Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, United States
Svitlana Tyekucheva
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States; Department of Data Science, Dana-Farber Cancer Institute, Boston, United States
Molin Wang
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, United States
Travis A Gerke
Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, United States
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States
Kathryn L Penney
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, United States
Philip W Kantoff
Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, United States
Stephen P Finn
Department of Pathology, St. James’s Hospital, Dublin, Ireland; Trinity College, Dublin, Ireland
Michelangelo Fiorentino
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States; Pathology Unit, Addarii Institute, S. Orsola-Malpighi Hospital, Bologna, Italy
Massimo Loda
Department of Pathology, Weill Cornell Medical College, New York, United States
Tamara L Lotan
Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, United States
Giovanni Parmigiani
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States
Lorelei A Mucci
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, United States
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 1448 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.