A new CRISPR-based method for tracing real-time cancer progression across thousands of cells has revealed novel insights into the rates, routes, and drivers of cancer metastasis. Using the lineage-tracing technique, Whitehead Institute member Jonathan Weissman, PhD, and colleagues were able to treat cancer cells in much the same way that evolutionary biologists might look at species, mapping out an intricately detailed family tree. The approach allowed the authors to generate phylogenies and follow the movement of metastatic human cancer cells over several months of growth and dissemination, in a lung cancer xenograft mouse model.
By examining the branches it is then possible to track cell lineage to find when a single tumor cell went rogue, spreading its progeny to the rest of the body. “With this method, you can ask questions like, ‘How frequently is this tumor metastasizing? Where did the metastases come from? Where do they go?'” said Weissman, who is a professor of biology at the Massachusetts Institute of Technology, and an investigator with the Howard Hughes Medical Institute. “By being able to follow the history of the tumor in vivo, you reveal differences in the biology of the tumor that were otherwise invisible.”
Through their studies, which are reported in Science, the investigators also identified distinct gene expression profiles associated with metastatic phenotypes, which revealed candidate genes with previously unknown roles in cancer progression. The team suggests that the new lineage tracing technique could inform many other difficult-to-observe facets of cellular cancer biology, including genetic mutations, microenvironment adaptation, and the acquisition of resistance to therapeutic agents.
Weissman’s lab, together with Howard Hughes Medical Institute colleague Jeffrey J. Quinn, PhD, and colleagues Nir Yosef, PhD, a computer scientist at the University of California, Berkeley, and Trever Bivona, MD, PhD, a cancer biologist at the University of California, San Francisco (UCSF), reported on their studies in in a paper titled, “Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts.”
When cancer is confined to one spot in the body, it can often be treated using surgery or other therapies. Much of the mortality associated with cancer, however, is due to its tendency to metastasize, and take root throughout the body. However, the exact moment of metastasis is fleeting, lost in the millions of divisions that take place in a tumor. “Metastasis is a particularly critical step in cancer progression to study because it is chiefly responsible for cancer-related mortality,” the authors wrote. “Yet because metastatic events are intrinsically rare, transient, and stochastic, they have proved challenging to monitor in real time. ” As Weissman added, “these events are typically impossible to monitor in real time.”
Scientists have, in the past, tracked the lineages of cancer cells by comparing shared mutations and other variations in their DNA blueprints. These methods, however, depend to a certain extent on there being enough naturally occurring mutations or other markers to accurately show relationships between cells.
“Classical lineage tracing strategies can infer tumor ancestry from the pattern of shared sequence variations across tumor subpopulations (e.g., naturally occurring mutations, like single-nucleotide polymorphisms or copy-number variations),” the team continued. However, they pointed out, as well as being limited by the number of natural distinguishing mutations such “retrospective” approaches to tracing have other limitations that can confound the conclusions.
Weissman and co-first authors Quinn, then a postdoctoral researcher in Weissman’s lab, and Matthew Jones, a graduate student in Weissman’s lab, saw an opportunity to use CRISPR technology—specifically, a method developed by Weissman Lab member Michelle Chan,PhD, to track embryo development—to facilitate lineage tracing. “The recent development of Cas9-enabled lineage tracing techniques with single-cell RNA-sequencing readouts provides the potential to explore cancer progression at vastly larger scales and finer resolution than has been previously possible with classical prospective or retrospective tracing approaches,” the team wrote.
Instead of simply hoping that a cancer lineage contained enough lineage-specific markers to track, the researchers used the new method to add in markers themselves. “Basically, the idea is to engineer a cell that has a genomic scratchpad of DNA, that then can be ‘written’ on using CRISPR,” Weissman said. This “writing” in the genome is done in such a way that it becomes heritable, meaning a cell’s grand-offspring would have the “writing” of its parent cells and grandparent cells recorded in its genome.
To create these special “scratchpad” cells, Weissman engineered human cancer cells with added genes: one for the bacterial protein Cas9—the “molecular scissors” used in CRISPR genome editing methods—others for glowing proteins for microscopy, and a few sequences that would serve as targets for the CRISPR technology. The investigators then implanted thousands of the modified human cancer cells into mice, to mimic a human lung tumor (a model developed by collaborator Bivona). And given that mice with human lung tumors often exhibit aggressive metastases, the researchers reasoned the model would be ideal for tracking cancer progression in real time.
As the cells began to divide, Cas9 made small cuts at these target sites. When the cell repaired the cuts, it patched in or deleted a few random nucleotides, leading to a unique repair sequence called an indel. This cutting and repairing happened randomly in nearly every generation. Writing in their paper, the authors explained, “Briefly, Cas9 cuts a defined genomic locus (hereafter ‘Target Site’), resulting in a stable insertion/deletion (indel) ‘allele’ that is inherited over subsequent generations; as the cells divide, they accrue more Cas9-induced indels at additional sites that further distinguish successive clades of cells.” This method effectively creates a map of cell divisions that Weissman and the team could then track using special computer models that they created by working with Yosef.
Tracking cells this way yielded some interesting results, the team reported. For example, it was found that individual tumor cells were much different from each other than the researchers had expected. The cells the researchers used were from an established human lung cancer cell line called A549. “You’d think they would be relatively homogeneous,” Weissman said. “But in fact, we saw dramatic differences in the propensity of different tumors to metastasize—even in the same mouse. Some had a very small number of metastatic events, and others were really rapidly jumping around.”
To find out where this heterogeneity was coming from, the team implanted two clones of the same cell in different mice. As the cells proliferated, the researchers found that their descendants metastasized at a remarkably similar rate. This was not the case with the offspring of different cells from the same cell line—the original cells had apparently evolved different metastatic potentials as the cell line was maintained over many generations.
The scientists next wondered which genes were responsible for this variability between cancer cells from the same cell line. So they began to look for genes that were expressed differently between nonmetastatic, weakly metastatic, and highly metastatic tumors.
Many genes stood out, some of which were previously known to be associated with metastasis, although it was not clear whether they were driving the metastasis or simply a side effect of it. “We report deeply resolved phylogenies for tens of thousands of cancer cells traced over months of growth and dissemination,” the team noted. “This revealed stark heterogeneity in metastatic capacity, arising from pre-existing and heritable differences in gene expression.” For example, the team found that one of these genes, KRT17, which codes for the protein Keratin 17, is much more strongly expressed in low metastatic tumors than in highly metastatic tumors. “When we knocked down or overexpressed Keratin 17, we showed that this gene was actually controlling the tumors’ invasiveness,” Weissman said.
Being able to identify metastasis-associated genes this way could help researchers answer questions about how tumors evolve and adapt. “It’s an entirely new way to look at the behavior and evolution of a tumor,” Weissman said. “We think it can be applied to many different problems in cancer biology.”
As the authors commented, “It will also be of interest to investigate the roles that other gene candidates identified here play in metastasis, as well as to elucidate the molecular mechanism by which KRT17 suppresses metastatic phenotype in vitro and in vivo—an unexpected role that this work uncovered.”
Weissman’s CRISPR method also allowed the researchers to track with more detail where metastasizing cells went in the body, and when. For example, the progeny of one implanted cancer cell underwent metastasis five separate times, spreading each time from the left lung to other tissues such as the right lung and liver. Other cells made a jump to a different area, and then metastasized again from there. These movements can be mapped neatly in phylogenetic trees, where each color represents a different location in the body. A very colorful tree shows a highly metastatic phenotype, where a cell’s descendants jumped many times between different tissues. A tree that is primarily one color represents a less metastatic cell.
“By applying our next-generation, Cas9-based lineage tracer to a mouse model of metastasis, we observed meaningful features of metastatic biology that were only apparent by virtue of subclonal lineage information,” the team stated. “Among these key insights were the broad range of metastatic rates for different tumor populations, the pre-existence and stable heritability of these heterogeneous metastatic phenotypes, and the complex, multidirectional tissue routes by which cancer cells disseminate in this model.
Mapping tumor progression in this way allowed Weissman and his team to make a few interesting observations about the mechanics of metastasis. For example, some clones seeded in a textbook way, traveling from the left lung, where they started, to distinct areas of the body. Others seeded more erratically, moving first to other tissues before metastasizing again from there. One such tissue, the mediastinal lymph tissue that sits between the lungs, appears to be a hub of sorts, said co-first author Quinn. “It serves as a waystation that connects the cancer cells to all of this fertile ground that they can then go and colonize.” And thinking therapeutically, the discovery of metastasis “hubs” could be extremely useful. “If you focus cancer therapies on those places, you could then slow down metastasis or prevent it in the first place,” Weissman said.
“Our work establishes that it is now possible to uniquely distinguish tens of thousands of cells over several months of growth in vivo, reconstruct deeply resolved and accurate cell phylogenies, and then interpret them to identify rare, transient events in the cells’ ancestry (here, metastasis) revealing otherwise unapparent distinctions in cellular phenotypes,” the authors concluded.
In the future, Weissman hopes to move beyond simply observing the cells and begin to predict their behavior. “It’s like with Newtonian mechanics—if you know the velocity and position and all the forces acting on a ball, you can figure out where the ball is going to go at any time in the future,” Weissman commented. “We’re hoping to do the same thing with cells. We want to construct essentially a function of what is driving differentiation of a tumor, and then be able to measure where they are at any given time, and predict where they’re going to be in the future.”
And extending beyond metastasis, the team says the approach could be used to provide new insights into other aspects of cancer biology, “… like the timing or order of genetic mutations during malignant transformation, adaptation to different tumor microenvironments, or the origin and mechanism by which tumor cells acquire resistance to therapeutic agents.”
The researchers are optimistic that being able to track the family trees of individual cells in real time will prove useful in other settings as well. “I think that it’s going to unlock a whole new dimension to what we think about as a measurable quantity in biology,” said co-first author Matthew Jones. “That’s what’s really cool about this field in general is that we’re redefining what’s invisible and what is visible.”
As the authors concluded, “ … beyond cancer, our approach has the potential to empower the study of the phylogenetic foundations of biological processes that transpire over many cell generations at unprecedented resolution and scale.”