Investigating molecular factors regulating cancer biology: from proteomics to multi-omics

Abstract

Recent technological advances in studying proteins using mass spectrometry (MS-based proteomics) have empowered in-depth profiling of the human proteome, such that MS-based proteomics can nowadays identify disease-specific proteins indicative of a particular cancer, and highlight molecular pathways associated with both sensitivity and resistance to drugs. However, in order to obtain a clearer picture of cancer biology, integrating information from multiple “-omic” data types such as genomics, transcriptomics, proteomics, metabolomics as well as different imaging techniques is considered vital. This thesis explores the application of multi-omics to study molecular mechanisms involved in the onset of cancer and drug resistance in several cancer models. From studying colorectal cancer, breast cancer and liposarcoma, the author demonstrates the benefits and challenges of integrating omics datatypes, which can assist in reaching the ultimate goal of improved therapeutic response in patients. To facilitate their data integration approaches, the author also developed a Python library named PaDuA for (phospho)proteomics analysis which provides a collection of tools that enable semi-automated data processing, filtering and statistical analysis. The ease with which PaDuA simplifies reproducibility and sharing of omics data strongly supports the standardization of analysis methods which is a recommended necessity for subsequent multi-omics data integration.