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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.
Chemical Research in Toxicology
|December 7, 2019
Summary
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.
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