Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA.
Department of Pharmacological Sciences, Drug Toxicity Signature Generation (DToxS) LINCS Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.
Cell Syst. 2019 Jul 24;9(1):35-48.e5. doi: 10.1016/j.cels.2019.06.005. Epub 2019 Jul 10.
Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays.
有证据表明，一些高影响力的生物医学研究结果无法重复，这激发了人们对生成可查找、可访问、可互操作和可重复使用（FAIR）数据的实践的兴趣。多篇论文已经确定了不可重复性的具体例子，但广泛研究使数据更具可重复性的实用方法尚未得到广泛研究。在这里，NIH LINCS 计划联盟的五个研究中心研究了一种典型的扰动测定法的可重复性：定量测定培养细胞对抗癌药物的反应性。此类测定对于药物开发、研究细胞网络和患者分层非常重要。虽然许多实验和计算因素会影响中心内和中心间的可重复性，但最难识别和控制的因素是那些与生物学背景有很强依赖性的因素。这些因素通常随分析的药物和生长条件而变化。我们提供了识别此类上下文敏感因素的方法，从而提高了基于细胞的可重复测定的理论和实践。
