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.

Fig. 1 |. Single-cell multi-omics for deciphering clonal evolution in cancer.

Analytic or experimental integrations of multiple data ‘omics’ modalities in single-cells advance our understanding of mechanisms of clonal evolution. a | Cancer cell representation with heritable traits that can be interrogated via multi-omics platforms. b | Extracting DNA methylation (DNAme) and transcriptomic information from the same cells experimentally has been achieved by modifying plate-based single-cell RNA sequencing (scRNA-seq) protocols (for example, Smart-seq2), in which both RNA and DNA are respectively isolated from the same cells for whole-transcriptome and DNAme data through bisulfite sequencing^,,. Heritable stochastic DNAme changes can then be exploited as native barcodes to directly infer the high-resolution phylogenetic history of tumour cells. c | scRNA-seq with integration of protein expression measurements can be performed in parallel for the same cells^,. DNA-barcoded antibodies, acting as synthetic transcripts, are used to convert the detection of proteins into a quantitative readout. This allows the immunophenotyping of cells to be integrated with an unbiased transcriptome analysis using existing single-cell sequencing approaches. d | High-sensitivity somatic genotyping in which, for instance, any mutation in mitochondrial DNA may serve as lineage markers. Interrogating these naturally occurring genetic barcodes within scATAC-seq (or scRNA-seq) provides high-resolution phylogenies coupled with cell state information. e | As an example of spatially aware platforms^,–,,, spatial transcriptomics utilizes molecular barcodes for the detection of mRNA molecules and maps them to their spatial positioning. gDNA, genomic DNA; indels, insertions or deletions; scRRBS, single-cell reduced-representation bisulfite sequencing; SNVs, single nucleotide variants.

Fig. 2 |. Phylogenetic inference for retrospective lineage tracing.

a | Bulk next-generation sequencing allows inference of clonal architecture phylogenetic trees of genetically heterogeneous populations. However, these data can resolve clonal and subclonal relationships to a limited extent by enabling the assessment of the order of acquisition of mutations (A–D) and are limited in their abilities to resolve the phylogenetic relationships of clones, especially at low cancer cell fractions (CCFs). b | Multi-sampling at different time points (T1, T2) during clonal evolution or at different regions (R1, R2) within a tumour (to assess for intratumoural clonal spatial composition) can provide higher-resolution phylogenetic relationships, even for subclones with low CCFs, owing to coordinated patterns of CCF fluctuations over time. c | Even though additional resolution is gained through multi-sampling, resolving phylogeny at single-cell resolution (by single-cell whole-genome sequencing or targeted sequencing) is required to derive the precise clonal dynamics and evolutionary history of a tumour. High-resolution trees pave the way for critical inferences derived directly from primary patient tumours for defining key parameters of somatic evolution. VAF, variant allele frequency.

Fig. 3 |. Interrogating native barcodes for retrospective lineage tracing.

Clonal architecture and/or phylogenies can be reconstructed from primary samples through naturally accumulated heritable marks, that is, ‘native barcodes’, such as copy number alterations (CNAs), single nucleotide variants (SNVs), small insertions or deletions (indels) in microsatellite repeat regions, DNA methylation changes, and mutations in mitochondrial DNA (mtDNA). Emerging and potential multi-omics technologies for lineage inference display a trade-off between throughput and lineage inference resolution (black bars). For instance, single-cell retrospective lineage tracing inference methods using CNAs (from single-cell whole-genome sequencing or single-cell RNA sequencing datasets) provide a low resolution of the underlying genetic diversity that fuels clonal evolution but can be applied to high number of cells. High-sensitivity somatic genotyping of a large number of loci may enable clonal reconstruction and a high-resolution retrospective lineage tracing with methods that interrogate mtDNA or microsatellite sites. Finally, heritable stochastic DNA methylation changes can serve as a molecular clock and therefore be exploited as native barcodes to infer phylogenetic history.

Fig. 4 |. Somatic mutations reshape differentiation topologies.

a | A schematic workflow of genotyping in single-cell RNA sequencing (scRNA-seq) by which cell state and somatic genotyping can be simultaneously captured for single cells. High-throughput digital scRNA-seq platforms (shown on the left) employ tagmentation or fragmentation for transcript-end biased cDNA short-read sequencing. Thus, loci harbouring somatic mutations are often lost. To overcome this limitation, these multi-omics techniques split the full-length cDNA for the targeted amplification of loci of interest on the one hand (shown on bottom panel with blue background) and for standard digital scRNA-seq on the other (shown on top panel with orange background). The two libraries are then intersected via shared cell barcodes analytically (not shown) to co-map somatic mutations and whole transcriptome data at single-cell resolution. b | The direct linking of somatic genotypes with whole transcriptome enables researchers to superimpose and chart two differentiation topologies within the same sample, namely the native wild type and the mutated one, thus turning the co-mingling of mutated and wild-type cells from a limitation to an advantage. The differentiation topologies (graphs) are built from scoring of stemness, pseudotime and differentiation states (that is, cell fate #1 and #2). c | By superimposing two differentiation topologies, we can identify the fitness impact of a somatic mutation within each cell state. Differential gene expression between mutant and wild-type cells can be identified (as shown by the volcano plots of differentially expressed genes). Pathway enrichment analysis of differentially regulated genes reveals activated or downregulated pathways (as shown by the annotated points on the volcano plots and heatmap, showing degree of enrichment). The differentially expressed genes and regulated pathways may vary as a function of cell state (that is, stem versus cell fate #1 versus cell fate #2). WT, wild type; MUT, mutant.

Fig. 5 |. An integrative model of cancer progression.

Single-cell multi-omics profiling of malignancies and clonal expansions in normal tissues may help unravel the underlying model through which rogue somatic evolutionary processes are suppressed in the multi cellular human host. Emerging data suggest that genetic constraints (that is, the time needed for the accumulation of multiple driver events) may need to act in concert with other mechanisms to suppress somatic evolution. One such mechanism may involve spatial constraints, as in the case of the colonic crypts that reduce the effective population size by splitting the overall population of colonic stem cells to isolated habitats, thereby favouring drift over selection^, (top panel). Greater spatial mixing with malignant progression can thus serve to amplify the selection and development of resistance to therapy. Another such mechanism may be the complex differentiation hierarchies that suppress positive selection. Thus, the patterns of de-differentiation related to the relaxation of epigenetic identity barriers in cancer may have the opposite effect, serving to amplify positive selection^, (bottom panel). Of note, these differentiation hierarchies may be encoded extrinsically in highly organized tissues, such as epithelial organs via cytokine gradients, or intrinsically in less spatially defined tissues, such as the bone marrow, through complex, deep epigenetic hierarchies.