De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection

Daria Beshnova, Jianfeng Ye, Oreoluwa Onabolu, Benjamin Moon, Wenxin Zheng, Yang Xin Fu, James Brugarolas, Jayanthi Lea, Bo Li

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The adaptive immune system recognizes tumor antigens at an early stage to eradicate cancer cells. This process is accompanied by systemic proliferation of the tumor antigen–specific T lymphocytes. While detection of asymptomatic early-stage cancers is challenging due to small tumor size and limited somatic alterations, tracking peripheral T cell repertoire changes may provide an attractive solution to cancer diagnosis. Here, we developed a deep learning method called DeepCAT to enable de novo prediction of cancer-associated T cell receptors (TCRs). We validated DeepCAT using cancer-specific or non-cancer TCRs obtained from multiple major histocompatibility complex I (MHC-I) multimer-sorting experiments and demonstrated its prediction power for TCRs specific to cancer antigens. We blindly applied DeepCAT to distinguish over 250 patients with cancer from over 600 healthy individuals using blood TCR sequences and observed high prediction accuracy, with area under the curve (AUC) ≥ 0.95 for multiple early-stage cancers. This work sets the stage for using the peripheral blood TCR repertoire for noninvasive cancer detection.

Original languageEnglish (US)
Article numbereaaz3738
JournalScience translational medicine
Volume12
Issue number557
DOIs
StatePublished - 2020

ASJC Scopus subject areas

  • Medicine(all)

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