Within the next years, we are going to witness dramatic changes in the treatment of cancer patients thanks to molecular and personalized medicine. Routine, genome-wide screening has already entered the clinics to complement and inform diagnosis and therapeutic choices, and it is expected to further benefit from the fast pace of development of multi-omic technologies. Indeed, it is now possible to measure multiple-omic data from the same sample, such as: transcriptomics, genomics (including indels and mutations), proteomics, metabolomics, as well as DNA copy number variants and rearrangements, and DNA/RNA methylation levels. In parallel, large-scale collaborative efforts, such as The Cancer Genome Atlas, have generated and made available to the scientific community petabytes of multi-omic data across different cancer types.
Multi-omic integrative analyses are sought to provide a comprehensive view of cancer mechanisms that disrupt normal cellular functions and lead to disease progression and resistance to therapy. As the majority of molecular aberrations identified in cancer have synergic interactions, it is important to jointly collect and analyze multiple types of data to improve patients’ prognosis and response to therapy. In addition to patients’ data, manually-curated knowledge about cancer processes, provided in the form of pathways, maps of cell signals and metabolism, and protein-protein interaction networks, can be integrated in the analysis as a priori information or a posteriori to guide result interpretation. However, while computational and statistical analyses of single-omics datasets are well established, approaches for integrating multi-omic data are still far from being standardized. To keep up with the pace of data generation and growth of biological knowledge, existing methods should be extended or generalized, and new computational tools need to be proposed to cope with the complexity and multi-level structure of the available information.
While the majority of the data generated so far are from bulk tumors (i.e. heterogeneous mixtures of cells), the breakthroughs in single-cell technologies have enabled the generation of multi-omic data from single cells. Integrative, single-cell analyses hold the promise to disentangle the cellular and spatial diversity of the whole tumor microenvironment and characterize the plasticity and functional diversity of tumor-infiltrating immune cells. However, the sparsity, noise and high dimensionality of these data, as well as the limited maturity and standardization of the available tools, pose additional challenges in the analysis of single-cell multi-omics.
To fully unlock the potential of multi-omics in oncology, computational and statistical methods for single-cell and bulk data need to be optimized or developed ad hoc to cope with specific challenges like data harmonization, integration, and joint analysis. In this Research Topic, we welcome submissions of Original Research articles, Reviews and Mini-Reviews that provide new insights into the following topics in the fields of oncology and immuno-oncology:
1. Multi-omic network and pathway analyses;
2. Bioinformatic tools for multi-omic data integration and visualization;
3. Statistical models for multi-omic data integration;
4. Integration of single-cell and bulk omic data;
5. Joint analysis of multi-omic single-cell data;
6. Integrative pan-cancer studies;
7. Multi-omic biomarker discovery;
8. Prediction of therapeutic response from multi-omic data integration;
9. Integration of high-throughput and spatially-resolved data;
10. Multi-omic data integration for prediction of tumor antigens.
Within the next years, we are going to witness dramatic changes in the treatment of cancer patients thanks to molecular and personalized medicine. Routine, genome-wide screening has already entered the clinics to complement and inform diagnosis and therapeutic choices, and it is expected to further benefit from the fast pace of development of multi-omic technologies. Indeed, it is now possible to measure multiple-omic data from the same sample, such as: transcriptomics, genomics (including indels and mutations), proteomics, metabolomics, as well as DNA copy number variants and rearrangements, and DNA/RNA methylation levels. In parallel, large-scale collaborative efforts, such as The Cancer Genome Atlas, have generated and made available to the scientific community petabytes of multi-omic data across different cancer types.
Multi-omic integrative analyses are sought to provide a comprehensive view of cancer mechanisms that disrupt normal cellular functions and lead to disease progression and resistance to therapy. As the majority of molecular aberrations identified in cancer have synergic interactions, it is important to jointly collect and analyze multiple types of data to improve patients’ prognosis and response to therapy. In addition to patients’ data, manually-curated knowledge about cancer processes, provided in the form of pathways, maps of cell signals and metabolism, and protein-protein interaction networks, can be integrated in the analysis as a priori information or a posteriori to guide result interpretation. However, while computational and statistical analyses of single-omics datasets are well established, approaches for integrating multi-omic data are still far from being standardized. To keep up with the pace of data generation and growth of biological knowledge, existing methods should be extended or generalized, and new computational tools need to be proposed to cope with the complexity and multi-level structure of the available information.
While the majority of the data generated so far are from bulk tumors (i.e. heterogeneous mixtures of cells), the breakthroughs in single-cell technologies have enabled the generation of multi-omic data from single cells. Integrative, single-cell analyses hold the promise to disentangle the cellular and spatial diversity of the whole tumor microenvironment and characterize the plasticity and functional diversity of tumor-infiltrating immune cells. However, the sparsity, noise and high dimensionality of these data, as well as the limited maturity and standardization of the available tools, pose additional challenges in the analysis of single-cell multi-omics.
To fully unlock the potential of multi-omics in oncology, computational and statistical methods for single-cell and bulk data need to be optimized or developed ad hoc to cope with specific challenges like data harmonization, integration, and joint analysis. In this Research Topic, we welcome submissions of Original Research articles, Reviews and Mini-Reviews that provide new insights into the following topics in the fields of oncology and immuno-oncology:
1. Multi-omic network and pathway analyses;
2. Bioinformatic tools for multi-omic data integration and visualization;
3. Statistical models for multi-omic data integration;
4. Integration of single-cell and bulk omic data;
5. Joint analysis of multi-omic single-cell data;
6. Integrative pan-cancer studies;
7. Multi-omic biomarker discovery;
8. Prediction of therapeutic response from multi-omic data integration;
9. Integration of high-throughput and spatially-resolved data;
10. Multi-omic data integration for prediction of tumor antigens.
