Project origin: DRAGON

Project type: Proof of concept

Project status: 10% complete

Partners involved: TopMD, COVID-19 Disease Maps Group, CIAO Project

Problem: How to identify and confirm AOPs of COVID-19 and Long COVID from single cell transcriptomics data as well as from gene expression, shedding light on the mechanistic and casual effects, together with personalised global pathway activity.

Fit with vision: Use of precision medicine and AI to help diagnose and predict outcomes early and stratify patients for potential new therapeutics.

Concept: Analyse differences and similarities of AOPs detected by COVID-19 Disease Maps and TopMD Maps and intertwine with the CIAO Project omics related AOPs.

Successful outcome: Identification of biomarkers essential in highlighting key events of disease critical state evolution

Next Steps: Publication, present outcome in a Spotlight Session and replicate exercise of other COVID AOPs.

Project origin: DRAGON

Project type: Initiation

Project status: 5% complete

Partners involved: Radiomics, ERS END COVID CRC, CDISC, Thirona, ICL

Problem: How adaptable is DistriM, a distributed machine learning solution, to set-up within the ERS CRC END COVID Registry for their research purposes.

Fit with vision: Developing a learning health system.

Concept: Assess END-COVID CRC needs against DistriM platform’s operational/technical solutions, data curation and model development needs for feasibility

Successful outcome: Easy translation of DistriM to facilitate END-COVID CRC research questions

Next Steps: Identify process and create feasibility outline, present outcome in a Spotlight Session, evaluate piloting DistriM in END COVID CRC sites.

Project origin: U-BIOPRED Alliance

Project type: Proof of Concept

Project status: 75% complete

Partners involved: Università Cattolica del Sacro Cuore, Imperial College London, Karolinska Institutet, University of Southampton

Problem: We don’t know if radiomics profiling can be used for severe asthma endotyping.

Fit with vision: Clarifying disease molecular mechanisms underlying severe asthma heterogeneity.

Concept: Assess if imaging outcomes (chest HRCT) relate to endotypes in severe asthma.

Successful outcome: Analysis report on correlations to U-BIOPRED dataset.

Next Steps: Publication and replicate in other datasets.

Project origin: U-BIOPRED Alliance

Project type: Proof of Concept

Project status: 30% complete

Partners involved: Breathomix, Università Cattolica del Sacro Cuore, Imperial College London, Karolinska Institutet, University of Southampton

Problem: The value of exhaled breath analysis has already been shown in multiple publications both for GC-MS and eNose. However, the value of the breathomics data collected in U-BIOPRED for phenotyping severe asthma patients has not been fully explored.

Fit with vision: Better understanding of the clinical value of the breathomics data can help improve the handprint of severe asthma patients.

Concept: Assess what patient clusters can be found based on breathomics data (eNOSE + GC/MS).

Successful outcome: Clinically relevant clusters/phenotypes..

Next Steps: Publication and present outcome in a Spotlight Session.

Project origin: U-BIOPRED Alliance

Project type: Proof of Concept

Project status: 15% complete

Partners involved: CIRO, Imperial College London, Karolinska Institutet

Problem: PRO’s don’t link well to physiological measurements but we do not know if they link well to OMIC signatures

Fit with vision: Link treatable mechanisms to patient centred outcomes

Concept: Reanalyse omic signatures (transcription-associated clusters (TACs)) in relation to PROs

Successful outcome: (New) Relationship(s) between certain OMIC signatures and PROs are identified.

Next Steps: Publication and present outcome in a Spotlight Session

Project origin: U-BIOPRED Alliance

Project type: Pilot

Project status: 45% complete

Partners involved: Università Cattolica del Sacro Cuore, Imperial College London, Karolinska Institutet, University of Southampton

Problem: We do not know if specific radiological profiles are linked with differences in physiological measures.

Fit with vision: Clarifying molecular heterogeneity in severe asthma

Concept: Assess if imaging outcomes (HRCT scans) link to physiological measure (and other measures) and if they relate to airway remodelling and/or airway inflammations

Successful outcome: Analysis report on U-BIOPRED data correlations

Next Steps: Publication and replicate in other datasets.

Project origin: U-BIOPRED Alliance

Project type: Proof of Concept

Project status: 5% complete

Partners involved: Università Cattolica del Sacro Cuore, Imperial College London, Karolinska Institutet, Amsterdam UMC

Problem: We do not know if severe asthma radiomic-associated clusters are linked to specific urinary and breath metabotypes.

Fit with vision: Maps accessible biomarkers to treatable mechanisms

Concept: Analyse existing data to see if imaging outcomes (HRCT scans) link to urinary and breath metabolomic endotypes and relate to airway remodelling

Successful outcome: Analysis report on U-BIOPRED data correlations

Next Steps: Publication and replicate in other datasets.

Project origin: U-BIOPRED Alliance

Project type: Proof of Concept

Project status:

Partners involved: Karolinska Institutet, Amsterdam UMC, Imperial College London, Universita Cattolica del Sacro Cuore

Problem: Disease mechanisms underlying asthma heterogeneity are not completely known. We have previously identified radiomic associated clusters (RACs) in the severe asthma U-BIOPRED cohort and showed that they are associated with specific transcriptomic endotypes. In this project we aim to clarify whether severe asthma RACs are linked to specific metagenomic pathways and their relationships with transcriptomic pathways involved in airway remodelling

Fit with vision: This project fits to the U-BIOPRED Alliance vision of clarifying the disease molecular mechanisms underlying severe asthma heterogeneity

Concept: We will use HRCT scan and sputum metagenomic data from the U-BIOPRED study and analyse them using machine learning and artificial intelligence

Successful outcome: Manuscript publication; research project proposals

Next Steps: Identification of the main research question, identification of metagenotypes and metagenomic endotypes in previously identified RACs, identification of the relationships between metagenomic endotypes and previously identified transcriptomic pathways in individual RACs

Project origin: U-BIOPRED Alliance

Project type: Proof of Concept

Project status:

Partners involved: Karolinska Institutet, Amsterdam UMC, Imperial College London, Universita Cattolica del Sacro Cuore

Problem: Disease mechanisms underlying asthma heterogeneity are not completely known. We have previously identified radiomic associated clusters (RACs) in the severe asthma U-BIOPRED cohort and showed that they are associated with specific transcriptomic endotypes. In this project we aim to clarify whether severe asthma RACs are linked to specific lipidomic pathways and their relationships with transcriptomic pathways involved in airway remodelling

Fit with vision: This project fits to the U-BIOPRED Alliance vision of clarifying the disease molecular mechanisms underlying severe asthma heterogeneity

Concept: We will use HRCT scan and urinary, plasma and sputum lipidomic data from the U-BIOPRED study and analyse them using machine learning and artificial intelligence

Successful outcome: Manuscript publication; research project proposals

Next Steps: Identification of the main research question, identification of lipidomic endotypes in previously identified RACs, identification of the relationships between lipidomic endotypes and previously identified transcriptomic pathways in individual RACs