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Biomedical subjects

A von Heydebreck

Publications and source records attributed to A von Heydebreck.

3 recordsLinked to original sources

Prognostic impacts of cytogenetic findings in clear cell renal cell carcinoma: gain of 5q31-qter predicts a distinct clinical phenotype with favorable prognosis.

To evaluate the prognostic significance of cytogenetic findings in clear cell renal cell carcinoma (RCC), cytogenetic results of 118 primary RCCs were evaluated in relation to classical indicators of prognosis and overall survival. Losses in 3p (98.3%) were most prevalent and included 32 (27.6%) monosomies of chromosome 3 and 84 (72.4%) structural aberrations involving 3p, of which 36 were unbalanced translocations, der(3)t(3;5)(p11-p22;q13-q31), resulting in duplication of 5q sequences. Patients with gain of 5q31-qter resulting from either polysomies or structural rearrangements of 5q, the most frequent of which was der(3)t(3;5), had a significantly better outcome than those without this aberration (P = 0.001). There was no association between gain of 5q or der(3)t(3;5) and any of the well-known variables for prognosis, including low versus high clinical stage and grade of malignancy. Among additional chromosomal aberrations, loss of chromosome 9/9p was associated with distant metastasis at diagnosis (P = 0.006). The data indicate that gain of 5q identifies a clinically favorable cytogenetic variant of clear cell RCC and demonstrate the impact of specific chromosome aberrations as additional prognostic indicators in clear cell RCC.

Adenocarcinoma, Clear Cell↗

Identifying splits with clear separation: a new class discovery method for gene expression data.

We present a new class discovery method for microarray gene expression data. Based on a collection of gene expression profiles from different tissue samples, the method searches for binary class distinctions in the set of samples that show clear separation in the expression levels of specific subsets of genes. Several mutually independent class distinctions may be found, which is difficult to obtain from most commonly used clustering algorithms. Each class distinction can be biologically interpreted in terms of its supporting genes. The mathematical characterization of the favored class distinctions is based on statistical concepts. By analyzing three data sets from cancer gene expression studies, we demonstrate that our method is able to detect biologically relevant structures, for example cancer subtypes, in an unsupervised fashion.

Algorithms↗

Identification and classification of differentially expressed genes in renal cell carcinoma by expression profiling on a global human 31,500-element cDNA array.

We investigated the changes in gene expression accompanying the development and progression of kidney cancer by use of 31,500-element complementary DNA arrays. We measured expression profiles for paired neoplastic and noncancerous renal epithelium samples from 37 individuals. Using an experimental design optimized for factoring out technological and biological noise, and an adapted statistical test, we found 1738 differentially expressed cDNAs with an expected number of six false positives. Functional annotation of these genes provided views of the changes in the activities of specific biological pathways in renal cancer. Cell adhesion, signal transduction, and nucleotide metabolism were among the biological processes with a large proportion of genes overexpressed in renal cell carcinoma. Down-regulated pathways in the kidney tumor cells included small molecule transport, ion homeostasis, and oxygen and radical metabolism. Our expression profiling data uncovered gene expression changes shared with other epithelial tumors, as well as a unique signature for renal cell carcinoma. [Expression data for the differentially expressed cDNAs are available as a Web supplement at http://www.dkfz-heidelberg.de/abt0840/whuber/rcc.]

Carcinoma, Renal Cell↗