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Selecting predictive biomarkers from genomic data.

Florian Frommlet1, Piotr Szulc2, Franz König1

  • 1Department of Medical Statistics, CEMSIIS, Medical University of Vienna, Vienna, Austria.

Plos One
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

Researchers developed methods to identify patient subgroups for effective treatments using genetic biomarkers. Modified Bayesian Information Criterion (mBIC2) and Sorted L-One Penalized Estimator (SLOBE) showed strong predictive performance with fewer biomarkers.

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Area of Science:

  • Biostatistics
  • Genetics
  • Personalized Medicine

Background:

  • Developing statistical methods to identify patient subgroups for targeted therapies is crucial.
  • Focus on selecting prognostic and predictive genetic biomarkers from a large set of Single Nucleotide Polymorphisms (SNPs).

Purpose of the Study:

  • To compare high-dimensional biomarker selection approaches for identifying treatment-responsive patient subgroups.
  • Evaluate the efficacy of adaptive lasso, SLOBE, and mBIC2 against classical multiple testing procedures.

Main Methods:

  • Utilized high-dimensional statistical models incorporating prognostic markers (main effects) and predictive markers (interaction effects with treatment).
  • Compared adaptive lasso, a Bayesian adaptive SLOBE, mBIC2, and classical multiple testing procedures.
  • Investigated methods for defining patient subgroups based on identified predictive markers.

Main Results:

  • Selection methods based on mBIC2 and SLOBE demonstrated predictive performance comparable to adaptive lasso.
  • mBIC2 and SLOBE included significantly fewer biomarkers than adaptive lasso.
  • Identified specific patient subgroups likely to benefit from certain treatments.

Conclusions:

  • mBIC2 and SLOBE are efficient methods for selecting genetic biomarkers to define treatment-responsive subgroups.
  • These methods offer a more parsimonious approach to biomarker discovery compared to adaptive lasso.
  • The study advances personalized medicine by improving the identification of effective treatment strategies for specific patient populations.