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. 2023 Feb 10;26(3):106174. doi: 10.1016/j.isci.2023.106174

Targeting the metastatic niche: Single-cell lineage tracing in prime time

Elijah R Sommer 1,2, Giulia C Napoli 1,2, Cindy H Chau 1, Douglas K Price 1, William D Figg 1,∗
PMCID: PMC9988656  PMID: 36895653

Summary

Identification of actionable drug targets remains a rate-limiting step of, and one of the most prominent barriers to successful drug development for metastatic cancers. CRISPR-Cas9, a tool for making targeted genomic edits, has given rise to various novel applications that have greatly accelerated discovery in developmental biology. Recent work has coupled a CRISPR-Cas9-based lineage tracing platform with single-cell transcriptomics in the unexplored context of cancer metastasis. In this perspective, we briefly reflect on the development of these distinct technological advances and the process by which they have become integrated. We also highlight the importance of single-cell lineage tracing in oncology drug development and suggest the profound capacity of a high-resolution, computational approach to reshape cancer drug discovery by enabling identification of novel metastasis-specific drug targets and mechanisms of resistance.

Subject areas: Phylogenetics, Bioinformatics, Genomic analysis, Cancer

Graphical abstract

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Highlights

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    CRISPR-Cas9 lineage tracing and single cell transcriptomics have progressed immensely

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    Together, these technologies enable tracking of metastatic disease progression

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    Insights on metastatic dynamics will inform metastasis-targeted drug discovery

Phylogenetics; Bioinformatics; Genomic analysis; Cancer

Introduction

Elucidating the mechanisms of cancer progression in a way that is both informative and actionable has been difficult. Existing preclinical models of many human cancers are invariably biased toward clonal selection and loss of tumoral heterogeneity that call into question how well they truly approximate clinical disease.1,2,3 This constraint has led to a disconnect between therapeutic avenues that exhibit promise in current preclinical models yet fall short when applied to human trials, highlighting a need for new means of adequately simulating the behavior of primary tumors. Descriptions of tumoral gene expression in current models have conventionally been retrospective in nature and reliant on endogenous genetic aberrations (e.g. single-nucleotide polymorphisms or copy-number variations). The presence of distinct cell populations within a tumor presents a challenge on how to effectively target and/or monitor cancer progression. Thus, the biological timing and tumor loci at which key molecular events occur over the course of disease progression remain largely obscured to investigators. Assays taking advantage of this natural feature have long been restricted to resolving the clonally dominant patterns of genetic aberration; however, such patterns do not always coincide with targets that would be most potent for the design of effective molecular therapies.

Metastatic progression remains the leading driver of cancer-related mortality.4,5,6 In certain cases, metastatic disease can be reliably controlled with systemic chemotherapy7; unfortunately, for most cancers of solid organs including those of prostate, lung, and breast, distal metastasis precludes curative intervention.2,8,9 A better understanding of the molecular mechanisms of metastasis has long been a major focus in cancer biology and developmental therapeutics. Identification of incident-specific changes in gene expression may implicate therapeutically actionable targets or groups of targets in metastasis, thus directing the development of agents capable of staving off mortality attributable to metastatic progression.

Recent studies have employed a CRISPR/Cas9-based single-cell lineage tracing formula to enhance the temporal resolution of subclonal molecular aberration detection during the process of metastatic spread in orthotopic murine xenografts. The findings of these groups serve as proof of concept for a new way of studying the interplay between the transcriptional dynamics of a cancer cell and its relative contribution to the phenotypically observed metastatic aggression of the tumor. This iteration of lineage tracing has the power to reveal how, when, and why precise metastatic events occur over the course of disease progression. It also sheds light on how we can better target the metastatic niche for drug development efforts. In this perspective, we highlight two technological advances: CRISPR-based lineage tracing and single-cell sequencing. We describe how the marriage of these two methodologies can impact future drug development. The ability to track patterns of tumor spread will enable our understanding of cancer cell behavior, how tumors acquire resistance and evade treatment, and ultimately open up new avenues for drug development, including the discovery of novel therapeutics designed to specifically target metastatic disease.

Tracing cell lineages

Lineage tracing is an established tool of developmental biology that enables the progenic tracking of an original cell undergoing a series of divisions into various subpopulations giving rise to a tissue, organ, or organism. In recent years, the technique has been expanded by the integration of molecular interventions, such as DNA barcoding,10 which enable genetic readouts of a stable, heritable marker introduced into more complex organisms and tissues. Lineage tracing has more recently been integrated into cancer research,11 given its ability to provide information about the composition and evolutionary dynamics of clonal subpopulations comprising tumor tissue. Although this approach can be informative, its most primitive forms are intrinsically limited by the amount of natural genetic variation that arises between clones.12 Furthermore, experimental designs for conventional barcoding require the introduction of exogenous genetic recorders into the object of study before initiation of a tracing experiment. Consequently, temporal stratification of transcriptional events had conventionally not been possible with in vivo models.

Recent evolution of lineage tracing methodology has coupled molecular editing with Cas9 endonuclease to explore cell fate actualization. In 2016, McKenna et al. documented their lineage tracing method dubbed genome editing of synthetic target arrays for lineage tracing or “GESTALT,” which used Cas9 editing of a construct containing CRISPR target sites to mutational diversity arising throughout cell division.13 This strategy enables the tracking of cells via the progressive marking of the construct over the course of multiple divisions, permitting reconstruction of fine-scale relationships between cell populations, even in an in vivo setting. Several groups have since used CRISPR-Cas9 barcoding to untangle the role of shifting tumoral heterogeneity on metastatic behavior and identify targets that may be of importance in cancer metastasis and progression. For instance, the technique was used in tandem with multicolor reporters in a mouse sarcoma model to identify genes involved in the suppression of lung-specific metastasis and observed that metastasis was driven by clonal selection.14 It has also been employed to model genetic mechanisms causing resistance to EGFR inhibitors in non-small cell lung cancer and to study the effects of repairing a mutated oncogene (receptor tyrosine kinase anaplastic lymphoma kinase [ALK]) in neuroblastoma cells.15 Although this methodology has shown promise in the tracking of tumor cell lineage evolution and mapping tumor-immune cell interactions, future developments need to address real-time monitoring of distant metastasis in in vivo models to understand how the tumor evolves to develop escape mechanisms in its microenvironment and better identify markers of treatment resistance.

Single-cell transcriptomics

Compared with bulk sequencing, which generates expression profiles for a population of cells, single-cell sequencing allows for analysis of intratumoral heterogeneity as well as identification and characterization of treatment-resistant and metastatic subclones.16,17 Over the past decade, single-cell (SC) omics has evolved from SC transcriptome sequencing to SC whole-genome sequencing, to the emerging fields of SC proteomics18 and spatial transcriptomics.19 Single-cell transcriptomics (SCT) is the product of several distinct steps: SC isolation, cDNA synthesis, and PCR amplification, followed by sequencing and data analysis. Flow-activated cell sorting (FACS), microfluidic technologies, and microdroplet-based microfluidics have emerged as the most widely used techniques for the challenging isolation step.20 Although SCT data initially suffered from strong 3′ bias,18 the development of unique molecular identifiers (UMIs) has reduced amplification noise and transcription bias.21 UMIs, 8–10 bp labels attached to each RNA molecule before whole transcriptome amplification, were a major development in the SCT field, as the reaction efficiency of cDNA synthesis dictates the RNA analyzed downstream. Furthermore, SC data science encounters many unique challenges arising from the small amount of input material and large quantity of output, including noisy data, mapping individual cells to a reference atlas, and difficulty identifying patterns in spatially resolved measurements.22 This is in addition to the overall complicated workflow and existing technical bias from RNA sequencing. Further improvements are also needed in the context of transcriptomics applied to aneuploid cancer cells. The continued development of SCT technology has unlocked a new realm of possibilities in the study of cancer progression and dynamics.

In cancer research, SCT has mainly been used to study tumor and immune heterogeneity, intercellular communication, and the tumor microenvironment. SCT has also been employed to investigate metastasis, allowing for the comparison of the molecular signatures of primary cells and metastatic cells. In particular, some of the most exciting insights have been those related to the epithelial-mesenchymal transition (EMT), a type of cellular reprograming that endows cancer cells with enhanced migratory qualities, and a potentially crucial step in the metastatic process.23 Puram and colleagues24 compared the transcriptomics of primary and metastatic tumor ecosystems from 18 patients with head and neck squamous cell carcinoma, identifying a “partial epithelial-to-mesenchymal transition” program lacking many of the transcription factors thought to drive EMT. Recently, SCT was used to identify five different endothelial cell (EC) phenotypes in various states of EC activation and blood brain barrier impairment in human glioblastoma patients, illuminating the distinction between ECs in the tumor periphery, the tumor core, and peripheral brain tissue.25

Circulating tumor cells (CTCs) have likewise proven a fitting target for SCT because of their scarcity. An SCT analysis of pancreatic cancer CTCs compared with primary tumors and tumor-derived cell lines showed that CTCs were enriched for Aldh1a2 (a stem cell-associated gene), co-expressed epithelial and mesenchymal markers, and overexpressed extracellular matrix proteins, including osteonectin (SPARC), a glycoprotein associated with metastatic migration.26 Cheng et al.27 used a hydrodynamic cell capture method (Hydro-Seq) coupled with SCT to detect drug targets for breast cancer in CTCs, including ER, PR, AR, and HER2. Analysis of these CTCs for EMT and mesenchymal-to-epithelial transition (MET)-related genes revealed that most of the CTCs studied were mesenchymal cells, although many exhibited the plasticity to perform EMT or MET and further differentiate into tumor regulator cells. Most recently, SCT has been coupled with Cas9 lineage tracing systems to enable lineage tracing at the SC level and trace the journey of metastatic progression.

Emergence of a dynamic duo

A timeline of landmark developments leading to Cas9-based single-cell lineage tracing in cancer biology is shown in Figure 1. In early 2018, a handful of groups became the first to integrate CRISPR-Cas9 barcode editing with SCT in zebrafish. This began with Raj et al.28 building upon the GESTALT platform13 to explore zebrafish brain developmental trajectories by coupling the barcode editing with SCT, which they dubbed scGESTALT. Shortly thereafter, Spanjaard et al.,29 using an analogous platform called LINNEAUS, reconstructed developmental lineage trees for multiple organs in adult fish. Similarly, Alemany and colleagues30 employed their platform ScarTrace to study how embryonic progenitors give rise to various organs of the fish and elucidate both developmental and regenerative cell fate determination. Subsequent work has extended into mammalian model systems to study cell fate during organism development as a function of cellular transcriptomes.31 In 2020, Bowling et al. engineered a doxycycline-inducible mouse line, CARLIN (CRISPR array repair lineage tracing), for doxycycline-inducible lineage tracing using unique barcodes, which they used to track blood progenitor clones to adulthood and examine the clonal behavior of blood replenishment after chemotherapeutic lympho-myeloablation.32

Figure 1.

Figure 1

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Timeline of major developments leading to application of Cas9 lineage tracing in cancer biology

Figure created with BioRender.

Further work in this space has sought to refine some of the computational barriers inherent in working at the single-cell level. Zafar et al.33 developed LinTIMaT, a statistical method that incorporates transcriptomic data into the reconstruction of lineage trees, alleviating some of the limitations of doing so based on mutational analysis alone. This method may importantly allow for the pooling of lineage tracing data from separate experiments within a cell line, enhancing the ease with which reproduction of results between scientific groups can be appraised for validity.

Limitations to the CRISPR-Cas9-based lineage tracing system include inefficiency of barcode capture rate and rapid mutations, which impacts lineage reconstruction. Barcode evolution approaches are in development to improve both technical and computational issues that would prevent data loss. Improvements in the genome-editing toolbox with base and prime editing enables targeted editing without generating double-stranded DNA breaks like traditional CRISPR. Thus, CRISPR lineage tracing would be more accurate and allow for lineage tracing of more complex metastatic models over time.

Dynamic lineage tracing and cancer metastasis

Recent studies have employed CRISPR/Cas9-based single-cell lineage tracing as a means of enhancing the temporal resolution with which to detect subclonal molecular aberrations occurring during the metastatic process in vivo.34,35 Quinn et al. investigated subclonal metastatic behavior in an orthotopic mouse model of lung adenocarcinoma using a Cas9-based, single-cell lineage tracer. Notably, their single-cell genomic and transcriptomic analyses showed a distinctive pattern of tissue distribution for each major clonal population examined with respect to incidence, directionality, and specificity of metastasis from the primary lung graft to five different metastatic sites. They also modeled subclonal dynamics using their computational data to develop a robust radial phylogenetic tree for each individual clone based on indel accrual, showing partitioning of subclones into various definable clades organized by evolved allelic variation in the recording construct. A significant finding from their data suggested that the metastatic potential of a given subclone seeded in vivo was effectively independent of its proliferative predisposition relative to other members of the tumor population. That is to say, tendency toward growth and distribution of cancer cells may be thought of as uncoupled phenomena. This would further enable the potential for not only monitoring metastatic spread but also identifying early prognostic biomarkers of disease recurrence.

The authors also used single-cell sequencing to investigate gene signatures involved in driving metastasis by comparing metastatic and non-metastatic clones, using the most strongly metastasis-correlated (or anticorrelated) markers in a functional assay as sgRNA targets in CRISPR interference or CRISPR activation in A549 cells to drive sequence-specific repression or gene activation, respectively. In a transwell invasion assay, knockdown or activation modulated invasiveness in accordance with the putative pro- or anti-metastatic functions of each respective target candidate. They used these targets functionally associated with metastasis to develop a “signature” for metastatic potential. Their approach demonstrates the power of the CRISPR-Cas9-based lineage tracing to identify candidate genes that are strongly associated with metastasis, use them to inform functional analysis of target activity, and subsequently derive predictive power from targets identified.

In a similar vein, Simeonov et al.35 developed macsGESTALT, a doxycycline-inducible tracing system, and applied it with scRNA-seq to a mouse model of metastatic pancreatic ductal adenocarcinoma (PDAC), focusing on describing the EMT. Of all clones, 85% were non-aggressive, transcriptionally similar, and showed elevated expression of epithelial markers; conversely, they found aggressive clones were rare, but transcriptionally divergent, and showed enriched expression of mesenchymal markers. UMAP (uniform manifold approximation and projection for dimension reduction) was employed for visualization of SCT data, representing cells as points on a 2D plane.36 Epithelial and mesenchymal UMAP regions were poorly segregated, pointing to a continuum of EMT states, instead of a binary.

Their trajectory analysis revealed that the main trajectory in the data corresponded to the EMT gene expression axis, namely “pseudoEMT.” Expression of epithelial markers was highest at the root of the trajectory, whereas both epithelial and mesenchymal markers alike rose and fell along the expression axis. Most clones were highly epithelial, leading the authors to conclude that the cells’ baseline states are epithelial, whereas mesenchymal EMT states appear in vivo. To clarify gene expression along the EMT axis in vivo, the most differentially expressed subset of genes was hierarchically clustered along the pseudoEMT spectrum. Early clusters were enriched for epithelial cell polarity genes, whereas later hybrid states displayed peaks in TGF-β signaling, an inducer of EMT and metastasis in the later phases of cancer progression.37 Within a particularly aggressive clone, the s100a gene family, implicated in tumor growth and metastasis for many cancer types,38 was 52-fold overenriched among genes positively associated with subclonal dissemination, identifying this gene family as a potential treatment target in PDAC. On the whole, Simeonov’s work elegantly illuminates how dynamic lineage tracing might help us better understand developmental reprogramming intrinsic to cancer cells, offering a technique for understanding the relationship between specific transcriptional and phenotypic changes exemplified by the EMT, and thereby those that coincide with metastasis.

The findings described in this section serve as proof of concept for a new way of studying the interplay between the metastatic activity and expression patterns of subclonal populations. These publications have prompted speculation on the broader implications this may have for cancer drug development. Indeed, such applications may help uncover actionable explanations for precise metastatic events occurring over the course of disease progression. The insights that may be gained from the application of dynamic lineage tracing to the study of treatment resistance could similarly point to better ways of precluding or circumventing its emergence. The improved resolution offered from coupling computational methods to robust cell-lineage tracing techniques may shift the paradigm for how drug developers view and approach the topic of cancer metastasis. An overview of this pipeline is offered in Figure 2.

Figure 2.

Figure 2

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Generalized workflow for dynamic lineage tracing, phylogenetic tree reconstruction, and applications to drug development

Figure created with BioRender.

Impact on drug discovery and development

The application of Cas9-based single-cell lineage tracing to cancer research reveals variation at the level of individual cells, dramatically enhancing the temporal resolution with which transcriptional changes associated with metastatic disease progression can be described. The methodologies highlighted herein illustrate the scale and power of computational biology, as well as the role this discipline can and will continue to play in understanding complex phenomena such as metastasis. We believe dynamic lineage tracing could become a standard mode of studying the progression of cancer and other diseases characterized by genomic and transcriptomic aberrancy. By coupling the precision of CRISPR-based tracer technology with scRNA sequencing, Quinn et al. demonstrated subclonal variation in the directionality and the rate with which tumor cells spread and propagate in vivo. Simeonov’s group similarly unveiled critical insights about the nature of the EMT phenomenon with macsGESTALT. The methods of these groups suggest a new means to monitor crucial changes in cellular activity that may be diminished or even imperceptible based on macroscopic observation of a tumor’s transcriptome through less elegant methods, allowing full encapsulation of subclonal heterogeneity.

Identification of distinct and reliable cellular signatures associated with heightened likelihood of metastatic events will be important for identification of better cancer drugs. Better understanding of the biology of metastatic potential unique to a given tumor subtype may inform therapeutic regimentation and timing strategies to best mitigate risk of metastasis on a patient-by-patient basis. In particular, Quinn et al.’s work draws an important distinction between the metastatic or invasive behavior and the proliferative characteristics of a tumor’s many clonal populations. The notion that a tumor clone’s growth is effectively uncoupled from its propensity to metastasize is a critical point—one that may call for a revised view of the practicality of universally using cytotoxic therapies, which target hyperproliferative cells, to fight metastasis. Preclinical development of oncology drug candidates may place inordinate focus on suppression of tumoral growth.39 The findings reflected upon here offer a caveat for the use of antiproliferative potential as a proxy marker by which drugs intended to target metastatic progression are preclinically evaluated. Given the immense financial burden of validating and bringing a drug candidate to market,40 robust early identification and minimization of false positives during the discovery phase is an imperative.

Insights acquired from high-resolution genomic and transcriptomic study of subclonal lineages have important implications for drug development as well. The conventional paradigm for cancer drug development relies on identification of discrete targets through comparative expression profiling. Yet, study of the metastatic process through a lower-resolution lens foregoes the opportunity to catch fluctuations in critical, transient mediators potentially representing otherwise viable drug targets, some of which may be even better suited to preventing disease progression than their more readily quantitated counterparts. Although metastasis is likely driven by a host of different pathways involving myriad target molecules,41 dynamic lineage tracing has the power to sort out how these markers involved fluctuate in relationship to one another over the time horizon of distal growth.

Studying cancer biology and the process of metastasis on finer spatial and temporal scales also represents an avenue for developing a better understanding of therapeutic resistance. By tracking the directionality of clonal metastatic dissemination in their mouse model, Quinn’s group noted some metastatic clones had reseeded the primary tumor site, which may factor into treatment resistance. Attempts to counter emergent drug resistance in clinic often involve therapeutic switching; identification and concurrent targeting of resistance mechanisms may maximize benefit to the patient. Further, insights gained from the study of treatment resistance via lineage tracing could point to better ways of stopping or delaying its emergence in cancer. In this very regard, single-cell lineage tracing has been used to demonstrate that human B-cell acute lymphoblastic leukemia clones exhibited distinct responses to chemotherapy, with consistent responses observed across multiple patient-derived xenografts for a given treatment regimen.42 Beyond this, better characterization of the early mechanisms of therapeutic resistance can similarly inform how available interventions might be optimally leveraged (with respect to treatment sequencing and/or scheduling) to prevent metastasis.

Metastasis remains responsible for the preponderance of cancer-related deaths from solid tumors. An enhanced focus on the prevention of metastasis early on in disease treatment is crucial for improving patient outcomes. Therefore, understanding the molecular features of metastatic disease remains a priority to improve drug development efforts to this end. Cas9-based single-cell lineage tracing may serve as a gateway through which these mechanisms can be more aptly appraised. It also has the power to elucidate the spatial dynamics behind metastatic progression and reveal details of the trafficking of metastatic cells to distant sites. In fact, the notion of metastatic directionality of a cancer cell as being transcriptomically determined suggests that dynamic lineage tracing may allow for the identification of common tissue choke points through which clonal populations of a given solid cancer preferentially progress during metastasis and/or accumulate disproportionate tumor burden. Exploration of metastatic driver genes and tissue-to-tissue cancer cell dissemination to "metastatic hubs" at the single-cell level can inform our understanding of the timing of hub instantiation at distant sites, as well as the development of agents that target the metastatic process. Although the prospect of prophylactic interventions against metastatic disease progression may seem far-fetched, technological advances indicate that it may be closer than ever. The identification of causative molecular events through technologies such as dynamic lineage tracing will propel the pursuit of such treatments.

Preclinical use of dynamic lineage tracing will likely become of increased importance to examine disease-specific metastasis mechanisms in vivo. Further application of combinatorial genomic and computational approaches to help elucidate cellular dynamics and therapeutic vulnerabilities will help define the usefulness of these methods. In this way, single-cell lineage tracing of the metastatic niche may eventually enable us to effectively combat and perhaps even override the phenomenon of metastasis for many cancers, improving survival outcomes for patients across the board.

Acknowledgments

This work was supported by the Intramural Research Program of the Center for Cancer Research, National Cancer Institute, National Institutes of Health (ZIA BC 010547). The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does it mention of trade names, commercial products, or organization imply endorsement by the U.S. Government.

Author contributions

Conceptualization, E.R.S, C.H.C., W.D.F.; Writing—Original Draft, E.R.S, G.C.N., and C.H.C.; Writing—Review and Editing, E.R.S, G.C.N., C.H.C., D.K.P., and W.D.F.; Funding Acquisition, W.D.F.; Supervision, C.H.C. and W.D.F. All authors contributed to the article and approved the submitted version.

Declaration of interests

None reported.

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