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

Association between community noise and children's cognitive and behavioral development: a prospective cohort study

BACKGROUND: Noise exposure has been associated with adverse cognitive and behavioral outcomes in children, but evidence on longitudinal associations between community noise and child development in low- and middle-income countries is rare. We investigated associations between community noise and behavioral and cognitive development in preschool children in Sao Paulo. METHODS: We linked child development data from the Sao Paulo Western Region Birth Cohort with average (Lden) and night-time (Lnight) community noise exposure at children's home, estimated by means of a land use regression model using various predictors (roads, schools, greenness, residential and informal settlements). Outcomes were the Strengths and Difficulties Questionnaire (SDQ) and Regional Project on Child Development Indicators (PRIDI) at 3 years of age and the Child Behavior Checklist (CBCL) and International Development and Early Learning Assessment (IDELA) at 6 years of age. We investigated the relationship between noise exposure and development using cross-sectional and longitudinal regression models. RESULTS: Data from 3385 children at 3 years of age and 1546 children at 6 years of age were analysed. Mean Lden and Lnight levels were 70.3 dB and 61.2 dB, respectively. In cross-sectional analyses a 10 dB increase of Lden above 70 dB was associated with a 32% increase in the odds of borderline or abnormal SDQ total difficulties score (OR = 1.32, 95% CI: 1.04; 1.68) and 0.72 standard deviation (SD) increase in the CBCL total problems z-score (95% CI: 0.55; 0.88). No cross-sectional association was found for cognitive development. In longitudinal analyses, each 10 dB increase was associated with a 0.52 SD increase in behavioral problems (95% CI: 0.28; 0.77) and a 0.27 SD decrease in cognition (95%-CI: 0.55; 0.00). Results for Lnight above 60 dB were similar. DISCUSSION: Our findings suggest that community noise exposure above Lden of 70 dB and Lnight of 60 dB may impair behavioral and cognitive development of preschool children

Land use regression modelling of community noise in São Paulo, Brazil

Noise pollution has negative health consequences, which becomes increasingly relevant with rapid urbanization. In low- and middle-income countries research on health effects of noise is hampered by scarce exposure data and noise maps. In this study, we developed land use regression (LUR) models to assess spatial variability of community noise in the Western Region of Sao Paulo, Brazil.We measured outdoor noise levels continuously at 42 homes once or twice during one week in the summer and the winter season. These measurements were integrated with various geographic information system variables to develop LUR models for predicting average A-weighted (dB(A)) day-evening-night equivalent sound levels (Lden) and night sound levels (Lnight). A supervised mixed linear regression analysis was conducted to test potential noise predictors for various buffer sizes and distances between home and noise source.Noise exposure levels in the study area were high with a site average Lden of 69.3 dB(A) ranging from 60.3 to 82.3 dB(A), and a site average Lnight of 59.9 dB(A) ranging from 50.7 to 76.6 dB(A). LUR models had a good fit with a R(2) of 0.56 for Lden and 0.63 for Lnight in a leave-one-site-out cross validation. Main predictors of noise were the inverse distance to medium roads, count of educational facilities within a 400 m buffer, mean Normalized Difference Vegetation Index (NDVI) within a 100 m buffer, residential areas within a 50 m (Lden) or 25 m (Lnight) buffer and slum areas within a 400 m buffer. Our study suggests that LUR modelling with geographic predictor data is a promising and efficient approach for noise exposure assessment in low- and middle-income countries, where noise maps are not available

A feature selection approach for identification of signature genes from SAGE data

<p>Abstract</p> <p>Background</p> <p>One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements.</p> <p>Results</p> <p>A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology.</p> <p>Conclusion</p> <p>The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS) or the recent Sequencing-By-Synthesis (SBS) technique. Some of such genes identified by the proposed method may be useful to generate classifiers.</p

Land use regression modelling of NO2 in São Paulo, Brazil

BACKGROUND: Air pollution is a major global public health problem. The situation is most severe in low- and middle-income countries, where pollution control measures and monitoring systems are largely lacking. Data to quantify the exposure to air pollution in low-income settings are scarce. METHODS: In this study, land use regression models (LUR) were developed to predict the outdoor nitrogen dioxide (NO2) concentration in the study area of the Western Region Birth Cohort in Sao Paulo. NO2 measurements were performed for one week in winter and summer at eighty locations. Additionally, weekly measurements at one regional background location were performed over a full one-year period to create an annual prediction. RESULTS: Three LUR models were developed (annual, summer, winter) by using a supervised stepwise linear regression method. The winter, summer and annual models explained 52 %, 75 % and 66 % of the variance (R(2)) respectively. Cross-holdout validation tests suggest robust models. NO2 levels ranged from 43.2 mug/m(3) to 93.4 mug/m(3) in the winter and between 28.1 mug/m(3) and 72.8 mug/m(3) in summer. Based on our annual prediction, about 67 % of the population living in the study area is exposed to NO2 values over the WHO suggested annual guideline of 40 mug/m(3) annual average. CONCLUSION: In this study we were able to develop robust models to predict NO2 residential exposure. We could show that average measures, and therefore the predictions of NO2, in such a complex urban area are substantially high and that a major variability within the area and especially within the season is present. These findings also suggest that in general a high proportion of the population is exposed to high NO2 levels