Related Experiment Video
Updated: Jan 2, 2026
A Strategy to Identify Compounds that Affect Cell Growth and Survival in Cultured Mammalian Cells at Low-to-Moderate Throughput
Published on: September 22, 2019
Can Transcriptomic Profiles from Cancer Cell Lines Be Used for Toxicity Assessment?
Zhichao Liu1, Liyuan Zhu1, Shraddha Thakkar1
1National Center for Toxicological Research , U.S. Food and Drug Administration , Jefferson , Arkansas 72079 , United States.
Abstract:
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.
Insights
Repurposing cancer cell line transcriptomic data shows promise for toxicity assessment, especially for predicting drug-induced liver injury (DILI). This approach may supplement traditional toxicogenomics (TGx) studies by leveraging existing data for broader toxicity screening.
Area of Science:
- Toxicogenomics
- Transcriptomics
- In vitro toxicology
Background:
- In vitro toxicogenomics (TGx) offers an alternative to animal testing but faces limitations in sample size and cell diversity.
- Large-scale transcriptomic datasets exist for cancer cell lines, primarily generated for drug efficacy screening.
- The utility of these cancer cell line transcriptomic data for toxicity assessment remains largely unexplored.
Purpose of the Study:
- To investigate the feasibility of using existing transcriptomic data from cancer cell lines for toxicity assessment.
- To compare the transcriptomic profiles of cancer cell lines with traditional TGx assays (primary hepatocytes, in vivo studies).
- To evaluate the potential of cancer cell line data in predicting specific toxicities like drug-induced liver injury (DILI).
Main Methods:
- Utilized the pair ranking (PRank) method to compare transcriptomic profiles.
- Compared data from three cancer cell lines (HL60, MCF7, PC3) from the CMap dataset against primary hepatocytes and in vivo studies from the Open TG-GATEs database.
- Focused analysis on drug-induced liver injury (DILI)-related compounds and genes.
Main Results:
- Observed significant similarity between HL60 cancer cells and human primary hepatocytes (PRank score = 0.70), suggesting potential interchangeability.
- Cancer cell lines demonstrated promise in DILI assessment compared to conventional TGx systems.
- Certain toxicity pathways (e.g., PPAR signaling, fatty acid metabolism) were conserved across different assay systems, indicating biological process-specific assay transferability.
- Identified potential applications for cancer cell line data in studying immune-related biological processes.
Conclusions:
- Repurposing cancer-related transcriptomic data holds significant potential for toxicity assessment, particularly for predicting DILI.
- The transferability of findings across assay systems is dependent on specific biological processes.
- The effectiveness of cancer cell lines in toxicity prediction is influenced by the selection of genes analyzed.
Related Concept Videos
01:16Cell Lines
01:25Mutagenicity and Carcinogenicity