Multi-Omics Integration for Personalised Medicine

Mentor: Florin Walter

Multi-Omics Integration for Personalised Medicine: Improving Prediction of Clinical Covariates for CLL patients

Personalised medicine has recently made promising strides with the advent of new targeted therapies, leveraging advancements in sequencing technologies that have drastically enhanced precision of genomic data. However, identifying the true underlying connections between various omics and many tumour phenotypes remains an open problem, presenting a complex challenge for comprehensive understanding and effective implementation in clinical settings. Especially robust and interpretable relationships across patients are missing to advance our understanding of the complex interplay between genetic variations, environmental factors, and disease phenotypes.

In this project we aim to identify key drivers for drug response in a chronic lymphocytic leukaemia (CLL) cohort of roughly 250 patients. The dataset combines ex vivo drug response measurements with transcriptome profiling, somatic mutation status and DNA methylation. To unravel important clinical markers for drug response, we will focus on integrating ‘omics using MOFA [1] and study its robustness in capturing comprehensive insights into the molecular landscape, aiming to enhance our ability to predict and optimise therapeutic outcomes.

GitHub page for the project: https://github.com/florinwalter/ebi_mofa_workshop

Reference:

[1] Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J.C., Buettner, F., Huber, W. and Stegle, O., 2018. Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets. Molecular systems biology, 14(6), p.e8124.

Dataset:

The CLL data were obtained from Dietrich et al. (2018) [2] and are available at the European Genome–Phenome Archive under accession EGAS00001001746. 

[2] Dietrich S, Oleś M, Lu J, Sellner L, Anders S, Velten B, Wu B, Hüllein J, da Silva Liberio M, Walther T (2018) Drug‐perturbation‐based stratification of blood cancer. J Clin Invest 128: 427–445