Biomedical subjects

A von Heydebreck

Publications and source records attributed to A von Heydebreck.

6 recordsLinked to original sources

PubMed2007

An oncogenetic tree model in gastrointestinal stromal tumours (GISTs) identifies different pathways of cytogenetic evolution with prognostic implications.

To model the cytogenetic evolution in gastrointestinal stromal tumour (GIST), an oncogenetic tree model was reconstructed using comparative genomic hybridization data from 203 primary GISTs (116 gastric and 87 intestinal GISTs, including 151 newly analysed cases), with follow-up available in 173 cases (mean 40 months; maximum 133 months). The oncogenetic tree model identified three major cytogenetic pathways: one initiated by -14q, one by -1p, and another by -22q. The -14q pathway mainly characterized gastric tumours with predominantly stable karyotypes and more favourable clinical course. On the other hand, the -1p pathway was more characteristic of intestinal GISTs, with an increased capacity for cytogenetic complexity and more aggressive clinical course. Loss of 22q, more closely associated with -1p than -14q, appeared to initiate the critical transition to an unfavourable cytogenetic subpathway. This -22q pathway included accumulation of +8q, -9p, and -9q, which could all predict disease-free survival in addition to tumour site. Thus, insights into the cytogenetic evolution obtained from oncogenetic tree models may eventually help to gain a better understanding of the heterogeneous site-dependent biological behaviour of GISTs.

Chromosome Aberrations↗

PubMed2003

Mathematical tree models for cytogenetic development in solid tumors.

We present a new approach for modeling the occurrence of genetic changes in human tumors over time. In solid tumors, data on genetic alterations are usually only available at a single point in time, allowing no direct insight into the sequential order of genetic events. In our approach, genetic tumor development and progression is assumed to follow a probabilistic tree model. We use maximum likelihood estimation to reconstruct a tree model for the genetic evolution of a given tumor type. The use of the proposed method is illustrated by an application to cytogenetic data from 173 cases of clear cell renal cell carcinoma, which results in a model for the karyotypic evolution of this tumor.

Carcinoma, Renal Cell↗

PubMed2002

Transcription profiling of renal cell carcinoma.

AIMS: Our aim was to prepare a comprehensive catalogue of the changes in gene expression accompanying the development and progression of renal cell carcinoma, and to correlate these with histo-pathological, cytogenetic and clinical findings. METHODS: mRNA samples from paired neoplastic and non-cancerous human kidney tissue were labeled and hybridized in duplicate against high-density cDNA arrays. Two array technologies were used: 31,500-element transcriptome-wide nylon arrays for hybridization with 37 radioactively labelled sample pairs, and 4200-element kidney- and cancer-specific glass microarrays for hybridization with 19 fluorescently labelled sample pairs. RESULTS: We identified more than 1700 cDNA clones that show differential transcription levels in kidney tumor tissue compared to normal kidney tissue. The functional classification of 389 annotated genes provided views of the changes in the activities of specific biological processes in renal cancer. Among the biological processes with a large proportion of up-regulated genes we found cell adhesion, signal transduction, and nucleotide metabolism. Down-regulated processes included small molecule transport, ion homeostasis, and oxygen and radical metabolism. Furthermore, we explored the feasibility of molecular diagnosis for renal cell tumors using cDNA microarrays on glass slides, investigating the association of transcription levels with tumor type, progression, and a putative prognostic variable. The experimental data is available from the GEO gene expression database (http://www.ncbi.nlm.nih.gov/geo; accession no. GSE3), and a comprehensive presentation of the results is available in the web supplement (http://www.dkfz-heidelberg.de/abt0840/whuber/rcc). CONCLUSION: Transcription profiling using high-density cDNA arrays is a powerful method with the potential to improve cancer diagnosis and prognosis. The identification and classification of differentially transcribed genes, as described in our study, is the beginning of a more complete understanding of kidney cancer.

Carcinoma, Renal Cell↗

PubMed2001

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↗

PubMed2001

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↗

PubMed2001

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↗