Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Abstract

Following cancer through the body The heterogeneity of mammalian tumors has been well documented, but it remains unknown how differences between individual cells lead to metastasis and spread throughout the body. Quinn et al. created a Cas9-based lineage tracer and used single-cell sequencing to generate phylogenies and follow the movement of metastatic human cancer cells implanted in the lung of a mouse xenograph model. Using this model, they found that within the same cell line, cancer cells exhibited diverse metastatic phenotypes. These subclones exhibited differential gene expression profiles, some of which were previously associated with metastasis. Science, this issue p. eabc1944 A Cas9-based lineage tracer elucidates the process of cancer metastasis. INTRODUCTION Cancer progression is fundamentally an evolutionary process, involving multiple distinct steps from oncogenic transformation to metastatic dissemination to development of therapeutic resistance. Defining the timing and molecular nature of each step in this process is critical to understanding cancer biology and to devising effective therapeutic strategies. Yet, it is challenging to directly observe these events because they unfold stochastically, in rare subpopulations of cells, and over long time scales. RATIONALE By reconstructing the phylogenetic relationships of tumor cells, it is possible to infer important features of these abstruse, past events, such as the timing or directionality of metastatic dissemination. Advances in Cas9-based molecular recording technologies have now enabled the high-resolution mapping of subclonal cellular lineages that, when paired with single-cell RNA sequencing, can be used to infer distant events in a cell’s past history and connect them to its present state. RESULTS We refined and applied our Cas9-based lineage recorder to study metastatic dissemination in implanted tumor cells in a mouse. These advances allowed us to resolve deep and accurate single-cell lineages for tens of thousands of metastatically disseminated cancer cells in a mouse over 2 months of growth. We leveraged the reconstructed phylogenetic trees to measure the frequency of metastatic dissemination at single-cell resolution. Unexpectedly, we found that tumors arising from individual implanted cancer cells showed dramatic differences in their capacity to spread to distant tissues, ranging from completely nonmetastatic (i.e., never leaving the primary site) to aggressively metastatic (i.e., frequently transiting between tissues). We paired this high-resolution lineage information with single-cell RNA sequencing to reveal characteristic sets of genes that underlie these divergent metastatic phenotypes. This analysis identified some genes that are known to modulate metastatic phenotype in lung cancers (such as ID3 and REG4) and others that were unexpected (such as KRT17). Using CRISPR-based perturbations in two distinct lung cancer cell lines, we validated that over- or underexpression of genes identified by this analysis was sufficient to modulate cancer invasion phenotypes. We then showed that the diversity of metastatic phenotypes observed in vivo stemmed from preexisting, heritable cellular states. Specifically, the heterogeneity in the expression of metastasis-associated genes existed before implantation into the mice, and cells with higher metastatic transcriptional signatures preimplantation went on to be more metastatic in vivo. Moreover, when cells from the same clone were implanted into different mice, they exhibited nearly identical metastatic behavior. Finally, we traced the complex and multidirectional routes through which the metastases disseminated from tissue to tissue and identified the mediastinal lymphatic tissue as a transit hub for metastasis. We also found numerous examples of different seeding topologies, including reseeding (i.e., cancer cells returning to the primary tumor site from a metastasis) and seeding cascades (multistep metastatic seeding). CONCLUSION High-resolution lineage recording of tumor cells revealed a rich diversity of metastatic phenotypes and behaviors that would have otherwise been unobservable. This illuminated aspects of cancer biology that are essential to understanding disease progression, such as the rates, tissue routes, and transcriptional drivers of metastasis. We anticipate that our approach will be broadly applicable to future studies of other aspects of cancer biology, such as the evolution of drug resistance, and, more generally, of biological processes that unfold over many cell generations. Exploring metastatic biology by tracing the lineages of engineered cancer cells implanted into mouse lungs. From cell ancestry information, we reconstructed high-resolution family trees of metastatically disseminated tumor cells and found populations with diverse metastatic behaviors. Pairing cell-state information with these lineages, we identified genes that drive metastatic capacity (1), observed heterogeneous metastatic states (2), and followed the tissue routes of metastatic dissemination (3). Detailed phylogenies of tumor populations can recount the history and chronology of critical events during cancer progression, such as metastatic dissemination. We applied a Cas9-based, single-cell lineage tracer to study the rates, routes, and drivers of metastasis in a lung cancer xenograft mouse model. We report deeply resolved phylogenies for tens of thousands of cancer cells traced over months of growth and dissemination. This revealed stark heterogeneity in metastatic capacity, arising from preexisting and heritable differences in gene expression. We demonstrate that these identified genes can drive invasiveness and uncovered an unanticipated suppressive role for KRT17. We also show that metastases disseminated via multidirectional tissue routes and complex seeding topologies. Overall, we demonstrate the power of tracing cancer progression at subclonal resolution and vast scale.