tx9b00288_si_002.xlsx (38.06 kB)
Can Transcriptomic Profiles from Cancer Cell Lines Be Used for Toxicity Assessment?
datasetposted on 2019-12-19, 21:45 authored by Zhichao Liu, Liyuan Zhu, Shraddha Thakkar, Ruth Roberts, Weida Tong
In vitro toxicogenomics (TGx) has the potential to replace or supplement animal studies. However, TGx studies often suffer from a limited sample size and cell types. Meanwhile, transcriptomic data have been generated for tens of thousands of compounds using cancer cell lines mainly for drug efficacy screening. Here, we asked the question of whether these types of transcriptomic data can be used to support toxicity assessment. We compared transcriptomic profiles from three cancer lines (HL60, MCF7, and PC3) from the CMap data set with those using primary hepatocytes or in vivo repeated dose studies from the Open TG-GATEs database by using our previously reported pair ranking (PRank) method. We observed an encouraging similarity between HL60 and human primary hepatocytes (PRank score = 0.70), suggesting the two cellular assays could be potentially interchangeable. When the analysis was limited to drug-induced liver injury (DILI)-related compounds or genes, the cancer cell lines exhibited promise in DILI assessment in comparison with conventional TGx systems (i.e., human primary hepatocytes or rat in vivo repeated dose). Also, some toxicity-related pathways, such as PPAR signaling pathways and fatty acid-related pathways, were preserved across various assay systems, indicating the assay transferability is biological process-specific. Furthermore, we established a potential application of transcriptomic profiles of cancer cell lines for studying immune-related biological processes involving some specific cell types. Moreover, if PRank analysis was focused on only landmark genes from L1000 or S1500+, the advantage of cancer cell lines over the TGx studies was limited. In conclusion, repurposing of existing cancer-related transcript profiling data has great potential for toxicity assessment, particularly in predicting DILI.