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Pharmacogenomics: Identification of New Drug Targets

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Identifying transcriptomic predictors of brodalumab response in psoriasis using CART analysis.

Vikram R Shaw1, Jay Patel2, Vamsi Varra3

  • 1School of Medicine, Baylor College of Medicine, Houston, TX, USA.

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Summary

Predicting psoriasis treatment response is key. This study used transcriptomics and clinical data to identify biomarkers for predicting response to brodalumab (a biologic), aiding precision medicine in dermatology.

Keywords:
BiologicOmicsPrecision medicinePsoriasis

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Area of Science:

  • Dermatology
  • Genomics
  • Precision Medicine

Background:

  • Psoriasis treatment involves novel biologics.
  • Predicting patient response to biologics is clinically valuable.
  • Precision medicine aims to tailor treatments for individual patients.

Purpose of the Study:

  • To develop a predictive model for treatment response to brodalumab in psoriasis patients.
  • To identify pre-treatment predictors of PASI75 and PASI90 response.
  • To assess the utility of transcriptomic data in predicting treatment outcomes.

Main Methods:

  • Utilized a classification and regression tree (CART) model.
  • Analyzed clinical variables and transcriptomic data from lesional biopsies.
  • Predicted week 12 PASI75 and PASI90 response rates.

Main Results:

  • Identified KRT16 RNA expression and BMI as predictors for PASI75 response.
  • Identified FERMT1, HLA_DQA1, TMPRSS11D, and S100P RNA expression as predictors for PASI90 response.
  • Achieved high AUC values (0.90 for PASI75, 0.88 for PASI90) with CART models.

Conclusions:

  • Focused transcriptomics can predict brodalumab treatment response in psoriasis.
  • Biomarkers like KRT16, BMI, FERMT1, HLA_DQA1, TMPRSS11D, and S100P show potential for clinical application.
  • This approach supports the advancement of precision medicine for psoriasis management.