Computational Oncology in the Multi-Omics Era: State of the Art

Abstract

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.

Keywords: cancer complexity; computational oncology; data integration; machine learning; multi-omics analysis; network science.

Figure 1

Hallmarks of cancer complexity. The defining features of cancer (3, 4) are intrinsically connected to the defining features of complex systems (2).

Figure 2

The many levels of interactions found in a cancer system. (A) Depicts intracellular interactions that can be measured via the different omic technologies, such as genomics, transcriptomics, metabolomics, lipidomics, and so on. (B) Shows intercellular interactions, such as the ones orchestrated through immune responses, microbial interactions (metagenomics) and other instances of cell-cell interactions.

Figure 3

Growth of interest in omics technologies in the twenty-first century: the number of Pubmed publications mentioning each omic technology in its title or abstract measured yearly since the year 2000.

Figure 4

Computational tools are needed for high-throughput data acquisition, data management in repositories, data processing, and high-end analysis.

Figure 5

Samples for omics analyses can be obtained from “bulk” tissue, single cell data, or heterogeneous populations, such as metagenomes. Most current omics data are generated using technologies either array-based, sequence-based, or mass spectrometry-based; although high-throughput imaging data is becoming important in the clinical setting. Complementary techniques exist for the analysis of epigenetic states. Each combination of sample type, omic measurement and analytical technology requires a specific bioinformatic pipeline for data acquisition and processing.

Figure 6

A representation of the data structure used in to store the Cancer Genome Atlas within the Genome Data Commons. This is represented as a directed graph. This is a simplified illustration of the one found at https://gdc.cancer.gov/developers/gdc-data-model/gdc-data-model-components.

Figure 7

Combinations between omics technologies. Width indicates number of co-occurrence in literature. Genomics, transcriptomics, and proteomics are the most common pairs.

Figure 8

Machine learning has many applications in cancer and multiomics.