Novel bispecific design improves CAR T cell immunotherapy for childhood leukemia

Scientists at St. Jude Children’s Research Hospital have improved chimeric antigen receptor (CAR) T-cell immunotherapy for acute myeloid leukemia (AML), demonstrating improved effectiveness in the laboratory.

To overcome common problems with CAR T cells, researchers created an additional way for the therapy to detect and eliminate cancer cells, using a small peptide. The study also showed how a computational approach integrating the protein patterns predicted by AlphaFold could help understand the impact of structure on antigen recognition and treatment efficiency.

Their findings are published in the journal Cell Reports Medicine.

Immunotherapy that reprograms a patient’s own immune cells to target a specific cancer protein, CAR T cell therapy, has shown success in treating certain recurrent leukemias. However, treatment sometimes fails because cancer cells that lack the targeted protein can still grow, escaping treatment and causing a relapse. The relapse rate of AML is high, leading to a poor prognosis for the disease as a whole.

The St. Jude group thought it might be possible to overcome the problem of immune evasion in AML models by targeting two different cancer-related proteins instead of just one.

Others have attempted a similar approach but encountered problems with the structure of the bispecific CAR. The scientists overcame these problems by adding a small peptide to the CAR to serve as a binder for the second targeted protein, then confirmed their results with computational structural analysis of their improved constructs.

“One of the most exciting aspects of the study is that this approach can be widely extrapolated to other tumors,” said senior corresponding author Paulina Velasquez, MD, of the St. Jude Department of Marrow Transplantation. bone and cell therapy. “We focused on leukemia, but combining the bispecific CAR design with computational predictions can be widely extrapolated to other tumors such as solid and brain tumors.”

Improve dual targeting by adding a second small barcode reader

The CAR created by the researchers is a unique design. It is a single molecule that includes the region of an antibody that binds to a specific target (its antigen) and a short peptide that binds to a distinct target.

“The two different binding domains of CAR are like having two barcode readers instead of one, searching for their appropriate barcode, targeted cancer-related proteins,” Velasquez said. “Normally, a CAR has a single barcode reader. Here we have placed two slightly different barcode readers on top of each other, and if either detects a code- appropriate target bars, the cancer immunotherapy response is activated.”

The two binding domains are joined by a linker to enable the binding of two different cancer-related proteins. This differs greatly from previous dual-targeting approaches in the field, which typically used two complete antibody-based linkers.

“We have shown interest in finding creative ways to perform dual-antigen targeting,” said first author Jaquelyn Zoine, Ph.D., of the St. Jude Department of Bone Marrow Transplantation and Cell Therapy. .

“Previous bispecific CAR approaches use two antibody-based single-chain variable fragments, which are physically large molecules and can interfere with each other, sometimes leading to weak or ineffective binding. Our approach instead added a small peptide, allowing our CAR to engage either platform to prevent immune evasion.”

Dual-targeted CARs performed better than single-targeted CARs in both in vitro and in vivo experiments, demonstrating their potential to improve CAR T cell function.

Untangling the performance of dual-target constructs using artificial intelligence

“We have shown proof of principle to explain and potentially expand the CAR design repertoire,” said co-author M. Madan Babu, Ph.D., FRS, director of the St. Jude Center of Excellence for Discovery. data-driven and George J Pedersen Endowed Chair in Biological Data Science in the Department of Structural Biology. “But then comes the challenge. How do you know which linkers to choose? How do you know how much physical flexibility is necessary?”

Since the physical structure of the targeting molecule and its linker that connects the two binding domains can cause internal interference that prevents binding to the cancer cell’s targets, identifying the most common types of linkers in therapeutics ineffective could lead to future improvements. Computational predictions of the structures and comparison of the structures with experimental results confirmed to the St. Jude group that shorter, more flexible linkers would work better in their models.

“If we have a rigid link connecting the barcode readers, it can only scan a small volume of cancer cells, which makes it less effective in finding the targets,” Babu said. “We found that when you have a linker that is flexible and shorter enough to not fold back on itself, it can scan a much larger volume and is more likely to find the target proteins on the cancer cell. You then you have a more effective tool.” pair of barcode readers that work together.

“We are one of the few groups in the world using AI-based structure prediction tools for CAR design,” said second author Kalyan Immadisetty, of the St. Jude Department of Bone Marrow Transplantation and cell therapy. Immadisetty confirmed the association between short, flexible linkers and greater anticancer efficacy by comparing 3D modeled structures. This information supported the performance of CAR in real experimental results.

“We were pleased that the structural predictions supported our experiments and informed us that a short, flexible linker would be the best configuration,” Zoine said. “As we performed the experiments, Immadisetty discovered that the structural components correlated almost exactly with what we showed functionally, even when we changed one of the binding domains of the targeting antibodies. We have now introduced the idea that these AI prediction tools can be extended to others. CAR constructions.”

“More importantly, others can now use our computational approach to design their CARs,” Immadisetty said. “And I hope this will help them understand the effectiveness of their CAR technology and lead to overall improvements for leukemia and other malignancies.”