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Lung Cancer Metastasis Study Relies on CRISPR-Based Method to Track Single-Cell Lineages

NEW YORK — Researchers have developed a CRISPR-based, single-cell lineage tracer method and used it to study how lung cancer metastasizes, with the goal of predicting metastatic behavior.

Phylogenetic trees based on the genomes of tumors and metastases can trace back mutation patterns that emerge during cancer development. By combining Cas9-based lineage tracing and single-cell RNA sequencing, researchers from the University of California, San Francisco, and elsewhere have now developed a way to prospectively track cancer progression in a mouse model.

As they reported in Science on Thursday, the researchers generated phylogenies for thousands of cancer cells and uncovered heritable differences in their ability to metastasize. They further uncovered genes that contribute to metastatic ability as well as those, like KRT17, that may suppress metastatic activity.

"With this method, you can ask questions like, 'How frequently is this tumor metastasizing? Where did the metastases come from? Where do they go?'" senior author Jonathan Weissman, a researcher at the Whitehead Institute and the Massachusetts Institute of Technology, said in a statement. "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."

For their new method, the researchers added the gene encoding the Cas9 protein and a luciferase gene into cells from a human KRAS-mutant lung adenocarcinoma cell line. They then introduced about 5,000 of these engineered cells into the left lungs of immunodeficient mice. After 54 days, they collected lung and other tissue samples from the mice for single-cell sequencing analysis. As the cells divided during that time, Cas9 made cuts leading to indels in the genomes of the daughter cells that the researchers could track.

While the scientists implanted about 2,150 clones, only about 100 engrafted and proliferated, indicating that only a portion of the engineered cells were competent to do so.

The clonal populations exhibited different distributions across the six sampled tissues. Some clones, for instance, were only found at the primary site, while others were more broadly found, suggesting metastatic heterogeneity, despite the cells all coming from the same cell line.

The researchers reconstructed these cells' phylogenies, uncovering patterns. For instance, non-metastatic populations were marked by all the clades residing in a single tissue, while highly metastatic populations had closely related cells found in different tissues. Intermediate populations, though, also had a distributed pattern.

At the same time, the researchers noted a modest link between a cell's transcriptional pattern and its tissue sample or metastatic rate. Through this, they homed in on genes associated with metastasis, such as IFI27, REG4, and TNNT1, and ones that damp down metastasis like NFKBIA, ID3, and ASS1.

KRT17 was strongly correlated with reduced metastatic potential, a finding the researchers noted was striking as it has previously been linked to promoting invasiveness and has been associated with poor prognosis in some cancers.

In a functional analysis, the researchers found that CRISPR knockdown of metastasis-associated genes like IFI6 and IFI27 led to decreased invasiveness, but knockdown of genes like KRT17, ID3, and ASS1 that are negatively associated with metastasis led to increased invasiveness. With these results, the researchers developed a metastatic signature that could, to a modest degree, gauge metastatic potential.

Further, they noted that this metastatic behavior was largely heritable, though one clone was an exception to that rule.

Going forward, the researchers hope to be able to make predictions about how cells will behave. "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 said. "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."

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