Gene trio signatures as molecular markers to predict response to doxorubicin cyclophosphamide neoadjuvant chemotherapy in breast cancerpatients

In breast cancer patients submitted to neoadjuvant chemotherapy (4 cycles of doxorubicin and cyclophosphamide, AC), expression of groups of three genes (gene trio signatures) could distinguish responsive from non-responsive tumors, as demonstrated by cDNA microarray profiling in a previous study by our group. In the current study, we determined if the expression of the same genes would retain the predictive strength, when analyzed by a more accessible technique (real-time RT-PCR). We evaluated 28 samples already analyzed by cDNA microarray, as a technical validation procedure, and 14 tumors, as an independent biological validation set. All patients received neoadjuvant chemotherapy (4 AC). Among five trio combinations previously identified, defined by nine genes individually investigated (BZRP, CLPTM1,MTSS1, NOTCH1, NUP210, PRSS11, RPL37A, SMYD2, and XLHSRF-1), the most accurate were established by RPL37A, XLHSRF-1based trios, with NOTCH1 or NUP210. Both trios correctly separated 86% of tumors (87% sensitivity and 80% specificity for predicting response), according to their response to chemotherapy (82% in a leave-one-out cross-validation method). Using the pre-established features obtained by linear discriminant analysis, 71% samples from the biological validation set were also correctly classified by both trios (72% sensitivity; 66% specificity). Furthermore, we explored other gene combinations to achieve a higher accuracy in the technical validation group (as a training set). A new trio, MTSS1, RPL37 and SMYD2, correctly classified 93% of samples from the technical validation group (95% sensitivity and 80% specificity; 86% accuracy by the cross-validation method) and 79% from the biological validation group (72% sensitivity and 100% specificity). Therefore, the combined expression of MTSS1, RPL37 and SMYD2, as evaluated by real-time RT-PCR, is a potential candidate to predict response to neoadjuvant doxorubicin and cyclophosphamide in breast cancer patients

Tumor slices as a model to evaluate doxorubicin in vitro treatment and expression of trios of genes PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2 in canine mammary gland cancer

<p>Abstract</p> <p>Background</p> <p>In women with breast cancer submitted to neoadjuvant chemotherapy based in doxorubicin, tumor expression of groups of three genes (PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2) have classified them as responsive or resistant. We have investigated whether expression of these trios of genes could predict mammary carcinoma response in dogs and whether tumor slices, which maintain epithelial-mesenchymal interactions, could be used to evaluate drug response <it>in vitro</it>.</p> <p>Methods</p> <p>Tumors from 38 dogs were sliced and cultured with or without doxorubicin 1 μM for 24 h. Tumor cells were counted by two observers to establish a percentage variation in cell number, between slices. Based on these results, a reduction in cell number between treated and control samples ≥ 21.7%, arbitrarily classified samples, as drug responsive. Tumor expression of PRSS11, MTSS1, CLPTM1 and SMYD2, was evaluated by real time PCR. Relative expression results were then transformed to their natural logarithm values, which were spatially disposed according to the expression of trios of genes, comprising PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2. Fisher linear discrimination test was used to generate a separation plane between responsive and non-responsive tumors.</p> <p>Results</p> <p>Culture of tumor slices for 24 h was feasible. Nine samples were considered responsive and 29 non-responsive to doxorubicin, considering the pre-established cut-off value of cell number reduction ≥ 21.7%, between doxorubicin treated and control samples. Relative gene expression was evaluated and tumor samples were then spatially distributed according to the expression of the trios of genes: PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2. A separation plane was generated. However, no clear separation between responsive and non-responsive samples could be observed.</p> <p>Conclusion</p> <p>Three-dimensional distribution of samples according to the expression of the trios of genes PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2 could not predict doxorubicin <it>in vitro </it>responsiveness. Short term culture of mammary gland cancer slices may be an interesting model to evaluate chemotherapy activity.</p

No-match ORESTES explored as tumor markers

Sequencing technologies and new bioinformatics tools have led to the complete sequencing of various genomes. However, information regarding the human transcriptome and its annotation is yet to be completed. The Human Cancer Genome Project, using ORESTES (open reading frame EST sequences) methodology, contributed to this objective by generating data from about 1.2 million expressed sequence tags. Approximately 30% of these sequences did not align to ESTs in the public databases and were considered no-match ORESTES. On the basis that a set of these ESTs could represent new transcripts, we constructed a cDNA microarray. This platform was used to hybridize against 12 different normal or tumor tissues. We identified 3421 transcribed regions not associated with annotated transcripts, representing 83.3% of the platform. The total number of differentially expressed sequences was 1007. Also, 28% of analyzed sequences could represent noncoding RNAs. Our data reinforces the knowledge of the human genome being pervasively transcribed, and point out molecular marker candidates for different cancers. To reinforce our data, we confirmed, by real-time PCR, the differential expression of three out of eight potentially tumor markers in prostate tissues. Lists of 1007 differentially expressed sequences, and the 291 potentially noncoding tumor markers were provided

No-match ORESTES explored as tumor markers

Sequencing technologies and new bioinformatics tools have led to the complete sequencing of various genomes. However, information regarding the human transcriptome and its annotation is yet to be completed. The Human Cancer Genome Project, using ORESTES (open reading frame EST sequences) methodology, contributed to this objective by generating data from about 1.2 million expressed sequence tags. Approximately 30% of these sequences did not align to ESTs in the public databases and were considered no-match ORESTES. On the basis that a set of these ESTs could represent new transcripts, we constructed a cDNA microarray. This platform was used to hybridize against 12 different normal or tumor tissues. We identified 3421 transcribed regions not associated with annotated transcripts, representing 83.3% of the platform. The total number of differentially expressed sequences was 1007. Also, 28% of analyzed sequences could represent noncoding RNAs. Our data reinforces the knowledge of the human genome being pervasively transcribed, and point out molecular marker candidates for different cancers. To reinforce our data, we confirmed, by real-time PCR, the differential expression of three out of eight potentially tumor markers in prostate tissues. Lists of 1007 differentially expressed sequences, and the 291 potentially noncoding tumor markers were provided

Gestational age acceleration is associated with epigenetic biomarkers of prenatal physiologic stress exposure

BACKGROUND: Physiological maternal stress response, such as imbalance in the glucocorticoid pathway and immune system seems to be mediated by DNA methylation (DNAm) and might translate intrauterine stress exposures into phenotypic changes in a sex-specific manner. DNAm in specific sites can also predict newborn gestational age and gestational age acceleration (GAA). GAA occurs when the predicted biological age is higher than the chronological age. In adults, poor health outcomes related to this deviance are well documented and raise questions for the interpretation and prediction in early stages of life. Boys seem to be more vulnerable to intrauterine stress exposure than girls; however, the mechanisms of adaptive sex-specific responses are still unclear. We hypothesize that intrauterine stress exposure is associated with GAA and could be different in boys and girls if inflammatory or glucocorticoid pathways exposure is considered. RESULTS: Using the Western Region Birth Cohort (ROC-Sao Paulo, Brazil) (n = 83), we calculated DNAm age and GAA from cord blood samples. Two epigenetic risk scores were calculated as an indirect proxy for low-grade inflammation (i-ePGS) and for glucocorticoid exposure (GES). Multivariate linear regression models were applied to investigate associations of GAA with prenatal exposures. The i-ePGS and GES were included in different models with the same co-variates considering sex interactions. The first multivariate model investigating inflammatory exposure (adj. R(2) = 0.31, p = < 0.001) showed that GAA was positively associated with i-ePGS (CI, 0.26-113.87, p = 0.049) and negative pregnancy-related feelings (CI, 0.04-0.48 p = 0.019). No sex interaction was observed. The second model investigating glucocorticoid exposure (adj. R(2) = 0.32, p = < 0.001) showed that the higher was the GAA was associated with a lower the lower was the GES in girls (CI, 0.04-2.55, p = 0.044). In both models, maternal self-reported mental disorder was negatively associated with GAA. CONCLUSION: Prenatal epigenetic score of exposure to low-grade inflammatory was a predictor of GAA for both sexes. Glucocorticoid epigenetic score seems to be more important to GAA in girls. This study supports the evidence of sex-specificity in stress response, suggesting the glucocorticoid as a possible pathway adopted by girls to accelerate the maturation in an adverse condition

Transcriptome signature of the adult mouse choroid plexus

<p>Abstract</p> <p>Background</p> <p>Although the gene expression profile of several tissues in humans and in rodent animal models has been explored, analysis of the complete choroid plexus (CP) transcriptome is still lacking. A better characterization of the CP transcriptome can provide key insights into its functions as one of the barriers that separate the brain from the periphery and in the production of cerebrospinal fluid.</p> <p>Methods</p> <p>This work extends further what is known about the mouse CP transcriptome through a microarray analysis of CP tissue from normal mice under physiological conditions.</p> <p>Results</p> <p>We found that the genes most highly expressed are those implicated in energy metabolism (oxidative phosphorylation, glycolysis/gluconeogenesis) and in ribosomal function, which is in agreement with the secretory nature of the CP. On the other hand, genes encoding for immune mediators are among those with lower expression in basal conditions. In addition, we found genes known to be relevant during brain development, and not previously identified to be expressed in the CP, including those encoding for various axonal guidance and angiogenesis molecules and for growth factors. Some of these are known to influence the neural stem cell niche in the subventricular zone, highlighting the involvement of the CP as a likely modulator of neurogenesis. Interestingly, our observations confirm that the CP transcriptome is unique, displaying low homology with that of other tissues. Of note, we describe here that the closest similarity is with the transcriptome of the endothelial cells of the blood-brain barrier.</p> <p>Conclusions</p> <p>Based on the data presented here, it will now be possible to further explore the function of particular proteins of the CP secretome in health and in disease.</p

GeoHealth: A Georeferenced System for Health Data Analysis in Primary Care

The Primary Care Information System (SIAB) concentrates basic healthcare information from all different regions of Brazil. The information is collected by primary care teams on a paper-based procedure that degrades the quality of information provided to the healthcare authorities and slows down the process of decision making. To overcome these problems we propose a new data gathering application that uses a mobile device connected to a 3G network and a GPS to be used by the primary care teams for collecting the families' data. A prototype was developed in which a digital version of one SIAB form is made available at the mobile device. The prototype was tested in a basic healthcare unit located in a suburb of Sao Paulo. The results obtained so far have shown that the proposed process is a better alternative for data collecting at primary care, both in terms of data quality and lower deployment time to health care authorities

Simcluster: clustering enumeration gene expression data on the simplex space

Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST &#x22;digital northern&#x22;, are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space.&#xd;&#xa;&#xd;&#xa;Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster.&#xd;&#xa;&#xd;&#xa;Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data