Biomedical subjects

Patrik Edén

Publications and source records attributed to Patrik Edén.

8 recordsLinked to original sources

PubMed2006

Gene expression profiles relate to SS18/SSX fusion type in synovial sarcoma.

We applied 27k spotted cDNA microarray slides to assess gene expression profiles in 26 samples from 24 patients with synovial sarcomas (SS). The data were analyzed in relation to histopathologic type, cytogenetic aberrations, gene fusion type and development of distant metastases. Supervised analysis based on gene fusion type in 12 SS with SS18/SSX1 and 9 with SS18/SSX2 revealed significant differences in gene expression profiles. Among the discriminators were several genes that have previously been found to be upregulated in SS, including AXL, ZIC2, SPAG7, AGRN, FOXC1, NCAM1 and multiple metallothioneins. Histopathology and degree of cytogenetic complexity did not significantly influence expression, whereas a genetic signature that related to development of metastases could be discerned, albeit with a high false-positive rate. In conclusion, our findings demonstrate differentially expressed genes for the 2 major gene fusion variants in SS, SS18/SSX1 and SS18/SSX2, and thereby suggest that these result in different downstream effects.

Adolescent↗

PubMed2005

Molecular signatures in childhood acute leukemia and their correlations to expression patterns in normal hematopoietic subpopulations.

Global expression profiles of a consecutive series of 121 childhood acute leukemias (87 B lineage acute lymphoblastic leukemias, 11 T cell acute lymphoblastic leukemias, and 23 acute myeloid leukemias), six normal bone marrows, and 10 normal hematopoietic subpopulations of different lineages and maturations were ascertained by using 27K cDNA microarrays. Unsupervised analyses revealed segregation according to lineages and primary genetic changes, i.e., TCF3(E2A)/PBX1, IGH@/MYC, ETV6(TEL)/RUNX1(AML1), 11q23/MLL, and hyperdiploidy (>50 chromosomes). Supervised discriminatory analyses were used to identify differentially expressed genes correlating with lineage and primary genetic change. The gene-expression profiles of normal hematopoietic cells were also studied. By using principal component analyses (PCA), a differentiation axis was exposed, reflecting lineages and maturation stages of normal hematopoietic cells. By applying the three principal components obtained from PCA of the normal cells on the leukemic samples, similarities between malignant and normal cell lineages and maturations were investigated. Apart from showing that leukemias segregate according to lineage and genetic subtype, we provide an extensive study of the genes correlating with primary genetic changes. We also investigated the expression pattern of these genes in normal hematopoietic cells of different lineages and maturations, identifying genes preferentially expressed by the leukemic cells, suggesting an ectopic activation of a large number of genes, likely to reflect regulatory networks of pathogenetic importance that also may provide attractive targets for future directed therapies.

Bone Marrow↗

PubMed2005

Intratumor versus intertumor heterogeneity in gene expression profiles of soft-tissue sarcomas.

Soft-tissue sarcomas (STSs) constitute more than 30 histologic entities. In addition, within each entity, tumors are often heterogeneous in macroscopic features, genetic alterations, microscopic appearance, and clinical course. Therefore, there has been concern about whether a single tumor sample can provide a gene expression profile representative of the entire tumor. We used 27-k cDNA microarray slides to assess the importance of intratumor versus intertumor heterogeneity of the gene expression profiles of 2 morphologically heterogeneous STSs. Multiple pieces of tumor (8 and 10 pieces) were obtained from a myxoid variant of malignant fibrous histiocytoma (MFH) and a leiomyosarcoma (LMS), respectively, and the expression patterns were compared with single tumor samples from 20 MFHs and 16 LMSs. Hierarchical clustering analysis of the expression profiles showed that samples from the same tumor clustered together. The average intratumor distance was considerably shorter than the average intertumor distance in both LMS and MFH. In addition, tumor subclusters that distinguished different macroscopic parts of the tumor could be discerned. We concluded that intratumor variability exists but that accurate gene expression profiling also could be obtained using single samples from a large STS.

Adult↗

PubMed2004

Comparing functional annotation analyses with Catmap.

BACKGROUND: Ranked gene lists from microarray experiments are usually analysed by assigning significance to predefined gene categories, e.g., based on functional annotations. Tools performing such analyses are often restricted to a category score based on a cutoff in the ranked list and a significance calculation based on random gene permutations as null hypothesis. RESULTS: We analysed three publicly available data sets, in each of which samples were divided in two classes and genes ranked according to their correlation to class labels. We developed a program, Catmap (available for download at http://bioinfo.thep.lu.se/Catmap), to compare different scores and null hypotheses in gene category analysis, using Gene Ontology annotations for category definition. When a cutoff-based score was used, results depended strongly on the choice of cutoff, introducing an arbitrariness in the analysis. Comparing results using random gene permutations and random sample permutations, respectively, we found that the assigned significance of a category depended strongly on the choice of null hypothesis. Compared to sample label permutations, gene permutations gave much smaller p-values for large categories with many coexpressed genes. CONCLUSIONS: In gene category analyses of ranked gene lists, a cutoff independent score is preferable. The choice of null hypothesis is very important; random gene permutations does not work well as an approximation to sample label permutations.

Classification↗

PubMed2004

"Good Old" clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers.

We compared the power of gene expression measurements with that of conventional prognostic markers, i.e., clinical, histopathological, and cell biological parameters, for predicting distant metastases in breast cancer patients using both established prognostic indices (e.g., the Nottingham Prognostic Index (NPI)) and novel combinations of conventional markers. We used publicly available data on 97 patients, and the performance of metastasis prediction was represented by receiver operating characteristic (ROC) areas and Kaplan-Meier plots. The gene expression profiler did not perform noticeably better than indices constructed from the clinical variables, e.g., the well established NPI. When analysing separately subgroups, according to the oestrogen receptor (ER) status both approaches could predict clinical outcome more easily for the ER-positive than for the ER-negative cohort. Given the time it may take before microarray processing is used worldwide, particularly due to the costs and the lack of standards, it is important to pursue research using conventional markers. Our analysis suggests that it might be possible to improve the combination of different conventional prognostic markers into one prognostic index.

Adult↗

PubMed2004

Predicting continuous values of prognostic markers in breast cancer from microarray gene expression profiles.

The prognostic and treatment-predictive markers currently in use for breast cancer are commonly based on the protein levels of individual genes (e.g., steroid receptors) or aspects of the tumor phenotype, such as histological grade and percentage of cells in the DNA synthesis phase of the cell cycle. Microarrays have previously been used to classify binary classes in breast cancer such as estrogen receptor (ER)-alpha status. To test whether the properties and specific values of conventional prognostic markers are encoded within tumor gene expression profiles, we have analyzed 48 well-characterized primary tumors from lymph node-negative breast cancer patients using 6728-element cDNA microarrays. In the present study, we used artificial neural networks trained with tumor gene expression data to predict the ER protein values on a continuous scale. Furthermore, we determined a gene expression profile-directed threshold for ER protein level to redefine the cutoff between ER-positive and ER-negative classes that may be more biologically relevant. With a similar approach, we studied the prediction of other prognostic parameters such as percentage cells in the S phase of the cell cycle (SPF), histological grade, DNA ploidy status, and progesterone receptor status. Interestingly, there was a consistent reciprocal relationship in expression levels of the genes important for both ER and SPF prediction. This and similar studies may be used to increase our understanding of the biology underlying these markers as well as to improve the currently available prognostic markers for breast cancer.

Biomarkers↗

PubMed2003

A simple model for the arterial system.

We present a simple model for the arterial part of the cardiovascular system, based on Poiseuille flow constrained by the power dissipated into the cells lining the vessels. This, together with the assumption of a volume-filling network, leads to correct predictions for the evolution of vessel radii, vessel lengths and blood pressure in the human arterial system. The model can also be used to find exponents for allometric scaling, and gives good agreement with data on mammals.

Animals↗

PubMed2002

Expression profiling to predict outcome in breast cancer: the influence of sample selection.

Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-alpha status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-alpha-positive and estrogen receptor-alpha-negative tumors.

Breast Neoplasms↗