Data Sheet 1_Peripheral blood TCR repertoire improves early detection across multiple cancer types utilizing a cancer predictor.docx

IntroductionIn the early asymptomatic stages of cancer, the immune system initiates a targeted response against tumor-associated antigens. During this phase, the immune system specifically identifies tumor antigens and triggers the clonal expansion of tumor antigen-specific T cells, which recognize tumor antigen peptides presented by the major histocompatibility complex via the T-cell receptor (TCR) on their surface. Consequently, monitoring alterations in the TCR repertoire holds promise for evaluating an individual’s immune status for cancer detection.
MethodsIn this study, we introduced a deep learning framework named DeepCaTCR, designed to enhance the prediction of cancer-associated T-cell receptors. The framework employs a one-dimensional convolutional neural network with variable convolutional kernels, a bidirectional long short-term memory network, and a self-attention mechanism to facilitate feature extraction from amino acid fragments of varying lengths.
ResultsDeepCaTCR demonstrates superior performance in cancer-associated TCR recognition, achieving an area under the receiver operating characteristic curve (AUC) of 0.863 and an F1-score of 0.669, thereby outperforming prevailing deep learning models. Validation result indicates that DeepCaTCR effectively distinguishes between tumor-infiltrating lymphocytes (TILs) and healthy peripheral blood samples, achieving an AUC greater than 0.95. It also exhibits high sensitivity (62.5%) and specificity (over 98%) in peripheral blood testing for early-stage cancer patients. To further enhance detection efficacy, we introduced a variance-based repertoire scoring strategy to quantify the dynamic heterogeneity of TCR clonal amplification, resulting in an increased AUC of 0.967 for pan-cancer early screening.
DiscussionThis study introduces a novel tool for analyzing the tumor immune microenvironment, offering significant translational potential for early cancer diagnosis. Its key feature is a new scoring method based on variance, not the average method.

引言：在癌症早期无症状阶段，免疫系统会启动针对肿瘤相关抗原的靶向免疫应答。在此阶段，免疫系统会特异性识别肿瘤抗原，并触发肿瘤抗原特异性T细胞的克隆扩增；这类T细胞可通过其表面的T细胞受体（T-cell receptor, TCR）识别由主要组织相容性复合体（major histocompatibility complex, MHC）呈递的肿瘤抗原肽段。因此，监测T细胞受体库的变化，有望通过评估个体免疫状态实现癌症检测。

方法：本研究提出了一款名为DeepCaTCR的深度学习框架，旨在优化癌症相关T细胞受体的预测任务。该框架采用带可变卷积核的一维卷积神经网络、双向长短期记忆网络以及自注意力机制，以实现对不同长度氨基酸片段的特征提取。

结果：DeepCaTCR在癌症相关T细胞受体识别任务中表现优异，受试者工作特征曲线下面积（area under the receiver operating characteristic curve, AUC）达0.863，F1值为0.669，性能优于当前主流深度学习模型。验证结果显示，DeepCaTCR可有效区分肿瘤浸润淋巴细胞（tumor-infiltrating lymphocytes, TILs）与健康外周血样本，AUC超过0.95。在针对早期癌症患者的外周血检测中，该模型亦表现出较高的灵敏度（62.5%）与特异性（98%以上）。为进一步提升检测效能，本研究提出了一种基于方差的库评分策略，以量化T细胞克隆扩增的动态异质性，使泛癌早期筛查的AUC提升至0.967。

讨论：本研究提出了一款用于分析肿瘤免疫微环境的新型工具，在早期癌症诊断领域具备可观的转化应用潜力。其核心特色在于采用基于方差的新型评分方法，而非传统的均值评分法。