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Pan-cancer single-cell landscape of drug-metabolizing enzyme genes

Wei Mao1, Tao Zhou1, Feng Zhang2

  • 1Department of Laboratory Medicine/Research Centre of Clinical Laboratory Medicine, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan.

Pharmacogenetics and Genomics
|May 30, 2024
PubMed
Abstract

Insights

Drug-metabolizing enzyme (DME) gene expression varies in the tumor microenvironment (TME). Understanding these DME patterns can help predict patient response to chemotherapy and personalize cancer treatment strategies.

Area of Science:

  • Cancer Biology
  • Pharmacogenomics
  • Single-cell Analysis

Background:

  • Drug-metabolizing enzyme (DME) gene expression significantly influences cancer treatment outcomes.
  • Variations in DME genes affect drug efficacy and duration.
  • The tumor microenvironment (TME) plays a crucial role in cancer progression and treatment response.

Purpose of the Study:

  • To analyze the transcriptional profile of DME genes within the TME at a single-cell level.
  • To investigate the impact of DME gene expression on individual responses to anticancer therapies.
  • To identify potential biomarkers for personalized chemotherapy selection.

Main Methods:

  • Integrated analysis of over 1.3 million cells from 481 normal/tumor samples across 9 solid cancer types.
  • Single-cell RNA sequencing to profile DME gene expression.
  • Ridge regression modeling using the PRISM database to correlate DME expression with drug sensitivity.

Main Results:

  • Identified distinct single-cell expression patterns of DME genes across various cancer types.
  • Found specific DME genes enriched in epithelial cells (e.g., GPX2, TST, CYP3A5) and TME components (e.g., CYP4F3 in monocytes).
  • Correlated elevated GPX2 and CYP3A5 expression, and reduced TST expression, with enhanced sensitivity to multiple chemotherapeutic agents.

Conclusions:

  • DME gene expression exhibits significant heterogeneity within cancer cells and the TME.
  • These varied DME expression patterns hold potential as predictive biomarkers for chemotherapy.
  • Findings support the use of DME profiling for optimizing anticancer agent selection in personalized medicine.

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