Associate Team presentation
The DESTRIER Associate Team is part of the Inria@SiliconValley program, and aims at DEfining Surrogacy of early Transcriptomics foR vaccInE Response.
Research Objective
DESTRIER brings together the expertise of:
in an effort to improve RNAseq data analysis methods and in particular assess the capacity of such measuurements to be used as a surrogate for evaluating early vaccine response.
Overview
With the COVID-19 pandemic raging around the world, the danger of emerging infectious diseases can hardly be overstated. Because it plays a central role in protein production and largely determines cellular function, gene expression (or the means by which DNA is turned into RNA and eventually proteins) may hold the key to a fast, reliable biomarker of vaccine immune response and efficacy as it usuually precedes more traditional measures of immune function (like antibody production or T-cell activity) by weeks or months.
A gene expression signature measured in the hours or days after vaccination could thus serve as a marker of vaccine response that could replace or supplement traditional measures of immune response. In the context of COVID-19, the ability to identify the persons most likely to benefit from an additional booster dose would help prioritize, especially considering the global tension on vaccines. Another such disease is Ebola, a particularly deadly disease associated with at least 34 outbreaks since 1976 causing more than 14,000 deaths and increased morbidity among survivors.
Because vaccines are the single most effective intervention against infectious disease, efficient vaccine development and administration is fundamental to contain and prevent the most dangerous outbreaks and epidemics. Surrogate markers for vaccine efficacy are mandatory to speed up vaccine development, facilitate licensure, and monitor effectiveness.
Scientific achievements
- “Doubly-robust evaluation of high-dimensional surrogate markers” article in Biostatistics 24(4): 985-999, 2023. (DOI: 10.1093/biostatistics/kxac020)
- “Post-clustering difference testing: valid inference and practical considerations” preprint on arXiv (arxiv:2210.13172)
- “Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives” preprint on bioRxiv (2022.05.10.490529)
- crossurr R package on CRAN
- VALIDICLUST R package on CRAN
Research directions
DESTRIER sets out to build a framework for creating, evaluating, and using optimal gene expression signatures to capture vaccine effectswith the following objectives:
- develop methods to quantify how much of a vaccine effect is mediated by gene expression, establishing if gene expression should be used for this task, and apply them to COVID-19 and Ebola vaccine trials
- develop methods to construct an optimal gene expression signature for capturing the vaccine effect and operationalize its use in future vaccine studies
