CRISPR Combined with DNA Barcodes to Monitor Tumor Growth

CRISPR Combined with DNA Barcodes to Monitor Tumor Growth

Researchers at Stanford University have combined CRISPR gene editing technology with DNA barcoding allowing them to monitor individual cells in tumors and accurately model human cancer.

The researchers used CRISPR to make edits to cancer-related genes in the lungs of mice and insert genetic barcodes which can be used to track the growth of tumors. By adding a unique identifier to tumor cells, each cell divides, both new cells get a copy of the bar code, which can be easily tracked.

Previously, in order to accurately track the growth of tumors it was necessary to excise the tumor and measure its size. While that could be done, there are problems with this approach, such as when two different tumors are stuck together or when tumors are in irregular shapes and are therefore difficult to remove.

This new method makes analysis much more straightforward. Rather than excising the tumor, it is possible to take the entire tissue, grind it up, subject the resultant material to high-throughput DNA sequencing and computational analysis to count the individual copies of each barcode. This technique makes it far easier, more precise, and faster to compare tumor sizes.

“We can now generate a very large number of tumors with specific genetic signatures in the same mouse and follow their growth individually at scale and with high precision. The previous methods were both orders of magnitude slower and much less quantitative,” said Dmitri Petrov, senior author of the study.

Using standard techniques for mapping tumors and tumor suppression mutations would have taken years and hundreds or thousands of mice, each with a specific type of tumor. This method greatly speeds up the process and only requires a few mice. “We’ve analyzed more genotypes of lung cancer tumors than the whole field has in 15 years,” said Monte Winslow, senior author of the study.

This new technique should allow researchers to develop human cancer models in mice that more accurately reflect the level of genetic diversity seen in human cancer patients. That would enable drugs to be tested on a wide variety of different tumors simultaneously to determine which work best and which are ineffective at treating certain genetic types of tumors.

The study also revealed that some tumor suppressor genes work in conjunction with other genes and the presence or absence of other genes affects cancer growth. “We are now in a good position to understand how key cancer drivers interact with each other, and why tumors with the same mutations sometimes grow to be very large and sometimes not,” said study co-first author Christopher McFarland.

Such tests would enable researchers to determine why certain therapies are not effective in some patients while others respond well to the same treatment. Petrov suggests that the reason why certain drugs fail in some patients is due to the genetic identity of their tumors.

The research is detailed in the paper – Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice – was recently published in Nature Genetics.

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