Literature DB >> 15744349

Purity for clarity: the need for purification of tumor cells in DNA microarray studies.

D de Ridder1, C E van der Linden, T Schonewille, W A Dik, M J T Reinders, J J M van Dongen, F J T Staal.   

Abstract

It is now well established that gene expression profiling using DNA microarrays can provide novel information about various types of hematological malignancies, which may lead to identification of novel diagnostic markers. However, to successfully use microarrays for this purpose, the quality and reproducibility of the procedure need to be guaranteed. The quality of microarray analyses may be severely reduced, if variable frequencies of nontarget cells are present in the starting material. To systematically investigate the influence of different types of impurity, we determined gene expression profiles of leukemic samples containing different percentages of nonleukemic leukocytes. Furthermore, we used computer simulations to study the effect of different kinds of impurity as an alternative to conducting hundreds of microarray experiments on samples with various levels of purity. As expected, the percentage of erroneously identified genes rose with the increase of contaminating nontarget cells in the samples. The simulations demonstrated that a tumor load of less than 75% can lead to up to 25% erroneously identified genes. A tumor load of at least 90% leads to identification of at most 5% false-positive genes. We therefore propose that in order to draw well-founded conclusions, the percentage of target cells in microarray experiment samples should be at least 90%.

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Year:  2005        PMID: 15744349     DOI: 10.1038/sj.leu.2403685

Source DB:  PubMed          Journal:  Leukemia        ISSN: 0887-6924            Impact factor:   11.528


  16 in total

1.  Hierarchical paracrine interaction of breast cancer associated fibroblasts with cancer cells via hMAPK-microRNAs to drive ER-negative breast cancer phenotype.

Authors:  Sanket H Shah; Philip Miller; Marta Garcia-Contreras; Zheng Ao; Leah Machlin; Emilio Issa; Dorraya El-Ashry
Journal:  Cancer Biol Ther       Date:  2015-07-17       Impact factor: 4.742

2.  Highly efficient and selective isolation of rare tumor cells using a microfluidic chip with wavy-herringbone micro-patterned surfaces.

Authors:  Shunqiang Wang; Antony Thomas; Elaine Lee; Shu Yang; Xuanhong Cheng; Yaling Liu
Journal:  Analyst       Date:  2016-04-07       Impact factor: 4.616

Review 3.  Computational deconvolution: extracting cell type-specific information from heterogeneous samples.

Authors:  Shai S Shen-Orr; Renaud Gaujoux
Journal:  Curr Opin Immunol       Date:  2013-10-19       Impact factor: 7.486

4.  Routine use of microarray-based gene expression profiling to identify patients with low cytogenetic risk acute myeloid leukemia: accurate results can be obtained even with suboptimal samples.

Authors:  Diane Raingeard de la Blétière; Odile Blanchet; Pascale Cornillet-Lefèbvre; Anne Coutolleau; Laurence Baranger; Franck Geneviève; Isabelle Luquet; Mathilde Hunault-Berger; Annaelle Beucher; Aline Schmidt-Tanguy; Marc Zandecki; Yves Delneste; Norbert Ifrah; Philippe Guardiola
Journal:  BMC Med Genomics       Date:  2012-01-30       Impact factor: 3.063

5.  ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles.

Authors:  Catalina V Anghel; Gerald Quon; Syed Haider; Francis Nguyen; Amit G Deshwar; Quaid D Morris; Paul C Boutros
Journal:  BMC Bioinformatics       Date:  2015-05-14       Impact factor: 3.169

6.  Inferring tumour purity and stromal and immune cell admixture from expression data.

Authors:  Kosuke Yoshihara; Maria Shahmoradgoli; Emmanuel Martínez; Rahulsimham Vegesna; Hoon Kim; Wandaliz Torres-Garcia; Victor Treviño; Hui Shen; Peter W Laird; Douglas A Levine; Scott L Carter; Gad Getz; Katherine Stemke-Hale; Gordon B Mills; Roel G W Verhaak
Journal:  Nat Commun       Date:  2013       Impact factor: 14.919

7.  Confounding Factors in the Transcriptome Analysis of an In-Vivo Exposure Experiment.

Authors:  Oskar Bruning; Wendy Rodenburg; Paul F K Wackers; Conny van Oostrom; Martijs J Jonker; Rob J Dekker; Han Rauwerda; Wim A Ensink; Annemieke de Vries; Timo M Breit
Journal:  PLoS One       Date:  2016-01-20       Impact factor: 3.240

8.  Microarray study reveals that HIV-1 induces rapid type-I interferon-dependent p53 mRNA up-regulation in human primary CD4+ T cells.

Authors:  Michaël Imbeault; Michel Ouellet; Michel J Tremblay
Journal:  Retrovirology       Date:  2009-01-15       Impact factor: 4.602

9.  The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies.

Authors:  Roel G W Verhaak; Frank J T Staal; Peter J M Valk; Bob Lowenberg; Marcel J T Reinders; Dick de Ridder
Journal:  BMC Bioinformatics       Date:  2006-03-02       Impact factor: 3.169

10.  Negative selection of chronic lymphocytic leukaemia cells using a bifunctional rosette-based antibody cocktail.

Authors:  Salim Essakali; Dennis Carney; David Westerman; Peter Gambell; John F Seymour; Alexander Dobrovic
Journal:  BMC Biotechnol       Date:  2008-01-29       Impact factor: 2.563

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