| Literature DB >> 34329587 |
Emily F Davis-Marcisak1, Atul Deshpande2, Genevieve L Stein-O'Brien1, Won J Ho2, Daniel Laheru2, Elizabeth M Jaffee2, Elana J Fertig3, Luciane T Kagohara4.
Abstract
Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.Entities:
Keywords: computational biology; single-cell proteomics; single-cell transcriptomics; spatial proteomics; spatial transcriptomics; translational medicine; tumor immunology
Mesh:
Year: 2021 PMID: 34329587 PMCID: PMC8406623 DOI: 10.1016/j.ccell.2021.07.004
Source DB: PubMed Journal: Cancer Cell ISSN: 1535-6108 Impact factor: 38.585