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Application of statistical machine learning in biomarker selection.

Ritwik Vashistha1, Zubdahe Noor2, Shibasish Dasgupta3,4

  • 1Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, TX, USA.

Scientific Reports
|October 26, 2023
PubMed
Summary
This summary is machine-generated.

This study evaluated variable selection methods for identifying biomarkers in advanced urothelial cancer (aUC) patients from the JAVELIN Bladder 100 trial. Some methods showed promise, but high collinearity in the data presented challenges for biomarker discovery.

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Area of Science:

  • Oncology
  • Biostatistics
  • Genomic Medicine

Background:

  • Advanced urothelial cancer (aUC) treatment benefits from maintenance therapy, as shown by the JAVELIN Bladder 100 trial.
  • Identifying biomarkers is crucial for precision medicine in aUC, but challenges exist due to high collinearity and low signal in genomic data.
  • The JAVELIN Bladder 100 dataset's characteristics make it difficult to reliably identify biomarkers using standard variable selection methods.

Purpose of the Study:

  • To evaluate the performance of various variable selection methods in discovering prognostic and predictive biomarkers for aUC patients.
  • To compare penalized regression, random survival forests, and Bayesian variable selection approaches.
  • To propose a modified Bayesian Information Criterion (BIC) thresholding rule for Bayesian methods.

Main Methods:

  • A simulation study assessed popular variable selection techniques for high-dimensional data.
  • Penalized regression models, random survival forests, and Bayesian variable selection were employed.
  • The methods were applied to the JAVELIN Bladder 100 dataset to identify survival-associated biomarkers.

Main Results:

  • Variable selection methods generally had low false discovery rates but struggled with high collinearity.
  • Lasso-related methods identified potentially biologically relevant variables in the JAVELIN Bladder 100 data.
  • Stochastic search variable selection and random survival forest showed limitations with the dataset's collinearity and low signal.

Conclusions:

  • No single variable selection method is universally superior for biomarker discovery in high-dimensional, collinear aUC data.
  • Further research is needed to develop novel variable selection methods for robust biomarker identification in this patient population.
  • The study highlights the complexities of biomarker discovery in advanced urothelial cancer and informs future research directions.