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.

适应性免疫系统在早期识别肿瘤抗原以消灭癌细胞。这一过程伴随着肿瘤抗原特异性 T 淋巴细胞的全身增殖。虽然由于肿瘤尺寸小和有限的体细胞改变，无症状早期癌症的检测具有挑战性，但追踪外周 T 细胞库的变化可能为癌症诊断提供有吸引力的解决方案。在这里，我们开发了一种名为 DeepCAT 的深度学习方法，能够从头预测癌症相关 T 细胞受体 (TCR)。我们使用从多个主要组织相容性复合物 I (MHC-I) 多聚体分选实验中获得的癌症特异性或非癌症 TCR 验证了 DeepCAT，并证明了其对癌症抗原特异性 TCR 的预测能力。我们盲目地应用 DeepCAT 使用血液 TCR 序列区分超过 250 名癌症患者和超过 600 名健康个体，并观察到较高的预测准确性，多种早期癌症的曲线下面积 (AUC) ≥ 0.95。这项工作为使用外周血 TCR 库进行非侵入性癌症检测奠定了基础。