癌症的异质性为癌症治疗的精确治疗策略提出了巨大的挑战。癌症亚型的识别旨在检测具有不同分子特征的患者，并为有效的临床治疗提供新的线索。虽然已经进行了大量的努力，但是开发能够高效整合多组学数据集的强大计算方法仍然具有挑战性。在本文中，我们提出了一种名为Deep Multi-view Contrastive Learning（DMCL）的新型自监督学习模型用于癌症亚型识别。具体而言，通过将重建损失、对比损失和聚类损失结合到一个统一框架中，我们的模型将样本的判别信息编码到提取的特征表示中，并在嵌入空间中有效地保持样本的聚类结构。此外，DMCL是一个端到端的框架，癌症亚型可以直接从模型输出中获得。我们将DMCL与八种替代方法进行比较，涵盖了经典癌症亚型识别方法到最新发展的最先进系统，使用了10个广泛使用的癌症多组学数据集以及一个综合数据集进行实验，实验结果验证了我们方法优越的性能。我们进一步对肝癌进行了案例研究，分析结果表明不同的亚型对所选择的化疗药物可能有不同的反应。© 作者 2023。牛津大学出版社保留所有权利。请发送电子邮件至：journals.permissions@oup.com以获取权限。

Cancer heterogeneity has posed great challenges in exploring precise therapeutic strategies for cancer treatment. The identification of cancer subtypes aims to detect patients with distinct molecular profiles and thus could provide new clues on effective clinical therapies. While great efforts have been made, it remains challenging to develop powerful computational methods that can efficiently integrate multi-omics datasets for the task. In this paper, we propose a novel self-supervised learning model called Deep Multi-view Contrastive Learning (DMCL) for cancer subtype identification. Specifically, by incorporating the reconstruction loss, contrastive loss and clustering loss into a unified framework, our model simultaneously encodes the sample discriminative information into the extracted feature representations and well preserves the sample cluster structures in the embedded space. Moreover, DMCL is an end-to-end framework where the cancer subtypes could be directly obtained from the model outputs. We compare DMCL with eight alternatives ranging from classic cancer subtype identification methods to recently developed state-of-the-art systems on 10 widely used cancer multi-omics datasets as well as an integrated dataset, and the experimental results validate the superior performance of our method. We further conduct a case study on liver cancer and the analysis results indicate that different subtypes might have different responses to the selected chemotherapeutic drugs.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.