Abbas-Aghababazadeh Farnoosh, Xu Wei, Haibe-Kains Benjamin
Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Front Genet. 2023 Jan 4;13:1027345. doi: 10.3389/fgene.2022.1027345. eCollection 2022.
With rapid advancements in high-throughput sequencing technologies, massive amounts of "-omics" data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in independent cohorts. Meta-analysis, a set of statistical tools to integrate independent studies addressing similar research questions, has been proposed to improve the accuracy and robustness of new biological insights. However, it is common practice among biomarker discovery studies using preclinical pharmacogenomic data to borrow molecular profiles of cancer cell lines from one study to another, creating dependence across studies. The impact of violating the independence assumption in meta-analyses is largely unknown. In this study, we review and compare different meta-analyses to estimate variations across studies along with biomarker discoveries using preclinical pharmacogenomics data. We further evaluate the performance of conventional meta-analysis where the dependence of the effects was ignored simulation studies. Results show that, as the number of non-independent effects increased, relative mean squared error and lower coverage probability increased. Additionally, we also assess potential bias in the estimation of effects for established meta-analysis approaches when data are duplicated and the assumption of independence is violated. Using pharmacogenomics biomarker discovery, we find that treating dependent studies as independent can substantially increase the bias of meta-analyses. Importantly, we show that violating the independence assumption decreases the generalizability of the biomarker discovery process and increases false positive results, a key challenge in precision oncology.
随着高通量测序技术的迅速发展，如今几乎在每个生物医学领域都有大量的“组学”数据可用。由于生物模型和分析方法的差异，临床和生物学研究的结果在独立队列中进行测试时往往无法推广。荟萃分析作为一组整合针对相似研究问题的独立研究的统计工具，已被提出用于提高新生物学见解的准确性和稳健性。然而，在使用临床前药物基因组学数据的生物标志物发现研究中，从一项研究借用癌细胞系的分子特征到另一项研究是常见做法，这就造成了研究之间的依赖性。在荟萃分析中违反独立性假设的影响在很大程度上尚不清楚。在本研究中，我们回顾并比较了不同的荟萃分析，以估计研究之间的差异以及使用临床前药物基因组学数据进行生物标志物发现的情况。我们还通过模拟研究进一步评估了忽略效应依赖性的传统荟萃分析的性能。结果表明，随着非独立效应数量的增加，相对均方误差和较低的覆盖概率也会增加。此外，当数据被复制且独立性假设被违反时，我们还评估了既定荟萃分析方法在效应估计中的潜在偏差。通过药物基因组学生物标志物发现，我们发现将相关研究视为独立研究会大幅增加荟萃分析的偏差。重要的是，我们表明违反独立性假设会降低生物标志物发现过程的可推广性并增加假阳性结果，这是精准肿瘤学中的一个关键挑战。
