School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China.
Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China.
J Cell Mol Med. 2024 Sep;28(18):e70101. doi: 10.1111/jcmm.70101.
Colorectal cancer (CRC) is a relatively common malignancy clinically and the second leading cause of cancer-related deaths. Recent studies have identified T-cell exhaustion as playing a crucial role in the pathogenesis of CRC. A long-standing challenge in the clinical management of CRC is to understand how T cells function during its progression and metastasis, and whether potential therapeutic targets for CRC treatment can be predicted through T cells. Here, we propose DeepTEX, a multi-omics deep learning approach that integrates cross-model data to investigate the heterogeneity of T-cell exhaustion in CRC. DeepTEX uses a domain adaptation model to align the data distributions from two different modalities and applies a cross-modal knowledge distillation model to predict the heterogeneity of T-cell exhaustion across diverse patients, identifying key functional pathways and genes. DeepTEX offers valuable insights into the application of deep learning in multi-omics, providing crucial data for exploring the stages of T-cell exhaustion associated with CRC and relevant therapeutic targets.
结直肠癌（CRC）是临床上较为常见的恶性肿瘤，也是癌症相关死亡的第二大主要原因。最近的研究表明，T 细胞耗竭在 CRC 的发病机制中起着关键作用。在 CRC 的临床管理中，长期存在的一个挑战是了解 T 细胞在其进展和转移过程中的功能，以及是否可以通过 T 细胞预测 CRC 治疗的潜在治疗靶点。在这里，我们提出了 DeepTEX，这是一种多组学深度学习方法，它集成了跨模型数据来研究 CRC 中 T 细胞耗竭的异质性。DeepTEX 使用域自适应模型来对齐来自两种不同模态的数据分布，并应用跨模态知识蒸馏模型来预测不同患者中 T 细胞耗竭的异质性，确定关键的功能途径和基因。DeepTEX 为深度学习在多组学中的应用提供了有价值的见解，为探索与 CRC 相关的 T 细胞耗竭阶段和相关治疗靶点提供了关键数据。
