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Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

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RISE: Two-Stage Rank-Based Identification of High-Dimensional Surrogate Markers Applied to Vaccinology.

Arthur Hughes1,2, Layla Parast3, Rodolphe Thiébaut1,2,4

  • 1INSERM, INRIA, BPH, U1219, SISTM, University of Bordeaux, Bordeaux, France.

Statistics in Medicine
|September 5, 2025
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Summary

We developed a new method, Rank-based Identification of high-dimensional SurrogatE Markers (RISE), to find surrogate markers for vaccine efficacy. RISE successfully identified gene expression signatures that predict immune responses in influenza vaccine trials.

Keywords:
high‐dimensionalnonparametric statisticssurrogate markertranscriptomicsvaccine

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Area of Science:

  • Immunology
  • Bioinformatics
  • Vaccinology

Background:

  • Identifying surrogate markers is crucial for long-term vaccine trial follow-up, offering early efficacy indicators and accelerating development.
  • High-throughput technologies like RNA-sequencing generate complex, high-dimensional data, posing challenges for traditional surrogate marker identification.
  • Existing methods struggle with the small sample, high-dimensional data common in modern vaccine studies.

Purpose of the Study:

  • To introduce a novel statistical approach, Rank-based Identification of high-dimensional SurrogatE Markers (RISE), for identifying surrogate markers in high-dimensional biological data.
  • To evaluate the performance of RISE in simulation studies, assessing its ability to control type I error rates and maintain empirical power.
  • To apply RISE to identify gene expression surrogates for immune responses in a human inactivated influenza vaccine trial.

Main Methods:

  • RISE utilizes a non-parametric univariate test for initial variable screening, followed by surrogate evaluation on independent datasets.
  • Simulation studies were conducted to assess RISE's statistical properties under various conditions.
  • The method was applied to RNA-sequencing data from an inactivated influenza vaccine trial to identify gene expression markers correlating with immune response.

Main Results:

  • RISE demonstrated desirable statistical properties, including accurate control of type I error rates and good empirical power in simulations.
  • Application of RISE to influenza vaccine data identified a gene expression signature serving as a surrogate for neutralizing antibody response.
  • The identified gene signature was enriched for pathways involved in innate antiviral signaling and interferon stimulation, offering clear biological relevance.

Conclusions:

  • RISE is a robust and effective method for identifying surrogate markers in small sample, high-dimensional settings typical of vaccine research.
  • The identified gene expression signature provides a potential biomarker for predicting immune response to inactivated influenza vaccines.
  • The findings highlight the utility of integrating high-throughput data with novel statistical approaches for advancing vaccinology.