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teff: estimation of Treatment EFFects on transcriptomic data using causal random forest.

Alejandro Cáceres1, Juan R González1

  • 1Instituto de Salud Global de Barcelona (ISGlobal), 08003 Barcelona, Spain.

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We developed teff, an R package for causal inference on gene expression data. It identifies patients likely to benefit most from treatment, enabling personalized medicine. This tool aids in selecting optimal therapies before intervention.

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Area of Science:

  • Genomics
  • Biostatistics
  • Computational Biology

Background:

  • Causal inference is crucial for identifying patient subgroups who benefit from treatments.
  • High-dimensional transcriptomic data presents challenges for existing causal inference methods.
  • Personalized treatment selection requires robust computational tools for gene expression data.

Purpose of the Study:

  • To develop a user-friendly implementation for causal inference on gene expression data.
  • To identify patient profiles that predict differential treatment effects.
  • To enable personalized patient targeting for optimal therapeutic outcomes.

Main Methods:

  • Application of random causal forests to high-dimensional gene expression data.
  • Development of the 'teff' R package for accessible causal inference.
  • Extraction of patient profiles based on expected treatment effects.

Main Results:

  • Identification of a patient profile benefiting from brodalucimab for psoriasis, linked to T cell abundance.
  • Validation of the profile's predictive power in an independent study with etanercept.
  • Demonstration of teff's capability to target individuals with high expected treatment effects.

Conclusions:

  • Causal inference profiling using gene expression data can guide personalized treatment decisions.
  • The 'teff' package provides a valuable tool for transcriptomic data analysis in precision medicine.
  • Individualized patient targeting can improve treatment selection efficacy before intervention.