Abstract 893: Batch effects in tumor biomarker studies using tissue microarrays: Extent, impact, and remediation

Abstract

Abstract Introduction: Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. It is unknown to what extent TMAs are affected by batch effects, i.e., measurement error in biomarker levels between batches (slides from TMAs), what impact batch effects have on scientific inference from TMAs, and how to correct for batch effects in the analysis phase. Methods: We leveraged 14 prostate cancer TMAs from the tumors of 1,464 men, prospectively diagnosed 1982-2009 and followed for survival through 2017. Twenty protein biomarkers were measured, using eye-scoring or computational scoring of a median of 3 cores per tumor. We quantified the maximal extent of potential batch effects (between-TMA variance) using intraclass correlation coefficients. We developed a free R package (batchtma) and implemented several methods for batch effect correction, focusing on batch means (simple means, marginal standardization, inverse probability weighting), mean and variance (ComBat), background and dynamic range (quantile regression), ranks (quantile normalization), and stratification-based approaches. Some methods attempted to retain biomarker differences from differing clinical and pathological characteristics of TMAs. We evaluated the performance of correction methods using plasmode simulation and quantified the impact of batch effect correction on common longitudinal biomarker analyses. Results: In 10 of the 20 biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1 to 47%). We observed differences in clinical and pathological characteristics between TMAs with secular changes; for example, between 11% and 32% of tumors per TMA were high-grade (Gleason 8-10). In parallel, we confirmed batch effects between multiple cores from the same tumors included on >1 TMA and between tumors with similar characteristics across TMAs. Batch effect-corrected biomarker levels were more similar between different correction methods compared to uncorrected values. For biomarkers with little between-TMA differences, associations of biomarker levels and survival outcomes were similar with and without batch effect correction. For biomarkers with higher between-TMA differences, Cox regression results after batch effect correction were substantially different from results based on uncorrected values, with up to twofold differences in hazard ratios. In plasmode simulation, all correction methods were better than no batch effect correction. Conclusions: Some extent of batch effects should be expected in TMA-based cancer biomarker studies. Batch effects should be quantified and, if necessary, corrected in any biomarker study using more than one TMA. Anticipating batch effects during study design and, for biomarkers found to have batch effects, utilizing straightforward analytical correction approaches increase the validity of TMA-based studies. Citation Format: Konrad H. Stopsack, Molin Wang, Svitlana Tyekucheva, Travis A. Gerke, J. Bailey Vaselkiv, Kathryn L. Penney, Philip W. Kantoff, Stephen P. Finn, Michelangelo Fiorentino, Massimo Loda, Giovanni Parmigiani, Lorelei A. Mucci. Batch effects in tumor biomarker studies using tissue microarrays: Extent, impact, and remediation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 893.