By better understanding the similarities and differences between T-cell receptors, researchers will be better able to develop personalized cancer immunotherapies.
Researchers may have a whole new avenue for combating cancer. Scientists at St. Jude Children’s Research Hospital and Fred Hutchinson Cancer Research Center have developed an algorithm that can help decipher how the immune system recognizes and binds antigens. The research, which has been published in the journal Nature, could help in the development of more personalized cancer immunotherapy and advance diagnosis and treatment of infectious diseases.
The researchers conducted an in-depth characterization of 10 epitope-specific T-cell receptor repertoires of CD8+ T cells from mice and humans. They also developed analytical tools to characterize the epitope-specific repertoires. The analyses demonstrated that each epitope-specific repertoire had a clustered group of receptors with shared core sequence similarities. Through this approach, the researchers were able to observe key conserved residues driving essential elements of T-cell receptor recognition.
“We developed a method to determine the degree of similarity between any two T-cell receptors, which are key players in the immune system that recognize pieces of foreign pathogens or mutated proteins from cancer cells. This similarity measure, which we called TCRdist (for distance) was then applied to groups of receptors,” said investigator Paul Thomas, PhD, who is an associate member of the St. Jude Department of Immunology in Nashville, Tennessee.
He said this approach paves the way for a better understand of some of the basic rules for how T-cell recognition works. With this algorithm, there is now a system to identify critical features of T-cell receptors that recognize the same antigen and how they interact. Thomas said this lays the groundwork for designing receptors to recognize cancer.
“The applications for cancer are one of the most exciting areas for us. We can now take tumor-infiltrating lymphocytes and cluster them using this measure (TCRdist), which will help us determine which T-cell receptors to study to best represent the anti-tumor response. Further, as we extend the method, it might provide a way of designing optimized T-cell receptors to mediate anti-tumor activity,” Thomas told OncoTherapy Network.
The researchers trained the algorithm with more than 4,600 T-cell receptors and then used it to correctly assign 81% of the human T cells and 78% of mouse T cells to one of 10 different viral epitopes. The “training data” were generated from 78 mice infected with influenza or the cytomegalovirus (CMV) and 32 humans infected with flu, CMV, or the Epstein-Barr virus. The epitope of each T cell had been determined previously using a different, more labor-intensive method.
The team tested the algorithm on three flu-infected mice without knowledge of the epitope-receptor recognition. The algorithm was able to predict with up to 90% accuracy the flu epitopes recognized by these cells.