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Genetically regulated expression underlies cellular sensitivity to chemotherapy in diverse populations
Ashley J Mulford1,2, Claudia Wing3, M Eileen Dolan3
1Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA.
Human Molecular Genetics
|February 12, 2021
Summary
Genetic variations influence cancer drug effectiveness. This study used lymphoblastoid cell lines to identify genetic factors impacting chemotherapy response, finding key genes and loci associated with drug cytotoxicity.
Area of Science:
- Pharmacogenomics
- Cancer Biology
- Human Genetics
Background:
- Chemotherapeutic agents exhibit variable efficacy among cancer patients, highlighting the impact of genetic variation on drug response.
- Lymphoblastoid cell lines (LCLs) from diverse ancestral populations offer a valuable model for investigating genetic influences on drug cytotoxicity.
Purpose of the Study:
- To identify genetic loci and genes associated with chemotherapy-induced cytotoxicity across diverse ancestral populations.
- To understand the genetic basis of differential responses to cancer chemotherapeutic agents.
Main Methods:
- Utilized lymphoblastoid cell lines from three 1000 Genomes Project populations.
- Performed genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) on drug response phenotypes (IC50, AUC).
- Conducted functional studies involving gene knockdown in a lung cancer cell line (A549).
Main Results:
- Identified four unique loci through GWAS and three genes through TWAS significantly associated with chemotherapy-induced cytotoxicity.
- Discovered that increased STARD5 predicted expression correlated with decreased etoposide half-maximal inhibitory concentration (IC50).
- Demonstrated that STARD5 knockdown in A549 cells led to decreased sensitivity to etoposide.
Conclusions:
- Genetic factors significantly contribute to variations in chemotherapy drug response.
- STARD5 is implicated as a gene influencing sensitivity to etoposide.
- Findings advance the understanding of pharmacogenomics for personalized cancer treatment development.
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