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Single-cell transcriptomics in cancer: computational challenges and opportunities

Experimental & Molecular Medicine 2020년 52권 9호 p.16 ~ 16
Fan Jean, Slowikowski Kamil, Zhang Fan,
소속 상세정보
 ( Fan Jean ) - Johns Hopkins University Department of Biomedical Engineering
 ( Slowikowski Kamil ) - Massachusetts General Hospital Center for Immunology and Inflammatory Diseases
 ( Zhang Fan ) - Brigham and Women’s Hospital Center for Data Sciences

Abstract


Intratumor heterogeneity is a common characteristic across diverse cancer types and presents challenges to current standards of treatment. Advancements in high-throughput sequencing and imaging technologies provide opportunities to identify and characterize these aspects of heterogeneity. Notably, transcriptomic profiling at a single-cell resolution enables quantitative measurements of the molecular activity that underlies the phenotypic diversity of cells within a tumor. Such high-dimensional data require computational analysis to extract relevant biological insights about the cell types and states that drive cancer development, pathogenesis, and clinical outcomes. In this review, we highlight emerging themes in the computational analysis of single-cell transcriptomics data and their applications to cancer research. We focus on downstream analytical challenges relevant to cancer research, including how to computationally perform unified analysis across many patients and disease states, distinguish neoplastic from nonneoplastic cells, infer communication with the tumor microenvironment, and delineate tumoral and microenvironmental evolution with trajectory and RNA velocity analysis. We include discussions of challenges and opportunities for future computational methodological advancements necessary to realize the translational potential of single-cell transcriptomic profiling in cancer.

키워드

Cancer; Computational biology and bioinformatics

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