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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Gene isoforms as expression-based biomarkers predictive of drug response in vitro

Zhaleh Safikhani1,2, Petr Smirnov1, Kelsie L Thu1,3

  • 1Princess Margaret Cancer Centre, University Health Network, 101 College Street, Toronto, ON, Canada, M5G1L7.

Nature Communications
|October 26, 2017
PubMed

Insights

This study identifies specific RNA splicing patterns as predictive biomarkers for cancer drug response. These transcriptomic biomarkers offer new avenues for personalized medicine and improved cancer treatment strategies.

Area of Science:

  • Genomics and Transcriptomics
  • Pharmacogenomics
  • Cancer Biology

Background:

  • Next-generation sequencing, including RNA-sequencing, is crucial for characterizing cancer cell lines at genomic and transcriptomic levels.
  • Alternative mRNA splicing is frequent in cancers, presenting an opportunity to link splicing patterns to drug response for biomarker discovery.

Purpose of the Study:

  • To develop a meta-analytical framework for identifying robust transcriptomic biomarkers predictive of drug response across multiple studies.
  • To validate candidate biomarkers using independent pan-cancer and breast cancer pharmacogenomic datasets.

Main Methods:

  • Meta-analysis of pharmacological data from two large-scale drug screening datasets.
  • RNA-sequencing for profiling alternatively spliced transcripts.
  • Validation using independent pan-cancer and breast cancer pharmacogenomic datasets.

Main Results:

  • Identified specific isoforms of IGF2BP2, NECTIN4, ITGB6, and KLHDC9 associated with drug response.
  • Found significant associations between these isoforms and specific drugs (AZD6244, lapatinib, erlotinib, paclitaxel) in breast cancer datasets.
  • Demonstrated the robustness of identified biomarkers across multiple cancer types.

Conclusions:

  • Isoform expression levels are a valuable resource for discovering biomarkers predictive of drug response in cancer.
  • This approach can advance personalized medicine by enabling more precise patient stratification for targeted therapies.

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