Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics

Abstract

Cancer represents an evolutionary process through which growing malignant populations genetically diversify, leading to tumour progression, relapse and resistance to therapy. In addition to genetic diversity, the cell-to-cell variation that fuels evolutionary selection also manifests in cellular states, epigenetic profiles, spatial distributions and interactions with the microenvironment. Therefore, the study of cancer requires the integration of multiple heritable dimensions at the resolution of the single cell — the atomic unit of somatic evolution. In this Review, we discuss emerging analytic and experimental technologies for single-cell multi-omics that enable the capture and integration of multiple data modalities to inform the study of cancer evolution. These data show that cancer results from a complex interplay between genetic and non-genetic determinants of somatic evolution.

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Fig. 1: Single-cell multi-omics for deciphering clonal evolution in cancer.
Fig. 2: Phylogenetic inference for retrospective lineage tracing.
Fig. 3: Interrogating native barcodes for retrospective lineage tracing.
Fig. 4: Somatic mutations reshape differentiation topologies.
Fig. 5: An integrative model of cancer progression.

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The authors contributed equally to all aspects of the article.

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Correspondence to Dan A. Landau.

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Glossary

Somatic mutations

Alterations in the DNA acquired post-conception (versus germline mutations) and able to be passed onto progeny of mutated cells. Somatic mutations can be detected by sequencing in otherwise histologically normal appearing tissue, are often associated with age and environmental exposures, and can manifest in cancer driver genes.

Clonal

A population of cells with the same underlying genetic make-up (that is, with the same somatic mutations), which can beget subclonal populations that have acquired additional genetic aberrancies.

Drivers

Somatic mutations that increase tumour cell fitness.

Cell states

A cell’s phenotype, as inferred by transcriptional or protein markers, that are often transitional (for example, intermediate states within a developmental system such as haematopoiesis or epithelium).

Single-cell multi-omics

Analytic or experimental integrations of multiple data ‘omics’ modalities in single cells.

Variant allele frequencies

Frequencies of mutated alleles in the sequenced reads from next-generation sequencing. Variant allele frequencies reflect copy number, zygosity, tumour purity and the fraction of cancer cells that harbour the mutation (for example, clonal versus subclonal).

Tumour purity

Per cent of a tumour mass composed of tumour cells, versus admixed non-neoplastic cells, such as tumour-infiltrating immune cells and stromal cells.

Cancer cell fractions

(CCFs). Fractions of cells that harbour a given mutation.

Copy number alterations

(CNAs). Changes in the number of copies of a DNA segment due to deletions or gains in the genome.

Retrospective lineage tracing

Clonal architecture and/or phylogenetic reconstruction of primary samples through naturally accumulated heritable marks, such as copy number alterations, single nucleotide variants or DNA methylation, as opposed to prospective lineage tracing, in which lineage barcodes are artificially introduced into a model organism.

Molecular clock

A method to deduce the temporal history (often in terms of number of divisions) of a cell or group of cells based on genetic or epigenetic changes that reflect time (or number of divisions).

Epimutation

Heritable stochastic errors in epigenetic marks (best described in DNA methylation).

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Nam, A.S., Chaligne, R. & Landau, D.A. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat Rev Genet (2020). https://doi.org/10.1038/s41576-020-0265-5

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