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Search Results: 1 - 10 of 52119 matches for " Carlos Caldas "
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Primeiro Encontro Nacional da ANPTECRE
Carlos Caldas
REVER : Revista de Estudos da Religi?o , 2008,
Da MPB como Fonte para Estudo da Religi o: Análise do Elemento Religioso Presente em “Anuncia o” de Alceu Valen a e “Um índio” de Caetano Veloso
Carlos Caldas
REVER : Revista de Estudos da Religi?o , 2006,
Abstract: The Brazilian tradition of religious studies is characterized by an intensive use of auxiliary tools in the field of the Social Sciences, especially Sociology. Nevertheless, it is possible to use other auxiliary tools in order to study the religious phenomenon or the religious experience. This element has been, by and large, ignored by Brazilian academy. This essay intends to work with Brazilian Popular Music – MPB – as a source to the study of religion. In order to reach this goal, the essay work with Jacques Derrida’s idea of track (trace) of the sacred, as well as Calvin Seerveld’s concept of allusiveness it will do an analysis of the religious element found in Anuncia o, by Alceu Valen a, and Um índio, by Caetano Veloso. This essay starts from the basic presupposition that the religious element found in the cited poems is messianism. Messianism will be analyzed from the Henri Desroche’s theoretical framework perspective.
Bonhoeffer no Brasil – Uma análise do neopentecostalismo brasileiro a partir de uma perspectiva bonhoefferiana
Carlos Caldas
Caminhando , 2008,
Abstract: Este artigo pretende apresentar: 1) a recep o da teologia de Dietrich Bonhoeffer no Brasil; 2) o conceito de gra a conforme entendido por Bonhoeffer como ferramenta crítica para analisar o neopentecostalismo brasileiro. “Discipulado” (Nachfolge), de Bonhoeffer é fonte primária deste artigo.
The breast cancer genome - a key for better oncology
Hans Vollan, Carlos Caldas
BMC Cancer , 2011, DOI: 10.1186/1471-2407-11-501
Abstract: The diversity of breast cancer has been acknowledged for decades, but recent technological advances in molecular biology have given detailed knowledge on how extensive this heterogeneity really is. Traditional classification based on morphology has given limited clinical value; mostly because the majority of breast carcinomas are classified as invasive ductal carcinomas, which show a highly variable response to therapy and outcome [1]. The first molecular sub-classification with a major impact on breast cancer research was proposed by Perou and colleagues where the tumors were subdivided according to their pattern of gene expression [2,3]. Five groups were identified and named Luminal A, Luminal B, Basal-like, Normal-like and the HER-2-enriched subgroups. These intrinsic subgroups have been shown to be different in terms of biology, survival and recurrence rate [3,4]. The molecular subgroups have been extended to also include a sixth subgroup which has been named the claudin- low group, based on its low expression level of tight junction genes (the claudin genes) [5]. Different methods for the assignment of individual tumors to its molecular subgroup is proposed; each based on the expression levels of different sets of genes [4,6,7]. The agreement between methods on how to classify individual tumors are not optimal and how to establish more robust single sample predictors is actively debated [8-11].Aneuploidy is the presence of an abnormal number of parts of or whole chromosomes and is one feature that clearly separates cancer cells from normal cells. This was proposed as being important in cancer nearly a century ago by Theodor Boveri [12]. With array-based comparative genomic hybridization (aCGH) a genome wide profile of the copy number alterations in the tumor can be obtained. These patterns are related to the molecular subtypes with distinct differences in the number of alterations between the subtypes [13-16]. These copy number alterations (CNAs) alter the dosa
José Carlos Caldas
Odonto , 2007,
A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer
Andrew E Teschendorff, Carlos Caldas
Breast Cancer Research , 2008, DOI: 10.1186/bcr2138
Abstract: Building on a previously identified seven-gene prognostic immune response module for ER- breast cancer, we developed a novel statistical tool based on Mixture Discriminant Analysis in order to build a classifier that could accurately identify ER- patients with a good prognosis.We report the construction of a seven-gene expression classifier that accurately predicts, across a training cohort of 183 ER- tumours and six independent test cohorts (a total of 469 ER- tumours), ER- patients of good prognosis (in test sets, average predictive value = 94% [range 85 to 100%], average hazard ratio = 0.15 [range 0.07 to 0.36] p < 0.000001) independently of lymph node status and treatment.This seven-gene classifier could be used in a polymerase chain reaction-based clinical assay to identify ER- patients with a good prognosis, who may therefore benefit from less aggressive treatment regimens.Oestrogen receptor (ER) negative (-) breast cancer accounts for about 30% of all breast cancer cases and generally has a worse prognosis compared with ER positive (+)disease [1,2]. Nevertheless, a significant proportion of ER- cases have shown a favourable outcome and could potentially benefit from a less aggressive course of therapy [3]. Reliable identification of such ER- patients with a good prognosis is, however, difficult and at present only possible through examining histopathological factors.Recently, attempts have been made to explain the observed clinical heterogeneity of ER- disease in terms of gene expression signatures [4-7]. However, most of these studies clearly indicated the difficulty of identifying a prognostic gene expression signature for ER- disease [4,6,7], unlike ER+ breast cancer where a multitude of alternative prognostic signatures have been identified [3,8-11]. Nevertheless, using an integrative analysis of gene expression microarray data from three untreated (no chemotherapy) ER- breast cancer cohorts (a total of 186 patients) [3,8,10] and a novel feature selection
A quantum leap in our knowledge of breast cancer mutations
Carlos Caldas, Paul AW Edwards
Breast Cancer Research , 2006, DOI: 10.1186/bcr1624
Abstract: Although we believe that breast cancer is caused by alterations to genes, we have very limited knowledge of what genes get altered. It only takes a moment's thought to realise how big a limitation this is: with a catalogue of gene alterations we could classify breast tumours, probably predict their response to therapy, and design new targeted drugs. We would also begin to understand the cell biology of breast cancer – what distinguishes a benign from a malignant breast tumour, which signalling pathways are disturbed, and so on.Sj?blom and colleagues [1] determined the sequence of all genes present on the consensus coding sequences database (CCDS) in two common human cancers, breast and colorectal. The work was divided into a discovery screen and a validation screen. In the discovery screen the sequences of exons and exon/intron boundaries of 14,661 transcripts corresponding to 13,023 genes in CCDS were determined in 11 breast cancer cell lines, 11 colorectal cancer xenografts and 2 normal samples. This represented a total of 456 megabases of sequence data, corresponding to 91% of targeted bases in CCDS. Sorting out this massive amount of data to discover 'real' somatic mutations and discard the remaining changes (which, besides some 'noise', represent a treasure trove of valuable data, for example of germline genetic variation) was no simple task, both experimentally and computationally. In total 816,986 putative nucleotide changes were found, which then had to be filtered; 557,029 of these were non-synonymous and were taken forward. The exclusion of false positive calls and changes present in either of the two normal controls or in single-nucleotide polymorphism databases removed a further 96%. Resequencing removed 9,295 more. The 19,986 'real' nucleotide changes were sequenced in matched normal DNA from the patients, showing that 18,414 were present in the germline as unknown polymorphisms and leaving (after a further filtering step) a final tally of 1,307 confirm
The breast cancer somatic 'muta-ome': tackling the complexity
Andrew E Teschendorff, Carlos Caldas
Breast Cancer Research , 2009, DOI: 10.1186/bcr2236
Abstract: The most prominent feature in the breast cancer copy-number muta-ome is amplification of the HER2 locus, present in about 10% to 15% of all breast tumours. It is remarkable that since the discovery of this amplification no further ERBB2-like oncogene has been conclusively identified. Although two recent large-scale (145 and 171 tumours) genome-wide profiling studies combining high-resolution copy-number and matched gene expression data have confirmed candidate oncogenes in well-known regions of recurrent amplification (notably, 8p12, 8q24, 11q13-14, 17q21-24, and 20q13), none of these appears to be as frequently amplified as ERBB2 and they rarely exhibit amplification profiles that clearly point at a specific genomic location or target [1,2]. Instead, the amplification profiles are complex and multi-modal, suggesting that multiple targets may coexist within these regions. This identification problem is compounded by the fact that a relatively high proportion of variation at the gene expression level (approximately 20%) is driven by copy-number changes; thus, focusing on regions of expression bias that are driven by underlying amplifications (so-called 'hotspots') still leaves an unmanageably large number of targets. Nevertheless, by focusing within these hotspots on genes that are also druggable, Chin and colleagues [1] prioritised a smaller set of eight targets, FGFR1, IKBKB, PROSC, ADAM9, FNTA, ACACA, PNMT, and NR1D1, including ERBB2. Confirming the robustness of these findings, all of these were also found to reside in amplification hotspots in an independent breast cancer cohort [2] (A.E. Teschendorff and C. Caldas, unpublished data). In spite of this agreement, the two studies were discordant when hotspots were associated with clinical outcome, mirroring the disagreements of initial gene expression studies. Thus, whereas in [1] associations with survival and recurrence were restricted to the amplicons on 8p11-12 and 17q11-12, in [2] outcome-associated amplicons
Does massively parallel transcriptome analysis signify the end of cancer histopathology as we know it?
Samuel AJR Aparicio, Carlos Caldas, Bruce Ponder
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-3-reviews1021
Abstract: The traditional way of classifying tumors is by histopathology, the staining and analysis of tissue samples. Now, the ability to analyse changes in the levels of the transcripts and/or protein products for literally thousands of genes promises interesting possibilities as a research tool - for understanding the underlying molecular mechanisms, but also for automated tissue diagnosis. Such approaches to biology and medicine have been termed 'massively parallel analysis'. Although the technologies which permit massively parallel analysis of the transcriptome (the transcribed fraction of genes in a genome) or the proteome (the expressed fraction of genes in a genome) are still in a phase of rapid evolution, the first studies applying these techniques and addressing the most obvious initial questions are now being published.A key question in assessing the utility of these techniques is whether sufficiently dense and accurate sampling of gene expression in any given tissue would allow objective molecular classification of that tissue. If this were to prove possible, then objective and automated diagnosis within an intact tissue would become a realistic possibility. A potentially formidable obstacle to reaching this goal is that tissues are multicellular by definition, and they therefore contain cells in different states and in varying quantities. It is widely assumed that in order to obtain meaningful data, it would be necessary physically to separate different cell populations in a given tissue sample, before undertaking expression analysis. Another potential concern, specific to studies of cancer, is that genetic heterogeneity between tumor cells with unstable genomes would lead to heterogeneous and uninterpretable expression data. In a recent article published in Nature [1], the groups of Botstein and Brown show that, at least in the case of advanced breast tumors, not only does each tumor have a unique transcriptome signature but sub-classification of tumor types is
Prognostic gene network modules in breast cancer hold promise
Andrew E Teschendorff, Yan Jiao, Carlos Caldas
Breast Cancer Research , 2010, DOI: 10.1186/bcr2774
Abstract: An outstanding problem in the clinical management of breast cancer is overtreatment. It is estimated that approximately 55 to 75% of breast cancer patients who receive adjuvant chemotherapy would do equally well without it [1], but identifying this low-risk population with a high enough predictive value (≥ 90%) is not possible using standard prognostic factors such as lymph node status or tumour size. Several recently developed gene expression classifiers have shown promise of achieving the required predictive values.One such classifer is Oncotype DX, a prognostic test based on the expression levels of 21 genes, which has been shown to identify low-risk patients with an accuracy of at least 90%, but is restricted to lymph node-negative oestrogen receptor-positive (ER+) breast cancer [2]. Another classifier is the 7 gene immune response (IR) module, which allows identification of low-risk patients in oestrogen receptor-negative (ER-) breast cancer [3]. Both of these signatures appear to be robust, demonstrating a high predictive value across many different breast cancer cohorts [2,3]. Gene Ontology (GO) analyses of prognostic signatures [2-6] have shown that specific biological processes play particularly important roles and that this is subgroup-specific. Thus, while cell-proliferation is strongly prognostic in ER+ breast cancer [6], the clinical heterogeneity of ER- breast cancers appears to be explained mainly by differential expression of genes related to immune response pathways, highlighting the need to conduct survival analysis within specific breast cancer subgroups [7-10].In line with this, Li and colleagues [11] have recently conducted a novel bioinformatic analysis of existing breast cancer expression data sets in order to identify gene expression modules that may predict patients at low risk of distant metastasis in specific breast cancer sub-groups. A common difficulty in identifying robust prognostic gene signatures is the presence of noise and spurious
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