the expression of mouse Gr-1(+) and human CD15(+) myeloid progenitor cells and showed that the pattern of gene expression of these two cell populations had some similarities. We also compared the expression of mouse Gr-1(+) myeloid progenitor cells with that of mouse brain tissue and found a highly tissue-specific manner of gene expression in these two samples. Our data provide a basis for studying altered gene expression in myeloid disorders using mouse models.