Volume 19, Issue 18 1900235
Editorial
Free Access

Special Issue on Single-Cell Multiomics for Immuno-Oncology and Cancer Systems Biology

Wei Wei

Corresponding Author

Wei Wei

Institute for Systems Biology, Seattle, WA, 98109 USA

E-mail: [email protected]Search for more papers by this author
Rong Fan

Rong Fan

Department of Biomedical Engineering, Yale University, New Haven, CT, 06520 USA

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First published: 14 August 2019

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Proteomics is inviting submissions to a special issue on single-cell multiomics for immuno-oncology and cancer systems biology. This issue is tentatively scheduled for publication in spring/summer 2020. Recent advances in cancer immunotherapies have provided paradigm shifts in treating patients with advanced malignancies. The development and therapeutic use of various immunotherapy regimens rely on the systems-level understanding of cellular composition, interaction, and dynamics of the tumor ecosystem.1 Tumors are infiltrated with immune, stromal, and other cell types, leading to extraordinary intratumor heterogeneity, which plays a vital role in tumor progression and requires new methods such as single-cell multiomics approaches and systems-level analysis to tackle. The objective of this special issue is to cover a variety of emergent single-cell omics technologies and showcase their applications in immuno-oncology and cancer systems biology, aiming at highlighting the synergy of these two exciting and fast-evolving fields. We welcome manuscripts from all related areas of single-cell omics and cancer systems biology. The types of manuscripts such as research articles, review articles, technical briefs, dataset briefs, and viewpoint articles will all be considered.

A suite of toolkits that permit the analysis of single-cell variability in different classes of biomolecules, including genome, epigenome, transcriptome, proteome, and metabolome, have been developed at a lightning pace.2 Tools for simultaneous quantification of multiple omics from the same single cells also emerge in the last couple of years. Such measurements significantly improve our understanding of the extent, cause, and consequences of cellular heterogeneity in cancer and allow researchers to ask questions from perspectives previously unattainable. Importantly, single-cell multiomics are moving toward the clinical arena with more and more exciting translational and clinical applications demonstrated, as exemplified by immuno-oncology.3

Single-cell multiomics approaches have been used to depict the tumor-immune ecosystem of many cancer types at unprecedented resolution.4 Such analysis would allow resolving the tumor clonal structure and dissecting the tumor microenvironment, which is critical to understanding antitumor immune responses, immune evasion, and metastasis. In addition to precisely dissecting the complex architecture of tumors, single-cell omics approaches can also identify and characterize rare and significant cell types, such as circulating tumor cells or antigen-reactive T cells in blood samples.5 The rarity of these cell types precludes their analysis at the bulk level. Non- or minimal invasiveness of blood cell sampling permits longitudinal monitoring of patients to examine the dynamic evolution of tumor and immune cell repertoire. It also allows for frequent examination of therapeutic response and resistance development. Single-cell profiling has also been used to monitor the polyfunctionality of engineered T cells for adoptive cell transfer immunotherapy including T cell receptor-engineered T cell (TCR-T) and chimeric antigen receptor T cell (CAR-T) therapies,6 and to identify novel T cell populations that are predictive for checkpoint immunotherapy.7

Accompanying these exciting translational studies is the increasingly large amount of high-dimensional single-cell data sets to help improve our understanding of disease etiology and treatment mechanisms. However, how to develop new analytical algorithms to analyze and make sense of such high-dimensional data has been a major challenge.8 The development of computational and systems biology approaches specifically for analyzing single-cell data represents a pressing need in this field. This special issue, therefore, aims to also solicit contributions in computational and informatics technologies for single-cell omics.

The regular submission is due by October 15, 2019. However, late submissions will still be given full consideration, but may run into the risk that the accepted manuscript might miss the production schedule to be included in the special issue. While this special issue will appear in the spring of 2020, all manuscripts can be published online upon acceptance ahead of inclusion in the issue, fully citable by digital object identifier (DOI). This special issue will serve as a forum to showcase latest progress in single-cell omics with applications to immuno-oncology and/or cancer systems biology.

Acknowledgements

The authors acknowledge Institute for Systems Biology and Yale University.

    Conflict of Interest

    The authors declare no conflict of interest.