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Review
. 2016 Jun 16;9(2):33.
doi: 10.3390/ph9020033.

Tumor Heterogeneity, Single-Cell Sequencing, and Drug Resistance

Affiliations
Review

Tumor Heterogeneity, Single-Cell Sequencing, and Drug Resistance

Felix Schmidt et al. Pharmaceuticals (Basel). .

Abstract

Tumor heterogeneity has been compared with Darwinian evolution and survival of the fittest. The evolutionary ecosystem of tumors consisting of heterogeneous tumor cell populations represents a considerable challenge to tumor therapy, since all genetically and phenotypically different subpopulations have to be efficiently killed by therapy. Otherwise, even small surviving subpopulations may cause repopulation and refractory tumors. Single-cell sequencing allows for a better understanding of the genomic principles of tumor heterogeneity and represents the basis for more successful tumor treatments. The isolation and sequencing of single tumor cells still represents a considerable technical challenge and consists of three major steps: (1) single cell isolation (e.g., by laser-capture microdissection), fluorescence-activated cell sorting, micromanipulation, whole genome amplification (e.g., with the help of Phi29 DNA polymerase), and transcriptome-wide next generation sequencing technologies (e.g., 454 pyrosequencing, Illumina sequencing, and other systems). Data demonstrating the feasibility of single-cell sequencing for monitoring the emergence of drug-resistant cell clones in patient samples are discussed herein. It is envisioned that single-cell sequencing will be a valuable asset to assist the design of regimens for personalized tumor therapies based on tumor subpopulation-specific genetic alterations in individual patients.

Keywords: RNA sequencing; cancer treatment; circulating tumor cells; flow cytometry; individualized therapy; intratumoral heterogeneity; laser-capture microdissection; micromanipulation; multi-region sequencing; next generation sequencing; precision medicine; single-cell sequencing; tumor ecosystems; whole genome amplification; xenograft tumor models.

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Figures

Figure 1
Figure 1
The work-flow of single-cell sequencing.

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