Blocking and randomization to improve molecular biomarker discovery

Li Xuan Qin, Qin Zhou, Faina Bogomolniy, Liliana Villafania, Narciso Olvera, Magali Cavatore, Jaya M. Satagopan, Colin B. Begg, Douglas A. Levine

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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 micro RNA 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.

Original languageEnglish (US)
Pages (from-to)3371-3378
Number of pages8
JournalClinical Cancer Research
Volume20
Issue number13
DOIs
StatePublished - Jul 1 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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