Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays

Konrad H. Stopsack, Svitlana Tyekucheva, Molin Wang, Travis A. Gerke, Bailey Vaselkiv, Kathryn L. Penney, Philip W. Kantoff, Stephen P. Finn, Michelangelo Fiorentino, Massimo Loda, Tamara L. Lotan, Giovanni Parmigiani, Lorelei A. Mucci

Research output: Contribution to journalArticlepeer-review


Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1,448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1–48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA.

Original languageEnglish (US)
Article numbere71265
StatePublished - Dec 2021


  • Batch effects
  • Batchtma R package
  • Measurement error
  • Tissue microarray

ASJC Scopus subject areas

  • General Neuroscience
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology


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