Cancer genomics: one cell at a time

Nicholas E Navin

Correspondence: Nicholas E Navin nnavin@mdanderson.org

Department of Genetics Unit 1010, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston 77030, TX, USA

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston 77030, TX, USA

The University of Texas Graduate School of Biomedical Sciences, 1515 Holcombe Blvd, Houston 77030, TX, USA

Genome Biology 2014, 15:452 doi:10.1186/s13059-014-0452-9

The electronic version of this article is the complete one and can be found online at:http://genomebiology.com/2014/15/8/452

Published:

30 August 2014

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Abstract

The study of single cancer cells has transformed from qualitative microscopic images to quantitative genomic datasets. This paradigm shift has been fueled by the development of single-cell sequencing technologies, which provide a powerful new approach to study complex biological processes in human cancers.

Introduction

Biologists have been studying single cancer cells since the invention of the microscope by Antonie van Leeuwenhoek in 1665. Many initial observations were based on the morphological differences between tumor cells, as recorded in the late 1800s by early pathologists, such as Rudolf Virchow[1]. These observations were greatly improved by the development of cellular staining techniques, such as hematoxylin and eosin. In the 1980s, the development of cytogenetic techniques, including spectral karyotyping (SKY) and fluorescence in situ hybridization (FISH), galvanized the field by allowing researchers to visualize the genomic diversity of chromosome aberrations directly in single tumor cells [2]–[4]. However, only in the past four years has the field moved from qualitative imaging data to quantitative datasets that are amenable to statistical and computational analysis. This paradigm shift has largely been fueled by the development of whole-genome amplification (WGA) and whole-transcriptome amplification (WTA), methods that can amplify the genome or transcriptome of a single cell from picogram-to-microgram quantities. By combining these methods with next-generation sequencing (NGS) technologies, it is now possible to obtain genome-wide mutational and transcriptional datasets on individual cancer cells.