Roberto Hirata

Comparative analysis of amplified and nonamplified RNA for hybridization in cDNA microarray

Limiting amounts of RNA is a major issue in cDNA microarray, especially when one is dealing with fresh tissue samples. Here we describe a protocol based on template switch and T7 amplification that led to efficient and linear amplification of 1300x. Using a glass-array containing 368 genes printed in three or six replicas covering a wide range of expression levels and ratios, we determined quality and reproducibility of the data obtained from one nonamplified and two independently amplified RNAs (aRNA) derived from normal and tumor samples using replicas with dye exchange (dye-swap measurements). Overall, signal-to-noise ratio improved when we used aRNA (1.45-fold for channel 1 and 2.02-fold for channel 2), increasing by 6% the number of spots with meaningful data. Measurements arising from independent aRNA samples showed strong correlation among themselves (r(2)=0.962) and with those from the nonamplified sample (r(2)=0.975), indicating the reproducibility and fidelity of the amplification procedure. Measurement differences, i.e, spots with poor correlation between amplified and nonamplified measurements, did not show association with gene sequence, expression intensity, or expression ratio and can, therefore, be compensated with replication. In conclusion, aRNA can be used routinely in cDNA microarray analysis, leading to improved quality of data with high fidelity and reproducibility.

Molecular classifiers for gastric cancer and nonmalignant diseases of the gastric mucosa

High incidence of gastric cancer-related death is mainly due to diagnosis at an advanced stage in addition to the lack of adequate neoadjuvant therapy. Hence, new tools aimed at early diagnosis would have a positive impact in the outcome of the disease. Using cDNA arrays having 376 genes either identified previously as altered in gastric tumors or known to be altered in human cancer, we determined expression signature of 99 tissue fragments representing normal gastric mucosa, gastritis, intestinal metaplasia, and adenocarcinomas. We first validated the array by identifying molecular markers that are associated with intestinal metaplasia, considered as a transition stage of gastric adenocarcinomas of the intestinal type as well as markers that are associated with diffuse type of gastric adenocarcinomas. Next, we applied Fisher’s linear discriminant analysis in an exhaustive search of trios of genes that could be used to build classifiers for class distinction. Many classifiers could distinguish between normal and tumor samples, whereas, for the distinction of gastritis from tumor and for metaplasia from tumor, fewer classifiers were identified. Statistical validations showed that trios that discriminate between normal and tumor samples are powerful classifiers to distinguish between tumor and nontumor samples. More relevant, it was possible to identify samples of intestinal metaplasia that have expression signature resembling that of an adenocarcinoma and can now be used for follow-up of patients to determine their potential as a prognostic test for malignant transformation.

Microarray gridding by mathematical morphology

DNA chips (i.e., microarrays) biotechnology is a hybridization (i.e., DNA matching) based process that makes it possible to quantify the relative abundance of mRNA from two distinct samples by analysing their fluorescence signals. This technique requires robotic placement (i.e., spotting) of thousands of cDNAs (i.e., complementary DNA) in an array format on glass microscope slides which provide gene-specific hybridization targets. The two different samples of mRNA, usually labeled with Cy3 and Cy5 fluorochromes, are cohybridized onto each spotted gene and two digital images, one for each fluorochrome, are acquired after hybridization. Before estimating the signal and background of each spot, it is necessary to locate the region of the spot in order to map the gene information with the corresponding spot. Therefore, these images must be segmented for analysis, that is, the spotting geometric structure must be found. That implies segmenting the subarrays (i.e., the set of grouped spots), and then the positions of the spots in each subarray. The authors introduce a new technique using morphological operators that performs automatic gridding procedures (i.e., subarrays and spot segmentation). This technique has been implemented and tested in a variety of microarray images with success.

Segmentation of microarray images by mathematical morphology

DNA chips (i.e., microarrays) biotechnology is a hybridization (i.e., matching of pairs of DNA)-based process that makes possible to quantify the relative abundance of mRNA of two distinct samples by analyzing their fluorescence signals. This technique requires robotic placement (i.e., spotting) of thousands of cDNAs (i.e., complementary DNA) in an array format on glass microscope slides. The spotted cDNAs are the hybridization targets for the mRNA samples. The two different samples of mRNA, usually labeled with Cy3 and Cy5 fluorochromes, are cohybridized onto each spotted gene. After hybridization, one digital image is acquired for each fluorochrome wavelength. Then, it is necessary to recognize each gene by its position in the array and to estimate its signal (i.e., hybridization information). For that, it is necessary to segment the image in three classes of objects: subarrays (i.e., set of grouped spots), spot box (i.e., the rectangular neighborhood that contains a spot) and spot (i.e., region of the image where there exists signal). In this paper, we present a technique based on mathematical morphology that performs this segmentation. In the website http://www.vision.ime.usp.br/demos/ microarray/detailed experimental results are presented.

Automatic Programming of Morphological Machines by PAC Learning

An important aspect of mathematical morphology is the description of complete lattice operators by a formal language, the Morphological Language (ML), whose vocabulary is composed of infimum, supremum, dilations, erosions, anti-dilations and anti-erosions. This language is complete (i.e., it can represent any complete lattice operator) and expressive (i.e., many useful operators can be represented as phrases with relatively few words). Since the sixties special machines, the Morphological Machines (MMachs), have been built to implement the ML restricted to the lattices of binary and gray-scale images. However, designing useful MMach programs is not an elementary task. Recently, much research effort has been addressed to automate the programming of MMachs. The goal of the different approaches for this problem is to find suitable knowledge representation formalisms to describe transformations over geometric structures and to translate them automatically into MMach programs by computational systems. We present here the central ideas of an approach based on the representation of transformations by collections of observed-ideal pairs of images and the estimation of suitable operators from these data. In this approach, the estimation of operators is based on statistical optimization or, equivalently, on a branch of Machine Learning Theory known as PAC Learning. These operators are generated as standard form morphological operators that may be simplified (i.e., transformed into equivalent morphological operators that use fewer vocabulary words) by syntactical transformations.

Early Changes in Gene Expression Induced by Tobacco Smoke: Evidence for the Importance of Estrogen within Lung Tissue

Lung cancer is the leading cause of cancer deaths in the United States, surpassing breast cancer as the primary cause of cancer-related mortality in women. The goal of the present study was to identify early molecular changes in the lung induced by exposure to tobacco smoke and thus identify potential targets for chemoprevention. Female A/J mice were exposed to either tobacco smoke or HEPA-filtered air via a whole-body exposure chamber (6 h/d, 5 d/wk for 3, 8, and 20 weeks). Gene expression profiles of lung tissue from control and smoke-exposed animals were established using a 15K cDNA microarray. Cytochrome P450 1b1, a phase I enzyme involved in both the metabolism of xenobiotics and the 4-hydroxylation of 17β-estradiol (E2), was modulated to the greatest extent following smoke exposure. A panel of 10 genes were found to be differentially expressed in control and smoke-exposed lung tissues at 3, 8, and 20 weeks (P < 0.001). The interaction network of these differentially expressed genes revealed new pathways modulated by short-term smoke exposure, including estrogen metabolism. In addition, E2 was detected within murine lung tissue by gas chromatography-coupled mass spectrometry and immunohistochemistry. Identification of the early molecular events that contribute to lung tumor formation is anticipated to lead to the development of promising targeted chemopreventive therapies. In conclusion, the presence of E2 within lung tissue when combined with the modulation of cytochrome P450 1b1 and other estrogen metabolism genes by tobacco smoke provides novel insight into a possible role for estrogens in lung cancer. Cancer Prev Res; 3(6); 707–17. ©2010 AACR.

Expression Profile of Malignant and Nonmalignant Lesions of Esophagus and Stomach: Differential Activity of Functional Modules Related to Inflammation and Lipid Metabolism

Adenocarcinomas of stomach and esophagus are frequently associated with preceding inflammatory alterations of the normal mucosa. Whereas intestinal metaplasia of the gastric mucosa is associated with higher risk of malignization, Barretts disease is a risk factor for adenocarcinoma of the esophagus. Barretts disease is characterized by the substitution of the squamous mucosa of the esophagus by a columnar tissue classified histopathologically as intestinal metaplasia. Using cDNA microarrays, we determined the expression profile of normal gastric and esophageal mucosa as well as intestinal metaplasia and adenocarcinomas from both organs. Data were explored to define functional alterations related to the transformation from squamous to columnar epithelium and the malignant transformation from intestinal metaplasia to adenocarcinomas. Based on their expression profile, adenocarcinomas of the esophagus showed stronger correlation with intestinal metaplasia of the stomach than with Barretts mucosa. Second, we identified two functional modules, lipid metabolism and cytokine, as being altered with higher statistical significance. Whereas the lipid metabolism module is active in samples representing intestinal metaplasia and inactive in adenocarcinomas, the cytokine module is inactive in samples representing normal esophagus and esophagitis. Using the concept of relevance networks, we determined the changes in linear correlation of genes pertaining to these two functional modules. Exploitation of the data presented herein will help in the precise molecular characterization of adenocarcinoma from the distal esophagus, avoiding the topographical and descriptive classification that is currently adopted, and help with the proper management of patients with Barretts disease.

Differentially expressed genes in gastric tumors identified by cDNA array

Using cDNA fragments from the FAPESP/lICR Cancer Genome Project, we constructed a cDNA array having 4512 elements and determined gene expression in six normal and six tumor gastric tissues. Using t-statistics, we identified 80 cDNAs whose expression in normal and tumor samples differed more than 3.5 sample standard deviations. Using Self-Organizing Map, the expression profile of these cDNAs allowed perfect separation of malignant and non-malignant samples. Using the supervised learning procedure Support Vector Machine, we identified trios of cDNAs that could be used to classify samples as normal or tumor, based on single-array analysis. Finally, we identified genes with altered linear correlation when their expression in normal and tumor samples were compared. Further investigation concerning the function of these genes could contribute to the understanding of gastric carcinogenesis and may prove useful in molecular diagnostics.

Differential expression of IGFBP-5 and two human ESTs in thyroid glands with goiter, adenoma and papillary or follicular carcinomas.

Here, we describe the identification of three human genes with altered expression in thyroid diseases. One of them corresponds to insulin-like growth factor binding protein 5 (IGFBP5), which has already been described as over expressed in other cancers and, for the first time, is identified as overexpressed in thyroid tumors. The other genes, named 44 and 199, are ESTs with yet unknown function and were mapped on human chromosomes seven and four, respectively. We determined by RT-PCR the expression level of these genes in ten samples of disease-free thyroid, ten of goiter, nine of papillary carcinoma, ten of adenoma and seven of follicular carcinoma and the significance of observed differences was statistically determined. IGFBP-5 and gene 44 were significantly overexpressed in papillary carcinoma when compared to normal and goiter. Genes 44 and 199 were differentially expressed in follicular carcinoma and adenoma when compared to normal thyroid tissue.

Gene expression profiles in breast tumors regarding the presence or absence of estrogen and progesterone receptors

Estrogen acts via its receptor (ER) to stimulate cell growth and differentiation in the mammary gland. ER and progesterone receptor (PR), which is regulated by estrogen via ER, have been used as prognostic markers in clinical management of breast cancer patients. Patients with ER− breast tumors have a poorer prognosis than patients with ER+ tumors. The aim of the present study was the identification of tumor‐associated genes differentially expressed in breast tumors regarding the presence or absence of ER and PR hybridized with cDNA microarrays containing 4,500 tumor‐derived expressed sequence tags generated using the ORESTES technique. Samples of human primary breast carcinomas from 38 patients were analyzed. The experiments were performed in triplicates and data from each element were acquired by phosphoimage scanning. Data acquisition was performed using the ArrayVision software. After normalization statistical analysis was applied. In a preliminary analysis, 98 differentially expressed transcripts were identified, 46 were found to be more expressed in ER+/PR+ and 52 were found to be more expressed in ER−/PR− breast tumors. The biochemical functions of the genes in the reported expression profile are diverse and include metabolic enzymes, protein kinases, helicases, transcription factors, cell cycle regulators and apoptotic factors. ER−/PR− breast tumors displayed increased levels of transcripts of genes associated with neurodegeneration and genes associated with proliferation were found in ER+/PR+ tumors.