A 71-gene signature of TRAIL sensitivity in cancer cells

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Jun-Jie Chen1, Steen Knudsen, Wiktor Mazin

  • 1Division of Therapeutic Proteins, Office of Biotechnology Products, Center for Drug Evaluation and Research, Food and Drug Administration, Bethesda, Maryland 20892, USA.

Molecular Cancer Therapeutics
|October 27, 2011
PubMed

Insights

Researchers identified a 71-gene signature that predicts cancer cell sensitivity to TNF-related apoptosis-inducing ligand (TRAIL) therapy. This discovery could improve the clinical use of TRAIL for cancer treatment.

Area of Science:

  • Molecular Biology
  • Cancer Research
  • Immunology

Background:

  • TNF-related apoptosis-inducing ligand (TRAIL) selectively induces apoptosis in cancer cells, showing therapeutic promise.
  • Cancer cell resistance to TRAIL limits its clinical efficacy.
  • Predictive biomarkers are needed to identify patients likely to respond to TRAIL-based therapies.

Purpose of the Study:

  • To identify gene expression profiles associated with TRAIL sensitivity in cancer cells.
  • To develop a predictive signature for TRAIL responsiveness.
  • To explore the role of interferon (IFN) and MHC genes in TRAIL sensitivity.

Main Methods:

  • Analysis of genome-wide mRNA expression profiles from the NCI60 cancer cell line panel.
  • Differential gene expression analysis comparing TRAIL-sensitive versus TRAIL-resistant cell lines.
  • Validation of the identified gene signature in independent test sets of human cancer cell lines.

Main Results:

  • A 71-gene signature was identified, with higher expression in TRAIL-sensitive cell lines.
  • This 71-gene signature accurately predicted TRAIL sensitivity across multiple cell line datasets.
  • The signature is enriched for interferon-induced genes and MHC genes; IFN-γ treatment enhanced TRAIL-induced apoptosis.

Conclusions:

  • A 71-gene expression signature can predict cancer cell sensitivity to TRAIL.
  • Interferon-induced and MHC genes play a significant role in TRAIL response.
  • This signature holds potential for clinical evaluation in predicting tumor response to TRAIL-related therapies.

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