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


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.

SUBMITTER: Beshnova D 

PROVIDER: S-EPMC7887928 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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

Beshnova Daria D   Ye Jianfeng J   Onabolu Oreoluwa O   Moon Benjamin B   Zheng Wenxin W   Fu Yang-Xin YX   Fu Yang-Xin YX   Brugarolas James J   Lea Jayanthi J   Li Bo B  

Science translational medicine 20200801 557


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 pr  ...[more]

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