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Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase01:11

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Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...

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A Quantitative Assay to Study Protein:DNA Interactions, Discover Transcriptional Regulators of Gene Expression, and Identify Novel Anti-tumor Agents
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Chemosensitivity prediction by transcriptional profiling.

J E Staunton1, D K Slonim, H A Coller

  • 1Whitehead/Massachusetts Institute of Technology Center for Genome Research, Cambridge, MA 02139, USA.

Proceedings of the National Academy of Sciences of the United States of America
|September 13, 2001
PubMed
Summary

This study developed a genomics-based algorithm to predict cancer cell drug response using gene expression profiles. The gene expression signatures of untreated cells accurately predicted chemosensitivity for a significant subset of compounds.

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Area of Science:

  • Genomics
  • Pharmacogenomics
  • Computational Biology

Background:

  • Predicting drug response is crucial for effective cancer therapy.
  • Genomic data offers a potential avenue for personalized medicine approaches.
  • Existing methods for predicting chemosensitivity have limitations.

Purpose of the Study:

  • To develop and validate a genomics-based algorithm for predicting cancer cell line chemosensitivity.
  • To assess the sufficiency of gene expression profiles in predicting drug response.
  • To create tissue-of-origin-independent classifiers for chemosensitivity.

Main Methods:

  • Oligonucleotide microarrays were used to measure gene expression levels in 60 human cancer cell lines (NCI-60).
  • Gene expression data from untreated cells was used to build predictive classifiers for 232 compounds.
  • Classifiers were evaluated on independent datasets to determine prediction accuracy.

Main Results:

  • Gene expression signatures alone were sufficient to predict chemosensitivity for a subset of compounds.
  • Eighty-eight out of 232 (37.8%) expression-based classifiers showed significant accuracy (P < 0.05) on independent test data.
  • This accuracy was substantially higher than the 12 classifiers expected by chance.

Conclusions:

  • Genomic approaches, utilizing gene expression profiles, are feasible for predicting chemosensitivity.
  • This study demonstrates the potential of a genomics-based strategy for personalized cancer treatment.
  • Further research can refine these genomic classifiers for broader clinical application.