Multiomics-based data-driven analysis unravels subtypes of complex diseases

This blog is written based on my invited talk at the Molecular Epidemiology Research Lab, Max-Delbrück-Centrum für Molekulare Medizin (MDC) in Germany. I would like to thank Dr. Sara Moazzen (Postdoctoral Researcher) and Prof. Dr. Tobias Pischon (Molecular Epidemiology Research Group Leader) for the invitation and for arranging my talk.

Disclaimer: Blogs constitute only the opinion of the author.

Introduction

A complex disease, also called multifactorial disease, is a disease caused by a combination of genetic, environmental, and lifestyle factors, most of which have not yet been identified. Complex diseases account for 70% of all deaths globally. Common examples of complex diseases include common noncommunicable diseases, including cancer, diabetes, cardiovascular diseases, Parkinson’s disease, depressive disorders, and psychotic spectrum disorders.

Complex diseases are highly heterogeneous regarding signs and symptoms, underlying causal mechanisms, and the number of underlying genetic and nongenetic risk factors. Disease subtyping is a method for clustering patients into discrete homogeneous subgroups based on multiomics data – phenomics (e.g., clinical and imaging data), metabolomics, epigenomics, proteomics, transcriptomics, and genomics. It is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis.

The availability of high-throughput genome-wide profiling technologies, electronic health records (EHR), large-scale cohort and birth registry data, and novel data-driven methods, offers promise for successful subtyping of complex diseases and tailoring disease prevention and treatment considering individual differences. Data-driven methods, also known as unsupervised machine learning algorithms, include clustering models such as group-based trajectory modeling, latent class growth analysis, growth mixture modeling, general growth mixture modeling, latent class analysis, and latent transition analysis. iCluster and Biclustering methods are also contemporary approaches for data integration and disease subtyping.

Here, I summarized findings from eight systematic reviews on data-driven subtypes of diabetes mellitus, cancer, depressive disorders, Parkinson’s diseases, and schizophrenia spectrum disorders identified using multiomics data. I also highlighted challenges and potential solutions to harness the full potential of data-driven methods.