Illustration by Victoria Comfort / Fred Hutch News Service
Like searching through the unnumbered pages of a scattered manuscript, scientists seeking to engineer cancer-fighting immune cells have faced a daunting task: sorting through a disjointed assortment of genetic information to find the members of a key gene pairing.
Dr. Harlan Robins, head of Fred Hutchinson Cancer Research Center’s Herbold Computational Biology Program, has developed an analytical method called PairSEQ to quickly and accurately re-associate vital gene duos from thousands of immune cells called T cells. Published Aug. 19 in Science Translational Medicine, Robins’ approach has the potential to dramatically accelerate development of T cell–based immunotherapies.
“It’s actually a simple concept — which is how you know you have a good idea,” Robins said.
Reorganizing information lost in the shuffle
T cells have specialized molecules on their surface called T-cell receptors, or TCRs. These TCRs — unique to each T cell and its descendants — give them the ability to identify a specific target cell and initiate its destruction. Dangerous cells, including tumor cells or virally infected cells, can be eliminated this way. Many cutting-edge immunotherapies involve identifying anti-cancer T cells and then either growing more of them or using their TCR genes to engineer other T cells to create an entire army of cancer fighters.
Ideally, this new army will be based on the best T cell: the one with the TCR that binds most strongly to its cancer target and triggers the most vigorous response. But researchers are hindered by a large hurdle: the TCR is made of two proteins, each encoded by a gene on a separate chromosome.
This poses a problem for anyone attempting to analyze the TCR genes of many T cells at once, said Robins. “Once you crack open the T cells, the genes get mixed. Now you don’t know which go together,” he said.
Each TCR gene is like a page of a two-page manuscript. If a nicely ordered pile of thousands of different manuscripts is dropped, all the pages mix and it suddenly becomes a great challenge to recreate each individual manuscript.
There are several potential ways to address this problem. One way is by ensuring that the manuscripts never mix — by isolating each individual T cell before exposing its DNA and sequencing its TCR genes. However, this single-cell analysis requires specialized equipment and is currently impossible to perform quickly for the many thousands of unique T cells from even a single patient.
Robins instead took a different tack: strategically mix TCR genes and use a little logic to help match the correct genes.
Rather than sequence TCR genes T cell by T cell, Robins divides the large collection of T cells into several smaller pools. Within any large sample of T cells, a certain percentage will be identical, made up of a specific T cell and its descendants, all carrying the same TCR genes like carbon copies of the same document. By chance, if there are enough secondary pools, these copies will end up in different ones. The smaller pools of T cells are then “cracked open” and all the TCR genes from each pool are easily sequenced together.
Illustration by Victoria Comfort / Fred Hutch News Service
Robins’ new combinatorial algorithms streamline comparison of the genes from each pool. If two genes are never separated and always show up together in the same pools, researchers know they are from the same T cell and can then read the complete TCR gene sequence. Robins demonstrated that he could use this method to recreate the distinct TCRs from hundreds of thousands of T cells taken from patients’ blood or from the thousands of T cells found in solid tumors.
A boost to immunotherapy development
Robins’ colleague, Dr. Phil Greenberg, is using PairSEQ to enhance his efforts to develop T-cell therapies for cancer patients. Greenberg’s approach is to engineer a patient’s own T cells with a TCR that excels at recognizing target cancer cells and initiating their destruction.
Although his strategy is already showing great promise in an ongoing clinical trial for patients with acute myeloid leukemia who have a specific tissue type, Greenberg and his colleagues need to find more standout TCRs in order to make additional versions of the therapy available to patients with different tissue types. Greenberg and colleagues Drs. Aude Chapuis and Tom Schmitt are also working to extend this strategy beyond blood cancers and offer it to patients with other malignancies, including pancreatic and lung cancers.
PairSEQ has the potential to be “a complete game changer,” said Greenberg, who expects that the method will dramatically accelerate the pace at which his team can identify the most promising anti-cancer TCRs.
Using the older methods, it took one year to identify the first TCR that has moved to the clinic, said Greenberg. His team had to grow and analyze each of the thousands of T cells taken from the blood of as many as 70 donors before sequencing the TCR genes from the most promising candidates — a process that took many months for each TCR. Now, it should take “no more than two months” to find each promising TCR, said Greenberg, and because so many can be sought in parallel, those two months can actually produce several candidates.
Robins is also collaborating with Dr. Steven Rosenberg at the National Cancer Institute to apply PairSEQ to another form of T-cell therapy for cancer. Rosenberg’s method involves removing anti-cancer T cells from patients’ tumors, multiplying them to large numbers in the laboratory, and re-infusing them into patients so they can fight the disease. But often the vigor of these particular T cells has been blunted by the tumor. So Rosenberg is using PairSEQ to analyze the TCRs from thousands of these T cells, then inserting the best TCRs — the ones known to target that patient’s own tumor — into more robust T cells taken from the patient’s blood.
Adaptive Biotechnologies, a Fred Hutch spin-off company that is developing immunoSEQ, Robins’ technique for accurately sequencing just one TCR gene from thousands to millions of T cells, is also taking the reins to further develop and automate PairSEQ. A modified strategy to provide the same quick-yet-thorough analysis of the spectrum of antibodies, small infection-fighting proteins, is also nearing completion, Robins said.
“PairSEQ is truly a high-throughput and cost-effective way to analyze many [T-cell] samples at once,” said Robins. With this new tool, he hopes to equip scientists with a method to fast-track immunotherapy development and improve patient care even sooner.
Dr. Sabrina Richards, a staff writer at Fred Hutchinson Cancer Research Center, has written about scientific research and the environment for The Scientist and OnEarth Magazine. She has a Ph.D. in immunology from the University of Washington, an M.A. in journalism and an advanced certificate from the Science, Health and Environmental Reporting Program at New York University. Reach her at email@example.com.
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