Biomedical subjects

Henrik Edgren

Publications and source records attributed to Henrik Edgren.

6 recordsLinked to original sources

PubMed2005

NMD microarray analysis for rapid genome-wide screen of mutated genes in cancer.

Gene mutations play a critical role in cancer development and progression, and their identification offers possibilities for accurate diagnostics and therapeutic targeting. Finding genes undergoing mutations is challenging and slow, even in the post-genomic era. A new approach was recently developed by Noensie and Dietz to prioritize and focus the search, making use of nonsense-mediated mRNA decay (NMD) inhibition and microarray analysis (NMD microarrays) in the identification of transcripts containing nonsense mutations. We combined NMD microarrays with array-based CGH (comparative genomic hybridization) in order to identify inactivation of tumor suppressor genes in cancer. Such a "mutatomics" screening of prostate cancer cell lines led to the identification of inactivating mutations in the EPHB2 gene. Up to 8% of metastatic uncultured prostate cancers also showed mutations of this gene whose loss of function may confer loss of tissue architecture. NMD microarray analysis could turn out to be a powerful research method to identify novel mutated genes in cancer cell lines, providing targets that could then be further investigated for their clinical relevance and therapeutic potential.

Animals↗

PubMed2004

Optimized LOWESS normalization parameter selection for DNA microarray data.

BACKGROUND: Microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS) algorithm. However, a much overlooked concern with the LOWESS normalization strategy deals with choosing the appropriate parameters. Parameters are usually chosen arbitrarily, which may reduce the efficiency of the normalization and result in non-optimally normalized data. Thus, there is a need to explore LOWESS parameter selection in greater detail. RESULTS AND DISCUSSION: In this work, we discuss how to choose parameters for the LOWESS method. Moreover, we present an optimization approach for obtaining the fraction of data points utilized in the local regression and analyze results for local print-tip normalization. The optimization procedure determines the bandwidth parameter for the local regression by minimizing a cost function that represents the mean-squared difference between the LOWESS estimates and the normalization reference level. We demonstrate the utility of the systematic parameter selection using two publicly available data sets. The first data set consists of three self versus self hybridizations, which allow for a quantitative study of the optimization method. The second data set contains a collection of DNA microarray data from a breast cancer study utilizing four breast cancer cell lines. Our results show that different parameter choices for the bandwidth window yield dramatically different calibration results in both studies. CONCLUSIONS: Results derived from the self versus self experiment indicate that the proposed optimization approach is a plausible solution for estimating the LOWESS parameters, while results from the breast cancer experiment show that the optimization procedure is readily applicable to real-life microarray data normalization. In summary, the systematic approach to obtain critical parameters in the LOWESS technique is likely to produce data that optimally meets assumptions made in the data preprocessing step and thereby makes studies utilizing the LOWESS method unambiguous and easier to repeat.

Algorithms↗

PubMed2004

Are data from different gene expression microarray platforms comparable?

Many commercial and custom-made microarray formats are routinely used for large-scale gene expression surveys. Here, we sought to determine the level of concordance between microarray platforms by analyzing breast cancer cell lines with in situ synthesized oligonucleotide arrays (Affymetrix HG-U95v2), commercial cDNA microarrays (Agilent Human 1 cDNA), and custom-made cDNA microarrays from a sequence-validated 13K cDNA library. Gene expression data from the commercial platforms showed good correlations across the experiments (r = 0.78-0.86), whereas the correlations between the custom-made and either of the two commercial platforms were lower (r = 0.62-0.76). Discrepant findings were due to clone errors on the custom-made microarrays, old annotations, or unknown causes. Even within platform, there can be several ways to analyze data that may influence the correlation between platforms. Our results indicate that combining data from different microarray platforms is not straightforward. Variability of the data represents a challenge for developing future diagnostic applications of microarrays.

Cell Line, Tumor↗

PubMed2004

Differentially expressed genes in nonsmall cell lung cancer: expression profiling of cancer-related genes in squamous cell lung cancer.

The expression patterns of cancer-related genes in 13 cases of squamous cell lung cancer (SCC) were characterized and compared with those in normal lung tissue and 13 adenocarcinomas (AC), the other major type of nonsmall cell lung cancer (NSCLC). cDNA array was used to screen the gene expression levels and the array results were verified using a real-time reverse-transcriptase-polymerase chain reaction (RT-PCR). Thirty-nine percent of the 25 most upregulated and the 25 most downregulated genes were common to SCC and AC. Of these genes, DSP, HMGA1 (alias HMGIY), TIMP1, MIF, CCNB1, TN, MMP11, and MMP12 were upregulated and COPEB (alias CPBP), TYROBP, BENE, BMPR2, SOCS3, TIMP3, CAV1, and CAV2 were downregulated. The expression levels of several genes from distinct protein families (cytokeratins and hemidesmosomal proteins) were markedly increased in SCC compared with AC and normal lung. In addition, several genes, overexpressed in SCC, such as HMGA1, CDK4, IGFBP3, MMP9, MMP11, MMP12, and MMP14, fell into distinct chromosomal loci, which we have detected as gained regions on the basis of comparative genomic hybridization data. Our study revealed new candidate genes involved in NSCLC.

Adult↗

PubMed2003

A novel strategy for microarray quality control using Bayesian networks.

MOTIVATION: High-throughput microarray technologies enable measurements of the expression levels of thousands of genes in parallel. However, microarray printing, hybridization and washing may create substantial variability in the quality of the data. As erroneous measurements may have a drastic impact on the results by disturbing the normalization schemes and by introducing expression patterns that lead to incorrect conclusions, it is crucial to discard low quality observations in the early phases of a microarray experiment. A typical microarray experiment consists of tens of thousands of spots on a microarray, making manual extraction of poor quality spots impossible. Thus, there is a need for a reliable and general microarray spot quality control strategy. RESULTS: We suggest a novel strategy for spot quality control by using Bayesian networks, which contain many appealing properties in the spot quality control context. We illustrate how a non-linear least squares based Gaussian fitting procedure can be used in order to extract features for a spot on a microarray. The features we used in this study are: spot intensity, size of the spot, roundness of the spot, alignment error, background intensity, background noise, and bleeding. We conclude that Bayesian networks are a reliable and useful model for microarray spot quality assessment. SUPPLEMENTARY INFORMATION: http://sigwww.cs.tut.fi/TICSP/SpotQuality/.

Algorithms↗

PubMed2003

Abnormal expression of apoptosis-related genes in haematological malignancies: overexpression of MYC is poor prognostic sign in mantle cell lymphoma.

The expression of apoptosis-related genes BCL2, BAX, BCL2L1, BCL2A1, MCL1, DAPK1 and MYC was studied by quantitative real-time polymerase chain reaction on total RNA samples from patients with acute lymphoblastic leukaemia (ALL, n = 16), acute myeloid leukaemia (AML, n = 27), chronic myeloid leukaemia (CML, n = 12), mantle cell lymphoma (MCL, n = 19) and chronic lymphoid leukaemia (CLL, n = 32). BCL2, BAX, BCL2A1, MCL1, DAPK1 and MYC were overexpressed in all patient groups. BCL2L1 was underexpressed in CLL and CML, but not in AML, ALL and MCL. MCL1 levels were significantly higher in CD13 and CD33-positive ALL, and in CD56-positive AML samples. BCL2, BCL2L1, BCL2A1 and MCL1 were overexpressed and DAPK1 was underexpressed in CLL samples with a 11q23 deletion. MYC overexpression was significantly associated with shorter overall survival in MCL (P < 0.01). AML patients with a normal karyotype showed a higher frequency of BCL2A1 overexpression (P < 0.001) than those with an abnormal karyotype.

Adult↗