The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritization.

ABSTRACT: In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and

SUBMITTER: Pham TMQ 

PROVIDER: S-EPMC11517254 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature