Unravelling the complexity of the cancer microenvironment with multidimensional genomic and cytometric technologiess

Abstract: Cancers are characterized by extensive heterogeneity that occurs intratumorally, between lesions, and across patients. To study cancer as a complex biological system, multidimensional analyses of the tumor microenvironment are paramount. Single-cell technologies such as flow cytometry, mass cytometry, or single-cell RNA-sequencing have revolutionized our ability to characterize individual cells in great detail and, with that, shed light on the complexity of cancer microenvironments. However, a key limitation of these single-cell technologies is the lack of information on spatial context and multicellular interactions. Investigating spatial contexts of cells requires the incorporation of tissue-based techniques such as multiparameter immunofluorescence, imaging mass cytometry, or in situ detection of transcripts. In this Review, we describe the rise of multidimensional single-cell technologies and provide an overview of their strengths and weaknesses. In addition, we discuss the integration of transcriptomic, genomic, epigenomic, proteomic, and spatially-resolved data in the context of human cancers. Lastly, we will deliberate on how the integration of multi-omics data will help to shed light on the complex role of cell types present within the human tumor microenvironment, and how such system-wide approaches may pave the way toward more effective therapies for the treatment of cancer.

Natasja L. de Vries, Ahmed Mahfouz, Frits Koning, and Noel F. de Miranda. Unravelling the complexity of the cancer microenvironment with multidimensional genomic and cytometric technologiess. Frontiers in Oncology, 2020.
@article { bib:2020_FrontOncol_review,
author = { Natasja L. de Vries and Ahmed Mahfouz and Frits Koning and Noel F. de Miranda },
title = { Unravelling the complexity of the cancer microenvironment with multidimensional genomic and cytometric technologiess },
journal = { Frontiers in Oncology },
year = { 2020 },
doi = { 10.3389/fonc.2020.01254 },
}
Multi-omic single cell data integration in cancer screenshot

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