Blending tumours for better cancer clues

Scientists discovered a groundbreaking method of testing tumours by homogenising cancer tissue in a blender. They are now sharing their method with researchers around the world.

Roche Diagnostics scientists have discovered a new, data-rich method for testing tumours - one that involves homogenising cancer tissue in a common household blender to better capture the genetic diversity of each solid tumour, and ultimately determine how to best kill it.

Through the recent publication of the team’s research on the cover of the open access journal STAR Protocols , the Roche team, along with partners in the UK, is now providing cancer researchers globally with step-by-step instructions for their method, known as representative sampling.

The goal? Improving testing and treatment through greater understanding of the genetic mutations that drive cancer and promote drug resistance.

The Roche Diagnostics representative sampling team
We are committed to sharing knowledge, which is what scientists do,” said Roche Diagnostics Research Leader Nelson Alexander. “Getting this method out there in the world is really important to us.

Through this new type of genetic testing, entire tumours are blended and sequenced, providing a more complete picture of the prevalence of genomic cancer biomarkers for each patient. As use of the method grows, so will researchers’ understanding of how to factor this new information into developing treatment plans, said researcher Lisa Gallegos. Lisa is one of the Roche Diagnostics authors of the paper, published in STAR Protocols. The article in this Cell Press open-access journal provides instructions on how to conduct experiments. (Watch the following video to see how representative sampling works)

“We’d like to extend the learning that we've had around representative sampling to anybody who wants to try it,” Lisa said. “Hopefully that would help the method spread to many other sites, including other research hospitals.”

Better data, improved patient care

The new testing concept dates back to 2014, when Roche Diagnostics scientists were puzzling over how to test an entire tumour, not just a couple of thin slices of tissue. With conventional tissue sampling for cancer diagnosis, as little as five in a million cells - or 0.0005% of a tumour - are tested, and what remains is typically incinerated - along with potentially vital information.

To develop the representative sampling method, Roche researchers have been collaborating with investigators from the Royal Marsden NHS Foundation Trust and the Francis Crick Institute. The teams have processed more than 250 cases of kidney, colon, lung, breast, ovarian and other cancer types, with preliminary results published in 2020 in Cell Reports . Those samples are being analysed to understand the medical value that potentially can be generated.

The recent STAR Protocols publication, which was prepared at the request of the journal, is expected to help researchers visualise how the method might work in hospitals and other research and clinical settings, Gallegos said.

Roche research is featured on the cover of STAR Protocols, with the colorful tree representing their tumour testing method

While using a kitchen blender is pretty far outside the norm for most cancer researchers, the publication could help scientists become at ease with this unusual technique, Gallegos said. Ideally, lab-grade equipment for processing leftover tumour tissue will one day make the workflow even more simple to conduct.

Next steps include determining what kinds of data can be mined from a large tissue sample repository, including data about the tumour microenvironment - the normal cells, molecules and blood vessels that surround and interact with tumour cells.

“This method provides a chance for us to detect pre-existing resistance to certain treatments and be able to say, okay, perhaps these patients should receive a different type of treatment, with the hope of maybe curing their cancer, instead of having it come back,” Lisa said. “That is my greatest hope for representative sampling.”

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