Chromosomal instability (CIN), an increased rate of chromosome segregation errors during mitosis, is a hallmark of cancer cells. CIN leads to karyotype differences between cells and thus large-scale heterogeneity among individual cancer cells; therefore, it plays an important role in cancer evolution. Studying CIN and its consequences is technically challenging, but various technologies have been developed to track karyotype dynamics during tumorigenesis, trace clonal lineages and link genomic changes to cancer phenotypes at single-cell resolution. These methods provide valuable insight not only into the role of CIN in cancer progression, but also into cancer cell fitness. In this Cell Science at a Glance article and the accompanying poster, we discuss the relationship between CIN, cancer cell fitness and evolution, and highlight techniques that can be used to study the relationship between these factors. To that end, we explore methods of assessing cancer cell fitness, particularly for chromosomally unstable cancer.

Aneuploidy is a state in which cells have an abnormal number of chromosomes, and includes the gain and loss of whole chromosomes (numerical aneuploidy), as well as the gain and loss of fragments of chromosomes (structural aneuploidy; see poster) (Orr et al., 2015). Aneuploidy can result from chromosomal instability (CIN), a process leading to increased rates of chromosome mis-segregation during mitosis. CIN manifests through different mitotic errors that lead to the gain or loss of chromosomes or parts of chromosomes; these errors include lagging chromatids (with or without a centromere), anaphase bridges, multipolar spindles, misalignment of chromosomes and premature sister chromatid separation (see poster) (Mirkovic et al., 2015; Orr et al., 2015). Beyond large-scale chromosomal copy number changes, CIN can also manifest as smaller structural alterations, including chromothripsis, which is an extensive chromosome shattering event leading to complex focal amplifications and deletions (Stephens et al., 2011). Underlying defects in DNA repair, for instance due to replication stress, can also cause structural gains and deletions (Burrell et al., 2013; Orr et al., 2015). Although CIN leads to aneuploidy, the concepts are not interchangeable. Aneuploidy refers to a cellular state of having an incorrect number of chromosomes, which can remain static over time, whereas CIN refers to the process that leads to aneuploidy (Schukken and Foijer, 2018). Therefore, aneuploid cells do not necessarily display CIN (Zerbib et al., 2023 preprint). However, there is increasing evidence that aneuploidy can predispose cells to CIN phenotypes (Passerini et al., 2016; Sheltzer et al., 2011).

See Supplementary information for a high-resolution version of the poster.

See Supplementary information for a high-resolution version of the poster.

Chromosome segregation errors lead to considerable changes in the quantity of genetic material and thus significantly impact the genetic makeup and physiology of a cell. In non-cancer cells, aneuploidy is detrimental and is associated with reduced viability and growth, increased proteotoxic stress and increased immune surveillance (Oromendia et al., 2012; Pfau et al., 2016; Santaguida and Amon, 2015; Santaguida et al., 2017). Interestingly, aneuploidy is a defining feature of cancer cells and is present in most tumours (Santaguida and Amon, 2015). In addition, aneuploidy and CIN have been associated with more aggressive disease, metastasis, treatment resistance and, thus, poor prognosis (Bakhoum et al., 2018; Oltmann et al., 2018; Walther et al., 2008). One way to explain this apparent paradox is the effect CIN has on cancer cell evolution and fitness. As such, understanding how karyotype dynamics drive fitness is vital to developing better strategies to eradicate cancers exhibiting CIN. In this Cell Science at a Glance article and the accompanying poster, we describe the dynamic relationship between CIN, cell fitness and cancer evolution; discuss tools that can be used to investigate fitness in tumours exhibiting CIN; and explore how phenotypic implications of fitness changes can be assessed.

Cancer cell evolution is a dynamic process. Cancer cells acquire genomic alterations over time, and the most beneficial alterations for cancer cell survival, proliferation and dissemination are selected for (Nowell, 1976), leading to distinct evolutionary patterns. Several models for cancer cell evolution that can explain these patterns have been proposed and reviewed elsewhere (Vendramin et al., 2021). In the next section, we will discuss the influence of CIN on cancer evolution and fitness.

Cancer evolution and CIN

Large-scale genomic changes might have greater potential to speed up cancer cell evolution than small, gradual changes (Chen et al., 2015; Selmecki et al., 2015). CIN is a powerful mechanism to introduce such large-scale changes as it promotes the gain and loss of chromosomes or fragments of chromosomes, generating cells with different aneuploidies within a tumour and thus substantial intratumour heterogeneity. This expedites selection and survival of the fittest cancer cell (see poster). Although most chromosome gains or losses are initially deleterious to developing cancer cells, the constantly changing intratumour heterogeneity landscape fuelled by ongoing CIN can become beneficial when tumours encounter selection pressures such as immune surveillance, exposure to chemotherapeutics or those faced when seeding in another tissue environment (see poster). When exposed to such evolutionary pressures, CIN promotes formation of a large pool of genetically diverse clones, which gives the tumour a better chance of adapting to its new environment; thus, CIN is an important driver of therapy resistance and metastasis (Ippolito et al., 2021; Lukow et al., 2021). Genomic pliability driven by CIN has been shown to facilitate many processes in cancer evolution, including acquisition of therapy resistance, likely by increasing heterogeneity under selective pressure (Chen et al., 2015; Ippolito et al., 2021; Lukow et al., 2021). CIN also promotes a stress state in cells that triggers an inflammatory response. Cancer cells exhibiting CIN rely on this response for survival (Hong et al., 2022) and modulate it to promote immune evasion and metastasis (Bakhoum et al., 2018; Li et al., 2023; Schubert et al., 2021 preprint). Taken together, the evidence supports a significant role for CIN in promoting karyotype evolution, which helps cancer cells adapt to their environment, and thus CIN facilitates selection of those karyotypes that provide the biggest fitness advantage to the cells at any point during tumorigenesis (Girish et al., 2023; Shih et al., 2023).

Cancer cell fitness

What does fitness mean in the context of cancer evolution? Although the term ‘fitness’ is widely used in biology, it is not well-defined in the context of cancer and is mostly used as a measure of growth (see Box 1). The environment and thus selection pressures are constantly changing in developing tumours, such as during metastasis, when cancer cells disseminate to remote tissues. Furthermore, when cancers are diagnosed and treated, cancer cells exploit genetic drift to acquire drug resistance in response to selection pressures induced by chemotherapeutics (Ippolito et al., 2021; Lipinski et al., 2016; Lukow et al., 2021).

Box 1. Fitness

In population genetics, the fittest individuals are defined as those with the highest proportion of progeny in the next generation (Freeman et al., 2014). Genetic drift of species over time modulates their fitness, and the resulting evolution favours survival of the fittest, allowing species to adapt to their constantly changing environment. Similar concepts apply to cancer cell evolution, in which genetic drift allows cells to change their behaviour and to become fitter than their non-transformed neighbours by dividing more frequently and overtaking their originating tissue.

When comparing cancer cell evolution to evolution of species, an ‘individual’ can be defined as a genetic clone (or even single cell) within the tumour cell population, much like in a group of asexually reproducing individuals (Sprouffske et al., 2012). Even in healthy tissues, fitter individuals ‘win’ over their less fit counterparts in a process termed cell competition (Bowling et al., 2019). What defines a ‘winner’, and thus a fitter cell, however, is context dependent. For instance, the fittest clone in a primary tumour might not be the fittest clone in a metastasis, as their environments are different, and the composition of primary tumours and metastases also often differs (Al Bakir et al., 2023; Turajlic et al., 2018a). Alternatively, a clone with low fitness in a naive tumour could become the fittest clone once the tumour is treated with chemotherapeutics (Salehi et al., 2021).

A simplistic way to measure fitness in the context of cancer is to quantify the number of cells present in a certain genetic clone, assuming bigger clones to be fitter. However, it is important to note that this is a snapshot measurement and might therefore underestimate fitness of smaller highly fit clones that arose recently. Fitness of cancer cell clones can also be inferred from the shape of genealogical trees (Neher et al., 2014). Similarly, the fitness effects of point mutations or chromosome aberrations can be estimated from their prevalence compared to background rates (Martincorena et al., 2017; Shih et al., 2023).

Fitness is determined not only by maximum proliferation potential, but also by other factors, such as reduced apoptosis, resistance to therapeutics and the capacity of cancer cells to evade the immune system (see poster). CIN and aneuploidy can significantly influence these factors and are therefore considered key factors that shape the fitness landscape (the relationship between genotype and fitness) of cancer cells. Indeed, it has recently been shown that in cancer certain aneuploidies are selected for over others based on their effect on fitness, providing a clear link between CIN and fitness (Shih et al., 2023). For example, recurrent loss of the short arm of chromosome 8 (8p) has been shown to be selected for in certain cancers; and loss of the WRN gene, which has been found to increase cell viability, is likely the driver of this preferential loss (Shih et al., 2023). This is a prime example of how fitness features can be translated into concrete drivers of cancer, and potentially identify new therapeutic targets.

Building tumour phylogenies

To identify the fittest clone in a population, the composition of the entire population first needs to be assessed. Cancer cells are, to some extent, subject to the same evolutionary principles as asexually reproducing species. Therefore, analyses developed to better understand the evolution of species can also be used to study tumour cell evolution over time. Tumour phylogeny is a method that uses evolutionary biology approaches to build the evolutionary tree of a tumour (or group of tumours) based on genetic information (Schwartz and Schäffer, 2017) (see poster). Using information such as the frequency and penetrance of certain mutations within the tumour, the relative timing of the appearance of individual mutations can be inferred, which can be used to draw an evolutionary tree retrospectively (Schwartz and Schäffer, 2017). Alternatively, introduction of lineage-tracing tools into cancer cell populations can facilitate subsequent monitoring of cancer cell evolution over time by comparing mutational landscapes with the proportions of lineage tracers (Gerrits et al., 2010). Both methods allow for determination of the number and size of (sub)clones in a tumour, which are important factors when assessing fitness.

Bulk DNA sequencing has long been used for building phylogenies using naturally occurring point mutations or copy number variations (CNVs) (Schwartz and Schäffer, 2017). When assessing tumour phylogeny in samples from individuals with cancer, multiregional bulk analyses are commonly used to capture clonal heterogeneity throughout the tumour. This is also known as multiregional sampling (see poster) (Jamal-Hanjani et al., 2014; Turajlic et al., 2018a,b; Zhao et al., 2016). Although these methods allow an assessment of regional heterogeneity and, to some extent, heterogeneity within a single biopsy, they fail to detect heterogeneity at the single-cell level or even to distinguish microscopic clones of a few hundred cells. Therefore, such methods grossly underestimate heterogeneity, particularly in tumours with substantial genomic plasticity, such as cells exhibiting CIN (Miura et al., 2020). Elucidating the evolutionary history and fitness consequences of cancer cell karyotypes in such cancers requires the application of single-cell genomics.

To better understand how highly dynamic cancers such as those that display CIN evolve, various methods for in vitro and in vivo single-cell lineage tracing can be used (see poster). A frequently used method for tracking individual cells is to introduce unique DNA barcodes into cells, for instance, by using viral transduction (Contreras-Trujillo et al., 2021; Sankaran et al., 2022). These barcodes enable longitudinal tracing of multiple (sub)groups of the same population in vitro or in vivo; this has been implemented in multiple different types of cancer (both solid and liquid) and on multiple timescales, ranging from weeks to months (Gerrits et al., 2010; Jacobs et al., 2020; Nolan-Stevaux et al., 2013; Seth et al., 2019). Mapping the barcode distribution throughout a timecourse experiment allows reconstruction of the dynamics of the barcoded clones. Although barcoding has been used extensively to track cells in vitro and in vivo, various new methods using CRISPR-Cas9 have recently been developed that make the tracing of cells in vivo even more effective (Bowling et al., 2020; Yang et al., 2022). These tools use tandem single guide RNAs (sgRNAs) to create a unique ‘scar’ in an engineered reporter sequence. Combining this with a system of conditional activation (for example, doxycycline-dependent activation) removes the necessity for ex vivo barcode introduction (Bowling et al., 2020; Yang et al., 2022). Some lineage-tracing tools also yield unique mRNAs so that lineage tracing can be combined with single-cell RNA sequencing (scRNAseq), which facilitates linking of clonal dynamics to expression of genes that influence fitness parameters (Bowling et al., 2020; Contreras-Trujillo et al., 2021; Yang et al., 2022).

However, an important limitation of these linage-tracing tools is that they cannot be used for studies in individuals or tissue biopsies. When assessing single-cell dynamics in primary samples, naturally occurring mutations and passenger mutations can be used to reconstruct the phylogenetic makeup of a tumour retrospectively (see poster) (Frumkin et al., 2005, 2008; Lee-Six et al., 2018; Shlush et al., 2012). Alternatively, the inherent variability of mitochondrial DNA can be used to construct tumour phylogenies (Lareau et al., 2021; Ludwig et al., 2019; Xu et al., 2019). Finally, copy number alterations (CNAs; gain or loss of chromosomes or chromosome fragments) can be used to partly reconstruct the evolutionary history of tumours (Lareau et al., 2021; Ludwig et al., 2019; Xu et al., 2019). However, assessing CNAs alone might also lead to an underestimation of evolutionary dynamics, as CNAs can appear and disappear without leaving a trace in the genome (Navin et al., 2011; Wang et al., 2014). Phylogenetic approaches have also been used to extrapolate disease progression to predict the risk of relapse and metastasis in patients (Schwartz and Schäffer, 2017; Zhao et al., 2016).

Assessing fitness using lineage tracing

Once the composition of a cancer cell population is known, phylogenetic trees and lineage tracing can be used to assess which clones in a population have higher or lower fitness. In its most simplistic form, this is done by counting the number of cells present in each clone, assuming that clones of higher fitness are larger than clones that are unfit. More advanced fitness algorithms combine clone size with other factors, such as branch length (genetic divergence) or number of clones, to determine the fitness landscape and map individual clones within this landscape (see poster) (Neher et al., 2014; Skums et al., 2019). However, the amount of information gained from these landscapes changes depending on sampling frequency within the population or which methods are used to deduce the landscape (point mutations or lineage tracing). For example, if a population is only sampled once, one needs to assume that the largest clone at the time of sampling is the fittest, as the past clonal dynamics of the population are not fully known. Longitudinal sampling reveals information about new clones that have emerged or clones that have disappeared from the population, thus yielding important insights into evolution and fitness dynamics (Salehi et al., 2021) (see poster).

Genomic information of single cells reveals their karyotype and identifies to which karyotypic (sub)clone they belong, while lineage tracing helps to disentangle how and when a clone developed and which clone is assumed to be most fit. However, this information does not reveal the mechanisms underlying why some clones are fitter than others. We will next discuss techniques that can be used to answer this question (see poster).

To understand how cell physiology is affected by chromosome copy number changes, and thus which molecular factors determine cellular fitness, single-cell measurements at the level of cell effectors (i.e. RNA and protein molecules) are required. One way to achieve this is by combining transcriptome analysis with lineage-tracing barcodes, as described above (Bowling et al., 2020). Although bulk RNA sequencing approaches are very powerful tools to map changes in global transcript expression in cancer tissue, they fail to detect transcriptional heterogeneity at the single-cell level. The advent of scRNAseq platforms has resolved this limitation and allows for profiling of the transcriptomes of individual cells. Combining this single-cell transcriptome data with lineage-tracing tools can reveal possible new drivers of cancer evolution. Furthermore, such analyses allow translation of basal findings about the impact of genome evolution on the transcriptome of individual cells towards new clinical intervention strategies (Gavish et al., 2023) (see poster). scRNAseq data can also be used to infer copy number states, taking advantage of the fact that RNA expression levels generally scale with DNA copy numbers. This allows for assigning of karyotypes to single cells and clones for which the transcriptome is known; thus, copy number state can be linked to particular fitness features (for example, the number of cycling cells) and to the fitness of smaller and larger clones (see https://github.com/broadinstitute/inferCNV; Gao et al., 2021). However, a caveat of this method is that, due to gene dosage compensation, scaling between DNA and RNA is not always proportional (Schukken and Sheltzer, 2022). This might skew ploidy inference in some cases (Chunduri et al., 2021; Schukken and Sheltzer, 2022). To overcome this limitation, techniques to sequence DNA and RNA from the same cell have recently been developed (Dey et al., 2015; Macaulay et al., 2015). In addition to dual DNA–RNA-omics, several other multi-omic approaches are being designed and combined (see Box 2 for selected examples).

Box 2. Examples of multi-omics approaches

Multi-omics is a quickly advancing field, with many approaches being developed to measure as many variables as possible (DNA, RNA and protein) within a single cell (see poster). Several types of multimodal measurements have been developed; for instance, the so-called assay for transposase-accessible chromatin using sequencing (ATAC-seq), which assesses open versus closed chromatin regions, has been used in conjunction with RNA sequencing (Xing et al., 2020); and other combined approaches have been used to correlate mRNA expression levels with protein levels (Genshaft et al., 2016; Gong et al., 2017). Another example using an ATAC-seq-based approach is PHAGE-ATAC, which employs phage-based protein measurements with ATAC-seq and retrospective mitochondrial DNA-based lineage tracing (Fiskin et al., 2022).

Regional proteomics could, in the future, also be combined with single-cell ATAC-seq on tissue slides. Such strategies could help to determine which signalling pathways contribute to a phenotype through the activation or repression of specific genes; this could be linked to karyotypes, especially now that algorithms are being developed to simultaneously obtain copy number information from ATAC-seq data (Nikolic et al., 2021; Ramakrishnan et al., 2023). Another approach is spatial multi-omics (SM-Omics), which combines spatial transcriptomics with spatial detection of selected proteins in sectioned tissue samples (Vickovic et al., 2022). Although this technique does not yet allow multiplexed detection of proteins, it shows promising future possibilities for linking transcriptomics to proteomics to better understand intratumour heterogeneity. scONE-seq, a method for combined DNA and RNA sequencing from single cells in frozen tumour samples, is another promising platform for the study of heterogeneity in primary human samples, and it has been used to determine CNVs and the resulting effects on the transcriptomes in a frozen astrocytoma sample (Yu et al., 2023).

Because CIN can lead to loss of protein complex stoichiometry, measuring the impact of CIN on the proteome will be key to identifying the mechanisms that allow cancer cells to cope with the resulting genomic abnormalities (Brennan et al., 2019; Dephoure et al., 2014; Oromendia et al., 2012). Importantly, because changes in the transcriptome are not always reflected in the proteome (Schukken and Sheltzer, 2022), especially in single cells (Genshaft et al., 2016), single-cell proteomes are considered to be more representative of the phenotypic consequences of a given karyotype. Therefore, single-cell proteomics is required to complement single-cell transcriptomics to explain phenotypic characteristics of individual cancer cells (Brunner et al., 2022). However, single-cell proteomics is still in its infancy, with several techniques currently in development (Mund et al., 2022; Reed et al., 2022; Vistain and Tay, 2021). Until such methods become widely available, immunohistochemistry approaches can be used to increase the regional resolution of bulk proteomic measurements (Clair et al., 2016; Zhu et al., 2018). Such approaches have the potential to reveal changes in protein expression that can be linked to fitness of a local clone. However, to correlate single-cell or regional proteomics to the fitness of (local) karyotype subpopulations, proteome data need to be linked to DNA or RNA sequencing data from the same individual cells or cells within the same region. One possible future approach is to combine regional proteomics with spatial scRNAseq (see Box 2); however, regional low-input proteomic methods are still in very early development (Vandereyken et al., 2023). Further work is required to develop such multimodal approaches and design bioinformatic tools that can integrate all of this data into ‘multi-omic’ quantifications of individual cells (Cui et al., 2023 preprint). With many multi-omics platforms becoming accessible as mainstream tools, the next challenge will be to integrate all the resulting data. For this, machine learning and artificial intelligence will likely be instrumental (Cui et al., 2023 preprint; Ma et al., 2020) (see poster).

Finally, in addition to single-cell DNA and RNA profiles, fitness effects of CIN on cancer cells and their environment can also be measured using other readouts. For example, CIN might influence the expression of surface proteins, the secretion of extracellular vesicles (EVs) or the cellular composition of the tumour microenvironment (TME) (Barkal et al., 2019; Chennakrishnaiah et al., 2020; Ekström et al., 2022; Ma et al., 2019; Zhang et al., 2023). Although we are only beginning to understand the impact of CIN on these factors in bulk samples, single-cell studies will be required to understand the complex interactions between aneuploid cells and the TME.

CIN can drive large-scale genomic reorganization, yielding pleiotropic consequences at the level of the transcriptome as well as the proteome. In addition, cancer cell evolution and cancer cell fitness are highly dynamic. Because studying cancer cell evolution and fitness each require advanced research methodologies, both concepts have mostly been studied separately. However, to understand how CIN shapes cancer cell genomes, integrating these two topics will be crucial, and steps are being made towards such integrated approaches. Indeed, combining these research fields will allow researchers to address important questions about fitness to determine which is the fittest clone in a tumour (using lineage tracing), what the genomes of these clones look like (using single-cell DNA or RNA sequencing), why certain clones are more or less fit than their counterparts (using integrated multi-omic analyses) and how these fitness effects further shape cancer cell evolution. Because recent work has shown that CIN can affect cells beyond their karyotype, for example by influencing immune responses (Li et al., 2023), it will be important to determine the various ways CIN can influence cancer cell fitness at different stages of disease progression (see poster).

Ultimately, a better understanding of how CIN shapes cancer cell fitness will help us to intervene in cancers that exhibit CIN either by promoting processes that reduce cancer cell fitness or, conversely, by inhibiting those that promote it. However, CIN has many pleiotropic effects that need to be measured at different levels (DNA, RNA, protein, immunity and phenotypic behaviours such as metastasis). In addition, how CIN affects fitness is likely influenced by context-dependent selective pressures. Therefore, to fully understand the complex relationship between CIN and cellular fitness and how this relationship can be exploited for new therapies will require a combination of multi-omic measurements at the single-cell level alongside novel lineage-tracing tools.

We are grateful to the members of the Genomic Instability in Development and Disease lab for fruitful discussion.

Funding

Our work in this area is funded by KWF Kankerbestrijding (grant number 18-RUG-11457 to F.F.).

High-resolution poster and poster panels

A high-resolution version of the poster and individual poster panels are available for downloading at https://journals.biologists.com/jcs/article-lookup/doi/10.1242/jcs.260199#supplementary-data.

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Competing interests

The authors declare no competing or financial interests.