In chronic-phase chronic myeloid leukemia (CML) patients, the lack of a major cytogenetic response (< 36% Ph ؉ metaphases) to imatinib within 12 months indicates failure and mandates a change of therapy. To identify biomarkers predictive of imatinib failure, we performed gene expression array profiling of CD34 ؉ cells from 2 independent cohorts of imatinib-naive chronic-phase CML patients. The learning set consisted of retrospectively selected patients with a complete cytogenetic response or more than 65% Ph ؉ metaphases within 12 months of imatinib therapy. Based on analysis of variance P less than .1 and fold difference 1.5 or more, we identified 885 probe sets with differential expression between responders and nonresponders, from which we extracted a 75-probe set minimal signature (classifier) that separated the 2 groups. On application to a prospectively accrued validation set, the classifier correctly predicted 88% of responders and 83% of nonresponders. Bioinformatics analysis and comparison with published studies revealed overlap of classifier genes with CML progression signatures and implicated ␤-catenin in their regulation, suggesting that chronic-phase CML patients destined to fail imatinib have more advanced disease than evident by morphologic criteria. Our classifier may allow directing more aggressive therapy upfront to the patients most likely to benefit while sparing good-risk patients from unnecessary toxicity. (Blood. 2010;115:315-325)

Size and shape are critical determinants of the mechanical properties of skeletal elements and can be anticipated to be highly heritable. Moreover, the genes responsible may be independent of those that regulate bone mineral density (BMD). To begin to identify the heritable determinants of skeletal geometry, we have examined femoral cross-sectional area (

Bone mineral density (BMD) is determined by both environmental influences and polygenic inheritance. The extreme difficulty of dissecting out environmental factors from genetic ones in humans has motivated the investigation of animal models. Previously, we used quantitative trait locus (QTL) analysis to examine peak BMD in 24 recombinant inbred (RI) mouse strains, derived from a cross between C57BL/6 (B6) and DBA/2 (D2) progenitors (RI-BXD). The distribution of BMD values among these strains indicated strong genetic influences and a number of chromosomal sites linked to BMD were identified provisionally. Using three additional independent mapping populations derived from the same progenitors, we have confirmed loci on chromosomes 1, 2, and 4, and 11 that contain genes that influence peak BMD.

Background: Peripheral blood is an accessible and informative source of transcriptomal information for many human disease and pharmacogenomic studies. While there can be significant advantages to analyzing RNA isolated from whole blood, particularly in clinical studies, the preparation of samples for microarray analysis is complicated by the need to minimize artifacts associated with highly abundant globin RNA transcripts. The impact of globin RNA transcripts on expression profiling data can potentially be reduced by using RNA preparation and labeling methods that remove or block globin RNA during the microarray assay. We compared four different methods for preparing microarray hybridization targets from human whole blood collected in PAXGene tubes. Three of the methods utilized the Affymetrix one-cycle cDNA synthesis/in vitro transcription protocol but varied treatment of input RNA as follows: i. no treatment; ii. treatment with GLOBINclear; or iii. treatment with globin PNA oligos. In the fourth method cDNA targets were prepared with the Ovation amplification and labeling system.

The quality of gene expression microarray data has improved dramatically since the first arrays were introduced in the late 1990s. However, the reproducibility of data generated at multiple laboratory sites remains a matter of concern, especially for scientists who are attempting to combine and analyze data from public repositories. We have carried out a study in which a common set of RNA samples was assayed five times in four different laboratories using Affymetrix GeneChip arrays. We observed dramatic differences in the results across laboratories and identified batch effects in array processing as one of the primary causes for these differences. When batch processing of samples is confounded with experimental factors of interest it is not possible to separate their effects, and lists of differentially expressed genes may include many artifacts. This study demonstrates the substantial impact of sample processing on microarray analysis results and underscores the need for randomization in the laboratory as a means to avoid confounding of biological factors with procedural effects.