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Fabian Model

Publications and source records attributed to Fabian Model.

5 recordsLinked to original sources
PubMed2006

Aristaless-like homeobox-4 gene methylation is a potential marker for colorectal adenocarcinomas.

BACKGROUND & AIMS: The identification of novel genetic and epigenetic markers indicative of changes in the pathogenesis of colon cancer, along with easier-to-use, more sensitive assay methods, may improve the detection, treatment, and overall prognosis of this malignancy. METHODS: Using methylation-specific arbitrarily primed polymerase chain reaction, a fragment of the Aristaless-like homeobox-4 (ALX4) gene that was highly methylated in colon adenomas and cancer was identified. Methylation of ALX4 was analyzed in colorectal adenomas and cancers, in the liver metastases of patients with colorectal cancer, and in 61 other neoplasias, including gastric, esophageal, and hepatocellular cancer and cholangiocarcinoma. ALX4 methylation was also analyzed in the serum of 30 patients with colon cancer. RESULTS: ALX4 gene methylation was confirmed in colon adenomas (11/13) and more frequently present in primary colorectal cancers (30/47) compared with the normal colon mucosa (0/21) (P < .0001). In addition, ALX4 methylation was frequently observed in adenocarcinomas of the esophagus (12/14), stomach (11/15), and bile ducts (4/5) compared with all other cancers (P < .001). ALX4 gene methylation was also more frequently found in sera of patients with colon cancer compared with noncancer controls (P < .0001). Using a cutoff of 41.4 pg/mL, sensitivity and specificity were 83.3% and 70%, respectively. CONCLUSIONS: Apart from colon adenomas and primary and metastatic colorectal cancers, ALX4 is frequently methylated in adenocarcinomas of the gastrointestinal tract. ALX4 gene methylation in sera of patients with cancer may thus serve as a methylation-specific test for colon and other gastrointestinal cancers.

Adenocarcinoma↗
PubMed2005

Association of DNA methylation of phosphoserine aminotransferase with response to endocrine therapy in patients with recurrent breast cancer.

To understand the biological basis of resistance to endocrine therapy is of utmost importance in patients with steroid hormone receptor-positive breast cancer. Not only will this allow us prediction of therapy success, it may also lead to novel therapies for patients resistant to current endocrine therapy. DNA methylation in the promoter regions of genes is a prominent epigenetic gene silencing mechanism that contributes to breast cancer biology. In the current study, we investigated whether promoter DNA methylation could be associated with resistance to endocrine therapy in patients with recurrent breast cancer. Using a microarray-based technology, the promoter DNA methylation status of 117 candidate genes was studied in a cohort of 200 steroid hormone receptor-positive tumors of patients who received the antiestrogen tamoxifen as first-line treatment for recurrent breast cancer. Of the genes analyzed, the promoter DNA methylation status of 10 genes was significantly associated with clinical outcome of tamoxifen therapy. The association of the promoter hypermethylation of the strongest marker, phosphoserine aminotransferase (PSAT1) with favorable clinical outcome was confirmed by an independent quantitative DNA methylation detection method. Furthermore, the extent of DNA methylation of PSAT1 was inversely associated with its expression at the mRNA level. Finally, also at the mRNA level, PSAT1 was a predictor of tamoxifen therapy response. Concluding, our work indicates that promoter hypermethylation and mRNA expression of PSAT1 are indicators of response to tamoxifen-based endocrine therapy in steroid hormone receptor-positive patients with recurrent breast cancer.

Adult↗
PubMed2002

Tumour class prediction and discovery by microarray-based DNA methylation analysis.

Aberrant DNA methylation of CpG sites is among the earliest and most frequent alterations in cancer. Several studies suggest that aberrant methylation occurs in a tumour type-specific manner. However, large-scale analysis of candidate genes has so far been hampered by the lack of high throughput assays for methylation detection. We have developed the first microarray-based technique which allows genome-wide assessment of selected CpG dinucleotides as well as quantification of methylation at each site. Several hundred CpG sites were screened in 76 samples from four different human tumour types and corresponding healthy controls. Discriminative CpG dinucleotides were identified for different tissue type distinctions and used to predict the tumour class of as yet unknown samples with high accuracy using machine learning techniques. Some CpG dinucleotides correlate with progression to malignancy, whereas others are methylated in a tissue-specific manner independent of malignancy. Our results demonstrate that genome-wide analysis of methylation patterns combined with supervised and unsupervised machine learning techniques constitute a powerful novel tool to classify human cancers.

Algorithms↗
PubMed2002

Statistical process control for large scale microarray experiments.

MOTIVATION: Maintaining and controlling data quality is a key problem in large scale microarray studies. In particular systematic changes in experimental conditions across multiple chips can seriously affect quality and even lead to false biological conclusions. Traditionally the influence of these effects can be minimized only by expensive repeated measurements, because a detailed understanding of all process relevant parameters seems impossible. RESULTS: We introduce a novel method for microarray process control that estimates quality based solely on the distribution of the actual measurements without requiring repeated experiments. A robust version of principle component analysis detects single outlier microarrays and thereby enables the use of techniques from multivariate statistical process control. In particular, the T(2) control chart reliably tracks undesired changes in process relevant parameters. This can be used to improve the microarray process itself, limits necessary repetitions to only affected samples and therefore maintains quality in a cost effective way. We prove the power of the approach on 3 large sets of DNA methylation microarray data.

Algorithms↗