Tumour heterogeneity and metastasis at single-cell resolution - PubMed (original) (raw)

Review

Tumour heterogeneity and metastasis at single-cell resolution

Devon A Lawson et al. Nat Cell Biol. 2018 Dec.

Abstract

Tumours comprise a heterogeneous collection of cells with distinct genetic and phenotypic properties that can differentially promote progression, metastasis and drug resistance. Emerging single-cell technologies provide a new opportunity to profile individual cells within tumours and investigate what roles they play in these processes. This Review discusses key technological considerations for single-cell studies in cancer, new findings using single-cell technologies and critical open questions for future applications.

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Figures

Fig. 1 ∣

Fig. 1 ∣. Common types of intratumour heterogeneity and its regulation by intrinsic and extrinsic factors.

Tumours comprise a heterogeneous population of cells, which is regulated by both intrinsic and extrinsic factors. Tumour cells vary in biomarker expression, epigenetic landscape, hypoxic state, metabolic state, stage of differentiation, invasive potential and genotype due to genomic instability. The tumour microenvironment can also be heterogeneous, in which different types of fibroblasts, pro-tumour and anti-tumour immune infiltrate, vascular and lymphatic vessel density and extracellular matrix (ECM) composition affect tumour cell heterogeneity and function.

Fig. 2 ∣

Fig. 2 ∣. Deciphering subclonal composition and cell types and states in single-cell omics data.

a, Inferring clonal trajectories and subclonal heterogeneity from bulk primary tumour genome sequencing data. In this example experiment, a tumour is sampled at a single time point (dotted lines). The table shows the frequency of each detected mutation. The panels show three (of many) possible clonal trajectories that can be inferred. The nodes represent points at which a mutation occurred, and overlapping coloured regions indicate that each of the mutations is present within any cell that is part of that population. b, Challenges associated with deciphering the genotype of a metastatic founder clone and subclonal trajectories from bulk genome sequencing of paired metastatic and primary tumours. The table shows the observed mutation frequencies in an example experiment in which a metastatic tumour from the individual in a was sequenced. The panels show three possible explanations for the observed frequencies. c, Cell types and states found in normal tissues. Tissues comprise different mature ‘cell types’ (labelled 1–5), which carry out specified functions. Cells within a ‘type’ can exist in a spectrum of allowable ‘cell states’ depending on the physiological status of the tissue. Mature cell types are derived from stem cells through a series of discrete differentiation intermediates or progenitors. The circles represent single cells, and the colour clouds represent the spectrum of allowable states. The density of circles represents the probability of observing a cell with that phenotype in a scRNA-seq experiment. d, Tumour cell types and states differ from normal tissue. Single-cell studies have shown that tumours contain stem-like cells (CSCs) and that differentiation is often noisy, skewed towards specific cell lineages.

Fig. 3 ∣

Fig. 3 ∣. Genetic and phenotypic properties of metastasis-initiating cells at the single-cell level.

Metastasis is a rare event, in which most cancer cells cannot progress through major bottlenecks associated with invasion, intravasation, extravasation, seeding and colonization to produce a malignant macrometastatic tumour. In this model, cancer cells are heterogeneous in genotype (nuclei) and phenotype (cytoplasm), and metastasis-initiating cells possess a distinct combination of both. Dashed arrow indicates that cancer cells within micrometastases can die. Death rates within micrometastases can balance proliferation rates, and thereby prevent progression to macrometastasis by the failure to produce net positive growth.

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