Unsupervised analysis reveals two molecular subgroups of serous ovarian cancer with distinct gene expression profiles and survival.

PURPOSE: Ovarian cancer is typically diagnosed at late stages, and thus, patients' prognosis is poor. Improvement in treatment outcomes depends, at least partly, on better understanding of ovarian cancer biology and finding new molecular markers and therapeutic targets. METHODS: An unsupervised method of data analysis, singular value decomposition, was applied to analyze microarray data from 101 ovarian cancer samples; then, selected genes were validated by quantitative PCR. RESULTS: We found that the major factor influencing gene expression in ovarian cancer was tumor histological type. The next major source of variability was traced to a set of genes mainly associated with extracellular matrix, cell motility, adhesion, and immunological response. Hierarchical clustering based on the expression of these genes revealed two clusters of ovarian cancers with different molecular profiles and distinct overall survival (OS). Patients with higher expression of these genes had shorter OS than those with lower expression. The two clusters did not derive from high- versus low-grade serous carcinomas and were unrelated to histological (ovarian vs. fallopian) origin. Interestingly, there was considerable overlap between identified prognostic signature and a recently described invasion-associated signature related to stromal desmoplastic reaction. Several genes from this signature were validated by quantitative PCR; two of them-DSPG3 and LOX-were validated both in the initial and independent sets of samples and were significantly associated with OS and disease-free survival. CONCLUSIONS: We distinguished two molecular subgroups of serous ovarian cancers characterized by distinct OS. Among differentially expressed genes, some may potentially be used as prognostic markers. In our opinion, unsupervised methods of microarray data analysis are more effective than supervised methods in identifying intrinsic, biologically sound sources of variability. Moreover, as histological type of the tumor is the greatest source of variability in ovarian cancer and may interfere with analyses of other features, it seems reasonable to use histologically homogeneous groups of tumors in microarray experiments.

Gene expression profile of B 16(F10) murine melanoma cells exposed to hypoxic conditions in vitro.

Hypoxia is an important feature of tumor microenvironment, exerting far-reaching effects on cells and contributing to cancer progression. Previous studies have established substantial differences in hypoxia response between various cell lines. Investigating this phenomenon in melanoma cells contributes to a better understanding of cell lineage-specific hypoxia response and could point out novel hypoxia-regulated genes. We investigated transcriptional activity of B 16(F10) murine melanoma cells cultured for 24 h under hypoxic (nominal 1% O2, 15 samples including controls) and hypoxia-mimicking conditions (cobalt chloride, 100 or 200 microM, 6 samples including controls). Gene expression profiles were analyzed using MG-U74Av2 oligonucleotide microarrays. Data analysis revealed 2541 probesets (FDR <5%) for 1% oxygen experiment and 364 probesets (FDR <5%) for cobalt chloride, which showed differences in expression levels. Analysis of hypoxia-regulated genes (true hypoxia, 1% O2) by stringent Family-Wise Error Rate estimation indicated 454 significantly changed transcripts (p < 0.05). The most upregulated genes were Lgals3, Selenbpl, Nppb (more than ten-fold increase). We observed significant differences in expression levels of genes regulating glycolysis (Pfkp, Hk2, Aldo3, Eno2), apoptosis (Bnip3, Bnip31, Cdknla), transcription (Bhlhb2, Sap30, Atf3, Mxil), angiogenesis (Vegfa, Adm, Anxa2, Ctgf), adhesion (Pkp2, Itga4, Mcam), migration (Cnn2, Tmsb4x), and other processes. Both true hypoxia and hypoxia mimicry induced HIF-1-regulated genes. However, unsupervised analysis (Singular Value Decomposition) revealed distinct differences in gene expression between these two experimental conditions. Contrary to hypoxia, cobalt chloride caused suppression of gene expression rather than stimulation, especially concerning transcripts related to proliferation, immune response, DNA repair, and melanin biosynthesis.

[Gene expression profile of medullary thyroid carcinoma--preliminary results]. / Profil eksresji genów w raku rdzeniastym tarczycy--doniesienie wstepne.

INTRODUCTION: Medullary thyroid carcinoma occurs both as a sporadic and a familial disease. Inherited MTC (iMTC) patients usually exhibit better prognosis than patients with sporadic form of MTC (sMTC), however, in both subtypes the outcome is unpredictable. No molecular markers contributing to the prognosis or predicting the type of therapy have been introduced to clinical practice until now. The aim of this study was to analyze gene expression pattern of MTC by high density oligonucleotide microarray. MATERIAL AND METHODS: 24 samples were studied: 12 MTC and 12 corresponding normal tissues, (Affymetrix HG-U 133A). Among MTC patients there were half inherited cases and half sporadic ones. RESULTS: First, the differences between MTC and thyroid tissue were analyzed by Singular Value Decomposition (SVD) which indicated three main modes determining the variability of gene expression profile: the first two were related to the tumor/normal tissue difference and the third one was related to the immune response. The characteristic expression pattern, beside of numerous changes within cancer- related genes, included many up-regulated genes specific for thyroid C cells. Further analysis of the second component revealed two subgroups of MTC, but the subdivision was not related to the iMTC/sMTC difference. Recursive Feature Replacement (RFR) confirmed the very similar expression profile in both forms of MTC. With subsequent ANOVA analysis some genes with differential expression could be specified, among them monoamine oxidase B (MAOB) and gamma-aminobutyric acid receptor (GABRR1) which were consistently up-regulated in sMTC. In contrary, some genes involved in regulation of cell proliferation: opioid growth factor receptor(OGFR) and synaptotagmin V (SYT 5) were up-regulated in iMTC. CONCLUSIONS: The obtained data indicate a very similar gene expression pattern in inherited and sporadic MTC. Minor differences in their molecular profile require further analysis.

Gene Expression Profiling in Hereditary, BRCA1-linked Breast Cancer: Preliminary Report.

Global analysis of gene expression by DNA microarrays is nowadays a widely used tool, especially relevant for cancer research. It helps the understanding of complex biology of cancer tissue, allows identification of novel molecular markers, reveals previously unknown molecular subtypes of cancer that differ by clinical features like drug susceptibility or general prognosis. Our aim was to compare gene expression profiles in breast cancer that develop against a background of inherited predisposing mutations versus sporadic breast cancer. In this preliminary study we analysed seven hereditary, BRCA1 mutation-linked breast cancer tissues and seven sporadic cases that were carefully matched by histopathology and ER status. Additionally, we analysed 6 samples of normal breast tissue. We found that while the difference in gene expression profiles between tumour tissue and normal breast can be easily recognized by unsupervised algorithms, the difference between those two types of tumours is more discrete. However, by supervised methods of data analysis, we were able to select a set of genes that may differentiate between hereditary and sporadic tumours. The most significant difference concerns genes that code for proteins engaged in regulation of transcription, cellular metabolism, signalling, proliferation and cell death. Microarray results for chosen genes (TOB1, SEPHS2) were validated by real-time RT-PCR.

Gene expression profile of papillary thyroid cancer: sources of variability and diagnostic implications.

The study looked for an optimal set of genes differentiating between papillary thyroid cancer (PTC) and normal thyroid tissue and assessed the sources of variability in gene expression profiles. The analysis was done by oligonucleotide microarrays (GeneChip HG-U133A) in 50 tissue samples taken intraoperatively from 33 patients (23 PTC patients and 10 patients with other thyroid disease). In the initial group of 16 PTC and 16 normal samples, we assessed the sources of variability in the gene expression profile by singular value decomposition which specified three major patterns of variability. The first and the most distinct mode grouped transcripts differentiating between tumor and normal tissues. Two consecutive modes contained a large proportion of immunity-related genes. To generate a multigene classifier for tumor-normal difference, we used support vector machines-based technique (recursive feature replacement). It included the following 19 genes: DPP4, GJB3, ST14, SERPINA1, LRP4, MET, EVA1, SPUVE, LGALS3, HBB, MKRN2, MRC2, IGSF1, KIAA0830, RXRG, P4HA2, CDH3, IL13RA1, and MTMR4, and correctly discriminated 17 of 18 additional PTC/normal thyroid samples and all 16 samples published in a previous microarray study. Selected novel genes (LRP4, EVA1, TMPRSS4, QPCT, and SLC34A2) were confirmed by Q-PCR. Our results prove that the gene expression signal of PTC is easily detectable even when cancer cells do not prevail over tumor stroma. We indicate and separate the confounding variability related to the immune response. Finally, we propose a potent molecular classifier able to discriminate between PTC and nonmalignant thyroid in more than 90% of investigated samples.

A note on estimation of dynamics of multiple gene expression based on singular value decomposition.

Recently, data on multiple gene expression at sequential time points were analyzed, using singular value decomposition (SVD) as a means to capture dominant trends, called characteristic modes, followed by fitting of a linear discrete-time dynamical system in which the expression values at a given time point are linear combinations of the values at a previous time point. We attempt to address several aspects of the method. To obtain the model we formulate a non-linear optimization problem and present how to solve it numerically using standard MATLAB procedures. We use publicly available data to test the approach. For reader's convenience, we provide a straightforward, ready-to-use, procedure in MATLAB, which employs its standard features to analyze data of this kind. Then, we investigate the sensitivity of the method to missing measurements and its possibilities to reconstruct missing data. Also, we discuss the possible consequences of data regularization, called sometimes 'polishing', on the outcome of analysis, especially when model is to be used for prediction purposes. Summarizing we point out that approximation of multiple gene expression data preceded by SVD provides some insight into the dynamics but may also lead to unexpected difficulties, like overfitting problems.