Blocking and randomization to improve molecular biomarker discovery Journal Article

Authors: Qin, L. X.; Zhou, Q.; Bogomolniy, F.; Villafania, L.; Olvera, N.; Cavatore, M.; Satagopan, J. M.; Begg, C. B.; Levine, D. A.
Article Title: Blocking and randomization to improve molecular biomarker discovery
Abstract: Randomization and blocking have the potential to prevent the negative impacts of nonbiologic effects on molecular biomarker discovery. Their use in practice, however, has been scarce. To demonstrate the logistic feasibility and scientific benefits of randomization and blocking, we conducted a microRNA study of endometrial tumors (n = 96) and ovarian tumors (n 96) using a blocked randomization design to control for nonbiologic effects; we profiled the same set of tumors for a second time using no blocking or randomization. We assessed empirical evidence of differential expression in the two studies. We performed simulations through virtual rehybridizations to further evaluate the effects of blocking and randomization. There was moderate and asymmetric differential expression (351/3,523, 10%) between endometrial and ovarian tumors in the randomized dataset. Nonbiologic effects were observed in the nonrandomized dataset, and 1,934 markers (55%) were called differentially expressed. Among them, 185 were deemed differentially expressed (185/351, 53%) and 1,749 not differentially expressed (1,749/3,172, 55%) in the randomized dataset. In simulations, when randomization was applied to all samples at once or within batches of samples balanced in tumor groups, blocking improved the true-positive rate from 0.95 to 0.97 and the false-positive rate from 0.02 to 0.002; when sample batches were unbalanced, randomization was associated with the true-positive rate (0.92) and the false-positive rate (0.10) regardless of blocking. Normalization improved the detection of true-positive markers but still retained sizeable false-positive markers. Randomization and blocking should be used in practice to more fully reap the benefits of genomics technologies. Clin Cancer Res; 20(13); 3371-8. (C) 2014 AACR.
Keywords: rna; targets; breast-cancer; feedback loop; statistical design
Journal Title: Clinical Cancer Research
Volume: 20
Issue: 13
ISSN: 1078-0432
Publisher: American Association for Cancer Research  
Date Published: 2014-07-01
Start Page: 3371
End Page: 3378
Language: English
ACCESSION: WOS:000338920100003
DOI: 10.1158/1078-0432.ccr-13-3155
PMCID: PMC4079727
PUBMED: 24788100
Notes: Article -- Source: Wos
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MSK Authors
  1. Colin B Begg
    234 Begg
  2. Jaya M Satagopan
    137 Satagopan
  3. Douglas A Levine
    356 Levine
  4. Qin Zhou
    120 Zhou
  5. Li-Xuan Qin
    114 Qin
  6. Narciso Olvera
    71 Olvera