Precision profiling and components of variability analysis for affymetrix microarray assays run in a clinical context

被引:13
作者
Daly, TM [1 ]
Dumaual, CM [1 ]
Dotson, CA [1 ]
Farmen, MW [1 ]
Kadam, SK [1 ]
Hockett, RD [1 ]
机构
[1] Eli Lilly & Co, Lilly Corp Ctr, Genom Med Grp, Div Expt Med, Indianapolis, IN 46240 USA
关键词
D O I
10.1016/S1525-1578(10)60570-3
中图分类号
R36 [病理学];
学科分类号
100104 ;
摘要
Although gene expression profiling using microarray technology is widely used in research environments, adoption of microarray testing in clinical laboratories is currently limited. in an attempt to determine how such assays would perform in a clinical laboratory, we evaluated the analytical variability of Affymetrix microarray probesets using two generations of human Affymetrix chips (U95Av2 and U133A). The study was designed to mimic potential clinical applications by using multiple operators, machines, and reagent lots, and by performing analyses throughout a period of several months. A mixed model analysis was used to evaluate the relative contributions of multiple factors to overall variability, including operator, instrument, run, cRNA/cDNA synthesis, and changes in reagent lots. Under these conditions, the average probeset coefficient of variation (CV) was relatively low for present probesets on both generations of chips (mean coefficient of variation, 21.9% and 27.2% for U95Av2 and U133A chips, respectively). The largest contribution to overall variation was chip-to-chip (residual) variability, which was responsible for between 40 to 60% of the total variability observed. Changes in Individual reagent lots and instrumentation contributed very little to the overall variability. We conclude that the approach demonstrated here could be applied to clinical validation of Affymetrix-based assays and that the analytical precision of this technique is sufficient to answer many biological questions.
引用
收藏
页码:404 / 412
页数:9
相关论文
共 17 条
[11]  
Littell RC., 1996, SAS SYSTEM MIXED MOD
[12]   Expression monitoring by hybridization to high-density oligonucleotide arrays [J].
Lockhart, DJ ;
Dong, HL ;
Byrne, MC ;
Follettie, MT ;
Gallo, MV ;
Chee, MS ;
Mittmann, M ;
Wang, CW ;
Kobayashi, M ;
Horton, H ;
Brown, EL .
NATURE BIOTECHNOLOGY, 1996, 14 (13) :1675-1680
[13]   Characterization of variability in large-scale gene expression data: Implications for study design [J].
Novak, JP ;
Sladek, R ;
Hudson, TJ .
GENOMICS, 2002, 79 (01) :104-113
[14]   The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma [J].
Rosenwald, A ;
Wright, G ;
Chan, WC ;
Connors, JM ;
Campo, E ;
Fisher, RI ;
Gascoyne, RD ;
Muller-Hermelink, HK ;
Smeland, EB ;
Staudt, LM .
NEW ENGLAND JOURNAL OF MEDICINE, 2002, 346 (25) :1937-1947
[15]   Molecular diagnosis of primary mediastinal B cell lymphoma identifies a clinically favorable subgroup of diffuse large B cell lymphoma related to Hodgkin lymphoma [J].
Rosenwald, A ;
Wright, G ;
Leroy, K ;
Yu, X ;
Gaulard, P ;
Gascoyne, RD ;
Chan, WC ;
Zhao, T ;
Haioun, C ;
Greiner, TC ;
Weisenburger, DD ;
Lynch, JC ;
Vose, J ;
Armitage, JO ;
Smeland, EB ;
Kvaloy, S ;
Holte, H ;
Delabie, J ;
Campo, E ;
Montserrat, E ;
Lopez-Guillermo, A ;
Ott, G ;
Muller-Hermelink, HK ;
Connors, JM ;
Braziel, R ;
Grogan, TM ;
Fisher, RI ;
Miller, TP ;
LeBlanc, M ;
Chiorazzi, M ;
Zhao, H ;
Yang, LM ;
Powell, J ;
Wilson, WH ;
Jaffe, ES ;
Simon, R ;
Klausner, RD ;
Staudt, LM .
JOURNAL OF EXPERIMENTAL MEDICINE, 2003, 198 (06) :851-862
[16]  
Schultz RM, 1999, SEMIN ONCOL, V26, P68
[17]   Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations [J].
Shippy, R ;
Sendera, TJ ;
Lockner, R ;
Palaniappan, C ;
Kaysser-Kranich, T ;
Watts, G ;
Alsobrook, J .
BMC GENOMICS, 2004, 5 (1)