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David S Johnson,
Wei Li,
D Benjamin Gordon,
Arindam Bhattacharjee,
Bo Curry,
Jayati Ghosh,
Leonardo Brizuela,
Jason S Carroll,
Myles Brown,
Paul Flicek, [......],
Annie Yang,
Zarmik Moqtaderi,
Heather Hirsch,
Hennady P Shulha,
Yutao Fu,
Zhiping Weng,
Kevin Struhl,
Richard M Myers,
Jason D Lieb,
X Shirley Liu
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ABSTRACT: The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols, and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated.
Genome Research 04/2008; 18(3):393-403. · 13.61 Impact Factor
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Ivo Meinhold-Heerlein,
Dirk Bauerschlag,
Yingyao Zhou,
Lisa M Sapinoso, Keith Ching,
Henry Frierson,
Karen Bräutigam,
Jalid Sehouli,
Elmar Stickeler,
Dominique Könsgen,
Felix Hilpert,
Constantin S von Kaisenberg,
Jacobus Pfisterer,
Thomas Bauknecht,
Walter Jonat,
Norbert Arnold,
Garret M Hampton
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ABSTRACT: Cancer of the ovary confers the worst prognosis among women with gynecologic malignancies, underscoring the need to develop new biomarkers for detection of early disease, particularly those that can be readily monitored in the blood.
We developed an algorithm to identify secreted proteins encoded among approximately 22,500 genes on commercial oligonucleotide arrays and applied it to gene expression profiles of 67 stage I to IV serous papillary carcinomas and 9 crudely enriched normal ovarian tissues, to identify putative diagnostic markers. ELISAs were used to validate increased levels of secreted proteins in patient sera encoded by genes with differentially high expression.
We identified 275 genes predicted to encode secreted proteins with increased/decreased expression in ovarian cancers (<0.5- or >2-fold, P < 0.001). The serum levels of four of these proteins (matrix metalloproteinase-7, osteopontin, secretory leukoprotease inhibitor, and kallikrein 10) were significantly elevated in a series of 67 independent patients with serous ovarian carcinomas compared with 67 healthy controls (P < 0.001, Wilcoxon rank sum test). Optimized support vector machine classifiers with as few as two of these markers (osteopontin or kallikrein 10/matrix metalloproteinase-7) in combination with CA-125 yielded sensitivity and specificity values ranging from 96% to 98.7% and 99.7% to 100%, respectively, with the ability to discern early-stage disease from normal, healthy controls.
Our data suggest that this assay combination warrants further investigation as a multi-analyte diagnostic test for serous ovarian adenocarcinoma.
Clinical Cancer Research 02/2007; 13(2 Pt 1):458-66. · 7.74 Impact Factor
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ABSTRACT: Half of patients treated for locally advanced bladder cancer relapse with often fatal metastatic disease to the lung. We have recently shown that reduced expression of the GDP dissociation inhibitor, RhoGDI2, is associated with decreased survival of patients with advanced bladder cancer. However, the effectors by which RhoGDI2 affects metastasis are unknown. Here we use DNA microarrays to identify genes suppressed by RhoGDI2 reconstitution in lung metastatic bladder cancer cell lines. We identify such RNAs and focus only on those that also increase with tumor stage in human bladder cancer samples to discover only clinically relevant targets of RhoGDI2. Levels of endothelin-1 (ET-1), a potent vasoconstrictor, were affected by both RhoGDI2 reconstitution and tumor stage. To test the hypothesis that the endothelin axis is important in lung metastasis, lung metastatic bladder carcinoma cells were injected in mice treated with the endothelin receptor-specific antagonist, atrasentan, thereby blocking engagement of the up-regulated ET-1 ligand with its cognate receptor. Endothelin antagonism resulted in a dramatic reduction of lung metastases, similar to the effect of reexpressing RhoGDI2 in these metastatic cells. Taken together, these experiments show a novel approach of identifying therapeutic targets downstream of metastasis suppressor genes. The data also suggest that blockade of the ET-1 axis may prevent lung metastasis, a new therapeutic concept that warrants clinical evaluation.
Cancer Research 09/2005; 65(16):7320-7. · 7.86 Impact Factor
