Abstract 1356: Meta-analysis and lack of independence assumption: Application in biomarker discovery
Authors: Abbas-Aghababazadeh Farnoosh , Haibe-Kains Benjamin 
Published: July 2021

Abstract
Due to the developments of high-throughput sequencing (HTS) technologies, massive amounts of data are currently available in cancer research which has provided a remarkable opportunity for the identification of biomarkers. However, biomarkers obtained from different studies of the same condition often show lack of agreement with each other due to concerns of inconsistency between laboratories in experimental designs and technical platforms for high throughput measurements and data processing computational methods. In addition, the large number of features and relatively small sample sizes can also lead findings from clinical and biological studies that are often not reproducible and can be biased when tested in independent cohorts. To address this issue, meta-analyses can be performed to reach more general and reliable conclusions along with increasing statistical power. In application, due to the limitations of experimental techniques and cost of HTS experiments, molecular data for some samples is not measured and is duplicated from available datasets in meta-analysis study. Therefore, in this scenario, the conventional meta-analysis procedures can be misleading when key assumptions such as independence of datasets or effect sizes are ignored. We assess the impact of ignoring the assumption of independence in applying the effect-size meta-analysis approaches to estimate heterogeneity across studies along with combined effect sizes using pharmacogenomic datasets.
We considered molecular and drug responses data from Breast cancer and Pan-cancer cell lines sensitivity screenings obtained from the PharmacoGx package including CCLE, GDSC, GRAY, gCSI, CTRP and UHNBreast. Under each study, the meta preprocess including missing drug response imputation via multiple imputation by chained equations method using classification and regression trees, gene matching or filtering, and scaling or normalization are considered. For each study, the association between drug sensitivity and expression data is assessed by fitting (adjusted) linear regression models to identify cancer biomarkers. We evaluate the deviation from the assumption of independence by applying the integration analyses using non-independent effect sizes (i.e., standardized regression coefficient) related to the duplicated expression data. In addition, the relationship between increasing in the number of duplicated expression data and violation of non-independent effect-sizes is studied. The results indicate that duplicating gene expression data, therefore violating the independence assumption, can substantially increase the deviation of meta-estimates of effect sizes.
Citation Format: Farnoosh Abbas-Aghababazadeh, Benjamin Haibe-Kains. Meta-analysis and lack of independence assumption: Application in biomarker discovery [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 1356.