Diagnostic model and assay for personalized vaccine

Introduction

Seasonal influenza poses a major global public health challenge, causing 3-5 million cases of severe illness and 290,000-650,000 deaths annually. Influenza vaccines are crucial for preventing illness, reducing the severity of infection, and limiting virus transmission. However, their efficacy varies across populations due to factors such as immune status and pre-existing comorbidities.

Vulnerability of Immunocompromised Patients

Immunocompromised patients (e.g., rheumatoid arthritis, HIV, or organ transplant recipients) are particularly vulnerable to influenza. Consequent complications include hospitalization and death, as their immune systems often fail to mount adequate responses to standard vaccines.

Data Analysis and Findings

Leveraging data from our previous ERC-St grant-funded studies, we analyzed the serological and multi-omics profiles of various cohorts across 5 time points over 4 influenza seasons. This analysis, covering multiple virus strains, identified robust molecular biomarkers linked to vaccine responses, which were validated through wet-lab experiments.

Machine Learning Models

Machine learning models based on pre-vaccination biomarkers were able to predict vaccine response in independent samples. Building on these findings, we propose to develop a diagnostic assay to measure these biomarkers and apply our established prediction model to stratify patients based on their responsiveness to the influenza vaccine.

Sample Collection and Validation

Samples from various patient cohorts, provided by our collaboration partners, will be used for verification and validation. Once validated, these biomarkers will be integrated into an innovative, fast, and reliable diagnostic test to predict vaccine responsiveness, through collaboration with an experienced industrial partner.

User-Interface Development

Additionally, our team will create a user-interface app to translate biomarker measurements into diagnostic outcomes. By enabling personalized vaccine strategies particularly for patients, this project has the potential to significantly improve influenza vaccine efficacy, reduce disease burden, and ultimately save lives.