Multi-omics biomarker approaches in precision medicine

Introduction

Exponential developments in molecular omics technologies such as mass spectrometry and next generation sequencing have enabled us to obtain increasing insights in the molecular components of human biology and their interactions. Novel personalized diagnostics and high precision therapies that modulate selected disease mechanisms are now driving the new paradigm of precision medicine. However, we are rediscovering that human biology is a highly complex system and that we need multiple viewing angles to begin to understand disease mechanisms, identify its key nodes and define optimal personalized therapies. Measuring health using multiple biomarkers in a multi-omics approach can be a powerful way to achieve this. Proteomics takes up a unique part in multi-omics approaches, as it provides insights in enzymatic and structural components of biological pathways, yielding candidate biomarkers for diagnosis, prognosis, therapy prediction and monitoring. Optimally, multi-omics biomarker discovery, validation and development to clinical applications should be done in an integrated manner.

Methods

In the selected case studies that will be presented, we applied an LC-MS proteomics workflow using the Evosep One LC (Evosep) and timsTOF Pro MS (Bruker Daltonics), in combination with the PaSER (Parallel Search Engine in Real-time; Bruker Daltonics) platform that enables real-time processing of PASEF DDA and dia-PASEF spectral data streamed from the timsTOF Pro to the PaSER box. Proteomics analysis was aimed at either expression proteomics or glycoproteomics in untargeted or targeted (glyco)peptide analysis. Other omics analysis (genomics by WES, methylomics by ME-Seq, transcriptomics by RNA-Seq, metabolomics by (un)targeted LC-MS, lipidomics by (un)targeted LC-MS, glycoproteomics by (un)targeted LC-MS) were performed at laboratories that are part of the Netherlands X-omics Consortium or as part of the EATRIS Biomarker Platform. Omics data analyses using various workflows including Multi-Omics Factor Analysis and Similarity Network Fusion were done at the X-omics data team at Radboudumc.

Results

Lessons learned from two multi-omics case studies will be presented. A cohort of 120 healthy individuals was analysed on 7 omics levels, yielding information on natural variation and inter-individual differences of analytes in a healthy population. Induced pluripotent stem cells derived from patients with three types of inherited diseases were analysed by 8 omics platforms, yielding information on differences between patients and between iPSC clones. Two examples will be discussed where targeted proteomics analysis of (glyco)peptides is being translated to certified clinical diagnostics.