Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

Abstract

In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.

Keywords: cancer; machine learning; multi-omics; oncology; systematic review; tools.

Figure 1

(A) Linkage between different methodological categories. References to papers (see Table 1). That could be categorized in different groups are reported near the link. (B) Publications by year of publication and Field-Weighted Citation Impact. Different colors indicate exploited methods, shapes aims, and outputs. Papers with red borders have source code or provide a tool. Papers in the “Subgroup identification” group and/or with free tool result to be the most cited across years. The reference numbers are reported in Table 1.