Scientists from Fred Hutch and St. Jude Children’s Research Hospital have developed an algorithm that functions like a Rosetta Stone to help decipher how the immune system recognizes and binds antigens. The study, published online in the journal Nature, should aid development of more personalized cancer immunotherapy, and advance diagnosis and treatment of infectious diseases.

The immune system depends on molecules called T–cell receptors on the surface of T cells to recognize and respond to foreign antigens from virus–infected cells, tumors and other threats. Genomic rearrangement means that a large number of different T–cell receptors are possible. Each person can have about 100 million different receptors, referred to as their T–cell repertoire, with little overlap even in identical twins. Each receptor in the repertoire is capable of recognizing a different antigen and rallying the immune response to address that threat.

“Our immune repertoires contain detailed information on pathogen exposures, autoimmune diseases and cancer, but this information is encoded in the protein sequences of immune receptors and we don't currently have the ability to interpret these sequences, said Fred Hutch computational biologist Dr. Philip Bradley, co–corresponding author on the study along with St. Jude immunologist Dr. Paul Thomas. “Our study is aimed at developing tools that would allow us to decode T–cell receptor sequences, which could improve diagnosis and treatment of a variety of human diseases and cancers.”

The algorithm was built using tools the researchers developed to define how T–cell receptors recognize a part of the antigen called the epitope. Epitopes are displayed on the surface of circulating immune cells and are where T cells bind antigens to fuel the immune response. Multiple epitopes are produced from the same virus, tumor or other threat. Each epitope is targeted by a pool of T cells bearing different, but specific, T–cell receptors to recognize and respond.

“These analytical tools helped us to understand the T–cell repertoire against a particular antigen in a more coherent way than we have been able to do before. Grouping T–cell receptors for a given epitope revealed underlying common features that characterized the bulk of the repertoire,” said first author Dr. Pradyot Dash, a staff scientist in Thomas’ laboratory.

Like the Rosetta Stone that scholars used to decode hieroglyphics, researchers trained the algorithm with more than 4,600 T–cell receptors and then used it to correctly assign 81 percent of the human T cells and 78 percent of mouse T cells to one of 10 different viral epitopes. The “training data” were generated from 78 mice infected with influenza or cytomegalovirus (CMV) and 32 humans infected with flu, CMV or Epstein–Barr virus. The epitope of each T cell had been determined previously using a different, more labor–intensive method.

Researchers 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 percent accuracy the flu epitopes recognized by these cells.